diff --git a/.gitignore b/.gitignore
index 1e6df0bc..972726f5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -128,9 +128,16 @@ dmypy.json
 # Pyre type checker
 .pyre/
 
+# sandboxes
+**sandbox.py
+parsing/sandbox*
+
 # defined by Ansong
 .venv
 data/
 debug-tmp/
 wandb/
 results/
+
+# defined by Troy
+.DS_Store
diff --git a/execution/spider_execution.py b/execution/spider_execution.py
index e0fcc579..8c86bb5d 100644
--- a/execution/spider_execution.py
+++ b/execution/spider_execution.py
@@ -1,6 +1,9 @@
 import sqlite3
 import pandas as pd
 import numpy as np
+import re
+import keyword
+import math
 
 from typing import List, Dict, Any, Union, Tuple
 
@@ -29,8 +32,11 @@ def spider_execution_sql(sql: str, conn: sqlite3.Connection, return_error_msg: b
 def db_to_df_dict(conn: sqlite3.Connection) -> Dict[str, pd.DataFrame]:
     df_dict = {}
     for table_name in conn.execute("SELECT name FROM sqlite_master WHERE type='table'").fetchall():
-        df_dict[table_name[0]] = pd.read_sql_query(f"SELECT * FROM {table_name[0]}", conn)
-        df_dict[table_name[0]].rename(columns=lambda x: x.lower(), inplace=True)
+        # modify to change everything including labels lower case
+        df = pd.read_sql_query(f"SELECT * FROM {table_name[0]}", conn)
+        df = df.applymap(lambda s: s.lower() if type(s) == str else s)
+        df_dict[table_name[0].lower()] = df
+        df_dict[table_name[0].lower()].rename(columns=lambda x: x.lower(), inplace=True)
     return df_dict
 
 def spider_execution_py(code: str, df_dict: Dict[str, pd.DataFrame], return_error_msg: bool = False) -> Any:
@@ -39,8 +45,22 @@ def spider_execution_py(code: str, df_dict: Dict[str, pd.DataFrame], return_erro
     # use the tables as part of the code context
     table_vars_code = "import pandas as pd\n"
     for table_name in df_dict.keys():
-        table_vars_code += f"# {' '.join(list(df_dict[table_name].columns))}\n{table_name} = df_dict['{table_name}']\n"
-    code = table_vars_code + "\n" + code
+        # table names may be reserved words like "class"
+        if table_name in keyword.kwlist:
+            table_vars_code += f"_{table_name} = df_dict['{table_name}']\n"
+            # but we have to make sure that table columns are not changed
+            # code = code.replace(table_name, f"_{table_name}")
+            code = re.sub("((?<!_)class(?!_))", "_class", code)
+        else:
+            table_vars_code += f"{table_name} = df_dict['{table_name}']\n"
+
+    # lower everything in quotes
+    code = re.sub(r"'(.*?)'", lambda p: f"'{p.group(1).lower()}'", code)
+    # move select statements after sorting or drop_dup
+    # TODO further processing needed, case 784, 1721,
+    #  and select, drop_duplicate, followed by sorting
+    code = re.sub(r"(.*(?<!\[))(\[\[?.*?\]?\])(\.sort_values.*)", r"\1\3\2", code)
+    code = table_vars_code + "\n" + f"answer = {code}"
 
     # execute the code
     try:
@@ -58,7 +78,7 @@ def spider_execution_py(code: str, df_dict: Dict[str, pd.DataFrame], return_erro
         else:
             return None
 
-def flatten_list_of_list(l: List[List[Any]]) -> List[Any]:
+def flatten_list_of_list(l: List[List[Any]], sort: bool = False) -> List[Any]:
     result = []
     for sublist in l:
         if isinstance(sublist, list) or isinstance(sublist, tuple):
@@ -66,35 +86,69 @@ def flatten_list_of_list(l: List[List[Any]]) -> List[Any]:
         else:
             result.append(sublist)
 
-    return result
+    if sort:
+        result.sort(key = str)
+        return result
+    else:
+        return result
 
-def spider_answer_eq(prediction: Union[pd.DataFrame, pd.Series, List[Tuple[Any]]], 
-                     gold_answer: Union[List[Tuple[Any]], int]) -> bool:
+def list_to_lower_case(l: List[Any]):
+    result = []
+    for object in l:
+        if isinstance(object, str):
+            result.append(object.lower())
+        else:
+            result.append(object)
+    return result
 
-    if isinstance(prediction, int) or isinstance(prediction, float):
+def compare_lists(l1: List[Any], l2: List[Any]) -> bool:
+    if len(l1) != len(l2):
+        return False
+    else:
+        for i in range(len(l1)):
+            if type(l1[i]) == float:
+                if not math.isclose(l1[i], l2[i]):
+                    return False
+                else:
+                    continue
+            elif l1[i] != l2[i]:
+                return False
+        return True
+
+def spider_answer_eq(prediction: Union[pd.DataFrame, pd.Series, List[Tuple[Any]]],
+                     gold_answer: Union[List    [Tuple[Any]], int],
+                     sort: bool = False) -> bool:
+
+    if isinstance(prediction, int) or isinstance(prediction, float) or (not isinstance(prediction, list) and not isinstance(prediction, pd.DataFrame) and not isinstance(prediction, np.ndarray) and not isinstance(prediction, tuple) and np.issubdtype(prediction, np.integer)):
         prediction = [prediction]
-    
+
     if isinstance(prediction, list) or isinstance(prediction, np.ndarray):
         if isinstance(gold_answer, list):
-            gold_flattened = flatten_list_of_list(gold_answer)
-            pred_flattened = flatten_list_of_list(prediction)
-            result = pred_flattened == gold_flattened
+            gold_flattened = list_to_lower_case(
+                flatten_list_of_list(gold_answer, sort))
+            pred_flattened = flatten_list_of_list(prediction, sort)
+            result = compare_lists(pred_flattened, gold_flattened)
         else:
             result = False
     elif isinstance(prediction, pd.DataFrame):
         if isinstance(gold_answer, list):
-            # convert the dataframe to a list of tuples and check
-            pred_list = flatten_list_of_list(list(prediction.itertuples(index=False, name=None)))
-            gold_list = flatten_list_of_list(gold_answer)
-            result = pred_list == gold_list
+            # we include the index only when it exists
+            pred_list = flatten_list_of_list(list(prediction.itertuples(
+                index=bool(prediction.index.name), name=None)), sort)
+            gold_list = list_to_lower_case(flatten_list_of_list(gold_answer, sort))
+            result = compare_lists(pred_list, gold_list)
         else:
             result = False
     elif isinstance(prediction, pd.Series):
         if isinstance(gold_answer, list):
             # convert the series to a list of tuples and check
-            pred_list = flatten_list_of_list(prediction.tolist())
-            gold_list = flatten_list_of_list(gold_answer)
-            result = pred_list == gold_list 
+            # we include the index only when it exists
+            if prediction.index.name:
+                pred_list = flatten_list_of_list(list(prediction.items()), sort)
+            else:
+                pred_list = flatten_list_of_list(prediction.tolist(), sort)
+            gold_list = list_to_lower_case(flatten_list_of_list(gold_answer, sort))
+            result = compare_lists(pred_list, gold_list)
         else:
             result = False
     else:
diff --git a/parsing/clean_query.py b/parsing/clean_query.py
new file mode 100644
index 00000000..166eedd1
--- /dev/null
+++ b/parsing/clean_query.py
@@ -0,0 +1,36 @@
+import re
+from helpers import trim_front_and_back
+
+
+# TODO:
+# - more robust parenthesis handling
+# - more robust extra space removal?
+
+
+# sql2pandas requires single quotes in SQL queries
+def replace_quotes(sql_query):
+    return sql_query.replace("\"", "\'")
+
+
+# Remove extra spaces
+def remove_consecutive_spaces(sql_query):
+    sql_query = sql_query.strip()
+    sql_query = re.sub(r"\s+", " ", sql_query)
+    sql_query = re.sub(r"\( ", "(", sql_query)
+    return sql_query
+
+
+# Add semi-colon at end of SQL query for consistency
+def add_semicolon(sql_query):
+    return sql_query if sql_query[-1:] == ";" else sql_query + ";"
+
+
+# Basic string preprocessing/cleanup for SQL queries
+def basic_clean_query(sql_query):
+    sql_query = replace_quotes(sql_query)
+    sql_query = remove_consecutive_spaces(sql_query)
+    # TODO: ensure balance for front/back parentheses
+    sql_query = trim_front_and_back(sql_query, "(", ")")
+
+    sql_query = add_semicolon(sql_query)
+    return sql_query
diff --git a/parsing/data.json b/parsing/data.json
new file mode 100644
index 00000000..e69de29b
diff --git a/parsing/helpers.py b/parsing/helpers.py
new file mode 100644
index 00000000..b35195ef
--- /dev/null
+++ b/parsing/helpers.py
@@ -0,0 +1,190 @@
+from typing import Tuple
+
+
+# Removes any characters in `chars_to_remove` from the front of `s`
+def trim_front(s, chars_to_remove):
+    while s[0] in chars_to_remove:
+        s = s[1:]
+    return s
+
+
+# Removes any characters in `chars_to_remove` from the back of `s`
+def trim_back(s, chars_to_remove):
+    while s[-1:] in chars_to_remove:
+        s = s[:-1]
+    return s
+
+
+# Removes characters like parentheses from front/end of `s`
+def trim_front_and_back(s, char_front, char_back):
+    while s[0] == char_front and s[-1:] == char_back:
+        s = s[1:-1]
+    return s
+
+
+# Find corresponding balanced closing parenthesis for opening parenthesis at index `open_idx-1`
+def find_closing_parenthesis(s, open_idx):
+    if s[open_idx-1] != '(':
+        print('[find_closing_parenthesis] input open_idx error')
+        return -1
+
+    idx = open_idx
+    ct_open = 0
+    while idx < len(s):
+        if s[idx] == '(':
+            ct_open += 1
+        elif s[idx] == ')':
+            if ct_open == 0:
+                return idx
+            ct_open -= 1
+
+        idx += 1
+
+    return -1
+
+
+# Determines if first non-whitespace char in `partial_sql_query` is "SELECT"
+def is_next_token_select(partial_sql_query):
+    return partial_sql_query.strip().find("SELECT") == 0
+
+
+def is_idx_at_token_start(sql_query: str, idx: int):
+    if idx >= len(sql_query):
+        return False
+
+    if sql_query[idx] == " ":
+        print("[is_idx_at_token_start] idx not in word")
+        return False
+
+    if idx > 0 and not sql_query[idx-1] == " ":
+        print("[is_idx_at_token_start] idx not at start of token")
+        return False
+
+    return True
+
+
+def get_next_token_idx(sql_query: str, idx: int):
+    while idx < len(sql_query) and sql_query[idx] != " ":
+        idx += 1
+
+    while idx < len(sql_query) and sql_query[idx] == " ":
+        idx += 1
+
+    return idx
+
+
+def get_prev_token(sql_query: str, idx: int):
+    if idx == 0:
+        print("[get_prev_token] no prev token")
+        return None
+
+    if not is_idx_at_token_start(sql_query, idx):
+        return None
+
+    finish_idx = idx - 1
+    while finish_idx - 1 >= 0 and sql_query[finish_idx-1] == " ":
+        finish_idx -= 1
+
+    start_idx = finish_idx - 1
+    while start_idx - 1 >= 0 and sql_query[start_idx - 1] != " ":
+        start_idx -= 1
+
+    return sql_query[start_idx:finish_idx]
+
+def get_second_last_token(sql_query: str):
+    length = len(sql_query)
+    if length < 2:
+        print("[get_second_last_token] no second last token")
+        return None
+
+    finish_idx = length - 1
+    while finish_idx > 0 and sql_query[finish_idx] != " ":
+        finish_idx -= 1
+
+    start_idx = finish_idx - 1
+    while start_idx > 0 and sql_query[start_idx] != " ":
+        start_idx -= 1
+
+    return sql_query[start_idx:finish_idx].strip()
+
+def get_cur_token(sql_query: str, idx: int):
+    if not is_idx_at_token_start(sql_query, idx):
+        return None
+
+    finish_idx = idx
+    while finish_idx < len(sql_query) and sql_query[finish_idx] != " ":
+        finish_idx += 1
+
+    return sql_query[idx:finish_idx]
+
+
+def get_next_token(sql_query: str, idx: int):
+    if idx >= len(sql_query) - 1:
+        print("[get_prev_token] no next token")
+        return None
+
+    if not is_idx_at_token_start(sql_query, idx):
+        return None
+
+    start_idx = get_next_token_idx(sql_query, idx)
+    return get_cur_token(sql_query, start_idx)
+
+
+def remove_prev_token(s: str, idx: int) -> Tuple[str, int]:
+    """Removes previous token from idx, where idx is at start of token.
+
+    Args:
+        s (str): String from which to remove previous token
+        idx (int): Index of start of token, where previous token from idx is removed.
+
+    Returns:
+        Tuple[str, int]: Redacted string, and new position of idx
+    """
+    if idx == 0:
+        print("[get_prev_token] no prev token")
+        return None
+
+    if not is_idx_at_token_start(s, idx):
+        return None
+
+    finish_idx = idx - 1
+    while finish_idx - 1 >= 0 and s[finish_idx-1] == " ":
+        finish_idx -= 1
+
+    start_idx = finish_idx - 1
+    while start_idx - 1 >= 0 and s[start_idx - 1] != " ":
+        start_idx -= 1
+
+    return s[:start_idx] + s[finish_idx:], idx - (finish_idx - start_idx)
+
+
+def extract_table_column(join_on_col: str) -> str:
+    """For a table column of the form TABLE.COLUMN (as in JOIN), extract COLUMN.
+
+    Args:
+        join_on_col (str): Full name of column, potentially with table specified.
+
+    Returns:
+        str: Extracted column (without specified table, if specified).
+    """
+    dot_idx = join_on_col.find(".")
+    return join_on_col if dot_idx < 0 else join_on_col[dot_idx+1:]
+
+
+def get_first_token(s: str) -> str:
+    idx = s.find(" ")
+    if idx < 0:
+        idx = len(s)
+    return s[:idx]
+
+def subtract_sql_to_pandas(sql: str, simple: bool) -> str:
+    """If simple subtract, removes the SELECT and parenthesis and ; from the sql for a subtract sql 
+    Otherwise, replaces subtraction with pandas and returns the new sql with subtraction replaced
+
+    TODO fill args
+    """
+    if simple:
+        ret = sql.replace("SELECT ", "").replace(";", "").replace("(", "").replace(")", "")
+    else:
+        ret = None
+    return ret
\ No newline at end of file
diff --git a/parsing/node_to_pandas_snippet.py b/parsing/node_to_pandas_snippet.py
new file mode 100644
index 00000000..e60d3f96
--- /dev/null
+++ b/parsing/node_to_pandas_snippet.py
@@ -0,0 +1,44 @@
+from processed_query import ProcessedSQLQueryNodeType, ProcessedSQLQueryNode
+
+
+# TODO:
+# - UNION ALL, other unsupported SQL keywords?
+
+
+def symbols_to_pandas(s1: str, s2: str, node_type: ProcessedSQLQueryNodeType) -> str:
+    """Given two symbols and SQL operation type on them, return corresponding pandas snippet."""
+
+    if node_type == ProcessedSQLQueryNodeType.NESTED_SELECT:
+        return s1
+
+    if node_type == ProcessedSQLQueryNodeType.UNION:
+        return f"pd.concat([{s1}, {s2}]).drop_duplicates()"
+
+    if node_type == ProcessedSQLQueryNodeType.INTERSECT:
+        return f"pd.merge({s1}, {s2}, how='inner')"
+
+    if node_type == ProcessedSQLQueryNodeType.EXCEPT:
+        return f"{s1}[~({s1}.isin({s2}).all(axis=1))]"
+
+    if node_type == ProcessedSQLQueryNodeType.SUBTRACT:
+        return f"{s1} - {s2}"
+    
+    raise ValueError(f"Unsupported SQL operation type: {node_type}")
+
+
+def extract_pandas_code_snippet_from_node(sql_query_node: ProcessedSQLQueryNode) -> str:
+    """Extract pandas snippet representing one node in SQL query decomposition tree, using internal symbol.
+
+    Args:
+        sql_query_node (ProcessedSQLQueryNode): Node for which to generate snippet
+
+    Returns:
+        str: One pandas code snippet of the form "symbol = f(left_symbol, r_symbol)".
+    """
+    symbol = sql_query_node.internal_symbol
+    if sql_query_node.node_type == ProcessedSQLQueryNodeType.LEAF or sql_query_node.node_type == ProcessedSQLQueryNodeType.SUBTRACT:
+        return f"{symbol} = {sql_query_node.pandas_query}"
+
+    left_symbol = sql_query_node.left_node.internal_symbol
+    right_symbol = sql_query_node.right_node.internal_symbol
+    return f"{symbol} = {symbols_to_pandas(left_symbol, right_symbol, sql_query_node.node_type)}"
diff --git a/parsing/preprocess.py b/parsing/preprocess.py
new file mode 100644
index 00000000..e20e7666
--- /dev/null
+++ b/parsing/preprocess.py
@@ -0,0 +1,342 @@
+from typing import Dict, List, Union
+from helpers import find_closing_parenthesis, is_next_token_select, get_second_last_token, subtract_sql_to_pandas
+from clean_query import basic_clean_query
+from node_to_pandas_snippet import extract_pandas_code_snippet_from_node
+from process_table_expr import extract_table_expr_from_query, substitute_symbol_for_table_expr
+from processed_query import ProcessedSQLQueryNode, ProcessedSQLQueryNodeType, ProcessedSQLQueryTree, ProcessedSQLTableExpr
+from sql2pandas import sql2pandas
+import re
+
+
+# TODO:
+# - convert table_expr to pandas (https://pandas.pydata.org/docs/getting_started/comparison/comparison_with_sql.html)
+# - handle RIGHT/LEFT OUTER/INNER/FULL JOIN
+# - handle UNION ALL
+# - remove original table name from table_expr (or replace all instances of AS alias with orig table name)
+# - return None tree if SQL query generates error (error in sql2pandas, parenthesis imbalance, etc)
+
+
+def extract_select_subquery(sql_query: str, query_type: ProcessedSQLQueryNodeType) -> Union[str, None]:
+    """Finds and extracts SELECT subquery from complex SQL query.
+
+    Args:
+        sql_query (str): SQL query from which to find and extract SELECT subquery.
+        query_type (ProcessedSQLQueryNodeType): Type of SELECT subquery.
+
+    Returns:
+        Union[str, None]: Extracted subquery (starting from SELECT) if found, None else.
+    """
+    query_type_token = query_type.value
+
+    start_idx = sql_query.find(query_type_token)
+    if start_idx < 0:
+        return None
+
+    start_idx += 1 if query_type == ProcessedSQLQueryNodeType.NESTED_SELECT else len(
+        query_type_token)
+    if not is_next_token_select(sql_query[start_idx:]) and query_type != ProcessedSQLQueryNodeType.SUBTRACT:
+        return None
+
+    # TODO: better finishing idx
+    finish_idx = len(sql_query)
+    if query_type == ProcessedSQLQueryNodeType.NESTED_SELECT:
+        finish_idx = find_closing_parenthesis(sql_query, start_idx)
+        if finish_idx == -1:
+            print(
+                f"[handle_nested_select] parenthesis imbalance detected: {sql_query}")
+            return None
+
+    extracted_subquery = sql_query[start_idx:finish_idx]
+    return extracted_subquery
+
+
+def convert_query_to_tree_node(sql_query: str, internal_symbol: str, tree_header: ProcessedSQLQueryTree) -> ProcessedSQLQueryNode:
+    """If there are SELECT subqueries to extract, extracts one layer of SELECT subquery into a tree,
+    with recursion to generate subtrees on remaining layers.
+
+    Args:
+        sql_query (str): Full SQL query string to decompose.
+        tree_header (ProcessedSQLQueryTree): Tree header.
+
+    Returns:
+        ProcessedSQLQueryNode: Tree node rooted at tree containing decomposed SQL query.
+    """
+
+    sql_query = basic_clean_query(sql_query)
+
+    query_type, subquery = None, None
+    for find_query_type in ProcessedSQLQueryNodeType:
+        found_subquery = extract_select_subquery(sql_query, find_query_type)
+        if found_subquery != None:
+            query_type = find_query_type
+            subquery = found_subquery
+            break
+
+    # Base case: LEAF node
+    print("A: ", sql_query)
+    if query_type == None or subquery == None:
+        print("leaf", sql_query)
+        table_expr_str = extract_table_expr_from_query(sql_query)
+        print("a", table_expr_str)
+        table_expr_symbol_key = tree_header.get_symbol_key()
+        table_expr = ProcessedSQLTableExpr(
+            orig_table_expr=table_expr_str, table_expr_symbol_key=table_expr_symbol_key, get_symbol=tree_header.get_symbol_key)
+
+        final_sql_query = substitute_symbol_for_table_expr(
+            sql_query, table_expr_str, table_expr_symbol_key)
+
+        leaf_node = ProcessedSQLQueryNode(
+            node_type=ProcessedSQLQueryNodeType.LEAF,
+            internal_symbol=internal_symbol,
+            sql_query=final_sql_query,
+            sql_query_table_expr=table_expr,
+            pandas_query=sql2pandas(final_sql_query),
+            left_node=None,
+            right_node=None
+        )
+
+        tree_header.add_key_value_to_symbol_table(
+            internal_symbol, sql_query, leaf_node)
+        return leaf_node
+
+    idx = sql_query.find(subquery)
+    if idx < 0:
+        print("[preprocess.py] ERROR: could not find subquery in sql_query")
+        return sql_query
+
+    left_symbol_key = tree_header.get_symbol_key()
+    right_symbol_key = tree_header.get_symbol_key()
+
+    # Handle nested SELECT separately because of external key
+    if query_type == ProcessedSQLQueryNodeType.NESTED_SELECT:
+        left_query = sql_query[0:idx] + \
+            right_symbol_key + sql_query[idx+len(subquery):]
+
+        print("left", left_query)
+        print("sub", subquery)
+
+        left_node = convert_query_to_tree_node(
+            left_query, left_symbol_key, tree_header)
+        left_node.set_external_symbol(right_symbol_key)
+
+        right_node = convert_query_to_tree_node(
+            subquery, right_symbol_key, tree_header)
+
+        root_node = ProcessedSQLQueryNode(
+            node_type=query_type,
+            internal_symbol=internal_symbol,
+            sql_query=None,
+            sql_query_table_expr=None,
+            pandas_query=None,
+            left_node=left_node,
+            right_node=right_node
+        )
+
+        tree_header.add_key_value_to_symbol_table(
+            internal_symbol, sql_query, root_node)
+        return root_node
+
+    # Handle subtraction case separately because there exists no table (i.e. no "FROM")
+    if query_type == ProcessedSQLQueryNodeType.SUBTRACT:
+        print("OG left:",sql_query[0:idx-len(query_type.value)])
+        left_query = sql_query[0:idx] + \
+            right_symbol_key + sql_query[idx+len(subquery):]
+
+        print("MOD left", left_query)
+        print("sub", subquery)
+        left_query = sql_query[0:idx-len(query_type.value)]
+
+        second_last_token = get_second_last_token(left_query)
+        if second_last_token == "SELECT":
+            leaf_node = ProcessedSQLQueryNode(
+                node_type=ProcessedSQLQueryNodeType.SUBTRACT,
+                internal_symbol=internal_symbol,
+                sql_query=subquery,
+                sql_query_table_expr=None,
+                pandas_query=subtract_sql_to_pandas(subquery, simple=True),
+                left_node=None,
+                right_node=None
+            )
+
+            tree_header.add_key_value_to_symbol_table(
+                internal_symbol, sql_query, leaf_node)
+            return leaf_node
+        else:
+            root_node = ProcessedSQLQueryNode(
+                node_type=query_type,
+                internal_symbol=internal_symbol,
+                sql_query=None,
+                sql_query_table_expr=None,
+                pandas_query=None,
+                left_node=left_node,
+                right_node=None
+            )
+
+            tree_header.add_key_value_to_symbol_table(
+                internal_symbol, sql_query, root_node)
+        return root_node
+
+        
+
+    left_query = sql_query[0:idx-len(query_type.value)]
+    left_node = convert_query_to_tree_node(
+        left_query, left_symbol_key, tree_header)
+    right_node = convert_query_to_tree_node(
+        subquery, right_symbol_key, tree_header)
+
+    root_node = ProcessedSQLQueryNode(
+        node_type=query_type,
+        internal_symbol=internal_symbol,
+        sql_query=None,
+        sql_query_table_expr=None,
+        pandas_query=None,
+        left_node=left_node,
+        right_node=right_node
+    )
+
+    tree_header.add_key_value_to_symbol_table(
+        internal_symbol, sql_query, root_node)
+    return root_node
+
+
+def preprocess_sql_query_into_tree(sql_query: str) -> ProcessedSQLQueryTree:
+    """Processes SQL query string into ProcessedSQLQueryTree.
+
+    Args:
+        sql_query (str): SQL query string to decompose.
+
+    Returns:
+        ProcessedSQLQueryTree: SQL query string tree decomposition.
+    """
+    tree = ProcessedSQLQueryTree()
+
+    root_symbol = tree.get_symbol_key()
+    root_node = convert_query_to_tree_node(sql_query, root_symbol, tree)
+
+    tree.reset_root_node(root_node)
+    return tree
+
+
+def extract_pandas_table_aliases_dfs(node: ProcessedSQLQueryNode, code_snippets: List[str]):
+    """Helper function to extract all table aliases into executable Python code snippets"""
+    if node == None:
+        return
+
+    if node.node_type == ProcessedSQLQueryNodeType.LEAF:
+        node.sql_query_table_expr.extract_table_aliases(code_snippets)
+        return
+
+    extract_pandas_table_aliases_dfs(node.right_node, code_snippets)
+    extract_pandas_table_aliases_dfs(node.left_node, code_snippets)
+
+
+def extract_pandas_table_expr_symbols_dfs(node: ProcessedSQLQueryNode, code_snippets: List[str]):
+    """Helper function to extract all table expression substitute symbols into executable Python code snippets"""
+    if node == None:
+        return
+
+    if node.node_type == ProcessedSQLQueryNodeType.LEAF:
+        code_snippets.extend(
+            node.sql_query_table_expr.table_expr_pandas_snippets)
+        return
+
+    extract_pandas_table_expr_symbols_dfs(node.right_node, code_snippets)
+    extract_pandas_table_expr_symbols_dfs(node.left_node, code_snippets)
+
+
+def get_pandas_code_snippets_dfs(sql_query_node: ProcessedSQLQueryNode, code_snippets: List[str]):
+    """Helper function for get_pandas_code_snippet_from_tree"""
+    if sql_query_node == None:
+        return
+
+    # Postorder: right (nested) to left, then root
+    get_pandas_code_snippets_dfs(
+        sql_query_node.right_node, code_snippets)
+    get_pandas_code_snippets_dfs(
+        sql_query_node.left_node, code_snippets)
+
+    code_snippets.append(extract_pandas_code_snippet_from_node(sql_query_node))
+
+
+def get_pandas_code_snippets_from_tree(sql_query_tree: ProcessedSQLQueryTree) -> List[str]:
+    """Generate list of executable pandas code from SQL query tree decomposition.
+
+    Args:
+        sql_query_tree (ProcessedSQLQueryTree): Tree from which to extract pandas code.
+
+    Returns:
+        List[str]: List of executable pandas statements, in order.
+    """
+    code_snippets = list()
+    code_snippets.append("# Tables")
+    extract_pandas_table_aliases_dfs(
+        sql_query_tree.root_node, code_snippets)
+
+    extract_pandas_table_expr_symbols_dfs(
+        sql_query_tree.root_node, code_snippets)
+
+    code_snippets.append("\n# Query")
+    get_pandas_code_snippets_dfs(sql_query_tree.root_node, code_snippets)
+
+    # Temp: remove duplicate code snippets (from repeated tables in subqueries)
+    # TODO: move table aliases to tree metainformation
+    return list(dict.fromkeys(code_snippets))
+
+
+def check_processed_sql_tree_dfs(node: ProcessedSQLQueryNode) -> Union[str, None]:
+    """Helper function for check_processed_sql_tree."""
+    if node == None:
+        return None
+
+    if node.node_type == ProcessedSQLQueryNodeType.LEAF:
+        assert(node.left_node == None and node.right_node == None)
+        assert(node.sql_query != None)
+        assert(node.sql_query_table_expr != None)
+        assert(node.pandas_query != None)
+        # assert(node.pandas_query.find("Error:") < 0)
+        if node.pandas_query.find("Error:") >= 0:
+            return f"{node.sql_query} -> {node.pandas_query}"
+        return None
+
+    assert(node.left_node != None and node.right_node != None)
+    assert(node.sql_query == None)
+    assert(node.sql_query_table_expr == None)
+    assert(node.pandas_query == None)
+
+    left_res = check_processed_sql_tree_dfs(node.left_node)
+    if left_res != None:
+        return left_res
+
+    right_res = check_processed_sql_tree_dfs(node.right_node)
+    if right_res != None:
+        return right_res
+
+    return None
+
+
+def check_processed_sql_tree(sql_query_tree: ProcessedSQLQueryTree) -> Union[str, None]:
+    """Checks validity of ProcessedSQLQueryTree.
+
+    Ensure LEAF and non-LEAF nodes contain required fields (assertions fail if not),
+    and that pandas queries for LEAF nodes were generated without errors.
+
+    Args:
+        sql_query_tree (ProcessedSQLQueryTree): Tree to check validity.
+
+    Returns:
+        Union[str, None]: Error string if tree is invalid, or None if tree is valid.
+    """
+    return check_processed_sql_tree_dfs(sql_query_tree.root_node)
+
+
+def sql_query_to_pandas_code_snippets(sql_query: str) -> List[str]:
+    """Exposed function API to convert raw SQL query string into pandas code snippets.
+
+    Args:
+        sql_query (str): Raw SQL query.
+
+    Returns:
+        List[str]: Executable pandas lines, in order.
+    """
+    sql_tree = preprocess_sql_query_into_tree(sql_query=sql_query)
+    return get_pandas_code_snippets_from_tree(sql_query_tree=sql_tree)
diff --git a/parsing/process_table_expr.py b/parsing/process_table_expr.py
new file mode 100644
index 00000000..921706ec
--- /dev/null
+++ b/parsing/process_table_expr.py
@@ -0,0 +1,268 @@
+from typing import Dict, List, Tuple
+from clean_query import remove_consecutive_spaces
+from helpers import get_next_token_idx, get_cur_token, get_prev_token, get_next_token, extract_table_column, get_first_token
+import re
+
+
+# TODO:
+# - handle different types of JOIN
+# - remove original table names altogether from extracted table expression; i.e. only use AS aliases
+# - actually convert table expressions (i.e. JOIN ONs) into pandas snippets
+
+
+def extract_table_expr_from_query(simple_sql_query: str) -> str:
+    """Extracts combined table expression (including JOIN/ON and AS table aliases) from SQL query.
+
+    Args:
+        simple_sql_query (str): Simple SELECT SQL query.
+
+    Returns:
+        str: Substring containing table expression of SQL query.
+    """
+    simple_sql_query = remove_consecutive_spaces(simple_sql_query)
+    start_idx = simple_sql_query.find("FROM ")
+    if start_idx < 0:
+        print("[extract_table] no FROM in simple_sql_query")
+        return None
+
+    start_idx += len("FROM ")
+    while start_idx < len(simple_sql_query) and simple_sql_query[start_idx] == " ":
+        start_idx += 1
+    idx = get_next_token_idx(simple_sql_query, start_idx)
+    while idx < len(simple_sql_query):
+        cur_token = get_cur_token(simple_sql_query, idx)
+        if cur_token == "JOIN":
+            idx = get_next_token_idx(simple_sql_query, idx)
+            idx = get_next_token_idx(simple_sql_query, idx)
+        elif cur_token == "AS":
+            idx = get_next_token_idx(simple_sql_query, idx)
+            idx = get_next_token_idx(simple_sql_query, idx)
+        elif cur_token == "ON" or cur_token == "AND":
+            idx = get_next_token_idx(simple_sql_query, idx)
+            idx = simple_sql_query.find("=", idx)
+            if idx < 0:
+                return None
+            idx += 1
+            while simple_sql_query[idx] == " ":
+                idx += 1
+            idx = get_next_token_idx(simple_sql_query, idx)
+        else:
+            return simple_sql_query[start_idx:idx].strip()
+
+    return simple_sql_query[start_idx:idx].strip()
+
+
+def extract_table_aliases_from_table_expr(sql_table_expr: str) -> Dict[str, str]:
+    """Extracts AS aliases for tables in table expression.
+
+    Args:
+        sql_table_expr (str): SQL table expression from which to create alias table.
+
+    Returns:
+        Dict[str, str]: Dict of (key, value) pairs corresponding to (alias, table name).
+    """
+    table_alias_dict = dict()
+
+    idx = 0
+    while idx < len(sql_table_expr):
+        cur_token = get_cur_token(sql_table_expr, idx)
+        if cur_token == "AS":
+            table_name = get_prev_token(sql_table_expr, idx)
+            alias_name = get_next_token(sql_table_expr, idx)
+            table_alias_dict.setdefault(alias_name, table_name)
+            idx = get_next_token_idx(sql_table_expr, idx)
+            idx = get_next_token_idx(sql_table_expr, idx)
+        else:
+            idx = get_next_token_idx(sql_table_expr, idx)
+
+    return table_alias_dict
+
+
+def remove_table_aliases(sql_table_expr: str) -> str:
+    """Removes AS aliases for tables in table expression.
+
+    Args:
+        sql_table_expr (str): SQL table expression from which to create alias table.
+
+    Returns:
+        str: Redacted table expression, with just the aliases.
+    """
+
+    idx = 0
+    aliased_sql_table_expr = ""
+    while idx < len(sql_table_expr):
+        cur_token = get_cur_token(sql_table_expr, idx)
+        next_token = get_next_token(sql_table_expr, idx)
+        if next_token == "AS":
+            idx = get_next_token_idx(sql_table_expr, idx)
+            idx = get_next_token_idx(sql_table_expr, idx)
+        else:
+            aliased_sql_table_expr += cur_token + " "
+            idx = get_next_token_idx(sql_table_expr, idx)
+
+    return remove_consecutive_spaces(aliased_sql_table_expr)
+
+
+def substitute_symbol_for_table_expr(simple_sql_query: str, sql_table_expr: str, sub_symbol: str):
+    """Substitutes provided symbol for entire table expression in SQL query.
+
+    Args:
+        simple_sql_query (str): SQL query to redact.
+        sql_table_expr (str): Table expression to replace.
+        sub_symbol (str): Symbol with which to replace table expression.
+
+    Returns:
+        _type_: Redacted SQL query with symbol in place of table expression
+    """
+    idx = simple_sql_query.find(sql_table_expr)
+    if idx < 0:
+        print("[substitute_symbol_for_table] sql_table_expr not in simple_sql_query")
+        return simple_sql_query
+
+    return re.sub(sql_table_expr, sub_symbol, simple_sql_query)
+
+
+def extract_join_segments(aliased_sql_table_expr: str) -> List[str]:
+    """Extracts JOIN segments.
+
+    Each segment is of the form "TABLE_NAME [ON left_col = right_col AND ...]".
+
+    Args:
+        aliased_sql_table_expr (str): SQL table expression with aliases only.
+
+    Returns:
+        List[str]: Extract tokens between JOINs.
+    """
+    # TODO: replace find JOIN with find(token) for list of tokens
+    join_segments = []
+    idx = 0
+    while idx < len(aliased_sql_table_expr):
+        next_idx = aliased_sql_table_expr.find("JOIN", idx)
+        if next_idx < 0:
+            segment = aliased_sql_table_expr[idx:]
+            segment = remove_consecutive_spaces(segment)
+            join_segments.append(segment)
+            return join_segments
+
+        segment = aliased_sql_table_expr[idx:next_idx]
+        segment = remove_consecutive_spaces(segment)
+        join_segments.append(segment)
+        idx = next_idx + len("JOIN")
+
+    return join_segments
+
+
+def extract_on_cols_from_join_segment(join_segment: str) -> List[Tuple[str, str]]:
+    """Extracts all left/right ON cols from JOIN segment.
+
+    Args:
+        join_segment (str): JOIN segment from which to extract JOIN ON cols.
+
+    Returns:
+        List[Tuple[str, str]]: List of (left_col, right_col) pairs.
+    """
+    on_cols = []
+    idx = 0
+    while idx < len(join_segment):
+        cur_token = get_cur_token(join_segment, idx)
+        if cur_token == "ON":
+            idx = get_next_token_idx(join_segment, idx)
+            left_finish_idx = join_segment.find("=", idx)
+            if left_finish_idx < 0:
+                print("[extract_on_from_join_segment] left_finish_idx < 0")
+                return on_cols
+
+            left_col = join_segment[idx:left_finish_idx]
+            left_col = remove_consecutive_spaces(left_col)
+            idx = left_finish_idx + 1
+            idx = get_next_token_idx(join_segment, idx)
+            right_finish_idx = join_segment.find(" ", idx)
+            if right_finish_idx < 0:
+                right_finish_idx = len(join_segment)
+
+            right_col = join_segment[idx:right_finish_idx]
+            right_col = remove_consecutive_spaces(right_col)
+            on_cols.append((left_col, right_col))
+            idx = get_next_token_idx(join_segment, right_finish_idx)
+        else:
+            idx = get_next_token_idx(join_segment, idx)
+
+    return on_cols
+
+
+def join_two_tables(t1: str, t2: str, on_cols: List[Tuple[str, str]]) -> str:
+    """Given two tables, and the columns on which to join them, return pandas code to join those two tables.
+
+    Args:
+        t1 (str): Left table symbol.
+        t2 (str): Right table symbol.
+        on_cols (List[Tuple[str, str]]): Columns on which to JOIN left/right tables.
+
+    Returns:
+        str: One line of pandas code corresponding to JOIN operation.
+    """
+    # TODO: different types of JOINs
+    if len(on_cols) == 0:
+        return f"pd.merge({t1}, {t2})"
+
+    left_on = []
+    right_on = []
+    for l_on_col, r_on_col in on_cols:
+        # TODO: clean this up?
+        left_on.append(extract_table_column(l_on_col))
+        right_on.append(extract_table_column(r_on_col))
+    return f"pd.merge({t1}, {t2}, left_on={left_on}, right_on={right_on})"
+
+
+def sql_table_expr_join_segments_to_snippets(join_segments: List[Tuple[str, str]], left_symbol: str, right_idx: int, pandas_snippets: List[str], get_symbol) -> str:
+    """Recursively create pandas snippets to JOIN segments together.
+
+    Args:
+        join_segments (List[Tuple[str, str]]): Segments to JOIN together.
+        left_symbol (str): Symbol corresponding to left table.
+        right_idx (int): Index of JOIN segment corresponding to right table.
+        pandas_snippets (List[str]): Destination for executable snippets.
+        get_symbol (Function): When invoked, generates unique symbol.
+
+    Returns:
+        str: The last symbol created.
+    """
+    if right_idx >= len(join_segments):
+        return left_symbol
+
+    right_segment = join_segments[right_idx]
+    on_cols = extract_on_cols_from_join_segment(right_segment)
+    joined_table_expr = join_two_tables(
+        left_symbol, get_first_token(right_segment), on_cols)
+    new_symbol = get_symbol()
+    snippet = f"{new_symbol} = {joined_table_expr}"
+    pandas_snippets.append(snippet)
+
+    return sql_table_expr_join_segments_to_snippets(
+        join_segments, new_symbol, right_idx+1, pandas_snippets, get_symbol)
+
+
+def sql_table_expr_to_pandas_snippets(table_expr_symbol: str, aliased_sql_table_expr: str, get_symbol) -> List[str]:
+    """Given aliased SQL table expression (i.e. JOIN, ON, AND), return code snippets corresponding to pandas conversion.
+
+    Args:
+        table_expr_symbol (str): Symbol to assign to final result of JOIN.
+        aliased_sql_table_expr (str): Simple table expression (no AS aliases, only symbols).
+        get_symbol (Function): When invoked, generates unique symbol.
+
+    Returns:
+        List[str]: List of pandas snippets corresponding to aliased_sql_table_expr.
+    """
+    join_segments = extract_join_segments(aliased_sql_table_expr)
+
+    if len(join_segments) == 0:
+        return [join_segments[0]]
+
+    pandas_snippets = []
+    final_symbol = sql_table_expr_join_segments_to_snippets(
+        join_segments, join_segments[0], 1, pandas_snippets, get_symbol)
+
+    # Assign final symbol to result of recursive JOINs
+    pandas_snippets.append(f"{table_expr_symbol} = {final_symbol}")
+
+    return pandas_snippets
diff --git a/parsing/processed_query.py b/parsing/processed_query.py
new file mode 100644
index 00000000..715683a0
--- /dev/null
+++ b/parsing/processed_query.py
@@ -0,0 +1,163 @@
+from enum import Enum
+from typing import Any, Dict, List, Union
+
+from process_table_expr import extract_table_aliases_from_table_expr, remove_table_aliases, sql_table_expr_to_pandas_snippets
+
+
+class ProcessedSQLQueryNodeType(Enum):
+    LEAF = "LEAF"
+    NESTED_SELECT = "(SELECT"
+    INTERSECT = "INTERSECT "
+    UNION = "UNION "
+    EXCEPT = "EXCEPT "
+    SUBTRACT = " - "
+
+
+def dump_dict(dict_obj, indent=4):
+    if dict_obj == None:
+        print("None")
+    else:
+        indent_spaces = indent * " "
+        for key in dict_obj.keys():
+            print(f"{indent_spaces}{key}: {dict_obj[key]}")
+
+
+class ProcessedSQLTableExpr:
+    def __init__(
+        self,
+        orig_table_expr: str,
+        table_expr_symbol_key: str,
+        get_symbol,
+    ):
+        self.orig_table_expr = orig_table_expr
+        aliased_table_expr = remove_table_aliases(orig_table_expr)
+        self.aliased_table_expr = aliased_table_expr
+        self.table_expr_symbol_key = table_expr_symbol_key
+        self.table_aliases = extract_table_aliases_from_table_expr(
+            orig_table_expr)
+        self.table_expr_pandas_snippets = sql_table_expr_to_pandas_snippets(
+            table_expr_symbol=table_expr_symbol_key, aliased_sql_table_expr=aliased_table_expr, get_symbol=get_symbol)
+
+    def extract_table_aliases(self, code_snippets: List[str]):
+        for key in self.table_aliases:
+            code_snippets.append(f"{key} = {self.table_aliases[key]}")
+
+    def dump_table_expr(self, indent=4):
+        indent_spaces = indent * " "
+
+        print(f"{indent_spaces}orig_table_expr: {self.orig_table_expr}")
+        print(f"{indent_spaces}aliased_table_expr: {self.aliased_table_expr}")
+        print(f"{indent_spaces}table_expr_symbol_key: {self.table_expr_symbol_key}")
+        print(f"{indent_spaces}table_aliases:")
+        dump_dict(self.table_aliases, indent=2*indent)
+        print(
+            f"{indent_spaces}table_expr_pandas_snippets: {self.table_expr_pandas_snippets}")
+
+
+class ProcessedSQLQueryNode:
+    """Tree node class for processed SQL queries.
+
+    Attributes:
+        node_type (ProcessedSQLQueryNodeType): Specifies node type.
+        internal_symbol (str | None): Symbol in symbol_table.
+            Unique symbol corresponding to SQL query rooted at node.
+        processed_query (str | None): Stores sql2pandas-convertible SQL query if LEAF node.
+        pandas_query (str | None): Stores sql2pandas-converted SQL query (corresponding to processed_query) if LEAF node.
+        left_node (ProcessedSQLQueryNode | None): Left child node.
+            Contains redacted SELECT query (redacted sub-query stored in right_node).
+        right_node (ProcessedSQLQueryNode | None): Left child node.
+            Connects smaller SELECT sub-query (fits into redacted portion of left_node query).
+        external_symbol (str | None): Symbol in symbol_table.
+            If processed_query is redacted with external_symbol substitute, can be used to lookup redacted sub_query.
+            Only present if ancestor is NESTED_SELECT type.
+    """
+
+    def __init__(
+            self,
+            node_type: ProcessedSQLQueryNodeType,
+            internal_symbol: str,
+            sql_query: Union[str, None],
+            sql_query_table_expr: Union[ProcessedSQLTableExpr, None],
+            pandas_query: Union[str, None],
+            left_node: Union[Dict[str, Any], None],
+            right_node: Union[Dict[str, Any], None],
+            external_symbol: Union[str, None] = None):
+        self.node_type = node_type
+        self.internal_symbol = internal_symbol
+        self.sql_query = sql_query
+        self.sql_query_table_expr = sql_query_table_expr
+        self.pandas_query = pandas_query
+        self.left_node = left_node
+        self.right_node = right_node
+        self.external_symbol = external_symbol
+
+    def set_external_symbol(self, external_symbol: str):
+        self.external_symbol = external_symbol
+
+    def dump_processed_sql_tree(self):
+        """Print contents of tree rooted at this node."""
+        if not self.left_node == None:
+            self.left_node.dump_processed_sql_tree()
+
+        print(f"node_type: {self.node_type}")
+        print(f"internal_symbol: {self.internal_symbol}")
+
+        if self.node_type == ProcessedSQLQueryNodeType.LEAF:
+            print(f"processed_query: {self.sql_query}")
+            print("sql_query_table_expr:")
+            self.sql_query_table_expr.dump_table_expr()
+            print(f"pandas_query: {self.pandas_query}")
+            print(f"external_symbol: {self.external_symbol}")
+
+        print()
+
+        if not self.right_node == None:
+            self.right_node.dump_processed_sql_tree()
+
+
+class ProcessedSQLQueryTree:
+    """Represents ProcessedSQLQueryTree header node, with symbol table.
+
+    Attributes:
+        root_node (ProcessedSQLQueryNode): Root node of SQL query tree.
+        symbol_table (Dict[str, Tuple[str, ProcessedSQLQueryNode]]): Dict with all symbols, mapped to (query, node) tuple.
+        symbol_count (int): Number of symbols in symbol_table.
+    """
+
+    def __init__(self, root_node: Union[ProcessedSQLQueryNode, None] = None):
+        self.root_node = root_node
+        self.symbol_table = dict()
+        self.symbol_count = 0
+
+    def get_symbol_key(self):
+        """Generate symbol key based on number of symbols currently in tree."""
+        symbol_key = f"symbol_{self.symbol_count}"
+        self.symbol_count += 1
+        return symbol_key
+
+    def add_key_value_to_symbol_table(self, symbol_key: str, query_str: str, tree_node: ProcessedSQLQueryNode):
+        """Add new (key, value) to tree symbol_table.
+
+        Args:
+            symbol_key (str): Symbol key. Preferably generated by get_symbol_key().
+            query_str (str): Query for which key substitutes.
+            tree_node (ProcessedSQLQueryNode): Node in tree at which query_str is rooted.
+        """
+        self.symbol_table[symbol_key] = (query_str, tree_node)
+
+    def reset_root_node(self, new_root_node: ProcessedSQLQueryNode):
+        self.root_node = new_root_node
+
+    def dump_tree(self):
+        """Print symbol table/count, as well as tree contents."""
+        print("-------- ProcessedSQLQueryTree --------\n")
+        print("-------- symbol_table --------")
+        for key in self.symbol_table.keys():
+            (query_str, tree_node) = self.symbol_table[key]
+            print(f"{key}: {query_str} (rooted at node {tree_node})")
+
+        print("\n-------- symbol_count --------")
+        print(str(self.symbol_count))
+
+        print("\n-------- Processed SQL query tree: --------\n")
+        self.root_node.dump_processed_sql_tree()
diff --git a/parsing/queries.txt b/parsing/queries.txt
new file mode 100644
index 00000000..35a2e19a
--- /dev/null
+++ b/parsing/queries.txt
@@ -0,0 +1,11276 @@
+SELECT ( SELECT c5_number FROM w WHERE c1_number = 1 ) - ( SELECT c5_number FROM w WHERE c1_number = 2 );
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'atacama' order BY id desc limit 1 ) + 1;
+SELECT c4 FROM w WHERE c3 = 'jaime quintana';
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c3 FROM w WHERE id = 1;
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT c3 FROM w WHERE c5_number != 2005 AND c5_number != 2009;
+SELECT c5_number FROM w WHERE c3 = 'antonio horvath kiss';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'late-may 1965 tornado outbreak' ) + 1;
+SELECT COUNT( * ) FROM w WHERE c2_minimum_year = 1965;
+SELECT SUM( c4_number ) FROM w WHERE c2_minimum_year = 1960;
+SELECT c4 FROM w WHERE c5 = 'shoulder injury sustained from crash';
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'matteo bono' ) + 1;
+SELECT c5 FROM w WHERE c3 = 'daniel martin';
+SELECT COUNT( c1 ) FROM w WHERE c2_number < 5;
+SELECT COUNT( c3 ) FROM w WHERE c1 = 'dnf';
+SELECT c1 FROM w WHERE id = 1;
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( c3 ) desc limit 1;
+SELECT c3 FROM w WHERE c5 = 'broken arm sustained from crash in stage 2';
+SELECT COUNT( c3 ) FROM w WHERE c2_number < 6;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 4;
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( c2 ) desc limit 1;
+SELECT c2 FROM w WHERE c4 = 'conservative party' AND c5 = 'bank manager';
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'conservative party' AND c3_minimum_number > ( SELECT c3_minimum_number FROM w WHERE c2 = 'peder kalve' );
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( c2 ) desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5 IN ( 'fisherman' , 'farmer' );
+SELECT c4 FROM w WHERE c1_number = 15;
+SELECT COUNT( c2 ) FROM w WHERE c3_maximum_year - c3_minimum_year = 9;
+SELECT c3_maximum_year FROM w WHERE c4 IS NULL order BY c3_maximum_year desc limit 1;
+SELECT c2 FROM w order BY c3_minimum_year asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'republican' AND id < ( SELECT id FROM w WHERE c1 = 'wes watkins' );
+SELECT COUNT( * ) FROM w WHERE c4 = 'democratic';
+SELECT c1 FROM w order BY c2_parsed asc limit 1;
+SELECT c1 FROM w WHERE id > ( SELECT id FROM w WHERE c1 = 'carl albert' ) limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c2 != 'hmb2' AND c6 = ( SELECT c6 FROM w WHERE c2 = 'hmb2' );
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'scrapped';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'erlangener erprobungstrager (eet 01)' ) + 1;
+SELECT COUNT( * ) FROM ( SELECT c2_second FROM w GROUP BY c2_second HAVING COUNT( * ) > 1 );
+SELECT COUNT( c2 ) FROM w WHERE c2_second = 'nor';
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2_first = 'elia viviani' ) - 1;
+SELECT c5_number FROM w WHERE c2_first = 'arnaud demare';
+SELECT c3 FROM w WHERE c2_first = 'jose joaquin rojas';
+SELECT abs ( ( SELECT c5_number FROM w WHERE c2_first = 'nikolas maes' ) - ( SELECT c5_number FROM w WHERE c2_first = 'john degenkolb' ) );
+SELECT MIN( c5_number ) FROM w WHERE c5_number > 14;
+SELECT c4 FROM w WHERE c2_first = 'john degenkolb';
+SELECT c2 FROM w WHERE c5_number = 22;
+SELECT c3 FROM w WHERE c1_number < ( SELECT MAX( c1_number ) FROM w ) order BY c1_number desc limit 1;
+SELECT c2_first FROM w order BY c5_number desc limit 1;
+SELECT c4 FROM w WHERE c2_first = 'john degenkolb';
+SELECT c1 FROM w WHERE c1 != 'marc dos santos' AND c4_number = ( SELECT c4_number FROM w WHERE c1 = 'marc dos santos' );
+SELECT c1 FROM w WHERE c3 = 'san antonio scorpions' order BY c4_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'canada';
+SELECT c5_number - c4_number FROM w WHERE c1 = 'colin clarke' AND c3 = 'puerto rico islanders';
+SELECT c2 FROM w WHERE c1 = 'marc dos santos';
+SELECT c5_number - c4_number FROM w WHERE c1 = 'colin clarke' AND c3 = 'puerto rico islanders';
+SELECT c1 FROM w WHERE c1 IN ( 'jose manuel abundis' , 'alex pineda chacon' ) AND c3 = 'atlanta silverbacks' order BY c5_number - c4_number desc limit 1;
+SELECT c1 FROM w WHERE c3 = 'fc edmonton' AND c4_number < ( SELECT c4_number FROM w WHERE c1 = 'colin miller' ) order BY id desc limit 1;
+SELECT c1 FROM w WHERE id = 1;
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'democratic';
+SELECT COUNT( * ) FROM w;
+SELECT MAX( c12_number ) FROM w;
+SELECT c1_number FROM w order BY c5_number desc limit 1;
+SELECT c1_number FROM w order BY c3_number desc limit 1;
+SELECT c1_number FROM w WHERE c2_number > 8114 AND c2_number < 21783;
+SELECT COUNT( * ) FROM w WHERE c3 = '0.29 kuwaiti dinars';
+SELECT COUNT( c1 ) FROM w WHERE c4_number > 90;
+SELECT COUNT( c1 ) FROM w WHERE c2_number > 6000;
+SELECT c1 FROM w WHERE c4_number >= 100;
+SELECT c4 FROM w WHERE c1 = 'millbrook 27';
+SELECT c1 FROM w order BY c2_first_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c5_year > 1900;
+SELECT c1 FROM w WHERE c2_second_number < 30;
+SELECT COUNT( c1 ) FROM w WHERE c4_number = 0;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT SUM( c2_second_number ) FROM w WHERE c1 IN ( 'millbrook 27' , 'sheet harbour 36' );
+SELECT c1 FROM w order BY c2_second_number asc limit 1;
+SELECT c4 FROM w WHERE c1 = 'sheet harbour 36';
+SELECT c6_number FROM w WHERE c2 = 'february 26';
+SELECT COUNT( c3 ) FROM w WHERE c9_number > 135;
+SELECT c6_number FROM w WHERE c3 = 'tony stewart';
+SELECT c8 FROM w WHERE c1_number = 2004;
+SELECT COUNT( * ) FROM w WHERE c5 = 'chevrolet';
+SELECT c3 FROM w WHERE c3 IN ( 'jeff gordon' , 'jeremy mayfield' ) order BY c9_number desc limit 1;
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT c3 FROM w WHERE c1_number = 2014;
+SELECT c2 FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c2 = 'gustaf mannerheim' ) order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'mauno koivisto' , 'martti ahtisaari' ) order BY c4_number asc limit 1;
+SELECT c3 FROM w order BY c4_number asc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w order BY c1_number limit 1;
+SELECT c2 FROM w WHERE c3 = 'director and screenwriter';
+SELECT abs ( ( SELECT c1_number FROM w WHERE c2 = 'cry_wolf' ) - ( SELECT c1_number FROM w WHERE c2 = 'four christmases' ) );
+SELECT c2 FROM w WHERE c2 IN ( 'undefeated' , 'freakonomics' ) AND c1_number = 2011;
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = 'cry_wolf' ) order BY c1_number limit 1;
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'cameo appearance' ) + 1;
+SELECT MIN( c2_year ) FROM w WHERE c3 = 'finland' AND c4 = 'greece';
+SELECT COUNT( * ) FROM w WHERE c7 = 'loss';
+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c8 = 'helsinki olympic stadium';
+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c4 = 'finland';
+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c7 = 'win' AND c8 = 'helsinki olympic stadium';
+SELECT c9 FROM w WHERE id = 1;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c7 = 'win' AND c3 = 'finland' ) > ( SELECT COUNT( * ) FROM w WHERE c7 = 'loss' AND c3 = 'finland' );
+SELECT COUNT( DISTINCT ( c2 ) ) FROM w WHERE c7 = 'win' AND abs ( c6_number1 - c6_number2 ) > 3;
+SELECT c2 FROM w order BY c4_number limit 1;
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c2 FROM w WHERE c4_number > 75000000;
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_number > 1000000;
+SELECT c6_number FROM w WHERE c2 = 'pgnig sa';
+SELECT c5 FROM w order BY c5_number limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_number <= 3 AND c5_number = 10000;
+SELECT c4 FROM w WHERE c2 = 'all-africa games' AND c1 = 1999;
+SELECT c5 FROM w order BY c5_number desc limit 1;
+SELECT c3 FROM w WHERE c3_address IN ( 'portugal' , 'greece' ) order BY c4_number limit 1;
+SELECT COUNT( DISTINCT c3 ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c5_number != 10000;
+SELECT COUNT( * ) FROM w WHERE c5 = '10,000 m';
+SELECT MIN( c1_number ) FROM w WHERE c4_number > 2;
+SELECT SUM( c7_number ) FROM w WHERE c1_number >= 1984 AND c1_number <= 1988;
+SELECT SUM( c7_number ) FROM w WHERE c1_number = 1981;
+SELECT COUNT( DISTINCT c1 ) FROM w WHERE c3 = 'nl';
+SELECT COUNT( * ) FROM w WHERE c2 = 'did not enter';
+SELECT COUNT( * ) FROM w WHERE c2 = 'round 1';
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = 'withdrew due to rebel attack' ) order BY c1_number limit 1;
+SELECT MAX( c1_number ) FROM w WHERE c2 = 'did not qualify';
+SELECT COUNT( * ) FROM w WHERE c2_number = 2010;
+SELECT c1 FROM w WHERE c2_number = 2007;
+SELECT c4 FROM w WHERE id = 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'bbc one';
+SELECT c1 FROM w WHERE c2_number = 2010 order BY id desc limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'loose women' ) + 1;
+SELECT COUNT( * ) FROM w WHERE c2_number < ( SELECT c2_number FROM w WHERE c1 = 'over the rainbow' );
+SELECT COUNT( * ) FROM w WHERE c4 != 'herself';
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w WHERE c5_number <= 15;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w order BY c6_first_number desc limit 1;
+SELECT ( SELECT COUNT( c2 ) FROM w ) >= 10;
+SELECT c2 FROM w order BY c6_first_number asc limit 1;
+SELECT c2 FROM w WHERE id = 1;
+SELECT c4 FROM w order BY c8_number desc limit 1;
+SELECT c4 FROM w order BY c8_number desc limit 1;
+SELECT COUNT( c4 ) FROM w WHERE c1_number > 10;
+SELECT c4 FROM w order BY c1_number desc limit 1;
+SELECT c4 FROM w WHERE id = 1;
+SELECT COUNT( c4 ) FROM w WHERE c8_number > 150;
+SELECT c4 FROM w WHERE c7_number = 160 AND c8_number = 142;
+SELECT COUNT( c4 ) FROM w WHERE c8_number < 80;
+SELECT COUNT( c4 ) FROM w WHERE c8 IS NULL;
+SELECT c4 FROM w WHERE c6 = 't-bird' order BY c1_number asc limit 1;
+SELECT ( SELECT COUNT( c4 ) FROM w WHERE c7_number = 160 ) = 0;
+SELECT c4 FROM w WHERE c5 = 'roush racing' order BY c1_number asc limit 1;
+SELECT c1 FROM w order BY c2_number limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_number > 600;
+SELECT c1 FROM w WHERE c4 = 'boston red sox' order BY c2_number desc limit 1;
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c2 FROM w order BY c6_number asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c6_number > 2600;
+SELECT COUNT( c2 ) FROM w WHERE c6_number <= 2600;
+SELECT COUNT( * ) FROM w WHERE c3 = 'united states';
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'united states';
+SELECT c2 FROM w WHERE c6_number > 2600 AND c3 = 'canada';
+SELECT c2 FROM w WHERE c1 = 'ash';
+SELECT c1 FROM w WHERE c1 IN ( 'daft punk' , 'franz ferdinand' ) GROUP BY c1 order BY COUNT( c2 ) desc limit 1;
+SELECT c1 FROM w WHERE c2 = ''girl'' INTERSECT SELECT c1 FROM w WHERE c2 = ''e-pro'';
+SELECT c3_month FROM w GROUP BY c3_month order BY COUNT( c2 ) asc limit 1;
+SELECT c6 FROM w order BY c7_number desc limit 1;
+SELECT c3 FROM w order BY c3_parsed desc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c7_number FROM w order BY c3_parsed desc limit 1;
+SELECT ( SELECT SUM( c5_number ) FROM w WHERE c4 = 'buffalo sabres' ) + ( SELECT SUM( c7_number ) FROM w WHERE c6 = 'buffalo sabres' );
+SELECT COUNT( c3 ) FROM w WHERE c5_number >= 3 OR c7_number >= 3;
+SELECT COUNT( c2 ) FROM w WHERE c6 = 'buffalo sabres';
+SELECT COUNT( c2 ) FROM w WHERE ( c4 = 'buffalo sabres' AND c5_number >= 4 ) OR ( c6 = 'buffalo sabres' AND c7_number >= 4 );
+SELECT COUNT( c2 ) FROM w WHERE c4_number > 10;
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c5_number FROM w order BY c3_number limit 1;
+SELECT c2 FROM w WHERE c2 != 'hungary' AND c3_number = ( SELECT c3_number FROM w WHERE c2 = 'hungary' );
+SELECT c2 FROM w WHERE c3_number = 6 AND c2 != 'bulgaria';
+SELECT c6_number FROM w WHERE c2 = 'australia';
+SELECT c2 FROM w order BY c5_number limit 1;
+SELECT c2 FROM w order BY c5_number limit 1;
+SELECT c2 FROM w WHERE c2 != 'bulgaria' AND c6_number = ( SELECT c6_number FROM w WHERE c2 = 'bulgaria' );
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT c4_number FROM w order BY c3_number desc limit 1;
+SELECT c2_first_number1 + c2_first_number2 FROM w WHERE c1 = 'somalia' AND c3 = 'sudan';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'botswana' ) - 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( c1 ) + COUNT( c3 ) FROM w;
+SELECT COUNT( c1 ) + COUNT( c3 ) FROM w;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'dr congo' ) + 1;
+SELECT c1 , c3 FROM w WHERE c4 NOT NULL;
+SELECT ( SELECT COUNT( c3 ) FROM w ) > 4;
+SELECT COUNT( c1 ) FROM w WHERE c6_number < 1926;
+SELECT c1 FROM w order BY c6_number limit 1;
+SELECT MAX( c6_number ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c6_number > 1917;
+SELECT c1 FROM w WHERE c5_number = 2591;
+SELECT c7_number - c6_number FROM w WHERE c1 = 'mount harquahala, arizona';
+SELECT c1 FROM w order BY c6_number limit 1;
+SELECT ( SELECT c5_number FROM w WHERE c1 = 'mount montezuma, chile' ) - ( SELECT c5_number FROM w WHERE c1 = 'mount brukkaros, namibia' );
+SELECT c1 FROM w WHERE c7_number - c6_number = 37;
+SELECT COUNT( * ) FROM w WHERE c5 = 'indoor';
+SELECT c6 FROM w order BY c3_parsed desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_address = 'italy';
+SELECT c6 FROM w GROUP BY c6 HAVING COUNT( * ) = 2;
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( c5 ) asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3_year < 1985;
+SELECT c3_month FROM w GROUP BY c3_month order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c1 = 'winner';
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( c2 ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 IN ( 'grass' , 'hard' );
+SELECT c6 FROM w order BY id desc limit 1;
+SELECT c2 FROM w WHERE c1_number = 2007;
+SELECT c5 FROM w order BY c4_second_number limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'carlow';
+SELECT COUNT( * ) FROM w WHERE c6 = 'semple stadium';
+SELECT COUNT( c2 ) FROM w WHERE c6 = 'semple stadium';
+SELECT c7 FROM w WHERE c6 = 'gaelic grounds' order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE c7 NOT NULL order BY c1_number limit 1;
+SELECT abs ( c3_second_number - c4_second_number ) FROM w WHERE c1_number = 2000;
+SELECT c2 FROM w WHERE c1_number = 2013;
+SELECT c7 FROM w WHERE c7 NOT NULL order BY c1_number limit 1;
+SELECT c2 FROM w WHERE c1_number < 2013 order BY c1_number desc limit 1;
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c1 FROM w WHERE c3_number = 9;
+SELECT c1 FROM w order BY c6_number asc limit 1;
+SELECT SUM( c2_number ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c4_number >= 8;
+SELECT c1 FROM w WHERE c1 != '1984 winter paralympics' AND c6_number < 6;
+SELECT c1 FROM w WHERE c1 != '2010 winter paralympics' AND c3_number = ( SELECT c3_number FROM w WHERE c1 = '2010 winter paralympics' );
+SELECT COUNT( c1 ) FROM w WHERE c5_number <= 9;
+SELECT c3_number FROM w WHERE c2_number = 0;
+SELECT c2 FROM w WHERE c1_parsed > ( SELECT c1_parsed FROM w WHERE c1 = '10/9/2009' ) order BY c1_parsed limit 1;
+SELECT c2 FROM w order BY c1_parsed desc limit 1;
+SELECT c2 FROM w order BY c1_parsed limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 = '4 p.m';
+SELECT COUNT( * ) FROM w WHERE c3 = '2 p.m';
+SELECT COUNT( * ) FROM w;
+SELECT MIN( c3 ) FROM w;
+SELECT COUNT( * ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c2 = 'jimmie johnson';
+SELECT c2 FROM w GROUP BY c2 HAVING MAX( c1_number ) - MIN( c1_number ) = 11;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c3 = 'united states' ) > ( SELECT COUNT( * ) FROM w WHERE c3 = 'united kingdom' );
+SELECT COUNT( * ) FROM w WHERE c2 = 'jeff gordon';
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT DISTINCT c4 FROM w WHERE c4 != 'nascar';
+SELECT c2 FROM w WHERE c2 IN ( 'nigel mansell' , 'al unser, jr' , 'michael schumacher' , 'jeff gordon' ) GROUP BY c2 HAVING COUNT( * ) = 1;
+SELECT c2 FROM w WHERE id = 1 + 1;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c9_number < 20;
+SELECT COUNT( * ) FROM w WHERE c5_month = 9;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'arrow' ) - 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'ardent' ) + 1;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT c2 FROM w order BY c9_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c8_parsed > ( SELECT c8_parsed FROM w WHERE c2 = 'ardent' );
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'f172' ) + 1;
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT abs ( ( SELECT c3_number FROM w WHERE c2 = 'mundelein elementary school district 75' ) - ( SELECT c3_number FROM w WHERE c2 = 'fremont school district 79' ) );
+SELECT c3 FROM w WHERE c2 = 'mundelein high school';
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT ( SELECT c3_number FROM w WHERE c2 = 'village of mundelein' ) > ( SELECT c3_number FROM w WHERE c2 = 'mundelein elementary school district 75' );
+SELECT SUM( c3_number ) FROM w WHERE c1_number <= 3;
+SELECT COUNT( c2 ) FROM w WHERE c3_number >= 300;
+SELECT COUNT( c2 ) FROM w WHERE c3_number < 300;
+SELECT c2 FROM w WHERE c3_number >= 300;
+SELECT COUNT( c2 ) FROM w WHERE c3_number < 250;
+SELECT c5 FROM w WHERE c2 = 'design of death';
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 2010;
+SELECT ( SELECT COUNT( c2 ) FROM w ) > 15;
+SELECT COUNT( c2 ) FROM w;
+SELECT c5 FROM w WHERE c2 = 'the message';
+SELECT c1_number FROM w WHERE c1_number IN ( 2009 , 2011 ) GROUP BY c1_number order BY COUNT( c2 ) desc limit 1;
+SELECT c3 FROM w order BY c8_number limit 1;
+SELECT c3 FROM w WHERE c1_number <= 8 AND c4 = 'mclaren-mercedes' AND c1_number != 1;
+SELECT COUNT( c3 ) FROM w WHERE c8 NOT NULL;
+SELECT c3 FROM w WHERE c7_number = 2;
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'giancarlo fisichella' ) + 1;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w order BY c3_number asc limit 1;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_number <= 9;
+SELECT SUM( c2_number ) FROM w;
+SELECT c5 FROM w WHERE c1 = '2006 fiba world championship';
+SELECT c5 FROM w WHERE c1 = '2013 eurobasket';
+SELECT c3 FROM w WHERE c1_number = 1989 order BY c2_parsed desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'detroit lions';
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'chicago bears';
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'chicago bears';
+SELECT c2 FROM w WHERE c3 = 'canada' order BY c1_number asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'united states' AND c1_number <= 5;
+SELECT c2 FROM w WHERE c3 = 'japan' order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'aya terakawa' , 'erin gammel' ) order BY c1_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'australia' AND c1_number <= 10;
+SELECT c3 FROM w WHERE c1_number <= 8 GROUP BY c3 order BY COUNT( c2 ) desc limit 1;
+SELECT c2 FROM w order BY c1_number asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c2 = 'aya terakawa' );
+SELECT COUNT( c2 ) FROM w WHERE c5_number > 1.9;
+SELECT COUNT( * ) FROM w WHERE c2 = 'world indoor championships';
+SELECT COUNT( * ) FROM w WHERE c4_first_number <= 3;
+SELECT c2 FROM w order BY c5_number asc limit 1;
+SELECT c1 FROM w WHERE c3 = 'sherbrooke, canada';
+SELECT COUNT( c2 ) FROM w WHERE c4_first_number < 10 AND c4_first_number > 4;
+SELECT COUNT( c2 ) FROM w;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'adamson pep squad' ) + 1;
+SELECT AVG( c8_number ) FROM w WHERE id <= 3;
+SELECT SUM( c7_number ) FROM w;
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c1 FROM w WHERE c1 != 'ccp bobcats' AND c2_number > 60;
+SELECT COUNT( c1 ) FROM w WHERE c6_number > ( SELECT c6_number FROM w WHERE c1 = 'altas perp squad' );
+SELECT COUNT( c1 ) FROM w WHERE c6_number < ( SELECT c6_number FROM w WHERE c1 = 'adamson pep squad' );
+SELECT c1 FROM w WHERE c1 IN ( 'adamson pep squad' , 'pup stars' ) order BY c5_number desc limit 1;
+SELECT c5 FROM w WHERE id = ( SELECT id FROM w WHERE c5 = 'dynamite!! usa' ) - 1;
+SELECT c5 FROM w WHERE id = ( SELECT id FROM w WHERE c5 = 'ufc 121' ) + 1;
+SELECT COUNT( * ) FROM w WHERE c7_number >= 2;
+SELECT c5 FROM w WHERE c1 = 'win' AND c6_parsed < ( SELECT c6_parsed FROM w WHERE c5 = 'ufc 87' ) order BY c6_parsed desc limit 1;
+SELECT COUNT( * ) FROM w;
+SELECT c3 FROM w WHERE c1 = 'loss' order BY c6_parsed asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c1 = 'win';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'michigan technological university' ) + 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT abs ( ( SELECT c5_number FROM w WHERE id = 1 ) - ( SELECT c5_number FROM w WHERE id = 2 ) );
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c1 FROM w order BY c6_number limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_address = 'california';
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c2_address = 'california';
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT c4_length FROM w WHERE c3 = 'thunderball';
+SELECT c5 FROM w WHERE c2_parsed < ( SELECT c2_parsed FROM w WHERE c5 = 'phina' ) order BY c2_parsed desc limit 1;
+SELECT c3 FROM w WHERE c2_parsed > ( SELECT c2_parsed FROM w WHERE c3 = 'jungle falls' ) order BY c2_parsed asc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c2_month = 11 AND c2_year = 2006;
+SELECT c5 FROM w WHERE id = ( SELECT id FROM w WHERE c5 = 'jan' ) + 1;
+SELECT c4_list FROM w WHERE c1_first_number = 10 - 1;
+SELECT c1 FROM w WHERE c4_first_number >= 12000;
+SELECT abs ( ( SELECT c4_first_number FROM w WHERE c1 = 'al-7' ) - ( SELECT c4_first_number FROM w WHERE c1 = 'al-21' ) );
+SELECT c1 FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE c2_year < ( SELECT c2_year FROM w WHERE c1 = 's-18/vdr-3' );
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'tr-2' ) + 1;
+SELECT c1 FROM w WHERE c2_year > 1960;
+SELECT c4 FROM w order BY c4_first_number desc limit 1;
+SELECT c3 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_year = 1926;
+SELECT ( SELECT c4_year FROM w WHERE c1 = 'oregon route 69' ) > ( SELECT c4_year FROM w WHERE c1 = 'oregon route 90' );
+SELECT c1 FROM w WHERE c1 IN ( 'oregon route 402' , 'oregon route 220' ) AND c4_year = 2002;
+SELECT c1 FROM w order BY c5_first_year limit 1;
+SELECT c3 FROM w WHERE c5_list = '(1) 'we will rock you'';
+SELECT ( SELECT c1_parsed FROM w WHERE c6 = 'matinee and evening performances' ) > ( SELECT c1_parsed FROM w WHERE c6 = '8th anniversary' );
+SELECT ( SELECT c1_day FROM w WHERE c2 = 'stockholm' ) - 3;
+SELECT c2 FROM w order BY c1_parsed limit 1;
+SELECT c3 FROM w order BY c1_parsed desc limit 1;
+SELECT c1 FROM w order BY c1_parsed limit 1;
+SELECT c1 FROM w order BY c1_parsed desc limit 1;
+SELECT c1 FROM w order BY c3_number limit 1;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'manchester city' ) - 1;
+SELECT abs ( c3_number1 - c3_number2 ) FROM w WHERE c2 = 'birmingham city' AND c4 = 'wrexham';
+SELECT c2 FROM w WHERE c3 = '2-2';
+SELECT ( SELECT c3_number1 FROM w WHERE c2 = 'leicester city' ) + ( SELECT c3_number2 FROM w WHERE c4 = 'leicester city' );
+SELECT SUM( c3_number2 ) FROM w;
+SELECT c2_first FROM w WHERE c3_parsed > ( SELECT c3_parsed FROM w WHERE c2_first = 'nejat konuk' ) order BY c3_parsed limit 1;
+SELECT c2_first FROM w WHERE c5 = 'republican turkish party' AND c3_parsed > ( SELECT c3_parsed FROM w WHERE c2_first = 'ferdi sabit soyer' ) order BY c3_parsed limit 1;
+SELECT c2_first FROM w GROUP BY c2_first HAVING COUNT( * ) >= 3;
+SELECT c5 FROM w WHERE c2_first IN ( 'nejat konuk' , 'dervis eroglu' );
+SELECT COUNT( * ) FROM w WHERE c2_first = 'dervis eroglu';
+SELECT COUNT( DISTINCT c5 ) FROM w;
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( c2 ) desc limit 1;
+SELECT c3 FROM w WHERE id = 1;
+SELECT COUNT( c3 ) FROM w;
+SELECT c4 FROM w WHERE c3 = 'marcus hellner';
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'petter northug' ) + 1;
+SELECT c3 FROM w WHERE c1_number = 10;
+SELECT MAX( c1_number ) FROM w;
+SELECT c5 FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE c5 = 'news talk information' limit 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'simmons broadcasting';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'kbmk' ) - 1;
+SELECT c1 FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE id = 1;
+SELECT c3 FROM w WHERE c1 = 'kbmk';
+SELECT COUNT( c2 ) FROM w WHERE c6_number > 9;
+SELECT c2 FROM w WHERE c6_number >= 10;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'mexico' ) - 1;
+SELECT MAX( c6_number ) - MIN( c6_number ) FROM w;
+SELECT c2 FROM w WHERE c4_number > 5;
+SELECT c2 FROM w WHERE c3_number = 4 AND c5_number = 1;
+SELECT c2 FROM w WHERE c6_number > 30;
+SELECT ( SELECT c6_number FROM w WHERE c2 = 'cuba' ) - ( SELECT c6_number FROM w WHERE c2 = 'mexico' );
+SELECT c2 FROM w WHERE c2 IN ( 'colombia' , 'bahamas' ) order BY c4_number desc limit 1;
+SELECT SUM( c3_number ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c4_number > 2;
+SELECT c6 FROM w WHERE c2 = 'ecuador';
+SELECT c2 FROM w WHERE c3_number = 1;
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE c4_number = 3;
+SELECT SUM( c4_number ) FROM w WHERE c2 IN ( 'peru' , 'ecuador' );
+SELECT c2 FROM w WHERE c3_number = 1 AND c4_number = 1 AND c5_number = 1;
+SELECT c2 FROM w WHERE c6_number >= 6;
+SELECT c2 FROM w WHERE c2 != 'peru' AND c5_number = ( SELECT c5_number FROM w WHERE c2 = 'peru' );
+SELECT c2 FROM w WHERE c6_number > ( SELECT c6_number FROM w WHERE c2 = 'chile' );
+SELECT ( SELECT c3_number FROM w WHERE c2 = 'venezuela' ) - ( SELECT c3_number FROM w WHERE c2 = 'colombia' );
+SELECT c6 FROM w WHERE c2 = 'venezuela';
+SELECT c2 FROM w WHERE c3_number >= 2;
+SELECT c3 FROM w order BY c5 asc limit 1;
+SELECT c2 FROM w WHERE c1_number = 9 + 1;
+SELECT c2 FROM w order BY c5 asc limit 1;
+SELECT c4_length FROM w WHERE c2 = ''the joint right here (remix)'';
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'phd';
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c5_min >= 3;
+SELECT c2 FROM w WHERE c3 = 'q-tip';
+SELECT COUNT( c2 ) FROM w WHERE c4_length >= 2;
+SELECT c2 FROM w WHERE c5 = '4:26';
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c2_first FROM w WHERE c2_first != 'great britain' AND c5_number = ( SELECT c5_number FROM w WHERE c2_first = 'great britain' ) AND c3_number = 0;
+SELECT c2_first FROM w WHERE c4_number < ( SELECT MAX( c4_number ) FROM w ) order BY c4_number desc limit 1;
+SELECT c2_first FROM w WHERE c3_number = c5_number;
+SELECT c2_first FROM w order BY c3_number desc limit 1;
+SELECT SUM( c6 ) FROM w WHERE c1_number <= 3;
+SELECT c2_first FROM w order BY c4_number desc limit 1;
+SELECT ( SELECT c6_number FROM w WHERE c1_number = 1 ) - ( SELECT c6_number FROM w WHERE c1_number = 2 );
+SELECT c6 FROM w WHERE c1_number = 2010;
+SELECT MIN( c1_number ) FROM w;
+SELECT SUM( c6_number ) FROM w;
+SELECT AVG( c3_number ) FROM w WHERE c1_number IN ( 2011 , 2012 );
+SELECT c1_number - 1 FROM w WHERE c4_number = 7;
+SELECT AVG( c3_number ) FROM w WHERE c1_number >= 2008 AND c1_number <= 2013;
+SELECT c9 FROM w WHERE c1_number = 2008;
+SELECT c8 FROM w WHERE c1_number = 2008;
+SELECT c9 FROM w WHERE c1_number = 2013 - 1;
+SELECT MIN( c1_number ) FROM w WHERE c2_number > 2000;
+SELECT c1_number FROM w order BY c4_number desc limit 1;
+SELECT SUM( c2_number ) FROM w WHERE c1_number > 2005;
+SELECT c2_number FROM w WHERE c1_number = 2008;
+SELECT c2_number FROM w WHERE c1_number = 1998;
+SELECT c1_number FROM w WHERE c5_number = 114;
+SELECT COUNT( c1 ) FROM w WHERE c3_number + c4_number < 1500;
+SELECT c1_number FROM w order BY c2_number limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'uppsala';
+SELECT COUNT( * ) FROM w WHERE c3 = 'stockholm';
+SELECT ( SELECT c5_length FROM w WHERE c2_number = 2003 ) > ( SELECT c5_length FROM w WHERE c2_number = 2001 );
+SELECT c2_number FROM w order BY c5_length desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'gothenburg';
+SELECT c2_number FROM w WHERE c5 = 'michael swanwick';
+SELECT c2 FROM w WHERE c5_length = ( SELECT MIN( c5_length ) FROM w );
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'imagicon' ) + 1;
+SELECT c3 FROM w order BY c8_number asc limit 1;
+SELECT c3 FROM w order BY c8_number desc limit 1;
+SELECT MAX( c8_number ) - MIN( c8_number ) FROM w;
+SELECT c3 FROM w WHERE c3 IN ( 'alex tagliani' , 'paul tracy' ) order BY c1_number asc limit 1;
+SELECT c3 FROM w order BY c8_number asc limit 1;
+SELECT c3 FROM w WHERE c3 != 'charles zwolsman' AND c8_number = ( SELECT c8_number FROM w WHERE c3 = 'charles zwolsman' );
+SELECT c4 FROM w order BY c1_number limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'ajax';
+SELECT COUNT( c2 ) FROM w WHERE c6_number > 2010;
+SELECT COUNT( c2 ) FROM w WHERE c6_number > 2000;
+SELECT COUNT( c2 ) FROM w WHERE c5_number >= 8;
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_number >= 9;
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c7_number > 20000;
+SELECT present_ref - c6_year FROM w WHERE c2 = 'luandensis';
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'metropolitan archdiocese';
+SELECT COUNT( c1 ) FROM w;
+SELECT c2 FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE c7 = '1074';
+SELECT c1 FROM w WHERE c1 IN ( 'ondjiva' , 'dundo' ) order BY c7_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c6_year < 1990;
+SELECT abs ( ( SELECT c7_number FROM w WHERE c1 = 'lubango' ) - ( SELECT c7_number FROM w WHERE c1 = 'luanda' ) );
+SELECT c2 FROM w WHERE c1 < '2006/07' order BY c1 desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2_number = 5 AND c1 > '2005/06';
+SELECT c1 FROM w WHERE c2_number = 4 AND c1 != '2008/09';
+SELECT c3 FROM w WHERE c1 = '2005/06';
+SELECT c4 FROM w order BY c4_number asc limit 1;
+SELECT MIN( c2_number ) FROM w;
+SELECT c1 FROM w WHERE c4_number = 19;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( '2005/06' , '2006/07' ) order BY c4_number asc limit 1;
+SELECT ( SELECT id FROM w WHERE c4_number = 3 ) < ( SELECT id FROM w WHERE c4_number = 18 );
+SELECT c1 FROM w WHERE c4_number = 8;
+SELECT c1 FROM w WHERE c4_number <= 5 order BY c1 desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3 = '1a aut. pref';
+SELECT c1 FROM w WHERE c5 = 'home to the university's admissions offices';
+SELECT ( SELECT COUNT( c1 ) FROM w WHERE c2 = 'library' ) > 5;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'bonds hall' ) + 1;
+SELECT c1 FROM w order BY c3_minimum_year asc limit 1;
+SELECT c1_first FROM w WHERE c1_first IN ( 'strosacker hall' , 'presidents house' ) AND c3_minimum_number = 1965;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'chapel';
+SELECT c1 FROM w order BY c3_minimum_number asc limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c2_first FROM w WHERE c1_number = 2012;
+SELECT COUNT( * ) FROM w WHERE c2_first = 'ucla';
+SELECT COUNT( c1 ) FROM w WHERE c2_first = 'stanford';
+SELECT COUNT( * ) FROM w WHERE c2_first = 'stanford';
+SELECT abs ( ( SELECT c1_number FROM w WHERE c3 = 'frank tripucka' ) - ( SELECT c1_number FROM w WHERE c3 = 'dale armstrong' ) ) - 1;
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( c3 ) desc limit 1;
+SELECT MAX( c1_number ) FROM w;
+SELECT c3 FROM w WHERE c2_number > ( SELECT c2_number FROM w WHERE c3 = 'frank burns' ) order BY c2_number limit 1;
+SELECT c3 FROM w WHERE c2_number = 1;
+SELECT COUNT( c3 ) FROM w WHERE c5 = 'notre dame';
+SELECT c3 FROM w WHERE c2_number > ( SELECT c2_number FROM w WHERE c3 = 'roy lester' ) order BY c2_number limit 1;
+SELECT c3 FROM w WHERE c2_number > ( SELECT c2_number FROM w WHERE c3 = 'bob dean' ) order BY c2_number limit 1;
+SELECT c6_first FROM w WHERE c2_parsed > ( SELECT c2_parsed FROM w WHERE c6_first = 'lance armstrong' ) order BY c2_parsed limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c5_first_number < 100;
+SELECT ( SELECT c5_first_number FROM w WHERE c1_number = 20 ) - ( SELECT c5_first_number FROM w WHERE c1_number = 19 );
+SELECT COUNT( c1 ) FROM w WHERE c5_first_number >= 200;
+SELECT c6_second FROM w GROUP BY c6_second order BY COUNT( * ) desc limit 1;
+SELECT c3 FROM w WHERE c5_first_number >= 100;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c1 FROM w WHERE id > ( SELECT id FROM w WHERE c1 = '2002' order BY id desc limit 1 ) limit 1;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5_first_number1 > c5_first_number2;
+SELECT COUNT( * ) > 0 FROM w WHERE c4 NOT NULL;
+SELECT COUNT( * ) FROM w WHERE c3 = 'first round';
+SELECT c5 FROM w WHERE c2 = 'slovakia';
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT ( SELECT c4_number FROM w WHERE c2 = 'russia' ) - ( SELECT c4_number FROM w WHERE c2 = 'denmark' );
+SELECT AVG( c4_number ) FROM w WHERE c1_number <= 5;
+SELECT c2 FROM w WHERE c2 != 'japan' AND c3_number = ( SELECT c3_number FROM w WHERE c2 = 'japan' );
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c2 FROM w WHERE c3_number = 3;
+SELECT ( SELECT c4_number FROM w WHERE c2 = 'united states' ) - ( SELECT c4_number FROM w WHERE c2 = 'russia' );
+SELECT c2 FROM w WHERE c6_number > 20 order BY c4_number asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c2 != 'germany' AND c5_number = ( SELECT c5_number FROM w WHERE c2 = 'germany' );
+SELECT c6 FROM w WHERE c2 = 'ukraine';
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'm65' ) + 1;
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'pisces';
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'virgo';
+SELECT ( SELECT c4_number FROM w WHERE c1_list = 'm90' ) > ( SELECT c4_number FROM w WHERE c1_list = 'm63' );
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT abs ( ( SELECT c4_number FROM w WHERE c1_list = 'm31' ) - ( SELECT c4_number FROM w WHERE c1_list = 'm33' ) );
+SELECT c5 FROM w WHERE c1 = 'madison mallards';
+SELECT c1 FROM w order BY c5_number limit 1;
+SELECT c3 FROM w order BY id limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'christine';
+SELECT MAX( c8_number ) FROM w;
+SELECT c3 FROM w WHERE id = 1;
+SELECT c1 , c7 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'october 29' ) + 1;
+SELECT MAX( c7_number1 ) FROM w;
+SELECT c3 FROM w WHERE c3_home = 'home' order BY c8_number desc limit 1;
+SELECT abs ( ( SELECT c8_number FROM w WHERE c1 = 'november 5' ) - ( SELECT c8_number FROM w WHERE c1 = 'november 25' ) );
+SELECT c7 FROM w WHERE id = 1;
+SELECT c3_raw FROM w WHERE c1_month = 1;
+SELECT COUNT( * ) FROM w WHERE c6 = 'cbs';
+SELECT COUNT( * ) FROM w WHERE c7_result = 'w' AND id < ( SELECT id FROM w WHERE c1 = 'november 19' );
+SELECT c2 FROM w WHERE c1 > '2011-12' order BY c1 asc limit 1;
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT c3 FROM w WHERE id = ( SELECT MAX( id ) FROM w WHERE c3 = 'division de honor' ) + 1;
+SELECT COUNT( c1 ) FROM w WHERE c5 = 'league champion';
+SELECT MAX( c1 ) FROM w WHERE c4_number = 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number = 3;
+SELECT COUNT( * ) FROM w WHERE c3 = 'division de honor';
+SELECT c1 FROM w WHERE c1 IN ( '2007-08' , '2005-06' ) order BY c4_number asc limit 1;
+SELECT c3 FROM w WHERE c3 IN ( 'division de honor' , 'primera nacional' ) GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c1 FROM w order BY id asc limit 1;
+SELECT c2 FROM w WHERE c1 = 'five star charts (新儀象法要)';
+SELECT c1 FROM w order BY id asc limit 1;
+SELECT c3 FROM w order BY id asc limit 1;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number1 < c4_number2;
+SELECT c1 FROM w WHERE c1 IN ( 'larissa' , 'panathinaikos' ) order BY c2_number1 desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2_number1 = c2_number2;
+SELECT COUNT( * ) FROM w WHERE c4 = '1-0';
+SELECT ( SELECT c4_number1 FROM w WHERE c1 = 'kallithea' ) - ( SELECT c5_number1 FROM w WHERE c1 = 'kallithea' );
+SELECT c4 FROM w WHERE c1 = 'lamia' AND c3 = 'kastoria';
+SELECT c2 FROM w WHERE c7 != 'free';
+SELECT c5 FROM w WHERE c2 = 'ray price';
+SELECT ( SELECT c4_parsed FROM w WHERE c2 = 'arthur kaye' ) < ( SELECT c4_parsed FROM w WHERE c2 = 'brian hall' );
+SELECT c2 FROM w order BY c4_parsed asc limit 1;
+SELECT c6 FROM w order BY c6_parsed asc limit 1;
+SELECT c7 FROM w WHERE c7 != 'free';
+SELECT COUNT( c2 ) FROM w WHERE c1 = 'df';
+SELECT COUNT( * ) FROM w WHERE c3_raw = 'chicago cardinals';
+SELECT c1_number FROM w WHERE c1_number IN ( 5 , 9 ) order BY c5_number desc limit 1;
+SELECT c2 FROM w order BY c2_parsed desc limit 1;
+SELECT c3_raw FROM w WHERE c3_raw != 'philadelphia eagles' AND c2_month = 10;
+SELECT c2 FROM w WHERE c4_result = 'l' AND c5_number > 40000;
+SELECT c2 FROM w order BY c4_number1 + c4_number2 desc limit 1;
+SELECT c2 FROM w WHERE c5_number = c6_number;
+SELECT c2 FROM w WHERE c2 IN ( 'jamaica' , 'mexico' ) order BY c3_number desc limit 1;
+SELECT c4_number FROM w WHERE c2 = 'bermuda';
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'cayman islands' ) - 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'venezuela' ) + 1;
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE c4_number >= 5;
+SELECT COUNT( c2 ) FROM w WHERE c3_number = 1;
+SELECT c4_number FROM w WHERE c2 = 'venezuela';
+SELECT c2 FROM w WHERE c4_number = 0;
+SELECT COUNT( c2 ) FROM w WHERE c4_number > 1900;
+SELECT COUNT( * ) FROM w WHERE c4_number < 1900;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'karine ruby' , 'shaun white' ) order BY c5_number desc limit 1;
+SELECT c7_number FROM w WHERE c1 = 'shaun white';
+SELECT MIN( c3_minimum_number ) FROM w WHERE c1 = 'karine ruby';
+SELECT c1 FROM w order BY c4_number limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'shaun white' , 'kelly clark' ) order BY c7_number desc limit 1;
+SELECT c1 FROM w order BY c4_number limit 1;
+SELECT c1 FROM w order BY c7_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_first = 'united states';
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c1 FROM w WHERE c4_number = 0 AND c5_number != 0 AND c6_number = 0;
+SELECT c1 FROM w order BY c7_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_number >= 2;
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c1 FROM w order BY c6_first_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number > 2000;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w order BY c6_first_number asc limit 1;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT ( SELECT c4_number FROM w WHERE c1 = 'alexandria' ) > ( SELECT c4_number FROM w WHERE c1 = 'delaware' );
+SELECT abs ( ( SELECT c4_number FROM w WHERE c1 = 'easton' ) - ( SELECT c4_number FROM w WHERE c1 = 'reno' ) );
+SELECT COUNT( c1 ) FROM w WHERE c4_number > 2000;
+SELECT ( SELECT c6_first_number FROM w WHERE c1 = 'delaware' ) > 45;
+SELECT c2 FROM w order BY c1_number asc limit 1;
+SELECT c2 FROM w WHERE c8 = 'total';
+SELECT DISTINCT c2 FROM w WHERE c7 = 'riaa: gold';
+SELECT COUNT( c1 ) FROM w WHERE c2 = 1997;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT c2 FROM w order BY c4 desc limit 1;
+SELECT c7_number - c6_number FROM w WHERE c1 = 'chiang mai';
+SELECT c7 FROM w WHERE c1 = 'xiamen';
+SELECT COUNT( * ) FROM w WHERE c6_number = 2013;
+SELECT c5 FROM w WHERE id = ( SELECT id FROM w WHERE c5 = 'darwin international airport' ) + 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'china';
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w WHERE c7_first_number = 5;
+SELECT c1 FROM w WHERE id = 1;
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'contract terminated';
+SELECT c5 FROM w WHERE c2 = 'stuart pearce';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'manchester city' ) + 1;
+SELECT c2 FROM w order BY c6_parsed desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c6_month = 11 AND c6_year = 2007;
+SELECT c3 FROM w WHERE c6_address = 'hunslet-taylor';
+SELECT c3 FROM w WHERE c6 = 'franco-belge, belgium';
+SELECT c6 FROM w GROUP BY c6 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'south african railways';
+SELECT COUNT( * ) FROM w WHERE c5_minimum_year = 1911;
+SELECT MAX( c5_maximum_year ) - MIN( c5_minimum_year ) FROM w WHERE c3_address = 'southern fuegian railway';
+SELECT COUNT( * ) FROM w WHERE c3_raw = 'braves';
+SELECT c3_raw FROM w GROUP BY c3_raw order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w order BY c8_number desc limit 1;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT COUNT( * ) FROM w;
+SELECT c3 FROM w order BY c6_number desc limit 2;
+SELECT c2 FROM w WHERE c6_year = 25 AND c3 = 'japan';
+SELECT c2 FROM w WHERE c6_year > 50;
+SELECT COUNT( c2 ) FROM w WHERE c5_year = 2010;
+SELECT c2 FROM w WHERE c2 != 'bhumibol adulyadej' AND c4_month = 12;
+SELECT COUNT( * ) FROM w WHERE c6_year >= 50;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT c4 FROM w WHERE c1 = 'beijing';
+SELECT c3 FROM w WHERE c1 = 'zhengzhou';
+SELECT c1 FROM w WHERE c1 IN ( 'beijing' , 'shanghai' ) order BY c3_number desc limit 1;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c2 FROM w WHERE c3 = 'columbus' AND c7 = 'demolished';
+SELECT COUNT( c2 ) FROM w WHERE c7 = 'vacant';
+SELECT COUNT( c2 ) FROM w WHERE c7 = 'vacant';
+SELECT COUNT( c2 ) FROM w WHERE c7 = 'demolished';
+SELECT COUNT( c2 ) FROM w WHERE c4_number < 10000;
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4_number > ( SELECT c4_number FROM w WHERE c2 = 'kilifi' );
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'mombasa' , 'tana river' ) order BY c5_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_number > 500000;
+SELECT c2 FROM w WHERE c4_number > ( SELECT c4_number FROM w WHERE c2 = 'kilifi' );
+SELECT c2 FROM w WHERE c4_number < 1000;
+SELECT SUM( c5_number ) FROM w;
+SELECT SUM( c5_number ) FROM w;
+SELECT c5 FROM w WHERE c2 = 'kilifi';
+SELECT c5 FROM w WHERE c1 = '27 february 1994';
+SELECT MAX( c1_year ) - MIN( c1_year ) FROM w;
+SELECT c2 FROM w WHERE c3_number > 300000;
+SELECT c1 FROM w WHERE c3_number = 9046;
+SELECT c1_year FROM w WHERE c1_year IN ( 1994 , 2001 ) order BY c5_number desc limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT abs ( ( SELECT c5_number FROM w WHERE c1_year = 2001 ) - ( SELECT c5_number FROM w WHERE c1_year = 1998 ) );
+SELECT c2 FROM w WHERE c9 IS NULL order BY id desc limit 1;
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT SUM( c9_number2 ) FROM w;
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+SELECT c2 FROM w order BY c18_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4_number >= 7 AND c6_number > 30;
+SELECT c2 FROM w order BY c4_number asc limit 1;
+SELECT c2 FROM w WHERE c5_number < 20 order BY id asc limit 1;
+SELECT c1 FROM w WHERE c6_first_number = 2;
+SELECT c1 FROM w WHERE c1 IN ( 'clemson' , 'western michigan' ) order BY c5_first_number desc limit 1;
+SELECT c1 FROM w WHERE c6_first_number = ( SELECT MAX( c6_first_number ) FROM w );
+SELECT c1 FROM w WHERE c3_first_number < 20;
+SELECT COUNT( c1 ) FROM w WHERE c5_first_number > 2;
+SELECT c5_first_number FROM w WHERE c1 = 'oklahoma state';
+SELECT COUNT( c1 ) FROM w WHERE c6_second_number = 1955;
+SELECT c1 FROM w WHERE c5_first_number = 0;
+SELECT c1 FROM w WHERE c3_first_number <= 16;
+SELECT c1_first_number FROM w WHERE c7_number = 55 AND c1_first_number != 2001;
+SELECT c1_first_number FROM w order BY c4_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c7_number > 40;
+SELECT SUM( c7_number ) FROM w;
+SELECT SUM( c3_number ) FROM w WHERE c1_first_number >= 2000 AND c1_first_number <= 2005;
+SELECT ( SELECT c5_number FROM w WHERE c1_first_number = 2009 ) - ( SELECT c5_number FROM w WHERE c1_first_number = 2005 );
+SELECT c1_first_number FROM w order BY c5_number desc limit 1;
+SELECT c1_first_number FROM w order BY c2_number limit 1;
+SELECT AVG( c3_number ) FROM w;
+SELECT c2_number FROM w WHERE c1_first_number = 2001;
+SELECT c1_second FROM w WHERE c1_second IN ( 'bulldogs' , 'roosters' , 'tigers' ) GROUP BY c1_second order BY SUM( c5_number ) desc limit 1;
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+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT AVG( c2_number ) FROM w;
+SELECT c2_number FROM w WHERE c1 = 'total';
+SELECT c1 FROM w WHERE c6_number = 2;
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c3_number FROM w WHERE c1 = 'labour';
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+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
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+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'pennsylvania 200' ) + 1;
+SELECT COUNT( c3 ) FROM w;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'pocono 200' ) + 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'ricky stenhouse, jr';
+SELECT c4 FROM w WHERE c5 = 'frank kimmel' order BY c2_parsed desc limit 1;
+SELECT c3 FROM w WHERE c3 IN ( 'carolina 500' , 'kentucky' ) order BY c1_number desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c5 = 'ricky stenhouse, jr';
+SELECT c4 FROM w WHERE c5 = 'ricky stenhouse, jr' INTERSECT SELECT c4 FROM w WHERE c5 = 'justin allgaier';
+SELECT c5 FROM w WHERE c3 = 'carolina 500';
+SELECT ( SELECT COUNT( * ) FROM w WHERE c1 = 'solon borland' ) > 0;
+SELECT c1 FROM w WHERE c2 = 'minister resident' order BY julianday ( c4_parsed ) - julianday ( c3_parsed ) asc limit 1;
+SELECT c1 FROM w order BY c3_parsed desc limit 1;
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'april 17, 1854' ) + 1;
+SELECT c1 FROM w WHERE c5 = 'barack obama';
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT c1 FROM w WHERE c3_parsed > ( SELECT c3_parsed FROM w WHERE c1 = 'hewson a. ryan' ) order BY c3_parsed asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'theodore roosevelt';
+SELECT c1 FROM w WHERE c5 = 'franklin pierce';
+SELECT c1 FROM w WHERE c5 = 'woodrow wilson' order BY c4_parsed asc limit 1;
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+SELECT COUNT( * ) FROM w WHERE c1 = 'village roadshow queensland new filmmakers awards' AND c5 = 'won';
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+SELECT COUNT( c1 ) FROM w WHERE c2_number > 2012;
+SELECT c3 FROM w WHERE c1 = 'janison short sharp film festival';
+SELECT COUNT( * ) FROM w WHERE c5 = 'won';
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c4 = 'canada';
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'marco loughran' ) - 1;
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+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'james goddard' ) + 1;
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+SELECT COUNT( DISTINCT c4 ) FROM w;
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+SELECT c3 FROM w WHERE c4 = 'australia';
+SELECT COUNT( c3 ) FROM w WHERE c5_hour = 1;
+SELECT c3 FROM w WHERE c3 IN ( 'james goddard' , 'charles francis' ) order BY id asc limit 1;
+SELECT COUNT( c3 ) FROM w;
+SELECT c3 FROM w WHERE c3 IN ( 'ashley delaney' , 'charles francis' ) order BY c5 desc limit 1;
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+SELECT c1 FROM w WHERE c1_number IN ( 1 , 3 ) order BY c5_number desc limit 1;
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+SELECT c1 FROM w order BY c5_number asc limit 1;
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+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'disk'o' ) + 1;
+SELECT COUNT( DISTINCT c1 ) FROM w;
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+SELECT c1 FROM w WHERE c4_first_number = ( SELECT MAX( c4_first_number ) FROM w );
+SELECT c5 FROM w WHERE c1 = 'surf's up';
+SELECT c5 FROM w WHERE c1 = 'skater';
+SELECT COUNT( DISTINCT ( c1 ) ) FROM w WHERE c6_number = 600;
+SELECT c1 FROM w WHERE c1 != 'mega disk'o' AND c4_first_number = ( SELECT c4_first_number FROM w WHERE c1 = 'mega disk'o' );
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+SELECT c2_number - c1_number FROM w WHERE c3 = 'daubin';
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+SELECT COUNT( * ) FROM w WHERE c5 = 'draw';
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+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'portugal' AND c2_year = 1989 ) + 1;
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+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT c1_month FROM w GROUP BY c1_month order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_year = 1976;
+SELECT COUNT( c4 ) FROM w WHERE c5_year >= 30;
+SELECT c2 FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c2 = 'ron ng' ) order BY c1_number desc limit 1;
+SELECT COUNT( c4 ) FROM w;
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = 'jin au-yeung' ) order BY c1_number limit 1;
+SELECT c2 FROM w order BY c5_year limit 1;
+SELECT c2 FROM w WHERE c5_year >= 28;
+SELECT COUNT( * ) FROM w WHERE c3_year > 1972;
+SELECT COUNT( * ) FROM w WHERE c3_year = 1979;
+SELECT c2 FROM w WHERE c3_year = 1982;
+SELECT c2 FROM w WHERE c2 != 'amigo choi' AND c5_year = ( SELECT c5_year FROM w WHERE c2 = 'amigo choi' );
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+SELECT c1 FROM w order BY c2_number desc limit 3;
+SELECT c4_number FROM w WHERE c1 = 'liberal democratic league';
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+SELECT c1_number FROM w WHERE c1_number IN ( 2012 , 2007 ) order BY c4 limit 1;
+SELECT c1_number FROM w WHERE c5 != 'did not qualify';
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+SELECT c2 FROM w order BY c6_number desc limit 1;
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+SELECT c2 FROM w WHERE c3_number > 4 AND c4_number > 3;
+SELECT COUNT( c2 ) FROM w WHERE c4_number >= 10;
+SELECT COUNT( c2 ) FROM w WHERE c5_number = 0;
+SELECT COUNT( c2 ) FROM w WHERE c3_number = 0;
+SELECT COUNT( c2 ) FROM w;
+SELECT SUM( c6_number ) FROM w WHERE c2 IN ( 'poland' , 'south africa' );
+SELECT c6_number FROM w WHERE c2 = 'belarus';
+SELECT c2 FROM w order BY c7_number limit 1;
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE c2 != 'hon hai precision industry' AND c4_number = ( SELECT c4_number FROM w WHERE c2 = 'hon hai precision industry' );
+SELECT c7_number FROM w WHERE c2 = 'vitol';
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'oil and gas' AND c1_number <= 10;
+SELECT c2 FROM w WHERE c3 = 'automotive' order BY c4_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4_number > 200 AND c3 = 'oil and gas';
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'apple' ) + 1;
+SELECT SUM( c6_number ) FROM w WHERE c2 IN ( 'china' , 'japan' , 'south korea' );
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+SELECT SUM( c3_number ) FROM w WHERE c2 IN ( 'kazakhstan' , 'uzbekistan' , 'thailand' );
+SELECT c2 FROM w order BY c3_number desc limit 1;
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+SELECT COUNT( c2 ) FROM w WHERE c5_number = 0;
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c2 FROM w WHERE c6_number = 5;
+SELECT c2 FROM w order BY c5_number desc limit 1;
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+SELECT c2 FROM w order BY c1_number desc limit 1;
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+SELECT c3_address FROM w WHERE c3_address IN ( 'germany' , 'greece' ) GROUP BY c3_address order BY COUNT( c2 ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_number > 8;
+SELECT MIN( c4_number ) FROM w WHERE c1_number < 2010;
+SELECT COUNT( * ) FROM w WHERE c2 = 'world championships';
+SELECT MIN( c4_number ) FROM w WHERE c1 > 2008;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'barcelona, spain';
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c5_number > 9;
+SELECT COUNT( * ) FROM w WHERE c2 = 'olympic games';
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+SELECT c2 FROM w order BY c6_number asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_number < 1987;
+SELECT c2 FROM w WHERE c3 NOT NULL AND c6 IS NULL;
+SELECT MIN( c3_number ) FROM w WHERE c7 = 'bonafide';
+SELECT COUNT( c6 ) FROM w WHERE c7 = 'intentions';
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+SELECT COUNT( * ) FROM w WHERE c5 = 'dallara';
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+SELECT COUNT( * ) FROM w WHERE c1_number >= 2005 AND c1_number <= 2007 AND c3 = 'sebastien bourdais';
+SELECT MIN( c7_number ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c6 = 'honda';
+SELECT c2 FROM w WHERE id > ( SELECT id FROM w WHERE c2 = 'planet sheen' ) limit 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'saturday night live' AND c3 = 'host';
+SELECT c2 FROM w WHERE c3 = 'host';
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c6_number1 > c6_number2;
+SELECT c4 FROM w order BY id desc limit 1;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c7_number1 FROM w WHERE c4 = 'kotayk abovian';
+SELECT c4 FROM w order BY c7_number1 asc limit 1;
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+SELECT c4 FROM w WHERE c6 = 'greensboro, north carolina' order BY c4_parsed desc limit 1;
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+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'j-money' ) - 1;
+SELECT c2 FROM w WHERE c4 = 'june 16, 2007';
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+SELECT COUNT( * ) FROM w WHERE c3_number NOT NULL;
+SELECT c6 FROM w order BY c1_parsed asc limit 1;
+SELECT COUNT( c1 ) FROM w;
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+SELECT COUNT( c3 ) FROM w WHERE c1 = 'rocky view county';
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT SUM( c2_number ) FROM w;
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE id > ( SELECT id FROM w WHERE c3_list_first = 'hwy 9' );
+SELECT c3 FROM w WHERE c3 IN ( '14 street nw, range road 14' , 'range road 293, 36 street ne' ) order BY c2_number desc limit 1;
+SELECT c2 FROM w WHERE c3 = 'range road 293, 36 street ne';
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+SELECT c1 FROM w order BY id desc limit 1;
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+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( c5 ) desc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'takeovers panel' ) - 1;
+SELECT c2 FROM w WHERE c3 = 'labour';
+SELECT c5 FROM w WHERE c1 = 'broadcasting' AND c4 = 'ace';
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'ace';
+SELECT COUNT( * ) FROM w WHERE c1 = 'antonov';
+SELECT c3_number FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE c6_number > 5000;
+SELECT MAX( c5_number ) FROM w;
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+SELECT c2 FROM w order BY id desc limit 1;
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+SELECT COUNT( c2 ) FROM w WHERE c3 = 'liberal';
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+SELECT c1 FROM w WHERE c1_number IN ( 1998 , 2006 ) order BY c4_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c5_number < 0;
+SELECT c1 FROM w WHERE c4_number > 400 AND c6_number < 12;
+SELECT c5 FROM w WHERE c1_number = '1980';
+SELECT c4_number FROM w WHERE c1_number = 1996;
+SELECT COUNT( * ) FROM w WHERE c5_number < 0;
+SELECT c1 FROM w WHERE c2_number = 38;
+SELECT abs ( ( SELECT c3_number FROM w WHERE c1_number = 1995 ) - ( SELECT c4_number FROM w WHERE c1_number = 1995 ) );
+SELECT MAX( c1_number ) - MIN( c1_number ) FROM w;
+SELECT c3_number FROM w WHERE c1_number = 2002;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'alan smith' ) + 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'brian talbot' ) - 1;
+SELECT c2 FROM w WHERE c2 != 'tommy taylor' AND c4_number = ( SELECT c4_number FROM w WHERE c2 = 'tommy taylor' );
+SELECT COUNT( c2 ) FROM w WHERE c5_minimum_year <= 1990 AND c5_maximum_year >= 1990;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'ryan mills' ) + 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'austin kearns' ) - 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'pat burrell' ) + 1;
+SELECT c5 FROM w WHERE c2 = 'mark mulder';
+SELECT c3 FROM w WHERE c2 = 'cc sabathia';
+SELECT c5 FROM w WHERE c2 = 'carlos pena';
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'mark mulder' ) - 1;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w order BY c2_number desc limit 1;
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+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE c1 != 'jalajala' AND c2_number = ( SELECT c2_number FROM w WHERE c1 = 'jalajala' );
+SELECT c2 FROM w WHERE c1 = 'angono';
+SELECT c1 FROM w WHERE c2_number = 5;
+SELECT COUNT( c1 ) FROM w WHERE c2_number >= 10;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w WHERE c2_number = ( SELECT SUM( c2_number ) FROM w WHERE c1 IN ( 'morong' , 'rodriguez' ) );
+SELECT c1 FROM w WHERE c1 IN ( 'wayne edward alley' , 'james henry alesia' ) order BY c3_parsed limit 1;
+SELECT ( SELECT id FROM w WHERE c1 = 'morris s. arnold' ) < ( SELECT id FROM w WHERE c1 = 'maryanne trump barry' );
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'frank x. altimari' ) + 1;
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+SELECT c4 FROM w WHERE c4 IN ( 'anthony grant' , 'elliot benyon' ) order BY c9_number desc limit 1;
+SELECT COUNT( c4 ) FROM w WHERE c2 IN ( 'mf' , 'df' );
+SELECT c9_number FROM w WHERE c4 = 'freddy eastwood';
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'freddy eastwood' ) - 1;
+SELECT c4 FROM w WHERE c8_number = 3;
+SELECT COUNT( c4 ) FROM w WHERE c6_number != 0;
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+SELECT COUNT( c4 ) FROM w WHERE c7_number > 0;
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+SELECT c2 FROM w order BY c3_list_number desc limit 1;
+SELECT c2 FROM w WHERE c2_first IN ( 'larned building' , 'state tower building' ) order BY c3_list_number limit 1;
+SELECT COUNT( * ) FROM w;
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c6_number FROM w WHERE c2 = 'south africa';
+SELECT c3 FROM w WHERE c2 = 'kenya';
+SELECT c2 FROM w WHERE c4_number = 8 AND c3_number = 0;
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+SELECT c2 FROM w WHERE c6_number > ( SELECT c6_number FROM w WHERE c2 = 'nigeria' );
+SELECT c2 FROM w WHERE c2 != 'ghana' AND c3 = ( SELECT c3 FROM w WHERE c2 = 'ghana' );
+SELECT c2 FROM w WHERE c3_number > ( SELECT c3_number FROM w WHERE c2 = 'south africa' );
+SELECT c6_number FROM w WHERE c2 = 'madagascar';
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT SUM( c3 ) FROM w WHERE c2 IN ( 'ivory coast' , 'namibia' );
+SELECT c2 FROM w order BY c1_number desc limit 4;
+SELECT c2 FROM w order BY c1_number limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4_list = 'eddie murphy';
+SELECT c2 FROM w WHERE c4 = 'tom selleck, ted danson, and steve guttenberg';
+SELECT c1 FROM w WHERE c6_number > 5000;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'detach quad';
+SELECT c1 FROM w WHERE c1 != 'magic mile express' AND c7_number >= 20;
+SELECT c1 FROM w WHERE c5_number > 1000;
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+SELECT MAX( c8_number ) FROM w;
+SELECT c1 FROM w order BY c8_number limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'detach quad';
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+SELECT COUNT( c1 ) FROM w WHERE c11_number > 1991;
+SELECT c2 FROM w WHERE c2_first IN ( 'vietnam' , 'indonesia' ) order BY c6_number desc limit 1;
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c4 FROM w WHERE c2_first = 'saudi arabia';
+SELECT c2 FROM w WHERE c2_first IN ( 'iran' , 'indonesia' ) order BY c3_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_number = 0;
+SELECT c2 FROM w WHERE c4_number > 1 AND c5_number = 0;
+SELECT c2 FROM w WHERE c3 = 'south korea';
+SELECT COUNT( c2 ) FROM w WHERE id <= 15 AND c3 = 'united states';
+SELECT c4_number FROM w WHERE c2 = 'jim chapin';
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'soviet union';
+SELECT c3 FROM w WHERE id <= 10 GROUP BY c3 HAVING COUNT( c2 ) = ( SELECT COUNT( c2 ) FROM w WHERE id <= 10 AND c3 = 'united states' ) AND c3 != 'united states';
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'sweden';
+SELECT c2 FROM w WHERE c2 IN ( 'mikio oyama' , 'arnulf sunde' ) order BY c4_number limit 1;
+SELECT c2 FROM w WHERE c2 != 'jan bazen' AND c4_number = ( SELECT c4_number FROM w WHERE c2 = 'jan bazen' );
+SELECT c1 FROM w order BY c4_number asc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'greene county';
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'greene county';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'calciana' ) - 1;
+SELECT SUM( c2_number ) FROM w WHERE c1 IN ( 'in internment camps' , 'during the wartime flight' );
+SELECT COUNT( c1 ) FROM w WHERE c2 > 50000;
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c2_number FROM w WHERE c1 = 'violent deaths';
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+SELECT COUNT( * ) FROM w WHERE c1 = 'runner-up';
+SELECT c4 FROM w WHERE c3_parsed < ( SELECT c3_parsed FROM w WHERE c4 = 'rimini' ) order BY c3_parsed desc limit 1;
+SELECT c6 FROM w WHERE c4 = 'mestre';
+SELECT COUNT( * ) FROM w WHERE c1 = 'winner' AND c3_year < 2003;
+SELECT COUNT( c4 ) FROM w WHERE c3_year = 2002;
+SELECT COUNT( * ) FROM w WHERE c5 = 'clay';
+SELECT c4 FROM w WHERE c1 = 'runner-up' AND c3_year = 2001;
+SELECT c4 FROM w WHERE c4 != 'bergamo' AND c7 = ( SELECT c7 FROM w WHERE c4 = 'bergamo' );
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'uriah forrest' ) + 1;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'benjamin edwards' ) + 1;
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'know-nothing';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'barbara mikulski' ) + 1;
+SELECT c5 FROM w WHERE c5 IN ( 'anti-administration' , 'pro-administration' ) AND c2 = 'benjamin contee';
+SELECT COUNT( c2 ) FROM w WHERE c3_year < 1800;
+SELECT c2_year FROM w WHERE c2_year > 2010 order BY c2_parsed limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c2_year FROM w order BY c2_parsed limit 1;
+SELECT SUM( c5_number1 + c5_number2 ) FROM w WHERE c7 = 'euro 2012 qualifying';
+SELECT COUNT( * ) FROM w WHERE c6_number1 < c6_number2;
+SELECT c2 FROM w order BY c2_parsed limit 1;
+SELECT c4 FROM w order BY c5_number1 desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'san marino' AND c2_year = 2010;
+SELECT c2 FROM w WHERE c2 != 'systems and software engineering - system life cycle processes' AND c5_number = ( SELECT c5_number FROM w WHERE c2 = 'systems and software engineering - system life cycle processes' );
+SELECT COUNT( c2 ) FROM w WHERE c3_second_number = 2011;
+SELECT abs ( ( SELECT c3_second_number FROM w WHERE c1 = 'iso/iec 15288' ) - ( SELECT c3_second_number FROM w WHERE c1 = 'iso/iec 20000-1' ) );
+SELECT c1 FROM w order BY c3_second_number desc limit 1;
+SELECT c1 FROM w order BY c3_second_number limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT ( SELECT COUNT( c2 ) FROM w ) > 24;
+SELECT COUNT( c1 ) FROM w WHERE c3_second_number = 2011;
+SELECT COUNT( c1 ) FROM w WHERE c3_second_number < 2000;
+SELECT COUNT( c1_day ) FROM w WHERE c1_month = 8 AND c2_address = 'canada';
+SELECT COUNT( * ) FROM w WHERE c2_address = 'washington';
+SELECT c2 FROM w order BY c1_parsed desc limit 1;
+SELECT c2_address FROM w WHERE c2_address IN ( 'quebec' , 'british columbia' ) GROUP BY c2_address order BY COUNT( * ) desc limit 1;
+SELECT c2_address FROM w WHERE c2_address IN ( 'calgary' , 'toronto' ) AND c1_month = 8 AND c1_day = 11;
+SELECT c4 FROM w WHERE c4 NOT NULL AND c1_parsed > ( SELECT c1_parsed FROM w WHERE c4 = 'cadillac tramps' order BY c1_parsed desc limit 1 ) order BY c1_parsed asc limit 1;
+SELECT c3 FROM w WHERE c1_parsed > ( SELECT c1_parsed FROM w WHERE c3 = 'verdun auditorium' ) order BY c1_parsed limit 1;
+SELECT c4 FROM w WHERE c3 = 'viper room';
+SELECT c1 FROM w WHERE c5_number = 8;
+SELECT COUNT( c1 ) FROM w WHERE c4 = 'tv azteca';
+SELECT COUNT( c1 ) FROM w WHERE c5_number > ( SELECT c5_number FROM w WHERE c1 = 'canal de las estrellas' );
+SELECT c5_number FROM w WHERE c1 = 'canal de las estrellas';
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( c1 ) desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_list = 'soap operas';
+SELECT c1 FROM w WHERE c4 = 'tv azteca';
+SELECT COUNT( c3 ) FROM w WHERE c4 = 'claudio zoli';
+SELECT ( SELECT c5_number FROM w WHERE c1 = 'top 6' ) > ( SELECT c5_number FROM w WHERE c1 = 'top 8' );
+SELECT c2 FROM w WHERE c2 IN ( 'judge's choice' , 'birth year songs' ) order BY c5_number limit 1;
+SELECT COUNT( DISTINCT c1 ) FROM w WHERE c6 = 'safe';
+SELECT ( SELECT c5_number FROM w WHERE c1 = 'top 10' ) > ( SELECT c5_number FROM w WHERE c1 = 'top 7' );
+SELECT COUNT( DISTINCT c2 ) FROM w;
+SELECT COUNT( c3 ) FROM w WHERE c4 = 'claudio zoli';
+SELECT COUNT( * ) FROM w WHERE c6 = '24 february 1843';
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c2 = 'diekirch';
+SELECT c2 FROM w WHERE c6 = '24 february 1843' AND c5_number = '3966';
+SELECT COUNT( * ) FROM w WHERE c3_year = 2009;
+SELECT c1 FROM w WHERE c1 IN ( 'wisoon wichaya' , 'piyapong pue-on' ) order BY c5_parsed limit 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'ttm samut sakhon';
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT c1 FROM w WHERE c2 = 'ttm samut sakhon' AND c3_parsed > ( SELECT c3_parsed FROM w WHERE c1 = 'prajuk viengsong' ) order BY c3_parsed limit 1;
+SELECT c4 FROM w WHERE c5 = 'april 2009' AND c4 != 'kij meesrisuk';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'surasak tansurat' ) - 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'pattaya united';
+SELECT c3_month FROM w WHERE c1 = 'hans r. emser';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = ''marion the superfluous feed character'' ) + 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'adam miller';
+SELECT c2 FROM w WHERE id = 1;
+SELECT COUNT( c2 ) FROM w WHERE c6_month = 7 AND c6_year = 2010;
+SELECT c3 FROM w WHERE c3 NOT NULL order BY id desc limit 1;
+SELECT c1_first FROM w order BY c3_length desc limit 1;
+SELECT c3 FROM w order BY c5_number desc limit 1;
+SELECT c2 FROM w WHERE c4_result = 'w' AND c2_parsed > ( SELECT c2_parsed FROM w WHERE c4_result = 'w' order BY c2_parsed limit 1 ) order BY c2_parsed limit 1;
+SELECT c2 FROM w WHERE c5_number > 20000 order BY c5_number limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'pole vault' ) - 1;
+SELECT COUNT( * ) FROM w WHERE c2_list = 'skrein';
+SELECT c1 FROM w WHERE c1 < ''broken wings'' order BY c1 desc limit 1;
+SELECT c2_list FROM w GROUP BY c2_list order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_list = 'shameless';
+SELECT SUM( c4_number1 ) FROM w WHERE c2_month = 9;
+SELECT COUNT( * ) FROM w WHERE c2_month = 10 AND c2_year = 1990;
+SELECT c4_number1 + c4_number2 FROM w WHERE c1_number = 3;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c6 = 1 ) - 1;
+SELECT abs ( c4_number1 - c4_number2 ) FROM w WHERE c1 = 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_number < 20;
+SELECT abs ( ( SELECT c4_first_number FROM w WHERE c2 = 'key tower' ) - ( SELECT c4_first_number FROM w WHERE c2 = '55 public square' ) );
+SELECT COUNT( c2 ) FROM w WHERE c6_number > 1950;
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT c2 FROM w order BY c4_first_number desc limit 1;
+SELECT c2 FROM w WHERE c6_number < 1991 order BY c4_first_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'lillestrøm';
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( c3 ) limit 1;
+SELECT c3 FROM w WHERE c3 != 'rosenborg' GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c1_minimum_parsed < ( SELECT c1_minimum_parsed FROM w WHERE c2 = 'tippeligaen' ) order BY c1_minimum_parsed desc limit 1;
+SELECT c1 FROM w WHERE c3 = 'skeid';
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c5 FROM w WHERE c1_minimum_parsed < ( SELECT c1_minimum_parsed FROM w WHERE c5 = 'valerenga' AND c1 = '1960-61' ) order BY c1_minimum_parsed desc limit 1;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'beijing, china' ) - 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_number IN ( 2009 , 2010 , 2011 );
+SELECT COUNT( * ) FROM w WHERE c5_number = 800;
+SELECT c1 FROM w WHERE c3_address = 'kenya';
+SELECT COUNT( c5 ) FROM w WHERE c5_number = 800;
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( c2 ) desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_number > 2008;
+SELECT c3 FROM w WHERE c4_first_number = 8 AND c1_number = 2007;
+SELECT c3 FROM w WHERE c1_number = 2009 order BY c4_first_number desc limit 1;
+SELECT MIN( c6_first ) FROM w WHERE c5_number = 1500 AND c1_number < 2013;
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 2013;
+SELECT abs ( ( SELECT c3_number FROM w WHERE c1 = 'luis estrella martinez' ) - ( SELECT c3_number FROM w WHERE c1 = 'edgardo rivera garcia' ) );
+SELECT COUNT( * ) FROM w WHERE c2 = 'associate justice';
+SELECT c1 FROM w WHERE c4_number = 17;
+SELECT c4 FROM w WHERE c1 = 'roberto feliberti cintron';
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number > 10;
+SELECT COUNT( c1 ) FROM w WHERE c6_number = 2004;
+SELECT c1 FROM w WHERE c2 = 'associate justice' AND c6_number = 2010;
+SELECT c1 FROM w order BY c4_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'luis fortuno';
+SELECT c3 FROM w WHERE id = 1;
+SELECT c5 FROM w WHERE c1 = 'billy sharp';
+SELECT c5 FROM w WHERE c1 = 'walter langton';
+SELECT c7 FROM w WHERE c1 = 'kit lawlor' AND c3_list = '1950-1954';
+SELECT c1 FROM w WHERE c7_number = c6_number;
+SELECT c3_list_maximum_number - c3_list_minimum_number FROM w WHERE c1 = 'syd bycroft';
+SELECT c4_number FROM w WHERE c2 = 'chorrillo f.c';
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT c2 FROM w order BY c8_number desc limit 1;
+SELECT c2 FROM w order BY c7_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c7_number >= 17;
+SELECT AVG( c6_number ) FROM w;
+SELECT c2 FROM w WHERE c5_number = ( SELECT MAX( c5_number ) FROM w );
+SELECT c2 FROM w WHERE c8 = 'mckinley';
+SELECT c2 FROM w WHERE c8 = 'truman' order BY c1_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c6_minimum_number NOT NULL;
+SELECT c2 FROM w WHERE c9 = 'resignation' AND c1_number > ( SELECT c1_number FROM w WHERE c2 = 'alexander campbell king' ) order BY c1_number limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c8 = 'carter';
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( c2 ) desc limit 1;
+SELECT c1_number FROM w order BY c11_number limit 1;
+SELECT SUM( c7_number ) FROM w;
+SELECT c1_number FROM w order BY c5_number desc limit 1;
+SELECT SUM( c4_number ) FROM w;
+SELECT MIN( c1_number ) FROM w WHERE c7_number > 0;
+SELECT c1 FROM w WHERE c1_number != 2011 AND c3 = ( SELECT c3 FROM w WHERE c1_number = 2011 );
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1_number = 2011 ) - 1;
+SELECT c1 FROM w WHERE c3 = 'mexico city';
+SELECT MAX( c1_number ) - MIN( c1_number ) FROM w;
+SELECT c1 FROM w WHERE c4 = ( SELECT c2 FROM w WHERE c1 = 2011 );
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'april 8' ) + 1;
+SELECT c5 FROM w order BY c5_second_number desc limit 1;
+SELECT SUM( c4_number1 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c4_result = 'l';
+SELECT c4_number1 - c4_number2 FROM w WHERE c2 = 'april 9';
+SELECT c1_number FROM w order BY c6_second_number desc limit 1;
+SELECT c3_raw FROM w order BY c7_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_number2 = 0;
+SELECT abs ( ( SELECT c7_number FROM w WHERE c1_number = 1 ) - ( SELECT c7_number FROM w WHERE c1_number = 12 ) );
+SELECT c2 FROM w WHERE c4_result = 'w' order BY c4_number1 asc limit 1;
+SELECT c3_raw FROM w WHERE c6 = 'lambeau field';
+SELECT c5 FROM w WHERE c1_number = 10;
+SELECT c2 FROM w WHERE c5 = 'slovakia';
+SELECT abs ( ( SELECT id FROM w WHERE c3 = 'jesse joensuu' ) - ( SELECT id FROM w WHERE c3 = 'kim johansson' ) ) - 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'goalie';
+SELECT COUNT( c3 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c4 = 'goalie';
+SELECT c4 FROM w WHERE c4 != 'center' GROUP BY c4 HAVING COUNT( * ) = ( SELECT COUNT( * ) FROM w WHERE c4 = 'center' );
+SELECT COUNT( * ) FROM w WHERE c4 = 'center';
+SELECT COUNT( * ) FROM w;
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( c3 ) desc limit 1;
+SELECT c3 FROM w WHERE c5 = 'united states' AND c6_list_first = 'springfield jr. blues';
+SELECT c3 FROM w order BY c2_number asc limit 1;
+SELECT c5 FROM w WHERE c5 IN ( 'sweden' , 'canada' ) GROUP BY c5 order BY COUNT( c3 ) desc limit 1;
+SELECT c1 FROM w order BY c8_first_number1 desc limit 1;
+SELECT c1 FROM w WHERE c4 = 'toronto';
+SELECT c6 FROM w WHERE c1_number = 2003;
+SELECT c3 FROM w WHERE id > ( SELECT id FROM w WHERE c1_number = 2007 ) order BY c1_number limit 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'montreal';
+SELECT COUNT( * ) FROM w WHERE c4 = 'queen's';
+SELECT MAX( c1_number ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c8 = 'hold your fire';
+SELECT c2 FROM w WHERE c8 = 'firehouse' AND c3_number < 10;
+SELECT c2 FROM w WHERE c8 = 'firehouse' order BY c7_number limit 1;
+SELECT c2 FROM w WHERE c3_number < 10 order BY c1_number limit 1;
+SELECT c2 FROM w WHERE c7 IS NULL;
+SELECT c2 FROM w WHERE c1_number = 1995 AND id > ( SELECT id FROM w WHERE c2 = ''i live my life for you'' );
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = ''all she wrote'' ) order BY c1_number limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c1 FROM w WHERE c5_year < ( SELECT c5_year FROM w WHERE c1 = 'l'inganno innocente' ) order BY c5_year desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_number >= 3;
+SELECT c1 FROM w WHERE c5_year > ( SELECT c5_year FROM w WHERE c1 = 'ardelinda' ) order BY c5_year limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'la fortezza al cimento' , 'astarto' ) order BY c3_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5_number > ( SELECT c5_number FROM w WHERE c1 = 'zenone, imperator d'oriente' );
+SELECT c1 FROM w WHERE c5_year > ( SELECT c5_year FROM w WHERE c1 = 'candalide' ) order BY c5_year limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'artamene' , 'merope' ) order BY c5_year limit 1;
+SELECT c3_number FROM w WHERE c1 = 'il giustino';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'high school educated' ) + 1;
+SELECT c1 FROM w WHERE c1 IN ( 'grade school educated' , 'white collar' ) order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'white' , 'farmer' ) order BY c4_number desc limit 1;
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT ( SELECT c5_number FROM w WHERE c1 = 'college educated' ) > ( SELECT c4_number FROM w WHERE c1 = 'college educated' );
+SELECT c6_number FROM w WHERE c1 = 'union member';
+SELECT c1 FROM w WHERE c2_number < ( SELECT c2_number FROM w WHERE c1 = 'union member' ) order BY c2_number desc limit 1;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT abs ( ( SELECT c2_number FROM w WHERE c1 = 'white' ) - ( SELECT c2_number FROM w WHERE c1 = 'black' ) );
+SELECT COUNT( * ) FROM w WHERE c3 = 'michael jackson';
+SELECT ( SELECT COUNT( DISTINCT c1 ) FROM w ) >= 5;
+SELECT c2 FROM w WHERE c6 = 'daystar';
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( * ) desc limit 1;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c2 = 'evansville' ) > 4;
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'terre haute';
+SELECT COUNT( * ) FROM w WHERE c2 = 'fort wayne';
+SELECT c5 FROM w order BY c5_number asc limit 1;
+SELECT c3 FROM w WHERE c1_number = 1;
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'petra chocova' ) + 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( c3 ) FROM w;
+SELECT c4 FROM w WHERE c4 IN ( 'iceland' , 'finland' ) order BY c5_number asc limit 1;
+SELECT c3 FROM w WHERE c3 IN ( 'hrafnhildur luthersdottir' , 'jenna laukkanen' ) order BY c5 desc limit 1;
+SELECT c4 FROM w order BY c1_number desc limit 1;
+SELECT c3 FROM w WHERE c3 IN ( 'katharina stiberg' , 'ivana ninkovic' ) order BY c5_number asc limit 1;
+SELECT c5 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number > 3;
+SELECT abs ( ( SELECT c3_number FROM w WHERE c1_number = 1 ) - ( SELECT c3_number FROM w WHERE c1_number = 13 ) );
+SELECT COUNT( c1 ) FROM w WHERE c3_number > 0.75;
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT c1_number FROM w WHERE c1_number != 5 AND c3_number = ( SELECT c3_number FROM w WHERE c1_number = 5 );
+SELECT c2_month FROM w order BY c1_number desc limit 1;
+SELECT c4_number FROM w order BY c1_number desc limit 1;
+SELECT MIN( c1_number ) FROM w WHERE c3_number < 0.6;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT c1_number FROM w WHERE c2_month = 7 AND c2_year = 2012;
+SELECT COUNT( c1 ) FROM w WHERE c3_number >= 0.7;
+SELECT COUNT( * ) FROM w WHERE c2_month = 5;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w order BY c6_parsed desc limit 1;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number > 100000;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w order BY c4_number asc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c5_number >= 1000.0;
+SELECT c5 FROM w WHERE c1 = 'june 24';
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5_number >= 10000;
+SELECT c3 FROM w WHERE id = 1;
+SELECT COUNT( * ) FROM w WHERE c3_first_number1 > c3_first_number2;
+SELECT c3_first_number1 FROM w WHERE c1 = 'june 11';
+SELECT MIN( c4_second_number ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c2_address = 'belgrade';
+SELECT COUNT( * ) FROM w WHERE c6 != 'friendly' AND c1_year = 2010;
+SELECT COUNT( * ) FROM w WHERE c6 = 'friendly';
+SELECT c1 FROM w order BY c4_first_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c6 = 'friendly';
+SELECT COUNT( * ) FROM w WHERE c6 = 'friendly';
+SELECT COUNT( c1 ) FROM w WHERE c6_number > 40;
+SELECT COUNT( c5 ) FROM w WHERE c5 = 'retired';
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c2 FROM w WHERE c4_maximum_year > 1988 order BY c6_number desc limit 1;
+SELECT MAX( c12_number ) FROM w;
+SELECT COUNT( * ) FROM w;
+SELECT MAX( c12_number ) FROM w WHERE id < ( SELECT id FROM w WHERE c1 = 'july 6, 2004' );
+SELECT COUNT( * ) FROM w WHERE c12_number <= 50000;
+SELECT COUNT( * ) FROM w WHERE c5_number < 1000;
+SELECT c4 FROM w WHERE id = 1;
+SELECT c2 FROM w WHERE c3_number1 = 2 AND id > ( SELECT id FROM w WHERE c2 = 'aylesbury united' ) order BY id asc limit 1;
+SELECT c2 FROM w WHERE c3_number1 + c3_number2 > 4;
+SELECT c2 FROM w order BY c5_number asc limit 1;
+SELECT c2 FROM w WHERE c2 != 'gloucester city' AND c5_number > 1000;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'gloucester city' ) - 1;
+SELECT c2 FROM w order BY c5_number asc limit 1;
+SELECT c2 FROM w WHERE c5_number >= 4000;
+SELECT c2 FROM w order BY c2_number asc limit 1;
+SELECT 2015 - 1912;
+SELECT c2 FROM w order BY abs ( c4_number1 - c4_number2 ) desc limit 1;
+SELECT c3_raw FROM w WHERE c3_raw IN ( 'tennessee titans' , 'green bay packers' ) order BY c4_number1 desc limit 1;
+SELECT c2 FROM w WHERE c4_number2 < 10 order BY c2_parsed limit 1;
+SELECT SUM( c4_number1 + c4_number2 ) FROM w;
+SELECT abs ( ( SELECT c6_number FROM w WHERE c2_month = 9 order BY c2_parsed limit 1 ) - ( SELECT c6_number FROM w WHERE c2_month = 1 order BY c2_parsed desc limit 1 ) );
+SELECT c3_raw FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c7 = 1 );
+SELECT ( SELECT COUNT( * ) FROM w WHERE c3_raw = 'denver broncos' ) = 1;
+SELECT c4_number1 - c4_number2 FROM w WHERE c1_number = 13;
+SELECT abs ( c4_number1 - c4_number2 ) FROM w WHERE c1 = 'october 19, 2008 - 15:00';
+SELECT COUNT( * ) FROM w WHERE c4_result = 'lost';
+SELECT c2 FROM w WHERE c1 = 'may 17, 2009 - 15:00';
+SELECT COUNT( DISTINCT c2 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE id < ( SELECT id FROM w WHERE c1 = 'october 5, 2008 - 15:00' ) AND c4_result = 'won';
+SELECT c4_number1 + c4_number2 FROM w WHERE c1 = 'november 15, 2008 - 20:30';
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4_number > 200;
+SELECT c2 FROM w WHERE c4_number < ( SELECT c4_number FROM w WHERE c2 = 'nedeljko golubovic' ) order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE c8 = 'sacred heart';
+SELECT COUNT( c1 ) FROM w WHERE c2_number > 30;
+SELECT ( SELECT c3_number FROM w WHERE c1 = 'caledonian canal' ) - ( SELECT c3_number FROM w WHERE c1 = 'aberdeenshire canal' );
+SELECT c1 FROM w WHERE c2_number = ( SELECT c2_number FROM w WHERE c1 = 'crinan canal' ) / 0.5;
+SELECT c8_number FROM w WHERE c1 = 'forth and clyde canal';
+SELECT c2_number FROM w WHERE c1 = 'dingwall canal';
+SELECT c1 FROM w WHERE c6_number = ( SELECT MAX( c6_number ) FROM w );
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w order BY c6_number limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'executive producer';
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 2004;
+SELECT c2 FROM w WHERE c3 = 'executive producer' order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'monster' ) - 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_number < 1988;
+SELECT c2 FROM w WHERE c4 = 'starring kevin costner and joan allen';
+SELECT c1_number FROM w WHERE c2 = 'beyond the sea';
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'pune strykers' , 'karnataka lions' ) order BY c8_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_number < ( SELECT c3_number FROM w WHERE c1 = 'pune strykers' );
+SELECT c1 FROM w WHERE c3_number = 0;
+SELECT COUNT( c1 ) FROM w WHERE c3_number = 2;
+SELECT c1 FROM w WHERE c1 != 'delhi wizards' AND c8_number = ( SELECT c8_number FROM w WHERE c1 = 'delhi wizards' );
+SELECT c1 FROM w WHERE c3_number = ( SELECT MAX( c3_number ) FROM w );
+SELECT c1 FROM w WHERE c4_number > 0;
+SELECT c1 FROM w WHERE c1 IN ( 'delhi wizards' , 'karnataka lions' ) order BY c3_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c8_number = 100;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT SUM( c4_number ) FROM w;
+SELECT c2 FROM w WHERE c2 IN ( 'craig stadler' , 'joe inman' ) order BY c4_result desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 5;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'united states' AND c4_result = 139;
+SELECT c2 FROM w WHERE c2 != 'ed sneed' AND c4_result = ( SELECT c4_result FROM w WHERE c2 = 'ed sneed' );
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT COUNT( DISTINCT c3 ) FROM w WHERE c1 IN ( 'a' , 'b' , 'c' );
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c8 = 'yes';
+SELECT c1 , c2 FROM w WHERE id = 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'no';
+SELECT COUNT( c2 ) FROM w WHERE c1 = 'b';
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'd' ) - 1;
+SELECT c3 FROM w WHERE c4 = ''the triangle'';
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'won' AND c1_number > 1987;
+SELECT c1_number FROM w WHERE c4 = ''cheers: the motion picture'';
+SELECT SUM( c3_length ) FROM w WHERE c1_number = 1985;
+SELECT COUNT( c4 ) FROM w WHERE c2 = 'outstanding film editing for a series' AND c5 = 'won';
+SELECT c3 FROM w WHERE c1 = 'swat force';
+SELECT abs ( ( SELECT c2_number FROM w WHERE c1 = 'swat 4' ) - ( SELECT c2_number FROM w WHERE c1 = 'swat force' ) );
+SELECT c1 FROM w WHERE c2_number < ( SELECT c2_number FROM w WHERE c1 = 'swat 4' ) order BY c2_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_number < 2006;
+SELECT COUNT( c1 ) FROM w WHERE c3_list = 'playstation portable';
+SELECT c1 FROM w WHERE c1 != 'swat 4' AND c4 = ( SELECT c4 FROM w WHERE c1 = 'swat 4' );
+SELECT COUNT( c1 ) FROM w WHERE c2 = 2006;
+SELECT COUNT( c1 ) FROM w WHERE c3_list = 'microsoft windows';
+SELECT COUNT( c1 ) FROM w;
+SELECT SUM( c4_number1 ) FROM w WHERE c1_year = 1950;
+SELECT c3 FROM w WHERE c1_year = 1950 order BY c1_parsed desc limit 1;
+SELECT SUM( c4_number1 ) FROM w WHERE c5 = 'friendly';
+SELECT c3 FROM w WHERE c4_number1 = 0;
+SELECT c1 FROM w WHERE c4_number1 = 0;
+SELECT c3 FROM w WHERE c1_year = 1951 order BY c1_parsed limit 1;
+SELECT c1_number FROM w WHERE c2 = 'boston college' order BY c1_number limit 1;
+SELECT COUNT( * ) FROM w WHERE c3_first_number1 > 7 AND c2 = 'boston college';
+SELECT c1 FROM w order BY c3_first_number1 - c3_first_number2 desc limit 1;
+SELECT c4 FROM w WHERE c4 IN ( 'boston university' , 'harvard' ) GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'boston college';
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'boston college' AND c5 = 'john 'snooks' kelley';
+SELECT c3_first FROM w WHERE id = ( SELECT id FROM w WHERE c2 = '1950-08-27' ) - 1;
+SELECT c2 FROM w WHERE c5_first = '0-6' order BY c2_parsed desc limit 1;
+SELECT abs ( c5_first_number1 - c5_first_number2 ) FROM w order BY c2_parsed desc limit 1;
+SELECT c1_number FROM w WHERE c1_number IN ( 163 , 181 ) order BY c5_first_number1 + c5_first_number2 desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'poland';
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'jna stadium, belgrade (a)' ) + 1;
+SELECT c1 FROM w order BY c2_parsed limit 1;
+SELECT c3 FROM w WHERE c1 = 'raymond h. fogler';
+SELECT COUNT( DISTINCT c5 ) FROM w WHERE c6 = 'beauty and the beast';
+SELECT c1 FROM w WHERE c6 = 'king kong';
+SELECT COUNT( DISTINCT c4 ) FROM w WHERE c1 = 'howard ashman';
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+SELECT c1 FROM w WHERE c2_year > 1990 AND c2_year < 1992;
+SELECT abs ( ( SELECT c2_year FROM w WHERE c1 = 'frank churchill' ) - ( SELECT c2_year FROM w WHERE c1 = 'allen davey' ) );
+SELECT c1 FROM w WHERE c1 IN ( 'walt disney' , 'james dean' ) AND c7 = 'won' GROUP BY c1 order BY COUNT( c5 ) desc limit 1;
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+SELECT c2 FROM w WHERE c1 = 'larry johnson';
+SELECT COUNT( c1 ) FROM w;
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+SELECT ( SELECT c2_number FROM w WHERE c1 = 'allan houston' ) - ( SELECT c2_number FROM w WHERE c1 = 'mark jackson' );
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+SELECT COUNT( c1 ) FROM w;
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+SELECT COUNT( c2 ) FROM w WHERE c1_minimum_year < 1992;
+SELECT c4 FROM w WHERE c3 = 'bbc' order BY c1_maximum_year desc limit 1;
+SELECT c2 FROM w WHERE c6 = 'the first made-for-television film to address people with aids';
+SELECT COUNT( c2 ) FROM w WHERE c3 IN ( 'hbo' , 'mtv' );
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'cbs';
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+SELECT c2 FROM w WHERE c3 = 'abc' order BY c1_minimum_year desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5_year < 1986 OR ( c5_year = 1986 AND c5_month < 6 );
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+SELECT COUNT( c2 ) FROM w WHERE c3_number < 3200;
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'ruma,bandarban';
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+SELECT COUNT( c2 ) FROM w WHERE c3_number > 3200;
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+SELECT c1 FROM w order BY c7_number desc limit 1;
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+SELECT c1 FROM w WHERE c5_list_number = 5;
+SELECT c4 FROM w WHERE c1 = 'four-hands';
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+SELECT c4 FROM w order BY c2_number asc limit 1;
+SELECT c4 FROM w order BY c2_number asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'summer';
+SELECT COUNT( * ) FROM w WHERE c3 = 'summer';
+SELECT c4 FROM w order BY c1_number desc limit 1;
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+SELECT COUNT( DISTINCT c4 ) FROM w;
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+SELECT c2 FROM w order BY c4_number desc limit 1;
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+SELECT c2 FROM w order BY c4_number limit 1;
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+SELECT COUNT( c2 ) FROM w WHERE c3_number > ( SELECT c3_number FROM w WHERE c2 = 'silex' );
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+SELECT c2 FROM w order BY c3_number desc limit 1;
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+SELECT COUNT( c1 ) FROM w;
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+SELECT c4 FROM w WHERE c5_number > ( SELECT c5_number FROM w WHERE c4 = 'nanjing olympic stadium' );
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+SELECT COUNT( c1 ) FROM w;
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+SELECT c3 FROM w order BY c1_number desc limit 1;
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+SELECT COUNT( * ) FROM w WHERE c3 = 'anthony browne';
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+SELECT abs ( ( SELECT c1_number FROM w WHERE c2_first = 'angela carter' ) - ( SELECT c1_number FROM w WHERE c2_first = 'anthony browne' ) );
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+SELECT c3 FROM w WHERE c2 = 'olympic games' AND c1_number = 1984;
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+SELECT c1 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4_number <= 5;
+SELECT COUNT( * ) FROM w WHERE c2 = 'european championships';
+SELECT COUNT( * ) FROM w WHERE c4_number = 4;
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+SELECT c3 FROM w order BY c2_number asc limit 1;
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+SELECT c4 FROM w GROUP BY c4 order BY COUNT( c3 ) desc limit 1;
+SELECT c3 FROM w WHERE c3 IN ( 'tom anderson' , 'tom sundberg' ) order BY c2_number asc limit 1;
+SELECT c2 FROM w order BY id desc limit 1;
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+SELECT COUNT( c3 ) FROM w WHERE c4 = 'canada';
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+SELECT SUM( c5_number ) FROM w;
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+SELECT c4_number FROM w WHERE c2_list = 'daniel willemsen';
+SELECT c4_number FROM w WHERE c2_list = 'maris rupeiks';
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+SELECT COUNT( DISTINCT c2 ) FROM w;
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+SELECT c5 FROM w;
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+SELECT c2 FROM w order BY c5_number asc limit 1;
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+SELECT COUNT( c2 ) FROM w;
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+SELECT COUNT( c4 ) FROM w;
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+SELECT COUNT( c2 ) FROM w WHERE c3 = 'nitti' AND c4 NOT NULL;
+SELECT c2 FROM w WHERE c2 IN ( 't.v. sivaraopantulu' , 'l. suryalingam' ) order BY c1_maximum_year - c1_minimum_year desc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c1_minimum_year >= 1905 AND c1_maximum_year <= 1921;
+SELECT c2 FROM w WHERE c1_minimum_year > ( SELECT c1_minimum_year FROM w WHERE c2 = 'maturiramarao' ) order BY c1_minimum_year limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c1_maximum_year - c1_minimum_year FROM w WHERE c2 = 'paidisettijayanthi';
+SELECT c2 FROM w WHERE c2 IN ( 't.v. sivaraopantulu' , 'gynatevenkatarao' ) order BY c1_minimum_year limit 1;
+SELECT c1_maximum_year - c1_minimum_year FROM w WHERE c2 = 'shilpa bendi';
+SELECT c1_maximum_year - c1_minimum_year FROM w WHERE c2 = 'challanarasimhanaidu';
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+SELECT c2 FROM w WHERE c5 = 'bob balaban';
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+SELECT COUNT( c1 ) FROM w WHERE c3_month = 5 AND c3_year = 1995;
+SELECT c2 FROM w WHERE c2 IN ( ''birth of a legend'' , ''skeletons in the closet'' ) order BY c3_parsed asc limit 1;
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+SELECT COUNT( c1 ) FROM w WHERE c3_month = 7 AND c3_year = 1995;
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c4_list = 'bill dial';
+SELECT COUNT( c1 ) FROM w WHERE c3_month = 7;
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+SELECT c1 FROM w WHERE c1 IN ( 'jerel myers' , 'cecil moore' ) order BY c5_number desc limit 1;
+SELECT c1 FROM w order BY c3_number desc limit 1;
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+SELECT abs ( ( SELECT c5 FROM w WHERE c1 = 'raymond philyaw' ) - ( SELECT c5 FROM w WHERE c1 = 'jerel myers' ) );
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+SELECT SUM( c5_number ) FROM w WHERE c1 IN ( 'anthony hines' , 'boo williams' );
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+SELECT c2 FROM w order BY id desc limit 1;
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+SELECT c2 FROM w WHERE c3 = 'cameroon';
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+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w WHERE c1_minimum_month = 1;
+SELECT c1 FROM w WHERE c2 = 'tour of libya';
+SELECT c5 FROM w WHERE c1 = 'oklahoma';
+SELECT c1 FROM w order BY c4_number asc limit 1;
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+SELECT c2 FROM w WHERE id = 1;
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+SELECT COUNT( * ) FROM w WHERE c4 = 'deggendorfer sc ii';
+SELECT c5 FROM w order BY id desc limit 1;
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+SELECT COUNT( c1 ) FROM w WHERE c5_list_first = 'militia';
+SELECT COUNT( c1 ) FROM w WHERE c4_month = 10;
+SELECT c2 FROM w WHERE c2 IN ( 'liberal' , 'unionist' ) GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c5_list_first = 'royal canadian air force';
+SELECT COUNT( c1 ) FROM w WHERE c5_list_first = 'canadian army';
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+SELECT c1 FROM w WHERE c1 IN ( 'can-i-bus' , '2000 b.c. (before can-i-bus)' ) order BY c3 asc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_number <= 50 OR c4_number <= 50;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 194 ) + 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = '2000 b.c. (before can-i-bus)' ) - 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT MIN( MIN( c3_number ) , MIN( c4_number ) ) FROM w;
+SELECT c1 FROM w WHERE c16 = '5-5';
+SELECT abs ( ( SELECT c16_number1 FROM w WHERE c1 = 'wimbledon' ) - ( SELECT c16_number1 FROM w WHERE c1 = 'us open' ) );
+SELECT COUNT( c1 ) FROM w;
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+SELECT COUNT( * ) FROM w WHERE c6_number > 80000;
+SELECT c1 FROM w WHERE c6_number < 30000;
+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c2 FROM w order BY length ( c2 ) desc limit 1;
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'andrew carter' ) - 1;
+SELECT COUNT( c2 ) FROM w WHERE c4_list = 'ol';
+SELECT c3 FROM w WHERE c1_number = 25 + 1;
+SELECT c3 FROM w order BY c1_number asc limit 1;
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c4_list = 'ol' AND c5 = 'mcmaster';
+SELECT c4 FROM w WHERE c2 = 'montreal alouettes' AND c1_number < ( SELECT c1_number FROM w WHERE c3 = 'peter moore' );
+SELECT c6 FROM w WHERE c1 = 'college of mount st. joseph';
+SELECT c1 FROM w WHERE c6_number > ( SELECT c6_number FROM w WHERE c1 = 'carthage college' );
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+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c1 FROM w WHERE c6_number < ( SELECT c6_number FROM w WHERE c1 = 'elmhurst college' ) order BY c6_number desc limit 1;
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+SELECT c3 FROM w GROUP BY c3 order BY COUNT( c1 ) desc limit 1;
+SELECT c4 FROM w order BY c5_parsed desc limit 1;
+SELECT c2_list FROM w WHERE c1 = 'gold' INTERSECT SELECT c2_list FROM w WHERE c1 = 'silver';
+SELECT COUNT( * ) FROM w WHERE c1 = 'silver' OR c1 = 'bronze';
+SELECT c2 FROM w WHERE c4 = 'men's javelin throw';
+SELECT ( SELECT COUNT( * ) FROM w WHERE c1 = 'silver' ) - ( SELECT COUNT( * ) FROM w WHERE c1 = 'gold' );
+SELECT COUNT( * ) FROM w WHERE c1 = 'gold';
+SELECT COUNT( * ) FROM w WHERE c1 = 'silver';
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+SELECT COUNT( DISTINCT c1 ) FROM w WHERE c3_number = 10;
+SELECT c1 FROM w GROUP BY c1 order BY SUM( c4_list_maximum - c4_list_minimum ) asc limit 1;
+SELECT c1 FROM w WHERE c1_number IN ( 1894 , 1893 ) GROUP BY c1_number order BY SUM( c3_number ) desc limit 1;
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+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c1 FROM w order BY id desc limit 1;
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+SELECT c2 FROM w WHERE id = 1;
+SELECT SUM( c8_number ) FROM w WHERE c2 = 'diego dominguez';
+SELECT COUNT( * ) FROM w WHERE c4_number >= 24 AND c11_year < 2000;
+SELECT c2 FROM w WHERE c3 != 'fly-half';
+SELECT c4 FROM w WHERE c1_number = 2011 AND c2 = 'filmfare awards' INTERSECT SELECT c4 FROM w WHERE c1_number = 2011 AND c2 = 'screen awards';
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( * ) asc limit 1;
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( c2 ) desc limit 1;
+SELECT c4 FROM w GROUP BY c4 HAVING COUNT( c2 ) = 2;
+SELECT COUNT( * ) FROM w WHERE c2 = 'filmfare awards';
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( * ) asc limit 1;
+SELECT c2 FROM w WHERE c4 = 'sarabhai vs sarabhai' order BY id desc limit 1;
+SELECT c2 FROM w WHERE c7 = 'nominated' order BY c1_number asc limit 1;
+SELECT MAX( c1_number ) FROM w WHERE c2 = 'kelsey grammer' AND c4 = 'outstanding voice-over performance';
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c1_number = 2009;
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+SELECT COUNT( c3 ) FROM w WHERE c7 = 'nominated';
+SELECT COUNT( * ) FROM w WHERE c2 = 'nancy cartwright' AND c1_number >= 1992 AND c1_number < 2011;
+SELECT COUNT( * ) FROM w WHERE c3 = 'annie award' AND c1_number < 2009;
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+SELECT COUNT( c2 ) FROM w WHERE c3 = 'belfast';
+SELECT c2 FROM w WHERE c5 = 'later miss united kingdom 2005 and miss universe united kingdom 2005 2nd runner-up';
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c5 IS NULL;
+SELECT COUNT( c1 ) FROM w WHERE c5_number = 1974;
+SELECT c1 FROM w WHERE c5_number = 1989;
+SELECT COUNT( c1 ) FROM w WHERE c3_list = 'alfred waterhouse';
+SELECT c1 FROM w WHERE c5_number < 1974;
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+SELECT c5 FROM w GROUP BY c5 order BY COUNT( c1 ) desc limit 1;
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+SELECT c4 FROM w WHERE c3 = 'arven';
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+SELECT c3 FROM w WHERE c4 = 'kirkelig kulturverksted';
+SELECT c3 FROM w WHERE c5_number <= 10 order BY c1_number asc limit 1;
+SELECT abs ( ( SELECT c1_number FROM w WHERE c3 = 'tarpan seasons' ) - ( SELECT c1_number FROM w WHERE c3 = 'antologie' ) );
+SELECT COUNT( c1 ) FROM w;
+SELECT c2 FROM w order BY c5_minimum_number asc limit 1;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c2 FROM w WHERE id = 1;
+SELECT COUNT( c1 ) FROM w WHERE c1_list = 'federal building';
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+SELECT c5_minimum_year FROM w WHERE c3 = '10 court street';
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+SELECT COUNT( * ) FROM w WHERE c5_first_number > 7.70;
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+SELECT c1_number FROM w order BY c5_first_number desc limit 1;
+SELECT abs ( ( SELECT COUNT( * ) FROM w WHERE c4_first_number = 3 ) - ( SELECT COUNT( * ) FROM w WHERE c4_first_number = 1 ) );
+SELECT c2 FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c2 = 'east asian games' AND c1_number = 2001 ) order BY c1_number desc limit 1;
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+SELECT COUNT( c2 ) FROM w;
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+SELECT c3 FROM w WHERE c3_address = 'spain';
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+SELECT COUNT( * ) FROM w;
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+SELECT c7 FROM w WHERE c3 = 'v10 kleber';
+SELECT COUNT( * ) FROM w WHERE c4 = 'new zealand' AND c1_year = 2010;
+SELECT abs ( ( SELECT c3_number1 FROM w WHERE c5 = '2012 autumn international' ) - ( SELECT c3_number2 FROM w WHERE c5 = '2012 autumn international' ) );
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+SELECT COUNT( * ) FROM w WHERE c2 = 'ballymore, brisbane';
+SELECT c1_year FROM w GROUP BY c1_year order BY COUNT( * ) desc limit 1;
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+SELECT MIN( c1_year ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c4 = 'wales';
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+SELECT COUNT( * ) FROM w WHERE c5_second = 'ken';
+SELECT c3 FROM w GROUP BY c3 HAVING COUNT( * ) >= 3;
+SELECT c3 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c3_first = 'joshua kipkemboi' ) order BY c1_number asc limit 1;
+SELECT c3 FROM w order BY c7_number desc limit 1;
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+SELECT c3 FROM w order BY c9_number asc limit 1;
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+SELECT COUNT( * ) FROM w WHERE c5 = 'toyota';
+SELECT c3 FROM w order BY c9_number desc limit 1;
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+SELECT COUNT( c4 ) FROM w;
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+SELECT c6 FROM w WHERE c2 = 'steve maestas';
+SELECT COUNT( c3 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c2 = 'vacancy';
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT c3 FROM w WHERE id = 1;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'ratcliffe's inc' ) + 1;
+SELECT c2 FROM w WHERE c1 = '10th kyu';
+SELECT c1 FROM w WHERE c3 IS NULL;
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT COUNT( DISTINCT c2 ) FROM w;
+SELECT c1 FROM w WHERE id = 1;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_number = 2011;
+SELECT c5_list FROM w GROUP BY c5_list order BY COUNT( c1 ) desc limit 1;
+SELECT COUNT( c6 ) FROM w;
+SELECT COUNT( * ) FROM w;
+SELECT c2_number FROM w WHERE c1 = ''cheat on you'';
+SELECT c2 FROM w WHERE id = 1;
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT c3 FROM w WHERE c4 IS NULL;
+SELECT c6 FROM w WHERE id = 1;
+SELECT MIN( c1_number ) FROM w WHERE c2 = 'olympic games';
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+SELECT c2 FROM w WHERE c2 IN ( 'beautiful' , 'j'adore' ) order BY c5_year desc limit 1;
+SELECT c2 FROM w WHERE id = 1;
+SELECT c6 FROM w WHERE id = ( SELECT id FROM w WHERE c6 = 'universal music' ) - 1;
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+SELECT c2 FROM w WHERE c6 = 'rock records' order BY id desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c6 = 'rock records';
+SELECT c2 FROM w WHERE c6 IN ( 'b'in music' , 'universal music' );
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+SELECT c2 FROM w WHERE c3 != 'atlanta' AND c5_number > 25000;
+SELECT c2 FROM w WHERE c2 != 'our lady of the assumption catholic church' AND c4_number = ( SELECT c4_number FROM w WHERE c2 = 'our lady of the assumption catholic church' );
+SELECT COUNT( DISTINCT c3 ) FROM w;
+SELECT ( SELECT c5_number FROM w WHERE c2 = 'cathedral of christ the king' ) - ( SELECT c5_number FROM w WHERE c2 = 'most blessed sacrament catholic church' );
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+SELECT c2_first FROM w WHERE c2_second = 'switzerland' order BY c1_number asc limit 1;
+SELECT c5 FROM w WHERE c2_first = 'karl behting';
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+SELECT c4_number FROM w WHERE c2 = 'rita';
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+SELECT c2 FROM w order BY c4_number limit 1;
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+SELECT c3 FROM w order BY c4_number desc limit 1;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = ''layla layla'' ) - 1;
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+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w order BY c4_number limit 1;
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+SELECT COUNT( * ) FROM w WHERE c4_first = 'tom boonen';
+SELECT COUNT( * ) FROM w WHERE c5 = 'italy';
+SELECT COUNT( * ) FROM w WHERE c4_first = 'tom boonen';
+SELECT COUNT( * ) FROM w WHERE c3 = 'uci europe tour';
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+SELECT COUNT( * ) FROM w WHERE c2_list = 'tour of qatar';
+SELECT COUNT( * ) FROM w WHERE c4_first = 'tom boonen';
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+SELECT MAX( c1 ) FROM w WHERE c4 = 'broxburn athletic';
+SELECT c4 FROM w WHERE c4 IN ( 'fauldhouse united' , 'newtongrange star' ) GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w WHERE c2 = 'bo'ness united' order BY c1_minimum_year desc limit 1;
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+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'tayport' ) - 1;
+SELECT c3 FROM w WHERE c1_month = 11;
+SELECT COUNT( * ) FROM w WHERE c6_number2 = 0;
+SELECT c2_raw FROM w WHERE c2_raw IN ( 'nebraska' , 'colorado' ) order BY c7_number desc limit 1;
+SELECT c2 FROM w WHERE c1_month = 9 order BY c1_parsed asc limit 1;
+SELECT c7 FROM w WHERE c2_raw = 'texas';
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+SELECT COUNT( * ) FROM w WHERE c3_number = 2;
+SELECT c2 FROM w WHERE c1_parsed > ( SELECT c1_parsed FROM w WHERE c2_raw = 'usc' ) order BY c1_parsed asc limit 1;
+SELECT c3 FROM w WHERE c2_month = 1;
+SELECT c3 FROM w order BY c4_number desc limit 1;
+SELECT c3 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( DISTINCT c3 ) FROM w WHERE c4_number > 5;
+SELECT c3 FROM w order BY c2_parsed desc limit 1;
+SELECT c1 FROM w WHERE c4 = 'winner' order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'madina begum' ) + 1;
+SELECT c2 FROM w WHERE c4 = 'winner' order BY c1_number asc limit 1;
+SELECT c2 FROM w WHERE c5 NOT NULL AND c4 IS NULL;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1_number = 1997 ) + 1;
+SELECT COUNT( c2 ) FROM w WHERE c4 NOT NULL AND c6 NOT NULL;
+SELECT COUNT( c1 ) FROM w;
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT MIN( c1 ) FROM w;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w WHERE c1 IN ( 'snp' , 'conservative' ) order BY c4_number desc limit 1;
+SELECT c1 FROM w WHERE c2 = 'flash gordon approaching';
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+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'b.n. yugandhar' ) + 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_year >= 2000 AND c3_year <= 2009;
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'ias';
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'rajeshwar prasad' ) - 1;
+SELECT MAX( c1_number ) - MIN( c1_number ) FROM w;
+SELECT c3 FROM w order BY c1_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'olympic games';
+SELECT c5 FROM w WHERE c2 = 'european championships';
+SELECT COUNT( * ) FROM w;
+SELECT c2 FROM w WHERE c3_address = 'spain' AND c2 IN ( 'european championships' , 'european indoor championships' );
+SELECT COUNT( * ) FROM w WHERE c5 = 'heptathlon';
+SELECT COUNT( * ) FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c4_number = 7 );
+SELECT c1 FROM w WHERE c4_number = 24;
+SELECT COUNT( * ) FROM w WHERE c4_number < ( SELECT c4_number FROM w WHERE c1_number = 1995 AND c2 = 'world indoor championships' );
+SELECT MAX( c1_year ) - MIN( c1_year ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c1_year = 1999;
+SELECT COUNT( * ) FROM w WHERE c1_month = 6;
+SELECT c2 FROM w WHERE c1_parsed < ( SELECT c1_parsed FROM w WHERE c2 = 'chasing shadows' ) order BY c1_parsed desc limit 1;
+SELECT c2 FROM w WHERE c1_parsed > ( SELECT c1_parsed FROM w WHERE c2 = 'swing 48' ) order BY c1_parsed limit 1;
+SELECT c2 FROM w WHERE c2 != 'avalon' AND c1_month = ( SELECT c1_month FROM w WHERE c2 = 'avalon' );
+SELECT c2 FROM w WHERE c1_year = 2000;
+SELECT COUNT( c2 ) FROM w WHERE c1_year = 1999;
+SELECT c2 FROM w order BY c4_first_number desc limit 1;
+SELECT MAX( c2_maximum_year ) - MIN( c2_minimum_year ) FROM w;
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c6 = 'bruce springsteen';
+SELECT c3 FROM w order BY c2_parsed limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4 = 'ed sheeran';
+SELECT COUNT( c3 ) FROM w WHERE c2_month = 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'ed sheeran';
+SELECT COUNT( c1 ) FROM w WHERE c3 = ''i see fire'';
+SELECT c5 FROM w order BY id desc limit 1;
+SELECT c4 FROM w WHERE c4 IN ( 'ed sheeran' , 'ace wilder' ) GROUP BY c4 order BY COUNT( c3 ) desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3 = ''i see fire'';
+SELECT c3 FROM w WHERE id = 1;
+SELECT COUNT( DISTINCT c5 ) FROM w WHERE c3 = ''i see fire'';
+SELECT c2 FROM w WHERE c4 = 'ed sheeran' order BY c2_parsed limit 1;
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( * ) limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3 = ''timber'';
+SELECT c3_number FROM w WHERE c2 = 'spain';
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT c4_number FROM w WHERE c2 = 'sweden';
+SELECT c2 FROM w WHERE c2 IN ( 'france' , 'croatia' ) order BY c5_number desc limit 1;
+SELECT c2 FROM w WHERE c5_number = ( SELECT MIN( c5_number ) FROM w );
+SELECT c7_number FROM w WHERE c5 = 'philadelphia';
+SELECT COUNT( * ) FROM w WHERE c2 = 'uefa europa league';
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT MAX( c1_maximum_number ) - MIN( c1_minimum_number ) + 1 FROM w;
+SELECT MAX( c1_number ) FROM w WHERE c1_number < 2005;
+SELECT c1_number FROM w WHERE c5 = 'national semifinals';
+SELECT c1 FROM w WHERE c1_number IN ( 2003 , 2004 ) AND c5 = 'did not qualify';
+SELECT MAX( c1_number ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c5 != 'did not qualify';
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'russia';
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( c2 ) desc limit 1;
+SELECT c4 FROM w WHERE c1_number = 7;
+SELECT c2 FROM w WHERE id = 1;
+SELECT c4 FROM w WHERE c2 = 'anna zagorska';
+SELECT c4 FROM w WHERE c2 = 'anna zagorska';
+SELECT c4 FROM w order BY c2_parsed asc limit 1;
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+SELECT c7 FROM w GROUP BY c7 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'finland';
+SELECT COUNT( * ) FROM w WHERE c7 = 'international friendly';
+SELECT c3_address FROM w GROUP BY c3_address order BY COUNT( * ) desc limit 1;
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+SELECT c5 FROM w WHERE c2 = 'december 11, 1976';
+SELECT c2_month FROM w;
+SELECT c2 FROM w order BY c2_parsed desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'san antonio spurs';
+SELECT COUNT( * ) FROM w WHERE c4_number1 < 100 OR c4_number2 < 100;
+SELECT COUNT( * ) FROM w;
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+SELECT c6 FROM w WHERE c2 = 'misano world circuit';
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+SELECT c5 FROM w WHERE c6 = 'target racing' GROUP BY c5 HAVING COUNT( * ) = 1;
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+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'tm jones' ) - 1;
+SELECT c3 FROM w WHERE c6_number1 < 20;
+SELECT COUNT( * ) FROM w WHERE c7 = 'unseated rider';
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+SELECT COUNT( c2 ) FROM w WHERE c4_number > 10;
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+SELECT c3 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'brown diamond' , 'flamecap' ) order BY c4_number desc limit 1;
+SELECT SUM( c4_number ) FROM w WHERE c2 IN ( 'what a myth' , 'rough tweed' );
+SELECT COUNT( * ) FROM w WHERE c5 = '10-0';
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+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'dodgy dealer' ) - 1;
+SELECT c2 FROM w WHERE c1_number = 1997 AND c2 IN ( 'theme hospital' , 'mad tv 2' );
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+SELECT COUNT( c2 ) FROM w WHERE c1_number = 1984;
+SELECT COUNT( c2 ) FROM w WHERE c1_year < 1995;
+SELECT c2 FROM w WHERE c3_list = 'frog city';
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+SELECT c5_length FROM w WHERE c2 = 'm.u.l.e' AND c1_year = 1983;
+SELECT COUNT( c2 ) FROM w WHERE c1_year >= 1963 AND c1_year <= 1973;
+SELECT COUNT( * ) FROM w;
+SELECT abs ( c4_number1 - c4_number2 ) FROM w WHERE c2 = 'december 19';
+SELECT c4_number1 + c4_number2 FROM w WHERE c2 = 'november 4';
+SELECT c5 FROM w WHERE c2 = 'december 14';
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'november 11' AND c3 = 'boston bruins' ) + 1;
+SELECT c7 FROM w WHERE c2 = 'march 4';
+SELECT c3_number FROM w WHERE c1 = 'boxing';
+SELECT c1 FROM w WHERE c2_number < ( SELECT MAX( c2_number ) FROM w ) order BY c2_number desc limit 1;
+SELECT c2_number FROM w WHERE c1 = 'rowing';
+SELECT c1 FROM w WHERE c1 IN ( 'wrestling' , 'rowing' ) order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'canoeing' ) + 1;
+SELECT c3 FROM w WHERE c2_number = 1;
+SELECT ( SELECT c2_number FROM w WHERE c4 = 'roy williams' ) < ( SELECT c2_number FROM w WHERE c4 = 'ryan sims' );
+SELECT c3 FROM w WHERE c2_number = 1;
+SELECT c4 FROM w WHERE c3 = 'detroit lions' AND c7 != 'pac-10' order BY c2_number limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c7 = 'big east' AND c1_number <= 2;
+SELECT c3_number FROM w WHERE c1_number = 2008;
+SELECT c1_number FROM w WHERE c2_number = 4140;
+SELECT c2_number FROM w WHERE c1_number = 2012;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w WHERE c1 IN ( 'takaji mori' , 'junji kawano' ) order BY c8_first_number desc limit 1;
+SELECT ( SELECT c8_first_number FROM w WHERE c1 = 'mitsuo kamata' ) > 40;
+SELECT ( SELECT c8_first_number FROM w WHERE c1 = 'shigeo yaegashi' ) - ( SELECT c8_first_number FROM w WHERE c1 = 'mitsuo kamata' );
+SELECT c8 FROM w WHERE c1 = 'mitsuo kamata';
+SELECT c8_first_number FROM w WHERE c1 = 'masakatsu miyamoto';
+SELECT c2 FROM w WHERE id = 1;
+SELECT c2 FROM w WHERE c1 = 1975;
+SELECT c1 FROM w WHERE c2 = 'spain' AND c1_number > 1979 order BY c1_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2_list = 'france';
+SELECT c2 FROM w GROUP BY c2_list order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c1_number < 2010 AND c2_list = 'ireland';
+SELECT c6 FROM w GROUP BY c6 order BY COUNT( c1 ) desc limit 1;
+SELECT c1 FROM w WHERE id < ( SELECT MIN( id ) FROM w WHERE c1 = 'diamond racing' ) order BY id desc limit 1;
+SELECT COUNT( * ) FROM ( SELECT c1 FROM w GROUP BY c1 HAVING COUNT( c3 ) >= 2 );
+SELECT c1 FROM w WHERE c1 IN ( 'team avanti' , 'motaworld racing' ) GROUP BY c1 order BY COUNT( c3 ) desc limit 1;
+SELECT c3 FROM w WHERE c3 != 'clivio piccione' AND c1 = 't-sport';
+SELECT c4 FROM w GROUP BY c4 order BY SUM( c5_number ) desc limit 1;
+SELECT c5 FROM w WHERE c2 = 'jatara';
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT c5_number FROM w WHERE c2 = 'chhatarpur';
+SELECT c2 FROM w WHERE c5 = 139110;
+SELECT c4 FROM w WHERE c4 != 'tikamgarh' AND agg = 0;
+SELECT COUNT( DISTINCT c4 ) FROM w;
+SELECT c2 FROM w WHERE c2 IN ( 'khargapur' , 'niwari' ) order BY c5_number desc limit 1;
+SELECT c2 FROM w WHERE c5_number < ( SELECT c5_number FROM w WHERE c2 = 'niwari' );
+SELECT COUNT( * ) FROM w WHERE c8_number >= 8000;
+SELECT SUM( c7_number1 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c2_month = 7;
+SELECT SUM( c8_number ) FROM w WHERE c2_month = 2;
+SELECT SUM( c8_number ) FROM w WHERE c2_month = 3;
+SELECT COUNT( c3 ) FROM w WHERE c2 != '19 aug' AND c8_number = ( SELECT c8_number FROM w WHERE c2 = '19 aug' );
+SELECT c6 FROM w WHERE c1_number = 1;
+SELECT c2 FROM w order BY c8_number desc limit 1;
+SELECT c3 FROM w WHERE c2_first = 'dzhebariki-khaya';
+SELECT c2 FROM w WHERE c1 = 'rural' order BY c4_first_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_number < 1000;
+SELECT c1 FROM w WHERE c3_number = 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'chicago' ) - 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'united states';
+SELECT c1 FROM w WHERE c5_number > 10000000 AND c5_number < 20000000;
+SELECT c5_number FROM w WHERE id = ( SELECT id FROM w WHERE c5_number = 5357422 ) - 1;
+SELECT c3_list FROM w GROUP BY c3_list HAVING COUNT( * ) >= 2;
+SELECT COUNT( c3_list ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 2003;
+SELECT COUNT( * ) FROM w WHERE c3 IS NULL;
+SELECT ( SELECT MIN( c1_number ) FROM w WHERE c4 = 'cts' ) - ( SELECT MIN( c1_number ) FROM w );
+SELECT COUNT( * ) FROM w WHERE c4 = 'tvb';
+SELECT COUNT( DISTINCT ( c4 ) ) FROM w;
+SELECT c1 FROM w WHERE c3_month = 10 AND c3_day = 8 AND c4 = 'phoenix';
+SELECT c4 FROM w order BY c3_parsed desc limit 1;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c1 = 'winner' AND c5_first = 'clay' ) > 5;
+SELECT c4 FROM w WHERE c1 = 'winner' GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT c5 FROM w WHERE c3_parsed < ( SELECT c3_parsed FROM w WHERE c3 = '12 november 1978' ) order BY c3_parsed desc limit 1;
+SELECT c4 FROM w WHERE c3_year = 1991;
+SELECT COUNT( * ) FROM w WHERE c5_first = 'grass';
+SELECT c6 FROM w GROUP BY c6 order BY COUNT( * ) desc limit 1;
+SELECT c4 FROM w WHERE c4 IN ( 'oldsmar' , 'los angeles' ) GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT c8 FROM w WHERE c3 = 'taiki tsuchiya';
+SELECT c3 FROM w WHERE c1 = 'loss' order BY c6_parsed limit 1;
+SELECT c3 FROM w WHERE c6_parsed > ( SELECT c6_parsed FROM w WHERE c3 = 'hatsu hioki' ) order BY c6_parsed limit 1;
+SELECT c1 FROM w WHERE c4_number < ( SELECT c4_number FROM w WHERE c1 = 'crettyard' ) order BY c4_number desc limit 1;
+SELECT c1 FROM w WHERE c4_number = 2009;
+SELECT COUNT( * ) FROM w WHERE c1 = 'confey';
+SELECT abs ( ( SELECT c4_number FROM w WHERE c1 = 'crettyard' ) - ( SELECT c4_number FROM w WHERE c1 = 'greystones' ) );
+SELECT c1 FROM w WHERE c4_number < 2008 order BY c4_number desc limit 1;
+SELECT c3 FROM w;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT SUM( c3_number ) FROM w;
+SELECT c3 FROM w WHERE c1 = 'confey';
+SELECT c2 FROM w GROUP BY c2 order BY SUM( c3_number ) desc limit 1;
+SELECT c4 FROM w;
+SELECT c1 FROM w WHERE c4_number > ( SELECT c4_number FROM w WHERE c1 = 'ballymore eustace' ) order BY c4_number asc limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'confey' ) - 1;
+SELECT c1 FROM w WHERE id = 1;
+SELECT c3 FROM w WHERE c1 = 'greystones';
+SELECT ( SELECT c4_number FROM w WHERE c1 = 'liberal party of canada' ) > ( SELECT c5_number FROM w WHERE c1 = 'liberal party of canada' );
+SELECT ( SELECT c3_number FROM w WHERE c1 = 'new democratic party' ) - ( SELECT c2_number FROM w WHERE c1 = 'new democratic party' );
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c1 FROM w order BY c4_number1 desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c1 = 'hair';
+SELECT c5 FROM w WHERE c1 != 'hair';
+SELECT c2 FROM w GROUP BY c2 HAVING COUNT( * ) >= 2;
+SELECT COUNT( * ) FROM w WHERE c5_year = 2011;
+SELECT COUNT( * ) FROM w WHERE c4_address = 'mexico city';
+SELECT COUNT( * ) FROM w WHERE c2_list = 'psycho clown';
+SELECT c1 FROM w WHERE c1 != 'hair';
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT abs ( ( SELECT c2_number FROM w WHERE c1 = '2000 (total population)' ) - ( SELECT c2_number FROM w WHERE c1 = '2005 (total population)' ) );
+SELECT c5 FROM w WHERE c4 = 'the lion king';
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'the cheetah girls' ) - 1;
+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT c5 FROM w WHERE c2 = ''look through my eyes'';
+SELECT c2 FROM w WHERE c2 IN ( ''can you feel the love tonight'' , ''candle on the water'' ) AND c4 = 'the lion king';
+SELECT c2 FROM w order BY c5 desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_min >= 4;
+SELECT c2 FROM w WHERE c2 != 'bob caudle' AND c4_list = 'announcing';
+SELECT COUNT( * ) FROM w WHERE c4_list = 'announcing';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'nelson royal' ) + 1;
+SELECT COUNT( * ) FROM w WHERE c1_number < 2009;
+SELECT c2_first FROM w WHERE c4 = 'wrestling and managing';
+SELECT COUNT( * ) FROM w WHERE c4_number > 45;
+SELECT COUNT( c3 ) FROM w;
+SELECT COUNT( c3 ) FROM w WHERE c8 != 'lethal injection';
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'lynda lyon block' ) + 1;
+SELECT c3 FROM w WHERE c3 != 'lois nadean smith' AND c2_year = ( SELECT c2_year FROM w WHERE c3 = 'lois nadean smith' );
+SELECT COUNT( c3 ) FROM w WHERE c8 = 'lethal injection';
+SELECT COUNT( c3 ) FROM w WHERE c7 = 'oklahoma';
+SELECT COUNT( c3 ) FROM w WHERE c2_year >= 2000 AND c2_year <= 2010;
+SELECT c1 FROM w order BY c8_number asc limit 1;
+SELECT ( SELECT c8_number FROM w WHERE c1 = 'ae 8/8' ) > ( SELECT c8_number FROM w WHERE c1 = 're 6/6' );
+SELECT abs ( ( SELECT c8_number FROM w WHERE c1 = '060-ea' ) - ( SELECT c8_number FROM w WHERE c1 = 'challenger' ) );
+SELECT SUM( c8_number ) FROM w WHERE c1 IN ( 'big boy' , 'm1' );
+SELECT COUNT( c1 ) FROM w;
+SELECT abs ( ( SELECT c8_number FROM w WHERE c1 = 're 465' ) - ( SELECT c8_number FROM w WHERE c1 = 'ae 6/6' ) );
+SELECT c6_number FROM w WHERE c1_number = 2011;
+SELECT c2_first FROM w WHERE c2_first != 'tiger woods' AND c1_number >= 2007 AND c1_number <= 2009;
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = 'justin rose' ) order BY c1_number limit 1;
+SELECT c2_first FROM w GROUP BY c2_first order BY SUM( c10_number ) desc limit 1;
+SELECT c2 FROM w WHERE c1_number = 2013;
+SELECT c2 FROM w WHERE c1_number = 2010;
+SELECT c2 FROM w order BY c6_number limit 1;
+SELECT COUNT( * ) FROM w WHERE c7 = 'cog hill golf & country club';
+SELECT COUNT( * ) FROM w WHERE c2_number < 1950;
+SELECT c1 FROM w WHERE c1 != 'eltham football club' AND c6_number = ( SELECT c6_number FROM w WHERE c1 = 'eltham football club' );
+SELECT c1 FROM w WHERE c7_number = ( SELECT c7_number FROM w WHERE c1 = 'heidelberg football club' ) - 1;
+SELECT COUNT( c1 ) FROM w WHERE c6_number > 1945;
+SELECT c1 FROM w WHERE c6_number = ( SELECT MIN( c6_number ) FROM w );
+SELECT abs ( ( SELECT c2_number FROM w WHERE c1 = 'heidelberg football club' ) - ( SELECT c2_number FROM w WHERE c1 = 'eltham football club' ) );
+SELECT COUNT( c1 ) FROM w WHERE c2_number > 1950;
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w WHERE c2_number < ( SELECT c2_number FROM w WHERE c1 = 'lalor football club' ) order BY c2_number desc limit 1;
+SELECT c2 FROM w order BY c3_number asc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w order BY c4_first_number desc limit 1;
+SELECT c2 FROM w order BY c4_first_number desc limit 1;
+SELECT c2 FROM w WHERE c3_number = 17;
+SELECT c2 FROM w WHERE c4_first_number > ( SELECT c4_first_number FROM w WHERE c2 = 'carolina ayala cromen' ) order BY c4_first_number asc limit 1;
+SELECT ( SELECT COUNT( c1 ) FROM w WHERE c3_number > ( SELECT c3_number FROM w WHERE c1 = 'alameda' ) ) > 0;
+SELECT abs ( ( SELECT c7_number FROM w WHERE c1 = 'marin' ) - ( SELECT c7_number FROM w WHERE c1 = 'santa cruz' ) );
+SELECT COUNT( c1 ) FROM w;
+SELECT c7 FROM w WHERE c1 = 'contra costa';
+SELECT c10 FROM w WHERE c1 = 'sacramento';
+SELECT c1 FROM w order BY c3_number asc limit 1;
+SELECT ( SELECT c2_number FROM w WHERE c1 = 'san francisco' ) - ( SELECT c2_number FROM w WHERE c1 = 'alameda' );
+SELECT c1 FROM w order BY c7_number asc limit 1;
+SELECT c1 FROM w WHERE c4_number = 15.79;
+SELECT ( SELECT c7_number FROM w WHERE c2 = 'australian open' ) - ( SELECT c7_number FROM w WHERE c2 = 'qatar total open' );
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'us open' ) - 1;
+SELECT c2 FROM w WHERE c4 = 'grand slam' order BY c1_minimum_parsed desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5_first = 'hard';
+SELECT COUNT( c2 ) FROM w WHERE c5_first = 'hard';
+SELECT COUNT( c2 ) FROM w WHERE c7_number <= 900;
+SELECT SUM( c8_number ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c7_number >= 100;
+SELECT c2 FROM w WHERE c5 = 'grass' order BY c1_minimum_parsed asc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c8_number - c7_number FROM w WHERE c2 = 'brisbane international';
+SELECT COUNT( * ) FROM w WHERE c9 = 'withdrew due to left foot injury';
+SELECT c1_first FROM w order BY c15_number limit 1;
+SELECT c1 FROM w WHERE id = 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'vavuniya';
+SELECT c1_first FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE id = 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'jaffna' ) + 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'vavuniya' ) - 1;
+SELECT c1_first FROM w WHERE c1_first IN ( 'gamini maha vidyalayam' , 'nelukkulam kalaimahal maha vidyalayam' ) order BY c4_number desc limit 1;
+SELECT c1 FROM w order BY c4_number limit 1;
+SELECT c5_number FROM w WHERE c1 = 'fred jordan';
+SELECT c1 FROM w WHERE c5_number = 1 order BY c2_list_minimum_year desc limit 1;
+SELECT ( SELECT c4_number FROM w WHERE c1 = 'mack erwin' ) > ( SELECT c4_number FROM w WHERE c1 = 'fred montsdeoca' );
+SELECT abs ( ( SELECT c7_number FROM w WHERE c1 = 'fred jordan' ) - ( SELECT c7_number FROM w WHERE c1 = 'chal port' ) );
+SELECT c1 FROM w WHERE c1 IN ( 'ed sabre' , 'mack erwin' ) order BY c3_number desc limit 1;
+SELECT c1 FROM w order BY c4_number + c5_number + c6_number limit 1;
+SELECT c1 FROM w WHERE c6_number = 2;
+SELECT c1 FROM w WHERE c4_number > 700;
+SELECT COUNT( c1 ) FROM w WHERE c4_number > 500;
+SELECT c7_number FROM w WHERE c2_list_minimum_year = 1948;
+SELECT c2 FROM w WHERE c5_list = 'ford triton engine';
+SELECT c2 FROM w WHERE id = 1;
+SELECT c2 FROM w order BY c6_number asc limit 1;
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT AVG( c6_number ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c3_address = 'ohio';
+SELECT COUNT( c2 ) FROM w WHERE c6_number > 2000;
+SELECT COUNT( c2 ) FROM w WHERE c6_number >= 1500;
+SELECT c1 FROM w WHERE c6 = 'petco park';
+SELECT MAX( c1_number ) FROM w WHERE c2_first = 'mike scott';
+SELECT COUNT( * ) FROM w WHERE c2_first = 'nolan ryan';
+SELECT c1 FROM w order BY abs ( c4_number1 - c4_number2 ) desc limit 1;
+SELECT c2 FROM w WHERE c6 = 'astrodome' order BY c1_number asc limit 1;
+SELECT c1 FROM w WHERE c13 IS NULL;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'germany' ) - 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'williams-bmw' AND id < ( SELECT id FROM w WHERE c3 = 'mark webber' );
+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT c3 FROM w WHERE c1_number = 1;
+SELECT c5_number FROM w WHERE c1_number = 1;
+SELECT c6 FROM w WHERE c3 = 'fernando alonso';
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'nick heidfeld' ) - 1;
+SELECT COUNT( c3 ) FROM w WHERE c1 = 'ret';
+SELECT COUNT( c3 ) FROM w WHERE c6 = 'engine';
+SELECT COUNT( * ) FROM w WHERE c4_number = 9;
+SELECT c1 FROM w WHERE c1 IN ( '2006/07' , '2007/08' ) order BY c4_number limit 1;
+SELECT c1 FROM w WHERE c1 != '2012/13' AND c2_number = 6;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_number = 4;
+SELECT c4 FROM w WHERE c1 > '2008/09' order BY c1 limit 1;
+SELECT c1 FROM w WHERE c4_number = 18;
+SELECT c1 FROM w order BY c6_number1 desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c6_number >= 10;
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'populism';
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_list = 'social democracy';
+SELECT c1 FROM w WHERE c2 = 'tampa bay' order BY c1_parsed limit 1;
+SELECT COUNT( * ) FROM w WHERE c7 = 'minneapolis';
+SELECT ( SELECT c10_number FROM w WHERE c1 = 'november 18, 1993' ) - ( SELECT c10_number FROM w WHERE c1 = 'november 9, 1993' );
+SELECT c2 FROM w WHERE c1_year = 1994 AND c1_month = 1 AND c1_day = 5 - 1;
+SELECT COUNT( * ) FROM w WHERE c7 = 'minneapolis';
+SELECT c1 FROM w order BY c10_number desc limit 1;
+SELECT c2 FROM w WHERE c1 = 'december 9, 1993';
+SELECT COUNT( DISTINCT c1 ) FROM w WHERE c2_list = 'porsche';
+SELECT COUNT( c6 ) FROM w WHERE c7 = 'all';
+SELECT DISTINCT c4 FROM w WHERE c1 = 'scuderia ferrari spa sefac';
+SELECT DISTINCT c5 FROM w;
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w WHERE id > ( SELECT id FROM w WHERE c1 = 'ecurie excelsior' ) limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'greenland' , 'mexico' ) order BY c7_number asc limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w WHERE c10_number = 1;
+SELECT c1 FROM w order BY c11_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3_raw = 'giants';
+SELECT COUNT( c7 ) FROM w WHERE c3_raw = 'marlins';
+SELECT COUNT( * ) FROM w WHERE c8_number > 30000;
+SELECT abs ( ( SELECT c8_number FROM w WHERE c2 = 'july 7' ) - ( SELECT c8_number FROM w WHERE c2 = 'july 8' ) );
+SELECT COUNT( * ) FROM w WHERE c8_number < 25000;
+SELECT abs ( ( SELECT c8_number FROM w WHERE c2 = 'july 7' ) - ( SELECT c8_number FROM w WHERE c2 = 'july 8' ) );
+SELECT COUNT( * ) FROM w WHERE c2_month = 7 AND c2_day < 4;
+SELECT c7_first FROM w GROUP BY c7_first order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 != 'usa';
+SELECT ( SELECT id FROM w WHERE c4_address = 'nara' ) < ( SELECT id FROM w WHERE c4_address = 'firenze' );
+SELECT ( SELECT COUNT( * ) FROM w WHERE c5 = 'canada' ) > 3;
+SELECT COUNT( * ) FROM w WHERE c4_address = 'san francisco';
+SELECT c4 FROM w WHERE c1_number < 2000 GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT c3 FROM w WHERE c1_number = 2012;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c5 = 'usa' ) > 7;
+SELECT COUNT( * ) FROM w WHERE c5 = 'usa';
+SELECT ( SELECT c7_number FROM w WHERE c2 = 'malaysia open super series' ) > ( SELECT c7_number FROM w WHERE c2 = 'french super series' );
+SELECT COUNT( * ) FROM w WHERE c7_number >= 500000;
+SELECT COUNT( * ) FROM w WHERE c7_number > 200000;
+SELECT SUM( c7_number ) FROM w;
+SELECT c2 FROM w WHERE c2 != 'french super series' AND c7_number = ( SELECT c7_number FROM w WHERE c2 = 'french super series' );
+SELECT c2 FROM w order BY c7_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_month = 1;
+SELECT c2 FROM w WHERE c6_month = 12;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c3_number < 1800 ) > 0;
+SELECT c2 FROM w WHERE c3_number < 1880;
+SELECT COUNT( c1 ) FROM w WHERE c4_first_number >= 18;
+SELECT 1889 - 1882;
+SELECT c2 FROM w WHERE c1 IN ( 'cardona island light' , 'caja de muertos light' );
+SELECT c1 FROM w order BY c4_first_number desc limit 1;
+SELECT c2 FROM w order BY c3_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5_list = 'trustybus';
+SELECT c1 FROM w WHERE c2 = 'broxbourne station' AND c3 = 'essex road industrial estate';
+SELECT c1 FROM w WHERE c1 != 'william stuart price' AND c3 = 'tulsa';
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'attorney';
+SELECT COUNT( c1 ) FROM w WHERE c3 IN ( 'lawton' , 'oklahoma city' );
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'banker';
+SELECT c1 FROM w WHERE c1 != 'ronald h. white, m.d' AND c3 = ( SELECT c3 FROM w WHERE c1 = 'ronald h. white, m.d' );
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'businessman';
+SELECT COUNT( c1 ) FROM w WHERE c5 NOT NULL;
+SELECT c1 FROM w WHERE c3 = 'tulsa' AND c1 != 'joseph l. parker jr';
+SELECT COUNT( c1 ) FROM w WHERE c4_number >= 2016;
+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c7 = 'euro 2000 qualifying';
+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c3_address = 'ostrava';
+SELECT c4 FROM w order BY id desc limit 1;
+SELECT c4 FROM w WHERE c7 = 'friendly';
+SELECT COUNT( c2 ) FROM w WHERE c2_year = 1999;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'wembley stadium, london' ) - 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'germany';
+SELECT c3 FROM w WHERE c2 = '18 september 1996';
+SELECT c4 FROM w order BY c5_number1 desc limit 1;
+SELECT c5_number FROM w WHERE c1 = 'full house';
+SELECT c2_number FROM w WHERE c1 = 'four aces';
+SELECT c4_number FROM w WHERE c1 = 'straight flush';
+SELECT ( SELECT c3_number FROM w WHERE c1 = 'full house' ) = ( SELECT c6_number FROM w WHERE c1 = 'three of a kind' );
+SELECT c1 FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE c1 IN ( 'straight' , 'flush' ) order BY c2 desc limit 1;
+SELECT abs ( ( SELECT c4_number FROM w WHERE c1 = 'straight flush' ) - ( SELECT c4_number FROM w WHERE c1 = 'royal flush' ) );
+SELECT c2_number FROM w WHERE c1 = 'royal flush';
+SELECT COUNT( c2 ) FROM w WHERE c3_number > ( SELECT c3_number FROM w WHERE c2 = 'arquata scrivia' );
+SELECT COUNT( c2 ) FROM w WHERE c3_number >= 25000;
+SELECT COUNT( c1 ) FROM w WHERE c5_number > 400;
+SELECT c2 FROM w order BY c4_number limit 1;
+SELECT c2 FROM w WHERE c3_number > 30000;
+SELECT c2 FROM w order BY c5_number limit 1;
+SELECT c2 FROM w order BY c3_number limit 1;
+SELECT c2 FROM w order BY c5_number limit 1;
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT ( SELECT c3_number FROM w WHERE c2 = 'ovada' ) - ( SELECT c3_number FROM w WHERE c2 = 'serravalle scrivia' );
+SELECT COUNT( c2 ) FROM w WHERE c2 != 'valenza' AND c3_number >= ( SELECT c3_number FROM w WHERE c2 = 'valenza' );
+SELECT c3_number FROM w WHERE c2 = 'tortona';
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'troy brown' ) - 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'baton rouge';
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w WHERE c5_number = 2005;
+SELECT c2 FROM w WHERE c2 IN ( 'jody amedee' , 'page cortez' ) order BY c5_number limit 1;
+SELECT c2 FROM w WHERE c2 != 'j.p. morrell' AND c5_number = 2008;
+SELECT c3 FROM w GROUP BY c3 HAVING COUNT( c2 ) = ( SELECT COUNT( c2 ) FROM w GROUP BY c3 order BY COUNT( c2 ) asc limit 1 );
+SELECT c3 FROM w WHERE id = 1;
+SELECT c2 FROM w WHERE c4_number >= 9 order BY c1_number asc limit 1;
+SELECT c2 FROM w WHERE c3 = 'kenya' order BY c1_number asc limit 1;
+SELECT ( SELECT c2_number FROM w WHERE c1 = 'zacarias hugo' ) > 18;
+SELECT c1 FROM w WHERE c6_address = 'california' order BY c4_year desc limit 1;
+SELECT COUNT( * ) FROM w;
+SELECT c5 FROM w WHERE c1 = 'david tobuk';
+SELECT c4 FROM w WHERE c1 = 'dr. bob piorkowski, alaska fish and game, and his wife';
+SELECT MIN( c2_minimum_number ) FROM w;
+SELECT c4_list FROM w GROUP BY c4_list order BY COUNT( c1 ) desc limit 1;
+SELECT c2 FROM w WHERE c1_number = 2007;
+SELECT COUNT( c2 ) FROM w WHERE c1 > 2010;
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( c2 ) desc limit 1;
+SELECT c2 , c1 FROM w WHERE c3 = 'policeman';
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'loris capirossi' ) + 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_number > 10;
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_number IS NULL;
+SELECT c1 FROM w WHERE c1 != 'nesccap electric double-layer capacitor' AND c4_number = ( SELECT c4_number FROM w WHERE c1 = 'nesccap electric double-layer capacitor' );
+SELECT AVG( c4_number ) FROM w WHERE c1 = 'nesccap electric double-layer capacitor';
+SELECT c3 FROM w WHERE id > ( SELECT id FROM w WHERE c3_number = 2.7 order BY id desc limit 1 ) order BY id limit 1;
+SELECT abs ( ( SELECT c3_number FROM w WHERE c1 = 'eestor eesu barium titanate supercapacitor' ) - ( SELECT c3_number FROM w WHERE c1 = 'act premlis lithium ion capacitor' ) );
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'hard';
+SELECT c4 FROM w order BY c3_parsed desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c3 != '1 august 2011' AND c1 = ( SELECT c1 FROM w WHERE c3 = '1 august 2011' );
+SELECT c6 FROM w WHERE c4 = 'lenzerheide, switzerland';
+SELECT COUNT( * ) FROM w WHERE c5 = 'clay';
+SELECT COUNT( * ) FROM w WHERE c3_year = 2011;
+SELECT c4 FROM w WHERE c1 = 'winner' AND c3_parsed < ( SELECT c3_parsed FROM w WHERE c4 = 'ribeirao preto, brazil' );
+SELECT MAX( c3_year ) - MIN( c3_year ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c5 = 'clay';
+SELECT c4 FROM w WHERE c4 != 'lorant olah' AND c6_number = ( SELECT c6_number FROM w WHERE c4 = 'lorant olah' );
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+SELECT SUM( c6_number ) FROM w;
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+SELECT ( SELECT c9_number FROM w WHERE c4 = 'leandro de almeida' ) - ( SELECT c9_number FROM w WHERE c4 = 'zsombor kerekes' );
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'peter biro' ) - 1;
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+SELECT COUNT( c1 ) FROM w;
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+SELECT COUNT( * ) FROM w WHERE c6 = 'espn';
+SELECT COUNT( DISTINCT c5 ) FROM w;
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+SELECT COUNT( c2 ) FROM w WHERE c3_number = 0;
+SELECT c6_number FROM w WHERE c2 = 'argentina';
+SELECT c2 FROM w WHERE c2 IN ( 'cuba' , 'brazil' ) order BY c4_number desc limit 1;
+SELECT SUM( c6_number ) FROM w;
+SELECT c1_number FROM w WHERE c2 = 'mexico';
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+SELECT c6_number FROM w WHERE c2 = 'brazil';
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+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT c1_number FROM w WHERE c2 IS NULL;
+SELECT c1_number FROM w order BY c5_number limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_month = 4;
+SELECT SUM( c4_number ) FROM w WHERE c2 IN ( 'salta open' , 'tandil open' );
+SELECT c2 FROM w WHERE c3 = 'rafael gomez' order BY c1_parsed asc limit 1;
+SELECT c2 FROM w WHERE c4_number > 550000;
+SELECT c2 FROM w order BY c4_number desc limit 1;
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+SELECT c2 FROM w order BY c4_number desc limit 1;
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+SELECT COUNT( c1 ) FROM w WHERE c4_first_number > 200;
+SELECT c1 FROM w order BY c4_first_number asc limit 1;
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+SELECT COUNT( c1 ) FROM w WHERE c3_number = 1922;
+SELECT c1 FROM w WHERE c1 IN ( 'old youngs bay bridge' , 'oregon city bridge' ) order BY c4_first_number desc limit 1;
+SELECT c3_first FROM w WHERE c5_number < ( SELECT c5_number FROM w WHERE c3_first = 'canada' ) order BY c5_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3_first = 'russia';
+SELECT c3_first FROM w WHERE c3_first IN ( 'germany' , 'russia' ) order BY c9 limit 1;
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c5_number >= 1000 AND c5_number <= 1200;
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+SELECT c1 FROM w order BY c5_number limit 1;
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+SELECT c2_year FROM w WHERE c3 = 'tucson open invitational' AND c2_year != 1960;
+SELECT abs ( ( SELECT c4_first_number FROM w WHERE c3 = 'san antonio texas open' ) - ( SELECT c4_first_number FROM w WHERE c3 = 'dallas centennial open' ) );
+SELECT COUNT( c3 ) FROM w WHERE c2_year = 1967;
+SELECT c3 FROM w order BY c5_number desc limit 1;
+SELECT c5 FROM w WHERE c2_year = 1963 AND c3 = 'tucson open invitational';
+SELECT COUNT( * ) FROM w WHERE c5_number >= 10;
+SELECT c5 FROM w order BY c5_number desc limit 1;
+SELECT c6_length FROM w WHERE c2 = 'jul 24, 1967';
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+SELECT c2 FROM w order BY c6_number limit 1;
+SELECT c3_number FROM w WHERE c1_number = 12;
+SELECT c6_number FROM w WHERE c2 = 'eric langton';
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+SELECT COUNT( * ) FROM w WHERE c3_number > 10;
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT MAX( c5_number ) FROM w;
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+SELECT c8_number FROM w WHERE c3 = 'jonas folger';
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+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'jonas folger' ) + 1;
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+SELECT COUNT( * ) FROM w WHERE c3_number >= 10000;
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+SELECT abs ( ( SELECT c8_number FROM w WHERE c1 = 'august 30' ) - ( SELECT c8_number FROM w WHERE c1 = 'november 1' ) );
+SELECT SUM( c8_number ) FROM w WHERE id <= 3;
+SELECT c3 FROM w WHERE c7_result = 'w' order BY c1_parsed asc limit 1;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'tetsuya harada' ) + 1;
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+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c2 FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c2 = 'marcellino lucchi' );
+SELECT c2 FROM w WHERE c5_number > 16 AND c5_number < 25;
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+SELECT COUNT( c2 ) FROM w WHERE c1_number <= 15 AND c3 = 'honda';
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+SELECT c3 FROM w WHERE c2 = 'max biaggi';
+SELECT c1 FROM w WHERE c4_number > 55;
+SELECT c1 FROM w WHERE c1 IN ( 'alston' , 'haydon bridge' ) order BY c3_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_number > 0.1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'staithes' ) - 1;
+SELECT abs ( ( SELECT c2_number FROM w WHERE c1 = 'allenheads' ) - ( SELECT c2_number FROM w WHERE c1 = 'seaham' ) );
+SELECT COUNT( c1 ) FROM w;
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+SELECT c1 FROM w WHERE c3_number = 26 order BY id asc limit 1;
+SELECT c3 FROM w WHERE c2_number = ( SELECT c2_number FROM w WHERE c3 = 'moses malone^' ) + 1;
+SELECT AVG( c8_number ) FROM w;
+SELECT ( SELECT c2 FROM w WHERE c1 = 'simona armstrong' ) - ( SELECT c2 FROM w WHERE c1 = 'leanne dobinson' );
+SELECT c1 FROM w order BY c2 asc limit 1;
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+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c2_number < 25;
+SELECT COUNT( c1 ) FROM w;
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+SELECT c1 FROM w WHERE c4 = 'pink';
+SELECT COUNT( * ) FROM w WHERE c4 = 'naval blockade';
+SELECT c2 FROM w order BY julianday ( c1_maximum_parsed ) - julianday ( c1_minimum_parsed ) desc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2_first FROM w WHERE c1_minimum_parsed > ( SELECT c1_minimum_parsed FROM w WHERE c2_first = 'operation maritime guard' ) order BY c1_minimum_parsed limit 1;
+SELECT c1_maximum_month - c1_minimum_month FROM w WHERE c2 = 'operation maritime monitor';
+SELECT AVG( c4_number ) FROM w;
+SELECT c2 FROM w WHERE c3 NOT NULL order BY c1_number asc limit 1;
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( * ) desc limit 1;
+SELECT c5 FROM w WHERE c2 = ''automatic'';
+SELECT c2 FROM w WHERE c5 = 'losing streak' AND c2 != ''dopeman'';
+SELECT abs ( ( SELECT c1_number FROM w WHERE c5 = 'losing streak' ) - ( SELECT c1_number FROM w WHERE c5 = 'gnv fla' ) );
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = ''overrated (everything is)'' ) + 1;
+SELECT c2 FROM w WHERE c2 IN ( ''dopeman'' , ''surrender'' ) order BY c1_number asc limit 1;
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+SELECT c2 FROM w order BY c3_number limit 1;
+SELECT c2 FROM w order BY c3_number limit 1;
+SELECT MAX( c3_number ) FROM w;
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+SELECT c2 FROM w order BY c3_number limit 1;
+SELECT MAX( c4 ) FROM w;
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+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'kaitlin sandeno' ) + 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 IS NULL;
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+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT SUM( c4_number ) FROM w WHERE c2_first IN ( 'france' , 'germany' );
+SELECT COUNT( * ) FROM w WHERE c6 = 'friendly';
+SELECT SUM( c7_number ) FROM w WHERE c2_year = 2005;
+SELECT c4 FROM w order BY id desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c6 = 'friendly';
+SELECT COUNT( * ) FROM w WHERE c5_number1 >= 3;
+SELECT c4 FROM w GROUP BY c4 HAVING COUNT( * ) > 1;
+SELECT c7_number FROM w WHERE c2 = '5 march 2002';
+SELECT c2 FROM w WHERE c6 = 'friendly' order BY c2_parsed limit 1;
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+SELECT c3 FROM w order BY c5_number limit 1;
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+SELECT c4 FROM w order BY c1_number desc limit 1;
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+SELECT COUNT( c4 ) FROM w;
+SELECT COUNT( c5 ) FROM w;
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+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'australia' ) - 1;
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+SELECT c3 FROM w WHERE c1 = 'france';
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+SELECT c2_length FROM w WHERE c1 = 'america west express';
+SELECT c1 FROM w order BY c2_length desc limit 1;
+SELECT c1 FROM w WHERE c1 != 'beaver aviation / bas airlines' AND c3 = ( SELECT c3 FROM w WHERE c1 = 'beaver aviation / bas airlines' );
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+SELECT c2 FROM w order BY c4_year - c3_year limit 1;
+SELECT c2 FROM w order BY c4_year - c3_year desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_list = 'calvin coolidge';
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT c2 FROM w WHERE c2 != 'david aaron kessler, m.d' AND c5_list = 'bill clinton';
+SELECT c2 FROM w order BY c3_parsed limit 1;
+SELECT c2 FROM w order BY c5_length desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'republican';
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+SELECT COUNT( c2 ) FROM w WHERE c3 = 'democratic';
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+SELECT c2 FROM w WHERE c4 = 'devils tower' order BY id asc limit 1;
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+SELECT c2 FROM w WHERE id = 1;
+SELECT c3_number FROM w WHERE c1_number = 1972;
+SELECT c5 FROM w WHERE c1_number = 1965;
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
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+SELECT COUNT( * ) FROM w WHERE c4_list = 'novelist';
+SELECT c2_maximum_year - c2_minimum_year FROM w WHERE c1 = 'william inge';
+SELECT c1 FROM w WHERE c1 IN ( 'evelyn irons' , 'arturo islas' ) order BY c2_maximum_year - c2_minimum_year desc limit 1;
+SELECT c1 FROM w order BY c2_minimum_number limit 1;
+SELECT c1 FROM w WHERE c2_maximum_year > ( SELECT c2_maximum_year FROM w WHERE c1 = 'elisabeth irwin' ) order BY c2_maximum_year limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_list_first_min < 5;
+SELECT COUNT( * ) FROM w WHERE c4 = 'joachim garraud';
+SELECT c2 FROM w WHERE c1 = 'radio edit';
+SELECT c2 FROM w WHERE c4 = 'y-front';
+SELECT c1 FROM w order BY c2 limit 1;
+SELECT c1 FROM w WHERE c5_number = 2005 order BY id desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_list_first_min >= 7;
+SELECT c1 FROM w order BY c2 limit 1;
+SELECT c2 FROM w order BY c1_number desc limit 1;
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+SELECT c3_first FROM w WHERE c2 = 'syracuse';
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+SELECT c2 FROM w order BY c3_second_number1 desc limit 1;
+SELECT MAX( c6_year ) - MIN( c6_year ) FROM w;
+SELECT SUM( c7_number ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c1 = 'win' AND c4_first = 'decision';
+SELECT COUNT( * ) FROM w WHERE c1 = 'win';
+SELECT COUNT( * ) FROM w WHERE c1_result = 'win';
+SELECT MIN( c8 ) FROM w WHERE c1_result = 'win';
+SELECT c3 FROM w WHERE c1 = 'loss' order BY c6_parsed asc limit 1;
+SELECT ( SELECT c4_number FROM w WHERE c1 = 'archie thompson' ) - ( SELECT c4_number FROM w WHERE c1 = 'billy celeski' );
+SELECT c2 FROM w WHERE c2 != 'australia' GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE c2 = 'england' limit 1;
+SELECT c1 FROM w WHERE c2 = 'australia' AND c3_number = 91;
+SELECT c4_number FROM w WHERE c1 = 'chris tadrosse';
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT SUM( c3_number ) FROM w WHERE c2 = 'brazil';
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+SELECT c2 FROM w WHERE c1_number = 11 limit 1;
+SELECT c2 FROM w order BY c1_number asc limit 1;
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+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( ''birds vs. worms'' , ''broke'' ) order BY c1 asc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = ''autumn beds'' ) + 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_first = 'epic';
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+SELECT c2 FROM w WHERE c5_first = 'hit or miss';
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+SELECT c1 FROM w WHERE c2 = 'rural community';
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+SELECT c1 FROM w order BY c4_number desc limit 1;
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+SELECT c1 FROM w WHERE c1 IN ( 'cd' , 'ef' ) order BY c3_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_number > 5000;
+SELECT COUNT( c1 ) FROM w;
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+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'ef' ) + 1;
+SELECT c1 FROM w WHERE c1 IN ( 'ca' , 'ce' ) order BY c4_number desc limit 1;
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+SELECT c1 FROM w WHERE c1 IN ( 'ca' , 'ce' ) order BY c4_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_number >= 200;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w WHERE c5_number = 724.307;
+SELECT ( SELECT c1_number FROM w WHERE c3 = ( SELECT MAX( c3 ) FROM w ) ) < 2000;
+SELECT COUNT( c1 ) FROM w WHERE c1_number >= 1990 AND c1_number <= 2004 AND c3_number >= 5;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE c1_number >= 1990 AND c1_number <= 2004 order BY c2 desc limit 1;
+SELECT c1 FROM w WHERE c2_list_first = 'south korea';
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+SELECT c5_number FROM w WHERE c1_list = 'evgeni plushenko';
+SELECT MAX( c4_number ) FROM w;
+SELECT ( SELECT c5_number FROM w WHERE c1_list = 'gillis grafstrom' ) - ( SELECT c5_number FROM w WHERE c1_list = 'sonja henie' );
+SELECT SUM( c7_number ) FROM w WHERE c2_list_first IN ( 'sweden' , 'norway' );
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+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'grant' ) + 1;
+SELECT c1 FROM w order BY c8_number desc limit 1;
+SELECT c3_number FROM w WHERE c1 = 'harding';
+SELECT abs ( ( SELECT c8_number FROM w WHERE c1 = 'otero' ) - ( SELECT c8_number FROM w WHERE c1 = 'harding' ) );
+SELECT c2 FROM w order BY c3_parsed limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'onn jaafar' ) + 1;
+SELECT c2 FROM w WHERE c3_parsed < ( SELECT c3_parsed FROM w WHERE c2 = 'abdullah jaafar' ) order BY c3_parsed desc limit 1;
+SELECT c4_year - c3_year FROM w WHERE c2 = 'jaafar mohamed';
+SELECT c4 FROM w order BY id desc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w order BY c4_year - c3_year desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4_year - c3_year >= 4;
+SELECT c2 FROM w WHERE c3_parsed > ( SELECT c3_parsed FROM w WHERE c2 = 'abdullah jaafar' ) order BY c3_parsed limit 1;
+SELECT c2 FROM w WHERE c3_parsed > ( SELECT c3_parsed FROM w WHERE c2 = 'mustapha jaafar' ) order BY c3_parsed limit 1;
+SELECT c4_year - c3_year FROM w WHERE c2 = 'ungku abdul aziz abdul majid';
+SELECT c2 FROM w WHERE c3_parsed > ( SELECT c3_parsed FROM w WHERE c2 = 'onn jaafar' ) order BY c3_parsed limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'girl' AND c4_first_number > ( SELECT c4_first_number FROM w WHERE c1 = 'chimaijem otto' );
+SELECT c2 FROM w order BY c4_first_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'boy';
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c3 != 'girl';
+SELECT c2_number FROM w WHERE c1_number = 2003;
+SELECT MIN( c1_number ) FROM w WHERE c3_number > 40;
+SELECT c4_number FROM w WHERE c1_number = 1991;
+SELECT c1_number FROM w WHERE c1_number IN ( 2003 , 2007 ) order BY c3_number desc limit 1;
+SELECT MAX( c1_number ) FROM w WHERE c3_number < 35;
+SELECT c4_number FROM w WHERE c1_number = 1994;
+SELECT COUNT( c1 ) FROM w WHERE c2_number > 200000;
+SELECT c1 FROM w WHERE c6_number = 1;
+SELECT c1 FROM w order BY c11_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'industrial';
+SELECT c1 FROM w order BY c7_number desc limit 1;
+SELECT SUM( c10_number ) FROM w;
+SELECT COUNT( c2 ) FROM w;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( c2 ) desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4 = '2pac';
+SELECT c2 FROM w order BY c5 desc limit 1;
+SELECT MAX( c5 ) FROM w;
+SELECT c2 FROM w WHERE c3 = 'mr. lee' order BY c1_number desc limit 1;
+SELECT c5 FROM w WHERE c1_number = 11;
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c1 IS NULL;
+SELECT COUNT( c1 ) FROM w;
+SELECT c3 FROM w WHERE c4 = 'u+22a1';
+SELECT c1 FROM w WHERE c5 = 'first round' GROUP BY c1 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c1 = 'west' AND c6 = 'arizona state';
+SELECT c3 FROM w WHERE c5 = 'champion';
+SELECT c3 FROM w order BY c7_number1 asc limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'west' ) - 1;
+SELECT COUNT( c3 ) FROM w WHERE abs ( c7_number1 - c7_number2 ) <= 3;
+SELECT COUNT( c3 ) FROM w WHERE c1 = 'midwest' AND c6 = 'southwestern louisiana';
+SELECT COUNT( * ) FROM w WHERE c13_number >= 20;
+SELECT COUNT( * ) FROM w WHERE c6_result = 'w';
+SELECT c3 FROM w WHERE c3_raw != 'western carolina' AND c4 = ( SELECT c4 FROM w WHERE c3_raw = 'western carolina' );
+SELECT c7_number FROM w WHERE c1 = '11/28/2012';
+SELECT COUNT( * ) FROM w WHERE c6_result = 'w';
+SELECT c3_raw FROM w WHERE id = ( SELECT id FROM w WHERE c3_raw = 'wku' ) + 1;
+SELECT c2 FROM w order BY c4_number limit 1;
+SELECT c4_number FROM w WHERE c2 = ''wrong place'';
+SELECT COUNT( c2 ) FROM w WHERE c1_number < 2000;
+SELECT c1_year FROM w GROUP BY c1_year order BY COUNT( c2 ) desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( ''workin\' it'' , ''catch a bad one'' ) order BY c1_number limit 1;
+SELECT c2 FROM w WHERE c4_number <= 10;
+SELECT c2 FROM w WHERE c4_number <= 30;
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = ''phoney phranchise'' ) order BY c1_number limit 1;
+SELECT c2 FROM w WHERE c1_number = 1993;
+SELECT c2 FROM w WHERE c4_number = 6;
+SELECT c2 FROM w order BY c3_number limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c5_result = 'w';
+SELECT c7_number FROM w WHERE c2 = 'january 9, 2009';
+SELECT MAX( c7_number ) FROM w;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT c4 FROM w WHERE c2_month = 3 order BY c7_number limit 1;
+SELECT c2 FROM w order BY abs ( c5_number1 - c5_number2 ) desc limit 1;
+SELECT SUM( c7_number ) FROM w WHERE c2_month = 1;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w order BY c3_number asc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'cadiz' , 'granada' ) order BY c3_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c5_number >= 100;
+SELECT COUNT( * ) FROM w;
+SELECT c1 FROM w order BY c4_first_number desc limit 1;
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'almeria' , 'jaen' ) order BY c4_first_number limit 1;
+SELECT c1 FROM w WHERE c3_number > ( SELECT c3_number FROM w WHERE c1 = 'malaga' );
+SELECT c1 FROM w order BY c4_second_number desc limit 1;
+SELECT ( SELECT c3_number FROM w WHERE c1 = 'cadiz' ) - ( SELECT c3_number FROM w WHERE c1 = 'jaen' );
+SELECT c1 FROM w WHERE c4_first_number = 72.4 AND c1 IN ( 'almeria' , 'cordoba' );
+SELECT c5_number FROM w WHERE c1 = 'cadiz';
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c9 FROM w GROUP BY c9 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c9 = 'flagged';
+SELECT SUM( c7_number ) FROM w;
+SELECT c1_number FROM w WHERE c8_number = ( SELECT MIN( c8_number ) FROM w );
+SELECT c2 FROM w WHERE c1_number = 1962;
+SELECT COUNT( * ) FROM w WHERE c6_number <= 3;
+SELECT c1_number FROM w WHERE c6_number <= 4;
+SELECT ( SELECT c4_number FROM w WHERE c1_number = 1967 ) - ( SELECT c4_number FROM w WHERE c1_number = 1965 );
+SELECT MIN( c6_number ) FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c6_number = 1 order BY c1_number limit 1 );
+SELECT COUNT( * ) FROM w WHERE c9 = 'running';
+SELECT c1_number FROM w WHERE c1_number < 1965 AND c3_number = 5;
+SELECT COUNT( c2 ) FROM w WHERE c2 = 'formula one';
+SELECT c2 FROM w WHERE c1 = 2001 order BY id limit 1;
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( c2 ) desc limit 1;
+SELECT c2 FROM w WHERE c1 = '2007-08';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'dungan' ) - 1;
+SELECT c1 FROM w WHERE c1 IN ( 'tatar' , 'tajik' ) order BY c2_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'russian' , 'ukrainian' , 'kazakh' ) order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'german' ) - 1;
+SELECT abs ( ( SELECT c5_number FROM w WHERE c1 = 'korean' ) - ( SELECT c5_number FROM w WHERE c1 = 'german' ) );
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'belorussian' ) + 1;
+SELECT c1 FROM w order BY c2_number asc limit 1;
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c1_number = 2;
+SELECT COUNT( c3 ) FROM w;
+SELECT c1 FROM w WHERE c3 = 'jarret stoll';
+SELECT c3 FROM w WHERE c2_number > ( SELECT c2_number FROM w WHERE c3 = 'patrick murphy' ) order BY c2_number limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c5_second = 'ncaa';
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( c3 ) limit 1;
+SELECT c3 FROM w WHERE c1_number = 9;
+SELECT COUNT( c3 ) FROM w WHERE c4 = 'canada';
+SELECT COUNT( * ) FROM w WHERE c4 = 'finland';
+SELECT c3 FROM w order BY c2_number limit 1;
+SELECT c3 FROM w WHERE c4 = 'finland';
+SELECT c3 FROM w WHERE c3 != 'patrick murphy' AND c1_number = 7;
+SELECT c1 FROM w WHERE c4_number > 40;
+SELECT abs ( SUM( c3_number ) - SUM( c4_number ) ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c5_number >= 100;
+SELECT c1 FROM w order BY c2_number limit 1;
+SELECT c1 FROM w order BY c6_number limit 1;
+SELECT c1 FROM w WHERE c6_number = 0;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_number > 5;
+SELECT c1 FROM w WHERE c3_number = 36 AND c4_number = 47;
+SELECT ( SELECT c5_number FROM w WHERE c1 = 'barcelona' ) > ( SELECT c5_number FROM w WHERE c1 = 'madrid' );
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c1 FROM w WHERE c2_number = 0;
+SELECT COUNT( c2 ) FROM w WHERE c2 != 'oliveira walewska' AND c7_first_number = ( SELECT c7_first_number FROM w WHERE c2 = 'oliveira walewska' );
+SELECT c2 FROM w order BY c4_first_number limit 1;
+SELECT c2 FROM w WHERE c4_first_number < ( SELECT c4_first_number FROM w WHERE c2 = 'thaisa menezes' ) order BY c4_first_number desc limit 1;
+SELECT c1 FROM w WHERE c2 = 'right' AND c4_number = 11.8;
+SELECT c1 FROM w WHERE c6_number > 300 AND c3_number > 10;
+SELECT c1 FROM w WHERE c2 = 'left' AND c3_number = 46.6;
+SELECT abs ( c3_number - c5_number ) FROM w WHERE c1 = 'orange';
+SELECT COUNT( c1 ) FROM w WHERE c4_number > c2_number;
+SELECT COUNT( c1 ) FROM w WHERE c4_number >= 60;
+SELECT COUNT( c1 ) FROM w WHERE c2_number >= 50;
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT abs ( ( SELECT c1_year FROM w WHERE c2 = 'cusco' ) - ( SELECT c1_year FROM w WHERE c2 = 'motul' ) );
+SELECT c4 FROM w WHERE c2 = 'aspero';
+SELECT c2 FROM w WHERE c3 = 'puerto rico' AND c1_parsed > ( SELECT c1_parsed FROM w WHERE c2 = 'caparra' ) order BY c1_parsed asc limit 1;
+SELECT abs ( ( SELECT c1_year FROM w WHERE c2 = 'rio de janeiro' ) - ( SELECT c1_year FROM w WHERE c2 = 'panama city' ) );
+SELECT c3_maximum_year FROM w WHERE c1 = 'harald v of norway';
+SELECT c1 FROM w WHERE c1 != 'vajiravhud' AND c2 = 'christ church';
+SELECT c1 FROM w WHERE c1 != 'seretse khama' AND c2 = ( SELECT c2 FROM w WHERE c1 = 'seretse khama' );
+SELECT c3_maximum_year - c3_minimum_year FROM w WHERE c1 = 'harald v of norway';
+SELECT c1 FROM w WHERE id = 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'spicy horse';
+SELECT c1 FROM w WHERE c3 = 'ipad';
+SELECT c1 FROM w WHERE c4 = 'spicy horse' order BY c2_number desc limit 1;
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT c1 FROM w WHERE c5 = 'electronic arts';
+SELECT c3_length FROM w WHERE c1 = 'american mcgee's grimm';
+SELECT c3 FROM w WHERE c1 = 'american mcgee's crooked house';
+SELECT COUNT( * ) FROM w WHERE c3_list = 'ipad';
+SELECT c1 FROM w WHERE c4 = 'spicy horse' order BY c2_number desc limit 1;
+SELECT MIN( c5_number ) FROM w;
+SELECT c5_number FROM w WHERE c1_number = 4110;
+SELECT c1 FROM w WHERE c5_number IS NULL;
+SELECT c3 FROM w WHERE c5_number = 402026;
+SELECT c1_number FROM w WHERE c4_parsed > ( SELECT c4_parsed FROM w WHERE c1_number = 4106 ) order BY c4_parsed limit 1;
+SELECT MAX( c5_number ) FROM w;
+SELECT c2 FROM w WHERE c5_number > 440000;
+SELECT c3 FROM w WHERE c1_number = 4102;
+SELECT c1 FROM w WHERE c1_number IN ( 4107 , 4103 ) order BY c3_parsed limit 1;
+SELECT c5_number FROM w WHERE c1_number = 2008;
+SELECT c5_number FROM w WHERE c1_number = 1995;
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = 'arazi' ) order BY c1_number limit 1;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'war pass' ) - 1;
+SELECT c2 FROM w WHERE c1_number = 1997 - 1;
+SELECT c2 FROM w WHERE c1_number = 1985;
+SELECT c5_number FROM w WHERE c1_number = 2013;
+SELECT MAX( c5_number ) FROM w;
+SELECT c1_number FROM w WHERE c5_number = ( SELECT MIN( c5_number ) FROM w );
+SELECT ( SELECT c5_number FROM w WHERE c3 = 'lpga championship' ) = ( SELECT c5_number FROM w WHERE c3 = 'sunstar classic' );
+SELECT c4_second_result FROM w WHERE c3 = 'greater baltimore classic';
+SELECT c3 FROM w WHERE id = 1;
+SELECT COUNT( c3 ) FROM w;
+SELECT c3 FROM w WHERE id = 1;
+SELECT COUNT( c3 ) FROM w WHERE c2_year < 1980;
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( c2 ) desc limit 1;
+SELECT c2 FROM w WHERE c1 < ( SELECT c1 FROM w WHERE c2 = 'mazowszanka pekaes pruszkow' ) order BY c1 desc limit 1;
+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c1_minimum_number >= 2000 AND c1_maximum_number <= 2005;
+SELECT COUNT( * ) FROM w WHERE c3 = 'anwil włocławek';
+SELECT COUNT( c5 ) FROM w WHERE c5 = 'ericsson bobry bytom';
+SELECT COUNT( * ) FROM w WHERE c2 = 'prokom trefl sopot';
+SELECT COUNT( c1 ) FROM w WHERE c4_number2 = 0;
+SELECT c3 FROM w WHERE c3 IN ( 'marco simoncelli' , 'hiroshi aoyama' ) order BY c5_number desc limit 1;
+SELECT c3 FROM w WHERE c3 IN ( 'marco simoncelli' , 'alvaro bautista' ) AND c1_number = 1;
+SELECT c4 FROM w WHERE c3 = 'marco simoncelli';
+SELECT c5_number FROM w WHERE c3 = 'imre toth';
+SELECT COUNT( c3 ) FROM w WHERE c4 = 'honda';
+SELECT COUNT( c3 ) FROM w;
+SELECT SUM( c16_number ) FROM w;
+SELECT SUM( c3_number ) FROM w;
+SELECT c2 FROM w WHERE c1_number > ( SELECT MAX( c1_number ) FROM w WHERE c2 = 'buf' ) order BY c1_number limit 1;
+SELECT c1_number FROM w WHERE c3_number >= 15;
+SELECT c2 FROM w order BY c3_number limit 1;
+SELECT c1_number FROM w WHERE c6_number = 1;
+SELECT c1_number FROM w order BY c4_number desc limit 1;
+SELECT SUM( c12_number ) FROM w;
+SELECT c6 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c6_number = 10 ) - 1;
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( c1 ) limit 1;
+SELECT MIN( c3_number ) FROM w WHERE c2 = 'buf';
+SELECT c1 FROM w WHERE c3_list = 'creative director' AND c4_list = 'warner bros. interactive entertainment';
+SELECT COUNT( c1 ) FROM w WHERE c3_list = 'creative director';
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'london';
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c1 FROM w order BY c7_number desc limit 1;
+SELECT c4 FROM w WHERE c1_number = 6;
+SELECT c1 FROM w order BY c7_number desc limit 1;
+SELECT abs ( ( SELECT c2_minimum_year FROM w WHERE c1 = 1 ) - ( SELECT c2_minimum_year FROM w WHERE c1_number = 13 ) );
+SELECT COUNT( c2 ) FROM w;
+SELECT SUM( c4_number ) FROM w;
+SELECT c3 FROM w order BY c6_number desc limit 1;
+SELECT abs ( ( SELECT c7_number FROM w WHERE c2 = 1986 ) - ( SELECT c7_number FROM w WHERE c2 = 1983 ) );
+SELECT c2 FROM w order BY c6_number asc limit 1;
+SELECT SUM( c8_number ) FROM w WHERE c1_number >= 1 AND c1_number <= 12;
+SELECT c2 FROM w WHERE c2_minimum_year != 1977 AND c7_number = 53;
+SELECT c1 FROM w order BY c6_number asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'choice tv sidekick';
+SELECT c4 FROM w WHERE c3 = 'won' GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT DISTINCT c2 FROM w WHERE c3 = 'won' AND c4 = 'outstanding supporting actor in a comedy series';
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'won';
+SELECT c2 FROM w WHERE c1 = 2002 AND c3 = 'won';
+SELECT c4 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'teen choice awards' AND c1_number >= 2004 AND c1_number <= 2007;
+SELECT MAX( c1_number ) FROM w;
+SELECT c1 FROM w WHERE c1 != '1995/96' AND c4_number = ( SELECT c4_number FROM w WHERE c1 = '1995/96' );
+SELECT COUNT( c1 ) FROM w WHERE c4_number < 5;
+SELECT c1 FROM w WHERE c4_number = 1;
+SELECT c1 FROM w WHERE c4_number = 1;
+SELECT c1 FROM w WHERE c4_number = 1;
+SELECT c1 FROM w order BY c4_number limit 1;
+SELECT abs ( ( SELECT c4_number FROM w WHERE c1 = '1988/89' ) - ( SELECT c4_number FROM w WHERE c1 < '1988/89' order BY c1 desc limit 1 ) );
+SELECT COUNT( c1 ) FROM w WHERE c4_number >= 10;
+SELECT c4 FROM w WHERE c1 > '1990/91' order BY c1 limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number = 3;
+SELECT c1 FROM w WHERE c2_number = 4;
+SELECT c1 FROM w order BY id asc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c5_number < 8.5;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_number > 9.000;
+SELECT COUNT( * ) FROM w WHERE c5_number < 8.700;
+SELECT COUNT( * ) FROM w WHERE c2_number >= 8.5;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT MAX( c4_number ) FROM w;
+SELECT c5 FROM w WHERE c1 = 'wests panthers';
+SELECT c1 FROM w order BY c6_length desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'brisbane' , 'townsville' ) GROUP BY c2 order BY COUNT( c1 ) desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'brisbane';
+SELECT COUNT( c1 ) FROM w WHERE id < ( SELECT id FROM w WHERE c1 = 'mackay sea eagles' );
+SELECT c1 FROM w WHERE c6_list = 2005;
+SELECT c1 FROM w WHERE c1 IN ( 'north queensland young guns' , 'toowoomba clydesdales' ) order BY c6_length desc limit 1;
+SELECT c3 FROM w WHERE id = ( SELECT MAX( id ) FROM w WHERE c3 = 'pony canyon, japan' AND c5_year = 2007 ) + 1;
+SELECT COUNT( * ) FROM w WHERE c4_list = 'lyricist';
+SELECT COUNT( * ) FROM w WHERE c5_year = 2013;
+SELECT c2 FROM w WHERE c5_parsed > ( SELECT c5_parsed FROM w WHERE c2 = 'enomoto atsuko' ) order BY c5_parsed limit 1;
+SELECT c5 FROM w order BY c5_parsed desc limit 1;
+SELECT c1 FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE id = 1;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT MAX( c4_number ) FROM w;
+SELECT c2 FROM w WHERE c2 != 'ibv' AND c4_number = 6;
+SELECT c2 FROM w order BY c4_number / c6_number desc limit 1;
+SELECT c5_number FROM w WHERE c2 = 'ibv';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'ks' ) - 1;
+SELECT c2 FROM w order BY c8_number desc limit 1;
+SELECT MAX( c4_number ) - MAX( c6_number ) FROM w;
+SELECT c2 FROM w WHERE c5_number = ( SELECT MAX( c5_number ) FROM w );
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT c7_number FROM w WHERE c2 = 'ks';
+SELECT COUNT( * ) FROM w WHERE c1_month = 10;
+SELECT COUNT( * ) FROM w WHERE c4_result = 'w';
+SELECT abs ( c4_number1 - c4_number2 ) FROM w WHERE c1 = 'october 17';
+SELECT c1 FROM w WHERE c4_result = 'l' AND c1_parsed < ( SELECT c1_parsed FROM w WHERE c2_raw = 'jackson state' ) order BY c1_parsed desc limit 1;
+SELECT SUM( c4_number1 ) FROM w WHERE c1_month = 11;
+SELECT COUNT( * ) FROM w WHERE c4_result = 'w' AND c1_month = 10;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT ( SELECT c10_number FROM w WHERE c2 = 'c.d. aguila' ) - ( SELECT c10_number FROM w WHERE c2 = 'chalatenango' );
+SELECT MAX( c7_number ) - MIN( c7_number ) FROM w;
+SELECT c2 FROM w WHERE c2 != 'c.d. arcense' AND c10_number = 17;
+SELECT SUM( c3 ) FROM w;
+SELECT c2 FROM w order BY c10_number desc limit 1;
+SELECT c2 FROM w order BY c8_number limit 1;
+SELECT c2 FROM w WHERE c10_number > 30 AND c2 != 'c.d. fas';
+SELECT c2 FROM w order BY c7_number desc limit 1;
+SELECT c10_number FROM w WHERE c2 = 'alianza f.c';
+SELECT c2 FROM w order BY c1_number limit 1;
+SELECT c2 FROM w order BY c10_number limit 1;
+SELECT c3 FROM w WHERE c1 = '8 = 23 + 0';
+SELECT c3 FROM w order BY id asc limit 5;
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'belgium' ) - 1;
+SELECT c1_number FROM w WHERE c3 = 'belgium';
+SELECT COUNT( c2 ) FROM w WHERE c4 IS NULL;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'jessie maclean' ) - 1;
+SELECT c2 FROM w WHERE c4 IS NULL;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT MAX( c1_maximum_year ) - MIN( c1_minimum_year ) FROM w;
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = 'berlin' ) order BY c1_number limit 1;
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'operational';
+SELECT c1 FROM w WHERE c3 = 'pink';
+SELECT COUNT( * ) FROM w WHERE c2_number < 1970;
+SELECT COUNT( * ) FROM w WHERE c5 = 'based on design elements from plymouth and blackburn tramcars';
+SELECT COUNT( * ) FROM w WHERE c2_number < 1960;
+SELECT COUNT( * ) FROM w WHERE c4 = 'operational';
+SELECT c2 FROM w WHERE id = 1;
+SELECT c2 FROM w WHERE c1_number = ( SELECT MAX( c1_number ) FROM w ) - 1;
+SELECT c2 FROM w order BY c8 desc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'italy (ita) italy i' ) + 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'italy (ita) italy ii' ) - 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_min < 3;
+SELECT COUNT( c2 ) FROM w WHERE c3_list = 'mike 'punch' harper';
+SELECT COUNT( c2 ) FROM w WHERE c5_min < 4;
+SELECT c2 FROM w WHERE c3 IS NULL;
+SELECT c5 FROM w WHERE c2 = ''die slow'';
+SELECT c5 FROM w WHERE c2 = ''horsemen'';
+SELECT c2 FROM w order BY c5 desc limit 1;
+SELECT c2 FROM w WHERE c3_list IN ( 'pillo jamel' , 'juju' );
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = ''phuk u'' ) - 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_list = 'chaos';
+SELECT abs ( ( SELECT c10_number FROM w WHERE c2 = 'koraput' ) - ( SELECT c10_number FROM w WHERE c2 = 'puri' ) );
+SELECT c2 FROM w WHERE c2 IN ( 'angul' , 'cuttack' ) order BY c4_number desc limit 1;
+SELECT c2 FROM w order BY c7_number limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c7_number > 15;
+SELECT c2 FROM w order BY c12_number limit 1;
+SELECT c6_number FROM w WHERE c2 = 'cuttack';
+SELECT c2 FROM w order BY c9_number desc limit 1;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'cosmos 300' ) - 1;
+SELECT MIN( c1_first_minimum_year ) FROM w WHERE c7 = 'success';
+SELECT COUNT( c3 ) FROM w WHERE c7 = 'success';
+SELECT COUNT( * ) FROM w WHERE c3 = 'cosmos 305';
+SELECT COUNT( * ) FROM w;
+SELECT c2 FROM w order BY c5_number asc limit 1;
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c7 FROM w order BY c4_number asc limit 1;
+SELECT c3_number FROM w WHERE c2 = 'sipocot';
+SELECT COUNT( c2 ) FROM w WHERE c5_number >= 50000;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( c7 ) FROM w WHERE c6 = '1st class';
+SELECT COUNT( * ) FROM w WHERE c3 = 'w';
+SELECT COUNT( * ) FROM w WHERE c2 = 'argentina' AND c6 = 'international friendly';
+SELECT c6 FROM w order BY c1_parsed limit 1;
+SELECT COUNT( DISTINCT c2 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c3 = 'w';
+SELECT c2 FROM w WHERE c1_maximum_year = 1976 - 3;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'mount leinster rangers' ) + 1;
+SELECT MAX( c1 ) FROM w WHERE abs ( c3_list_number1 - c3_list_number2 ) < 11;
+SELECT c2 FROM w WHERE c1_minimum_year < ( SELECT c1_minimum_year FROM w WHERE c2 = 'birr' ) order BY c1_minimum_year desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c6 = 'alain prost';
+SELECT c6 FROM w WHERE c2 = 'german grand prix';
+SELECT c2 FROM w WHERE c8 = 'benetton-ford';
+SELECT COUNT( * ) FROM w WHERE c5 = 'ayrton senna';
+SELECT COUNT( * ) FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c5 = 'alain prost' order BY c1_number limit 1 );
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'san marino grand prix' ) - 1;
+SELECT c4 FROM w GROUP BY c4 HAVING COUNT( * ) = 2;
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT c4 FROM w order BY c1_number desc limit 1;
+SELECT c4 FROM w WHERE c2_number < ( SELECT c2_number FROM w WHERE c4 = 'zurab zviadauri' ) order BY c2_number desc limit 1;
+SELECT c4 FROM w WHERE c2_number > ( SELECT c2_number FROM w WHERE c4 = 'ramaz nozadze' ) order BY c2_number asc limit 1;
+SELECT COUNT( c4 ) FROM w;
+SELECT c4 FROM w order BY c2_number desc limit 1;
+SELECT COUNT( c4 ) FROM w WHERE c3 = 'summer';
+SELECT COUNT( * ) FROM w WHERE c3 = 'summer';
+SELECT COUNT( * ) FROM w WHERE c4 = 'sofia akhmeteli';
+SELECT c4 FROM w order BY c2_number asc limit 1;
+SELECT ( SELECT c5_number FROM w WHERE c1_number = 2 ) - ( SELECT c5_number FROM w WHERE c1_number = 1 );
+SELECT COUNT( * ) FROM w WHERE c4_result = 'w' AND c2_month >= 12;
+SELECT c4_number1 FROM w WHERE c1_number = 13;
+SELECT COUNT( * ) FROM w WHERE c5_number >= 50000;
+SELECT ( SELECT c4_number1 FROM w WHERE c1_number = 7 ) > 15;
+SELECT COUNT( * ) FROM w WHERE c5_number >= 50000;
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3_raw = 'dallas cowboys';
+SELECT COUNT( c2 ) FROM w WHERE c1_number = ( SELECT MIN( c1_number ) FROM w );
+SELECT c2 FROM w WHERE c2 IN ( 'halaal ki kamai' , 'dariya dil' ) order BY c1_number limit 1;
+SELECT c1_number FROM w WHERE c4 = 'critically acclaimed role';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'love 86' ) + 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'dadagiri' ) + 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 1992;
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( c1 ) desc limit 1;
+SELECT c5 FROM w WHERE c1_number = 8;
+SELECT c2 FROM w order BY c7_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c5_number1 - c5_number2 > 2;
+SELECT c3_raw FROM w WHERE c1_number = 2;
+SELECT COUNT( c1 ) FROM w WHERE c3_home = 'home';
+SELECT c4_number FROM w WHERE c1_number = 1945;
+SELECT ( SELECT c2_number FROM w WHERE c1_number = 1968 ) - ( SELECT c2_number FROM w WHERE c1_number = 1974 );
+SELECT c6 FROM w WHERE c1_number = 1974;
+SELECT COUNT( * ) FROM w WHERE c3_number = 2;
+SELECT c2_number FROM w WHERE c1_number = 2013;
+SELECT c1_number FROM w order BY c2_number desc limit 1;
+SELECT c2 FROM w order BY c5_number asc limit 1;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT AVG( c5_number ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'ajax';
+SELECT c3 FROM w WHERE c2 = 'dario cvitanich' AND c4 = 'ajax';
+SELECT abs ( ( SELECT c5_number FROM w WHERE c1_number = 5 ) - ( SELECT c5_number FROM w WHERE c1_number = 11 ) );
+SELECT c4_list FROM w GROUP BY c4_list order BY COUNT( c1 ) desc limit 1;
+SELECT c7_length FROM w WHERE c1 = 'lego creationary';
+SELECT COUNT( c1 ) FROM w WHERE c7 NOT NULL;
+SELECT c1 FROM w WHERE c6_list = 'game boy advance' order BY c2_number desc limit 1;
+SELECT ( SELECT c5_length FROM w WHERE c1 = 'bionicle heroes' ) - ( SELECT c5_length FROM w WHERE c1 = 'bionicle: the game' );
+SELECT c1 FROM w order BY c2_number limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'action-adventure';
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+SELECT COUNT( * ) FROM w WHERE c2_number >= 88 AND c2_number <= 92;
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+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c4 = 'news and classical' ) - ( SELECT COUNT( * ) FROM w WHERE c4 = 'roots, rock, and jazz' );
+SELECT c3 FROM w WHERE c5 = 'minot public radio';
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+SELECT c3 FROM w GROUP BY c3_number order BY COUNT( c2 ) desc limit 1;
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+SELECT c1 FROM w order BY c3 desc limit 1;
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+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_number < 1900;
+SELECT COUNT( c1 ) FROM w WHERE c3_number < 1950;
+SELECT c1 FROM w WHERE c1 IN ( 'bryan college' , 'montreat college' ) order BY c3_number asc limit 1;
+SELECT c2 FROM w WHERE c1 = 'union college';
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+SELECT c3 FROM w WHERE c1 = 'winner' GROUP BY c3 order BY COUNT( * ) desc limit 1;
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+SELECT c1 FROM w GROUP BY c1 order BY COUNT( * ) desc limit 1;
+SELECT c3 FROM w WHERE c1 = 'winner' order BY c3_number asc limit 1;
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+SELECT COUNT( * ) FROM w WHERE c5 = '110 m hurdles';
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
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+SELECT COUNT( * ) FROM w;
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+SELECT c2 FROM w order BY c6_parsed asc limit 1;
+SELECT c2 FROM w order BY c6_parsed asc limit 1;
+SELECT c2 FROM w WHERE c6_year < 1900;
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+SELECT c1 FROM w order BY c7_number asc limit 1;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w GROUP BY c1 HAVING COUNT( c2 ) > 2;
+SELECT c3 FROM w WHERE c3 IN ( ''healing'' , ''boys & girls'' ) AND c5 = 'with trax';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'mama' ) + 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT abs ( ( SELECT c1_number FROM w WHERE c2 = 'only one' ) - ( SELECT c1_number FROM w WHERE c2 = 'toheart' ) );
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = ''one dream'' ) - 1;
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+SELECT COUNT( c2 ) FROM w WHERE c1_number > 2010;
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+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'once a cop' ) + 1;
+SELECT c1_number FROM w GROUP BY c1_number order BY COUNT( c2 ) desc limit 1;
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+SELECT COUNT( c2 ) FROM w;
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+SELECT c4_number FROM w WHERE c1 = 'rutgers-eagleton' AND c2 = 'march 2014';
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+SELECT c1 FROM w WHERE c4 > 50 AND c2_year = 2006;
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+SELECT COUNT( c2 ) FROM w;
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+SELECT COUNT( c2 ) FROM w;
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+SELECT COUNT( c2 ) FROM w WHERE c5_number > 5000;
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+SELECT c2 FROM w WHERE c4_number < ( SELECT c4_number FROM w WHERE c2 = 'haridwar' ) order BY c4_number desc limit 1;
+SELECT c4 FROM w order BY id desc limit 1;
+SELECT c5_number FROM w WHERE c3_list_first = 'felix loch';
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+SELECT c1 FROM w order BY c1_parsed limit 1;
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+SELECT c2_first FROM w order BY c6_number desc limit 1;
+SELECT c3 FROM w WHERE c2_first = 'russia';
+SELECT ( SELECT c6_number FROM w WHERE c2_first = 'slovakia' ) - ( SELECT c6_number FROM w WHERE c2_first = 'germany' );
+SELECT SUM( c6_number ) FROM w;
+SELECT ( SELECT c3_number FROM w WHERE c2_first = 'russia' ) - ( SELECT c3_number FROM w WHERE c2_first = 'great britain' );
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+SELECT c2_first FROM w order BY c6_number desc limit 1;
+SELECT c2 FROM w order BY c6_number desc limit 1;
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+SELECT c2 FROM w WHERE c3 = 'sierra nevada' order BY c4_list_number desc limit 1;
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+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = '2010-11' ) + 1;
+SELECT c4_number FROM w WHERE c2 = '2006-07';
+SELECT COUNT( * ) FROM w WHERE c6_number >= 10;
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+SELECT c4 FROM w WHERE c4 IN ( 'malayalam' , 'telugu' ) GROUP BY c4 order BY COUNT( c2 ) desc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w WHERE c4 = 'malayalam' AND c2 != 'koothara';
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'tamil';
+SELECT COUNT( c2 ) FROM w WHERE c5 IS NULL;
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 2010;
+SELECT c2 FROM w WHERE c4 = 'hindi' order BY c1_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 2012;
+SELECT COUNT( * ) FROM ( SELECT c1_number FROM w GROUP BY c1_number HAVING COUNT( c2 ) >= 3 );
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'filming';
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c3 FROM w WHERE c2 = 'queens';
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+SELECT c2 FROM w order BY c5_number desc limit 1;
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+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'henry cotton' ) - 1;
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+SELECT c1 FROM w WHERE c4_minimum_year >= 1900 order BY c4_minimum_year asc limit 1;
+SELECT c1 FROM w WHERE c4_maximum_year - c4_minimum_year >= 1 order BY c4_minimum_year desc limit 1;
+SELECT c5 FROM w order BY c4_maximum_year desc limit 1;
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w order BY c3_number limit 1;
+SELECT c1_number FROM w GROUP BY c1_number HAVING COUNT( c2 ) = 3;
+SELECT c5 FROM w order BY c3_number limit 1;
+SELECT c5 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c5 = 'the man in the mirror' order BY c1_number desc limit 1 ) order BY c1_number limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'singles only';
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+SELECT COUNT( c2 ) FROM w WHERE c1_number > 1977;
+SELECT c1_number FROM w GROUP BY c1_number HAVING COUNT( c2 ) = ( SELECT COUNT( c2 ) FROM w GROUP BY c1_number order BY COUNT( c2 ) limit 1 );
+SELECT COUNT( c2 ) FROM w WHERE c3_number <= 10;
+SELECT c2 FROM w order BY c3_number limit 1;
+SELECT c5 FROM w WHERE c2 = ''i\'ll be your fool tonight'';
+SELECT COUNT( * ) FROM w WHERE c1_number < 1949;
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+SELECT c2 FROM w order BY c1_number limit 1;
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+SELECT c5 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c5 = 'lin zexu' ) order BY c1_number limit 1;
+SELECT COUNT( * ) FROM w WHERE c1_number >= 1930 AND c1_number <= 1940;
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+SELECT c1 FROM w WHERE c1 != 'vaygach' AND c2_number = ( SELECT c2_number FROM w WHERE c1 = 'vaygach' );
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+SELECT c4 FROM w WHERE c2 = 'm1894 rifle';
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( * ) limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c2 != 'm1898 carbine' AND c3 = ( SELECT c3 FROM w WHERE c2 = 'm1898 carbine' );
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+SELECT c1 FROM w order BY c3_number desc limit 1;
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+SELECT c1 FROM w WHERE c7 = 'air sakha';
+SELECT c1 FROM w WHERE c7 = 'siberian sky';
+SELECT c1_first FROM w WHERE c3_list_second = 'game days';
+SELECT c1 FROM w WHERE c5_number < ( SELECT c5_number FROM w WHERE c1 = 'george halas' );
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+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c5_number > 100;
+SELECT c4_number FROM w WHERE c1 = 'ralf woods';
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c1 FROM w WHERE c4_number = 3;
+SELECT c1 FROM w WHERE c5_number = 2 order BY c2_number limit 1;
+SELECT c1 FROM w WHERE c5_number = ( SELECT MIN( c5_number ) FROM w );
+SELECT c1 FROM w WHERE c1 IN ( 'r.c. haas' , 'clyde alwood' ) order BY c2_number desc limit 1;
+SELECT c5 FROM w WHERE c1 = 'ralf woods';
+SELECT c4 FROM w WHERE c4 != 'jeremiah chechik' AND c6_parsed = ( SELECT c6_parsed FROM w WHERE c4 = 'jeremiah chechik' );
+SELECT c3 FROM w WHERE c6_parsed < ( SELECT c6_parsed FROM w WHERE c6 = 'july 5, 2007' ) order BY c6_parsed desc limit 1;
+SELECT c3 FROM w WHERE c6_parsed < ( SELECT c6_parsed FROM w WHERE c3 = ''unpaid debts'' ) order BY c6_parsed desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c5_list = 'matt nix';
+SELECT c3 FROM w WHERE c8_number = ( SELECT MAX( c8_number ) FROM w );
+SELECT c3 FROM w WHERE c5_list = 'nick thiel';
+SELECT COUNT( c3 ) FROM w WHERE c5_list = 'craig o'neill';
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+SELECT COUNT( c3 ) FROM w WHERE c6_month = 8;
+SELECT c3 FROM w order BY c2_number desc limit 1;
+SELECT COUNT( c3 ) FROM w;
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+SELECT COUNT( c3 ) FROM w;
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+SELECT c3 FROM w order BY c6_number desc limit 1;
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+SELECT COUNT( c2 ) FROM w;
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+SELECT c3 FROM w WHERE c1 = 'armando armani';
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+SELECT c1 FROM w order BY c1_number desc limit 1;
+SELECT MIN( c3_number ) FROM w;
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+SELECT c1 FROM w order BY c1_number desc limit 1;
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+SELECT c3 FROM w order BY c9_number desc limit 1;
+SELECT c3 FROM w WHERE c3 != 'carlin motorsport' AND c1_minimum_year = 2011;
+SELECT SUM( c5_number ) FROM w;
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+SELECT c3 FROM w WHERE c10_number = 1 AND c1_minimum_year < ( SELECT c1_minimum_year FROM w WHERE c3 = 'mrf challenge' );
+SELECT MIN( c9_number ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE id > ( SELECT id FROM w WHERE c2 = 'crucifixion of jesus' );
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c7 NOT NULL;
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+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'birth of john the baptist' ) - 1;
+SELECT COUNT( c2 ) FROM w WHERE c4_first = 'sergio garcia';
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+SELECT c2 FROM w WHERE c6_number >= 1100000;
+SELECT c3 FROM w WHERE c2 = 'mercedes championships';
+SELECT ( SELECT c5_first_number FROM w WHERE c2 = 'bob hope chrysler classic' ) - ( SELECT c5_first_number FROM w WHERE c2 = 'buick classic' );
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+SELECT c2 FROM w order BY id desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c4 = 'private';
+SELECT COUNT( c1 ) FROM w WHERE c4 = 'private';
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'hd';
+SELECT COUNT( c1 ) FROM w WHERE c4 = 'government';
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'hd';
+SELECT SUM( c4_number ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c3_number >= 1;
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+SELECT c2 FROM w WHERE c2 != 'thailand' AND c6_number = ( SELECT c6_number FROM w WHERE c2 = 'thailand' );
+SELECT COUNT( c2 ) FROM w WHERE c5_number > 3;
+SELECT SUM( c3_number ) FROM w;
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+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c2 = 'japan' );
+SELECT MAX( c1_maximum_number - c1_minimum_number ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c1 = 2005;
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+SELECT c2 FROM w WHERE c4 = 'cameo';
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+SELECT COUNT( c2 ) FROM w;
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+SELECT COUNT( c2 ) FROM w WHERE c1_minimum_number < 2000;
+SELECT c2 FROM w order BY c1_maximum_number - c1_minimum_number desc limit 1;
+SELECT SUM( c4_length ) FROM w;
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT COUNT( c5 ) FROM w;
+SELECT COUNT( c3 ) FROM w;
+SELECT c3 FROM w WHERE c1_number > 20;
+SELECT SUM( c1_number ) FROM w;
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+SELECT c1 FROM w order BY c7_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number > 1000;
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+SELECT c1 FROM w WHERE c7_number > ( SELECT c7_number FROM w WHERE c1 = 'smyrna' );
+SELECT c1 FROM w WHERE c9_number = ( SELECT MIN( c9_number ) FROM w );
+SELECT COUNT( c1 ) FROM w;
+SELECT c4_number FROM w WHERE c1 = 'burnett';
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c1 FROM w order BY c4_number asc limit 1;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'martyn bernard' ) - 1;
+SELECT COUNT( DISTINCT c4 ) FROM w;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'niki palli' ) + 1;
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+SELECT c3 FROM w WHERE c10 = '2.05';
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'ivan ukhov' ) + 1;
+SELECT c4 FROM w order BY id desc limit 1;
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'niki palli' ) - 1;
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+SELECT COUNT( c3 ) FROM w WHERE c4 = 'democratic' AND c2_list = 'frederick';
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+SELECT c4 FROM w WHERE c3 = 'wendell r. beitzel';
+SELECT c5 FROM w WHERE c3 = 'galen r. clagett';
+SELECT COUNT( DISTINCT c6 ) FROM w;
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+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c1 FROM w order BY c6_number desc limit 1;
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+SELECT c2 FROM w order BY id desc limit 1;
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+SELECT c2 FROM w WHERE c4 = 'live album' order BY c1_parsed limit 1;
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+SELECT c3 FROM w WHERE c1 = 'aspect';
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+SELECT COUNT( * ) FROM w WHERE c4_number1 > c4_number2;
+SELECT COUNT( * ) FROM w WHERE c3 = 'guam';
+SELECT COUNT( * ) FROM w WHERE c3 = 'guam';
+SELECT c2_address FROM w WHERE c2_address IN ( 'palau track and field stadium' , 'yap sports complex' ) GROUP BY c2_address order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = '20 july 2012' ) + 1;
+SELECT c3 FROM w order BY c1_parsed desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'palau';
+SELECT ( SELECT c1_number FROM w WHERE c2 = 'shipon junction' ) - ( SELECT c1_number FROM w WHERE c2 = 'mahanayim junction' );
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( c4 ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'leeds united';
+SELECT c4 FROM w WHERE c2 = 'queens park rangers' AND c3 = 'everton';
+SELECT c3 FROM w WHERE c2 = 'liverpool';
+SELECT c3 FROM w WHERE c2 = 'southampton' AND c5 = '8 may 1993';
+SELECT c2 FROM w WHERE c1 = 'john hendrie';
+SELECT c1 FROM w WHERE c1 != 'mark robins' AND c4 = ( SELECT c4 FROM w WHERE c1 = 'mark robins' );
+SELECT c1 FROM w WHERE c2 = 'tottenham hotspur';
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT MAX( c3_first_year ) - MIN( c3_first_year ) FROM w;
+SELECT c2 FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c2 = 'ellen van dijk' ) order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_number >= 47;
+SELECT c2 FROM w WHERE c3 = 'russia' order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE c3 = 'russia' order BY c1_number limit 1;
+SELECT c3 FROM w WHERE c1_number = 1;
+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT c3 FROM w WHERE c3 IN ( 'belarus' , 'lithuania' ) GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'irregular';
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c1 FROM w WHERE c2 = 'canada';
+SELECT c3 FROM w order BY c5_list_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2_list = 'united states';
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT MIN( c1 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c3 = 'montgomery county';
+SELECT ( SELECT COUNT( * ) FROM w WHERE c3 = 'pulaski county' ) > ( SELECT COUNT( * ) FROM w WHERE c3 = 'montgomery county' );
+SELECT MAX( c1 ) FROM w;
+SELECT c2 FROM w WHERE c3 = 'tokyo, japan' AND c4_number = 2;
+SELECT c1_number FROM w WHERE c4_number = 1 AND c5 = '10,000m';
+SELECT ( SELECT c1_number FROM w WHERE c4_number = 1 AND c5 = '10,000m' ) < ( SELECT c1_number FROM w WHERE c4_number = 28 AND c5 = '10,000m' );
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'fukuoka marathon';
+SELECT c4 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_number <= 2;
+SELECT COUNT( * ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c5 NOT NULL;
+SELECT c1 FROM w order BY c3_number limit 1;
+SELECT ( SELECT MIN( c3_number ) FROM w WHERE c3_number > 1999 ) - 1999;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'get lost' ) + 1;
+SELECT COUNT( c1 ) FROM w WHERE c5 NOT NULL;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_number >= 35;
+SELECT COUNT( c2 ) FROM w WHERE c3_number >= 40;
+SELECT c3 FROM w WHERE id = 1;
+SELECT COUNT( c4 ) FROM w WHERE c5_number < 15;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'tango' ) - 1;
+SELECT COUNT( * ) FROM w WHERE c4_list = 'henrik normann';
+SELECT c2_list FROM w WHERE c1 IN ( 'quickstep' , 'paso doble' ) order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE c8_number <= 75000;
+SELECT c2 FROM w WHERE c6_first = 'mile high stadium';
+SELECT COUNT( * ) FROM w WHERE c5_number1 - c5_number2 > 10;
+SELECT c8 FROM w order BY id desc limit 1;
+SELECT ( SELECT MAX( c8_number ) FROM w ) - ( SELECT MIN( c8_number ) FROM w );
+SELECT c4 FROM w WHERE id = 1;
+SELECT COUNT( * ) FROM w WHERE c5_number1 >= 2 * c5_number2;
+SELECT SUM( c9_number ) FROM w;
+SELECT c1 FROM w WHERE c1_minimum_year != 2002 AND c7_number = 2;
+SELECT c9_number FROM w WHERE c1_minimum_year = 2006;
+SELECT c1 FROM w WHERE c1 IN ( 1998 , 2002 ) order BY c4 desc limit 1;
+SELECT c1 FROM w WHERE c1_minimum_year IN ( 1998 , 2006 ) order BY c7_number limit 1;
+SELECT c4_number FROM w WHERE c1_minimum_year = 1998;
+SELECT COUNT( c1 ) FROM w WHERE c2 != 'budivelnyk';
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c1 = 2004 AND c2 = 'azovmash' ) order BY c1_number asc limit 1;
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c1_number > 2005 order BY c1_number asc limit 1;
+SELECT c2 FROM w WHERE c1_number = 1992;
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w GROUP BY c2 HAVING COUNT( * ) = 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number >= 10;
+SELECT c2 FROM w WHERE c2 IN ( 'lucimar ferreira da silva' , 'adriano leite ribeiro' ) AND c6 = 'colombia' order BY c4_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number >= 30;
+SELECT c4_number FROM w WHERE c1 = 'ze roberto';
+SELECT c2 FROM w WHERE c2 != 'adriano leite ribeiro' AND c4_number = ( SELECT c4_number FROM w WHERE c2 = 'adriano leite ribeiro' );
+SELECT c6_number FROM w WHERE c2 = 'nikolay davydenko';
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT ( SELECT c1_number FROM w WHERE c2 = 'gilles simon' ) < ( SELECT c1_number FROM w WHERE c2 = 'tommy haas' );
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'kim clijsters' , 'nikolay davydenko' ) AND c7 = 'broken wrist';
+SELECT COUNT( c2 ) FROM w WHERE c6_number > 2000;
+SELECT abs ( c4_number - c6_number ) FROM w WHERE c2 = 'juan martin del potro';
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w order BY c1_number limit 1;
+SELECT SUM( c4_number1 ) FROM w WHERE c2 = 'preston north end';
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c6_number FROM w WHERE c1_month = 2 AND c1_day = 11 AND c2 = 'huddersfield town';
+SELECT COUNT( * ) FROM w WHERE c1_month IN ( 9 , 11 );
+SELECT COUNT( * ) FROM w WHERE c6_number < 15000;
+SELECT abs ( c4_number1 - c4_number2 ) FROM w WHERE c1 = '2 january 1922';
+SELECT COUNT( * ) FROM w WHERE c1_month = 10 AND c1_year = 1921;
+SELECT c2 FROM w order BY c1_parsed desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5_list = 'sapsford';
+SELECT c1 FROM w WHERE c1 != 'pm10' AND c3 = ( SELECT c3 FROM w WHERE c1 = 'pm10' );
+SELECT c1 FROM w WHERE c5_number = 0;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'lb/mmbtu';
+SELECT COUNT( c3 ) FROM w WHERE c3 = '≤ 1.1';
+SELECT c1 FROM w order BY c5_number limit 1;
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'venezuela' ) + 1;
+SELECT c6_number FROM w WHERE c2 = 'colombia';
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT ( SELECT c3_number FROM w WHERE c2 = 'panama' ) > ( SELECT c3_number FROM w WHERE c2 = 'peru' );
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c2 FROM w WHERE c3_number = 0;
+SELECT ( SELECT c1_number FROM w WHERE c2 = 'chile' ) < ( SELECT c1_number FROM w WHERE c2 = 'bolivia' );
+SELECT c2 FROM w WHERE c3_number = 5;
+SELECT COUNT( c2 ) FROM w WHERE c4_number >= 3;
+SELECT COUNT( c2 ) FROM w WHERE c3_number = 0;
+SELECT ( SELECT COUNT( c2 ) FROM w WHERE c6 = 'general acute care' ) - ( SELECT COUNT( c2 ) FROM w WHERE c6 = 'rehabilitaion' );
+SELECT COUNT( c2 ) FROM w WHERE c5_number >= 600;
+SELECT c6 FROM w WHERE c2 = 'grossmont hospital';
+SELECT c2 FROM w WHERE c1_number IN ( 8 , 9 );
+SELECT c2 FROM w WHERE c4 = 'los angeles' AND c6 = 'rehabilitaion' AND c1_number <= 10;
+SELECT ( SELECT c5_number FROM w WHERE c2 = 'patton state hospital' ) > ( SELECT c5_number FROM w WHERE c2 = 'atascadero state hospital' );
+SELECT COUNT( c2 ) FROM w WHERE c5_number >= 1000;
+SELECT SUM( c3_number ) FROM w;
+SELECT c3_number FROM w WHERE c2 = 'dominican republic';
+SELECT c2 FROM w WHERE c2 IN ( 'ecuador' , 'chile' ) order BY c6_number desc limit 1;
+SELECT c5_number FROM w WHERE c2 = 'el salvador';
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c2 FROM w order BY c3_number limit 1;
+SELECT c2 FROM w WHERE c3_number = 0;
+SELECT c2 FROM w WHERE c2 IN ( 'venezuela' , 'chile' ) order BY c4_number desc limit 1;
+SELECT ( SELECT c3_number FROM w WHERE c2 = 'peru' ) > ( SELECT c3_number FROM w WHERE c2 = 'chile' );
+SELECT AVG( c3_number ) FROM w;
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT c2 FROM w WHERE c3_number >= 5;
+SELECT c2 FROM w WHERE c3 = 'alice lawson' AND c1_minimum_number != 1985 AND c2 != 'anne of green gables';
+SELECT c2 FROM w order BY c1_maximum_number - c1_minimum_number desc limit 1;
+SELECT c3 FROM w order BY c2_number desc limit 1;
+SELECT c4 FROM w WHERE c2_number = 1;
+SELECT c6_month FROM w GROUP BY c6_month HAVING COUNT( c3 ) >= 4;
+SELECT c2_number FROM w WHERE c2_number != 1 AND c5_list = 'will dixon' limit 1;
+SELECT c5_list FROM w WHERE c5_length = 1;
+SELECT c6 FROM w WHERE c2_number = 1;
+SELECT COUNT( * ) FROM w WHERE c6_year = 1996;
+SELECT COUNT( c3 ) FROM w WHERE c6_month = 11;
+SELECT COUNT( c3 ) FROM w;
+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE id < ( SELECT id FROM w WHERE c2 = 'hodu' AND c3 = 'book of esther' );
+SELECT COUNT( c2 ) FROM w WHERE c5 NOT NULL;
+SELECT COUNT( c2 ) FROM w WHERE c1_number >= 1994 AND c1_number <= 2005;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w order BY c5_number asc limit 1;
+SELECT c2 FROM w WHERE c1_number = 2009;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w WHERE id = 1;
+SELECT COUNT( c2 ) FROM w WHERE c1 > 2010;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c2 FROM w WHERE c2 != ''1000 times'' AND c1_number = 1999;
+SELECT c6 FROM w GROUP BY c6 order BY COUNT( c2 ) desc limit 1;
+SELECT c5 FROM w WHERE c5 NOT NULL;
+SELECT c2_raw FROM w WHERE c2_raw IN ( 'george washington' , 'syracuse' ) order BY c4_number1 desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_result = 'w' AND c1_year = 1941;
+SELECT COUNT( * ) FROM w WHERE c4_number1 >= 50;
+SELECT COUNT( * ) FROM w WHERE abs ( c4_number1 - c4_number2 ) > 5;
+SELECT COUNT( * ) FROM w WHERE c1_month = 2;
+SELECT c2 FROM w WHERE c4 = 'slovakia' AND c2_number != 2010;
+SELECT ( SELECT c2_number FROM w WHERE c4 = 'canada' ) > ( SELECT c2_number FROM w WHERE c4 = 'belgium' );
+SELECT c7_length FROM w WHERE c6 = 'dominika cibulkova';
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'world group ii' order BY id desc limit 1 ) + 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_number > 2010;
+SELECT c1_number FROM w GROUP BY c1_number HAVING COUNT( c2 ) >= 3;
+SELECT ( SELECT c1_number FROM w WHERE c2 = 'beginning blue' ) - ( SELECT c1_number FROM w WHERE c2 = 'schooled' );
+SELECT abs ( ( SELECT c1_number FROM w WHERE c2 = 'the watermelon' ) - ( SELECT c1_number FROM w WHERE c2 = 'the bacchae' ) );
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'the audacity of democracy' ) - 1;
+SELECT c1_number FROM w GROUP BY c1_number order BY COUNT( c2 ) desc limit 1;
+SELECT c5_year FROM w GROUP BY c5_year order BY COUNT( c3 ) desc limit 1;
+SELECT MIN( c5_year ) FROM w;
+SELECT c3 FROM w WHERE c2 = 'black sun productions';
+SELECT c3 FROM w WHERE c2 = 'coh' order BY c5_year limit 1;
+SELECT c4_list FROM w GROUP BY c4_list order BY COUNT( c3 ) desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c4_list = '7' vinyl';
+SELECT c2 FROM w GROUP BY c2 HAVING COUNT( c3 ) > 6;
+SELECT c1_number FROM w order BY c5_number1 limit 1;
+SELECT ( SELECT c4_number FROM w WHERE c1_number = 1976 ) > 30;
+SELECT c1_number FROM w WHERE c1_number IN ( 1994 , 2009 ) order BY c2_number limit 1;
+SELECT c3_number FROM w WHERE c1_number = 1987;
+SELECT c5 FROM w WHERE c1_number = 1994;
+SELECT c3_number FROM w WHERE c1_number = 1961;
+SELECT c1_number FROM w order BY c3_number limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'bobruisk';
+SELECT COUNT( * ) FROM w WHERE c2 = 'minsk';
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT c3 FROM w WHERE c4_number < ( SELECT c4_number FROM w WHERE c3 = 'spartak, bobruisk' );
+SELECT c1 FROM w WHERE c5_number = 1;
+SELECT COUNT( DISTINCT c3 ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'minsk';
+SELECT SUM( DISTINCT c4_number ) FROM w WHERE c3 IN ( 'central, vitebsk' , 'neman' );
+SELECT COUNT( c3 ) FROM w WHERE c4_number >= 10000;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'minsk';
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT c3 FROM w WHERE c1 = 'johann haraldsson' AND c2 = 'slalom';
+SELECT c5_list FROM w WHERE c5_list != 'desideria ampon' AND c1_number = ( SELECT c1_number FROM w WHERE c5_list = 'desideria ampon' );
+SELECT c5 FROM w WHERE c1_number = 1982;
+SELECT COUNT( * ) FROM w WHERE c2 = 'bangkok';
+SELECT MIN( c1_number ) FROM w WHERE c4 = 'tamarine tanasugarn';
+SELECT c3 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c3 = 'lee duk-hee' ) order BY c1_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5_number = 225;
+SELECT c8_number FROM w WHERE c3 = 'patrick carpentier';
+SELECT SUM( c8_number ) FROM w WHERE c1_number <= 3;
+SELECT c3 FROM w order BY c5_number limit 1;
+SELECT c3 FROM w WHERE c1_number = 9;
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'mauricio gugelmin' ) + 1;
+SELECT MAX( c8_number ) FROM w WHERE c5_number < 225;
+SELECT COUNT( * ) FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c3 = 'dario franchitti' );
+SELECT c3 FROM w order BY c8_number limit 1;
+SELECT c3 FROM w WHERE c8_number > 20;
+SELECT c3 FROM w WHERE c8_number = 25;
+SELECT COUNT( c1 ) FROM w;
+SELECT c3 FROM w WHERE c3 IN ( 'stefan bradl' , 'sergio gadea' ) order BY c1_number limit 1;
+SELECT c2 FROM w WHERE c3 != 'japan' AND c4_result = ( SELECT c4_result FROM w WHERE c3 = 'japan' );
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'seve ballesteros' ) + 1;
+SELECT COUNT( c2 ) FROM w WHERE c6_number >= 40000;
+SELECT ( SELECT c4_result FROM w WHERE c1_number = 6 ) - ( SELECT c4_result FROM w WHERE c1_number = 2 );
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'united states';
+SELECT AVG( c4_result ) FROM w WHERE c3 = 'united states';
+SELECT c2 FROM w WHERE c1_number = 4;
+SELECT COUNT( c2 ) FROM w WHERE c4_result < 283;
+SELECT c2 FROM w WHERE c3 != 'united states' order BY c1_number limit 1;
+SELECT abs ( ( SELECT c4_result FROM w WHERE c1_number = 1 ) - ( SELECT c4_result FROM w WHERE c1_number = 8 ) );
+SELECT c8_number2 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_number >= 0.2;
+SELECT COUNT( * ) FROM w WHERE c3_number >= 1.0;
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c1_number FROM w WHERE c2 IS NULL;
+SELECT c1_number FROM w WHERE c6_number = ( SELECT MIN( c6_number ) FROM w );
+SELECT MIN( c1_number ) FROM w WHERE c1_number > 1983 AND c3_number = 0.1;
+SELECT MAX( c1_number ) FROM w;
+SELECT c4_list FROM w WHERE c4_list IN ( 'lord finesse' , 'buckwild' ) AND c2 = ''ga head'';
+SELECT c4_list FROM w WHERE c2 = ''no main topic'';
+SELECT COUNT( c2 ) FROM w WHERE c4_list = 'buckwild';
+SELECT COUNT( DISTINCT c3_list ) FROM w WHERE c3_list != 'o. credle';
+SELECT c3_list FROM w GROUP BY c3_list order BY COUNT( c2 ) desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_length <= 2;
+SELECT COUNT( c2 ) FROM w WHERE c4_list = 'buckwild';
+SELECT c4_list FROM w GROUP BY c4_list order BY COUNT( c2 ) desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'lord finesse';
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c3_list FROM w GROUP BY c3_list HAVING COUNT( c2 ) = ( SELECT COUNT( c2 ) FROM w GROUP BY c3_list order BY COUNT( c2 ) asc limit 1 );
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'jack pickersgill' ) + 1;
+SELECT c2 FROM w WHERE c3 = 'thompson';
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'macdonald';
+SELECT c2_first FROM w WHERE c3 = 'laurier' INTERSECT SELECT c2_first FROM w WHERE c3 = 'king';
+SELECT COUNT( * ) FROM w WHERE c2_month = 9;
+SELECT c4 FROM w order BY abs ( c5_number1 - c5_number2 ) desc limit 1;
+SELECT c4 FROM w WHERE c2_month = 8 order BY c2_parsed desc limit 1;
+SELECT c4 FROM w WHERE c1_number = 1;
+SELECT ( SELECT c3_number FROM w WHERE c1 = 'australian labor party' ) > ( SELECT c3_number FROM w WHERE c1 = 'independent' );
+SELECT c6 FROM w WHERE c1 = 'australian labor party';
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c2 FROM w WHERE c1 = 'independent';
+SELECT c1 FROM w order BY c5_number asc limit 1;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c5 FROM w WHERE c1 = 'independent';
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c1 FROM w WHERE c3_number < ( SELECT c3_number FROM w WHERE c1 = 'liberal and country league' ) order BY c3_number desc limit 1;
+SELECT c2 FROM w WHERE c1_number = 2005;
+SELECT c1_number FROM w WHERE c1_number != 2012 AND c5_number = ( SELECT c5_number FROM w WHERE c1_number = 2012 );
+SELECT abs ( ( SELECT COUNT( * ) FROM w WHERE c2 = 'galaxy' ) - ( SELECT COUNT( * ) FROM w WHERE c2 = 'chivas' ) );
+SELECT COUNT( * ) FROM w WHERE c2 = 'galaxy';
+SELECT COUNT( * ) FROM w WHERE c2 = 'chivas';
+SELECT ( SELECT SUM( c4_number ) FROM w ) > 10;
+SELECT COUNT( * ) FROM w WHERE c1 = 'runner-up';
+SELECT COUNT( * ) FROM w WHERE c1 = 'winner';
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3_first = 'miami';
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'hard';
+SELECT ( SELECT c4_number2 FROM w WHERE c3 = 'belgium' ) - ( SELECT c5_number2 FROM w WHERE c3 = 'belgium' );
+SELECT COUNT( c1 ) + COUNT( c3 ) FROM w;
+SELECT COUNT( c1 ) FROM w;
+SELECT c5 FROM w WHERE c6 = 'win';
+SELECT c2 FROM w WHERE c6 = 'loss';
+SELECT COUNT( DISTINCT c4 ) FROM w;
+SELECT MAX( c2_year ) - MIN( c2_year ) FROM w;
+SELECT c1 FROM w order BY c5_number asc limit 1;
+SELECT c7 FROM w GROUP BY c7 order BY COUNT( c1 ) desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number > 100;
+SELECT COUNT( c1 ) FROM w WHERE c7 = 'alessia marcuzzi';
+SELECT c7 FROM w WHERE c2_parsed < ( SELECT c2_parsed FROM w WHERE c7 = 'barbara d'urso' ) order BY c2_parsed desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number > 100;
+SELECT c6 FROM w WHERE c2_parsed < ( SELECT c2_parsed FROM w WHERE c6 = 'flavio montrucchio' ) order BY c2_parsed desc limit 1;
+SELECT MAX( c5_number ) FROM w;
+SELECT c7 FROM w WHERE c2_parsed < ( SELECT c2_parsed FROM w WHERE c7 = 'barbara d'urso' ) order BY c2_parsed desc limit 1;
+SELECT c6 FROM w WHERE c8_first_number = ( SELECT MAX( c8_first_number ) FROM w );
+SELECT c1 FROM w order BY c9_number asc limit 1;
+SELECT MAX( c4_number ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c4_year = 1944;
+SELECT c1 FROM w WHERE c1 != 'wave victor' AND c4_year = ( SELECT c4_year FROM w WHERE c1 = 'wave victor' );
+SELECT c1 FROM w WHERE c4_parsed > ( SELECT c4_parsed FROM w WHERE c1 = 'wave emperor' ) order BY c4_parsed limit 1;
+SELECT c4 FROM w order BY c4_parsed limit 1;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c3 FROM w WHERE c2 = 'surfside 6';
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( DISTINCT c5 ) FROM w;
+SELECT c1 FROM w WHERE c5 = 'afrikaans' order BY c3_number desc limit 1;
+SELECT SUM( c4_number ) FROM w WHERE c1 IN ( 'makeleketla' , 'theunissen' );
+SELECT c5 FROM w order BY c4_number desc limit 1;
+SELECT c5 FROM w WHERE c5 != 'sotho';
+SELECT COUNT( c1 ) FROM w WHERE c5 = 'sotho';
+SELECT abs ( ( SELECT c4_number FROM w WHERE c1 = 'fora' ) - ( SELECT c4_number FROM w WHERE c1 = 'masilo' ) );
+SELECT MIN( c4_number ) FROM w;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c4_number FROM w WHERE c1 = 'beatrix mine';
+SELECT ( SELECT c4_number FROM w WHERE c1 = 'brandfort' ) = ( SELECT c4_number FROM w WHERE c1 = 'boipatong' );
+SELECT c2 FROM w WHERE c3_list = 'havoc' order BY c1_number limit 1;
+SELECT c5 FROM w order BY c5 asc limit 1;
+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT c4 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_min >= 4;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = ''street glory'' ) + 1;
+SELECT c5 FROM w order BY c5 desc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = ''fire'' ) - 1;
+SELECT c2 FROM w WHERE c2 IN ( ''money'' , ''die 4'' ) order BY c5 desc limit 1;
+SELECT c5 FROM w order BY c5 desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( ''fire'' , ''die 4'' ) order BY c5 desc limit 1;
+SELECT c1 FROM w WHERE c3_number >= 1000 order BY c1_number asc limit 1;
+SELECT c1 FROM w WHERE c2 < 38;
+SELECT c1 FROM w WHERE c3_number = 460;
+SELECT COUNT( c1 ) FROM w WHERE c4_number > 500 AND c4_number < 502;
+SELECT c3 FROM w GROUP BY c3 HAVING COUNT( * ) >= 2;
+SELECT c3 FROM w WHERE c3 IN ( 'frozen' , 'casese quien pueda' ) order BY c4_number desc limit 1;
+SELECT c3 FROM w order BY c4_number desc limit 1;
+SELECT c3 FROM w order BY c4_number desc limit 1;
+SELECT c3 FROM w WHERE c2_parsed < ( SELECT c2_parsed FROM w WHERE c2 = 'february 9, 2014' ) order BY c2_parsed desc limit 1;
+SELECT SUM( c4_number ) FROM w WHERE c3 = 'frozen';
+SELECT c3 FROM w WHERE c3 IN ( 'mr. peabody & sherman' , 'the lego movie' ) order BY c4_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'frozen';
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c1 FROM w order BY c6_number asc limit 1;
+SELECT c1 FROM w order BY id asc limit 1;
+SELECT c3 FROM w WHERE c1 = 'new national';
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c2_month = 10;
+SELECT COUNT( * ) FROM w;
+SELECT c3 FROM w WHERE c2_parsed < ( SELECT c2_parsed FROM w WHERE c3 = 'plymouth albion' ) order BY c2_parsed desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5_result = 'w';
+SELECT COUNT( * ) FROM w WHERE c7_number > 1500;
+SELECT c2 FROM w WHERE c7_number > 2000 order BY c1_number desc limit 1;
+SELECT c3 FROM w WHERE id = 1;
+SELECT c6_number1 - c6_number2 FROM w WHERE c2 = '3 october';
+SELECT COUNT( * ) FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c3 = 'bristol' );
+SELECT COUNT( * ) FROM w WHERE c7_number > 70000;
+SELECT c2_raw FROM w order BY c3_number1 desc limit 1;
+SELECT c2_raw FROM w WHERE c3_result = 'l' AND c1_number > 6 order BY c1_number limit 1;
+SELECT COUNT( DISTINCT ( c3 ) ) FROM w WHERE id < ( SELECT id FROM w WHERE c3 = 'united states' );
+SELECT c3 FROM w WHERE c2 = 'kaija mustonen';
+SELECT COUNT( c2 ) FROM w;
+SELECT ( SELECT c1_number FROM w WHERE c3 = 'sweden' ) <= 10;
+SELECT COUNT( c2 ) FROM w WHERE c1_number <= 30 AND c3 = 'norway';
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'paul london' ) + 1;
+SELECT c2 FROM w WHERE c2 IN ( 'super dragon' , 'scott lost' , 'paul london' ) order BY c3_number + c4_number desc limit 1;
+SELECT c2 FROM w WHERE c5 = '1';
+SELECT abs ( ( SELECT c5_number FROM w WHERE c2 = 'kevin steen' ) - ( SELECT c5_number FROM w WHERE c2 = 'davey richards' ) );
+SELECT COUNT( c2 ) FROM w WHERE c3_number >= 2;
+SELECT c5_number FROM w WHERE c2 = 'kevin steen';
+SELECT COUNT( c2 ) FROM w;
+SELECT c3_number FROM w WHERE c2 = 'joey ryan';
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c1_number FROM w order BY c5_number limit 1;
+SELECT MAX( c5_number ) FROM w;
+SELECT c1_number FROM w WHERE c6_number <= 10;
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+SELECT c1_number FROM w order BY c5_number desc limit 1;
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+SELECT MAX( c1_number ) FROM w WHERE c3_number > 0;
+SELECT c2 FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE c3 = 'military/public';
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+SELECT ( SELECT COUNT( c2 ) FROM w WHERE c3 = 'military/public' AND c2 != 'eagle farm airport' ) > 0;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'tarampa airfield' ) - 1;
+SELECT COUNT( c2 ) FROM w WHERE c1 = 'townsville';
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'military';
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( c2 ) desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1 = 'townsville';
+SELECT c2 FROM w order BY c1_number limit 1;
+SELECT ( ( SELECT c1_number FROM w WHERE c5 = 'first danish sound film' ) - ( SELECT MIN( c1_number ) FROM w ) );
+SELECT COUNT( * ) FROM w WHERE c5 = 'fy & bi film';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'lejla' ) + 1;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'william tell and son' ) - 1;
+SELECT COUNT( c2 ) FROM w GROUP BY c1_number order BY COUNT( c2 ) desc limit 1;
+SELECT c2 FROM w WHERE id = 1;
+SELECT c3 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c3 = 'love that lives' ) order BY c1_number limit 1;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT MAX( c1_number ) FROM w;
+SELECT COUNT( c3 ) FROM w;
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+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c4_list_number FROM w WHERE c1 = 'grazer ak' order BY c4_list_number asc limit 1;
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+SELECT c2_number FROM w WHERE c1 = 'sk sturm graz';
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+SELECT c1 FROM w order BY c7_number asc limit 1;
+SELECT c1 FROM w order BY c7_number asc limit 1;
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+SELECT c3 FROM w order BY c7_number desc limit 1;
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+SELECT COUNT( DISTINCT c3 ) FROM w;
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+SELECT c4 FROM w WHERE c1 = '2011/12';
+SELECT COUNT( c1 ) FROM w;
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+SELECT ( SELECT c4_number FROM w WHERE c1 = '2008/09' ) < 10;
+SELECT c4 FROM w WHERE c1 = '2002/03';
+SELECT COUNT( DISTINCT c2 ) FROM w;
+SELECT COUNT( c4 ) FROM w;
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+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'dil vil pyar vyar' ) + 1;
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+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'dhol' ) - 1;
+SELECT c6_length FROM w WHERE c1 = '1952-53';
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+SELECT c6_list FROM w WHERE c6_list IN ( 'vasilis goumas' , 'antonis christeas' ) GROUP BY c6_list order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c6_list = 'roderick blakney';
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+SELECT COUNT( * ) FROM w WHERE c1_maximum_number < 1977 AND c2 = 'champion';
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+SELECT MAX( c1 ) FROM w WHERE c2 NOT NULL;
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+SELECT COUNT( c1 ) FROM w WHERE c5_list >= present_ref - 10;
+SELECT c4 FROM w WHERE c1 = 'canterbury crusaders';
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+SELECT c3_number FROM w WHERE c2 = 'saint lucia';
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+SELECT c4 FROM w WHERE c2 = 'micoud';
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+SELECT COUNT( * ) FROM w WHERE c3 = 'preferente' AND c1_minimum_year >= 1990;
+SELECT COUNT( c1 ) FROM w WHERE c4_number >= 15;
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+SELECT c6_number FROM w WHERE c1 = '30-jan-68';
+SELECT MAX( abs ( c4_number1 - c4_number2 ) ) FROM w;
+SELECT c6_number FROM w WHERE c1 = '18-apr-68';
+SELECT COUNT( * ) FROM w WHERE c2 = 'bournemouth';
+SELECT COUNT( c1 ) FROM w WHERE c3_maximum_year = 1836;
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+SELECT COUNT( * ) FROM w WHERE c5 = 't';
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+SELECT c2 FROM w WHERE c5 = 'film' AND c1_number < 1996;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'the way to fight' ) + 1;
+SELECT MAX( c1_number ) - MIN( c1_number ) + 1 FROM w;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( c1 ) limit 1;
+SELECT c2 FROM w WHERE c4 = 'women's singles' AND c1 = 'silver';
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( c1 ) desc limit 1;
+SELECT c2 FROM w WHERE c3 = 'weightlifting' GROUP BY c2 HAVING COUNT( c1 ) = 2;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'satheesha rai';
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'shooting';
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( DISTINCT c3 ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'weightlifting';
+SELECT c1_number FROM w order BY c5_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2_month = 11;
+SELECT c4_number1 - c4_number2 FROM w WHERE c1_number = 10;
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+SELECT c3 FROM w WHERE c1_number = 4 + 1;
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'thailand' , 'south korea' ) order BY c5_number desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'japan' , 'china' ) order BY c6_number desc limit 1;
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c2 FROM w WHERE c3_number >= 1 AND c4_number >= 1 AND c5_number >= 1;
+SELECT SUM( c6_number ) FROM w;
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+SELECT c3_number FROM w WHERE c2 = 'japan';
+SELECT c2 FROM w order BY c6_number limit 1;
+SELECT c2 FROM w order BY c6_number limit 1;
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+SELECT c3 FROM w WHERE c2 = 'fairfax park';
+SELECT c1 FROM w order BY c3_number asc limit 1;
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+SELECT c1 FROM w order BY c3_number desc limit 1;
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+SELECT COUNT( * ) FROM w WHERE c4_minimum_year < 2010;
+SELECT c2_month FROM w GROUP BY c2_month order BY COUNT( * ) desc limit 1;
+SELECT c1_number FROM w WHERE c1_number IN ( 1 , 7 ) order BY c4_number1 + c4_number2 desc limit 1;
+SELECT abs ( c4_number1 - c4_number2 ) FROM w WHERE c1_number = 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'toronto';
+SELECT c3 FROM w WHERE id = 1;
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+SELECT COUNT( c2 ) FROM w;
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+SELECT c3 FROM w GROUP BY c3 HAVING COUNT( c1 ) >= 3;
+SELECT COUNT( * ) FROM w WHERE c5 = 'christian contemporary';
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+SELECT abs ( ( SELECT c3_number1 FROM w WHERE c2 = 'southampton' ) - ( SELECT c3_number1 FROM w WHERE c2 = 'sunderland' ) );
+SELECT COUNT( * ) FROM w WHERE c5 = '14 february 1976';
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+SELECT c2 FROM w WHERE id = 1;
+SELECT c2 FROM w WHERE c5 IS NULL;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'new worlds';
+SELECT c2 FROM w WHERE c1_number = 2006;
+SELECT c5 FROM w WHERE c3 = 'non-album single';
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 2007;
+SELECT c3 FROM w WHERE c2 = ''i want you to know'';
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+SELECT COUNT( c2 ) FROM w WHERE c1_year < 2000;
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+SELECT c2 FROM w order BY c1_parsed limit 1;
+SELECT c2 FROM w WHERE c1_year < 1984;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w order BY c1_parsed limit 1;
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+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_number = 1 AND c1_number > 1989;
+SELECT COUNT( * ) FROM w WHERE c4_number = 2 AND c2 = 'european championships';
+SELECT COUNT( * ) FROM w WHERE c4 = 'macau';
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+SELECT c5_number2 FROM w WHERE c4 = 'saudi arabia' AND c2_year = 2000;
+SELECT c2 FROM w WHERE c7 = '1998 fifa world cup qualification';
+SELECT COUNT( * ) FROM w WHERE c6 = 'won';
+SELECT MIN( c5_number1 ) FROM w;
+SELECT c3 FROM w WHERE id = 1;
+SELECT c3 FROM w WHERE c3 != 'tokyo, japan' AND c5 = '10-0';
+SELECT MAX( c2 ) FROM w;
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'ghimbav' , 'prejmer' ) order BY c3_number desc limit 1;
+SELECT c3_number FROM w WHERE c1 = 'predeal';
+SELECT SUM( c2_number ) FROM w;
+SELECT c4_number FROM w WHERE c1 = 'predeal';
+SELECT c1 FROM w order BY c3_number limit 1;
+SELECT c1 FROM w order BY c5_number limit 1;
+SELECT c3 FROM w WHERE c2 = 'octane';
+SELECT c2 FROM w WHERE c3 = 'ms. monica';
+SELECT c2 FROM w WHERE c4 = 'also producer';
+SELECT c1_number FROM w WHERE c2 = 'polio water';
+SELECT c3 FROM w WHERE c2 = 'the sixth sense';
+SELECT MIN( c1_number ) FROM w;
+SELECT c1 FROM w WHERE c2_list = 'olympics';
+SELECT c1 FROM w WHERE c2_list = 'world cup' order BY c3_number desc limit 1;
+SELECT c4 FROM w WHERE c1 = 'france';
+SELECT c1 FROM w WHERE c2_list = 'world cup' order BY c3_number asc limit 1;
+SELECT c1 FROM w WHERE c3_number > ( SELECT c3_number FROM w WHERE c1 = 'germany' ) order BY c3_number asc limit 1;
+SELECT MAX( c4_number ) FROM w;
+SELECT c4 FROM w WHERE c1 = 'italy';
+SELECT c1 FROM w WHERE c4 <= 1;
+SELECT c1 FROM w WHERE c5 = 'did not qualify';
+SELECT c1 FROM w WHERE c6 != 'did not qualify' AND c1_number > 2004 order BY c1_number asc limit 1;
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'conference finals';
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+SELECT c5 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c5 = 'champions' ) + 1;
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+SELECT c2 FROM w WHERE c3_number = 7;
+SELECT c6 FROM w WHERE c2 = 'russia';
+SELECT ( SELECT c6_number FROM w WHERE c2 = 'united states' ) > 10;
+SELECT c6 FROM w WHERE c2 = 'spain';
+SELECT SUM( c6_number ) FROM w WHERE c2 IN ( 'france' , 'cuba' );
+SELECT COUNT( * ) FROM w WHERE c5 = 'seyni kountche';
+SELECT COUNT( c1 ) FROM w WHERE c5 = 'ali saibou';
+SELECT c5 FROM w WHERE c5 IN ( 'ali saibou' , 'mamadou tandja' ) GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT c5 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 1974 ) + 1;
+SELECT COUNT( c1 ) FROM w WHERE id < ( SELECT id FROM w WHERE c4 = 'partly free' );
+SELECT COUNT( * ) FROM w WHERE id < ( SELECT id FROM w WHERE c3_number < 6 );
+SELECT COUNT( * ) FROM w WHERE c2_number = 7;
+SELECT c5 FROM w WHERE id = ( SELECT id FROM w WHERE c5 = 'mamadou tandja' ) - 1;
+SELECT c6_number FROM w WHERE c2 = 'indonesia (ina)';
+SELECT COUNT( c2 ) FROM w WHERE c3_number > 5;
+SELECT COUNT( c2 ) FROM w WHERE c4_number = 0;
+SELECT c2 FROM w WHERE c2_first != 'japan' AND c3_number = ( SELECT c3_number FROM w WHERE c2_first = 'japan' );
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT ( SELECT c6_number FROM w WHERE c2_first = 'india' ) - ( SELECT c6_number FROM w WHERE c2_first = 'pakistan' );
+SELECT SUM( c3_number ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c3_number > 0 AND c4_number > 0 AND c5_number > 0;
+SELECT SUM( c4_number ) FROM w WHERE c2_first IN ( 'china' , 'india' , 'japan' );
+SELECT c2_first FROM w WHERE c2_first IN ( 'philippines' , 'kazakhstan' ) order BY c6_number desc limit 1;
+SELECT SUM( c6_number ) FROM w;
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c4_number FROM w WHERE c2 = 'argentina';
+SELECT c2 FROM w order BY c1_number limit 1;
+SELECT c6_number FROM w WHERE c2 = 'puerto rico';
+SELECT c2 FROM w WHERE c4_number = 0 AND c5_number = 0;
+SELECT c6_number FROM w WHERE c2 = 'chile';
+SELECT COUNT( c2 ) FROM w WHERE c4_number > 0;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'chile' ) - 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT c1 FROM w order BY c4_parsed limit 1;
+SELECT MIN( c4_year ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c5 IN ( 'standard' , 'broad' );
+SELECT COUNT( c1 ) FROM w WHERE c5 != 'standard';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'bl29' ) - 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'joongang seoul marathon';
+SELECT COUNT( * ) FROM w WHERE c2 = 'frankfurt marathon';
+SELECT COUNT( * ) FROM w WHERE c4_number = 1;
+SELECT COUNT( * ) FROM w WHERE c4_number IS NULL;
+SELECT COUNT( * ) FROM w WHERE c2 = 'summer olympics';
+SELECT COUNT( * ) FROM w GROUP BY c1 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c4_number = 1 AND id < ( SELECT id FROM w WHERE c2 = 'helsinki marathon' ) order BY id desc limit 1;
+SELECT c2 FROM w GROUP BY c2 HAVING COUNT( * ) = 1;
+SELECT c1 FROM w WHERE c4_number = 1 order BY c1_number asc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'max biaggi' , 'ralf waldmann' ) order BY c5_number desc limit 1;
+SELECT c2 FROM w order BY c5_number asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'honda';
+SELECT c5_number FROM w WHERE c1_number = 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'yamaha';
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w WHERE c5_number > ( SELECT c5_number FROM w WHERE c2 = 'doriano romboni' );
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT c5_number FROM w WHERE c2 = 'nobuatsu aoki';
+SELECT c3 FROM w WHERE c2 IN ( 'loris capirossi' , 'ralf waldmann' );
+SELECT COUNT( * ) FROM ( SELECT c3 FROM w GROUP BY c3 HAVING COUNT( * ) > ( SELECT COUNT( * ) FROM w WHERE c3 = 'united states' ) );
+SELECT abs ( ( SELECT COUNT( * ) FROM w WHERE c3 = 'czech republic' ) - ( SELECT COUNT( * ) FROM w WHERE c3 = 'china' ) );
+SELECT c2 FROM w WHERE c1 = '4.08 m (13 ft 41⁄2 in)';
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c5_parsed > ( SELECT c5_parsed FROM w WHERE c2 = 'emma george' AND c5_year = 1999 ) order BY c5_parsed limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'australia';
+SELECT c3 FROM w GROUP BY c3 HAVING COUNT( * ) = 1;
+SELECT COUNT( * ) FROM w WHERE c6 = 'kathrin zettel';
+SELECT c4 FROM w WHERE c1 = '15 march 2006';
+SELECT c1 FROM w WHERE c1_number != 2008 AND c2_list = 'team essex';
+SELECT c3_list FROM w WHERE c1_number = 2004 AND c2_list = 'lister racing' AND c3_list != 'john nielsen';
+SELECT COUNT( * ) FROM w WHERE c7_number > 20;
+SELECT COUNT( * ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c4 = 'porsche rs spyder evo';
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT c3_list FROM w GROUP BY c3_list order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 IN ( 'martin carthy' , 'ewan maccoll' );
+SELECT abs ( ( SELECT c3_number FROM w WHERE c1 = 'the furrowed field' ) - ( SELECT c3_number FROM w WHERE c1 = 'songs' ) );
+SELECT c2 FROM w WHERE c2 IN ( 'ewan maccoll' , 'kornog' ) GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c1 FROM w WHERE c3_number < ( SELECT c3_number FROM w WHERE c1 = 'blood and roses vol 2' ) order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE c1 = c2;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c5 = 'clay' ) > ( SELECT COUNT( * ) FROM w WHERE c5 = 'hard' );
+SELECT c5 FROM w WHERE c5 IN ( 'clay' , 'hard' ) GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT c3 FROM w order BY c3_parsed desc limit 1;
+SELECT c1 FROM w WHERE id = 1;
+SELECT c3_year FROM w WHERE c3_year IN ( 2007 , 2008 ) GROUP BY c3_year order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5_first = 'hard';
+SELECT COUNT( * ) FROM w WHERE c7 = 'olympic stadium' AND c8_number > 12000;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c7 FROM w order BY c2_parsed asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c8_number > 18000;
+SELECT COUNT( * ) FROM w WHERE c3 > '6:00 p.m';
+SELECT COUNT( * ) FROM w WHERE c5_result = 'w';
+SELECT c2 FROM w order BY c2_parsed asc limit 1;
+SELECT c4_raw FROM w WHERE c2_parsed > ( SELECT c2_parsed FROM w WHERE c2 = 'sunday, may 20' AND c4_raw = 'frankfurt galaxy' ) order BY c2_parsed asc limit 1;
+SELECT c2 FROM w WHERE c2_parsed > ( SELECT c2_parsed FROM w WHERE c2 = 'saturday, april 14' ) AND c5_result = 'w';
+SELECT COUNT( * ) FROM w WHERE c7 = 'olympic stadium';
+SELECT c2 FROM w WHERE c2 IN ( 'theodis tarver' , 'david watson' ) AND c3_list = 'center';
+SELECT c2 FROM w order BY c5_number asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c6_number > ( SELECT c6_number FROM w WHERE c2 = 'michael baumer' );
+SELECT COUNT( c6 ) FROM w WHERE c6_number >= 90;
+SELECT COUNT( c2 ) FROM w WHERE c4_year > 1985;
+SELECT c2 FROM w WHERE c7 = 'chemosvit svit';
+SELECT COUNT( * ) FROM w WHERE c2 = 'jagex';
+SELECT c1 FROM w WHERE c2 = 'fubra';
+SELECT c3 FROM w WHERE c1 = 'cyber nations';
+SELECT c3 FROM w WHERE c1 = 'twilight heroes';
+SELECT c6 FROM w WHERE c1 = 'club penguin';
+SELECT COUNT( * ) FROM w WHERE c3_first_minimum_number = 2003;
+SELECT c2 FROM w WHERE c1 = 'castle of heroes';
+SELECT c1 FROM w WHERE id = 1;
+SELECT c5_number FROM w WHERE c1 = 'ismail isa';
+SELECT abs ( ( SELECT c5_number FROM w WHERE c1 = 'vladimir gadzhev' ) - ( SELECT c5_number FROM w WHERE c1 = 'yordan miliev' ) );
+SELECT COUNT( c1 ) FROM w WHERE c3_number = 0;
+SELECT SUM( c3_number ) + SUM( c4_number ) FROM w;
+SELECT c1 FROM w WHERE c4_number >= 4;
+SELECT c1 FROM w WHERE c2_number = 1;
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_number = 0;
+SELECT COUNT( c1 ) FROM w WHERE c5_number = 4;
+SELECT c1 FROM w WHERE c5 IS NULL;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'cootel' ) + 1;
+SELECT ( SELECT c7_number FROM w WHERE c1 = 'peak 2 peak gondola' ) - ( SELECT c7_number FROM w WHERE c1 = 'vanoise express' );
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'france';
+SELECT ( SELECT c8_number FROM w WHERE c1 = 'peak 2 peak gondola' ) < ( SELECT c8_number FROM w WHERE c1 = 'vanoise express' );
+SELECT ( SELECT c8_number FROM w WHERE c1 = 'sandia peak tramway' ) < ( SELECT c8_number FROM w WHERE c1 = '3s aerial tramway' );
+SELECT COUNT( c1 ) FROM w WHERE c8_number > 1970;
+SELECT c1 FROM w WHERE c8_number = ( SELECT c8_number FROM w WHERE c1 = '3s aerial tramway' ) - 1;
+SELECT c1 FROM w WHERE c1 IN ( '3s aerial tramway' , 'aiguille du midi' ) order BY c8_number limit 1;
+SELECT ( SELECT c5_list_number FROM w WHERE c1 = 'peak 2 peak gondola' ) - ( SELECT c5_list_number FROM w WHERE c1 = '3s aerial tramway' );
+SELECT c3 FROM w WHERE c4_number = 1 GROUP BY c3 HAVING COUNT( * ) = 3;
+SELECT COUNT( * ) FROM w;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'cynthia' ) - 1;
+SELECT c3 FROM w WHERE c1 = 'blandings';
+SELECT c2 FROM w WHERE c3 = 'itv2';
+SELECT COUNT( c1 ) FROM w WHERE c4_number = 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number >= 5;
+SELECT COUNT( * ) FROM w;
+SELECT ( SELECT c4_number FROM w WHERE c2 = 'cynthia' ) > 4;
+SELECT COUNT( c1 ) FROM w;
+SELECT c4 FROM w WHERE c2 = 'italy';
+SELECT COUNT( c2 ) FROM w WHERE c4_number >= 4;
+SELECT COUNT( c2 ) FROM w WHERE c5 IS NULL;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'chile' ) + 1;
+SELECT c2 FROM w WHERE c4 > ( SELECT c4 FROM w WHERE c2 = 'chile' );
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'germany' ) + 1;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'germany' ) + 1;
+SELECT c2 FROM w WHERE c2 IN ( 'ukraine' , 'united states' ) order BY c4 asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_length >= 5;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c2 = 'spain' );
+SELECT COUNT( c2 ) FROM w WHERE c2 != 'italy' AND c5 NOT NULL;
+SELECT c4 FROM w order BY c6 desc limit 1;
+SELECT c4 FROM w WHERE c4 IN ( 'turn me up' , 'make me feel' ) order BY c6 desc limit 1;
+SELECT c4 FROM w order BY c1_number asc limit 1;
+SELECT COUNT( c4 ) FROM w WHERE c3 = 'sandy';
+SELECT COUNT( c4 ) FROM w WHERE c2 = 'benassi bros';
+SELECT COUNT( c4 ) FROM w WHERE c3 = 'dhany';
+SELECT COUNT( c4 ) FROM w;
+SELECT COUNT( c4 ) FROM w;
+SELECT ( SELECT COUNT( * ) FROM w ) >= 5;
+SELECT c2 FROM w WHERE c5_number > 60;
+SELECT c2 FROM w WHERE c4_first_number > 200 order BY c1_minimum_number asc limit 1;
+SELECT c2 FROM w WHERE c1_maximum_number - c1_minimum_number = 0;
+SELECT c2 FROM w WHERE c1_maximum_year > ( SELECT c1_maximum_year FROM w WHERE c2 = 'toronto-dominion centre' ) order BY c1_maximum_year asc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'toronto';
+SELECT c2 FROM w WHERE c1_maximum_year < present_ref order BY c1_maximum_year desc limit 1;
+SELECT c5 FROM w WHERE c1_maximum_year = present_ref;
+SELECT c3 FROM w WHERE c2 = 'first canadian place';
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'ohio state';
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( c2 ) desc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'tony fisher' ) + 1;
+SELECT c3 FROM w WHERE c2 = 'tony fisher';
+SELECT COUNT( * ) FROM w WHERE c5 = 'ohio state';
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'ben mauk' ) + 1;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'robert smith' ) - 1;
+SELECT c2 FROM w WHERE c1_number = 2003;
+SELECT c2 FROM w order BY c3_number1 desc limit 1;
+SELECT c6_number FROM w WHERE c2 = 'hull' AND c4 = 'harlequins rl';
+SELECT COUNT( * ) FROM w WHERE c3 = 'philanthropist';
+SELECT c2 FROM w WHERE c1_number > 1952 order BY c1_number limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'lawyer' AND c1_number >= 1883 AND c1_number <= 2014;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'philanthropist';
+SELECT c2 FROM w WHERE c3 = 'surgeon & nobel prize winner';
+SELECT COUNT( * ) FROM w WHERE c3 = 'soldier';
+SELECT c2 FROM w WHERE c1_number < 1951 order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'edward preuss' ) - 1;
+SELECT ( SELECT c8_number FROM w WHERE c2 = 'ireland' ) > ( SELECT c8_number FROM w WHERE c2 = 'greece' );
+SELECT c4 FROM w order BY c8_number limit 1;
+SELECT COUNT( c4 ) FROM w WHERE c8_number >= 40;
+SELECT c2 FROM w WHERE c2 IN ( 'israel' , 'united kingdom' ) order BY c7_number limit 1;
+SELECT c8_number FROM w WHERE c2 = 'finland';
+SELECT c2 FROM w order BY c8_number desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'france' , 'spain' ) order BY c8_number desc limit 1;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c4 FROM w WHERE c8_number = 32;
+SELECT c4 FROM w WHERE c4 IN ( 'dina' , 'kali' ) order BY c8_number limit 1;
+SELECT c2 FROM w WHERE c8_number < ( SELECT MAX( c8_number ) FROM w ) order BY c8_number desc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'belgium' ) - 1;
+SELECT COUNT( c5 ) FROM w WHERE c8_number < 10;
+SELECT c1 FROM w WHERE c3 = 'mac harper';
+SELECT COUNT( c2 ) FROM w WHERE c3 != 'phineas bogg';
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c2 FROM w WHERE c1 = 'james bland catlett';
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c1 FROM w order BY c6_number asc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c6_number > 200;
+SELECT COUNT( c1 ) FROM w WHERE c4_address = 'ann arbor';
+SELECT c3 FROM w WHERE c1 = 'lawrence roehm';
+SELECT COUNT( c3 ) FROM w WHERE c4_length = 1;
+SELECT c3 FROM w WHERE id = 1;
+SELECT c2_maximum_day - c2_minimum_day + 1 FROM w WHERE c3 = 'fbn live';
+SELECT c2_maximum_day - c2_minimum_day + 1 FROM w WHERE c3 = 'fbn live';
+SELECT SUM( c5_number ) FROM w WHERE c2 IN ( 'toronto' , 'hamilton' );
+SELECT c3 FROM w GROUP BY c3 order BY SUM( c6_number ) desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c6_number IN ( SELECT c6_number FROM w order BY c6_number desc limit 10 ) AND c3 = 'ontario';
+SELECT c2 FROM w order BY c10_number desc limit 1;
+SELECT c2 FROM w WHERE c10_number > 0 order BY c10_number limit 1;
+SELECT ( SELECT c6_number FROM w WHERE c2 = 'ottawa' ) > ( SELECT c6_number FROM w WHERE c2 = 'vancouver' );
+SELECT c2 FROM w WHERE c2 IN ( 'edmonton' , 'winnipeg' ) order BY c5_number desc limit 1;
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT SUM( c7_number ) FROM w WHERE id IN ( SELECT id FROM w WHERE c3 = 'ontario' order BY c7_number desc limit 5 );
+SELECT c2 FROM w WHERE c6_number > 1000000;
+SELECT c2 FROM w WHERE id = 1;
+SELECT c2 FROM w WHERE c4 = 'regional municipality';
+SELECT COUNT( * ) FROM w WHERE c4 = 'bangalore';
+SELECT c5 FROM w order BY id desc limit 1;
+SELECT c5 FROM w WHERE id = ( SELECT id FROM w WHERE c5 = 'bangalore express' ) - 1;
+SELECT COUNT( c5 ) FROM w WHERE c4 = 'jolarpet junction';
+SELECT COUNT( c5 ) FROM w WHERE c4 = 'new delhi';
+SELECT COUNT( c5 ) FROM w WHERE c4 = 'trivandrum';
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = 'emotion' ) order BY c1_number asc limit 1;
+SELECT c2 FROM w order BY c1_number asc limit 1;
+SELECT MAX( c1_number ) - MIN( c1_number ) FROM w;
+SELECT c1 FROM w WHERE c3 = 'fontana';
+SELECT c2 FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c2 = 'celebration' ) order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'merry clayton' , 'keep your eye on the sparrow' ) order BY c4_number asc limit 1;
+SELECT c3 FROM w GROUP BY c3 HAVING COUNT( * ) > 1;
+SELECT MIN( c1_number ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'ode';
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'ode';
+SELECT MAX( c1_maximum_year ) FROM w;
+SELECT MIN( c1_minimum_year ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c1_minimum_year > 2004;
+SELECT COUNT( c2 ) FROM w;
+SELECT c3 FROM w WHERE c1_minimum_year < 1995 order BY c1_maximum_year desc limit 1;
+SELECT c2 FROM w WHERE c1_minimum_year = 2008 AND c5 = 'host';
+SELECT c1 FROM w WHERE c2 = 'lynch';
+SELECT COUNT( c2 ) FROM w WHERE c1_minimum_year >= 1998 AND c1_minimum_year <= 2002;
+SELECT c2 FROM w WHERE c2 IN ( 'kachorra' , 'sos mi vida' ) order BY c1_minimum_year asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'canal 9';
+SELECT MIN( c4_number ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c2_number > 10;
+SELECT ( SELECT c4_number FROM w WHERE c1 = 'john challen' ) - ( SELECT c4_number FROM w WHERE c1 = 'albert clapp' );
+SELECT SUM( c3_number ) FROM w WHERE c1 IN ( 'bill roe' , 'ted tyler' );
+SELECT c4 FROM w WHERE c1 = 'ted tyler';
+SELECT c3 FROM w WHERE c1 = 'albert clapp';
+SELECT c1 FROM w order BY c4_number asc limit 1;
+SELECT c1 FROM w WHERE c3_number <= 13;
+SELECT c1 FROM w WHERE c5_number > 25;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w order BY c6_number limit 1;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c6 = 1926;
+SELECT c2 FROM w WHERE c1 = 'altoona';
+SELECT c3 FROM w WHERE c2_parsed > ( SELECT c2_parsed FROM w WHERE c3 = 'slick 50 200' ) order BY c2_parsed asc limit 1;
+SELECT c3 FROM w WHERE c2_parsed < ( SELECT c2_parsed FROM w WHERE c3 = 'australian indycar grand prix' ) order BY c2_parsed desc limit 1;
+SELECT c5 FROM w order BY c2_parsed asc limit 1;
+SELECT COUNT( c4 ) FROM w;
+SELECT c3 FROM w order BY c2_parsed asc limit 1;
+SELECT c5 FROM w WHERE c2_parsed > ( SELECT c2_parsed FROM w WHERE c2 = 'march 5' ) order BY c2_parsed asc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c6 = 'michael andretti';
+SELECT c1 FROM w WHERE c7_number2 = 7;
+SELECT c3 FROM w order BY c7_number1 desc limit 1;
+SELECT ( SELECT c8_number FROM w WHERE c1 = 'november 7' ) - ( SELECT c8_number FROM w WHERE c1 = 'october 17' );
+SELECT COUNT( * ) FROM w WHERE c8_number >= 50000;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c7_number1 > 20 AND c7_number2 > 20;
+SELECT c1 FROM w WHERE id = 1;
+SELECT COUNT( c1 ) FROM w WHERE c8 > 0;
+SELECT COUNT( c1 ) FROM w WHERE c8 >= 1.0;
+SELECT ( SELECT c6 FROM w WHERE c1 = 'epsilon canis majoris' ) - ( SELECT c6 FROM w WHERE c1 = 'zeta sagittarii' );
+SELECT COUNT( c1 ) FROM w WHERE c6_number >= 30;
+SELECT ( SELECT c8_number FROM w WHERE c1_first = 'capella' ) > ( SELECT c8_number FROM w WHERE c1_first = 'vega' );
+SELECT MAX( c7_number ) - MIN( c7_number ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c8_number <= 0;
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c1 FROM w WHERE c6_number = 80;
+SELECT c3 FROM w WHERE c2_number < ( SELECT c2_number FROM w WHERE c3 = 'medaglia pontificia (pope's medal) anno xiii' ) order BY c2_number desc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c3 FROM w WHERE c2_number > ( SELECT c2_number FROM w WHERE c3 = 'international kim il sung prize certificate' ) order BY c2_number asc limit 1;
+SELECT c3 FROM w order BY c2_number desc limit 1;
+SELECT c2 FROM w WHERE c3 = 'fellowship award of the institute of governance and social research';
+SELECT c5 FROM w WHERE c3 = 'o.b.f.f.s';
+SELECT c3 FROM w order BY c2_number desc limit 1;
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( c4 ) desc limit 1;
+SELECT COUNT( c3 ) FROM w;
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'recognition granted';
+SELECT ( SELECT COUNT( * ) FROM w WHERE c2_number = 1988 ) - ( SELECT COUNT( * ) FROM w WHERE c2_number = 1995 );
+SELECT COUNT( * ) FROM w WHERE c2_second = 'hun';
+SELECT COUNT( * ) FROM w WHERE c4_first = 'krisztina papp';
+SELECT COUNT( * ) FROM w WHERE c6_number >= 5000;
+SELECT c2_second FROM w GROUP BY c2_second order BY COUNT( c2 ) desc limit 1;
+SELECT c2 FROM w WHERE c1_number = 2012;
+SELECT c6 FROM w WHERE c1_number = 1997;
+SELECT COUNT( * ) FROM w WHERE c1_number < 2001;
+SELECT c2 , c4 FROM w WHERE c1_number = 1996;
+SELECT c2 FROM w WHERE c3 = 'minnesota timberwolves';
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c3 = 'miami heat' order BY c5_list_maximum_year limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'new york knicks';
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( DISTINCT c3 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c3 = 'chicago bulls';
+SELECT COUNT( c3 ) FROM w WHERE c2 = 'charles barkley';
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c6 ) FROM w;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( DISTINCT c3 ) FROM w;
+SELECT SUM( c10_second_number ) FROM w WHERE c2 IN ( 'makalu' , 'cho oyu' );
+SELECT COUNT( c2 ) FROM w WHERE c3_number > 8000;
+SELECT c2 FROM w WHERE c2 IN ( 'lhotse' , 'makalu' ) order BY c3_number desc limit 1;
+SELECT c4_number FROM w WHERE c2_list = 'k2';
+SELECT ( SELECT c3_number FROM w WHERE c2_list = 'k2' ) > ( SELECT c3_number FROM w WHERE c2_list = 'kangchenjunga' );
+SELECT COUNT( c2 ) FROM w WHERE c8_number > 5000;
+SELECT COUNT( c2 ) FROM w WHERE c8_number > 5000;
+SELECT c2 FROM w WHERE c2_first IN ( 'sergio salazar' , 'eric walther' ) order BY c7_second_number desc limit 1;
+SELECT c2_second FROM w order BY c8_number asc limit 1;
+SELECT abs ( ( SELECT c8_number FROM w WHERE c2_first = 'andrey moiseev' ) - ( SELECT c8_number FROM w WHERE c2_first = 'marcin horbacz' ) );
+SELECT c2 FROM w order BY c8_number desc limit 1;
+SELECT c2 FROM w order BY c6_first_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c6_second_number > 1100;
+SELECT c1 FROM w WHERE c2 = 'darlington' AND c4 = 'notts county';
+SELECT COUNT( * ) FROM w WHERE c3_number1 + c3_number2 < 5;
+SELECT c4 FROM w order BY id desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c1 = 'replay';
+SELECT COUNT( * ) FROM w WHERE c3_number1 > c3_number2;
+SELECT COUNT( * ) FROM w WHERE c1 = 'replay';
+SELECT COUNT( * ) FROM w WHERE c5 = '22 november 1988';
+SELECT c3 FROM w WHERE c4 = 'ireland';
+SELECT c3 FROM w WHERE c1_number = 1;
+SELECT c3 FROM w WHERE c1_number = 1;
+SELECT c6 FROM w WHERE c3 = 'elin backman';
+SELECT c3 FROM w WHERE c1_number = 1;
+SELECT abs ( ( SELECT c6_number FROM w WHERE c3 = 'yelizaveta bryzhina' ) - ( SELECT c6_number FROM w WHERE c3 = 'ksenija balta' ) );
+SELECT c3 FROM w WHERE c6_number > 24.00;
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'sabina veit' ) + 1;
+SELECT c2 FROM w order BY id asc limit 1;
+SELECT c1_month FROM w WHERE c1_month IN ( 9 , 10 ) GROUP BY c1_month order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w order BY c3_first_number1 - c3_first_number2 desc limit 1;
+SELECT c1 FROM w WHERE c5 = 'considered second fortress of anjou, after angers';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'chateau de bourmont' ) + 1;
+SELECT c1 FROM w WHERE c5 = 'built as hunting lodge';
+SELECT c1 FROM w WHERE c5 = 'built as hunting lodge';
+SELECT COUNT( * ) FROM w WHERE c3 = 'ruins';
+SELECT c1 FROM w WHERE c2_minimum_year = 1455;
+SELECT c2 FROM w WHERE c5 = 'kannur';
+SELECT abs ( ( SELECT c7_number FROM w WHERE c3 = 'thunakkadavu' ) - ( SELECT c7_number FROM w WHERE c3 = 'peechi' ) );
+SELECT COUNT( * ) FROM w WHERE c4_number >= 20;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'chalakkudy' ) - 1;
+SELECT c3 FROM w WHERE c7_number > 100 AND c7_number < 500;
+SELECT COUNT( * ) FROM w WHERE c5 = 'palakkad';
+SELECT COUNT( * ) FROM w WHERE c7 = 'won';
+SELECT c5 FROM w order BY c6_list_number desc limit 1;
+SELECT c4 FROM w order BY c9_parsed desc limit 1;
+SELECT c9_year FROM w WHERE id = 1;
+SELECT ( SELECT c6_list_number FROM w WHERE c1_number = 2 ) > ( SELECT c6_list_number FROM w WHERE c1_number = 1 );
+SELECT COUNT( * ) FROM w;
+SELECT c3 FROM w WHERE c2 = 'keiji mutoh';
+SELECT c5_number FROM w WHERE c2 = 'genichiro tenryu';
+SELECT c2 FROM w WHERE c3_number = 3;
+SELECT c2 FROM w WHERE c3_number = 5;
+SELECT c4_number FROM w WHERE c2 = 'yuji nagata';
+SELECT c2 FROM w WHERE c2 != 'antonio inoki' AND c3_number = 1;
+SELECT abs ( c4_number - c5_number ) FROM w WHERE c1_number = 1;
+SELECT ( SELECT c4_number FROM w WHERE c2 = 'hiroshi tanahashi' ) - ( SELECT c4_number FROM w WHERE c2 = 'kensuke sasaki' );
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'turkey' ) + 1;
+SELECT c3_number FROM w WHERE c2 = 'japan';
+SELECT c2 FROM w WHERE c2 IN ( 'iran' , 'germany' ) AND c1_number > 10;
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT SUM( c3_number ) FROM w WHERE c2 IN ( 'japan' , 'france' );
+SELECT c2 FROM w WHERE c6_number = 1 AND c5_number = 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_number > 5;
+SELECT c2 FROM w WHERE c3_number > ( SELECT c3_number FROM w WHERE c2 = 'united states' );
+SELECT c4_number FROM w WHERE c2 = 'turkey';
+SELECT COUNT( * ) FROM w WHERE c1 = 'gold';
+SELECT COUNT( * ) FROM w WHERE c5_parsed > ( SELECT c5_parsed FROM w WHERE c5 = 'august 3' );
+SELECT c3 FROM w WHERE c2_list = 'louis chaillot';
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'team dressage' ) - 1;
+SELECT COUNT( * ) FROM w WHERE c1 = 'gold' AND c3 = 'weightlifting';
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( c1 ) desc limit 1;
+SELECT c3 FROM w WHERE c2_list = 'louis chaillot' AND c1 = 'silver';
+SELECT c5 FROM w WHERE id = ( SELECT id FROM w WHERE c5 = 'august 7' ) + 1;
+SELECT c3 FROM w WHERE id = 1;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c1 = 'gold' ) > ( SELECT COUNT( * ) FROM w WHERE c1 = 'silver' );
+SELECT c2 FROM w order BY c4 asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4 < ( SELECT c4 FROM w WHERE c2 = 'bill chisholm' );
+SELECT COUNT( * ) FROM w WHERE c3 = 'united states' AND c1 NOT NULL;
+SELECT COUNT( c2 ) FROM w WHERE c4_hour < 5;
+SELECT c2 FROM w WHERE c3 = 'great britain';
+SELECT COUNT( c1 ) FROM w;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'karl hahnel' ) + 1;
+SELECT c2 FROM w WHERE c3 = 'united states' order BY c4 desc limit 1;
+SELECT COUNT( c1 ) FROM w;
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+SELECT c1 FROM w WHERE id = 1;
+SELECT COUNT( c1 ) FROM w WHERE c4 = 'rose-pink';
+SELECT c1 FROM w WHERE c1 IN ( 'leonard messel' , 'royalty' ) order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE c1 != 'spring festival' AND c4 = 'pink';
+SELECT COUNT( c1 ) FROM w WHERE c1 != 'freedom bell' AND c5 = ( SELECT c5 FROM w WHERE c1 = 'freedom bell' );
+SELECT c3 FROM w WHERE c1 = 'cornish snow';
+SELECT COUNT( c1 ) FROM w WHERE c5 = 'semi-double';
+SELECT c6 FROM w WHERE c2 = 'france';
+SELECT c2 FROM w WHERE c6_number > ( SELECT c6_number FROM w WHERE c2 = 'bulgaria' );
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c6_number = 0;
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT SUM( c4_number ) FROM w;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c6 FROM w WHERE c2 = 'russia';
+SELECT MAX( c5_number ) FROM w WHERE c5_number < 74854;
+SELECT COUNT( * ) FROM w WHERE c3_raw = 'denver broncos';
+SELECT COUNT( * ) FROM ( SELECT c3_raw FROM w GROUP BY c3_raw HAVING COUNT( * ) = 2 );
+SELECT c2 FROM w WHERE id = 1;
+SELECT c2 FROM w WHERE c5 = 'coburn gore';
+SELECT ( SELECT c3_first_year FROM w WHERE c2 = 'bass boarding house' ) - ( SELECT c3_first_year FROM w WHERE c2 = 'ora blanchard house' );
+SELECT c2 FROM w WHERE id = 1;
+SELECT c2 FROM w WHERE c3_first = 'october 1, 1969';
+SELECT COUNT( * ) FROM w WHERE c5 = 'farmington';
+SELECT COUNT( c2 ) FROM w WHERE c3_first_year < 1970;
+SELECT c2 FROM w WHERE c2 IN ( 'mccleary farm' , 'nordica homestead' ) order BY c3_first_year asc limit 1;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'new sharon';
+SELECT c1 FROM w WHERE c1 IN ( 'star of lake tai' , 'star of nanchang' , 'melbourne star' ) order BY c3_number limit 1;
+SELECT c4 FROM w WHERE c2_first_number > 80 AND c3_number = 2008 GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w WHERE c3_number = 2008 AND c2_first_number = 165;
+SELECT c2 FROM w order BY c3_number asc limit 1;
+SELECT ( SELECT c6_number FROM w WHERE c2 = 'india' ) - ( SELECT c6_number FROM w WHERE c2 = 'nepal' );
+SELECT c2 FROM w WHERE id = 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_number > 10;
+SELECT MAX( c6_number ) - MIN( c6_number ) FROM w;
+SELECT ( SELECT c3_number FROM w WHERE c2 = 'nepal' ) - ( SELECT c3_number FROM w WHERE c2 = 'pakistan' );
+SELECT c4 FROM w WHERE c2 = 'pakistan';
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT c3 FROM w WHERE c2 = 'india';
+SELECT c5 FROM w WHERE c2 = 'sri lanka';
+SELECT SUM( c3_number ) FROM w;
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+SELECT c2 FROM w WHERE c6_number < 10;
+SELECT COUNT( * ) FROM w WHERE c1 = 'gis xxi';
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'june 2010' AND c2 = 'radio nacional de venezuela' ) + 1;
+SELECT SUM( c5_number ) FROM w WHERE c3_parsed > ( SELECT c3_parsed FROM w WHERE c3 = 'august 2010' );
+SELECT c5 FROM w WHERE c3 = 'may 2010';
+SELECT COUNT( DISTINCT c1 ) FROM w;
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+SELECT COUNT( c2 ) FROM w WHERE c3 = 'pop singles';
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+SELECT c2 FROM w order BY c8_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'fw';
+SELECT c4 FROM w WHERE c1 = 'ted davis';
+SELECT c1 FROM w WHERE id = 1;
+SELECT AVG( c9_number ) FROM w WHERE c2 = 'scotland';
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+SELECT COUNT( * ) FROM w WHERE c5 IS NULL;
+SELECT c3 FROM w WHERE c5_month = 10 AND c5_year = 1995 order BY c5_parsed asc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c5_month = 10;
+SELECT c3 FROM w WHERE c5_month = 10 AND c5_year = 1995 order BY c5_parsed asc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c4 NOT NULL AND c5 NOT NULL;
+SELECT COUNT( c3 ) FROM w WHERE c4 NOT NULL AND c5 IS NULL;
+SELECT COUNT( c3 ) FROM w;
+SELECT COUNT( DISTINCT c2 ) FROM w;
+SELECT c3 FROM w WHERE c5_month = 9 AND c5_year = 1995 order BY c5_parsed desc limit 1;
+SELECT c1 FROM w order BY c4_number limit 1;
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+SELECT c1 FROM w WHERE c2_first_number = 9.5 AND c3_first_number = 1531;
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+SELECT c1 FROM w order BY id desc limit 1;
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+SELECT c3 FROM w WHERE c2 = 'secretariat' AND c1_number = 1973;
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+SELECT c3 FROM w WHERE c1_number = 1978;
+SELECT MIN( c1_number ) FROM w;
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+SELECT COUNT( c3 ) FROM w;
+SELECT c3 FROM w order BY c5 limit 1;
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+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'nina ryabova' ) - 1;
+SELECT COUNT( c3 ) FROM w WHERE c4 = 'china';
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+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT c2 FROM w WHERE c4_number > 95.4;
+SELECT c2 FROM w order BY c5_number desc limit 1;
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+SELECT abs ( ( SELECT c3_number FROM w WHERE c2_first = 'sharon bowes' ) - ( SELECT c3_number FROM w WHERE c2_first = 'silvia sperber' ) );
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+SELECT c2 FROM w WHERE c1_number < 1981;
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+SELECT COUNT( * ) FROM w WHERE c2_first = c3;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w WHERE c4 = 'chile' OR c5 = 'chile';
+SELECT COUNT( * ) FROM w WHERE c3 = 'brazil' AND c1_number < 2000;
+SELECT COUNT( * ) FROM w WHERE c2_first = c4;
+SELECT COUNT( * ) FROM w WHERE c4 = 'chevrolet';
+SELECT COUNT( * ) FROM w WHERE c5 = 'braun racing';
+SELECT COUNT( * ) FROM w WHERE c4 = 'chevrolet';
+SELECT COUNT( * ) FROM w WHERE c4 = 'toyota';
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+SELECT c5 FROM w WHERE c5 IN ( 'braun racing' , 'michael waltrip racing' ) AND c3 = 'jason leffler';
+SELECT c1 FROM w WHERE c3 = 'benetton b198';
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+SELECT MAX( c5_number ) FROM w;
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT c3 FROM w WHERE c6 = 'collision damage';
+SELECT COUNT( c3 ) FROM w WHERE c5_number < 56;
+SELECT c4 FROM w WHERE c3 = 'heikki kovalainen';
+SELECT c6 FROM w WHERE c3 = 'jenson button';
+SELECT COUNT( c3 ) FROM w;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'kazuki nakajima' ) - 1;
+SELECT c1 FROM w WHERE c6_number > 1960 order BY c2_second_year desc limit 1;
+SELECT c2_second_year FROM w WHERE c1 = 'whoopi goldberg';
+SELECT c2_second_year FROM w WHERE c1 = 'richard rodgers';
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+SELECT c1 FROM w WHERE c1 != 'scott rudin' AND c4_number = ( SELECT c4_number FROM w WHERE c1 = 'scott rudin' );
+SELECT c1 FROM w order BY c2_second_year desc limit 1;
+SELECT ( SELECT c2_second_year FROM w WHERE c1 = 'scott rudin' ) > 30;
+SELECT c1 FROM w order BY c2_second_year limit 1;
+SELECT c1 FROM w order BY c2_first_number limit 1;
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE c3 = ''eu so quero'';
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT c4 FROM w WHERE c2 = 'florencia';
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( c2 ) desc limit 1;
+SELECT c2 FROM w WHERE c3 = 'sid marcus' order BY c1 desc limit 1;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( c2 ) desc limit 1;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'bob balsar';
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number > 15;
+SELECT COUNT( c1 ) FROM w;
+SELECT c7 FROM w WHERE c1_number < ( SELECT MIN( c1_number ) FROM w WHERE c7_list = 'colin eglin' ) order BY c1_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c7_list = 'colin eglin';
+SELECT COUNT( * ) FROM w WHERE c7_list = 'jan steytler';
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_number >= 10;
+SELECT MAX( c5_number ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'post abolished';
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( c2 ) desc limit 1;
+SELECT c3_maximum_year - c3_minimum_year FROM w WHERE c2 = 'frederik beichmann';
+SELECT COUNT( c2 ) FROM w WHERE c3 = '15 january 1931 - 1 february 1936';
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+SELECT c2 FROM w WHERE c1 = 'finland' order BY c3_minimum_year desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_minimum_year > 1930;
+SELECT c3_maximum_year - c3_minimum_year FROM w WHERE c4 = 'not re-elected';
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'post abolished';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'rafael erich' ) - 1;
+SELECT ( SELECT c3_minimum_year FROM w WHERE c2 = 'rafael erich' ) < ( SELECT c3_minimum_year FROM w WHERE c2 = 'mihajlo jovanovic' );
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+SELECT c1 FROM w WHERE c1 IN ( 'athens' , 'rome' ) order BY c5_number desc limit 1;
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+SELECT COUNT( c1 ) FROM w WHERE c8 NOT NULL;
+SELECT c1 FROM w order BY c5_number desc limit 1;
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+SELECT c1 FROM w GROUP BY c1 order BY COUNT( c2 ) desc limit 1;
+SELECT c5 FROM w WHERE c1_number = 1997 AND c2 = 'world championships';
+SELECT c1 FROM w GROUP BY c1 HAVING COUNT( c2 ) > 1;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'athens, greece' ) + 1;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'dakar, senegal' ) - 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT c1 FROM w WHERE c4_number < 5;
+SELECT c1 FROM w WHERE c1_number != 1999 AND c4_number = 2;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT COUNT( * ) FROM w;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'gothenburg, sweden' ) - 1;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT MAX( c1_number ) - MIN( c1_number ) FROM w;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = ''ain\'t no sunshine'' ) + 1;
+SELECT c2 FROM w WHERE id = 1;
+SELECT c3 FROM w WHERE c2 = ''sweat it out'';
+SELECT COUNT( c2 ) FROM w;
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+SELECT c12 FROM w WHERE c1 = 'netherlands';
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+SELECT c12 FROM w WHERE c1 = 'bolivia';
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+SELECT c4 FROM w WHERE c4 IN ( 'automobili o.s.c.a' , 'david brown' ) order BY c8_number desc limit 1;
+SELECT c4 FROM w order BY c8_number desc limit 1;
+SELECT c4 FROM w WHERE c8_number = 0;
+SELECT COUNT( * ) FROM w WHERE c8_number > 150;
+SELECT c8_number FROM w WHERE c4 = 'scuderia ferrari' AND c5_list = 'robert manzon';
+SELECT ( SELECT MAX( c8_number ) FROM w ) > 250;
+SELECT c1 FROM w WHERE c1 != 'chris hodgson' AND c2_number = ( SELECT c2_number FROM w WHERE c1 = 'chris hodgson' );
+SELECT c1 FROM w WHERE c1 IN ( 'nikki dinki' , 'viet pham' ) order BY c6 limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_number < 30;
+SELECT c1 FROM w order BY id limit 1;
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+SELECT c1 FROM w order BY id desc limit 1;
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+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'swansea city' , 'west ham united' ) order BY c2_number desc limit 1;
+SELECT c2_number FROM w WHERE c1 = 'manchester united';
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+SELECT c1 FROM w WHERE c2_number = 2 AND c3_number = 0;
+SELECT c1 FROM w WHERE c2_number = ( SELECT MIN( c2_number ) FROM w );
+SELECT c3_number FROM w WHERE c1 = 'arsenal';
+SELECT COUNT( c1 ) FROM w WHERE c2_number = 4;
+SELECT c2_number FROM w WHERE c1 = 'liverpool';
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE c2_number < 8 order BY c2_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_number >= 5;
+SELECT COUNT( * ) FROM w WHERE c4_first_number = 1;
+SELECT c6 FROM w WHERE c2 = 'commonwealth games';
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c4_first_number <= 3;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3_address = 'finland' ) - 1;
+SELECT MIN( c1_number ) FROM w;
+SELECT c3 FROM w WHERE c2 = 'olympic games';
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT MAX( c6_number ) - MIN( c6_number ) FROM w WHERE c5 = 'javelin throw';
+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT c3 FROM w WHERE c1_number = 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_number = 1;
+SELECT c2 FROM w WHERE c2 != 'you are not alone' AND c3 = 'michael jackson';
+SELECT c1_number FROM w WHERE c3 = 'madonna';
+SELECT COUNT( c2 ) FROM w WHERE c4_number >= 500000;
+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT abs ( ( SELECT c4_number FROM w WHERE c2 = 'think twice' ) - ( SELECT c4_number FROM w WHERE c2 = 'back for good' ) );
+SELECT COUNT( c2 ) FROM w WHERE c4_number >= 1000000;
+SELECT c1 FROM w WHERE c3_number = 1 AND c4_number = 1;
+SELECT abs ( ( SELECT c12_number FROM w WHERE c1 = ''revolving door'' ) - ( SELECT c12_number FROM w WHERE c1 = ''drowning'' ) );
+SELECT c2 FROM w WHERE c1 = ''drowning'' AND c4_number = 24;
+SELECT COUNT( c1 ) FROM w WHERE c9_number = 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w order BY c8_first_number desc limit 1;
+SELECT c1 FROM w WHERE c1 != 'mecklenburg county' order BY c7_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'alamance county' , 'alexander county' ) order BY c7_number desc limit 1;
+SELECT c1 FROM w WHERE c5 = 1919 AND c2 = 'country';
+SELECT c2 FROM w WHERE c1 = 'alan bird';
+SELECT abs ( ( SELECT c5_list_first_minimum_number FROM w WHERE c1 = 'allan fraser' ) - ( SELECT c5_list_first_minimum_number FROM w WHERE c1 = 'frank crean' ) );
+SELECT c2 FROM w WHERE c1_number < 1994 order BY c1_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_number >= 1988 AND c1_number <= 1993;
+SELECT COUNT( c1 ) > 5 FROM w WHERE c3_list = 'nes';
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'ghostbusters' AND c1_number = 1988 ) - 1;
+SELECT COUNT( * ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 1990;
+SELECT COUNT( c2 ) FROM w WHERE c4_list = 'activision';
+SELECT COUNT( c2 ) FROM w WHERE c8_address = 'dresden';
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'oper';
+SELECT c2 FROM w WHERE c6_minimum_year >= 1900 order BY c6_minimum_year asc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'elektra' , 'intermezzo' ) order BY c7_list_year - c6_minimum_year desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'manny ramjohn stadium';
+SELECT COUNT( * ) FROM w WHERE c2 = 'w connection';
+SELECT c5 FROM w GROUP BY c5 HAVING COUNT( * ) >= 5;
+SELECT c3_number1 + c3_number2 FROM w WHERE c1_number = 2010;
+SELECT abs ( ( SELECT COUNT( * ) FROM w WHERE c5 = 'manny ramjohn stadium' ) - ( SELECT COUNT( * ) FROM w WHERE c5 = 'marvin lee stadium' ) );
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w WHERE c2 = 'joe public';
+SELECT c2 FROM w WHERE c1_number >= 2000 AND c1_number <= 2012 GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT abs ( c3_number1 - c3_number2 ) FROM w WHERE c1_number = 2005;
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c4_number > 300;
+SELECT c3 FROM w WHERE c2 = 'sihag';
+SELECT ( SELECT c4_number FROM w WHERE c2 = 'punia' ) > ( SELECT c4_number FROM w WHERE c2 = 'godara' );
+SELECT c6_length FROM w WHERE c2 = 'punia';
+SELECT c2 FROM w WHERE c4_number < ( SELECT c4_number FROM w WHERE c2 = 'godara' ) order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'sihag' ) + 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c4_number FROM w WHERE c2 = 'johiya';
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c2_list = 'celine dion';
+SELECT c3 FROM w WHERE c2 = 'eiffel 65' AND c3 != ''blue (da ba dee)'';
+SELECT COUNT( * ) FROM w WHERE c6_number = 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_number > 900000;
+SELECT ( SELECT c5_number FROM w WHERE c3 = ''candle in the wind'' ) - ( SELECT c5_number FROM w WHERE c3 = ''freed from desire'' );
+SELECT c5_number FROM w WHERE c3 = ''tic, tic tac'';
+SELECT ( SELECT c5_number FROM w WHERE c2_list = 'elton john' ) - ( SELECT c5_number FROM w WHERE c2_list = 'lou bega' );
+SELECT c5 FROM w WHERE c3 = ''blue (da ba dee)'' AND c2 = 'eiffel 65';
+SELECT c3 FROM w WHERE c2 = 'elton john' AND c3 != ''candle in the wind'';
+SELECT MAX( c5_number ) - MIN( c5_number ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c1_month = 5 AND c1_year = 2010;
+SELECT COUNT( * ) FROM w WHERE c6_number > 1;
+SELECT c1 FROM w WHERE abs ( c4_number1 - c4_number2 ) > 50;
+SELECT COUNT( * ) FROM w WHERE c2 = 'craven park';
+SELECT c2 FROM w WHERE c6_number >= 3;
+SELECT COUNT( * ) FROM w WHERE c3 = 'wakefield trinity wildcats';
+SELECT COUNT( * ) FROM w WHERE c7 = 'win';
+SELECT MAX( c6_number1 - c6_number2 ) FROM w;
+SELECT c1_number FROM w GROUP BY c1_number order BY SUM( c6_number1 ) limit 1;
+SELECT COUNT( * ) FROM w WHERE c7_result = 'win';
+SELECT ( SELECT SUM( c6_number1 ) FROM w WHERE c1_number = 1990 ) > 10;
+SELECT c1_number FROM w WHERE c1_number IN ( 1992 , 1996 ) AND c7_result = 'win' GROUP BY c1_number order BY COUNT( * ) desc limit 1;
+SELECT c1_number FROM w WHERE c1_number IN ( 1990 , 1993 ) AND c2 = 'world group, 1st round' AND c7_result = 'win';
+SELECT c5 FROM w order BY id desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'spain';
+SELECT c5 FROM w WHERE id = ( SELECT id FROM w WHERE c1_number = 1990 AND c2 = 'world group, 1st round' ) + 1;
+SELECT c3 FROM w WHERE c5 = 'champion';
+SELECT COUNT( * ) FROM w WHERE c2 = 1;
+SELECT c1 FROM w WHERE id < ( SELECT id FROM w WHERE c1 = 'midwest' ) order BY id desc limit 1;
+SELECT c5 FROM w WHERE c3 = 'north carolina';
+SELECT c3 FROM w WHERE c5 = 'champion';
+SELECT COUNT( c3 ) FROM w;
+SELECT COUNT( c3 ) FROM w WHERE c1 = 'east';
+SELECT c1 FROM w WHERE c3 = 'summit, delaware';
+SELECT c1 FROM w order BY c2_length desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_address = 'maryland';
+SELECT c1 FROM w WHERE c3_address = 'delaware' AND c2_list = 'de 9';
+SELECT c4 FROM w WHERE c3 = 'best actress in a revival' AND c1_minimum_number = 1984;
+SELECT c3 FROM w WHERE c4 = 'once in a lifetime';
+SELECT c1 FROM w WHERE c4 = 'prime suspect' AND c2 = 'bafta tv award';
+SELECT c4 FROM w WHERE c3 = 'best featured in a play' AND c1 = 1981;
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 1998;
+SELECT c1 FROM w WHERE c1_number > 1995 order BY c1_number asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_number <= 25;
+SELECT c2 FROM w order BY c3_number asc limit 1;
+SELECT c2 FROM w order BY c1_number asc limit 1;
+SELECT c1 FROM w order BY c1_number asc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w order BY c3_number asc limit 1;
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c5 FROM w WHERE c1 = 'united states';
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c1 FROM w WHERE c1 != 'germany' order BY c5_number desc limit 1;
+SELECT c4 FROM w WHERE c1 = 'bulgaria';
+SELECT c3_raw FROM w WHERE c2_parsed > ( SELECT c2_parsed FROM w WHERE c3_raw = 'utah' ) order BY c2_parsed limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c5_first = 'chris kaman';
+SELECT MIN( c7_list_second_number ) FROM w;
+SELECT c7_list_first FROM w WHERE c2 = 'october 19';
+SELECT c1 FROM w WHERE c2_number IS NULL;
+SELECT COUNT( DISTINCT c1 ) FROM w WHERE c2_number >= 1953 AND c2_number <= 1958;
+SELECT c2 FROM w WHERE c1 = 'ch';
+SELECT ( SELECT c2_number FROM w WHERE c1 = 'e' ) < 1950;
+SELECT MIN( c2_number ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c5_list = 'private';
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT SUM( c6_number ) FROM w WHERE c1 IN ( 'brown university' , 'union college' );
+SELECT COUNT( c1 ) FROM w WHERE c7 = 'ivy league';
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c1 FROM w WHERE c3 = 'saints';
+SELECT ( SELECT COUNT( c3 ) FROM w WHERE c3_second = 'ken' ) + ( SELECT COUNT( c5 ) FROM w WHERE c5_second = 'ken' );
+SELECT c3 FROM w WHERE c2_minimum_year = 2013 UNION SELECT c5 FROM w WHERE c2_minimum_year = 2013;
+SELECT COUNT( * ) FROM w WHERE c3_first = 'philip singoei';
+SELECT c3 FROM w order BY c2_minimum_parsed limit 1;
+SELECT COUNT( * ) FROM w WHERE c1_number NOT NULL;
+SELECT COUNT( * ) FROM w WHERE c5_first = 'mieke hombergen';
+SELECT COUNT( * ) FROM w WHERE c4_number <= 5;
+SELECT COUNT( * ) FROM w WHERE c2 = 'world championships';
+SELECT MIN( c1_number ) FROM w WHERE c2 = 'asian games';
+SELECT c4 FROM w WHERE c2 = 'world race walking cup';
+SELECT COUNT( * ) FROM w WHERE c4_number <= 10;
+SELECT COUNT( * ) FROM w WHERE c4_number > 20;
+SELECT COUNT( * ) FROM w WHERE c2 = 'olympic games' AND c1_number > 1992;
+SELECT COUNT( * ) FROM w WHERE c2 = 'world championships' AND c1_number < 2001;
+SELECT c1 FROM w WHERE c4_number IN ( 3 , 2 );
+SELECT COUNT( * ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c2_number > 1000;
+SELECT COUNT( c1 ) FROM w WHERE c2_number >= 5 AND c2_number < 10;
+SELECT c5 FROM w WHERE c1 = 'famjin';
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c1 FROM w WHERE c2_number > ( SELECT c2_number FROM w WHERE c1 = 'klaksvik' );
+SELECT abs ( ( SELECT c2_number FROM w WHERE c1 = 'akrar' ) - ( SELECT c2_number FROM w WHERE c1 = 'dalur' ) );
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'akrar' ) + 1;
+SELECT c5 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'argir' ) - 1;
+SELECT c1 FROM w order BY c1 limit 1;
+SELECT ( SELECT c2_number FROM w WHERE c1 = 'argir' ) - ( SELECT c2_number FROM w WHERE c1 = 'glyvrar' );
+SELECT c2_number FROM w WHERE c1 = 'argir';
+SELECT c3_first FROM w WHERE c1_number = 1962;
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+SELECT c5_second FROM w GROUP BY c5_second order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c3_second = 'usa';
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+SELECT c5 FROM w WHERE c1 = 'medusa';
+SELECT c1 FROM w WHERE c2_number > 40 order BY id limit 1;
+SELECT c1 FROM w WHERE c2_number = 41 order BY id desc limit 1;
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+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'fairfield' ) + 1;
+SELECT c1 FROM w WHERE c1 IN ( 'no.1' , 'veteran' ) AND c2_number = 1847;
+SELECT c6 FROM w WHERE c6 IN ( 'inside' , 'outside' ) GROUP BY c6 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w WHERE c1 != 'fairfield' AND c2_number = 1847;
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+SELECT COUNT( * ) FROM w WHERE c2_number = 1847;
+SELECT c5 FROM w order BY id asc limit 1;
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+SELECT c3_address FROM w WHERE c3_address IN ( 'lionel roberts park' , 'andre kamperveen stadion' ) GROUP BY c3_address order BY COUNT( * ) desc limit 1;
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+SELECT c3 FROM w WHERE c2_year = 2012 order BY c2_parsed asc limit 1;
+SELECT c2 FROM w WHERE c4_number = ( SELECT MIN( c4_number ) FROM w );
+SELECT COUNT( c2 ) FROM w WHERE c6_number >= 3;
+SELECT SUM( c3 ) FROM w WHERE c2 IN ( 'italy' , 'belgium' , 'ireland' );
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c2 FROM w WHERE c2 != 'france' order BY c4 desc limit 1;
+SELECT SUM( c6_number ) FROM w;
+SELECT c2 FROM w WHERE c2 != 'scorpio, jr. and super crazy' AND c7 = 'iwl oktoberfest';
+SELECT c5 FROM w WHERE c2_first = 'ultimo gladiador and ultimo vampiro';
+SELECT c7 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( c7 ) FROM w WHERE c4_year = 2011;
+SELECT c2 FROM w order BY c5_number desc limit 1;
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+SELECT c7 FROM w order BY c4_parsed desc limit 1;
+SELECT c6 FROM w GROUP BY c6 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c6_list = 'earl ball';
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+SELECT COUNT( c1 ) FROM w WHERE c6_list = 'cooney checkaye';
+SELECT c1_number FROM w WHERE c2 IS NULL;
+SELECT c6_list FROM w WHERE c5 = 'indiana state champs';
+SELECT c2 FROM w WHERE c1_year = 1985;
+SELECT c2 FROM w order BY c1_parsed limit 1;
+SELECT abs ( ( SELECT c6_number FROM w WHERE c1 = 'february 10, 1979' ) - ( SELECT c6_number FROM w WHERE c1 = 'february 11, 1978' ) );
+SELECT c5 FROM w order BY c6_number limit 1;
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+SELECT COUNT( c1 ) FROM w WHERE c4 IS NULL;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_number > 1;
+SELECT c1 FROM w order BY c2_number desc limit 1;
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+SELECT MAX( c1 ) FROM w;
+SELECT c2 FROM w order BY c6_number limit 1;
+SELECT COUNT( c2 ) FROM w;
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+SELECT c2 FROM w WHERE c2 IN ( 'mark macneill' , 'karen olsson' ) order BY c6_number desc limit 1;
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c6_number > ( SELECT c6_number FROM w WHERE c2 = 'tamara lorincz' );
+SELECT c6_number FROM w WHERE c2 = 'megan leslie';
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+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'copper' ) - 1;
+SELECT COUNT( c3 ) FROM w WHERE c1_number > 25;
+SELECT c13_number FROM w WHERE c2 = 'mo';
+SELECT COUNT( c2 ) FROM w WHERE c2 != 'kr' AND c13_number NOT NULL;
+SELECT COUNT( c3 ) FROM w;
+SELECT c3 FROM w WHERE c3 IN ( 'titanium' , 'iron' ) AND c2 = 'fe';
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+SELECT c2 FROM w order BY c1_number asc limit 1;
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+SELECT c6 FROM w GROUP BY c6 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c6 = 'david dowd';
+SELECT COUNT( c1 ) FROM w WHERE c8_year >= 90;
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w WHERE c5 = 'republican' order BY c7_parsed asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'democratic';
+SELECT c4_list_maximum_number - c4_list_minimum_number FROM w WHERE c1 = 'william r. poage';
+SELECT c1 FROM w WHERE c1 IN ( 'sala burton' , 'harold earthman' ) order BY c7_parsed asc limit 1;
+SELECT ( SELECT c8_year FROM w WHERE c1 = 'harold earthman' ) > 79;
+SELECT AVG( c8_year ) FROM w WHERE id <= 3;
+SELECT c4_list_maximum_number - c4_list_minimum_number FROM w WHERE c1 = 'william r. poage';
+SELECT c3_length FROM w WHERE c1 = 'charles goodell';
+SELECT COUNT( * ) FROM w WHERE c5 = 'democratic' AND c4_list_maximum_number = c7_year;
+SELECT c4_list_maximum_number - c4_list_minimum_number FROM w WHERE c1 = 'sala burton';
+SELECT c3 FROM w WHERE id = 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT c2 FROM w WHERE c2 IN ( 'hindoocraft' , 'spokane' ) order BY c1_number limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'outbound' ) + 1;
+SELECT c5 FROM w WHERE c5 IN ( 'beverwyck stable' , 'milton young' ) order BY c1_number limit 1;
+SELECT COUNT( c4 ) FROM w;
+SELECT c5 FROM w WHERE id = ( SELECT id FROM w WHERE c5 = 'milton young' ) + 1;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'outbound' ) - 3;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT c6 FROM w WHERE c2 = 'spokane';
+SELECT COUNT( DISTINCT c3 ) FROM w;
+SELECT MIN( c5_number1 + c5_number2 ) FROM w;
+SELECT c5_number1 FROM w WHERE c4 = 'san marino';
+SELECT c4 FROM w order BY c2_parsed desc limit 1;
+SELECT c4 FROM w WHERE c4 != 'belgium' AND c2_year = 2001;
+SELECT MAX( c5_number1 + c5_number2 ) FROM w;
+SELECT c4 FROM w WHERE c6 = '4-0';
+SELECT c4 FROM w WHERE c2_parsed < ( SELECT c2_parsed FROM w WHERE c4 = 'faroe islands' ) order BY c2_parsed desc limit 1;
+SELECT c5_number1 FROM w WHERE c4 = 'faroe islands';
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_list = 'scotland';
+SELECT c3 FROM w WHERE c2 = 'gene sarazen';
+SELECT ( SELECT c1_number FROM w WHERE c2 = 'tommy armour' ) < ( SELECT c1_number FROM w WHERE c2 = 'denny shute' );
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+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = 'al espinosa' ) order BY c1_number limit 1 );
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+SELECT c1 FROM w WHERE c1 IN ( 'united states' , 'scotland' ) order BY c6_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c1 != 'canada' AND c6_number = ( SELECT c6_number FROM w WHERE c1 = 'canada' );
+SELECT SUM( c2_number ) FROM w WHERE c1 IN ( 'england' , 'wales' );
+SELECT c6 FROM w WHERE c1 = 'spain';
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c5 FROM w WHERE c1 = 'zimbabwe';
+SELECT c4 FROM w WHERE c1 = 'fiji';
+SELECT c2 FROM w WHERE c1_number = 1946;
+SELECT MAX( c1_number ) - MIN( c1_number ) + 1 FROM w;
+SELECT c2 FROM w WHERE c4 = 'nominated - academy award for best supporting actor';
+SELECT COUNT( c1 ) FROM w;
+SELECT ( SELECT c4_year - c3_year FROM w WHERE c1 = 'charles royer' ) > ( SELECT c4_year - c3_year FROM w WHERE c1 = 'paul schell' );
+SELECT c1 FROM w WHERE c3_year = 1890;
+SELECT c1 FROM w WHERE c3_year < ( SELECT c3_year FROM w WHERE c1 = 'john t. jordan' ) order BY c3_year desc limit 1;
+SELECT c1 FROM w WHERE c3_number > 1900 order BY c3_parsed asc limit 1;
+SELECT c1 FROM w WHERE c3_year = 1871;
+SELECT abs ( ( SELECT c6_number FROM w WHERE c3 = 'enping' ) - ( SELECT c6_number FROM w WHERE c3 = 'heshan' ) );
+SELECT c3 FROM w WHERE c1 = 'city proper' order BY c7_number asc limit 1;
+SELECT c3 FROM w order BY c6_number desc limit 1;
+SELECT ( SELECT c8_number FROM w WHERE c3 = 'enping' ) > ( SELECT c8_number FROM w WHERE c3 = 'kaiping' );
+SELECT c3 FROM w order BY c8_number asc limit 1;
+SELECT c3 FROM w order BY c6_number desc limit 1;
+SELECT c3 FROM w WHERE c1 = 'satellite cities' order BY c6_number desc limit 1;
+SELECT c4 FROM w order BY c1_parsed desc limit 1;
+SELECT ( SELECT c3_number1 + c3_number2 FROM w order BY c1_parsed limit 1 ) > ( SELECT c3_number1 + c3_number2 FROM w order BY c1_parsed desc limit 1 );
+SELECT c2 FROM w WHERE c2 IN ( 'butler' , 'purdue' ) order BY c3_number1 + c3_number2 desc limit 1;
+SELECT c2 FROM w WHERE c1_parsed > ( SELECT c1_parsed FROM w WHERE c1 = 'january 29, 1949' ) order BY c1_parsed limit 1;
+SELECT c2 FROM w order BY c1_parsed desc limit 1;
+SELECT c1 FROM w WHERE c1 != 'january 10, 1949' AND c6_number = ( SELECT c6_number FROM w WHERE c1 = 'january 10, 1949' );
+SELECT COUNT( * ) FROM w WHERE c4_first_result = 'win' AND c1_year = 1949;
+SELECT COUNT( * ) FROM w WHERE c4_first_result = 'win' AND c1_year = 1948;
+SELECT c3 FROM w WHERE c1 = 'lyubomir popov' AND c2 = 'giant slalom';
+SELECT c6 FROM w WHERE c1 = 'stefan shalamanov' AND c2 = 'slalom';
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+SELECT COUNT( DISTINCT c1 ) FROM w;
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+SELECT c1 FROM w WHERE c2 = 'slalom' order BY c5 desc limit 1;
+SELECT c1 FROM w order BY c6_number asc limit 1;
+SELECT abs ( ( SELECT c3_number FROM w WHERE c1 = 'petar popangelov' ) - ( SELECT c4_number FROM w WHERE c1 = 'petar popangelov' ) );
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5_number >= 3;
+SELECT c2 FROM w WHERE c5_number > ( SELECT c5_number FROM w WHERE c3 = 'hertz bay hill classic' );
+SELECT COUNT( * ) FROM w WHERE c3 = 'u.s. open';
+SELECT c2_month FROM w GROUP BY c2_month order BY COUNT( * ) desc limit 1;
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+SELECT COUNT( * ) FROM w WHERE c6_list = 'scott simpson';
+SELECT c4 FROM w order BY c4_first_number limit 1;
+SELECT COUNT( * ) FROM w WHERE c2_month = 4;
+SELECT AVG( c3_number1 ) FROM w;
+SELECT c1 FROM w WHERE c3_number1 = 0;
+SELECT COUNT( * ) FROM w WHERE c3_number1 = c3_number2;
+SELECT c1 FROM w WHERE c3_number1 = 0;
+SELECT SUM( c3_number2 ) FROM w WHERE c1_month = 12;
+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 10 , 12 ) order BY c4_first_number1 desc limit 1;
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+SELECT c1 FROM w WHERE c1 IN ( 9 , 11 ) order BY abs ( c4_first_number1 - c4_first_number2 ) desc limit 1;
+SELECT c3_number - c2_number FROM w WHERE c1 = 'alberto ginastera';
+SELECT COUNT( c1 ) FROM w WHERE c4_list = 'french';
+SELECT COUNT( c1 ) FROM w WHERE c5_length >= 2;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c4_list = 'american' ) > 4;
+SELECT c3_number - c2_number FROM w WHERE c1 = 'soulima stravinsky';
+SELECT COUNT( c1 ) FROM w WHERE c3_number < 1960;
+SELECT COUNT( c1 ) FROM w WHERE c4_list = 'english';
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_number >= 2;
+SELECT ( SELECT c6_number FROM w WHERE c2 = 'china' ) - ( SELECT c6_number FROM w WHERE c2 = 'japan' );
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'hong kong' , 'syria' ) order BY c4_number desc limit 1;
+SELECT c2 FROM w order BY c5_number limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_number > 2;
+SELECT c2 FROM w WHERE c6_number = ( SELECT MIN( c6_number ) FROM w );
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 7;
+SELECT SUM( c5_number ) FROM w WHERE c2 IN ( 'south korea' , 'india' );
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w order BY c4_minimum_number limit 1;
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT c1 FROM w GROUP BY c1 order BY SUM( c5_number ) desc limit 1;
+SELECT SUM( c5_number ) FROM w WHERE c3 = 'tank' AND c1 = 'china';
+SELECT SUM( c4_number1 ) FROM w WHERE c3 = 'buffalo bills';
+SELECT c2 FROM w order BY c5_number asc limit 1;
+SELECT c3_raw FROM w WHERE c3_home = 'home' order BY c2_parsed asc limit 1;
+SELECT c4_number1 FROM w WHERE c3_raw = 'tampa bay buccaneers' AND c1_number = 4;
+SELECT COUNT( c3 ) FROM w;
+SELECT c2 FROM w order BY c8_number desc limit 1;
+SELECT c2 FROM w WHERE c3_raw = 'reds' order BY c2_parsed asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c8_number >= 40000;
+SELECT c7_second FROM w WHERE c2 = 'july 3';
+SELECT c3_raw FROM w WHERE c8_number > 35000 order BY c2_parsed asc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'july 1' , 'july 2' ) order BY c8_number desc limit 1;
+SELECT c6 FROM w order BY c6_parsed limit 1;
+SELECT c1 FROM w order BY c3_length desc limit 1;
+SELECT SUM( c4_length ) FROM w;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'arctic recon' ) + 1;
+SELECT c1 FROM w WHERE c2_list = 'oasis' AND c3_length = 2;
+SELECT c6 FROM w WHERE c6_month = 7 order BY c6_parsed desc limit 1;
+SELECT ( SELECT c5_parsed FROM w WHERE c1 = '4-6' ) > ( SELECT c6_parsed FROM w WHERE c1 = '4-6' );
+SELECT SUM( c2_length ) FROM w;
+SELECT c3 FROM w WHERE c3 NOT NULL order BY c5_parsed limit 1;
+SELECT c5 FROM w order BY c5_parsed desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_number = 2003;
+SELECT COUNT( c1 ) FROM w WHERE c3_number = 2003;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_number = 2005;
+SELECT COUNT( c1 ) FROM w WHERE c5_first_number < 2010;
+SELECT c3_number FROM w GROUP BY c3_number order BY COUNT( c1 ) limit 1;
+SELECT c1 FROM w WHERE c5 IS NULL;
+SELECT COUNT( c1 ) FROM w WHERE c3_number = 2002;
+SELECT c2 FROM w WHERE c3_number = 2008;
+SELECT COUNT( * ) FROM w WHERE c4_first_result = 'w';
+SELECT COUNT( * ) FROM w WHERE c3_raw = 'green bay packers';
+SELECT COUNT( * ) FROM w WHERE c2_month = 11;
+SELECT COUNT( * ) FROM w WHERE c4_first_result = 'w' AND c2_parsed < ( SELECT c2_parsed FROM w WHERE c2 = 'october 26, 1978' );
+SELECT c1 FROM w WHERE c1 IN ( 1 , 12 ) order BY c4_first_number1 desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3_raw = 'tampa bay buccaneers';
+SELECT c3 FROM w order BY c2_parsed desc limit 1;
+SELECT c3 FROM w order BY c5_number asc limit 1;
+SELECT c3 FROM w WHERE c4_first_result = 't';
+SELECT MAX( c1_number ) FROM w WHERE c2 = 'no competition';
+SELECT MIN( c1_number ) FROM w WHERE c2 NOT NULL;
+SELECT c1 FROM w WHERE c5_second_number < 25 order BY c1_number desc limit 1;
+SELECT c3_second_number + c4_second_number + c5_second_number FROM w WHERE c1_number = 2009;
+SELECT c5_first FROM w GROUP BY c5_first order BY COUNT( * ) desc limit 1;
+SELECT SUM( c3_second_number ) FROM w WHERE c1 > 2008;
+SELECT COUNT( * ) FROM w WHERE c1_number < 2008 AND c4_first = 'sweden';
+SELECT c3_first FROM w GROUP BY c3_first order BY COUNT( * ) desc limit 1;
+SELECT c3 FROM w order BY c5_number desc limit 1;
+SELECT c4 FROM w order BY c2 desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_number < 0.9;
+SELECT c5_number FROM w WHERE c1_number = 6;
+SELECT ( SELECT c6_number FROM w WHERE c1_number = 6 ) > ( SELECT c6_number FROM w WHERE c1_number = 11 );
+SELECT c3 FROM w WHERE c5_number >= 1.0;
+SELECT c5_number FROM w WHERE c3 = ''together we are one'';
+SELECT COUNT( c3 ) FROM w WHERE c6_number = 11;
+SELECT COUNT( c3 ) FROM w;
+SELECT c5_number FROM w order BY c2 desc limit 1;
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+SELECT c10 FROM w WHERE c2 = ''hard woman'';
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+SELECT c4 FROM w WHERE c2 = ''listen!!!'';
+SELECT c4 FROM w WHERE c2 = ''a dream'';
+SELECT c4 FROM w GROUP BY c4 HAVING COUNT( * ) >= 3;
+SELECT c2 FROM w order BY c3_length desc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = ''keep ya head up'' ) + 1;
+SELECT c3_length FROM w WHERE c2 = ''hip hop hooray'';
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+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT c3 FROM w order BY c8_number desc limit 1;
+SELECT c5 FROM w WHERE c3 = 'dan clarke';
+SELECT c3 FROM w WHERE c2_number = c1_number;
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+SELECT COUNT( c3 ) FROM w WHERE c5_number < 60;
+SELECT c3 FROM w WHERE c3 IN ( 'oriol servia' , 'katherine legge' ) order BY c5_number desc limit 1;
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+SELECT c1 FROM w WHERE c3_number = 50;
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+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT c1 FROM w order BY c3_number desc limit 1;
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+SELECT c1_number FROM w WHERE c3_number <= 4;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'modern rock tracks';
+SELECT COUNT( DISTINCT c3 ) FROM w WHERE c2 = ''run'';
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+SELECT c3_number + c2_number FROM w WHERE c1 = 'richard pyrah';
+SELECT SUM( c8_number ) FROM w WHERE c1 IN ( 'craig white' , 'richard pyrah' , 'adam lyth' );
+SELECT COUNT( c1 ) FROM w;
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+SELECT c2 FROM w GROUP BY c2 HAVING COUNT( c1 ) = 2;
+SELECT c5_number FROM w WHERE c1 = 'riverhead county center';
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+SELECT COUNT( c3 ) FROM w;
+SELECT c3 FROM w WHERE c1_parsed < ( SELECT c1_parsed FROM w WHERE c3 = ''can\'t buy me love'' ) order BY c1_parsed desc limit 1;
+SELECT COUNT( DISTINCT c2_first_number ) FROM w;
+SELECT c3 FROM w WHERE c5 = 'free choice' order BY c1_parsed desc limit 1;
+SELECT c3 FROM w WHERE c4 = 'the beatles' order BY c1_parsed asc limit 1;
+SELECT SUM( c5_number ) FROM w;
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c3 FROM w WHERE c2 = 'djurgardens if';
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+SELECT SUM( c6 ) FROM w;
+SELECT SUM( c5_number ) FROM w;
+SELECT c2_year FROM w GROUP BY c2_year order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c4 = 'vinyl lp' order BY c2_parsed asc limit 1;
+SELECT c1 FROM w WHERE c4 = 'vinyl lp';
+SELECT c1 FROM w GROUP BY c1 HAVING COUNT( DISTINCT c4 ) > 1;
+SELECT c1 FROM w order BY c2_parsed desc limit 1;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c4 = 'compact disc' ) - ( SELECT COUNT( * ) FROM w WHERE c4 = 'cassette tape' );
+SELECT COUNT( DISTINCT ( c1 ) ) FROM w WHERE c2_year < 1990;
+SELECT c5 FROM w WHERE c2 = 'running man';
+SELECT c1 FROM w WHERE c1 IN ( 2008 , 2009 ) AND c4 = 'mnet';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'music bank' ) - 1;
+SELECT c1 FROM w WHERE c2 = 'tears of the antarctic';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'running man' ) - 1;
+SELECT MAX( c1_maximum_year ) FROM w;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'music bank' ) - 1;
+SELECT c4 FROM w order BY id desc limit 1;
+SELECT c3 FROM w WHERE c2 = 'rajanna';
+SELECT c2 FROM w order BY c5_length desc limit 1;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( c2 ) desc limit 1;
+SELECT c5_length FROM w WHERE c2 = 'prema katha';
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'suresh krishna';
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+SELECT c3 FROM w order BY julianday ( c5_first_parsed ) - julianday ( c4_parsed ) limit 1;
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+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'sevilla fc' ) + 1;
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+SELECT COUNT( c2 ) FROM w;
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+SELECT c1 FROM w order BY c1_parsed asc limit 1;
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+SELECT COUNT( * ) FROM w WHERE c3_min >= 5;
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+SELECT COUNT( * ) FROM w;
+SELECT c2 FROM w WHERE c3_parsed < ( SELECT c3_parsed FROM w WHERE c2 = 'alexander mckim' ) order BY c3_parsed desc limit 1;
+SELECT COUNT( DISTINCT c2 ) FROM w;
+SELECT c2 FROM w order BY c3_parsed limit 1;
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+SELECT c4 FROM w WHERE c2 = 'peter little' order BY c4_parsed desc limit 1;
+SELECT c5 FROM w order BY c3_parsed limit 1;
+SELECT COUNT( DISTINCT c2 ) FROM w;
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+SELECT c2 FROM w WHERE c3_year = 1803 order BY c3_parsed limit 1;
+SELECT c4_year - c3_year FROM w WHERE c2 = 'alexander mckim';
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+SELECT COUNT( c2 ) FROM w;
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+SELECT c2 FROM w order BY c4_number limit 1;
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+SELECT c2 FROM w WHERE c1_number = 1946;
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+SELECT c4 FROM w WHERE c1 = 'october 7';
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+SELECT c2 FROM w WHERE c3 = '1998-2007';
+SELECT SUM( c5_number ) FROM w WHERE c2 IN ( 'jean-pierre rives' , 'michel crauste' );
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+SELECT COUNT( c2 ) FROM w WHERE c3_maximum_year - c3_minimum_year > 3;
+SELECT c3 FROM w WHERE c2 = 'michel crauste';
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+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c5_number FROM w WHERE c2 = 'guy basquet';
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+SELECT c6 FROM w WHERE c5 = 'mile high stadium';
+SELECT c2_month FROM w GROUP BY c2_month order BY COUNT( * ) desc limit 1;
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+SELECT c5 FROM w WHERE c6_number > 70000;
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+SELECT abs ( c4_first_number1 - c4_first_number2 ) FROM w WHERE c2 = 'november 19, 1978';
+SELECT c3 FROM w order BY c1_number desc limit 1;
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+SELECT COUNT( c1 ) FROM w;
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+SELECT c4 FROM w WHERE c1 > ( SELECT c1 FROM w WHERE c4_number = 19 ) order BY c1 asc limit 1;
+SELECT c4 FROM w WHERE c1 = '2001/02';
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+SELECT c3 FROM w WHERE c4_number < 60;
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+SELECT c2 FROM w order BY c4_number desc limit 1;
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+SELECT c3 FROM w WHERE c4_number < 60;
+SELECT c2 FROM w WHERE c4_number > ( SELECT c4_number FROM w WHERE c2 = 'linda martin and friends' );
+SELECT c5_number FROM w WHERE c3 = 'new york giants' AND c2 = 'october 25, 1981';
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+SELECT ( SELECT COUNT( * ) FROM w WHERE c7_first = 'lidge' ) > 7;
+SELECT abs ( c4_first_number1 - c4_first_number2 ) FROM w WHERE c2 = 'september 15';
+SELECT COUNT( c2 ) FROM w;
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+SELECT MIN( c1_number ) FROM w WHERE c4 = 'fifth place (tie)' AND c1_number > 1967;
+SELECT c3 FROM w WHERE c2 = 'gardenside ll';
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+SELECT c1_number FROM w WHERE c4 = 'eighth place';
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+SELECT COUNT( * ) FROM w WHERE c3_first_year <= 2010;
+SELECT c1 FROM w WHERE c3 = 'whitney' GROUP BY c1 order BY COUNT( c5 ) desc limit 1;
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+SELECT COUNT( c3 ) FROM w WHERE c5 = 'gold';
+SELECT COUNT( * ) FROM w WHERE c1_number < 1993 AND c4 = 'album';
+SELECT COUNT( c5 ) FROM w WHERE c6_result = 'won' AND c3 = 'whitney';
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+SELECT c2 FROM w WHERE c1 = '2013-2014';
+SELECT c1 FROM w WHERE c9 NOT NULL;
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+SELECT c1 FROM w order BY id desc limit 1;
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+SELECT c3 FROM w WHERE c1_minimum_year = ( SELECT c1_minimum_year FROM w WHERE c3 = 'spinning a tale' ) - 2;
+SELECT c1_number FROM w WHERE c2_first = ''superwoman'';
+SELECT c1_number FROM w WHERE c2 = ''am i too late'';
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+SELECT COUNT( * ) FROM w WHERE c3 = 'carpet';
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+SELECT c5 FROM w order BY c1_number desc limit 1;
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+SELECT c1 FROM w order BY c2_number desc limit 1;
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+SELECT c3 FROM w WHERE c2_first = 'gideon brand van zyl';
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+SELECT COUNT( c3 ) FROM w WHERE c6_number = c8_number;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'anja parson' ) - 1;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'federica brignone' ) + 1;
+SELECT c4 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( c3 ) FROM w;
+SELECT MIN( c1_year ) FROM w;
+SELECT ( SELECT c1_parsed FROM w WHERE c2 = ''hot love'' ) < ( SELECT c1_parsed FROM w WHERE c2 = ''run to me'' );
+SELECT c2 FROM w WHERE c4_number > ( SELECT c4_number FROM w WHERE c2 = ''who\'s that lady with my man'' ) order BY c4_number asc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( ''sister mary' (duet with joe dolan)' , ''run to me'' ) order BY c1_parsed asc limit 1;
+SELECT c2 FROM w WHERE c6 NOT NULL AND c3 IS NULL;
+SELECT COUNT( c2 ) FROM w WHERE c6 NOT NULL AND c5 NOT NULL;
+SELECT COUNT( c2 ) FROM w WHERE c3_number <= 50;
+SELECT c2 FROM w WHERE c3_list = 'warren beatty' AND c4 = 'won' INTERSECT SELECT c2 FROM w WHERE c3_list = 'jeremy pikser' AND c4 = 'won';
+SELECT COUNT( c1 ) FROM w WHERE c3_list = 'warren beatty';
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2_year = 2005;
+SELECT COUNT( c1 ) FROM w WHERE c3_year = 2010;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'discontinued';
+SELECT c5 FROM w WHERE c1 = 'crest whitestrips 3d professional effects';
+SELECT c1 FROM w WHERE c1 != 'crest whitestrips 3d vivid' AND c3_year = ( SELECT c3_year FROM w WHERE c1 = 'crest whitestrips 3d vivid' );
+SELECT COUNT( c1 ) FROM w WHERE c5_number = 12;
+SELECT c1 FROM w WHERE c1 != 'crest whitestrips 3d advanced vivid' AND c3 = ( SELECT c3 FROM w WHERE c1 = 'crest whitestrips 3d advanced vivid' );
+SELECT SUM( c5_number ) FROM w;
+SELECT c1 FROM w WHERE c4 = 'national polytechnic institute';
+SELECT c4 FROM w WHERE c1 = 'azteca 7';
+SELECT c1 FROM w WHERE c3_list = 'sports' AND c4 != 'televisa';
+SELECT abs ( ( SELECT c5_number FROM w WHERE c1 = 'galavision' ) - ( SELECT c5_number FROM w WHERE c1 = 'azteca 13' ) );
+SELECT COUNT( c1 ) FROM w WHERE c4 = 'televisa';
+SELECT COUNT( c1 ) FROM w WHERE c4 = 'tv azteca';
+SELECT c5 FROM w WHERE c1 = 'galavision';
+SELECT AVG( c5_number ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c4 = 'televisa';
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT c3 FROM w WHERE c2_parsed > ( SELECT c2_parsed FROM w WHERE c3 = 'cagnes-sur-mer, france $100,000' ) order BY c2_parsed limit 1;
+SELECT c3 FROM w WHERE c1_number = 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'hard';
+SELECT COUNT( * ) FROM w WHERE c5 = 'maria sharapova';
+SELECT COUNT( c3 ) FROM w WHERE id < ( SELECT id FROM w WHERE c3 = 'sedona, usa $25,000' );
+SELECT COUNT( c3 ) FROM w WHERE c4 = 'clay';
+SELECT COUNT( * ) FROM w WHERE c4 != 'clay';
+SELECT c2 FROM w WHERE c4 = 'grass';
+SELECT COUNT( c2 ) FROM w WHERE c4_number < 10;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'boston marathon' ) - 1;
+SELECT c1_number FROM w WHERE c2 = 'olympic games' order BY c1_number limit 1;
+SELECT COUNT( * ) FROM w WHERE c3_address = 'united states';
+SELECT MIN( c6 ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c3_address = 'japan';
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT c3 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c3 = 'rome, italy' ) order BY c1_number limit 1;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3_address = 'virginia avenue to us 522 north' ) - 1;
+SELECT COUNT( c3 ) FROM w WHERE c1 = 'woodmont';
+SELECT COUNT( c3 ) FROM w WHERE c1 = 'hancock';
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c2 FROM w WHERE c3 = 'us 522 south - berkeley springs, wv';
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3_address = 'limestone road north' ) + 1;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'us 40 scenic west (national pike)' ) + 1;
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( c3 ) desc limit 1;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'us 522 south - berkeley springs, wv' ) + 1;
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'former md 453' ) - 1;
+SELECT c2 FROM w order BY c5_list_number asc limit 1;
+SELECT ( SELECT c3_number FROM w WHERE c2 = 'maccabi tel aviv' ) - ( SELECT SUM( c3_number ) FROM w WHERE c2 != 'maccabi tel aviv' );
+SELECT c4 FROM w WHERE c2 = 'hapoel holon';
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+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT SUM( c4_number ) FROM w;
+SELECT c2 FROM w order BY c4_number asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_number >= 1;
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT COUNT( c2 ) FROM w WHERE c2 != 'maccabi tel aviv' AND c4_number = ( SELECT c4_number FROM w WHERE c2 = 'maccabi tel aviv' );
+SELECT c2 FROM w WHERE c5_list_number = 2008;
+SELECT SUM( c3_number ) FROM w;
+SELECT c1 FROM w WHERE c4_number - c3_number = 7 AND c2 = 'liberal';
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'conservative';
+SELECT c1 FROM w WHERE c2 = 'progressive' AND id = ( SELECT id FROM w WHERE c2 = 'liberal' ) + 1;
+SELECT c4 - c3 FROM w WHERE c1 = 'arthur berry';
+SELECT c1 FROM w WHERE c2 = 'conservative';
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'conservative';
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'conservative';
+SELECT c1 FROM w WHERE c2 = 'liberal';
+SELECT MIN( c3_number ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c2_number >= 15000;
+SELECT COUNT( c1 ) FROM w WHERE c5_number >= 11000;
+SELECT c1 FROM w WHERE c2_first = 'kevin martin';
+SELECT COUNT( * ) FROM w WHERE c5 = 'marcel rocque';
+SELECT COUNT( c1 ) FROM w WHERE c2_first = 'david nedohin';
+SELECT COUNT( c1 ) FROM w WHERE c4 = 'scott pfeifer';
+SELECT c4 FROM w WHERE c4 IN ( 'scott pfeifer' , 'sean nedohin' ) GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT c5 FROM w WHERE id = ( SELECT id FROM w WHERE c5 = 'pat mccallum' ) - 1;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( c1 ) desc limit 1;
+SELECT c6 FROM w WHERE c1_number = 8;
+SELECT COUNT( c2 ) FROM w;
+SELECT c1 FROM w WHERE c4 = 'i want to let her attend school';
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c5_list_month = 4;
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c4 = 'goodbye mother' ) + 1;
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+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT MAX( c3_number ) FROM w;
+SELECT c5 FROM w WHERE c4 = 'north american x-15';
+SELECT c5 FROM w WHERE c4 = 'mit monarch b';
+SELECT c1 FROM w WHERE c4 = 'westland lynx 800 g-lynx';
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+SELECT c1 FROM w WHERE c2_number < ( SELECT c2_number FROM w WHERE c1 = 'rocket-powered aircraft' ) order BY c2_number desc limit 1;
+SELECT c3 FROM w GROUP BY c3 HAVING COUNT( * ) = 1;
+SELECT c3 FROM w WHERE c3 IN ( 'honor b' , 'asobal' ) order BY c2_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'promoted';
+SELECT SUM( c2_number ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c2_number = 1;
+SELECT c1 FROM w WHERE c5 = 'relegated';
+SELECT COUNT( * ) FROM w WHERE c4_number = 2;
+SELECT COUNT( c1 ) FROM w WHERE c4_number <= 3;
+SELECT c1 FROM w WHERE c1 IN ( '2012-13' , '2011-12' ) order BY c4_number limit 1;
+SELECT c1 FROM w WHERE c1 IN ( '2002-03' , '2003-04' ) order BY c4_number limit 1;
+SELECT c1 FROM w WHERE c2_first_number > 0;
+SELECT COUNT( * ) FROM w WHERE c1 = 'jamaica' AND c2_second_list_number > ( SELECT MAX( c2_second_list_number ) FROM w WHERE c1 = 'trinidad and tobago' );
+SELECT c1 FROM w order BY c2_first_number / c3_first_number desc limit 1;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c6_number < 25;
+SELECT COUNT( c1 ) FROM w WHERE c5_number > 60;
+SELECT abs ( c5_number - c6_number ) FROM w WHERE c2 = 'february 28-march 3, 2014';
+SELECT COUNT( c1 ) FROM w WHERE c8_number > 12;
+SELECT ( SELECT c3_number FROM w WHERE c1 = 'siena' ) > ( SELECT c3_number FROM w WHERE c1 = 'quinnipiac' );
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c2 FROM w WHERE c6_number > 25;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w WHERE c1 != 'duke of coimbra' AND c2 = ( SELECT c2 FROM w WHERE c1 = 'duke of coimbra' );
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'duke of albuquerque' ) - 1;
+SELECT c1 FROM w order BY c2_parsed desc limit 1;
+SELECT c3 FROM w WHERE c3 IN ( 'braganca' , 'avila' ) order BY id limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w order BY c4_number asc limit 1;
+SELECT c1 FROM w WHERE c1 != 'remington-beals navy model revolver' AND c4 = ( SELECT c4 FROM w WHERE c1 = 'remington-beals navy model revolver' );
+SELECT c1 FROM w order BY c5_first_number desc limit 1;
+SELECT MAX( c3_maximum_number ) FROM w;
+SELECT c1 FROM w order BY c5_first_number asc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c3 FROM w WHERE c1_number = 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 < '5:56.21';
+SELECT c2 FROM w WHERE c1_number = 10;
+SELECT c3 FROM w WHERE c2_first = 'peter matheka mutuku';
+SELECT COUNT( c2 ) FROM w WHERE c3_number < 6;
+SELECT c5 FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE c1 != '0.1' AND c3 = ( SELECT c3 FROM w WHERE c1 = '0.1' );
+SELECT COUNT( c1 ) FROM w;
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+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = '0.8' ) + 2;
+SELECT c1 FROM w WHERE c3 = 'beta' order BY id desc limit 1;
+SELECT c1 FROM w WHERE c3 = 'alpha';
+SELECT COUNT( c2 ) FROM w WHERE c6_number >= 400;
+SELECT ( SELECT c5_number FROM w WHERE c2 = 'betty jameson' ) - ( SELECT c5_number FROM w WHERE c2 = 'patty berg' );
+SELECT c3 FROM w WHERE c3 != 'united states';
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c2 FROM w WHERE c1 = 'gshp, ground at 10 °c';
+SELECT COUNT( c1 ) FROM w WHERE c7 NOT NULL;
+SELECT COUNT( c1 ) FROM w WHERE c2 IS NULL;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c8 NOT NULL;
+SELECT COUNT( c1 ) FROM w WHERE c3_number > 5.0;
+SELECT c4 FROM w WHERE c3 = 'lau nim yat';
+SELECT COUNT( c4 ) FROM w;
+SELECT c3 FROM w WHERE id = 1;
+SELECT c2 FROM w WHERE c3 = 'li haiqiang';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1_number = 7 ) - 1;
+SELECT COUNT( c3 ) FROM w WHERE c4_first = 'sun pegasus';
+SELECT c4 FROM w WHERE c3 = 'lau nim yat';
+SELECT c2 FROM w WHERE c1_number = 3;
+SELECT c6 FROM w WHERE c3 = 'wong chin hung';
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'mauro rafael da silva' ) - 1;
+SELECT COUNT( c3 ) FROM w WHERE c6 = '9 july 2012';
+SELECT COUNT( * ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c2_month = 1;
+SELECT c2 FROM w WHERE id = 1;
+SELECT ( SELECT c4 FROM w WHERE c2 = 'december 26' ) = ( SELECT c4 FROM w WHERE c2 = 'january 2' );
+SELECT c4_number1 FROM w WHERE c2 = 'december 26';
+SELECT c2_month FROM w GROUP BY c2_month order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'oldsmobile';
+SELECT SUM( c6_number1 ) FROM w;
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'san marino';
+SELECT c4 FROM w order BY c5_number1 desc limit 1;
+SELECT c2 FROM w order BY id limit 1;
+SELECT c5_number1 FROM w WHERE c2 = '16 november 2005' AND c4 = 'spain';
+SELECT c2 FROM w order BY c3_number limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 NOT NULL;
+SELECT c5 FROM w WHERE c2_month = 12 AND c2_year = 2010;
+SELECT COUNT( * ) FROM w WHERE c5_number = 2;
+SELECT c5 FROM w order BY c2_parsed desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2_year = 2010;
+SELECT c5 FROM w WHERE c2_year = 2009 AND c3_address = 'aspen';
+SELECT c3 FROM w WHERE c5_number = 1 order BY c2_parsed asc limit 1;
+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c5_min < 3 OR ( c5_min = 3 AND c5_sec < 3 );
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'spain' ) - 1;
+SELECT c5 FROM w WHERE c3 = 'france';
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'russia' ) - 1;
+SELECT c3 FROM w order BY c5 limit 1;
+SELECT c5 FROM w WHERE c3 = 'germany';
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+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'teri meri kahaani' ) - 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'hindi';
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'kurradu' ) + 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'teri meri kahaani' ) + 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number > 210;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'derek cockroft' ) + 1;
+SELECT c1 FROM w order BY c5_parsed limit 1;
+SELECT c1 FROM w WHERE c1_first IN ( 'bobby brown' , 'jose olmeda' ) order BY c3 desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4_min >= 8;
+SELECT COUNT( * ) FROM w WHERE c5 IS NULL;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'spain';
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( c2 ) desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4_number < 8;
+SELECT COUNT( c2 ) FROM w WHERE c4_number >= 8;
+SELECT COUNT( c2 ) FROM w WHERE c4_number < 8;
+SELECT c2 FROM w WHERE id = 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 != 'cyprus';
+SELECT COUNT( * ) FROM w WHERE c3 = 'spain' AND id <= 10;
+SELECT COUNT( c3 ) FROM w WHERE c5_number != 95;
+SELECT COUNT( c3 ) FROM w WHERE c4 = 'team rahal';
+SELECT c3 FROM w WHERE c6 = 'fire';
+SELECT c4 FROM w WHERE id = 1;
+SELECT c3 FROM w order BY c5_number limit 1;
+SELECT c5_number FROM w WHERE c3 = 'scott pruett';
+SELECT ( SELECT c1_number FROM w WHERE c3 = 'robby gordon' ) < ( SELECT c1_number FROM w WHERE c3 = 'bobby rahal' );
+SELECT c1_number FROM w WHERE c3 = 'richie hearn';
+SELECT c2_number FROM w WHERE c1_number = 1;
+SELECT c3 FROM w WHERE c3 IN ( 'alex barron' , 'gil de ferran' ) order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE c5_first = 'ken hill';
+SELECT c4 FROM w WHERE c3 = 'expos' order BY c2_parsed desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c8_number > 40000;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'august 4' AND c3 = 'pirates' ) - 1;
+SELECT ( SELECT ( SELECT c7 FROM w WHERE c2 = 'august 3' ) = ( SELECT c7 FROM w WHERE c2 = 'august 11' ) ) AND ( SELECT ( SELECT c7 FROM w WHERE c2 = 'august 4' ) = ( SELECT c7 FROM w WHERE c2 = 'august 11' ) );
+SELECT COUNT( * ) FROM w WHERE c4_first_number1 < c4_first_number2;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'august 7' ) + 1;
+SELECT COUNT( DISTINCT c3 ) FROM w WHERE id > ( SELECT id FROM w WHERE c2 = 'august 4' );
+SELECT c1 FROM w WHERE c5_number = 8 AND c4_first_number = 240;
+SELECT c4_first_number FROM w WHERE c1 = 'ids tower';
+SELECT c1 FROM w WHERE c1 != 'lumber exchange building' AND c5_number = ( SELECT c5_number FROM w WHERE c1 = 'lumber exchange building' );
+SELECT c5_number FROM w WHERE c1 = 'foshay tower';
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+SELECT c1 FROM w order BY c4_first_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'metropolitan building' , 'lumber exchange building' ) order BY c4_first_number desc limit 1;
+SELECT c1 FROM w order BY id asc limit 1;
+SELECT c1 FROM w WHERE c1 != 'ids tower' order BY c4_first_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_first_number > 200;
+SELECT COUNT( c3 ) FROM w WHERE c4 = 'nominated';
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c4 = 'nominated' order BY c1_number limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'best foreign performer';
+SELECT COUNT( * ) FROM w WHERE c3 = 'best actor';
+SELECT c1_number FROM w WHERE c3 = 'best actor' order BY c1 desc limit 1;
+SELECT c3 FROM w WHERE c1_number = 1994;
+SELECT COUNT( c3 ) FROM w WHERE c4 = 'won';
+SELECT c2 FROM w WHERE c2 != 'hardwick wood' AND c6_number = ( SELECT c6_number FROM w WHERE c2 = 'hardwick wood' );
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = 'buff wood' ) order BY c1_number asc limit 1;
+SELECT c6 FROM w order BY id desc limit 1;
+SELECT SUM( c6_number ) FROM w WHERE c2 IN ( 'aversley wood' , 'brampton wood' );
+SELECT ( SELECT c6_number FROM w WHERE c2 = 'hayley wood' ) - ( SELECT c6_number FROM w WHERE c2 = 'thorpe wood' );
+SELECT SUM( c6_number ) FROM w WHERE c2 IN ( 'langley wood' , 'little paxton wood' );
+SELECT abs ( ( SELECT c6_number FROM w WHERE c2 = 'madingley wood' ) - ( SELECT c6_number FROM w WHERE c2 = 'hayley wood' ) );
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c1_number = '16' ) - 1;
+SELECT COUNT( * ) FROM w WHERE c3_number < 15;
+SELECT c1 FROM w WHERE c4 NOT NULL;
+SELECT COUNT( * ) FROM w WHERE c5 = 'kita-ku, okayama';
+SELECT ( SELECT id FROM w WHERE c1 = 'kibitsu' ) - ( SELECT id FROM w WHERE c1 = 'bizen-mikado' ) - 1;
+SELECT c1 FROM w WHERE c3_number > 1 AND c3_number < 2;
+SELECT c1 FROM w WHERE c1 IN ( 'hattori' , 'kibitsu' ) order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'nfl blitz 2000' ) + 1;
+SELECT ( SELECT c2_list_number FROM w WHERE c1 = 'nfl blitz pro' ) < ( SELECT c2_list_number FROM w WHERE c1 = 'blitz: the league' );
+SELECT COUNT( c1 ) FROM w WHERE c6 = 'arcade';
+SELECT c2 FROM w WHERE c1 IN ( 'nfl blitz special edition' , 'nfl blitz 20-02' );
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( DISTINCT c3_list ) FROM w;
+SELECT c1 FROM w WHERE c1 != 'nfl blitz special edition' AND c2_list_number = ( SELECT c2_list_number FROM w WHERE c1 = 'nfl blitz special edition' );
+SELECT c1_first FROM w order BY c3_parsed limit 1;
+SELECT c2 FROM w order BY c5_first_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'nbc';
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'june 20, 2008' ) + 1;
+SELECT c5_first_number FROM w WHERE c1_number = 22 - 1;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c3 FROM w order BY c5_first_number desc limit 1;
+SELECT c1 FROM w WHERE c1_number != 18 AND c5_first_number = ( SELECT c5_first_number FROM w WHERE c1_number = 18 );
+SELECT c3 FROM w order BY c5_first_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_number > 0;
+SELECT COUNT( c2 ) FROM w WHERE c3_number = 0;
+SELECT c3 FROM w WHERE c2 = 'japan';
+SELECT COUNT( c2 ) FROM w WHERE c3_number = 0;
+SELECT c2 FROM w WHERE c2 != 'switzerland' AND c5_number = 3;
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c2 FROM w WHERE c5_number > 10;
+SELECT SUM( c4_number ) FROM w WHERE c2 IN ( 'russia' , 'norway' , 'sweden' );
+SELECT c3 FROM w WHERE c2 = 'france';
+SELECT COUNT( c2 ) FROM w WHERE c5_number > ( SELECT c5_number FROM w WHERE c2 = 'sweden' );
+SELECT MIN( c1_number ) FROM w WHERE c3 = 'usl second division';
+SELECT COUNT( * ) FROM w WHERE c3 = 'usl pdl';
+SELECT COUNT( c1 ) FROM w WHERE c5 = 'did not qualify';
+SELECT MAX( c1_number ) FROM w WHERE c3 = 'usl second division';
+SELECT COUNT( * ) FROM w WHERE c5 = 'did not qualify' AND c6 = 'did not qualify';
+SELECT c4_number1 - c4_number2 FROM w WHERE c2_year = 1976;
+SELECT c2 FROM w WHERE c5 = 'houston, tx' order BY c2_parsed asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'houston, tx';
+SELECT c3 FROM w WHERE c2_year = 2008;
+SELECT ( SELECT c4_number1 + c4_number2 FROM w WHERE c2_year = 2003 ) > 35;
+SELECT c2 FROM w order BY c4_number1 desc limit 1;
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 IS NULL;
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'appealing for peace' ) + 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_month = 11;
+SELECT COUNT( c2 ) FROM w WHERE c5_month = 12;
+SELECT c5 FROM w WHERE c5_parsed > ( SELECT c5_parsed FROM w WHERE c5 = '1 december 1918' ) order BY c5_parsed limit 1;
+SELECT c3 FROM w WHERE id = 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c5_month != 1 AND c5_year = 1921;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'sacra propediem' ) + 1;
+SELECT c4 FROM w WHERE c5 = '23 may 1920';
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE c1 = '11 may 1944' AND c3 = 'soviet union' AND c5_first != 'sunk';
+SELECT DISTINCT c3 FROM w WHERE c2 IN ( 'flandria' , 'patria' );
+SELECT c5_first FROM w GROUP BY c5_first order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c1_year = 1940 AND c1_month = 5 order BY c1_parsed limit 1;
+SELECT SUM( c4_number ) FROM w WHERE c3 = 'sweden';
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'french navy' ) - 1;
+SELECT COUNT( * ) FROM w WHERE c1_year < 2011 AND c5_number1 > c5_number2;
+SELECT ( SELECT COUNT( DISTINCT c2 ) FROM w ) > 6;
+SELECT c4 FROM w WHERE c5_number2 >= 6;
+SELECT COUNT( * ) FROM w WHERE c5_number2 = 6;
+SELECT COUNT( * ) FROM w WHERE c1_year = 2012;
+SELECT COUNT( * ) FROM w WHERE c5_number1 > c5_number2;
+SELECT ( SELECT COUNT( c4 ) FROM w ) > 10;
+SELECT COUNT( * ) FROM w WHERE c5_number1 > c5_number2;
+SELECT c1 FROM w order BY c3_number limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'unparished area';
+SELECT COUNT( * ) FROM w WHERE c2 = 'civil parish' AND c3_number >= 10000;
+SELECT c3_number FROM w WHERE c1 = 'formby';
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c3 FROM w WHERE c1 = 'bold';
+SELECT c1 FROM w WHERE c1 IN ( 'aintree village' , 'maghull' ) AND c2 = 'civil parish';
+SELECT COUNT( c1 ) FROM w WHERE c2_month = 2;
+SELECT COUNT( c1 ) FROM w WHERE c2_year = 1983;
+SELECT COUNT( c1 ) FROM w WHERE c2_month = 2;
+SELECT COUNT( * ) FROM w;
+SELECT c5 FROM w order BY id desc limit 1;
+SELECT c5 FROM w WHERE c5 IN ( 'jalapa' , 'villa nueva' ) order BY c4_first_number desc limit 1;
+SELECT c1 FROM w WHERE c1 != 'chimaltenango' AND c3_number = 19;
+SELECT c2 FROM w order BY c4_first_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_number < 20;
+SELECT COUNT( c2 ) FROM w WHERE c3_number <= 20;
+SELECT c5 FROM w WHERE c5 IN ( 'izabal' , 'jalapa' ) order BY c3_number desc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c3_number < 20;
+SELECT c2 FROM w WHERE c2 != 'rita elizabeth meda cojulun' AND c3_number = ( SELECT c3_number FROM w WHERE c2 = 'rita elizabeth meda cojulun' );
+SELECT COUNT( c1 ) FROM w WHERE c2_length > 3;
+SELECT SUM( c3_number ) FROM w WHERE id <= 4;
+SELECT c2 FROM w order BY c3_number asc limit 1;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'uup' , 'dup' ) order BY c3_number desc limit 1;
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT c1 FROM w order BY c3_number asc limit 1;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'kosh' , 'erebuni' ) order BY c4_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_address = 'yerevan';
+SELECT c4 FROM w WHERE c1 = 'artik';
+SELECT c1 FROM w WHERE c5 = 'for former policemen and military officers' AND c1 IN ( 'abovyan' , 'vardashen' );
+SELECT c1 FROM w order BY c4_number asc limit 1;
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+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c4 FROM w WHERE c1 = 'hrazdan';
+SELECT COUNT( c1 ) FROM w WHERE c3_address = 'nubarashen';
+SELECT c1 FROM w WHERE c1 != 'vardashen' AND c4_number < 200;
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+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c1 FROM w order BY c5_number1 asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5_number1 >= 20;
+SELECT COUNT( * ) FROM w WHERE c5_result = 'w';
+SELECT c2_raw FROM w WHERE c1 = 'september 8';
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c3_list FROM w WHERE c3_list != 'audio';
+SELECT c6 FROM w WHERE c2 = 'qcelp';
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT c4 FROM w WHERE c1 = '2010-11';
+SELECT c4 FROM w order BY c5_first_number1 desc limit 1;
+SELECT COUNT( c4 ) FROM w;
+SELECT COUNT( c4 ) FROM w WHERE c1 = '2011-12';
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+SELECT COUNT( c3 ) FROM w;
+SELECT c3 FROM w order BY c1_maximum_year - c1_minimum_year desc limit 1;
+SELECT MAX( c1_maximum_year ) - MIN( c1_minimum_year ) FROM w;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'pooja' ) - 1;
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+SELECT c2 FROM w order BY c7_number limit 1;
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+SELECT c7 FROM w WHERE c2 = 'bob neyret';
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+SELECT COUNT( * ) FROM w WHERE c6 = 'non-album singles';
+SELECT c1 FROM w WHERE c2 > 2012;
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c2_number = 2011;
+SELECT COUNT( c1 ) FROM w;
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+SELECT COUNT( * ) FROM w WHERE c6_month = 8 AND c6_year = 1939;
+SELECT c1 FROM w order BY c5_number asc limit 1;
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+SELECT COUNT( c1 ) FROM w;
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+SELECT c1_number FROM w WHERE c2 = ''wide open road'';
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+SELECT c2 FROM w order BY c5_number limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c6 = 'single-only release';
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT c2 FROM w order BY c3_number limit 1;
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = ''wide open road'' ) order BY c1_number limit 1;
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+SELECT c1 FROM w WHERE c1 IN ( '50 km race walk' , 'marathon' ) order BY c2 asc limit 1;
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+SELECT c2 FROM w WHERE c3 = 'dayron robles' AND c1 = '110 m hurdles';
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+SELECT c5 FROM w WHERE c5_address = 'china' order BY id desc limit 1;
+SELECT COUNT( DISTINCT c4 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c4 = 'ethiopia';
+SELECT COUNT( * ) FROM w WHERE c6_month = 8;
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+SELECT MAX( c1_number ) FROM w WHERE c3_number >= 1;
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+SELECT MAX( c1_number ) FROM w;
+SELECT c1_number FROM w GROUP BY c1_number HAVING COUNT( * ) = 1;
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+SELECT COUNT( * ) FROM w WHERE c2 = 'world championships';
+SELECT MAX( c1_number ) FROM w;
+SELECT c2 FROM w order BY c4_number limit 1;
+SELECT c2 FROM w order BY c6 limit 1;
+SELECT c1_number FROM w WHERE c3_address = 'china';
+SELECT c1_number FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE c1_number IN ( 1980 , 1982 );
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'jnp';
+SELECT c3 FROM w WHERE c3 IN ( 'shri tej bhahdur' , 'shri anish ahemd khan' ) GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c4 FROM w WHERE c3 = 'shri durga prasad';
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+SELECT c1 FROM w order BY c5_number desc limit 1;
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+SELECT MAX( c2_number ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c4 = 'express';
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c6 FROM w WHERE c2 = 'kitchen';
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+SELECT COUNT( * ) FROM w WHERE c3 = 'kyal and kara';
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+SELECT COUNT( c1 ) FROM w WHERE c5 = 'chantelle and steve';
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+SELECT COUNT( c1 ) FROM w WHERE c6 IS NULL;
+SELECT c8_number FROM w WHERE c1 = 'brazil';
+SELECT c2_number FROM w WHERE c1 = 'rwanda';
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+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'nigeria' , 'rwanda' ) order BY c7_number desc limit 1;
+SELECT c2_number FROM w WHERE c1 = 'world';
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+SELECT c5 FROM w WHERE c1_first_number > 450 order BY c5_parsed limit 1;
+SELECT c2 FROM w WHERE c1_first_number < ( SELECT MIN( c1_first_number ) FROM w WHERE c2 = 'mlx01' ) order BY c1_first_number desc limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w WHERE c3_parsed > ( SELECT c3_parsed FROM w WHERE c1 = 'pete rouse' ) order BY c3_parsed limit 1;
+SELECT c4_year - c3_year FROM w WHERE c1 = 'karl rove';
+SELECT c1 FROM w WHERE c3_year = 2013 order BY c3_parsed desc limit 1;
+SELECT c1 FROM w WHERE c1 != 'karl rove' AND c5 = ( SELECT c5 FROM w WHERE c1 = 'karl rove' );
+SELECT c4_year - c3_year FROM w WHERE c1 = 'david plouffe';
+SELECT c1 FROM w WHERE c3_parsed < ( SELECT c3_parsed FROM w WHERE c1 = 'pete rouse' ) order BY c3_parsed desc limit 1;
+SELECT c2 FROM w WHERE c1 = 'citizens' committee' order BY c3_number desc limit 1;
+SELECT SUM( c3_number ) FROM w WHERE c1 = 'independent';
+SELECT c2 FROM w order BY c3_number asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1 = 'independent';
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+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c2 FROM w WHERE c1 = 'labour' order BY c3_number asc limit 1;
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+SELECT c2 FROM w order BY c4_number1 limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'win';
+SELECT COUNT( * ) FROM w WHERE c5 = 'win';
+SELECT COUNT( * ) FROM w WHERE c4_number >= 30;
+SELECT COUNT( * ) FROM w WHERE c2_month = 10;
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+SELECT COUNT( * ) FROM w WHERE c1 = 'mainland' AND c5_number < 100000;
+SELECT c5 FROM w WHERE c2 = 'angus';
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+SELECT c2 FROM w WHERE c3_number > ( SELECT c3_number FROM w WHERE c2 = 'argyll and bute' );
+SELECT c2 FROM w order BY c3_number asc limit 1;
+SELECT ( SELECT c3_number FROM w WHERE c2 = 'angus' ) - ( SELECT c3_number FROM w WHERE c2 = 'fife' );
+SELECT SUM( c3_number ) FROM w WHERE c2 IN ( 'east lothian' , 'angus' , 'dundee city' );
+SELECT abs ( c6_first_number1 - c6_first_number2 ) FROM w WHERE c1_number = 1996;
+SELECT c5 FROM w WHERE c8 = 'quarterfinal' order BY c6_first_number2 desc limit 1;
+SELECT c5 FROM w WHERE c1_number = 1984;
+SELECT COUNT( * ) FROM w WHERE c3 = 'braly stadium';
+SELECT c5 FROM w WHERE c1_number = 1996 INTERSECT SELECT c5 FROM w WHERE c1_number = 1997;
+SELECT AVG( c4_first_number ) FROM w WHERE c5 = 'thomas lloyd';
+SELECT c1 FROM w WHERE c2 != 'california league';
+SELECT ( SELECT COUNT( * ) FROM w WHERE c6 = 'league champs' ) >= 5;
+SELECT c5_list FROM w WHERE c5_list != 'thomas lloyd' AND c2 = 'sunset league';
+SELECT c5_list FROM w WHERE c2 = 'california league' AND c1_number < ( SELECT c1_number FROM w WHERE c5_list = 'ray perry' ) order BY id desc limit 1;
+SELECT c1 FROM w WHERE c1_number IN ( 1970 , 1975 ) order BY c4_first_number asc limit 1;
+SELECT c1 FROM w WHERE c1_number IN ( 1949 , 1961 ) order BY c4_first_number asc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'sunset league' AND c5 = 'thomas lloyd';
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+SELECT c2 FROM w WHERE c10_number = ( SELECT c10_number FROM w WHERE c2 = 'c.d. atletico balboa' ) AND c2 != 'c.d. atletico balboa';
+SELECT SUM( c7_number ) FROM w;
+SELECT c7_number FROM w WHERE c2 = 'a.d. isidro metapan';
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w order BY c10_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c10_number >= 25;
+SELECT c2 FROM w WHERE c6_number = ( SELECT MIN( c6_number ) FROM w );
+SELECT ( SELECT COUNT( * ) FROM w WHERE c4 = 'mtv' ) > ( SELECT COUNT( * ) FROM w WHERE c4 = 'mtv india' );
+SELECT c2 FROM w WHERE c1_minimum_year > 2009 order BY c1_minimum_year asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_minimum_number = 2010;
+SELECT c2 FROM w WHERE c3 = 'video jockey' AND c2 != 'fantastic 5';
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c3 FROM w order BY c1_minimum_year asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'anchor';
+SELECT COUNT( c3 ) FROM w WHERE c1_maximum_year < 2010;
+SELECT c5_number FROM w WHERE c2 = 'russia';
+SELECT ( SELECT c3_number FROM w WHERE c2 = 'netherlands' ) - ( SELECT c3_number FROM w WHERE c2 = 'italy' );
+SELECT c2 FROM w WHERE c2 IN ( 'sweden' , 'russia' ) order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE c4_number < ( SELECT MAX( c4_number ) FROM w ) order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE c6_number < ( SELECT c6_number FROM w WHERE c2 = 'spain' ) order BY c6_number desc limit 1;
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT c3 FROM w WHERE c2 = 'netherlands';
+SELECT c3_number FROM w WHERE c2 = 'italy';
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT SUM( c3_number ) FROM w WHERE c2 IN ( 'netherlands' , 'italy' );
+SELECT c2 FROM w WHERE c5_number > 7;
+SELECT c1 FROM w WHERE c3_number > 500;
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_number > 30;
+SELECT MAX( c6_number ) FROM w WHERE c3_number < 500;
+SELECT c1 FROM w WHERE c1 != 'diatonic semitone' AND c2_number = ( SELECT c2_number FROM w WHERE c1 = 'diatonic semitone' );
+SELECT c1 FROM w WHERE c1 IN ( 'tridecimal major third' , 'tridecimal neutral third' ) order BY c2_number desc limit 1;
+SELECT ( SELECT c3_number FROM w WHERE c1 = 'tridecimal major third' ) - ( SELECT c3_number FROM w WHERE c1 = 'whole tone, minor tone' );
+SELECT c1 FROM w WHERE c2_number > 40;
+SELECT c1 FROM w WHERE c3_number = 150;
+SELECT COUNT( c1 ) FROM w WHERE c2_number < 10;
+SELECT c2 FROM w WHERE c2_number != 316 AND c1_year = 1883;
+SELECT c1_year FROM w GROUP BY ( c1_year ) order BY COUNT( * ) desc limit 1;
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c2_number IN ( 283 , '317' ) order BY c4_second_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c1_year > 1940;
+SELECT c5 FROM w order BY c1_parsed desc limit 1;
+SELECT c6 FROM w WHERE c7 = 'bressingham steam museum' order BY c1_parsed desc limit 1;
+SELECT abs ( ( SELECT c4_second_number FROM w WHERE c2_number = 541 ) - ( SELECT c4_second_number FROM w WHERE c2_number = 542 ) );
+SELECT c2_number FROM w WHERE c1_year = 1882;
+SELECT c2 FROM w WHERE c12 = 'charles wood';
+SELECT MIN( c1_year ) FROM w WHERE c8_number = 1;
+SELECT COUNT( * ) FROM w WHERE c8_number = 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'epsom downs';
+SELECT c2 FROM w WHERE c1_year = 1880 order BY c11 limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_year = 1881;
+SELECT c4 FROM w WHERE c2_parsed < ( SELECT c2_parsed FROM w WHERE c4 = 'portugal' ) order BY c2_parsed desc limit 1;
+SELECT c3 FROM w WHERE c2_parsed > ( SELECT c2_parsed FROM w WHERE c2 = '22 june 1930' ) order BY c2_parsed limit 1;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'madrid, spain';
+SELECT c5_number1 + c5_number2 FROM w order BY ( c5_number1 + c5_number2 ) desc limit 1;
+SELECT c4 FROM w WHERE c4 IN ( 'mexico' , 'italy' ) AND c7 = 'friendly' GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT AVG( c3_number ) FROM w;
+SELECT c3 FROM w WHERE c2 = 'joseph black';
+SELECT c2 FROM w WHERE c1_minimum_year < ( SELECT c1_minimum_year FROM w WHERE c2 = 'ardis smith' ) order BY c1_minimum_year desc limit 1;
+SELECT c3 FROM w WHERE c2 = 'thomas stouch';
+SELECT c2 FROM w WHERE c1_maximum_year > ( SELECT c1_maximum_year FROM w WHERE c2 = 'b. l. noojin' ) order BY c1_maximum_year limit 1;
+SELECT c2 FROM w WHERE c3_number = 6;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'tilden campbell' ) - 1;
+SELECT c3_number FROM w WHERE c2 = 'wallace wade';
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT SUM( c3_number ) FROM w WHERE c2 IN ( 'thomas stouch' , 'schwartz' );
+SELECT c3_number FROM w WHERE c2 = 'd. v. graves';
+SELECT c2 FROM w WHERE c1_minimum_year > ( SELECT c1_minimum_year FROM w WHERE c2 = 'hank crisp' ) order BY c1_minimum_year limit 1;
+SELECT ( SELECT c3_number FROM w WHERE c2 = 'd. v. graves' ) - ( SELECT c3_number FROM w WHERE c2 = 'j. f. jenkins' );
+SELECT c2 FROM w WHERE c1_minimum_year > ( SELECT c1_minimum_year FROM w WHERE c2 = 'thomas stouch' ) order BY c1_minimum_year limit 1;
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'gene kiniski' , 'ric flair' ) order BY c4_number desc limit 1;
+SELECT c4 FROM w WHERE c2 = 'orville brown';
+SELECT COUNT( c3_list ) FROM w WHERE c4_list = 'england';
+SELECT c1 FROM w WHERE c3_list = 'jane anderson';
+SELECT COUNT( c1 ) FROM w WHERE c4_list = 'france';
+SELECT c1 FROM w WHERE c3_list = 'angela evers hughey';
+SELECT COUNT( c1 ) FROM w;
+SELECT c5_list FROM w GROUP BY c5_list order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w WHERE c3_list = 'jeff london' AND c1 IN ( 'before night falls' , 'and then came summer' );
+SELECT COUNT( * ) FROM w WHERE c5 = 'crime thriller';
+SELECT COUNT( c1 ) FROM w WHERE c4_list = 'argentina';
+SELECT c2_number FROM w WHERE id = 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'roaming';
+SELECT COUNT( * ) FROM w WHERE c4 = '4g';
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_month = 8;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'sfp 250' ) - 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_address = 'fort worth';
+SELECT c3 FROM w order BY c4_parsed desc limit 1;
+SELECT c2 FROM w order BY c4_parsed asc limit 1;
+SELECT c2 FROM w order BY c4_parsed asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'fox';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'sfp 250' ) + 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c1 FROM w WHERE c2 = 'geno hayes';
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c2 != 'nick roach' AND c4_day = ( SELECT c4_day FROM w WHERE c2 = 'nick roach' );
+SELECT COUNT( c2 ) FROM w WHERE c4_month = 3;
+SELECT c2 FROM w WHERE c4_parsed < ( SELECT c4_parsed FROM w WHERE c2 = 'troy nolan' ) order BY c4_parsed desc limit 1;
+SELECT COUNT( * ) FROM w;
+SELECT c2 FROM w WHERE c4_month = 7;
+SELECT COUNT( c2 ) FROM w WHERE c1 IN ( 'cb' , 'og' );
+SELECT COUNT( c2 ) FROM w WHERE c4_month = 3;
+SELECT COUNT( DISTINCT c5 ) FROM w;
+SELECT c3 FROM w WHERE c1 = 'volume 6';
+SELECT c2 FROM w WHERE c1_year = ( SELECT c1_year FROM w WHERE c2 = 'niji-iro no sneaker' ) - 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_month = 11;
+SELECT c2 FROM w WHERE id = 2;
+SELECT COUNT( c2 ) FROM w WHERE c1_year > 2000;
+SELECT c2 FROM w WHERE c1_parsed > ( SELECT c1_parsed FROM w WHERE c2 = 'whatever' ) order BY c1_parsed asc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'pulse';
+SELECT c4 FROM w WHERE c2 = 'pulse';
+SELECT c2 FROM w order BY c1_parsed desc limit 1;
+SELECT c2 FROM w order BY c1_parsed asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4_number <= 10;
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT c1 FROM w WHERE c2 = 'tyros';
+SELECT c3 FROM w WHERE c5 = 'sunk' GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT MAX( c7_number ) FROM w;
+SELECT MAX( c1_number ) - MIN( c1_number ) FROM w WHERE c2 = 'banned';
+SELECT c3_raw FROM w WHERE c3_home = 'home' order BY c5_number desc limit 1;
+SELECT c1_number FROM w WHERE c5_number > 80000;
+SELECT c2 FROM w WHERE c5_number > 80000;
+SELECT c2 FROM w WHERE c4_result = 'w' AND c4_number1 - c4_number2 = 27;
+SELECT c3_raw FROM w WHERE c2_parsed > ( SELECT c2_parsed FROM w WHERE c3_raw = 'detroit lions' ) order BY c2_parsed limit 1;
+SELECT c3_raw FROM w WHERE c3_home = 'home' order BY c1_number desc limit 1;
+SELECT c1_number FROM w WHERE c3 IS NULL;
+SELECT COUNT( c1 ) FROM w WHERE c4_list_number = 1960;
+SELECT abs ( ( SELECT c4_list_number FROM w WHERE c1 = 'clio' ) - ( SELECT c4_list_number FROM w WHERE c1 = 'atherton' ) );
+SELECT COUNT( c1 ) FROM w WHERE c5 = 'big nine conference';
+SELECT c1 FROM w WHERE c1 != 'fenton' AND c4_list_number = ( SELECT c4_list_number FROM w WHERE c1 = 'fenton' );
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'bendle' ) - 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_list_number < ( SELECT c4_list_number FROM w WHERE c1 = 'ainsworth' );
+SELECT c1 FROM w WHERE c2_list_number = 1976;
+SELECT COUNT( c1 ) FROM w WHERE c2_list_number < 1960;
+SELECT c1 FROM w order BY c2_list_number desc limit 1;
+SELECT c4 FROM w WHERE c1 = 'sr-3';
+SELECT ( SELECT c3_number FROM w WHERE c1_number = 1975 ) - ( SELECT c3_number FROM w WHERE c1_number = 1963 );
+SELECT c1_number FROM w order BY c2_number desc limit 1;
+SELECT MIN( c4_number ) FROM w WHERE c1_number < 2003;
+SELECT c2_number FROM w WHERE c1_number = 1990;
+SELECT c2_number FROM w WHERE c1_number = 1971;
+SELECT SUM( c2 ) FROM w WHERE c1_number > 2000;
+SELECT MAX( c5_number ) FROM w WHERE c1_number < 1990;
+SELECT c1_number FROM w order BY c5_number limit 1;
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT MAX( c4_number ) FROM w;
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c4 FROM w WHERE c7 = '2006 fifa world cup qualification' order BY c2_parsed asc limit 1;
+SELECT c4 FROM w WHERE id = 1;
+SELECT c3 FROM w order BY c2_parsed desc limit 1;
+SELECT c3 FROM w WHERE c2 = '10 september 2013';
+SELECT COUNT( * ) FROM w WHERE c4 = 'honduras';
+SELECT c6 FROM w WHERE c3 = 'italy';
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c8 = 'troy bayliss';
+SELECT c5 FROM w order BY c1_number desc limit 1;
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'germany' ) - 1;
+SELECT c3 FROM w WHERE c3 IN ( 'germany' , 'australia' ) GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c6 FROM w GROUP BY c6 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT c4 FROM w order BY c5_parsed desc limit 1;
+SELECT COUNT( DISTINCT c3 ) FROM w;
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'mexico' , 'spain' ) order BY c4_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c6_number = 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_number = 1;
+SELECT c5_number FROM w WHERE c2 = 'france';
+SELECT c2 FROM w WHERE c3_number < ( SELECT c3_number FROM w WHERE c2 = 'south korea' ) order BY c3_number desc limit 1;
+SELECT c2 FROM w WHERE c4_number = ( SELECT SUM( c4_number ) FROM w WHERE c2 IN ( 'mexico' , 'turkey' ) );
+SELECT c2 FROM w WHERE c3_number = 1 AND c4_number = 0;
+SELECT SUM( c6_number ) FROM w;
+SELECT ( SELECT c5_number FROM w WHERE c2 = 'united states' ) - ( SELECT c5_number FROM w WHERE c2 = 'west germany' );
+SELECT COUNT( c2 ) FROM w WHERE c6_number > 6;
+SELECT c1_number FROM w WHERE c1_number IN ( 1979 , 1985 ) order BY c5_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c5_number = 7;
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+SELECT COUNT( c2 ) FROM w WHERE c2 = 'penske';
+SELECT c2 FROM w WHERE c1_number = 1989 - 1;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c1_number FROM w WHERE c3 = 'buick' AND c4_number = 1;
+SELECT c5_number - c4_number FROM w WHERE c1_number = 1979 AND c3 = 'cosworth';
+SELECT COUNT( * ) FROM w WHERE c5_number <= 10;
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( c3 ) desc limit 1;
+SELECT c5 FROM w WHERE c5 IN ( 'onna no ehon' , 'music tree' ) AND c1 != 2004;
+SELECT MIN( c1_number ) FROM w;
+SELECT c3 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c3 = 'utabito (歌人 singer?)' ) order BY c1_number asc limit 1;
+SELECT c5 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'boku wa do kana (僕はとうかな what should i do?)' ) - 1;
+SELECT COUNT( * ) FROM w WHERE c1_number = 2005;
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT MAX( c1_number ) FROM w;
+SELECT c3 FROM w WHERE c3 != 'manten no hoshi no yoru (満天の星の夜 night with a sky full of stars?)' AND c4 = ( SELECT c4 FROM w WHERE c3 = 'manten no hoshi no yoru (満天の星の夜 night with a sky full of stars?)' );
+SELECT c2 FROM w order BY c5_number2 desc limit 1;
+SELECT c2_raw FROM w order BY id desc limit 1;
+SELECT c5 FROM w order BY id asc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'october 23' AND c2_raw = 'indiana' ) - 1;
+SELECT c2 FROM w WHERE id = 1;
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+SELECT ( SELECT c4_number FROM w WHERE c1 = 'ascension island' ) - ( SELECT c4_number FROM w WHERE c1 = 'gough island' );
+SELECT COUNT( c1 ) FROM w WHERE c4_number < 500;
+SELECT COUNT( * ) FROM w WHERE c5 = 'edinburgh of the seven seas';
+SELECT COUNT( c1 ) FROM w WHERE c4_number >= 500;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'nightingale island' ) - 1;
+SELECT c1 FROM w order BY c3_number limit 1;
+SELECT COUNT( DISTINCT c5 ) FROM w;
+SELECT c4_number FROM w WHERE id = 1;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c3 FROM w WHERE c1 = 'yanglin';
+SELECT COUNT( c1 ) FROM w WHERE c3_number >= 15000;
+SELECT abs ( ( SELECT c3_number FROM w WHERE c1 = 'daping' ) - ( SELECT c3_number FROM w WHERE c1 = 'shaoshan' ) );
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_number >= 15000;
+SELECT ( SELECT c3_number FROM w WHERE c1 = 'ruyi' ) = ( SELECT c3_number FROM w WHERE c1 = 'yongyi' );
+SELECT c1 FROM w WHERE c3_number < 15000;
+SELECT SUM( c3_number ) FROM w WHERE c1 IN ( 'qingxi' , 'ruyi' , 'daping' );
+SELECT c1 FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE c1 IN ( 'bank of montreal' , 'bonsecours market' ) order BY c3_number asc limit 1;
+SELECT c2 FROM w WHERE id = 1;
+SELECT abs ( c2_list_first_number - c3_number ) FROM w WHERE c1 = 'grey nuns' hospital';
+SELECT c1 FROM w WHERE id = 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = '19 october 2013' ) - 1;
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'japan' ) + 1;
+SELECT COUNT( * ) FROM w WHERE c7 = '2013 asian indoor-martial arts games';
+SELECT COUNT( * ) FROM w WHERE c4 = 'china';
+SELECT COUNT( DISTINCT c4 ) FROM w;
+SELECT c5 FROM w WHERE id = 1;
+SELECT c4 FROM w WHERE id = 1;
+SELECT COUNT( * ) FROM w WHERE c3_address = 'brazil';
+SELECT c3 FROM w WHERE c2_parsed < ( SELECT c2_parsed FROM w WHERE c3 = 'ginasio chico neto, maringa' ) order BY c2_parsed desc limit 1;
+SELECT c2 FROM w order BY c2_parsed desc limit 1;
+SELECT c1 FROM w WHERE c4 = 'st. louis cardinals' order BY c2_list_minimum_number limit 1;
+SELECT c1 FROM w WHERE c3_list = ''robbie'';
+SELECT c2_list_maximum_number - c2_list_minimum_number FROM w WHERE c1 = 'hank aaron';
+SELECT c1 FROM w WHERE c3_list = ''tom terrific'' INTERSECT SELECT c1 FROM w WHERE c3_list = ''the franchise'';
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( c1 ) desc limit 1;
+SELECT c2_list_maximum_number - c2_list_minimum_number FROM w WHERE c1 = 'cap anson';
+SELECT COUNT( c1 ) FROM w WHERE c4 = 'philadelphia phillies';
+SELECT c2 FROM w WHERE c2 IN ( 'dexter fields' , 'ovie soko' ) order BY c4_number desc limit 1;
+SELECT AVG( c4_number ) FROM w WHERE c2 IN ( 'jamarr sanders' , 'robert williams' );
+SELECT abs ( ( SELECT c4_number FROM w WHERE c2 = 'dexter fields' ) - ( SELECT c4_number FROM w WHERE c2 = 'quincy taylor' ) );
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w WHERE id = 1;
+SELECT COUNT( * ) FROM w WHERE c6 = 'jr';
+SELECT c2 FROM w order BY c6_number limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c2 IN ( SELECT c2 FROM w order BY c6_number desc limit 10 ) AND c3 = '20th century fox';
+SELECT COUNT( c2 ) FROM w WHERE c3 = '20th century fox';
+SELECT c2 FROM w WHERE c5 = 'burt lancaster, paul scofield, jeanne moreau, michel simon' AND c3 = 'united artists';
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'united artists';
+SELECT c2 FROM w WHERE c3 = 'walt disney productions' order BY c6_number desc limit 1;
+SELECT c2 FROM w WHERE c5_list = 'elizabeth taylor' INTERSECT SELECT c2 FROM w WHERE c5_list = 'richard burton';
+SELECT COUNT( * ) FROM w WHERE c3 = '20th century fox';
+SELECT c4 FROM w order BY c6_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5 != 'the academy';
+SELECT c2 FROM w WHERE c2 IN ( 'leonardo burian' , 'darwin torres' ) order BY c4_first_parsed asc limit 1;
+SELECT c2 FROM w order BY c4_first_parsed asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'df';
+SELECT COUNT( c2 ) FROM w WHERE c6 = 'reserve team player';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'santiago romero' ) + 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'ks tomori' ) - 1;
+SELECT c1 FROM w WHERE c1 IN ( 'kf laci' , 'ks bylis' ) order BY c4_number desc limit 1;
+SELECT c1 , c3 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'kf laci' ) + 1;
+SELECT COUNT( c1 ) FROM w WHERE c1 != 'ks flamurtari' AND c4_number = ( SELECT c4_number FROM w WHERE c1 = 'ks flamurtari' );
+SELECT AVG( c5_number ) FROM w WHERE id <= 3;
+SELECT c5 FROM w WHERE c1 = 'dinamo tirane';
+SELECT COUNT( * ) FROM w WHERE c7_number > 25;
+SELECT c3_number FROM w WHERE agg = 1;
+SELECT AVG( c5_number ) FROM w;
+SELECT c2 FROM w WHERE c3_number = 7;
+SELECT COUNT( c2 ) FROM w WHERE c5_number >= 3;
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c3_number + c4_number FROM w WHERE c2 = 'china';
+SELECT c5 FROM w WHERE c2 = 'iran';
+SELECT ( SELECT c6_number FROM w WHERE c2 = 'hong kong' ) - ( SELECT c6_number FROM w WHERE c2 = 'chinese taipei' );
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c6_number FROM w WHERE c2 = 'japan';
+SELECT COUNT( c2 ) FROM w WHERE c3_number = 0;
+SELECT c2 FROM w WHERE c3_number = 3;
+SELECT c1 FROM w WHERE c6 = 'awarded 416th oak leaves 2 march 1944';
+SELECT COUNT( c1 ) FROM w WHERE c5_year < 1940;
+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c2 != 'heer';
+SELECT c5 FROM w WHERE c1 = 'joe clark';
+SELECT c2_list_first FROM w GROUP BY c2_list_first order BY COUNT( * ) asc limit 1;
+SELECT c1 FROM w order BY c5_list_first_minimum_number limit 1;
+SELECT c2 FROM w WHERE c1 = 'tom burke';
+SELECT c3 FROM w WHERE c1_number = 9;
+SELECT c2 FROM w WHERE c1 IN ( SELECT c1 FROM w GROUP BY c1 HAVING COUNT( * ) > 1 );
+SELECT COUNT( c1 ) FROM w;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'shatshruti dhaivata' ) - 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'panchama' ) + 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'antara gandhara' ) + 1;
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT c3 FROM w WHERE c1_parsed > ( SELECT c1_parsed FROM w WHERE c1 = '30 november 2001' );
+SELECT c1 FROM w WHERE c3 = 'andy hughes';
+SELECT c5 FROM w WHERE c3 = 'kevin watson';
+SELECT c3 FROM w WHERE c3 IN ( 'andy hughes' , 'john salako' ) order BY c5_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'free';
+SELECT c3 FROM w WHERE c1_parsed > ( SELECT c1_parsed FROM w WHERE c3 = 'john salako' ) order BY c1_parsed limit 1;
+SELECT COUNT( * ) FROM w WHERE c2_number = 6;
+SELECT COUNT( c4 ) FROM w WHERE c5_number < 44;
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'great britain' ) + 1;
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c4_list = 'josiah ng' ) + 1;
+SELECT COUNT( c5 ) FROM w WHERE c5_number >= ( SELECT c5_number FROM w WHERE c3 = 'united states' );
+SELECT ( SELECT id FROM w WHERE c3 = 'germany' ) < ( SELECT id FROM w WHERE c3 = 'malaysia' );
+SELECT c3 FROM w order BY c5_number limit 1;
+SELECT c3 FROM w order BY c5_number limit 1;
+SELECT c3 FROM w WHERE c3 IN ( 'australia' , 'france' ) order BY c1_number limit 1;
+SELECT abs ( ( SELECT c5_number FROM w WHERE c1_number = 1 ) - ( SELECT c5_number FROM w WHERE c1_number = 2 ) );
+SELECT COUNT( c5 ) FROM w WHERE c5_number >= 45;
+SELECT COUNT( * ) FROM w WHERE c5_number < 45;
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( c5_number1 > c5_number2 ) desc limit 1;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'ullevaal stadion, oslo, norway' ) + 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'romania';
+SELECT COUNT( * ) FROM w WHERE c2_month < 9 AND c7 = 'friendly';
+SELECT COUNT( * ) FROM w WHERE c5_number1 + c5_number2 >= 2;
+SELECT c2_month FROM w GROUP BY c2_month order BY COUNT( * ) limit 1;
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT c1_number - 2008 FROM w WHERE c5 != 'did not qualify' AND c1_number > 2008;
+SELECT c1_number FROM w WHERE c6 != 'did not qualify';
+SELECT COUNT( * ) FROM w WHERE c5 = 'did not qualify';
+SELECT c1_number FROM w WHERE c6 = '2nd round';
+SELECT COUNT( * ) FROM w WHERE c6 = '2nd round';
+SELECT c1 FROM w WHERE c1_number != 2012 AND c4 = ( SELECT c4 FROM w WHERE c1_number = 2012 );
+SELECT c5 FROM w order BY id desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3_number = 1987;
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'taito';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'omega race' ) + 1;
+SELECT COUNT( * ) FROM w WHERE c6_number >= 3;
+SELECT c1 FROM w order BY c3_number limit 1;
+SELECT COUNT( * ) FROM w WHERE c3_number < 1979;
+SELECT c1 FROM w WHERE id = 1;
+SELECT MAX( c1_number ) FROM w;
+SELECT AVG( c2_number ) FROM w;
+SELECT c1_number FROM w order BY c5_number limit 1;
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c6_number FROM w WHERE c1 = 'avro anson';
+SELECT COUNT( c1 ) FROM w WHERE c3_list = 'france';
+SELECT c6_number FROM w WHERE c1 = 'bell griffon';
+SELECT c1 FROM w WHERE c1 IN ( 'avro 504' , 'douglas digby' ) order BY c6_number desc limit 1;
+SELECT c3_list FROM w WHERE c3_list IN ( 'uk' , 'us' ) GROUP BY c3_list order BY COUNT( * ) desc limit 1;
+SELECT c6 FROM w WHERE c1 = 'airco dh.4';
+SELECT c5_maximum_year - c5_minimum_year FROM w WHERE c1 = 'canadair sabre';
+SELECT c1 FROM w WHERE c1 IN ( 'hawker hart' , 'hawker hind' ) order BY c6_number desc limit 1;
+SELECT c2 FROM w WHERE c2 != 'central american championships' AND c1 = 2010;
+SELECT COUNT( * ) FROM w WHERE c4_number = 1;
+SELECT COUNT( * ) FROM w WHERE c3_address = 'guatemala';
+SELECT c3 FROM w WHERE c4_number = 64;
+SELECT c2 FROM w WHERE c1_number = 2007 AND c4_number = 2;
+SELECT c2 FROM w WHERE c4_number = 2 order BY c1_number desc limit 1;
+SELECT c2 FROM w order BY c1_number asc limit 1;
+SELECT c5 FROM w WHERE c5 IN ( '10,000 m' , '5000 m' ) GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4 IS NULL;
+SELECT c4 FROM w WHERE c3 = 'holon city arena';
+SELECT c1 FROM w WHERE c1 != 'maccabi tel aviv' AND c5_first_number = 1;
+SELECT c3 FROM w order BY c4_number desc limit 1;
+SELECT c4_number FROM w WHERE c3 = 'holon city arena';
+SELECT COUNT( c1 ) FROM w;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'begin arena' ) + 1;
+SELECT c4_number FROM w WHERE c3 = 'begin arena';
+SELECT COUNT( c3 ) FROM w WHERE c4_number > 2000;
+SELECT c2 FROM w GROUP BY c2 HAVING COUNT( c1 ) >= 2;
+SELECT c3 FROM w WHERE c3 IN ( 'yeshurun' , 'holon city arena' ) order BY c4_number desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c4_number > 2000;
+SELECT c2 FROM w WHERE c1 = 1999 AND c3_list = 'mandy murphy';
+SELECT COUNT( * ) FROM w WHERE c4 = 'television movie';
+SELECT c2 FROM w order BY c4_length desc limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w WHERE c4_number > ( SELECT c4_number FROM w WHERE c1 = 'titans' AND c2 = '2011-12 miway t20 challenge' ) AND c2 = '2011-12 miway t20 challenge' order BY c4_number asc limit 1;
+SELECT c1 FROM w WHERE c5_parsed = ( SELECT MAX( c5_parsed ) FROM w );
+SELECT c1 FROM w WHERE c4_number = 1 AND c1 != 'perth scorchers' AND c2 = ( SELECT c2 FROM w WHERE c1 = 'perth scorchers' );
+SELECT c5 FROM w WHERE c1 = 'auckland aces';
+SELECT c1 FROM w WHERE c1 IN ( 'titans' , 'delhi daredevils' ) AND c3 = 'winners';
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT c3 FROM w order BY c2_parsed desc limit 1;
+SELECT c5 FROM w WHERE c2_parsed < ( SELECT c2_parsed FROM w WHERE c4 = 'benin' ) order BY c2_parsed desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c7 = 'friendly';
+SELECT COUNT( * ) FROM w WHERE c7 = 'friendly';
+SELECT COUNT( * ) FROM w WHERE c7 = 'friendly' AND c5 = '4-1';
+SELECT c2 FROM w WHERE c4 != 'benin' AND c5 = ( SELECT c5 FROM w WHERE c4 = 'benin' );
+SELECT c7 FROM w WHERE c2_parsed > ( SELECT c2_parsed FROM w WHERE c4 = 'egypt' ) order BY c2_parsed limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'february 8, 2009' , 'april 4, 2009' ) order BY c6_number desc limit 1;
+SELECT c5_result FROM w WHERE c1_number = 13;
+SELECT c6_number FROM w WHERE c1_number = 6;
+SELECT AVG( c5_number1 + c5_number2 ) FROM w WHERE c2_month = 2;
+SELECT COUNT( c1 ) FROM w WHERE c6_number >= 5000;
+SELECT c2_month FROM w GROUP BY c2_month order BY COUNT( c1 ) limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4 = 'prudential center';
+SELECT c3 FROM w WHERE c2_parsed > ( SELECT c2_parsed FROM w WHERE c3 = 'toronto rock' ) order BY c2_parsed limit 1;
+SELECT present_ref - c3_list_year FROM w WHERE c1 = 'don january';
+SELECT c1 FROM w order BY c3_length desc limit 1;
+SELECT c3_length FROM w WHERE c1 = 'gene sarazen';
+SELECT c1 FROM w order BY c3_length desc limit 1;
+SELECT c6_number FROM w WHERE c1 = 'gene sarazen';
+SELECT c4_number FROM w WHERE c3_first = 'manjeet kaur';
+SELECT c4_number FROM w WHERE c1_number = 5;
+SELECT COUNT( c3 ) FROM w WHERE c4_number < ( SELECT c4_number FROM w WHERE c3_first = 'pinki pramanik' );
+SELECT c3_first FROM w WHERE id = 1;
+SELECT c3 FROM w WHERE c3_second = 'sri';
+SELECT c3_first FROM w WHERE c4_number > 54;
+SELECT COUNT( c3 ) FROM w WHERE c4_number >= 53;
+SELECT c3_first FROM w WHERE id = 1;
+SELECT c3_first FROM w WHERE id = ( SELECT id FROM w WHERE c3_first = 'manjeet kaur' ) + 1;
+SELECT abs ( ( SELECT c4_number FROM w WHERE c3_first = 'olga tereshkova' ) - ( SELECT c4_number FROM w WHERE c3_first = 'manjeet kaur' ) );
+SELECT COUNT( c1 ) FROM w WHERE c2_number >= 85;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE c2_number > 100;
+SELECT c1 FROM w WHERE c2_number >= 100;
+SELECT c1 FROM w order BY c2_number limit 1;
+SELECT c1 FROM w WHERE c1 != 'new york yankees' AND c3_number = ( SELECT c2_number FROM w WHERE c1 = 'new york yankees' );
+SELECT c1 FROM w order BY c4_number limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_number <= 75;
+SELECT c1 FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'boston red sox' ) + 1;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c1 , c4 FROM w WHERE id = 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'slate';
+SELECT COUNT( * ) FROM w WHERE c2 = 'slate';
+SELECT COUNT( * ) FROM w WHERE c3_number <= 1892;
+SELECT c5_maximum_number - c5_minimum_number FROM w WHERE c1 = 'cesail';
+SELECT SUM( c2_number ) FROM w WHERE c1 != 'australian labor party';
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c5 FROM w WHERE c1 = 'independent';
+SELECT c1 FROM w order BY c2_number asc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'nationals sa' , 'independent' ) order BY c2_number desc limit 1;
+SELECT SUM( c3_number ) FROM w WHERE c1 IN ( 'nationals sa' , 'independent' );
+SELECT c1 FROM w WHERE c6_number = 0;
+SELECT c1 FROM w WHERE c3_number < 5;
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c1 FROM w WHERE c3 = 'south asia';
+SELECT COUNT( c1 ) FROM w WHERE c4 IS NULL;
+SELECT COUNT( DISTINCT c2_first ) FROM w WHERE c3 = 'africa';
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c3 FROM w WHERE id = 1;
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'africa';
+SELECT COUNT( c1 ) FROM w WHERE c4 IS NULL;
+SELECT c1 FROM w WHERE c3 = 'south asia';
+SELECT COUNT( c1 ) FROM w WHERE c4_number = 2015;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'arab dinar';
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'east african shilling';
+SELECT COUNT( c1 ) FROM w WHERE c4 = 'temagami';
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+SELECT COUNT( * ) FROM w WHERE c4 = 'temagami';
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+SELECT COUNT( c2 ) FROM w;
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+SELECT COUNT( c1 ) FROM w WHERE c2 = 'male';
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+SELECT COUNT( c1 ) FROM w;
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+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c4 = 'wr';
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+SELECT c1_number FROM w order BY id desc limit 1;
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+SELECT SUM( c2_number ) FROM w;
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+SELECT SUM( c4_number ) FROM w WHERE c5_year = 2007;
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+SELECT c2 FROM w WHERE c1_number = 2001;
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+SELECT c2 FROM w order BY c1_number limit 1;
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+SELECT c4 FROM w order BY id desc limit 1;
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+SELECT COUNT( c2 ) FROM w WHERE c5 = 'hed arzi';
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+SELECT c2 FROM w WHERE c5_number >= 6;
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+SELECT c2 FROM w order BY c3_number desc limit 1;
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+SELECT c1 FROM w GROUP BY c1 order BY COUNT( * ) desc limit 1;
+SELECT ( SELECT c3_number FROM w WHERE c2_address = 'los angeles' ) - ( SELECT c3_number FROM w WHERE c2_address = 'saskatoon' );
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+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c5 FROM w WHERE c2 = 'steven smith';
+SELECT c4 FROM w WHERE id = 1;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'banjo calpito' ) - 1;
+SELECT c3 FROM w WHERE c3 != 'united states';
+SELECT ( SELECT COUNT( c2 ) FROM w WHERE c3 = 'united states' ) > 5;
+SELECT c1 FROM w WHERE c2 = 'steven smith';
+SELECT c2 FROM w WHERE c5 = 'the citadel' order BY c1_number asc limit 1;
+SELECT c2 FROM w WHERE c3 != 'united states';
+SELECT COUNT( * ) FROM w WHERE c3 = 'philippines';
+SELECT c4 FROM w WHERE c4 IN ( 'pop cola 800s' , 'gordon's gin boars' ) GROUP BY c4 order BY COUNT( c2 ) desc limit 1;
+SELECT SUM( c2_number ) FROM w;
+SELECT c2_number FROM w WHERE c1 = 'republican';
+SELECT c5 FROM w WHERE c1 = 'unaffiliated';
+SELECT SUM( c4_number ) FROM w WHERE c1 IN ( 'democratic' , 'republican' );
+SELECT c2_number FROM w WHERE c1 = 'democratic';
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT ( SELECT COUNT( c2 ) FROM w WHERE c7_number = 0 ) > 0;
+SELECT ( SELECT c4_number FROM w order BY c1_number asc limit 1 ) - ( SELECT c4_number FROM w order BY c1_number desc limit 1 );
+SELECT c2 FROM w WHERE c4_number = 27 AND c10_number = 1;
+SELECT ( SELECT c4_number FROM w order BY c1_number asc limit 1 ) - ( SELECT c4_number FROM w order BY c1_number desc limit 1 );
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+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'cordoba cf' ) + 1;
+SELECT c8 FROM w WHERE c2 = 'hercules cf';
+SELECT c2 FROM w WHERE c5_number = 4;
+SELECT c2 FROM w order BY c4_number desc limit 1;
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+SELECT c2 FROM w order BY c10_number asc limit 1;
+SELECT c2 FROM w WHERE c7_number > 21;
+SELECT c2 FROM w order BY c1_number asc limit 1;
+SELECT c2 FROM w WHERE c2 != 'granada cf' AND c9_number = ( SELECT c8_number FROM w WHERE c2 = 'granada cf' );
+SELECT abs ( ( SELECT c4_number FROM w WHERE c2 = 'cordoba cf' ) - ( SELECT c4_number FROM w WHERE c2 = 'cd villarrobledo' ) );
+SELECT c2 FROM w WHERE id = 1;
+SELECT c2 FROM w WHERE c1 > ( SELECT c1 FROM w WHERE c2 = 'european cup' AND c1 = '1985-86' ) order BY c1 asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c1 = '2005-06';
+SELECT c4 FROM w WHERE c2 = 'uefa cup' AND c1 = '2007-08' order BY id desc limit 1;
+SELECT COUNT( DISTINCT ( c1 ) ) FROM w WHERE c2 = 'european cup';
+SELECT COUNT( DISTINCT c2 ) FROM w;
+SELECT c6 FROM w WHERE c1 = '6 march 1985';
+SELECT MAX( c7_number ) FROM w;
+SELECT c7_number FROM w WHERE c1 = '19 september 1984';
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+SELECT c7_number FROM w WHERE c1 = '20 march 1985';
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+SELECT COUNT( c5 ) FROM w WHERE c5_first != 'clay' AND c5_first != 'hard';
+SELECT c4 FROM w order BY c9_number desc limit 1;
+SELECT c8 FROM w WHERE c8 IN ( 'europe' , 'asia' ) GROUP BY c8 order BY COUNT( * ) desc limit 1;
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'bnp paribas open' ) - 1;
+SELECT COUNT( c4 ) FROM w WHERE c5 = 'grass';
+SELECT c4 FROM w order BY id desc limit 1;
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'family circle cup' ) - 1;
+SELECT c3 FROM w order BY c1 asc limit 1;
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+SELECT COUNT( c3 ) FROM w WHERE c8_number >= 9;
+SELECT ( SELECT c1_number FROM w WHERE c3 = 'alex polita' ) < ( SELECT c1_number FROM w WHERE c3 = 'lorenzo lanzi' );
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+SELECT c2 FROM w order BY c6_number limit 1;
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+SELECT COUNT( * ) FROM w WHERE c2_month = 9 AND c2_year = 1978;
+SELECT COUNT( * ) FROM w WHERE c7_list_first = 'hinnigan';
+SELECT COUNT( c3 ) FROM w WHERE c6 = 'engine';
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+SELECT COUNT( c3 ) FROM w WHERE c4 != 'brabham-repco';
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'jack brabham' ) + 1;
+SELECT abs ( ( SELECT c8_number FROM w WHERE c3 = 'chris amon' ) - ( SELECT c8_number FROM w WHERE c3 = 'jim clark' ) );
+SELECT COUNT( c3 ) FROM w WHERE c4 = 'brabham-repco';
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+SELECT c2 FROM w WHERE c1_number >= 2012;
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+SELECT c8_number FROM w WHERE c2 = 'germany';
+SELECT c2 FROM w order BY c8_number desc limit 1;
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+SELECT COUNT( * ) FROM w WHERE c4 = 'honda';
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+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'nicky hayden' ) + 1;
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+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'sylvain guintoli' ) + 1;
+SELECT c3 FROM w WHERE c8_number = 25;
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+SELECT COUNT( * ) FROM w WHERE c3 = 'model 25';
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+SELECT COUNT( * ) FROM w WHERE c4 = '8 mhz intel 8086';
+SELECT COUNT( c4 ) FROM w WHERE c7 = 'desktop with integrated monochrome display';
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+SELECT c2 FROM w WHERE c4 = 'physiology or medicine' order BY c1_number limit 1;
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+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'richard kuhn' ) + 1;
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+SELECT c2 FROM w WHERE c4 = 'nobel peace prize';
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+SELECT COUNT( c2 ) FROM w WHERE c4 = 'physics';
+SELECT COUNT( c1 ) FROM w;
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+SELECT COUNT( c1 ) FROM w WHERE id > ( SELECT id FROM w WHERE c1 = 'tarmo' );
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+SELECT c1 FROM w order BY c2_number desc limit 1;
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+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'radical civic union (ucr)' , 'democratic progressive' ) order BY c3_number desc limit 1;
+SELECT c1 FROM w order BY c4_number asc limit 1;
+SELECT c2 FROM w order BY c5 asc limit 1;
+SELECT c3 FROM w order BY c1_number desc limit 1;
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+SELECT c2 FROM w WHERE c2 != 'topeng' AND c5 = ( SELECT c5 FROM w WHERE c2 = 'topeng' );
+SELECT COUNT( c2 ) FROM w;
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+SELECT COUNT( c2 ) FROM w WHERE c4 = 'ost. alexandria';
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+SELECT COUNT( c2 ) FROM w;
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+SELECT c3 FROM w order BY c1_number limit 1;
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+SELECT MIN( c1_number ) FROM w WHERE c6_first_number < 51000;
+SELECT COUNT( * ) FROM w WHERE c4_first_number > c5_first_number;
+SELECT COUNT( c1 ) FROM w WHERE c2_list = 'r';
+SELECT c1 FROM w WHERE c3_number > ( SELECT c3_number FROM w WHERE c1 = 'coast visible' ) AND c1 IN ( 'chara array' , 'coast infrared' );
+SELECT SUM( c3_number ) FROM w WHERE c1 IN ( 'coast visible' , 'coast infrared' );
+SELECT COUNT( c1 ) FROM w WHERE c6_number < 3000;
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT c5 , c2 FROM w WHERE c1 = 'olle ahlund';
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+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c1 FROM w WHERE c4_number >= 6;
+SELECT SUM( c4_number ) FROM w;
+SELECT c4_number FROM w WHERE c1 = 'olle ahlund';
+SELECT ( SELECT COUNT( * ) FROM w WHERE c5 = 'won' AND c3 = 'outstanding actress in a daytime drama series' ) > ( SELECT COUNT( * ) FROM w WHERE c5 != 'won' AND c3 = 'outstanding actress in a daytime drama series' );
+SELECT COUNT( * ) FROM w WHERE c5 = 'nominated';
+SELECT COUNT( * ) FROM w WHERE c5 = 'nominated';
+SELECT c1 FROM w order BY c1_number asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'won';
+SELECT c3 FROM w WHERE c5 >= 7;
+SELECT c5 FROM w WHERE c2 = ''don\'t cry for me argentina'';
+SELECT c3 FROM w order BY c5_first_number desc limit 1;
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+SELECT c5_first_number FROM w WHERE c2 = ''don\'t cry for me argentina'';
+SELECT c2 FROM w order BY c5_first_number desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'lee young-sun' , 'emika yoshida' ) order BY c4_number desc limit 1;
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+SELECT c3 FROM w WHERE c4_number <= 40;
+SELECT c3 FROM w order BY id desc limit 1;
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+SELECT c2 FROM w WHERE c1 IS NULL AND c3 != 'south korea';
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w WHERE c4_number < ( SELECT c4_number FROM w WHERE c2 = 'liliya dusmetova' ) order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE id = 1;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'nadeeka lakmali' ) - 1;
+SELECT COUNT( DISTINCT c3 ) FROM w WHERE c1_number > 5;
+SELECT COUNT( * ) FROM w WHERE c5_month = 1;
+SELECT COUNT( c4 ) FROM w;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'on the church in bavaria' ) - 1;
+SELECT c4 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c4 = 'on the effects of the jubilee' ) - 1;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( c4 ) FROM w;
+SELECT c2 FROM w order BY c5_number asc limit 1;
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+SELECT COUNT( c2 ) FROM w;
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+SELECT c3 FROM w WHERE c2 = 'super trio series 2: movie buff championship';
+SELECT c1 FROM w WHERE c5 = 'tie';
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+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c3_list_number >= 5;
+SELECT COUNT( * ) FROM w WHERE c5_list = 'charmaine sheh';
+SELECT c1 FROM w WHERE c7_number > 7;
+SELECT c1 FROM w WHERE c1 != ''89 u12 bluebird ltd' AND c2 = ( SELECT c2 FROM w WHERE c1 = ''89 u12 bluebird ltd' );
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+SELECT COUNT( * ) FROM w WHERE c2 = 'redtop';
+SELECT c3 FROM w WHERE c1 = ''90-'94 n14 pulsar gti-r';
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+SELECT c3 FROM w order BY c2_number desc limit 1;
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+SELECT c3 FROM w order BY c6_parsed limit 1;
+SELECT c2_number FROM w WHERE c3 = 'wolverine';
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+SELECT COUNT( c6 ) FROM w WHERE c6_month = 4;
+SELECT c3 FROM w WHERE c6_year = 2013 AND c6_month = 12;
+SELECT c3 FROM w WHERE c6 = '27 dec 2013';
+SELECT COUNT( c3 ) FROM w WHERE c8_number >= 4;
+SELECT MIN( c5_number ) FROM w;
+SELECT c6 FROM w WHERE c3 = 'jo bonnier';
+SELECT COUNT( * ) FROM w WHERE c4 = 'ferrari';
+SELECT c5_number FROM w WHERE c3 = 'phil hill';
+SELECT COUNT( c3 ) FROM w WHERE c4 = 'ferrari';
+SELECT c3 FROM w order BY c8_number desc limit 1;
+SELECT c5 FROM w WHERE c2 = 'george hees';
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+SELECT c5 FROM w GROUP BY c5 order BY COUNT( c2 ) desc limit 1;
+SELECT c2 FROM w order BY c3_parsed limit 1;
+SELECT c2 FROM w WHERE c3_parsed > ( SELECT c3_parsed FROM w WHERE c2 = 'c. d. howe' ) order BY c3_parsed limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'liberal' AND c6_second = 'st. laurent';
+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c5 = 'liberal';
+SELECT c5 FROM w WHERE c2 = 'joseph-enoil michaud';
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'delhi' ) - 1;
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c2 FROM w WHERE c3_number < 224000;
+SELECT c4_number FROM w WHERE c2 = 'thanjavur';
+SELECT c2 FROM w order BY c4_number limit 1;
+SELECT c2 FROM w WHERE c3_number > 1000000 AND c5 = 'punjab';
+SELECT c3_number FROM w WHERE c2 = 'patna';
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+SELECT c2 FROM w WHERE c1_number = 2010 - 1;
+SELECT c4 FROM w order BY c1_number desc limit 1;
+SELECT c5 FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c5 = 'jt marvelous' ) order BY c1_number desc limit 1;
+SELECT c3 FROM w WHERE c1_number > 2008 order BY c1_number limit 1;
+SELECT c3 FROM w WHERE c1_number = 2012;
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+SELECT COUNT( * ) FROM w WHERE c1_month IN ( 7 , 8 );
+SELECT COUNT( * ) FROM w WHERE c5_result = 'w';
+SELECT c5_number1 FROM w WHERE c3 = 'home' order BY c5_number1 desc limit 1;
+SELECT c3 FROM w WHERE c5_result = 'w' GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT abs ( c5_number1 - c5_number2 ) FROM w WHERE c1 = 'june 2';
+SELECT c6_first FROM w GROUP BY c6_first HAVING COUNT( c3 ) <= 4;
+SELECT c7 FROM w WHERE c2_number = 4;
+SELECT c6 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'hank stein' ) - 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'f/c';
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+SELECT c3 FROM w WHERE c7 = 'notre dame';
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+SELECT COUNT( c3 ) FROM w WHERE c6_first = 'st. louis hawks';
+SELECT c4 FROM w WHERE c4 IN ( 'g' , 'c' ) GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT c7 FROM w WHERE c2_number = ( SELECT c2_number FROM w WHERE c3 = 'bob anderegg' ) - 1;
+SELECT c1 FROM w order BY c3_number desc limit 1;
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+SELECT SUM( c3_number ) FROM w WHERE c1 IN ( 'battleford' , 'oxbow' );
+SELECT c3 FROM w WHERE c2 = 'maria paris';
+SELECT c2 FROM w WHERE id = 1;
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+SELECT COUNT( * ) FROM w WHERE c4_minimum_month IN ( 3 , 4 );
+SELECT COUNT( * ) FROM w WHERE c4_maximum_year > 2009;
+SELECT c5 FROM w WHERE c2 = 'over the hill with the sword of a thousand men';
+SELECT c2 FROM w order BY c1_number desc limit 1;
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+SELECT COUNT( c2 ) FROM w WHERE c5_min >= '3';
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+SELECT COUNT( c2 ) FROM w;
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+SELECT c2 FROM w order BY c1_number desc limit 1;
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+SELECT COUNT( c2 ) FROM w WHERE c4_number >= 2000;
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+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w order BY c4_number asc limit 1;
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+SELECT c2 FROM w order BY c4_number desc limit 1;
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+SELECT c2 FROM w order BY c1_number asc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'united states';
+SELECT c2 FROM w order BY c4_result asc limit 1;
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+SELECT MAX( c6_first_number ) FROM w;
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+SELECT COUNT( * ) FROM w WHERE c4 = 'united states' AND c1 NOT NULL;
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+SELECT SUM( c6_first_number ) FROM w WHERE c4 = 'germany';
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+SELECT c3 FROM w order BY c5_number limit 1;
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+SELECT COUNT( c3 ) FROM w;
+SELECT c3 FROM w order BY c5_number limit 1;
+SELECT c3 FROM w order BY c5_number limit 1;
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+SELECT c2 FROM w WHERE c3 = 'three';
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_number >= 50000;
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT c2 FROM w WHERE c5_number > 200000;
+SELECT c1_year FROM w GROUP BY c1_year order BY SUM( c5_number ) desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_year = 1975;
+SELECT c6 FROM w WHERE id = 1;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c6 IS NULL ) > 0;
+SELECT c1 FROM w order BY c7 limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'liberty' , 'australia ii' ) GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT c6_number1 - c6_number2 FROM w WHERE c1 = 'september 15, 1983';
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c2 = 'australia ii' ) - ( SELECT COUNT( * ) FROM w WHERE c2 = 'liberty' );
+SELECT c1 FROM w WHERE c6_number1 = c6_number2;
+SELECT c4 FROM w WHERE id = 1;
+SELECT c4 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c5_list = 'epping forest' ) + 1;
+SELECT c2 FROM w order BY c3_parsed limit 1;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c2 FROM w WHERE c3_parsed > ( SELECT c3_parsed FROM w WHERE c2 = 'the south' ) order BY c3_parsed limit 1;
+SELECT c2 FROM w order BY c3_parsed desc limit 1;
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'chris jackson' ) - 1;
+SELECT c2 FROM w WHERE c5_list = 'river severn';
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c1_year = 1979;
+SELECT c5_length FROM w WHERE id = 1;
+SELECT c5_list_first FROM w GROUP BY c5_list_first order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c1_month > 6;
+SELECT c2 FROM w WHERE c4 = 'kannur';
+SELECT c4 FROM w WHERE id = 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'passenger';
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'subrahmanya road' ) - 1;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = '56661' ) + 1;
+SELECT c4 FROM w WHERE c5 = 'passenger' order BY id limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'passenger';
+SELECT COUNT( * ) FROM w WHERE c5 = 'demu';
+SELECT COUNT( * ) FROM w WHERE c3 = 'mangalore central';
+SELECT c1 FROM w WHERE c2 = 'united kingdom';
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( c1 ) desc limit 1;
+SELECT c1 FROM w WHERE id = 1;
+SELECT c1 FROM w order BY c5_number limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'united states';
+SELECT c1 FROM w WHERE c5_number >= 50;
+SELECT ( SELECT id FROM w WHERE c1 = 'am general hmmwv' ) < ( SELECT id FROM w WHERE c1 = 'maxi-ambulance' );
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c5_number FROM w WHERE c1 = 'maxi-ambulance';
+SELECT c2 FROM w WHERE c3 = 'boston bruins' order BY c1_minimum_number limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'toronto maple leafs';
+SELECT c5 FROM w WHERE c1 = 'bath abbey';
+SELECT c2 FROM w WHERE c4_number = 2;
+SELECT c2 FROM w WHERE c7_number = 0;
+SELECT c1 FROM w WHERE c6_number = 6;
+SELECT c1 FROM w WHERE c6 = 0;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c1 FROM w WHERE c7_number = 2;
+SELECT COUNT( c1 ) FROM w WHERE c6 = 0;
+SELECT c5 FROM w WHERE c1 = '04/05';
+SELECT c1 FROM w WHERE c5 = 'nimes-ales-camargue-cevennes airport (garons airport)';
+SELECT c1 FROM w order BY c2_list_number desc limit 1;
+SELECT c4_list_number FROM w WHERE c3 = 'luxembourg';
+SELECT c1 FROM w order BY c2_list_number desc limit 1;
+SELECT SUM( c6_number ) FROM w;
+SELECT abs ( ( SELECT SUM( c3_number ) FROM w ) - ( SELECT SUM( c5_number ) FROM w ) );
+SELECT c3 FROM w WHERE c2_first = 'vietnam';
+SELECT SUM( c3_number ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c3_number = 0;
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c6 FROM w WHERE c2_first = 'iraq';
+SELECT c2_first FROM w WHERE c2_first IN ( 'vietnam' , 'south korea' ) order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( ''i can go deep'' , ''don\'t rush'' ) order BY c3_number limit 1;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c5 = 'fuji tv' ) - ( SELECT COUNT( * ) FROM w WHERE c5 = 'tbs' );
+SELECT COUNT( c4 ) FROM w WHERE c1_number = 2008;
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'fuji tv';
+SELECT c3 FROM w order BY id asc limit 1;
+SELECT MAX( c1 ) - MIN( c1 ) FROM w;
+SELECT c1 FROM w GROUP BY c1 HAVING COUNT( c2 ) = ( SELECT COUNT( c2 ) FROM w GROUP BY c1 order BY COUNT( c2 ) desc limit 1 );
+SELECT COUNT( c4 ) FROM w WHERE c1_number = 2009;
+SELECT c2 FROM w order BY c6_number asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c6_number > 1000000;
+SELECT ( SELECT c4_number FROM w WHERE c2_list = 'chicago' ) - ( SELECT c4_number FROM w WHERE c2_list = 'toronto' );
+SELECT ( SELECT c6_number FROM w WHERE c2_list = 'toronto' ) > ( SELECT c6_number FROM w WHERE c2_list = 'hamilton' );
+SELECT c2 FROM w WHERE c2 IN ( 'detroit' , 'cleveland' ) order BY c6_number desc limit 1;
+SELECT c2 FROM w WHERE id = 1;
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT c6 FROM w WHERE c7_parsed > ( SELECT c7_parsed FROM w WHERE c6 = 'dong biwu' ) order BY c7_parsed asc limit 1;
+SELECT c5_year - c4_year FROM w WHERE c2 = 'wang guangmei';
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'jiang qing' ) + 1;
+SELECT c2 FROM w WHERE c6 = 'hu jintao';
+SELECT c2 FROM w WHERE c4 IS NULL;
+SELECT c2 FROM w WHERE c2 IN ( 'wang guangmei' , 'liu yongqing' ) order BY c4_parsed asc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'he lianying' ) - 1;
+SELECT c6 FROM w WHERE c7_parsed < ( SELECT c7_parsed FROM w WHERE c6 = 'dong biwu' ) order BY c7_parsed desc limit 1;
+SELECT c6 FROM w order BY c8_year - c7_year asc limit 1;
+SELECT COUNT( c5 ) FROM w;
+SELECT c1 FROM w WHERE c4_list = 'spain';
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_list = 'england';
+SELECT c1 FROM w order BY c3_number limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'brown' , 'jones' ) order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'davis' ) - 1;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'smith' , 'johnson' ) order BY c3_number desc limit 1;
+SELECT c3 FROM w order BY c4_parsed limit 1;
+SELECT c1 FROM w WHERE c4_parsed < ( SELECT c4_parsed FROM w WHERE c1 = 'h. w. whillock' ) order BY c4_parsed desc limit 1;
+SELECT c1 FROM w WHERE c4_parsed > ( SELECT c4_parsed FROM w WHERE c1 = 'dirk kempthorne' ) order BY c4_parsed limit 1;
+SELECT SUM( c3_number ) FROM w;
+SELECT c2 FROM w WHERE c3_number = 0 order BY c6_number desc limit 1;
+SELECT c2 FROM w WHERE c2 != 'france' AND c1_number = 5;
+SELECT SUM( c6_number ) FROM w WHERE c1_number = 11;
+SELECT ( SELECT SUM( c5_number ) FROM w ) - ( SELECT SUM( c4_number ) FROM w );
+SELECT c2 FROM w WHERE c5_number > ( SELECT c5_number FROM w WHERE c2 = 'mexico' );
+SELECT MAX( c6_number ) - MIN( c6_number ) FROM w;
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT ( SELECT c3_number FROM w WHERE c2 = 'spain' ) + ( SELECT c3_number FROM w WHERE c2 = 'egypt' );
+SELECT COUNT( c2 ) FROM w WHERE c3_number >= 2;
+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c3_address = 'vasil levski national stadium';
+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c2_year = 2003;
+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c7 = 'friendly';
+SELECT c7 FROM w WHERE c2 = '9 may 2006';
+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c7 = 'friendly';
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'belgium' ) + 1;
+SELECT c7 FROM w GROUP BY c7 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c2_month = 3;
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT c4 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c4 = 'georgia' order BY c1_number desc limit 1 ) + 1;
+SELECT ( SELECT c8_number FROM w WHERE c2 = 'saturday, april 13' ) > ( SELECT c8_number FROM w WHERE c2 = 'saturday, may 11' );
+SELECT c2 FROM w WHERE c5_result = 'l' order BY c2_parsed asc limit 1;
+SELECT SUM( c8_number ) FROM w WHERE c1_number IN ( 1 , 2 );
+SELECT c4_raw FROM w order BY c5_number2 asc limit 1;
+SELECT c2 FROM w order BY c8_number asc limit 1;
+SELECT MAX( c5_number2 ) FROM w WHERE c4_raw = 'rhein fire';
+SELECT c6 FROM w order BY c1_number desc limit 1;
+SELECT c4_raw FROM w WHERE c2_parsed < ( SELECT c2_parsed FROM w WHERE c2 = 'saturday, june 8' ) AND c5_result = 'w' order BY c2_parsed desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c7 = 'waldstadion';
+SELECT c1 FROM w WHERE c1_number != 6 AND c3 = ( SELECT c3 FROM w WHERE c1_number = 6 );
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = '1:57.08' ) + 1;
+SELECT c4 FROM w WHERE c2 NOT NULL;
+SELECT c2 FROM w order BY c4 limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'rex favero' ) + 1;
+SELECT c2 FROM w WHERE c3 = 'united states' order BY c4 desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'united states';
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT ( SELECT c3_number FROM w WHERE c1 = 'stade toulousain' ) - ( SELECT c3_number FROM w WHERE c1 = 'ca brive' );
+SELECT c1 FROM w WHERE c1 != 'montpellier rc' AND c8_number = 10;
+SELECT COUNT( c1 ) FROM w WHERE c4 IS NULL;
+SELECT c1 FROM w WHERE c9_number <= 50;
+SELECT c1 FROM w WHERE c1 IN ( 'su agen' , 'rc toulonnais' ) order BY c5_number desc limit 1;
+SELECT ( SELECT c9_number FROM w WHERE c1 = 'biarritz olympique' ) - ( SELECT c9_number FROM w WHERE c1 = 'su agen' );
+SELECT c1 FROM w WHERE c1 IN ( 'su agen' , 'castres olympique' ) order BY c9_number desc limit 1;
+SELECT c1 FROM w order BY c9_number limit 1;
+SELECT c1 FROM w WHERE c1 != 'asm clermont' AND c3_number = ( SELECT c3_number FROM w WHERE c1 = 'asm clermont' );
+SELECT COUNT( c1 ) FROM w WHERE c7_address = 'nc';
+SELECT COUNT( c1 ) FROM w WHERE c5_number > 200;
+SELECT COUNT( c1 ) FROM w WHERE c3 != 'guard';
+SELECT COUNT( * ) FROM w WHERE c6 = 'freshman';
+SELECT COUNT( c1 ) FROM w WHERE c6 != 'junior';
+SELECT COUNT( c1 ) FROM w WHERE id < ( SELECT id FROM w WHERE c1 = 'dixon' );
+SELECT COUNT( * ) FROM w WHERE c3 = 'weld county';
+SELECT c1 FROM w WHERE c4_number = 80110;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'deer park' ) - 1;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT ( SELECT COUNT( c1 ) FROM w ) > 30;
+SELECT c1 FROM w WHERE c1 != 'dixon' AND c3 = 'larimer county';
+SELECT COUNT( c4 ) FROM w;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'dallas' ) + 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'won';
+SELECT c4 FROM w WHERE id = 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'nominated';
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'won';
+SELECT c4 FROM w WHERE c5 = 'won' GROUP BY c4 order BY COUNT( DISTINCT c2 ) desc limit 1;
+SELECT abs ( ( SELECT COUNT( * ) FROM w WHERE c5 = 'won' ) - ( SELECT COUNT( * ) FROM w WHERE c5 = 'nominated' ) );
+SELECT COUNT( * ) FROM w WHERE c5 = 'nominated';
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'won' AND c1_number > 2012;
+SELECT COUNT( c5 ) FROM w WHERE c5 = 'won';
+SELECT COUNT( * ) FROM w WHERE c5 = 'nominated';
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'challenge';
+SELECT c2 FROM w WHERE c2 != 'reyna royo' AND c3_number = 24;
+SELECT COUNT( c2 ) FROM w WHERE c3_number = 20;
+SELECT abs ( ( SELECT c4_number FROM w WHERE c2 = 'reyna royo' ) - ( SELECT c4_number FROM w WHERE c2 = 'michelle krisko sugasti' ) );
+SELECT c2 FROM w WHERE id = 1;
+SELECT c2 FROM w WHERE c2 IN ( 'karol guevara' , 'patricia de leon' ) order BY c3_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_number >= 21;
+SELECT c2 FROM w WHERE c3_number = 21;
+SELECT COUNT( c2 ) FROM w WHERE c4_number < 1.75;
+SELECT COUNT( * ) FROM w WHERE c3 = 'republican';
+SELECT COUNT( c2 ) FROM w WHERE c5_list = 're-elected';
+SELECT COUNT( * ) FROM w WHERE c3 = 'democratic' AND c5_list = 're-elected';
+SELECT COUNT( * ) FROM w WHERE c5_list = 'democratic gain';
+SELECT COUNT( * ) FROM w WHERE c5_list = 'democratic gain';
+SELECT COUNT( * ) FROM w WHERE c3 = 'republican' AND c5_list = 're-elected' AND c4_number > 1920;
+SELECT c3 FROM w WHERE c3 IN ( 'republican' , 'democratic' ) GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w WHERE c5 IS NULL;
+SELECT c1 FROM w WHERE c2_number = 5;
+SELECT c3 FROM w WHERE c3 != 'npsl';
+SELECT MAX( c1 ) FROM w WHERE c4 = '3rd, atlantic';
+SELECT COUNT( * ) FROM w WHERE c4 = '3rd, atlantic';
+SELECT COUNT( c1 ) FROM w WHERE c5 = 'did not qualify';
+SELECT COUNT( * ) FROM w WHERE c3 = 'division 1' AND c1_number >= 2000;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) asc limit 1;
+SELECT c5 FROM w order BY c5_number desc limit 1;
+SELECT abs ( ( SELECT c5_number FROM w WHERE c1_number = 2007 ) - ( SELECT c5_number FROM w WHERE c1_number = 2008 ) );
+SELECT c1 FROM w order BY c1_number desc limit 1;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w order BY c1_number asc limit 1;
+SELECT c1 FROM w WHERE c1_number IN ( 2006 , 2010 ) order BY c6_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c6_number < 11;
+SELECT c6 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c4 = 'k-mart racing team' AND c6_number = 3 ) order BY c1_number asc limit 1;
+SELECT c3 FROM w WHERE c1_number = 2012 AND c2 = 'hamilton street circuit';
+SELECT COUNT( * ) FROM w WHERE c6_number = 1;
+SELECT c5 FROM w order BY c1_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c6_number < 4;
+SELECT c1 FROM w WHERE c4_number IS NULL;
+SELECT c1 FROM w WHERE c4_number = 1;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_number = 2;
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c4_number = 1;
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) asc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_number = 3;
+SELECT c1_minimum_number FROM w WHERE c2 = 'scare tactics' AND c3_list = 'host';
+SELECT c5_number FROM w WHERE c1 = 'castleton';
+SELECT COUNT( c1 ) FROM w WHERE c7_number > 2000;
+SELECT c1 FROM w WHERE c1 IN ( 'wall lands' , 'hurdlow meadows' ) order BY c4_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'carver's rocks' , 'cawdor quarry' ) order BY c5_number desc limit 1;
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT COUNT( c3 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c4_address = 'scotland';
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'don cherry' ) + 1;
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT c2 FROM w WHERE id = 1;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'wayne gretzky' ) - 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'wayne gretzky' ) + 1;
+SELECT c2 FROM w WHERE c3 = 'environmentalist';
+SELECT c2 FROM w WHERE id = 1;
+SELECT COUNT( * ) FROM w;
+SELECT c2 FROM w WHERE c4_address = 'british columbia';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'devakanya' ) - 1;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT c2_length FROM w WHERE c1 = 'arunthathi';
+SELECT c2 FROM w WHERE c1 = 'diwan bahadur';
+SELECT COUNT( c1 ) FROM w;
+SELECT c4 FROM w WHERE c1 = 'arunthathi';
+SELECT c1 FROM w WHERE c2 = 'r. padmanaban';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'dhaasippen or jothi malar' ) + 1;
+SELECT c1 FROM w WHERE c2 = 'c. v. raman';
+SELECT c1 FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'devakanya' ) - 1;
+SELECT abs ( c2_number - c3_number ) FROM w WHERE c1_number = 1801;
+SELECT c1_number FROM w order BY c9_number desc limit 1;
+SELECT MAX( c1_number ) FROM w WHERE c4_number < 10000;
+SELECT c15_number FROM w WHERE c1_number = 1801;
+SELECT abs ( c3_number - c4_number ) FROM w WHERE c1_number = 1831;
+SELECT c8 FROM w WHERE c1_number = 1821 - 10;
+SELECT c4 FROM w WHERE c3 = 'bruce bowen';
+SELECT c3 FROM w WHERE c2 != 'free agent';
+SELECT c3 FROM w order BY c5_number asc limit 1;
+SELECT c5_number FROM w WHERE c3 = 'mark madsen';
+SELECT COUNT( c2 ) FROM w WHERE c5_number > 12;
+SELECT MAX( c5_number ) - MIN( c5_number ) FROM w;
+SELECT c3 FROM w order BY c5_number desc limit 1;
+SELECT c3 FROM w order BY c5_number desc limit 1;
+SELECT c5 FROM w order BY c4_year - c3_year desc limit 1;
+SELECT c2 FROM w WHERE c3_year = 2000 order BY c3_parsed asc limit 1;
+SELECT c2 FROM w WHERE c3_year = 2000;
+SELECT COUNT( c2 ) FROM w WHERE c4_year - c3_year < 1;
+SELECT c3_year FROM w WHERE c2 = 'christos folias';
+SELECT COUNT( c2 ) FROM w WHERE c3_year < 2010;
+SELECT c2 FROM w WHERE c2 IN ( 'evangelos venizelos' , 'dimitris sioufas' ) order BY c4_year - c3_year desc limit 1;
+SELECT c2 FROM w order BY c3_parsed asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'new democracy';
+SELECT c5 FROM w WHERE c2 = 'dimitris sioufas';
+SELECT c2 FROM w WHERE c3_parsed < ( SELECT c3_parsed FROM w WHERE c2 = 'dimitris sioufas' ) order BY c3_parsed desc limit 1;
+SELECT c2 FROM w WHERE c3_year = 2007;
+SELECT c2 FROM w WHERE c5 = 'new democracy' order BY c3_parsed asc limit 1;
+SELECT COUNT( DISTINCT c4 ) FROM w;
+SELECT abs ( ( SELECT c5_number FROM w WHERE c3 = 'alex figge' ) - ( SELECT c5_number FROM w WHERE c3 = 'will power' ) );
+SELECT c4 FROM w WHERE c3 = 'justin wilson';
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'alex figge' ) - 1;
+SELECT c5 FROM w WHERE c3 = 'alex figge';
+SELECT COUNT( c3 ) FROM w WHERE c8_number >= 20;
+SELECT c3 FROM w order BY c8_number desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c6 = 'contact';
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'paul tracy' ) + 1;
+SELECT c3 FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c3 = 'simon pagenaud' );
+SELECT COUNT( DISTINCT c4 ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c5_number = 2002;
+SELECT abs ( ( SELECT c5_number FROM w WHERE c2 = 'hugh farley' ) - ( SELECT c5_number FROM w WHERE c2 = 'james seward' ) );
+SELECT ( SELECT COUNT( * ) FROM w WHERE c3 = 'democratic' AND c6_list = 'brooklyn' ) >= 5;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c3 = 'republican' ) = ( SELECT COUNT( * ) FROM w WHERE c3 = 'democratic' );
+SELECT COUNT( * ) FROM w WHERE c3 = 'democratic';
+SELECT c2 FROM w WHERE c6_list = 'erie' order BY id desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'republican' AND c5_number = 2010;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( c2 ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'republican' AND c5_number > 2000;
+SELECT c2 FROM w WHERE c1 = 'independent';
+SELECT c5_number FROM w WHERE c1 = 'atlantic coast conference';
+SELECT c1 FROM w WHERE c5_number = 2;
+SELECT c1 FROM w WHERE c1 != 'colonial athletic association' AND c2_number = ( SELECT c2_number FROM w WHERE c1 = 'colonial athletic association' );
+SELECT ( SELECT c2_number FROM w WHERE c1 = 'atlantic coast conference' ) > ( SELECT c2_number FROM w WHERE c1 = 'colonial athletic association' );
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number = 0;
+SELECT c1 FROM w WHERE c1 IN ( 'atlantic coast conference' , 'independent' ) order BY c4 desc limit 1;
+SELECT c4_number FROM w WHERE c1 = 'colonial athletic association';
+SELECT c1 FROM w WHERE c4_number > ( SELECT c4_number FROM w WHERE c1 = 'atlantic coast conference' );
+SELECT c1 FROM w WHERE c2_number > ( SELECT c2_number FROM w WHERE c1 = 'jule' ) order BY c2_number limit 1;
+SELECT c1 FROM w WHERE c3 = 'bainbridge-class destroyer' AND c7_month < 12;
+SELECT c1 FROM w WHERE c1_first IN ( 'uss lawrence' , 'uss macdonough' ) order BY c8_parsed limit 1;
+SELECT c4 FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE c3 = 'lightvessel';
+SELECT COUNT( * ) FROM w WHERE c6 IS NULL;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'caprice' ) - 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'caprice' ) - 1;
+SELECT c1 FROM w WHERE c4 = 'clapp';
+SELECT c1 FROM w WHERE c5_parsed > ( SELECT c5_parsed FROM w WHERE c5 = 'february 6, 2000' ) order BY c5_parsed asc limit 1;
+SELECT c2 FROM w WHERE c4_list_minimum_year = 1965;
+SELECT c1 FROM w WHERE c5_year < 1989;
+SELECT c4_list_maximum_year - c4_list_minimum_year FROM w WHERE c2 = 'wayne gretzky';
+SELECT c2 FROM w WHERE c3 = 'rw';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'harry howell' ) + 1;
+SELECT c2 FROM w WHERE c4_length = 2;
+SELECT c2 FROM w order BY c4_list_maximum_year - c4_list_minimum_year desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'eta zeta' , 'eta alpha' ) order BY c3_parsed asc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_address = 'oh';
+SELECT COUNT( c1 ) FROM w WHERE c3_year <= 1950;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'zeta beta' ) - 1;
+SELECT c1 FROM w WHERE c1 IN ( 'zeta eta' , 'zeta omega' ) order BY c3_year asc limit 1;
+SELECT c4 FROM w order BY c4_parsed limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c4 FROM w order BY c4_parsed desc limit 1;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c4 = '14 november 1933';
+SELECT c6_year FROM w GROUP BY c6_year order BY COUNT( * ) desc limit 1;
+SELECT c4 FROM w order BY c1_number asc limit 1;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT c3 FROM w WHERE c1_number = 1;
+SELECT c2 FROM w WHERE c2 != 'luis serrado' AND c4 = ( SELECT c4 FROM w WHERE c2 = 'luis serrado' );
+SELECT c4 FROM w WHERE c2 = 'luis serrado';
+SELECT c5 FROM w WHERE c2 = 'giandomenico basso';
+SELECT c2 FROM w WHERE c2 IN ( 'filipe freitas' , 'jose camacho' ) order BY c1_number asc limit 1;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'jose camacho' ) + 1;
+SELECT c5 FROM w WHERE c2 = 'luca betti';
+SELECT c1 FROM w WHERE c6_number IS NULL AND c7_number IS NULL;
+SELECT MAX( c3_number ) FROM w;
+SELECT c1 FROM w order BY c8_number desc limit 1;
+SELECT c1 FROM w order BY c5_number - c3_number desc limit 1;
+SELECT c1 FROM w order BY c6_number asc limit 1;
+SELECT MIN( c5_number ) FROM w;
+SELECT c1 FROM w order BY c7_number desc limit 1;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c5_number > 10000;
+SELECT COUNT( * ) FROM w WHERE c3 = 'brussels';
+SELECT c1_number FROM w WHERE c1_number IN ( 2012 , 2007 ) order BY c5_number limit 1;
+SELECT c3 FROM w WHERE c3 != 'brussels' order BY c2 limit 1;
+SELECT c1_number FROM w order BY c2 limit 1;
+SELECT c5 FROM w WHERE c3 = 'paris';
+SELECT c3 FROM w order BY c2 limit 1;
+SELECT c1 FROM w order BY c2 desc limit 1;
+SELECT c3 FROM w WHERE c3 IN ( 'ostrava' , 'berlin' ) order BY c2 limit 1;
+SELECT COUNT( * ) FROM w;
+SELECT COUNT( * ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c5_list = 'mark skaife';
+SELECT c1 FROM w WHERE c5 = 'glenn seton' order BY id desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_address = 'new south wales';
+SELECT c7 FROM w GROUP BY c7 order BY COUNT( * ) desc limit 1;
+SELECT c5 FROM w WHERE id = 1 + 1;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'symmons plains raceway' ) + 1;
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+SELECT c6 FROM w WHERE c2 = 'atcc round 1' AND c5 = 'mark skaife';
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+SELECT c1 FROM w order BY c2_list_first_number1 asc limit 1;
+SELECT COUNT( c4 ) FROM w WHERE c3_number = 2;
+SELECT c1 FROM w WHERE id = 1;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT c3_number FROM w WHERE c1 = 'hurdles';
+SELECT SUM( c6_number ) FROM w WHERE c5 = 'private/catholic';
+SELECT c6 FROM w WHERE c1 = 'misericordia university';
+SELECT c1 FROM w WHERE c6_number > 4000;
+SELECT COUNT( c2 ) FROM w WHERE c1 = 'yeovil town';
+SELECT c3 FROM w WHERE c2_parsed > ( SELECT c2_parsed FROM w WHERE c3 = 'san francisco 49ers' ) order BY c2_parsed limit 1;
+SELECT MIN( c7_number1 ) FROM w WHERE c7_result = 'w';
+SELECT COUNT( * ) FROM w WHERE c4_hour >= 4;
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT c3 FROM w WHERE c5 = 'soldier field' order BY c2_parsed desc limit 1;
+SELECT c2 FROM w WHERE c3 = 'atlanta falcons' order BY c2_parsed desc limit 1;
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+SELECT COUNT( * ) FROM w WHERE c5_year > 1800;
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+SELECT COUNT( * ) FROM ( SELECT c1 FROM w GROUP BY c1 HAVING COUNT( * ) >= 2 );
+SELECT COUNT( * ) FROM w WHERE c1 = 'cheshunt';
+SELECT c1 FROM w WHERE c1 IN ( 'cromer' , 'chipperfield' , 'cheshunt' ) GROUP BY c1 order BY COUNT( c2 ) desc limit 1;
+SELECT COUNT( * ) FROM w;
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+SELECT COUNT( * ) FROM w;
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+SELECT c2 FROM w WHERE c3_second = '2ot';
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'won' AND c4 = 'my girlfriend is an agent';
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'won';
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'won' AND c1_number < 2010;
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+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'hawthorn' ) + 1;
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+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c2 FROM w WHERE julianday ( c4_parsed ) = ( SELECT julianday ( c4_parsed ) FROM w WHERE c2 = 'bo grahn' ) - 1;
+SELECT c6_number FROM w WHERE c2 = 'december 7, 1969';
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+SELECT c1_number FROM w WHERE c1_number IN ( 4 , 9 ) order BY c6_number desc limit 1;
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+SELECT c4 FROM w order BY c4_first_number1 - c4_first_number2 desc limit 1;
+SELECT MAX( c4_first_number1 + c4_first_number2 ) FROM w;
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+SELECT c2 FROM w WHERE c5 = 'germany';
+SELECT c5 FROM w WHERE c2 = 'david konecny';
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+SELECT COUNT( * ) FROM w WHERE c1 = 'cape girardeau';
+SELECT COUNT( * ) FROM w WHERE c1 = 'cape girardeau';
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+SELECT COUNT( * ) FROM w WHERE c6 = 'cbs';
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+SELECT AVG( c3_number ) FROM w;
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+SELECT c2 FROM w order BY c6_number desc limit 1;
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+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT c5 FROM w WHERE c3 = 'ryosuke irie';
+SELECT COUNT( c3 ) FROM w;
+SELECT c4 FROM w WHERE c4 IN ( 'russia' , 'japan' ) order BY c5 desc limit 1;
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+SELECT c6_number FROM w WHERE c2_first = 'australia';
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+SELECT c2 FROM w order BY c5_number limit 1;
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+SELECT c6_number FROM w WHERE c2_first = 'australia';
+SELECT c6_number - c3_number FROM w WHERE c2_first = 'china';
+SELECT c4_number FROM w WHERE c2_first = 'poland';
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+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c6 FROM w WHERE c5_first = 'camilla benjaminsson';
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+SELECT c2 FROM w WHERE c3_second = 'ind' order BY c2_first_number desc limit 1;
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+SELECT COUNT( c3 ) FROM w WHERE c2_first_number >= 2000 AND c3_second = 'ken';
+SELECT COUNT( c5 ) FROM w WHERE c5_second = 'rus';
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+SELECT c8_number FROM w WHERE c1_number = 1988;
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+SELECT c2 FROM w;
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+SELECT COUNT( * ) FROM w WHERE c3_address = 'greece';
+SELECT COUNT( * ) FROM w WHERE c2 = 'summer olympics';
+SELECT c1_number FROM w WHERE c1_number IN ( 1959 , 1960 ) order BY c4_number desc limit 1;
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+SELECT COUNT( c2 ) FROM w WHERE c4 = 'patrick massett & john zinman';
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'brad falchuk';
+SELECT COUNT( c2 ) FROM w WHERE c5 IS NULL;
+SELECT COUNT( c2 ) FROM w WHERE c5 IS NULL;
+SELECT COUNT( c2 ) FROM w WHERE c4_list = 'john zinman' AND c2 != ''reunion'';
+SELECT c2 FROM w order BY id desc limit 1;
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+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'anna jochemsen' ) - 1;
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+SELECT c3 FROM w WHERE c3 IN ( 'katja saarinen' , 'petra smarzova' ) order BY c8_number asc limit 1;
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+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'melania corradini' ) + 1;
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'mariya papulova' ) + 1;
+SELECT c1 FROM w WHERE c4_number = ( SELECT c4_number FROM w WHERE c1 = 'taib yatırım bank' ) * 4;
+SELECT COUNT( * ) FROM w WHERE c4 = 'telugu';
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+SELECT COUNT( c2 ) FROM w WHERE c1 = 2008;
+SELECT c2 FROM w order BY c1_number asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 2005;
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'telugu';
+SELECT c4 FROM w WHERE c4 IN ( 'tamil' , 'telugu' ) GROUP BY c4 order BY COUNT( c2 ) desc limit 1;
+SELECT c2 FROM w WHERE c1_number = 2006;
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+SELECT COUNT( c1 ) FROM w;
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+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c2_number > 500;
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+SELECT MAX( c3_number ) FROM w;
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+SELECT COUNT( * ) FROM w WHERE c2_number > 500;
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+SELECT COUNT( * ) FROM w WHERE c3_number1 + c3_number2 >= 4;
+SELECT c2 FROM w WHERE c3 = '3-1' order BY c5_parsed asc limit 1;
+SELECT SUM( c3_number1 + c3_number2 ) FROM w;
+SELECT c2 FROM w WHERE c2 != 'aston villa' AND c3 = ( SELECT c3 FROM w WHERE c2 = 'aston villa' ) AND c5 = '28 january 1922';
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+SELECT COUNT( c2 ) FROM w WHERE c2_second = 'jpn';
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+SELECT abs ( ( SELECT c7_number FROM w WHERE c1_number = 1 ) - ( SELECT c7_number FROM w WHERE c1_number = 2 ) );
+SELECT c2 FROM w WHERE c2_first != 'juno stover-irwin' AND c2_second = ( SELECT c2_second FROM w WHERE c2_first = 'juno stover-irwin' );
+SELECT COUNT( c2 ) FROM w WHERE c3 NOT NULL;
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+SELECT c4 FROM w order BY c4_number asc limit 1;
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+SELECT c1 FROM w order BY c4_number asc limit 1;
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+SELECT COUNT( c2 ) FROM w;
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+SELECT c2 FROM w WHERE id = 1;
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+SELECT c1_number FROM w WHERE c3 NOT NULL;
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+SELECT COUNT( * ) FROM w WHERE c3 IS NULL;
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+SELECT COUNT( * ) FROM w WHERE c3_list = 'k-def';
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = ''grand groove'' ) - 1;
+SELECT COUNT( c2 ) FROM w;
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+SELECT c2 FROM w order BY c1_number desc limit 1;
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+SELECT c8 FROM w WHERE c5_list = 'ronnie bucknum';
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+SELECT c3 FROM w order BY c1_number desc limit 1;
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+SELECT c2 FROM w order BY id desc limit 1;
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+SELECT c1 FROM w order BY c2_number desc limit 1;
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+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'michigan state' , 'colorado college' ) order BY c2_number desc limit 1;
+SELECT c3 FROM w WHERE c1 = 'michigan state';
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+SELECT c3 FROM w WHERE id = 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'china';
+SELECT COUNT( * ) FROM w WHERE c3 = 'thailand';
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+SELECT c3 FROM w WHERE id <= 3 GROUP BY c3 order BY COUNT( * ) desc limit 1;
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+SELECT COUNT( c3 ) FROM w WHERE c5_first_number < 6;
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+SELECT c3 FROM w order BY c6_first_number asc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c6_first_number < 200;
+SELECT c1 FROM w WHERE c3_list = 'genuine games';
+SELECT COUNT( c1 ) FROM w WHERE c3_list = 'rockstar north';
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+SELECT c1 FROM w WHERE c5_list = 'driving';
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+SELECT c1 FROM w order BY c2_parsed desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_list IN ( 'ubisoft' , 'hothouse creations' );
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w order BY c2_parsed asc limit 1;
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'matt dallas' ) - 1;
+SELECT c1 FROM w order BY id desc limit 1;
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+SELECT SUM( c3_number ) FROM w WHERE c1_number <= 3;
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+SELECT c2 FROM w order BY c3_number desc limit 1;
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+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c2 FROM w WHERE c3_number > ( SELECT c3_number FROM w WHERE c2 = 'japan' );
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+SELECT c4 FROM w GROUP BY c4 order BY COUNT( * ) desc limit 1;
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+SELECT c1 FROM w WHERE c1 IN ( ''\'til the day'' , ''2nd amendment'' ) order BY c2_parsed limit 1;
+SELECT c2_year FROM w WHERE c1 = ''try not to think'';
+SELECT c2 FROM w order BY c2_parsed limit 1;
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+SELECT COUNT( c1 ) FROM w WHERE c5 NOT NULL;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( ''\'til the day'' , ''bleach'' ) order BY c5_number limit 1;
+SELECT COUNT( * ) FROM w;
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+SELECT c5 FROM w WHERE c8_number > 100000;
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+SELECT COUNT( * ) FROM w WHERE c5 = 'notre dame stadium • notre dame, in';
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+SELECT c2 FROM w WHERE c2 IN ( 'switzerland' , 'great britain' ) order BY c1_number asc limit 1;
+SELECT c2 FROM w order BY c1_number desc limit 1;
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+SELECT COUNT( * ) FROM w;
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+SELECT c2 FROM w WHERE c4 = 'ferrari';
+SELECT COUNT( * ) FROM w;
+SELECT c1 FROM w order BY c4_number asc limit 1;
+SELECT c5 FROM w order BY id desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number >= 5;
+SELECT COUNT( c1 ) FROM w WHERE c3_number > 100;
+SELECT COUNT( c1 ) FROM w WHERE c3_number > 100;
+SELECT abs ( ( SELECT c4_number FROM w WHERE c5 = 'naga' ) - ( SELECT c4_number FROM w WHERE c5 = 'bacolod' ) );
+SELECT COUNT( c1 ) FROM w WHERE c4_number = 5;
+SELECT c4 FROM w WHERE c5 = 'davao';
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3_number > 95;
+SELECT c1 FROM w WHERE c3_number < 90;
+SELECT c1 FROM w order BY c3_number asc limit 1;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'heredia' ) + 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT ( SELECT c5_list_number FROM w WHERE c3 = 'alajuela' ) - ( SELECT c5_list_number FROM w WHERE c3 = 'puntarenas' );
+SELECT COUNT( c1 ) FROM w;
+SELECT c4 FROM w order BY c5_list_number desc limit 1;
+SELECT c4 FROM w WHERE c5_list_number > 20000;
+SELECT c5 FROM w WHERE c1 = 'deportivo saprissa' AND c4 = 'ricardo saprissa';
+SELECT SUM( c5_list_number ) FROM w WHERE c3 IN ( 'tarrazu' , 'guapiles' );
+SELECT COUNT( * ) FROM w WHERE c2 = 'scribe';
+SELECT COUNT( * ) FROM w WHERE c2 = 'scribe';
+SELECT c2 FROM w order BY c1_number asc limit 1;
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = 'p-money' ) order BY c1_number asc limit 1;
+SELECT c2 FROM w WHERE c1_number IN ( 2008 , 2009 );
+SELECT c1 FROM w WHERE c4_length = 1;
+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT c1 FROM w WHERE c1_number != 2011 AND c2 = ( SELECT c2 FROM w WHERE c1_number = 2011 );
+SELECT c3 FROM w WHERE c1_number > 2010 order BY c1_number asc limit 1;
+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT c3 FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c3 = 'borrowed time' ) order BY c1_number desc limit 1;
+SELECT c3_number FROM w WHERE c3_number IN ( 39 , 29 ) AND c1 = 'wivm-ld';
+SELECT c1 FROM w WHERE c1 IN ( 'wivx-ld' , 'wivm-ld' ) order BY c4_number desc limit 1;
+SELECT c5 FROM w GROUP BY c5_first order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'loudonville' , 'newcomerstown' ) GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'canton';
+SELECT COUNT( * ) FROM w WHERE c3_number >= 29;
+SELECT c1 FROM w WHERE c5_first = 'faith ministries radio';
+SELECT c5 FROM w WHERE c1 = 'wivn-ld' AND c4_number = 29.2;
+SELECT COUNT( * ) FROM w WHERE c2 = 'canton';
+SELECT c4 FROM w WHERE c4 IN ( 'quarterback' , 'defensive end' ) GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT c4 FROM w WHERE c3 = 'ed bauer';
+SELECT c4 FROM w WHERE c3 = 'karl farmer';
+SELECT c3 FROM w WHERE c4 = 'linebacker' order BY c1_number limit 1;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c2 = 'miami dolphins';
+SELECT c3 FROM w WHERE c2 = 'cincinnati bengals' order BY c1_number limit 1;
+SELECT abs ( ( SELECT c1_number FROM w WHERE c3 = 'greg schaum' ) - ( SELECT c1_number FROM w WHERE c3 = 'ed bauer' ) );
+SELECT ( SELECT c1_number FROM w WHERE c3 = 'david williams' ) - ( SELECT c1_number FROM w WHERE c3 = 'greg schaum' );
+SELECT COUNT( * ) FROM w WHERE c4 = 'defensive back';
+SELECT COUNT( * ) FROM w WHERE c4 = 'defensive back';
+SELECT c1 FROM w order BY c4_number limit 1;
+SELECT c1 FROM w WHERE c1 != '2001/02' AND c4_number = ( SELECT c4_number FROM w WHERE c1 = '2001/02' );
+SELECT ( SELECT c4_number FROM w WHERE c1 = '1998/99' ) - ( SELECT c4_number FROM w WHERE c1 = '2003/04' );
+SELECT MIN( c4_number ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 4;
+SELECT c1 FROM w WHERE c4_number = 2 order BY c1 desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number < 7;
+SELECT COUNT( * ) FROM w WHERE c4_number = 9;
+SELECT c1 FROM w WHERE c1 != '1998/99' AND c4_number = ( SELECT c4_number FROM w WHERE c1 = '1998/99' );
+SELECT c2 FROM w order BY c4_number desc limit 1;
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+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c2 FROM w order BY c4_number limit 1;
+SELECT c3_number FROM w WHERE c2 = 'seriate';
+SELECT c5 FROM w order BY c1_number desc limit 1;
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w WHERE c3_first_number = c4_first_number;
+SELECT c2 FROM w WHERE c5_number < 38;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_first_number >= 19.000;
+SELECT c2 FROM w WHERE c2 IN ( 'ukraine' , 'bulgaria' ) order BY c4_first_number desc limit 1;
+SELECT c2 FROM w order BY c3_first_number asc limit 1;
+SELECT abs ( ( SELECT c3_first_number FROM w WHERE c2 = 'bulgaria' ) - ( SELECT c3_first_number FROM w WHERE c2 = 'belarus' ) );
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT c2 FROM w WHERE c2 != 'spain' AND c5_number > ( SELECT c5_number FROM w WHERE c2 = 'russia' );
+SELECT COUNT( c1 ) FROM w;
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT MAX( c4_number ) FROM w;
+SELECT c2_list FROM w GROUP BY c2_list order BY SUM( c5_number ) desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_list = 'joni mitchell';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = ''life is a carnival'' ) + 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = ''georgia on my mind'' ) - 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c2 FROM w WHERE id = 1;
+SELECT MAX( c2_list_maximum_year ) FROM w;
+SELECT MAX( c2_list_maximum_year ) FROM w WHERE c1 = 'television scores';
+SELECT c4 FROM w WHERE c2_list_minimum_year > ( SELECT c2_list_minimum_year FROM w WHERE c4 = 'requiem for strings' ) AND c1 = 'orchestral' order BY c2_list_minimum_year asc limit 1;
+SELECT c2_list FROM w GROUP BY c2_list order BY COUNT( * ) desc limit 1;
+SELECT c3 FROM w WHERE c4 = 'gerry chiniquy' order BY c3_parsed desc limit 1;
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( * ) limit 1;
+SELECT c2 FROM w WHERE c5 = 'tony benedict';
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'jim ryan';
+SELECT c2 FROM w WHERE c4 = 'robert mckimson' AND c3_parsed > ( SELECT c3_parsed FROM w WHERE c2 = 'le quiet squad' ) order BY c3_parsed limit 1;
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+SELECT c2 FROM w WHERE c7_number = 774;
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'fallon' , 'phillips' ) order BY c7_number desc limit 1;
+SELECT c2 FROM w WHERE id = 1;
+SELECT c2 FROM w WHERE id = 1;
+SELECT abs ( ( SELECT c3_number FROM w WHERE c2 = 'richland' ) - ( SELECT c3_number FROM w WHERE c2 = 'sheridan' ) );
+SELECT ( SELECT c6_number FROM w WHERE c2 = 'richland' ) < 10000;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'usb' ) - 1;
+SELECT c4 FROM w WHERE c1 = 'cpu';
+SELECT COUNT( c1 ) FROM w;
+SELECT ( SELECT COUNT( c1 ) FROM w ) >= 13;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'bluetooth' ) + 1;
+SELECT ( SELECT id FROM w WHERE c4 = 'austria wien' ) < ( SELECT id FROM w WHERE c4 = 'jeunesse esch' );
+SELECT c1 FROM w WHERE id = 1;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c5_number1 > c5_number2 AND c1 = '2010-11' ) > 1;
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT DISTINCT c1 FROM w WHERE c5_number1 > c5_number2;
+SELECT c1 FROM w WHERE c5 = '1-1';
+SELECT c4 FROM w order BY id desc limit 1;
+SELECT c4 FROM w WHERE c3 = '1qr' AND c1 = '2010-11';
+SELECT abs ( c6_number1 - c6_number2 ) FROM w order BY c1_parsed desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c6_number1 - c6_number2 >= 10;
+SELECT MAX( c6_number1 - c6_number2 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c4_address = 'ben hill griffin stadium • gainesville';
+SELECT COUNT( * ) FROM w;
+SELECT c4 FROM w order BY c3_number limit 1;
+SELECT MAX( c6_first_number ) FROM w;
+SELECT c3 FROM w order BY c4_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c6_second NOT NULL;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w WHERE c2 != 'leonardo costa' AND c3 = 'brazil';
+SELECT c3 FROM w WHERE id <= 3 GROUP BY c3 HAVING COUNT( * ) = 2;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'nate boyle' ) - 1;
+SELECT ( SELECT c3 FROM w WHERE c2 = 'scott tucker' ) = ( SELECT c3 FROM w WHERE c2 = 'yannick lupien' );
+SELECT c2 FROM w WHERE id = 1;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT COUNT( * ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c10_number >= 4;
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+SELECT COUNT( c2 ) FROM w WHERE c5 = 'partial' AND c3_parsed < ( SELECT MIN( c3_parsed ) FROM w WHERE c5 = 'annular' );
+SELECT c2_number FROM w WHERE c2_number IN ( 8 , 21 ) order BY c9_number desc limit 1;
+SELECT c3 FROM w WHERE c3_parsed > ( SELECT c3_parsed FROM w WHERE c3 = 'may 24, 2199' ) order BY c3_parsed limit 1;
+SELECT c10 FROM w WHERE c2 = 18;
+SELECT abs ( ( SELECT c8_number FROM w WHERE c3 = 'may 13, 2181' ) - ( SELECT c8_number FROM w WHERE c3 = 'may 24, 2199' ) );
+SELECT c4 FROM w WHERE c3 = 'october 3, 2415';
+SELECT c3 FROM w WHERE c8_number > 1.00 order BY c3_parsed limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_number > 2000;
+SELECT MIN( c1_number ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'willy deville' AND c3 NOT NULL;
+SELECT c2 FROM w WHERE c5 = 'willy deville' AND c1_number > ( SELECT c1_number FROM w WHERE c2 = 'savoir faire' ) order BY c1_number limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'willy deville';
+SELECT MAX( c1_number ) FROM w WHERE c5 = 'willy deville';
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'emi';
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 1997;
+SELECT MIN( c1_number ) FROM w;
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w order BY c5_list_number desc limit 1;
+SELECT MAX( c5_list_number ) FROM w WHERE c1 = 'velvet';
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'medium genomes (e.g. e.coli)';
+SELECT COUNT( * ) FROM w WHERE c4 = 'sahli, m. & shibuya, t';
+SELECT COUNT( * ) FROM w WHERE c3_list = 'all';
+SELECT c6 FROM w WHERE c6 IN ( 'os' , 'c' ) GROUP BY c6 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT SUM( c6_number ) FROM w WHERE c2 IN ( 'switzerland' , 'france' );
+SELECT c2 FROM w WHERE c4_number = ( SELECT MIN( c4_number ) FROM w );
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c4_number = 0;
+SELECT c4_number FROM w WHERE c2 = 'italy';
+SELECT COUNT( c2 ) FROM w WHERE c6_number >= 2;
+SELECT c2 FROM w WHERE c2 IN ( 'russia' , 'georgia' ) order BY c3_number desc limit 1;
+SELECT MAX( c6_number ) - MIN( c6_number ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c2 != 'germany' AND c4_number = ( SELECT c4_number FROM w WHERE c2 = 'germany' );
+SELECT COUNT( c2 ) FROM w WHERE c5_number >= 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_number = 0;
+SELECT c2 FROM w WHERE c3 = ''two loves i have, of comfort and despair'';
+SELECT c2 FROM w WHERE c3 = ''as it fell upon a day'';
+SELECT c1 FROM w order BY c11_number asc limit 1;
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+SELECT COUNT( c1 ) FROM w WHERE c2_list = 'yellow';
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+SELECT COUNT( c1 ) FROM w WHERE c3 = 'monoclinic';
+SELECT c1 FROM w WHERE c11_number >= 15;
+SELECT c1 FROM w order BY c11_number desc limit 1;
+SELECT SUM( c11_number ) FROM w WHERE c1 IN ( 'pao' , 'pao2' );
+SELECT COUNT( c2 ) FROM w WHERE c1_number IS NULL;
+SELECT c5 FROM w WHERE c2 = 'innes ireland';
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( c2 ) desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'philadelphia';
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+SELECT c2 FROM w WHERE c4 = 'vault';
+SELECT AVG( c8_number ) FROM w WHERE c2 = 'u.s. championships';
+SELECT c2 FROM w WHERE c2 IN ( 'olympic trials' , 'american cup' ) GROUP BY c2 order BY COUNT( c4 ) desc limit 1;
+SELECT c2 FROM w WHERE c1_number = 2008 AND c4 = 'team';
+SELECT COUNT( c4 ) FROM w WHERE c3 = 'beijing';
+SELECT MAX( c8_number ) FROM w WHERE c4 = 'all around';
+SELECT c2 FROM w GROUP BY c2 HAVING COUNT( c4 ) = 1;
+SELECT MIN( c7_number ) FROM w;
+SELECT c6 FROM w WHERE c1 = 'win' AND c4_first = 'decision' order BY c6_parsed limit 1;
+SELECT c3 FROM w WHERE c1 = 'loss' AND c3 != 'cody goodale';
+SELECT COUNT( * ) FROM w WHERE c1 = 'loss';
+SELECT c7_number FROM w WHERE c3 = 'ovince st. preux';
+SELECT c4_first FROM w WHERE c1 = 'win' AND c4_first IN ( 'submission' , 'tko' ) GROUP BY c4_first order BY COUNT( * ) desc limit 1;
+SELECT c8 FROM w WHERE c3 = 'william richey';
+SELECT c3 FROM w WHERE c6_parsed < ( SELECT c6_parsed FROM w WHERE c3 = 'tom hubert' ) order BY c6_parsed desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_first = 'submission';
+SELECT COUNT( * ) FROM w;
+SELECT c7_number FROM w WHERE c3 = 'cody goodale';
+SELECT COUNT( * ) FROM w WHERE c2 = 'johan museeuw';
+SELECT COUNT( * ) FROM w WHERE c4_list = 'johan bruyneel';
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'tom boonen' , 'sven nys' ) order BY c1_number asc limit 1;
+SELECT c2 FROM w WHERE c3 != 'united states';
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'murle breer' ) + 1;
+SELECT c2 FROM w WHERE c2 != 'sandra haynie' AND c4_result = ( SELECT c4_result FROM w WHERE c2 = 'sandra haynie' );
+SELECT c4 FROM w order BY c4_number limit 1;
+SELECT c3_raw FROM w order BY c4_number limit 1;
+SELECT abs ( c7_number1 - c7_number2 ) FROM w WHERE c3_raw = 'michigan state';
+SELECT ( SELECT c7_number1 - c7_number2 FROM w WHERE c1 = 'september 29' ) < 5;
+SELECT c3_raw FROM w WHERE c1_parsed > ( SELECT c1_parsed FROM w WHERE c3_raw = 'unlv' ) order BY c1_parsed limit 1;
+SELECT c4 FROM w WHERE c1_parsed > ( SELECT c1_parsed FROM w WHERE c1 = 'november 10' ) order BY c1_parsed limit 1;
+SELECT c7_number2 FROM w WHERE c3_raw = 'miami (oh)';
+SELECT c3_list_first FROM w WHERE c3_list_first != 'frank glieber' AND c2 = 'cbs' AND c1_number = 1965;
+SELECT c4 FROM w order BY c1_number desc limit 1;
+SELECT c3 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c3_list_first = 'ray scott' ) order BY c1_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3_list_first IN ( 'chris schenkel' , 'chuck thompson' );
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3_list = 'ray scott' ) - 1;
+SELECT c4 FROM w WHERE c1_number = 1968;
+SELECT COUNT( * ) FROM w WHERE c3_list_first = 'chuck thompson';
+SELECT MAX( c1_number ) FROM w;
+SELECT SUM( c3_number ) FROM w WHERE c1 IN ( 'conservative' , 'rainbow dream ticket' );
+SELECT c2 FROM w order BY c5_number limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w WHERE c2_number = 2004 limit 1;
+SELECT ( SELECT COUNT( c1 ) FROM w WHERE c2_number = 2005 ) - ( SELECT COUNT( c1 ) FROM w WHERE c2_number = 2003 );
+SELECT COUNT( c1 ) FROM w WHERE c2_number = 2004;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'sengoku cannon' ) + 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'gunbird special edition / gunbird 1&2' ) - 1;
+SELECT c1 FROM w WHERE c2_number = 2004;
+SELECT COUNT( c1 ) FROM w WHERE c4 = 'psp';
+SELECT c2 FROM w GROUP BY c2_number order BY COUNT( c1 ) desc limit 1;
+SELECT c1 FROM w WHERE c5 = 'released and published in europe by play it as 1945 i & ii: the arcade games';
+SELECT c2 FROM w WHERE c4_number = ( SELECT MIN( c4_number ) FROM w );
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'philips';
+SELECT c2 FROM w WHERE c4_number > 20;
+SELECT COUNT( * ) FROM w WHERE c4_number <= 10;
+SELECT c2 FROM w order BY c1_number limit 1;
+SELECT c2 FROM w order BY c1_number limit 1;
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = ''melting pot'' ) order BY c1_number limit 1;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c3_home = 'home' AND c5_result = 'w' ) > ( SELECT COUNT( * ) FROM w WHERE c3_home = 'away' AND c5_result = 'w' );
+SELECT COUNT( * ) FROM w WHERE id < ( SELECT id FROM w WHERE c2 = 'march 4, 2005' ) AND c5_result = 'w';
+SELECT COUNT( * ) FROM w WHERE c5_number1 - c5_number2 > 2;
+SELECT ( SELECT c1_number FROM w WHERE c6_first = 'relegated' ) - 1999;
+SELECT MAX( c1_number ) - MIN( c1_number ) FROM w;
+SELECT c1 FROM w WHERE c2 = 'tier 6' order BY c1_number asc limit 1;
+SELECT c5 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c5_number < 4;
+SELECT c1 FROM w order BY c5_number asc limit 1;
+SELECT c2 FROM w WHERE c1_number < 2007;
+SELECT COUNT( * ) FROM w WHERE c5_number <= 3;
+SELECT COUNT( c1 ) FROM w WHERE c5_number <= 5;
+SELECT COUNT( c1 ) FROM w WHERE c1 != 1999 AND c6_second = 'spring series';
+SELECT COUNT( * ) FROM w WHERE c5_number = 2;
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT COUNT( * ) FROM w WHERE c6 = 'waterford';
+SELECT c2 FROM w WHERE id = 1;
+SELECT c5_number FROM w WHERE c2 = 'joe hennessy';
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT COUNT( DISTINCT c2 ) FROM w;
+SELECT c2 FROM w WHERE c5_number = 10;
+SELECT MIN( c5_number ) FROM w;
+SELECT SUM( c5_number ) FROM w;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'mark corrigan' ) - 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'vietnam';
+SELECT COUNT( * ) FROM w WHERE c5_number >= 2;
+SELECT COUNT( * ) FROM w WHERE c4 = 'laos';
+SELECT c6_number1 + c6_number2 FROM w WHERE c2 = '24 march 2007';
+SELECT c2_month FROM w WHERE c2_month IN ( 4 , 12 ) GROUP BY c2_month order BY COUNT( * ) desc limit 1;
+SELECT c3 FROM w order BY c6_number1 + c6_number2 desc limit 1;
+SELECT c3 FROM w WHERE id = 1;
+SELECT c3 FROM w WHERE c2_parsed > ( SELECT c2_parsed FROM w WHERE c3 = 'colombo, sri lanka' ) order BY c2_parsed limit 1;
+SELECT c6 FROM w WHERE c3_parsed > ( SELECT c3_parsed FROM w WHERE c6 = 'marc lopez' AND c3_month = 5 AND c3_year = 2011 ) order BY c3_parsed asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c1 = 'winner';
+SELECT c6 FROM w WHERE c4_address = 'atp world tour finals';
+SELECT COUNT( * ) FROM w WHERE c5_first = 'hard';
+SELECT COUNT( * ) FROM w WHERE c1 = 'runner-up';
+SELECT COUNT( * ) FROM w WHERE c1 = 'runner-up';
+SELECT c4 FROM w WHERE c3_parsed > ( SELECT c3_parsed FROM w WHERE c4_address = 'kremlin cup' ) order BY c3_parsed asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c1 = 'winner';
+SELECT c4 FROM w order BY c2_number desc limit 1;
+SELECT c3_first FROM w order BY c2_number desc limit 1;
+SELECT c7 FROM w GROUP BY c7 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c7 = 'new zealand';
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'pankaj roy' ) + 1;
+SELECT c9 FROM w WHERE c8_parsed > ( SELECT c8_parsed FROM w WHERE c8 = '15 january 1954' ) order BY c8_parsed limit 1;
+SELECT COUNT( c3 ) FROM w;
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( DISTINCT c2_second ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c2_second = 'chn';
+SELECT COUNT( * ) FROM w WHERE c2_second = 'chn';
+SELECT ( SELECT c1_number FROM w WHERE c2_first = 'wang xin' ) < ( SELECT c1_number FROM w WHERE c2_first = 'qiu lianhai' );
+SELECT c3 FROM w WHERE c2_first = 'byamba enkh-amgalan';
+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c1_list_first_year = 2005;
+SELECT c1_list_first_year FROM w WHERE c1_list_first_year IN ( 2004 , 2005 ) GROUP BY c1_list_first_year order BY COUNT( c2 ) desc limit 1;
+SELECT c2 FROM w order BY id asc limit 1;
+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c1_list_first_year = 2004;
+SELECT c2 FROM w WHERE c4 = 'california' order BY c5_first_number desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'cerritos' , 'la palma' ) order BY c1_number limit 1;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT c2 FROM w order BY c5_first_number desc limit 1;
+SELECT c2 FROM w order BY c1_number limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c5_first_number > 15;
+SELECT c3 FROM w GROUP BY c3 order BY SUM( c5_first_number ) desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'new jersey' AND c5_first_number > 15;
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'new jersey' AND c1_number <= 10;
+SELECT COUNT( * ) FROM w WHERE c4 = 'new jersey';
+SELECT c1 FROM w WHERE c4 = 'september 23, 2013' AND c1 != 'france';
+SELECT COUNT( c1 ) FROM w WHERE c4_list_month = 9 AND c4_list_year = 2011;
+SELECT COUNT( c3 ) FROM w;
+SELECT c3 FROM w WHERE c1 = 'loss';
+SELECT COUNT( * ) FROM w WHERE c8 = 'golden gloves';
+SELECT c2 FROM w WHERE c2 IN ( 'italy' , 'spain' ) order BY c4_number desc limit 1;
+SELECT c4_number + c5_number FROM w WHERE c2 = 'russia';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'france' ) - 1;
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT ( SELECT c5_number FROM w WHERE c2 = 'france' ) - ( SELECT c5_number FROM w WHERE c2 = 'russia' );
+SELECT c1_number FROM w WHERE c2 = 'germany';
+SELECT c6 FROM w WHERE c2 = 'germany';
+SELECT SUM( c3_number ) FROM w WHERE c2 IN ( 'belgium' , 'france' , 'turkey' );
+SELECT c2 FROM w WHERE c3_number = 6;
+SELECT c2 FROM w WHERE c6_number > ( SELECT c6_number FROM w WHERE c2 = 'belgium' );
+SELECT c3 FROM w WHERE c2 = 'poland';
+SELECT c6 FROM w WHERE c2 = 'portugal';
+SELECT c6 FROM w WHERE c2 = 'belgium';
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT ( SELECT c6_number FROM w WHERE c2 = 'germany' ) - ( SELECT c6_number FROM w WHERE c2 = 'spain' );
+SELECT COUNT( c1 ) FROM w WHERE c4_minimum_month = 11;
+SELECT c4 FROM w order BY id desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'st. paul open' , 'charlotte open' ) order BY id asc limit 1;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5_first = 'gene rhoda';
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'denver, colorado' ) + 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'florida lanes' ) + 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w WHERE id = 1;
+SELECT c6 FROM w WHERE c1_number = 1;
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1_list = 'james iii' ) - 1;
+SELECT c1 FROM w order BY id asc limit 1;
+SELECT c1 FROM w WHERE id > ( SELECT id FROM w WHERE c1_list = 'james iii' ) limit 1;
+SELECT c1 FROM w WHERE c3_list = 'no children';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'ben e. king' ) - 1;
+SELECT COUNT( c3 ) FROM w WHERE c1_number = 1998;
+SELECT c3 FROM w WHERE c2 = 'eddie harris & les mccann' AND c3 != 'swiss movement';
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'hold on, i'm coming' ) + 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'donny hathaway' ) - 1;
+SELECT c4_number1 FROM w WHERE c2 = 'dan pohl';
+SELECT ( SELECT c5_number FROM w WHERE c2 = 'isao aoki' ) - ( SELECT c5_number FROM w WHERE c2 = 'larry nelson' );
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c2 FROM w WHERE c3 = 'australia';
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT ( SELECT c4_result FROM w WHERE c2 = 'fuzzy zoeller' ) - ( SELECT c4_result FROM w WHERE c2 = 'larry nelson' );
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 4;
+SELECT c5_number - c4_number FROM w WHERE c2_first = 'hallgrimur sveinsson';
+SELECT c2 FROM w WHERE c2_first IN ( 'helgi thordersen' , 'geir vidalin' ) order BY c5_number - c4_number desc limit 1;
+SELECT c2 FROM w WHERE c4_year >= 1846 AND c5_year <= 1866;
+SELECT COUNT( c2 ) FROM w;
+SELECT c5_number - c4_number FROM w WHERE c2_first = 'geir vidalin';
+SELECT c2 FROM w order BY c5_number - c4_number desc limit 1;
+SELECT c2 FROM w order BY c4_parsed asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_month = 2;
+SELECT COUNT( c2 ) FROM w;
+SELECT c4_year - c3_year FROM w WHERE c2 = 'anne churchill (later spencer)';
+SELECT COUNT( c2 ) FROM w WHERE c4_year > 1675;
+SELECT COUNT( * ) FROM w WHERE c3_raw = 'white sox';
+SELECT abs ( ( SELECT c8_number FROM w WHERE c2 = 'july 7' ) - ( SELECT c8_number FROM w WHERE c2 = 'july 8' ) );
+SELECT c2 FROM w order BY c4_first limit 1;
+SELECT c2 FROM w WHERE c5_first = 'murray' AND c2_day > 6 order BY c2_day limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c8_number < 10000;
+SELECT COUNT( * ) FROM w WHERE c2_month = 7;
+SELECT c1 FROM w WHERE id = 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number > 5;
+SELECT COUNT( c1 ) FROM w WHERE c7_number < 1.0;
+SELECT abs ( ( SELECT c5_number FROM w WHERE c1 = '1/10'' ) - ( SELECT c5_number FROM w WHERE c1 = '1/8'' ) );
+SELECT c1 FROM w WHERE c1 IN ( 'blackmagic pocket cinema camera' , '1/2.7'' ) order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'super 8mm film frame' , 'imax film frame' ) order BY c2_number desc limit 1;
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c2 FROM w WHERE c1 = '1/10'';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = '1/1.6'' ) + 1;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c1 != '1/3.6' (nokia lumia 720)' AND c4_number = ( SELECT c4_number FROM w WHERE c1 = '1/3.6' (nokia lumia 720)' ) ) > 0;
+SELECT c1 FROM w WHERE c7_number > 20;
+SELECT c1 FROM w order BY c7_number desc limit 1;
+SELECT abs ( ( SELECT c4_number FROM w WHERE c1_number = 2011 ) - ( SELECT c4_number FROM w WHERE c1_number = 2012 ) );
+SELECT c5 FROM w WHERE c1_number = 2010;
+SELECT MIN( c1_number ) FROM w WHERE c4_number <= 10;
+SELECT c1_number FROM w WHERE c4_number = ( SELECT MAX( c4_number ) FROM w );
+SELECT c1_number FROM w order BY c3_number limit 1;
+SELECT MAX( c5_number ) FROM w;
+SELECT c4_number FROM w WHERE c1_minimum_number = 1982;
+SELECT COUNT( c6 ) FROM w WHERE c3 != 'cancelled';
+SELECT c6 FROM w WHERE id = ( SELECT id FROM w WHERE c6_first = 'romeo bonzo' ) + 1;
+SELECT c6_first FROM w GROUP BY c6_first order BY COUNT( * ) desc limit 1;
+SELECT c7 FROM w WHERE c1_minimum_number = 1998;
+SELECT c6 FROM w WHERE id = ( SELECT id FROM w WHERE c6_first = 'wong kam-po' ) - 1;
+SELECT COUNT( * ) FROM w WHERE c6_first = 'carlo guieb';
+SELECT COUNT( * ) FROM w WHERE c3_number1 - c3_number2 >= 5;
+SELECT COUNT( * ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c3_number1 + c3_number2 <= 10;
+SELECT MAX( abs ( c3_number1 - c3_number2 ) ) FROM w;
+SELECT COUNT( * ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c4_minimum_year > 1980;
+SELECT COUNT( c1 ) FROM w WHERE c4_minimum_year = 1949;
+SELECT c2 FROM w WHERE id = 1;
+SELECT COUNT( DISTINCT c3 ) FROM w WHERE c2 = 'baldwin locomotive works';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'ss&iw' ) - 1;
+SELECT c1 FROM w WHERE c4_minimum_year > ( SELECT c4_minimum_year FROM w WHERE c2 = 'ss&iw' ) order BY c4_minimum_year limit 1;
+SELECT c1 FROM w WHERE c2 = 'edwards rail car company';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'juk faat wan' ) + 1;
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'sek6 lam4' ) - 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'gwong wai' , 'ma ling-yee' ) order BY id asc limit 1;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'shi lin' ) + 1;
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'juk faat wan' ) - 1;
+SELECT length ( c2 ) FROM w WHERE c1 = 'juk faat wan';
+SELECT c3 FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE id = 1;
+SELECT c3 FROM w WHERE id = 1;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'plymouth argyle' , 'reading' ) order BY c4_number2 desc limit 1;
+SELECT MAX( c4_number1 ) FROM w;
+SELECT c1 FROM w order BY c1_parsed limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 != 'h';
+SELECT c5_number FROM w WHERE c1_month = 11 AND c2 = 'sunderland';
+SELECT c2 FROM w WHERE c2 IN ( 'cardiff city' , 'reading' ) order BY c5_number desc limit 1;
+SELECT c3 FROM w WHERE c1 = 'bull-dog drummond' order BY c3_number limit 1;
+SELECT c2 FROM w WHERE c2 != 'h. c. mcneile' AND c3_number > ( SELECT c3_number FROM w WHERE c2 = 'h. c. mcneile' ) order BY c3_number limit 1;
+SELECT c1 FROM w WHERE c3_number < 1921;
+SELECT c2 FROM w GROUP BY c2 HAVING MIN( c4_number ) > 300;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'gerard fairlie';
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( c1 ) asc limit 1;
+SELECT c2 FROM w WHERE id = 1;
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'teen choice awards';
+SELECT COUNT( * ) FROM w WHERE c4 = 'nominated';
+SELECT ( SELECT AVG( c4 ) FROM w ) > 2;
+SELECT c2 FROM w WHERE c5_first = 'nathan brown';
+SELECT c5 , c6 FROM w WHERE c2 = 'copa america de ciclismo';
+SELECT c5_second FROM w WHERE c5_second IN ( 'usa' , 'canada' ) GROUP BY c5_second order BY COUNT( * ) desc limit 1;
+SELECT c5_first FROM w WHERE c5_first IN ( 'oscar sevilla' , 'oscar sanchez' ) GROUP BY c5_first order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w order BY c1_minimum_parsed asc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c4 FROM w WHERE c1 = 'santa maria';
+SELECT c1 FROM w WHERE c4_number < ( SELECT c4_number FROM w WHERE c1 = 'lima' ) order BY c4_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c7 = 'suncorp stadium';
+SELECT c2 FROM w order BY c8_number desc limit 1;
+SELECT c7 FROM w order BY c8_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'salvage tug';
+SELECT c1 FROM w order BY c4_number limit 1;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c4 FROM w WHERE c1 = 'rari class';
+SELECT abs ( ( SELECT c4_number FROM w WHERE c1 = 'malabar class' ) - ( SELECT c4_number FROM w WHERE c1 = 'ut507 class' ) );
+SELECT c1 FROM w WHERE c1 IN ( 'ut507 class' , 'ut515 class' ) order BY c4_number desc limit 1;
+SELECT c3 FROM w WHERE c4 = 'poland' order BY c5_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2_number = 2;
+SELECT c3 FROM w WHERE c4 = 'poland' order BY c1_number asc limit 1;
+SELECT c3 FROM w WHERE c3 IN ( 'adrienne power' , 'kim wall' ) order BY c5_number asc limit 1;
+SELECT c4 FROM w order BY c1_number desc limit 1;
+SELECT abs ( ( SELECT c5_number FROM w WHERE c4 = 'mexico' ) - ( SELECT c5_number FROM w WHERE c4 = 'brazil' ) );
+SELECT c4 FROM w WHERE c4 != 'russia' AND c1_number <= 3;
+SELECT c3 FROM w WHERE c4 = 'brazil';
+SELECT COUNT( c3 ) FROM w WHERE c4 = 'russia';
+SELECT COUNT( DISTINCT c5 ) FROM w;
+SELECT c4 FROM w WHERE c6_list_first = 'doris lessing';
+SELECT c4 FROM w WHERE c2_number > ( SELECT c2_number FROM w WHERE c4 = 'yokohama' ) order BY c2_number asc limit 1;
+SELECT c2 FROM w order BY c7_first_number desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c2_number < 1970 AND c7_first_number > 1000;
+SELECT COUNT( c3 ) FROM w WHERE c5 != 'united states';
+SELECT COUNT( * ) FROM w WHERE c4_address = 'chicago';
+SELECT c2 FROM w order BY c2_number desc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c4_address = 'chicago';
+SELECT c2 FROM w WHERE c3 = 'love story' order BY c1_number limit 1;
+SELECT c2 FROM w WHERE c2 != 'kshana kshana' AND c3 = 'suspense thriller';
+SELECT COUNT( c2 ) FROM w WHERE c1_number >= 2000 AND c1_number <= 2006;
+SELECT c2 FROM w WHERE c3 = 'romance';
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = 'parva' ) order BY c1_number limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'love story';
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c2 = 'marma' ) order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'parva' ) + 1;
+SELECT COUNT( * ) FROM w WHERE c4_list = 'ananth nag';
+SELECT c3 FROM w WHERE c1 = '30 may 1963';
+SELECT COUNT( * ) FROM w WHERE c3 = 'fine gael';
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c1_year = 1964;
+SELECT COUNT( * ) FROM w WHERE c4 = 'national progressive democrats';
+SELECT COUNT( * ) FROM w WHERE c4 = 'labour party';
+SELECT c5 FROM w WHERE c3 = 'helsinki, finland';
+SELECT COUNT( * ) FROM w WHERE c4_number = 1 AND c1_number > 2008;
+SELECT COUNT( DISTINCT c1 ) FROM w WHERE c5 = '20 km';
+SELECT c1_number FROM w WHERE c5 = '5 km';
+SELECT c5 FROM w order BY c5_number limit 1;
+SELECT c3 FROM w WHERE c5 = '5 km';
+SELECT COUNT( * ) FROM w WHERE c2 = 'olympic games';
+SELECT MAX( c1_number ) FROM w WHERE c4_number = 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'world championships';
+SELECT COUNT( c1 ) FROM w WHERE c2_number >= 75;
+SELECT SUM( c3_number ) FROM w WHERE c1 IN ( 'del norte' , 'el dorado' );
+SELECT c1 FROM w WHERE c1 IN ( 'amador' , 'humboldt' , 'lake' ) order BY c2_number asc limit 1;
+SELECT c1 FROM w order BY c2_number asc limit 1;
+SELECT c1 FROM w order BY c8_number desc limit 1;
+SELECT c8 FROM w WHERE c1 = 'amador';
+SELECT ( SELECT c4_number FROM w WHERE c1 = 'alameda' ) > ( SELECT c2_number FROM w WHERE c1 = 'alameda' );
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'del norte' ) - 1;
+SELECT COUNT( c2 ) FROM w WHERE c4_number > 7000;
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'ss eros' ) - 1;
+SELECT c2 FROM w WHERE c1_month = 8 AND c1_day = 25 order BY id desc limit 1;
+SELECT c2 FROM w order BY c4_number limit 1;
+SELECT MAX( c4_number ) - MIN( c4_number ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'united kingdom';
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c8_first_number < 1000;
+SELECT c1 FROM w WHERE id = 1;
+SELECT c1 FROM w order BY c2_first_number desc limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c5 FROM w WHERE c1 = 'russians';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'russians' ) - 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'qender' ) + 1;
+SELECT COUNT( * ) FROM w WHERE c6_number > 50;
+SELECT c6 FROM w WHERE c1 = 'fratar';
+SELECT abs ( ( SELECT c2_second_number FROM w WHERE c2_first = 'dallandyshe allkaj' ) - ( SELECT c3_second_number FROM w WHERE c3_first = 'ilir cela' ) );
+SELECT abs ( ( SELECT c2_second_number FROM w WHERE c2_first = 'sabire hoxhaj' ) - ( SELECT c3_second_number FROM w WHERE c3_first = 'astrit sejdinaj' ) );
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'coalition for the future';
+SELECT c2 FROM w WHERE c3 = 'regina';
+SELECT COUNT( * ) FROM w WHERE c3 = 'the evil queen';
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = 'cha-cha' ) order BY c1_number limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = '1/8' ) + 1;
+SELECT c2_number FROM w WHERE id = ( SELECT id FROM w WHERE c2_number = 0.093 ) + 1;
+SELECT c1 FROM w WHERE c5 = 'number drill 26 (3.7 mm)';
+SELECT ( SELECT c3_number FROM w WHERE c1 = '1/16' ) - ( SELECT c3_number FROM w WHERE c1 = '1/8' );
+SELECT MAX( c3_number ) FROM w;
+SELECT c3 FROM w WHERE c1 = '9/16';
+SELECT SUM( c2_number ) FROM w WHERE id <= 2;
+SELECT c2 FROM w WHERE c1 = '1/8';
+SELECT c1 FROM w WHERE c3_number = 5;
+SELECT c1 FROM w WHERE c1 != '3/16' AND c3_number = ( SELECT c3_number FROM w WHERE c1 = '3/16' );
+SELECT c2_number FROM w order BY id desc limit 1;
+SELECT MIN( c2_number ) FROM w;
+SELECT c1 FROM w WHERE id = 1 + 1;
+SELECT c4 FROM w WHERE c1_parsed < ( SELECT c1_parsed FROM w WHERE c4 = 'john george montagu' ) order BY id desc limit 1;
+SELECT ( SELECT c1_parsed FROM w WHERE c5 = 'john lee' ) < ( SELECT c1_parsed FROM w WHERE c5 = 'lord hugh seymour' );
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'richard ford' ) + 1;
+SELECT c4 FROM w WHERE c4 IN ( 'charles william wyndham' , 'marquess of worcester' ) AND c6 = 'chose to sit for bristol';
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT ( SELECT c5_first_number FROM w WHERE c1 = 'english canyon' ) - ( SELECT c5_first_number FROM w WHERE c1 = 'munger creek' );
+SELECT c1 FROM w WHERE c2_length >= 3;
+SELECT c1 FROM w WHERE c5_first_number = 1;
+SELECT COUNT( * ) FROM w WHERE c4_number1 >= 3;
+SELECT COUNT( c2 ) FROM w;
+SELECT c4_number1 FROM w order BY c4_number1 desc limit 1;
+SELECT SUM( c4_number1 ) FROM w WHERE c1_month = 4;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'birmingham city' ) + 1;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT c1_month FROM w GROUP BY c1_month order BY COUNT( c2 ) desc limit 1;
+SELECT c4 FROM w WHERE id = 1;
+SELECT COUNT( * ) FROM w WHERE c4_number1 = 0;
+SELECT COUNT( * ) FROM w WHERE c2 = 'newcastle united';
+SELECT c4_number1 - c4_number2 FROM w WHERE c2 = 'bury';
+SELECT c1 FROM w WHERE c4_number1 = 2 order BY id desc limit 1;
+SELECT c1 FROM w order BY id asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 NOT NULL;
+SELECT c3 FROM w WHERE c2 = 'asian games' order BY c1_number asc limit 1;
+SELECT MIN( c4_first_number ) FROM w WHERE c2 = 'olympic games' AND c1_number > 1996;
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 2002;
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 2003;
+SELECT c2 FROM w WHERE c4_first_number = 1;
+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c1_number > 2000 AND c4_first_number = 1;
+SELECT c2 FROM w WHERE c4_first_number = 1;
+SELECT c2 FROM w WHERE c4_first_number = 1;
+SELECT c3 FROM w WHERE c2 = 'world championships' AND c1_number > ( SELECT c1_number FROM w WHERE c2 = 'world championships' AND c3_address = 'seville' ) order BY c1_number limit 1;
+SELECT COUNT( * ) FROM w WHERE c1_number < 2004 AND c4_first_number <= 5;
+SELECT COUNT( * ) FROM w WHERE c7 = 'friendly match';
+SELECT COUNT( * ) FROM w WHERE c7 != 'friendly match';
+SELECT c4 FROM w WHERE c2_parsed > ( SELECT c2_parsed FROM w WHERE c4 = 'germany' ) order BY c2_parsed limit 1;
+SELECT c5_number1 FROM w WHERE c4 = 'lithuania';
+SELECT c4 FROM w WHERE c7 = 'friendly match' AND c2_parsed < ( SELECT c2_parsed FROM w WHERE c4 = 'germany' ) order BY c2_parsed desc limit 1;
+SELECT COUNT( DISTINCT c4 ) FROM w;
+SELECT c2_first FROM w WHERE c1_number = ( SELECT c1_number FROM w order BY c1_number desc limit 1 );
+SELECT COUNT( c2 ) FROM w WHERE c6_number > ( SELECT c6_number FROM w WHERE c2_first = 'brazil' );
+SELECT COUNT( c2 ) FROM w WHERE c5_number >= 2;
+SELECT ( SELECT c3_number FROM w WHERE c2_first = 'jamaica' ) - ( SELECT c3_number FROM w WHERE c2_first = 'cuba' );
+SELECT ( SELECT c5_number FROM w WHERE c2_first = 'canada' ) - ( SELECT c5_number FROM w WHERE c2_first = 'venezuela' );
+SELECT c2_first FROM w order BY id desc limit 1;
+SELECT c2 FROM w WHERE c3_number >= 20;
+SELECT c3_number FROM w WHERE c2_first = 'puerto rico';
+SELECT c6 FROM w WHERE c2_second = 'usa';
+SELECT COUNT( c2 ) FROM w WHERE c2_first != 'mexico' AND c3_number = ( SELECT c3_number FROM w WHERE c2_first = 'mexico' );
+SELECT COUNT( c2 ) FROM w WHERE c6_number >= 10;
+SELECT SUM( c5_number ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c6_number > 15;
+SELECT COUNT( * ) FROM w WHERE c10 != 'friendly';
+SELECT COUNT( c7 ) FROM w WHERE c7 = 'tarpley';
+SELECT c4 FROM w WHERE c7 = 'wambach';
+SELECT c3 FROM w WHERE c2_parsed > ( SELECT c2_parsed FROM w WHERE c3 = 'usafrisco tx' ) order BY c2_parsed limit 1;
+SELECT c6_number FROM w WHERE c1_number = 1;
+SELECT c4 FROM w WHERE c1_number = 1;
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4 = 'nigeria';
+SELECT SUM( c9_number1 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c6_number >= 60000;
+SELECT c5 FROM w WHERE c6_number < 50000;
+SELECT COUNT( * ) FROM w WHERE c5 = 'the meadowlands';
+SELECT SUM( c6_number ) FROM w WHERE c3_raw = 'new england patriots';
+SELECT abs ( ( SELECT c6_number FROM w order BY c2_parsed limit 1 ) - ( SELECT c6_number FROM w order BY c2_parsed desc limit 1 ) );
+SELECT c5 FROM w order BY c6_number desc limit 1;
+SELECT c5 FROM w order BY c6_number limit 1;
+SELECT c2 FROM w WHERE c5 = 'the meadowlands' order BY c2_parsed limit 1;
+SELECT c3_raw FROM w WHERE c4_first_number1 > 40 AND c3_raw != 'miami dolphins';
+SELECT MIN( c5_number ) FROM w;
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT ( SELECT c4_number FROM w WHERE c2 = 'maria' ) - ( SELECT c4_number FROM w WHERE c2 = 'carolus' );
+SELECT COUNT( c2 ) FROM w WHERE c4_number < 1000;
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4_number > 2000;
+SELECT c2 FROM w WHERE c4_number = 425;
+SELECT c1 FROM w WHERE id = 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w WHERE c2_first = 'hantu tinta';
+SELECT COUNT( c3 ) FROM w;
+SELECT ( SELECT c3_number FROM w WHERE c2 = 'at&t stadium' ) - ( SELECT c3_number FROM w WHERE c2 = 'ford field' );
+SELECT c4 FROM w WHERE c2 = 'miller park';
+SELECT COUNT( c2 ) FROM w WHERE c3_number > 70000;
+SELECT c2 FROM w WHERE c2 IN ( 'wembley stadium' , 'at&t stadium' ) order BY c3_number desc limit 1;
+SELECT c5 FROM w WHERE c3_number > 70000 GROUP BY c5 order BY COUNT( c2 ) desc limit 1;
+SELECT c2 FROM w WHERE c5 = 'kazakhstan';
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_number > 60000;
+SELECT c5 FROM w order BY c3_number asc limit 1;
+SELECT c3 FROM w WHERE c4_first_number = 1 order BY c1_number desc limit 1;
+SELECT c6 FROM w WHERE c5 = '4x400 m relay' AND c2 = 'universiade' AND c1 = 2005;
+SELECT COUNT( * ) FROM w WHERE c4_first_number = 3;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'world indoor championships' AND c1_number = 2008 ) + 1;
+SELECT COUNT( * ) FROM w WHERE c4_first_number = 1;
+SELECT c6 FROM w WHERE c5 = 'medley relay' AND c1_number = 2001;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3_address = 'turkey' ) - 1;
+SELECT c1 FROM w WHERE c1_number IN ( 2005 , 2003 ) order BY c6 asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_first_number = 1 AND c5 = '4x400 m relay';
+SELECT c6 FROM w WHERE c5 = '400 m' AND c3 = 'erfurt, germany';
+SELECT COUNT( * ) FROM w WHERE c4_first_number <= 5;
+SELECT COUNT( * ) FROM w WHERE c8_number > 30000;
+SELECT COUNT( * ) FROM w;
+SELECT MAX( abs ( c4_first_number1 - c4_first_number2 ) ) FROM w;
+SELECT ( SELECT c3_number FROM w WHERE c2 = 'holm park' ) > ( SELECT c3_number FROM w WHERE c2 = 'the oval' );
+SELECT c3_number FROM w WHERE c2 = 'windsor park';
+SELECT c3_number FROM w WHERE c2 = 'holm park';
+SELECT c2 FROM w WHERE c3_number > ( SELECT c3_number FROM w WHERE c2 = 'ballymena showgrounds' ) AND c3_number < ( SELECT c3_number FROM w WHERE c2 = 'windsor park' );
+SELECT ( SELECT c3_number FROM w WHERE c2 = 'taylors avenue' ) - ( SELECT c3_number FROM w WHERE c2 = 'dixon park' );
+SELECT c3_number FROM w WHERE c2 = 'windsor park';
+SELECT c4 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_number >= 6000 AND c3_number <= 8000;
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'belfast';
+SELECT c2 FROM w WHERE c2 != 'knockrammer park' AND c3_number = ( SELECT c3_number FROM w WHERE c2 = 'knockrammer park' );
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c5 FROM w WHERE c1_number = 1;
+SELECT c5 FROM w WHERE c7_first = 'hardcore tv #6';
+SELECT c2 FROM w WHERE c2_list_first IN ( 'the super destroyers' , 'the dudley boyz' ) GROUP BY c2_list_first order BY COUNT( * ) desc limit 1;
+SELECT c7 FROM w WHERE id = ( SELECT id FROM w WHERE c7_first = 'hardcore tv #14' ) - 1;
+SELECT COUNT( * ) FROM w WHERE c2_list_first = 'the suicide blondes';
+SELECT COUNT( * ) FROM w WHERE c1 = 'winner';
+SELECT c6 FROM w WHERE id = ( SELECT id FROM w WHERE c6 = 'jurgen fassbender' ) - 1;
+SELECT c6 FROM w GROUP BY c6 order BY COUNT( * ) desc limit 1;
+SELECT c6 FROM w WHERE c5 = 'indoor' order BY c3_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c6 = 'jimmy connors';
+SELECT c4 FROM w WHERE c5 = 'indoor';
+SELECT COUNT( c4 ) FROM w WHERE c4 = 'baltimore, u.s';
+SELECT c5 FROM w WHERE id = ( SELECT id FROM w WHERE c5 = 'carpet' ) - 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number < 1900;
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'fr frank thorpe';
+SELECT c1 FROM w WHERE c3 IS NULL;
+SELECT c1 FROM w WHERE c1 != 'st mary' AND c3 = 'fr frank thorpe';
+SELECT c1 FROM w WHERE c4_number > 1950;
+SELECT c1 FROM w WHERE c5 NOT NULL;
+SELECT SUM( c6_number ) FROM w WHERE c1_number = 8;
+SELECT c2 FROM w WHERE c6_number = ( SELECT MIN( c6_number ) FROM w );
+SELECT c2 FROM w WHERE c6_number = ( SELECT MIN( c6_number ) FROM w );
+SELECT c2 FROM w WHERE c2 != 'peru' AND c5_number = 0;
+SELECT c2 FROM w WHERE c5_number = ( SELECT MIN( c5_number ) FROM w );
+SELECT ( SELECT c3_number FROM w WHERE c2 = 'chile' ) > ( SELECT c3_number FROM w WHERE c2 = 'panama' );
+SELECT c2 FROM w WHERE c3_number < ( SELECT c3_number FROM w WHERE c2 = 'colombia' ) order BY c3_number desc limit 1;
+SELECT ( SELECT c3_number FROM w WHERE c2 = 'brazil' ) - ( SELECT c3_number FROM w WHERE c2 = 'argentina' );
+SELECT c4 FROM w WHERE c2 = 'chile';
+SELECT c2 FROM w WHERE c2 != 'uruguay' AND c4_number = ( SELECT c4_number FROM w WHERE c2 = 'uruguay' );
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'peru' ) - 1;
+SELECT c2 FROM w WHERE c2 IN ( 'colombia' , 'venezuela' ) order BY c6_number desc limit 1;
+SELECT c3 FROM w WHERE c2 = 'brazil';
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'kenya';
+SELECT c2 FROM w WHERE c4 < ( SELECT c4 FROM w WHERE c2 = 'emebet anteneh mengistu' );
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'ethiopia';
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'ethiopia' AND id <= 5;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'ethiopia' AND id <= 9;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'rwanda';
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_number >= 100000;
+SELECT c1 FROM w order BY c5_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_number >= 800000;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c5_number > 0;
+SELECT COUNT( c1 ) FROM w;
+SELECT c5 FROM w WHERE c1_number = 2011;
+SELECT c3 FROM w WHERE c5 = 'scotland' AND c1_number < ( SELECT c1_number FROM w WHERE c3 = 'alan brazil' );
+SELECT c1 FROM w WHERE c1_number > 1986 order BY c1_number asc limit 1;
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( c3 ) desc limit 1;
+SELECT c3 FROM w WHERE c5 = 'new zealand';
+SELECT c3 FROM w WHERE c5 = 'england' order BY c1_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = 'terry butcher';
+SELECT c3 FROM w WHERE c5 = 'new zealand';
+SELECT c5 FROM w order BY id desc limit 1;
+SELECT COUNT( DISTINCT c2 ) FROM w;
+SELECT c2 FROM w WHERE c2 IN ( 'routh' , 'lake george' );
+SELECT COUNT( DISTINCT c5 ) FROM w;
+SELECT COUNT( DISTINCT c4 ) FROM w WHERE c1_number <= 5;
+SELECT c4 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c4 = 'lusty engineering' ) - 1;
+SELECT c5_list FROM w WHERE c4 = 'perkins engineering' AND c5_list != 'larry perkins';
+SELECT ( SELECT c7 FROM w WHERE c1_number = 1 ) - ( SELECT c7 FROM w WHERE c1_number = 24 );
+SELECT c4 FROM w WHERE c5_list = 'john faulkner';
+SELECT c5_list FROM w WHERE c4 = 'phil ward racing' AND c5_list != 'phil ward';
+SELECT COUNT( c4 ) FROM w WHERE c8_number < 5;
+SELECT c1 FROM w WHERE c3 = 'keene';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = '1999-2000' ) - 1;
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'friendswood';
+SELECT COUNT( * ) FROM w WHERE c2 = 'garden city';
+SELECT COUNT( * ) FROM w WHERE c7_number > 120;
+SELECT COUNT( * ) FROM w WHERE c2_year = 2012;
+SELECT c1 FROM w WHERE c1 != 's3300' AND c5_list_number = 2.7;
+SELECT c1 FROM w WHERE c5_list_number = 3 AND c7_number >= 210;
+SELECT c1 FROM w order BY c7_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c7_number >= 100 AND c7_number <= 200;
+SELECT COUNT( c1 ) FROM w WHERE c5_list_number < 3;
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT c1 FROM w WHERE c7_number >= 15;
+SELECT SUM( c15_number ) FROM w;
+SELECT c1 FROM w WHERE c7_number > 10 order BY c1_number asc limit 1;
+SELECT c1 FROM w order BY c9_number desc limit 1;
+SELECT c2 FROM w GROUP BY c2 order BY SUM( c3_number ) desc limit 1;
+SELECT SUM( c7_number ) FROM w WHERE c2 = 'hou';
+SELECT c1 FROM w order BY c9_number limit 1;
+SELECT c9_number FROM w WHERE c1 = 'portugal';
+SELECT c1 FROM w order BY c8_number desc limit 1;
+SELECT c1 FROM w order BY c9_number desc limit 1;
+SELECT c4 FROM w WHERE c2 = 'nynex';
+SELECT c3 FROM w WHERE c2 = 'southern bell telephone co' AND c1_number = 1955;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c4 FROM w WHERE c1 = 1971;
+SELECT COUNT( * ) FROM w WHERE c3_number > 200000;
+SELECT SUM( c3_number ) FROM w WHERE c2 = 'verizon';
+SELECT c4 FROM w WHERE c2 = 'at&t' AND c1_number = 2012;
+SELECT SUM( c4_day ) FROM w WHERE c2 = 'at&t';
+SELECT COUNT( * ) FROM w WHERE c1_number < 1989;
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE c4 IN ( 'marc kalenga' , 'gavin rae' );
+SELECT c4 FROM w WHERE c4 IN ( 'gavin rae' , 'bajram fetai' ) order BY c10_number desc limit 1;
+SELECT COUNT( c4 ) FROM w WHERE c10 = 'free';
+SELECT c8 FROM w GROUP BY c8 order BY COUNT( * ) desc limit 1;
+SELECT c4 FROM w order BY c10_number desc limit 1;
+SELECT COUNT( c4 ) FROM w WHERE c5_number = 21 OR c5_number = 33;
+SELECT c6 FROM w WHERE c4 = 'henning berg';
+SELECT ( SELECT c5 FROM w WHERE c4 = 'paolo vanoli' ) - ( SELECT c5 FROM w WHERE c4 = 'marc kalenga' );
+SELECT c4 FROM w WHERE c4 != 'henning berg' AND c8 = 'summer' AND c9_number = 2004;
+SELECT c4 FROM w WHERE c10_number = 0.67;
+SELECT c1 FROM w WHERE c1 IN ( 'jewish' , 'roman catholic' ) order BY c5_number desc limit 1;
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c2_list FROM w WHERE c2_list IN ( 'spain' , 'south africa' ) GROUP BY c2_list order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w WHERE c4_parsed > ( SELECT c4_parsed FROM w WHERE c1 = 'geotrupes spiniger marsham' ) order BY c4_parsed asc limit 1;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c3 FROM w WHERE c1 = 'liatongus militaris castelanu';
+SELECT ( SELECT c3_number FROM w WHERE c1 = 'bubas bison' ) - ( SELECT c3_number FROM w WHERE c1 = 'copris hispanus linnaeus' );
+SELECT COUNT( DISTINCT c1 ) FROM w WHERE c4_year = 1971;
+SELECT abs ( ( SELECT c3_number FROM w WHERE c1 = 'onthophagus obliquus' ) - ( SELECT c3_number FROM w WHERE c1 = 'sisyphus rubrus paschalidis' ) );
+SELECT COUNT( DISTINCT c1 ) FROM w WHERE c2_list_first = 'south africa';
+SELECT c1 FROM w WHERE c1 IN ( 'bubas bison' , 'copris hispanus linnaeus' ) order BY c3_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4_list = 'at least one tenth of the existing representatives or senators';
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'political party';
+SELECT c2 FROM w WHERE c6_number = 25;
+SELECT abs ( ( SELECT c6_number FROM w WHERE c2 = 'erwan nigon' ) - ( SELECT c6_number FROM w WHERE c2 = 'dirk heidolf' ) );
+SELECT COUNT( c2 ) FROM w WHERE c6 IS NULL;
+SELECT c2 FROM w WHERE c6_number = 2;
+SELECT c2 FROM w WHERE c2 IN ( 'alex debon' , 'toni elias' ) order BY c6_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c6_number >= 20;
+SELECT c2 FROM w WHERE c1_number <= 15 AND c6_number <= 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'cork' AND c1_number <= 15;
+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c5_number > 10;
+SELECT c6 FROM w WHERE c1_number = 27 limit 1;
+SELECT c2 FROM w WHERE c2 IN ( 'brendan cummins' , 'ger cuddy' ) order BY c5_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'cork';
+SELECT c2 FROM w WHERE c1_number <= 10 AND c3 = 'laois';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'declan lovett' ) + 1;
+SELECT c1 FROM w WHERE c2 = 'pat enright' AND c1_number IN ( 5 , 9 );
+SELECT c1 FROM w WHERE c1 != 'brazil' AND c2_number1 = 5;
+SELECT COUNT( * ) FROM w WHERE abs ( c2_number1 - c2_number2 ) >= 3;
+SELECT COUNT( * ) FROM w WHERE abs ( c2_number1 - c2_number2 ) >= 2;
+SELECT SUM( c2_number1 + c2_number2 ) FROM w;
+SELECT c7 FROM w GROUP BY c7 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c1 FROM w WHERE c2_number = 1989;
+SELECT c4 FROM w order BY id desc limit 1;
+SELECT c1 FROM w WHERE c2_number > ( SELECT c2_number FROM w WHERE c1 = 'susie' ) order BY c2_number limit 1;
+SELECT c1 FROM w WHERE c2_number > ( SELECT c2_number FROM w WHERE c1 = 'susie' ) order BY c2_number limit 1;
+SELECT c1 FROM w WHERE c2_number < ( SELECT c2_number FROM w WHERE c1 = 'distributor' ) order BY c2_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'mark 1' , 'distributor' ) order BY c2_number limit 1;
+SELECT ( SELECT id FROM w WHERE c1 = 'dean winstanley' ) < ( SELECT id FROM w WHERE c1 = 'kyle anderson' );
+SELECT c1 FROM w WHERE c1 != 'kyle anderson' AND c4 = 'lost' AND c2 = 2014;
+SELECT c1 FROM w WHERE c4 = 'won' order BY id desc limit 1;
+SELECT c1 FROM w WHERE c2 = 2011;
+SELECT c1 FROM w WHERE c4 = 'won' order BY c2_number asc limit 1;
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c8_number >= 10;
+SELECT c2 FROM w order BY c8_number asc limit 1;
+SELECT c2 FROM w order BY c8_number desc limit 1;
+SELECT c3 FROM w WHERE c1_number = 6;
+SELECT COUNT( * ) FROM w WHERE c8_number > 10;
+SELECT COUNT( c2 ) FROM w WHERE c3_number < ( SELECT c3_number FROM w WHERE c2 = 'carlow-kilkenny' );
+SELECT c2 FROM w WHERE id = 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_year <= 1910;
+SELECT c3 FROM w WHERE id = 1;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT c3 FROM w WHERE id = 1;
+SELECT c1 FROM w order BY c4_year - c3_year asc limit 1;
+SELECT c1 FROM w WHERE c1_number != 445 AND c4_year = ( SELECT c4_year FROM w WHERE c1_number = 445 );
+SELECT c1 FROM w WHERE c4_year < 1945;
+SELECT c3_year FROM w WHERE c1_number = 445;
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( DISTINCT c2 ) FROM w;
+SELECT c1 FROM w WHERE c4_year != 1957;
+SELECT COUNT( * ) FROM w WHERE c3_year <= 1950 AND c4_year >= 1950;
+SELECT c3_number FROM w WHERE c1 = 'ronnie baxter';
+SELECT COUNT( c1 ) FROM w WHERE c10_number > 97;
+SELECT c1 FROM w WHERE c4_number = ( SELECT MIN( c4_number ) FROM w );
+SELECT ( SELECT c10_number FROM w WHERE c1 = 'mark walsh' ) > 93;
+SELECT c4_number FROM w WHERE c1 = 'james wade';
+SELECT c1 FROM w WHERE c9_number = 116;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'mark walsh' ) + 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_number > 3;
+SELECT c1 FROM w WHERE c1 IN ( 'andy smith' , 'kevin painter' ) AND c10_number = 96.71;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE c9_number = 131;
+SELECT COUNT( c1 ) FROM w WHERE c3_number >= 30;
+SELECT MAX( c4_number ) FROM w;
+SELECT COUNT( c1 ) FROM w;
+SELECT c5_list FROM w GROUP BY c5_list order BY COUNT( c1 ) desc limit 1;
+SELECT c1 FROM w WHERE c5_number < 30;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w order BY c3_number asc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number > 300000;
+SELECT c1 FROM w WHERE c1 IN ( 'independencia' , 'barahona' ) order BY c4_number asc limit 1;
+SELECT abs ( ( SELECT c3_number FROM w WHERE c1 = 'san cristobal' ) - ( SELECT c3_number FROM w WHERE c1 = 'puerto plata' ) );
+SELECT ( SELECT c4_number FROM w WHERE c1 = 'san juan' ) - ( SELECT c4_number FROM w WHERE c1 = 'sanchez ramirez' );
+SELECT c1 FROM w WHERE c5_number < ( SELECT c5_number FROM w WHERE c1 = 'independencia' );
+SELECT c1 FROM w WHERE c1 != 'san juan' AND c5_number = ( SELECT c5_number FROM w WHERE c1 = 'san juan' );
+SELECT c3 FROM w WHERE c2_number = 1982;
+SELECT COUNT( c1 ) FROM w WHERE c2_number < 2000;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'lego the lord of the rings' ) - 1;
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'melbourne house';
+SELECT c1 FROM w order BY c5_length desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2_number > 1982;
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'melbourne house';
+SELECT c3_list FROM w GROUP BY c3_list order BY COUNT( c1 ) desc limit 1;
+SELECT c1 FROM w WHERE c2_number < 1990;
+SELECT COUNT( c2 ) FROM w WHERE c6_number > 100;
+SELECT ( SELECT c5_number FROM w WHERE c2 = 'guam' ) > ( SELECT c5_number FROM w WHERE c2 = 'palau' );
+SELECT ( SELECT c6_number FROM w WHERE c2 = 'fiji' ) - ( SELECT c6_number FROM w WHERE c2 = 'tonga' );
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE c3_number <= 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_number >= 20;
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( DISTINCT c4 ) FROM w;
+SELECT MIN( c1_first_year ) FROM w;
+SELECT c2 FROM w WHERE c1_first_year = 2012;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w WHERE c4 = 'zee network';
+SELECT c2 FROM w WHERE c3 = 'celebrity contestant';
+SELECT c2 FROM w WHERE c3 = 'herself';
+SELECT c2 FROM w WHERE c3 = 'host';
+SELECT c2 FROM w WHERE c3 = 'singing contestant';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'ek anhonee (tele-film)' ) + 1;
+SELECT c2 FROM w order BY c1_first_parsed desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'kannada';
+SELECT COUNT( DISTINCT ( c4 ) ) FROM w;
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( c2 ) desc limit 1;
+SELECT c4 FROM w GROUP BY c4 HAVING COUNT( c2 ) = 1;
+SELECT c2 FROM w WHERE c4 = 'kannada' order BY c1_number asc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c1_number >= 2007 AND c1_number <= 2009;
+SELECT COUNT( c2 ) FROM w WHERE c1_number IN ( 2010 , 2012 );
+SELECT c2 FROM w order BY c1_number asc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c5_list = 'germany';
+SELECT c2 FROM w WHERE id = 1;
+SELECT COUNT( * ) FROM w WHERE c3_list = 'soviet union';
+SELECT COUNT( * ) FROM w WHERE c3_list = 'soviet union';
+SELECT COUNT( c1 ) FROM w WHERE c1 != 'kabul' AND c8 = ( SELECT c8 FROM w WHERE c1 = 'kabul' );
+SELECT COUNT( c1 ) FROM w WHERE c7_list = 'pashto';
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'ghor' ) - 1;
+SELECT c1 FROM w order BY c5_number limit 1;
+SELECT c7 FROM w WHERE c1 = 'herat';
+SELECT c1 FROM w WHERE c4_number > 1000;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'buenos aires, argentina' ) + 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_first_address = 'united states';
+SELECT c2_maximum_day - c2_minimum_day FROM w WHERE c1 = 'wikimania 2011';
+SELECT c1 FROM w order BY c4_number limit 1;
+SELECT c2 FROM w WHERE c1 = 'wikimania 2014';
+SELECT c4_number FROM w WHERE c1 = 'wikimania 2005';
+SELECT c2 FROM w WHERE c3 = 'narrator';
+SELECT c2 FROM w WHERE id = 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'this morning' ) + 1;
+SELECT c1 FROM w WHERE c4 = '1220';
+SELECT COUNT( c1 ) FROM w;
+SELECT c4 FROM w WHERE c1_number = 2010;
+SELECT SUM( c7_number ) FROM w WHERE id <= 5;
+SELECT COUNT( c1 ) FROM w WHERE c3_number < 16;
+SELECT COUNT( c1 ) FROM w WHERE c6_number >= 20;
+SELECT AVG( c5_number ) FROM w;
+SELECT c9 FROM w WHERE c1_number = 2004;
+SELECT COUNT( c1 ) FROM w WHERE c9_number = 0;
+SELECT SUM( c7_number ) FROM w;
+SELECT c1 FROM w WHERE c7_number < 3;
+SELECT COUNT( c1 ) FROM w WHERE c4_number >= 70;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'tengku hadzali shah' ) - 1;
+SELECT c2 FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c2 = 's raj' ) order BY c1_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'ronald l briones';
+SELECT COUNT( c2 ) FROM w;
+SELECT c5_number - c6_number - c7_number FROM w WHERE c1_number = 2009;
+SELECT COUNT( c1 ) FROM w WHERE c4 IS NULL;
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = 'tengku hadzali shah' ) order BY c1_number asc limit 1;
+SELECT c4 FROM w WHERE c1_number = 1850;
+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c1 FROM w WHERE c1_number != 2011 order BY c5_number desc limit 1;
+SELECT c1 FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c3_number = 74.1 ) order BY c1_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3_number > 70;
+SELECT c1 FROM w order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE c2_number = 6981 AND c4_number = 19.4;
+SELECT COUNT( * ) FROM w WHERE c2_number >= 6000;
+SELECT c1 FROM w WHERE c1 != 'germany' AND c5 = ( SELECT c5 FROM w WHERE c1 = 'germany' );
+SELECT ( SELECT c2_parsed FROM w WHERE c3_list = 'cd single' ) < ( SELECT c2_parsed FROM w WHERE c3_list = 'digital ep' );
+SELECT c1 FROM w WHERE c1 IN ( 'france' , 'united states' ) GROUP BY c1 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w order BY c2_parsed limit 1;
+SELECT c1 FROM w order BY c2_parsed desc limit 1;
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c2 FROM w WHERE c1_number = 1;
+SELECT c1 FROM w order BY c6_number asc limit 1;
+SELECT c2_month FROM w GROUP BY c2_month order BY COUNT( c1 ) asc limit 1;
+SELECT c2 FROM w WHERE c1_number IN ( 1 , 2 );
+SELECT c2 FROM w WHERE c5 = 'louisiana state university';
+SELECT ( SELECT c1_number FROM w WHERE c2 = 'kiki jones' ) < ( SELECT c1_number FROM w WHERE c2 = 'greg gohr' );
+SELECT c2 FROM w order BY c1_number asc limit 1;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT c3 FROM w WHERE c3 IN ( 'seattle mariners' , 'baltimore orioles' , 'los angeles dodgers' , 'houston astros' ) AND c4 = 'rhp' order BY c1_number asc limit 1;
+SELECT ( SELECT c1_number FROM w WHERE c2 = 'steve hosey' ) < ( SELECT c1_number FROM w WHERE c2 = 'mo vaughn' );
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'federal building' order BY id desc limit 1 ) + 1;
+SELECT COUNT( * ) FROM ( SELECT c2 FROM w GROUP BY c2 HAVING COUNT( c1 ) > 1 );
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4 = 'n.d. miss';
+SELECT c1 FROM w WHERE c2 = 'gulfport';
+SELECT c2 FROM w WHERE c2 IN ( 'aberdeen' , 'greenville' ) GROUP BY c2 order BY COUNT( c1 ) desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'aberdeen';
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( c1 ) desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c6 NOT NULL;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'biloxi';
+SELECT COUNT( c1 ) FROM w;
+SELECT c3 FROM w WHERE c2 = 'murphy brown';
+SELECT c1_maximum_year - c1_minimum_year FROM w WHERE c2 = 'pee-wee's playhouse';
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( c3 ) desc limit 1;
+SELECT ( SELECT COUNT( c3 ) FROM w WHERE c1 = 1997 ) - ( SELECT COUNT( c3 ) FROM w WHERE c1 = 2007 );
+SELECT c5 FROM w WHERE c1 = '2004-05';
+SELECT c2 FROM w WHERE c5_number >= 15;
+SELECT ( SELECT SUM( c5_number ) FROM w WHERE c2 = 'hajduk split' ) > ( SELECT SUM( c5_number ) FROM w WHERE c2 = 'nk zagreb' );
+SELECT c2 FROM w order BY c5_number desc limit 1;
+SELECT MIN( c3_number ) FROM w WHERE c1 = 'winner';
+SELECT COUNT( c4 ) FROM w;
+SELECT c6 FROM w WHERE id = 1;
+SELECT c4 FROM w WHERE c1 = 'winner' order BY id desc limit 1;
+SELECT c4 FROM w WHERE c3_number = 1975;
+SELECT COUNT( * ) FROM w WHERE c4_address = 'u.s';
+SELECT COUNT( * ) FROM w WHERE c5 = 'clay';
+SELECT COUNT( c1 ) FROM w WHERE c2_number >= 35;
+SELECT c5 FROM w WHERE c1 = 'boone';
+SELECT c6 FROM w WHERE c1 = 'camden';
+SELECT c1 FROM w WHERE c1 IN ( 'barry' , 'benton' ) order BY c6_number asc limit 1;
+SELECT abs ( c3_number - c5_number ) FROM w WHERE c1 = 'benton';
+SELECT c1 FROM w order BY c5_number asc limit 1;
+SELECT c3_number - c5_number FROM w WHERE c1 = 'iron';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'krishna prema' ) + 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_number < 1950;
+SELECT COUNT( DISTINCT c3 ) FROM w;
+SELECT c2 FROM w WHERE c7 = 'pratiba' order BY c1_number asc limit 1;
+SELECT c2 FROM w order BY c1_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1 = 1941;
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w WHERE c2_number > 1990;
+SELECT c1 FROM w WHERE c2_number = ( SELECT MIN( c2_number ) FROM w );
+SELECT c1 FROM w WHERE c1 != 'ace' AND c2_number = ( SELECT c2_number FROM w WHERE c1 = 'ace' );
+SELECT ( SELECT COUNT( * ) FROM w WHERE c2_number < 2000 ) > ( SELECT COUNT( * ) FROM w w WHERE c2_number > 2000 );
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+SELECT COUNT( c1 ) FROM w WHERE c3 = 'algeria';
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'algeria';
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+SELECT COUNT( c3 ) FROM w;
+SELECT c3 FROM w WHERE c5 = 'kansas state';
+SELECT c4 FROM w GROUP BY c4 HAVING COUNT( c3 ) = ( SELECT COUNT( c3 ) FROM w GROUP BY c4 order BY COUNT( c3 ) limit 1 );
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'paul fanning' ) + 1;
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+SELECT COUNT( * ) FROM w WHERE c4 = 'guard';
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+SELECT c2 FROM w order BY c2_parsed desc limit 1;
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4 = 'venezuela' ) + 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = '27 march 1977' ) + 1;
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+SELECT c3 FROM w WHERE c1 = '1984 summer olympics';
+SELECT c1 FROM w order BY c5_number desc limit 1;
+SELECT c1 FROM w order BY c4_number asc limit 1;
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+SELECT COUNT( c1 ) FROM w;
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+SELECT COUNT( c1 ) FROM w WHERE c3 = 'print';
+SELECT c1 FROM w WHERE c2_number IS NULL;
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+SELECT c6 FROM w WHERE c2_number = 2011 order BY c8_number2 desc limit 1;
+SELECT c5 FROM w WHERE c2 = 2011 order BY c4_parsed desc limit 1;
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+SELECT c2 FROM w order BY c1_number desc limit 1;
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+SELECT c4 FROM w WHERE c2 = 'claudia poll';
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'mexico';
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+SELECT COUNT( c2 ) FROM w WHERE c1 IS NULL;
+SELECT COUNT( c2 ) FROM w WHERE c4_min <= 1;
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+SELECT COUNT( c2 ) FROM w WHERE c3 = 'mexico' AND c1_number <= 10;
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+SELECT c1 FROM w order BY c7_number desc limit 1;
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+SELECT c1 FROM w order BY c6_number desc limit 1;
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+SELECT c6 FROM w WHERE c1 = 'foyle college';
+SELECT COUNT( c1 ) FROM w WHERE c3_number > 5;
+SELECT c1 FROM w WHERE c1 != 'coleraine academical institution' AND c3_number = ( SELECT c3_number FROM w WHERE c1 = 'coleraine academical institution' );
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+SELECT COUNT( c3 ) FROM w WHERE c4 = 'united states';
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+SELECT COUNT( c1 ) FROM w WHERE c4_number > 2000;
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( c1 ) FROM w;
+SELECT c1 FROM w order BY c5_number asc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_number > 100;
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c1 FROM w order BY id desc limit 1;
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+SELECT c3 FROM w WHERE c3 IN ( 'steven davis' , 'chris baird' ) order BY c5_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'crystal palace';
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+SELECT MAX( c5_number ) FROM w;
+SELECT c4 FROM w WHERE c3 = 'aaron hughes';
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+SELECT COUNT( c1 ) FROM w WHERE c4_number < 100;
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+SELECT c3 FROM w WHERE c1 = 'ocz vertex 3';
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+SELECT c5 FROM w;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE id < ( SELECT id FROM w WHERE c1_number = 2007 );
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'xiamen international marathon' ) + 1;
+SELECT c2 FROM w order BY c1_number asc limit 1;
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+SELECT COUNT( c2 ) FROM w WHERE c5_year = 1977;
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+SELECT abs ( ( SELECT c1_number FROM w WHERE c2 = 'nancy pelosi' ) - ( SELECT c1_number FROM w WHERE c2 = 'john boehner' ) );
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'tom petri' ) + 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'cska moscow' ) - 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'porto' ) + 1;
+SELECT c1 FROM w order BY c3_number limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c5 = 'nebraska';
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+SELECT c4_list_maximum_year - c4_list_minimum_year FROM w WHERE c1 = 'w. harold browne';
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+SELECT COUNT( * ) FROM w WHERE c2 = '90 v';
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+SELECT COUNT( c1 ) FROM w WHERE c6_number >= 20;
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+SELECT c3 FROM w WHERE c5 = 'bank officer' AND c1_number > ( SELECT c1_number FROM w WHERE c3 = 'michel deuzet' );
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+SELECT c3 FROM w WHERE c5 = 'first secretary for the minister of finance';
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+SELECT c2 FROM w order BY c6_parsed desc limit 1;
+SELECT c2 FROM w order BY c6_parsed asc limit 1;
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+SELECT COUNT( * ) FROM w;
+SELECT MIN( julianday ( c6_parsed ) - julianday ( c4_parsed ) ) FROM w;
+SELECT abs ( ( SELECT c4_year FROM w WHERE c2 = 'royal blue' ) - ( SELECT c4_year FROM w WHERE c2 = 'crusader' ) );
+SELECT c2 FROM w order BY c5_number - c4_number desc limit 1;
+SELECT c2 FROM w WHERE c2 != 'wall street' AND c3_list = 'philadelphia';
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+SELECT c2 FROM w order BY c4_number limit 1;
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+SELECT SUM( c2_number ) FROM w;
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+SELECT c8 FROM w WHERE c1_number = 2011;
+SELECT abs ( ( SELECT c2_number FROM w WHERE c1_number = 2008 ) - ( SELECT c2_number FROM w WHERE c1_number = 2002 ) );
+SELECT COUNT( c1 ) FROM w;
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+SELECT c2 FROM w WHERE c1_number = 2010;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'england' AND c4_number > 15;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'england';
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT c2 FROM w WHERE c3 = 'england' AND c4_number = ( SELECT c4_number FROM w WHERE c2 = 'mel berry' );
+SELECT c3 FROM w WHERE c2 = 'lucy millard';
+SELECT COUNT( * ) FROM w WHERE c1_number <= 10 AND c3 = 'england';
+SELECT c2 FROM w WHERE c1_number = 20;
+SELECT SUM( c4_number ) FROM w WHERE c3 = 'france';
+SELECT c5 FROM w WHERE c3 = 'walt disney world speedway, florida';
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'march 22' ) - 1;
+SELECT c8 FROM w GROUP BY c8 order BY COUNT( * ) desc limit 1;
+SELECT c3 FROM w order BY c4_number desc limit 1;
+SELECT MAX( c7_number ) - MIN( c7_number ) FROM w;
+SELECT c1 FROM w WHERE c4_number = '0.018';
+SELECT c5 FROM w WHERE c1 = 'diamond';
+SELECT c1 FROM w WHERE c3_number > 2.6;
+SELECT ( SELECT c5_number FROM w WHERE c1 = 'diamond' ) - ( SELECT c5_number FROM w WHERE c1 = 'white sapphire' );
+SELECT c1 FROM w WHERE c7 NOT NULL AND c7 != 'excellent' AND c7 != 'poor';
+SELECT c1 FROM w WHERE c7 = 'high';
+SELECT COUNT( * ) FROM w WHERE c4 = 'hard';
+SELECT c5 FROM w WHERE id = ( SELECT id FROM w WHERE c5 = 'chanelle scheepers' ) - 1;
+SELECT COUNT( c3 ) FROM w;
+SELECT COUNT( c4 ) FROM w WHERE c4 = 'grass';
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'australian open' ) + 1;
+SELECT COUNT( * ) FROM w WHERE c3_list = 'left fielder';
+SELECT c3 FROM w order BY id desc limit 1;
+SELECT SUM( c5_number1 ) FROM w WHERE c3_address = 'los angeles';
+SELECT COUNT( * ) FROM w WHERE c5_number1 + c5_number2 >= 3;
+SELECT COUNT( * ) FROM w WHERE c3_address = 'los angeles';
+SELECT COUNT( * ) FROM w WHERE c2_month = 2 AND c2_year = 2000;
+SELECT SUM( c5_number1 ) FROM w WHERE c4 IN ( 'panama' , 'colombia' );
+SELECT c3 FROM w WHERE c2_parsed < ( SELECT c2_parsed FROM w WHERE c4 = 'colombia' ) order BY c2_parsed desc limit 1;
+SELECT c3 FROM w order BY c2_parsed desc limit 1;
+SELECT SUM( c5_number1 ) FROM w WHERE c4 = 'haiti';
+SELECT COUNT( c2 ) FROM w WHERE c5_number >= 10;
+SELECT abs ( ( SELECT c6_number FROM w WHERE c2_first = 'qatar' ) - ( SELECT c6_number FROM w WHERE c2_first = 'indonesia' ) );
+SELECT COUNT( c2 ) FROM w WHERE c3_number >= 10;
+SELECT c2 FROM w WHERE c2_first != 'north korea' AND c4_number = ( SELECT c4_number FROM w WHERE c2_first = 'north korea' );
+SELECT c6 FROM w WHERE c2_first = 'iran';
+SELECT 12 - ( SELECT c3_number FROM w WHERE c2_first = 'qatar' );
+SELECT c2 FROM w WHERE c2_first IN ( 'kuwait' , 'india' ) order BY c3_number desc limit 1;
+SELECT c6 FROM w WHERE c2_first = 'india';
+SELECT c5 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c5 = 'sukhrob nematov' ) order BY c1_number asc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c5 = 'vokhid shodiev';
+SELECT c1 FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE c1_number IN ( 2008 , 2004 ) order BY c3_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'alisher kholiqov';
+SELECT COUNT( * ) FROM w WHERE c4 = '1/4';
+SELECT c1 FROM w WHERE c3_number = 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'vokhid shodiev';
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c3 = '3 acts';
+SELECT c6_address FROM w GROUP BY c6_address order BY COUNT( c1 ) desc limit 1;
+SELECT MAX( c5_list_year ) FROM w;
+SELECT COUNT( DISTINCT c1 ) FROM w WHERE c5_list_month = 9;
+SELECT MAX( c5_list_year ) FROM w;
+SELECT c5_list_year FROM w order BY id desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 = '1 act';
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'never used';
+SELECT c1 FROM w WHERE c2 = '5 february 2014';
+SELECT COUNT( c1 ) FROM w WHERE c2_year = 1997;
+SELECT c1 FROM w WHERE c2 = '5 december 2005' AND c5 = 'lethal injection';
+SELECT c1 FROM w WHERE c2_parsed < ( SELECT c2_parsed FROM w WHERE c1 = 'arizona' ) order BY c2_parsed desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'delaware' , 'mississippi' ) order BY c2_parsed desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'never used';
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'never used';
+SELECT c5 FROM w WHERE c1 = 'alabama';
+SELECT COUNT( c1 ) FROM w WHERE c2_year = 2014;
+SELECT COUNT( c3 ) FROM w WHERE c6_number IS NULL;
+SELECT c5_number FROM w WHERE c3 = 'neel jani';
+SELECT c4 FROM w WHERE c3 = 'sebastien bourdais';
+SELECT c4 FROM w order BY c1_number limit 1;
+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'will power' ) + 1;
+SELECT c6 FROM w WHERE c3 = 'robert doornbos';
+SELECT c5_number FROM w WHERE c3 = 'paul tracy';
+SELECT c3 FROM w WHERE c3 IN ( 'mario dominguez' , 'dan clarke' ) AND c6 = '+ 3 laps';
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'tristan gommendy' ) - 1;
+SELECT c4 FROM w WHERE id = 1;
+SELECT c3 FROM w order BY c8_number limit 1;
+SELECT c5 FROM w WHERE c1 = 'half-bridge';
+SELECT ( SELECT c3_number FROM w WHERE c1 = 'flyback' ) > 1.5;
+SELECT c4_maximum FROM w WHERE c1 = 'flyback';
+SELECT c1 FROM w WHERE c4_first_number = 5 AND c4_second = '1 as sub';
+SELECT c5 FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'andy egli' ) + 1;
+SELECT AVG( c3_second_number ) FROM w WHERE c5 = 'grasshoppers';
+SELECT COUNT( c1 ) FROM w WHERE c5 = 'fc st. gallen';
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT c4_first_number FROM w WHERE c1 = 'andy egli';
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w order BY c3_second_number desc limit 1;
+SELECT c2 FROM w WHERE c1 = 2008 order BY id desc limit 1;
+SELECT SUM( c9_number ) FROM w WHERE c1 = 2008;
+SELECT c1 FROM w WHERE c1 IN ( 2009 , 2006 ) order BY c4_number desc limit 1;
+SELECT SUM( c4 ) FROM w WHERE c1 = 2003;
+SELECT c3 FROM w GROUP BY c3 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c4_number = 1 order BY c1_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_number = 1;
+SELECT COUNT( * ) FROM w WHERE c3_address = 'united states';
+SELECT c2 FROM w WHERE c3_address = 'pr china';
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'olympic games';
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+SELECT c1 FROM w WHERE c1 != 'lake ercek' AND c5_list = 'van';
+SELECT c4 FROM w order BY c4_list_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'lake bafa' , 'lake yay' ) order BY c3_number desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c3_number > 300;
+SELECT c5 FROM w order BY c3_number desc limit 1;
+SELECT c1 , c5 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3_number < 10;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c5 = 'denizli, afyonkarahisar' ) + 1;
+SELECT COUNT( * ) FROM w WHERE c3_number > 100;
+SELECT COUNT( * ) FROM w WHERE c5_list = 'van';
+SELECT c1 FROM w order BY c4_number desc limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'trainer 1' ) + 1;
+SELECT c1 FROM w order BY c7_number desc limit 1;
+SELECT c6 FROM w WHERE c1 = 'laxeralp 2';
+SELECT c1 FROM w WHERE c2 = 'cable car' order BY c7_number asc limit 1;
+SELECT c2 FROM w WHERE c1 = 'trainer 1';
+SELECT c1 FROM w WHERE c2 = 'cable car' AND c5_number = 2937;
+SELECT MAX( abs ( c4_number1 - c4_number2 ) ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c6_number <= 10000;
+SELECT COUNT( * ) FROM w WHERE c5 = 'war memorial stadium';
+SELECT c3_raw FROM w WHERE c1_number = 1;
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'war memorial stadium';
+SELECT COUNT( * ) FROM w WHERE c2_month = 11;
+SELECT c3 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c3 = 'oakland raiders' ) order BY c1_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c6_number >= 10000;
+SELECT c3_raw FROM w WHERE c4_result = 't';
+SELECT c2 FROM w WHERE c5 = 'war memorial stadium' order BY c2_parsed asc limit 1;
+SELECT COUNT( * ) FROM ( SELECT c2 FROM w GROUP BY c2 HAVING COUNT( c1 ) < ( SELECT COUNT( c1 ) FROM w WHERE c2 = 'bill manlove' ) );
+SELECT ( SELECT COUNT( c1 ) FROM w WHERE c2 = 'marty brill' ) - ( SELECT COUNT( c1 ) FROM w WHERE c2 = 'tom conley' );
+SELECT ( SELECT c3_number1 FROM w WHERE c1_number = 1933 ) > ( SELECT c3_number1 FROM w WHERE c1_number = 1933 - 1 );
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'marty brill' order BY c1_number desc limit 1 ) + 1;
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = 'archie stalcup' order BY c1_number desc limit 1 ) order BY c1_number limit 1;
+SELECT c3_number FROM w WHERE c2 = 'jamaica';
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c6_number = 1;
+SELECT COUNT( c2 ) FROM w WHERE c4_number > 1;
+SELECT c2 FROM w WHERE c3_number < ( SELECT c3_number FROM w WHERE c2 = 'united states' ) order BY c3_number desc limit 1;
+SELECT AVG( c3_number ) FROM w WHERE c1_number <= 5;
+SELECT COUNT( c2 ) FROM w WHERE c6_number > ( SELECT c6_number FROM w WHERE c2 = 'canada' );
+SELECT COUNT( c2 ) FROM w WHERE c3_number >= 2;
+SELECT c2 FROM w order BY c4_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3_number = 0;
+SELECT c2 FROM w WHERE c4_number >= 3;
+SELECT COUNT( * ) FROM w WHERE c4 <= 1906;
+SELECT DISTINCT c2 FROM w WHERE c5_number = 44;
+SELECT COUNT( * ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c2 = 'american car company';
+SELECT c1 FROM w order BY c3_parsed limit 1;
+SELECT c1 FROM w WHERE c3_year = 1999 AND c2 = 'boston red sox';
+SELECT c2 FROM w WHERE c5 = 'miller park' UNION SELECT c4 FROM w WHERE c5 = 'miller park';
+SELECT c3 FROM w WHERE c2 = 'detroit tigers' AND c4 = 'cleveland indians';
+SELECT c1 FROM w order BY c3_parsed desc limit 1;
+SELECT c1 FROM w WHERE c3_year = 1936;
+SELECT c4 FROM w WHERE c2 = 'boston red sox' AND c3 = 'july 27, 1946';
+SELECT COUNT( * ) FROM w WHERE c2 = 'boston red sox';
+SELECT COUNT( * ) FROM w;
+SELECT c2 FROM w order BY c1_first_minimum_number asc limit 1;
+SELECT c4 FROM w WHERE c1 > 1967 order BY c1 asc limit 1;
+SELECT c2 FROM w order BY c1_first_minimum_number asc limit 1;
+SELECT MAX( c1_first_maximum_number ) FROM w;
+SELECT c2 FROM w order BY c1_first_maximum_number desc limit 1;
+SELECT c2 FROM w GROUP BY c2 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c5_maximum_number = 1989;
+SELECT c1 FROM w order BY c5_maximum_number - c5_minimum_number asc limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'liberal';
+SELECT c2 FROM w WHERE c1 = 'phil lockyer';
+SELECT COUNT( c1 ) FROM w WHERE c3 = 'lower west';
+SELECT c2 FROM w WHERE c2 IN ( 'soviet union' , 'portugal' ) order BY c4_number1 desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'west germany';
+SELECT c1 FROM w WHERE id = 1;
+SELECT ( SELECT c4_number1 + c4_number2 FROM w WHERE c1 = 'june 14, 1982' ) > 6;
+SELECT COUNT( * ) FROM w;
+SELECT c2 FROM w WHERE c1_parsed > ( SELECT c1_parsed FROM w WHERE c2 = 'soviet union' ) order BY c1_parsed limit 1;
+SELECT COUNT( * ) FROM w WHERE c5_list_first = 'zico';
+SELECT c1 FROM w order BY c6_number desc limit 1;
+SELECT c2_number FROM w WHERE c1 = 'mexico';
+SELECT c8 FROM w order BY c8_number desc limit 1;
+SELECT c1 FROM w order BY c5_number limit 1;
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c4_number FROM w WHERE c1 = 'brazil';
+SELECT c1 FROM w order BY c8_number asc limit 1;
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c4 FROM w WHERE c1 = 'brazil';
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT c1 FROM w order BY c8_number desc limit 1;
+SELECT c1 FROM w WHERE c1 != 'clarke university' AND c7_number = ( SELECT c7_number FROM w WHERE c1 = 'clarke university' );
+SELECT c1 FROM w order BY c6_number asc limit 1;
+SELECT c1 FROM w WHERE c6_number > 2000;
+SELECT c3 FROM w WHERE c3 IN ( 'v-hawks' , 'mustangs' ) order BY c6_number asc limit 1;
+SELECT c1 FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE c1 IN ( 'viterbo university' , 'william penn university' ) order BY c6_number desc limit 1;
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = 'tainan' ) order BY c1_number asc limit 1;
+SELECT c3 FROM w WHERE c1_number > 2006 order BY c1_number asc limit 1;
+SELECT c4_first FROM w WHERE id = ( SELECT id FROM w WHERE c4_first = 'mary zorn' ) - 1;
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( DISTINCT c2 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c2 = 'madrid';
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c4_first = 'amandine bouillot' ) + 1;
+SELECT c6 FROM w WHERE c6 NOT NULL order BY c1_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'united states';
+SELECT c4 FROM w WHERE c2 = 'algeria';
+SELECT c1 FROM w WHERE c1 != 'asia' GROUP BY c1 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w order BY id desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c1 = 'asia';
+SELECT c1 FROM w GROUP BY c1 order BY COUNT( * ) asc limit 1;
+SELECT c3 FROM w WHERE c3 IN ( 'university of witwatersrand' , 'iit guwahati' ) order BY c7_number desc limit 1;
+SELECT DISTINCT c4 FROM w;
+SELECT c7_first_number FROM w WHERE c1 = 'russian-lipovan';
+SELECT c1 FROM w order BY c9_first_number desc limit 1;
+SELECT c2 FROM w WHERE c4_first_number = 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c2 = 'olympic games';
+SELECT COUNT( c2 ) FROM w;
+SELECT COUNT( * ) FROM w WHERE c6_number > 20;
+SELECT COUNT( * ) FROM w WHERE c2 = 'olympic games';
+SELECT c6 FROM w order BY c6_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c4_first_number = 1;
+SELECT c2 FROM w WHERE c4_first_number = 1;
+SELECT c2 FROM w order BY c4_first_number limit 1;
+SELECT c5 FROM w WHERE c4_first_number = 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w WHERE c6_number1 = 4;
+SELECT c2 FROM w WHERE id = 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'shakhtar donetsk';
+SELECT c4 FROM w WHERE id = 1;
+SELECT c6 FROM w WHERE c1_number = 1;
+SELECT c2 FROM w WHERE c2_parsed > ( SELECT c2_parsed FROM w WHERE c2 = '20 february 2008' );
+SELECT COUNT( * ) FROM w WHERE c6_number1 = 0 AND c6_number2 = 0;
+SELECT c5 FROM w order BY c2_parsed asc limit 1;
+SELECT c6 FROM w WHERE c2_month = 10;
+SELECT COUNT( c3 ) FROM w WHERE c3_year = 1972;
+SELECT COUNT( c4 ) FROM w WHERE c6 = 'bird sanctuary';
+SELECT COUNT( c2 ) FROM w WHERE c5 = 'maui';
+SELECT COUNT( * ) FROM w WHERE c6 = 'bird sanctuary';
+SELECT COUNT( c2 ) FROM w WHERE c6 IS NULL;
+SELECT c2 FROM w WHERE c2 != 'mauna kea' AND c5 = 'hawaiʻi';
+SELECT COUNT( * ) FROM w WHERE c6 = 'state monument';
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c4 IS NULL;
+SELECT c6 FROM w WHERE c2 = 'kanaha pond';
+SELECT c2 FROM w WHERE c6 = 'u.s. state high point';
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'morwell';
+SELECT ( SELECT c7_number FROM w WHERE c1 = 'churchill united' ) > ( SELECT c7_number FROM w WHERE c1 = 'falcons 2000' );
+SELECT COUNT( c1 ) FROM w WHERE c5_list = 'red';
+SELECT c7 FROM w WHERE c1 = 'fortuna 60';
+SELECT c1 FROM w WHERE c4 IS NULL;
+SELECT COUNT( c1 ) FROM w WHERE c2 = 'morwell';
+SELECT COUNT( * ) FROM w WHERE c3_list = 'mocho cota' AND c2_list = 'chamaco valaguez';
+SELECT COUNT( * ) FROM w WHERE c4 = 'mexico city, mexico';
+SELECT c2 FROM w WHERE c3_list = 'mocho cota' AND c4_address = 'mexico city' order BY c5_parsed asc limit 1;
+SELECT c2 FROM w WHERE c3_list = 'mocho cota' AND c5_year = 1994;
+SELECT COUNT( * ) FROM w WHERE c2_list = 'mocho cota' AND c5_year > 1983;
+SELECT c3 FROM w WHERE id = 1;
+SELECT COUNT( * ) FROM w WHERE c4_address = 'mexico city' AND c5_year IN ( 1983 , 1984 );
+SELECT c2 FROM w WHERE c4_address = 'cuernavaca' AND c2_list != 'mocho cota';
+SELECT c4_number - c6_number FROM w WHERE c1 = '2 april 2005';
+SELECT c1 FROM w WHERE c1 IN ( '15 february 2005' , '19 february 2005' ) order BY c4_number desc limit 1;
+SELECT MIN( c4_number ) FROM w;
+SELECT ( SELECT SUM( c4_number ) FROM w WHERE c3 = 'bedford' ) + ( SELECT SUM( c6_number ) FROM w WHERE c5 = 'bedford' );
+SELECT ( SELECT c4_number FROM w WHERE c1 = '2 april 2005' ) - ( SELECT c4_number FROM w WHERE c1 = '12 march 2005' );
+SELECT c4_number FROM w WHERE c3 = 'bedford' AND c4_number NOT NULL;
+SELECT ( SELECT COUNT( c2 ) FROM w WHERE c3 = 'bedford' AND c4_number < c6_number ) + ( SELECT COUNT( c2 ) FROM w WHERE c5 = 'bedford' AND c6_number < c4_number );
+SELECT COUNT( c2 ) FROM w;
+SELECT c1 FROM w WHERE c4_number IS NULL;
+SELECT c2 FROM w order BY c1_parsed limit 1;
+SELECT c2_raw FROM w WHERE c4_address = 'louisiana superdome • new orleans';
+SELECT COUNT( * ) FROM w WHERE c5_result = 'w';
+SELECT MAX( c5_number1 ) FROM w;
+SELECT c2 FROM w order BY c5_number2 limit 1;
+SELECT c2 FROM w WHERE c1 = 'october 24';
+SELECT c1 FROM w WHERE c5_number1 = 16 AND c5_number2 = 16;
+SELECT COUNT( * ) FROM w WHERE c1_month = 9;
+SELECT COUNT( c9 ) FROM w;
+SELECT COUNT( c1 ) FROM w WHERE c11 IS NULL;
+SELECT c2 FROM w WHERE c2 IN ( 'myst' , 'sharp shooters' ) order BY c1_number limit 1;
+SELECT ( SELECT c4 FROM w WHERE c1_number = 1991 ) IS NULL;
+SELECT c2 FROM w WHERE c1_number = 2014;
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = 'trumpet' ) order BY c1_number limit 1;
+SELECT c2 FROM w order BY c1_number limit 1;
+SELECT c2 FROM w WHERE c1_number > 1994 order BY c1_number limit 1;
+SELECT c1_number FROM w WHERE c1_number IN ( 2009 , 2010 ) AND c3 = 'blox';
+SELECT ( SELECT c4 FROM w WHERE c2_first = 'erik zabel' ) - ( SELECT c4 FROM w WHERE c2_first = 'francisco ventoso' );
+SELECT ( SELECT c4 FROM w WHERE c2_first = 'robbie mcewen' ) + ( SELECT c4 FROM w WHERE c2_first = 'cristian moreni' );
+SELECT COUNT( * ) FROM w WHERE c4_year < 1869;
+SELECT c2 FROM w WHERE c3_number <= 30000;
+SELECT ( SELECT c3_number FROM w WHERE c2 = 'netherlands - amsterdam' ) - ( SELECT c3_number FROM w WHERE c2 = 'spain - palma de mallorca' );
+SELECT SUM( c3_number ) FROM w WHERE c2_list = 'spain';
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w order BY c3_number desc limit 1;
+SELECT c2 FROM w order BY c3_number limit 1;
+SELECT AVG( c3_number ) FROM w;
+SELECT COUNT( c2 ) FROM w WHERE c2_list = 'spain';
+SELECT c2 FROM w WHERE c3_number > ( SELECT c3_number FROM w WHERE c2_list = 'united kingdom' );
+SELECT SUM( c3_number ) FROM w WHERE c2_list = 'spain';
+SELECT c2 FROM w WHERE c2_list = 'portugal';
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'caliber comics';
+SELECT c2 FROM w order BY c6_first_number desc limit 1;
+SELECT c2 FROM w WHERE c1_number = 2008 AND c3 = 'dc comics' AND c6_first_number > 10000000 AND c5 = 'lionsgate';
+SELECT c2 FROM w WHERE c1_number = 2012 AND c6_first_number < 100000000;
+SELECT c2 FROM w WHERE c1_number < 1966;
+SELECT c2 FROM w WHERE c6_first_number > 300000000 order BY c1_number limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 1978;
+SELECT COUNT( c2 ) FROM w WHERE c6 = 'television film';
+SELECT c2 FROM w order BY c6_first_number desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 2013;
+SELECT COUNT( c2 ) FROM w WHERE c6_number = 1;
+SELECT c2 FROM w WHERE c7_number <= 10;
+SELECT c4 FROM w GROUP BY c4 order BY COUNT( c2 ) desc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'jan ingman' ) + 1;
+SELECT c2 FROM w WHERE id = 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'norway';
+SELECT c3 FROM w WHERE c3 != 'sweden';
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'st. louis blues';
+SELECT c2 FROM w WHERE c3 = 'norway' order BY c5_number limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'norway';
+SELECT c4 FROM w WHERE c4 IN ( 'jeff maggert' , 'tiger woods' ) GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT c1_maximum_day - c1_minimum_day FROM w WHERE c2 = 'estoril open';
+SELECT c3 FROM w WHERE c5 = 'new tournament' order BY id limit 1;
+SELECT COUNT( * ) FROM w WHERE c2_number = 2000;
+SELECT c3 FROM w order BY c2_number desc limit 1;
+SELECT c2_number FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'normandy: the great crusade' ) - 1;
+SELECT COUNT( * ) FROM w WHERE c1 = 'short film';
+SELECT c2_number FROM w GROUP BY c2_number order BY COUNT( c5 ) desc limit 1;
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'the ref' ) + 1;
+SELECT COUNT( c2 ) FROM w WHERE c7_number >= 20;
+SELECT COUNT( * ) FROM w WHERE c3 = 'honda';
+SELECT c2 FROM w order BY c7_number desc limit 1;
+SELECT c2 FROM w WHERE c1 <= 15 order BY c7_number asc limit 1;
+SELECT c2 FROM w WHERE c6_number = 6;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'joan olive' ) + 1;
+SELECT COUNT( c2 ) FROM w WHERE c7_number IS NULL;
+SELECT COUNT( c1 ) FROM w WHERE c2_number >= 100000;
+SELECT COUNT( c1 ) FROM w WHERE c2_number < 5000;
+SELECT COUNT( c1 ) FROM w WHERE c3_number > 10;
+SELECT abs ( ( SELECT c2_number FROM w WHERE c1 = 'john mccain' ) - ( SELECT c2_number FROM w WHERE c1 = 'mitt romney' ) );
+SELECT c2_number FROM w WHERE c1 = 'alan keyes';
+SELECT COUNT( c1 ) FROM w WHERE c3_number > 5;
+SELECT c1 FROM w WHERE c1 IN ( 'duncan hunter' , 'alan keyes' ) order BY c2_number desc limit 1;
+SELECT COUNT( c2 ) FROM w;
+SELECT c2 FROM w order BY c3_number asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 NOT NULL AND c4 NOT NULL AND c5 IS NULL;
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 1974;
+SELECT c2 FROM w order BY c4_number asc limit 1;
+SELECT COUNT( c3 ) FROM w WHERE c5_number < 13;
+SELECT COUNT( c3 ) FROM w WHERE c4 = 'nigeria';
+SELECT c3 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c3 = 'christy akinremi' ) - 1;
+SELECT c3 FROM w WHERE c1_number = 1;
+SELECT c3 FROM w WHERE c1_number = 1 AND c2_number = 1;
+SELECT COUNT( c3 ) FROM w WHERE c4 = 'jamaica';
+SELECT COUNT( c3 ) FROM w WHERE c4 = 'england' AND c1_number <= 14;
+SELECT abs ( ( SELECT c5_number FROM w WHERE c3 = 'rachel king' ) - ( SELECT c5_number FROM w WHERE c3 = 'sriyani kulawansa' ) );
+SELECT COUNT( c3 ) FROM w WHERE c5_number = 13.29;
+SELECT COUNT( * ) FROM w WHERE c4 = 'eliminated week 1';
+SELECT COUNT( * ) FROM w WHERE c4 = 'eliminated week 1';
+SELECT COUNT( * ) FROM w WHERE c4 = 'eliminated week 1';
+SELECT AVG( c5_number ) FROM w WHERE id <= 3;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c6_number >= 10;
+SELECT ( SELECT c6_number FROM w WHERE c2 = 'egypt' ) - ( SELECT c6_number FROM w WHERE c2 = 'ivory coast' );
+SELECT c5_number FROM w WHERE c2 = 'togo';
+SELECT c6_number FROM w WHERE c2 = 'switzerland';
+SELECT COUNT( c1 ) FROM w WHERE c4_number >= 1;
+SELECT ( SELECT c4_number FROM w WHERE c2 = 'france' ) - ( SELECT c4_number FROM w WHERE c2 = 'egypt' );
+SELECT c2 FROM w order BY c6_number desc limit 1;
+SELECT c5 FROM w order BY c1_number desc limit 1;
+SELECT c2 FROM w order BY c4_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c3 NOT NULL;
+SELECT COUNT( c2 ) FROM w WHERE c1_number = 2010;
+SELECT c2 FROM w WHERE c1_number > 2011;
+SELECT c2 FROM w order BY c4_number asc limit 1;
+SELECT c2 FROM w order BY c1_number asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c3 NOT NULL;
+SELECT COUNT( c2 ) FROM w WHERE c1 <= 2009;
+SELECT COUNT( * ) FROM w WHERE c6 = 'jim mcmanus';
+SELECT ( SELECT COUNT( * ) FROM w WHERE c1 = 'winner' AND c5 = 'grass' ) >= 1;
+SELECT c3_number FROM w WHERE c3_number IN ( 1971 , 1972 ) AND c1 = 'winner' GROUP BY c3_number order BY COUNT( * ) desc limit 1;
+SELECT c8 FROM w WHERE c3_number = 1969;
+SELECT c4 FROM w order BY c3_number limit 1;
+SELECT c4 FROM w WHERE c3_number < 1970 order BY c3_number desc limit 1;
+SELECT COUNT( c4 ) FROM w WHERE c6 = 'jim mcmanus';
+SELECT COUNT( c4 ) FROM w;
+SELECT c6 FROM w GROUP BY c6 order BY COUNT( * ) desc limit 1;
+SELECT c1_number FROM w WHERE c13 = 'hallelujah - live volume 2';
+SELECT c3_raw FROM w order BY c1_parsed desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c6_result = 'w';
+SELECT COUNT( c1 ) FROM w WHERE c7_number > 60000;
+SELECT c1 FROM w order BY c7_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c7_number > 50000;
+SELECT c3_raw FROM w WHERE c1_parsed > ( SELECT c1_parsed FROM w WHERE c3_raw = 'jackson state' ) order BY c1_parsed limit 1;
+SELECT c1 FROM w WHERE c7_number = 31840;
+SELECT c7_number FROM w WHERE c1 = 'september 5';
+SELECT COUNT( * ) FROM w WHERE c2_list = 'hit';
+SELECT c1 FROM w WHERE c6_number > ( SELECT c6_number FROM w WHERE c1_first = 'terminal 6' );
+SELECT c2 FROM w WHERE id = 1;
+SELECT COUNT( c1 ) FROM w WHERE c6_number >= 8;
+SELECT c3 FROM w order BY c1_number desc limit 1;
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( c1 ) desc limit 1;
+SELECT COUNT( c1 ) FROM w;
+SELECT COUNT( c3 ) FROM w WHERE c4 != 'vitas';
+SELECT c3 FROM w WHERE c1_number = 1;
+SELECT c3 FROM w WHERE c5_list = 'v. shumsky';
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( c2 ) desc limit 1;
+SELECT c2 FROM w order BY c4_number limit 1;
+SELECT COUNT( * ) FROM w WHERE c1 = 't4';
+SELECT COUNT( DISTINCT c3 ) FROM w;
+SELECT c4_result FROM w WHERE c3 = 'united states' INTERSECT SELECT c4_result FROM w WHERE c3 = 'denmark';
+SELECT c2 FROM w WHERE c2 != 'david duval' AND c1 = 't2';
+SELECT COUNT( c2 ) FROM w WHERE id > ( SELECT id FROM w WHERE c2 = 'tiger woods' );
+SELECT COUNT( c2 ) FROM w WHERE c1_number < ( SELECT c1_number FROM w WHERE c2 = 'darren clarke' );
+SELECT c4_result FROM w WHERE c2 = 'ernie els';
+SELECT COUNT( * ) FROM w WHERE c2 = 'tiger woods';
+SELECT c4_number3 FROM w WHERE c2 = 'tiger woods';
+SELECT c2 FROM w WHERE c1 = 'four corners';
+SELECT c2 FROM w WHERE c1 = 'four corners';
+SELECT c1 FROM w WHERE c1 IN ( 'four corners' , 'west milby' ) order BY c3_number desc limit 1;
+SELECT c1 FROM w WHERE c2 IS NULL;
+SELECT COUNT( c1 ) FROM w;
+SELECT c3_number FROM w WHERE c1 = 'four corners';
+SELECT COUNT( DISTINCT c1 ) FROM w WHERE c3_parsed < ( SELECT c3_parsed FROM w WHERE c1 = 'horshu' );
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1 = 'keith walker' ) - 1;
+SELECT COUNT( * ) FROM w WHERE c4_address = 'japan';
+SELECT c1 FROM w WHERE c4_address = 'texas';
+SELECT c1 FROM w order BY c3_parsed desc limit 1;
+SELECT c3 FROM w WHERE c1 = 'steve corino' order BY c3_parsed limit 1;
+SELECT c2_number FROM w WHERE c1 = 'ricky landell';
+SELECT ( SELECT COUNT( * ) FROM w WHERE c2 = 'chipper adams' AND c3 = 'justin beyendeza' ) > 3;
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = 'ponsiano lwakataka' ) order BY c1_number asc limit 1;
+SELECT c1 FROM w WHERE c2 IS NULL;
+SELECT COUNT( * ) FROM w WHERE c2 = 'chipper adams' AND c3 = 'justin beyendeza';
+SELECT COUNT( * ) FROM w WHERE c4 = 'toyota supra';
+SELECT COUNT( * ) FROM w WHERE c2 = 'charlie lubega';
+SELECT c6 FROM w WHERE c3 = 'michael schumacher';
+SELECT c1 FROM w WHERE c3 = 'operational until august 2026';
+SELECT c2 FROM w WHERE c3 = 'el salvador';
+SELECT COUNT( c2 ) FROM w;
+SELECT c4 FROM w order BY c1_number desc limit 1;
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'diego cuenca' ) + 1;
+SELECT c2 FROM w WHERE c2 IN ( 'diego cuenca' , 'eduardo piccinini' ) order BY c4_number asc limit 1;
+SELECT c2 FROM w WHERE c4_number <= 54.20;
+SELECT c2 FROM w WHERE c1_number = 8;
+SELECT COUNT( c2 ) FROM w WHERE c4_number < 55;
+SELECT abs ( ( SELECT c4_number FROM w WHERE c2 = 'eduardo piccinini' ) - ( SELECT c4_number FROM w WHERE c2 = 'diego cuenca' ) );
+SELECT COUNT( c2 ) FROM w WHERE c3 = 'united states';
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = 'eduardo piccinini' ) - 1;
+SELECT COUNT( * ) FROM w;
+SELECT c1 FROM w WHERE c5 = 'won' GROUP BY c1 order BY COUNT( * ) desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'won';
+SELECT c3 FROM w WHERE id = 1;
+SELECT c5 FROM w GROUP BY c5 order BY COUNT( * ) desc limit 1;
+SELECT SUM( c6_number ) FROM w;
+SELECT c1 FROM w order BY c2_number asc limit 1;
+SELECT c1 FROM w WHERE c2_number = 5;
+SELECT c1 FROM w WHERE c3_number = ( SELECT MAX( c3_number ) FROM w );
+SELECT c1 FROM w order BY c2_number asc limit 1;
+SELECT abs ( ( SELECT c2_number FROM w WHERE c1 = 'beni' ) - ( SELECT c6_number FROM w WHERE c1 = 'beni' ) );
+SELECT ( SELECT c3_number FROM w WHERE c1 = 'oruro' ) > ( SELECT c3_number FROM w WHERE c1 = 'la paz' );
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT c1 FROM w WHERE c1 IN ( 'cochabamba' , 'chuquisaca' ) order BY c5_number desc limit 1;
+SELECT abs ( ( SELECT c3_number FROM w WHERE c1 = 'potosi' ) - ( SELECT c3_number FROM w WHERE c1 = 'beni' ) );
+SELECT c1 FROM w order BY c2_number desc limit 1;
+SELECT MIN( c2_number ) FROM w;
+SELECT c1 FROM w WHERE c5 = 'miami heat';
+SELECT SUM( c1 ) FROM w WHERE c6_year = 1978;
+SELECT SUM( c13 ) FROM w WHERE c4_first = 'moses malone';
+SELECT c6_year FROM w WHERE c6_year IN ( 1978 , 1979 ) GROUP BY c6_year order BY COUNT( * ) desc limit 1;
+SELECT c4 FROM w WHERE c5 = 'chicago bulls';
+SELECT COUNT( c4 ) FROM w WHERE c1_number = 30;
+SELECT ( SELECT c9_number FROM w WHERE c4_first = 'charles oakley' ) > ( SELECT c9_number FROM w WHERE c4_first = 'robert parish' );
+SELECT MAX( c5_number1 ) FROM w;
+SELECT c4 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = '21 december 2004' AND c4 = 'thailand' order BY id desc limit 1 ) + 1;
+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c4 = 'liechtenstein';
+SELECT c3 FROM w WHERE c7 = 'friendly' order BY c1_number asc limit 1;
+SELECT SUM( c5_number1 ) FROM w WHERE c2_year = 2007 AND c4 = 'romania';
+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c7 != 'friendly';
+SELECT COUNT( DISTINCT c2 ) FROM w WHERE c7 != 'friendly';
+SELECT c6 FROM w GROUP BY c6 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c4_number = 23;
+SELECT MAX( c1_number ) - MIN( c1_number ) FROM w;
+SELECT c2 FROM w WHERE c1_number > ( SELECT c1_number FROM w WHERE c2 = ''song i hate'' ) order BY c1_number asc limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c6 != 'non-album single';
+SELECT c2 FROM w WHERE c1_number = 1995 order BY c4_number asc limit 1;
+SELECT c2 FROM w order BY c1_number asc limit 1;
+SELECT c6 FROM w WHERE c2 = ''mister love'';
+SELECT c2 FROM w order BY c1_number asc limit 1;
+SELECT c2 FROM w WHERE id = ( SELECT id FROM w WHERE c2 = ''backslider'' ) - 1;
+SELECT COUNT( c4 ) FROM w;
+SELECT c4 FROM w WHERE id = 1;
+SELECT c1 FROM w WHERE c2_number < 10 order BY c1_number asc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2_number < 10;
+SELECT AVG( c3_number ) FROM w WHERE c4 = 'brussels';
+SELECT c1 FROM w WHERE c2_number = 9.72;
+SELECT c4 FROM w WHERE c4 != 'szczecin' AND c2_number = ( SELECT c2_number FROM w WHERE c4 = 'szczecin' );
+SELECT c4 FROM w order BY c2_number asc limit 1;
+SELECT c1 FROM w WHERE id = ( SELECT id FROM w WHERE c1_number = 2001 ) - 1;
+SELECT c1 FROM w WHERE c2_number > 10 order BY c1_number desc limit 1;
+SELECT COUNT( * ) FROM w WHERE c2 = 'chelsea wlliams';
+SELECT c3 FROM w WHERE c1 = '2008 telstra men's pro';
+SELECT COUNT( * ) FROM w WHERE c3 = 'josh constable';
+SELECT c1 FROM w WHERE c2 = 'chelsea wlliams' order BY c1 limit 1;
+SELECT c1 FROM w WHERE c2 = 'chelsea wlliams';
+SELECT c2 FROM w WHERE c1 = '2008 telstra men's pro';
+SELECT c1 FROM w WHERE c2 = 'taylor jensen' order BY c1 limit 1;
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'highland, new york';
+SELECT ( SELECT c2 FROM w WHERE c1_number = 2 ) != ( SELECT c2 FROM w WHERE c1_number = 4 );
+SELECT c4 FROM w WHERE c2 = 'brant's crossing';
+SELECT COUNT( c2 ) FROM w WHERE c4 = 'highland, new york';
+SELECT c2 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c2 = 'clark tannery' ) + 1;
+SELECT c3 FROM w WHERE c2 = 'early sawmill';
+SELECT COUNT( c2 ) FROM w WHERE c4 IN ( 'callicoon, new york' , 'forestburg, new york' );
+SELECT COUNT( DISTINCT c1 ) FROM w;
+SELECT c1 FROM w WHERE c5 = 'adam';
+SELECT c2 FROM w WHERE c4_length = 2 order BY c1_number desc limit 1;
+SELECT abs ( ( SELECT c4_length FROM w WHERE c1_number = 1 ) - ( SELECT c4_length FROM w WHERE c1_number = 2 ) );
+SELECT c4 FROM w order BY c1_number desc limit 1;
+SELECT c4 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c4 = 'patrick' ) + 1;
+SELECT c4 FROM w WHERE c1_number = ( SELECT c1_number FROM w WHERE c4 = 'patrick' ) - 1;
+SELECT COUNT( c1 ) FROM w WHERE c4 = 'none';
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'chicago rush' ) + 1;
+SELECT COUNT( * ) FROM w WHERE c4 = 'home';
+SELECT c3 FROM w WHERE id = ( SELECT id FROM w WHERE c3 = 'chicago rush' ) - 1;
+SELECT COUNT( * ) FROM w WHERE id < ( SELECT id FROM w WHERE c5_result = 'w' );
+SELECT COUNT( * ) FROM w WHERE c5_result = 'l' AND c2_month = 4;
+SELECT c4 FROM w WHERE c5_result = 'w' GROUP BY c4 order BY COUNT( * ) desc limit 1;
+SELECT c2 FROM w WHERE c4 = 'away' order BY c2_parsed limit 1;
+SELECT COUNT( c1 ) FROM w WHERE c4_number > 32;
+SELECT c4 FROM w WHERE c4 IN ( 'laron landry' , 'ted ginn jr' ) order BY c2_number asc limit 1;
+SELECT COUNT( c4 ) FROM w WHERE c5 = 'qb';
+SELECT c4 FROM w WHERE c2_number = ( SELECT c2_number FROM w WHERE c4 = 'jamarcus russell' ) + 1;
+SELECT COUNT( * ) FROM w WHERE c5 = 'qb' AND c1_number = 1;
+SELECT c6 FROM w WHERE c4 = 'calvin johnson';
+SELECT COUNT( c4 ) FROM w WHERE c6 = 'wisconsin' AND c1_number = 1;
+SELECT c4 FROM w WHERE c2_number = 1;
+SELECT c4 FROM w WHERE c1_number = 2002;
+SELECT ( SELECT COUNT( * ) FROM w WHERE c3 = 'chicago' ) > ( SELECT COUNT( * ) FROM w WHERE c3 = 'cal-berkeley' );
+SELECT c6 FROM w WHERE c6 != 'valencia cc' GROUP BY c6 order BY COUNT( * ) desc limit 1;
+SELECT c6 FROM w GROUP BY c6 order BY COUNT( * ) desc limit 1;
+SELECT c1 FROM w WHERE id = 1;
+SELECT c1 FROM w order BY id desc limit 1;
+SELECT c1 FROM w WHERE c2 = 'minnesota' order BY c1_number desc limit 1;
\ No newline at end of file
diff --git a/parsing/spider_queries.txt b/parsing/spider_queries.txt
new file mode 100644
index 00000000..0cb944ef
--- /dev/null
+++ b/parsing/spider_queries.txt
@@ -0,0 +1,7000 @@
+SELECT count(*) FROM head WHERE age  >  56;
+SELECT name ,  born_state ,  age FROM head ORDER BY age;
+SELECT creation ,  name ,  budget_in_billions FROM department;
+SELECT max(budget_in_billions) ,  min(budget_in_billions) FROM department;
+SELECT avg(num_employees) FROM department WHERE ranking BETWEEN 10 AND 15;
+SELECT name FROM head WHERE born_state != 'California';
+SELECT DISTINCT T1.creation FROM department AS T1 JOIN management AS T2 ON T1.department_id  =  T2.department_id JOIN head AS T3 ON T2.head_id  =  T3.head_id WHERE T3.born_state  =  'Alabama';
+SELECT born_state FROM head GROUP BY born_state HAVING count(*)  >=  3;
+SELECT creation FROM department GROUP BY creation ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name ,  T1.num_employees FROM department AS T1 JOIN management AS T2 ON T1.department_id  =  T2.department_id WHERE T2.temporary_acting  =  'Yes';
+SELECT count(DISTINCT temporary_acting) FROM management;
+SELECT count(*) FROM department WHERE department_id NOT IN (SELECT department_id FROM management);
+SELECT DISTINCT T1.age FROM management AS T2 JOIN head AS T1 ON T1.head_id  =  T2.head_id WHERE T2.temporary_acting  =  'Yes';
+SELECT T3.born_state FROM department AS T1 JOIN management AS T2 ON T1.department_id  =  T2.department_id JOIN head AS T3 ON T2.head_id  =  T3.head_id WHERE T1.name  =  'Treasury' INTERSECT SELECT T3.born_state FROM department AS T1 JOIN management AS T2 ON T1.department_id  =  T2.department_id JOIN head AS T3 ON T2.head_id  =  T3.head_id WHERE T1.name  =  'Homeland Security';
+SELECT T1.department_id ,  T1.name ,  count(*) FROM management AS T2 JOIN department AS T1 ON T1.department_id  =  T2.department_id GROUP BY T1.department_id HAVING count(*)  >  1;
+SELECT head_id ,  name FROM head WHERE name LIKE '%Ha%';
+SELECT count(*) FROM farm;
+SELECT count(*) FROM farm;
+SELECT Total_Horses FROM farm ORDER BY Total_Horses ASC;
+SELECT Total_Horses FROM farm ORDER BY Total_Horses ASC;
+SELECT Hosts FROM farm_competition WHERE Theme !=  'Aliens';
+SELECT Hosts FROM farm_competition WHERE Theme !=  'Aliens';
+SELECT Theme FROM farm_competition ORDER BY YEAR ASC;
+SELECT Theme FROM farm_competition ORDER BY YEAR ASC;
+SELECT avg(Working_Horses) FROM farm WHERE Total_Horses  >  5000;
+SELECT avg(Working_Horses) FROM farm WHERE Total_Horses  >  5000;
+SELECT max(Cows) ,  min(Cows) FROM farm;
+SELECT max(Cows) ,  min(Cows) FROM farm;
+SELECT count(DISTINCT Status) FROM city;
+SELECT count(DISTINCT Status) FROM city;
+SELECT Official_Name FROM city ORDER BY Population DESC;
+SELECT Official_Name FROM city ORDER BY Population DESC;
+SELECT Official_Name ,  Status FROM city ORDER BY Population DESC LIMIT 1;
+SELECT Official_Name ,  Status FROM city ORDER BY Population DESC LIMIT 1;
+SELECT T2.Year ,  T1.Official_Name FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID  =  T2.Host_city_ID;
+SELECT T2.Year ,  T1.Official_Name FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID  =  T2.Host_city_ID;
+SELECT T1.Official_Name FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID  =  T2.Host_city_ID GROUP BY T2.Host_city_ID HAVING COUNT(*)  >  1;
+SELECT T1.Official_Name FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID  =  T2.Host_city_ID GROUP BY T2.Host_city_ID HAVING COUNT(*)  >  1;
+SELECT T1.Status FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID  =  T2.Host_city_ID GROUP BY T2.Host_city_ID ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T1.Status FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID  =  T2.Host_city_ID GROUP BY T2.Host_city_ID ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T2.Theme FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID  =  T2.Host_city_ID WHERE T1.Population  >  1000;
+SELECT T2.Theme FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID  =  T2.Host_city_ID WHERE T1.Population  >  1000;
+SELECT Status ,  avg(Population) FROM city GROUP BY Status;
+SELECT Status ,  avg(Population) FROM city GROUP BY Status;
+SELECT Status FROM city GROUP BY Status ORDER BY COUNT(*) ASC;
+SELECT Status FROM city GROUP BY Status ORDER BY COUNT(*) ASC;
+SELECT Status FROM city GROUP BY Status ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Status FROM city GROUP BY Status ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Official_Name FROM city WHERE City_ID NOT IN (SELECT Host_city_ID FROM farm_competition);
+SELECT Official_Name FROM city WHERE City_ID NOT IN (SELECT Host_city_ID FROM farm_competition);
+SELECT Status FROM city WHERE Population  >  1500 INTERSECT SELECT Status FROM city WHERE Population  <  500;
+SELECT Status FROM city WHERE Population  >  1500 INTERSECT SELECT Status FROM city WHERE Population  <  500;
+SELECT Official_Name FROM city WHERE Population  >  1500 OR Population  <  500;
+SELECT Official_Name FROM city WHERE Population  >  1500 OR Population  <  500;
+SELECT Census_Ranking FROM city WHERE Status !=  'Village';
+SELECT Census_Ranking FROM city WHERE Status !=  'Village';
+SELECT T1.course_name FROM courses AS T1 JOIN student_course_registrations AS T2 ON T1.course_id = T2.course_Id GROUP BY T1.course_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.course_name FROM courses AS T1 JOIN student_course_registrations AS T2 ON T1.course_id = T2.course_Id GROUP BY T1.course_id ORDER BY count(*) DESC LIMIT 1;
+SELECT student_id FROM student_course_registrations GROUP BY student_id ORDER BY count(*) LIMIT 1;
+SELECT student_id FROM student_course_registrations GROUP BY student_id ORDER BY count(*) LIMIT 1;
+SELECT T2.first_name ,  T2.last_name FROM candidates AS T1 JOIN people AS T2 ON T1.candidate_id = T2.person_id;
+SELECT T2.first_name ,  T2.last_name FROM candidates AS T1 JOIN people AS T2 ON T1.candidate_id = T2.person_id;
+SELECT student_id FROM students WHERE student_id NOT IN (SELECT student_id FROM student_course_attendance);
+SELECT student_id FROM students WHERE student_id NOT IN (SELECT student_id FROM student_course_attendance);
+SELECT student_id FROM student_course_attendance;
+SELECT student_id FROM student_course_attendance;
+SELECT T1.student_id ,  T2.course_name FROM student_course_registrations AS T1 JOIN courses AS T2 ON T1.course_id = T2.course_id;
+SELECT T2.student_details FROM student_course_registrations AS T1 JOIN students AS T2 ON T1.student_id = T2.student_id ORDER BY T1.registration_date DESC LIMIT 1;
+SELECT T2.student_details FROM student_course_registrations AS T1 JOIN students AS T2 ON T1.student_id = T2.student_id ORDER BY T1.registration_date DESC LIMIT 1;
+SELECT count(*) FROM courses AS T1 JOIN student_course_attendance AS T2 ON T1.course_id = T2.course_id WHERE T1.course_name = 'English';
+SELECT count(*) FROM courses AS T1 JOIN student_course_attendance AS T2 ON T1.course_id = T2.course_id WHERE T1.course_name = 'English';
+SELECT count(*) FROM courses AS T1 JOIN student_course_attendance AS T2 ON T1.course_id = T2.course_id WHERE T2.student_id = 171;
+SELECT count(*) FROM courses AS T1 JOIN student_course_attendance AS T2 ON T1.course_id = T2.course_id WHERE T2.student_id = 171;
+SELECT T2.candidate_id FROM people AS T1 JOIN candidates AS T2 ON T1.person_id = T2.candidate_id WHERE T1.email_address = 'stanley.monahan@example.org';
+SELECT T2.candidate_id FROM people AS T1 JOIN candidates AS T2 ON T1.person_id = T2.candidate_id WHERE T1.email_address = 'stanley.monahan@example.org';
+SELECT candidate_id FROM candidate_assessments ORDER BY assessment_date DESC LIMIT 1;
+SELECT candidate_id FROM candidate_assessments ORDER BY assessment_date DESC LIMIT 1;
+SELECT T1.student_details FROM students AS T1 JOIN student_course_registrations AS T2 ON T1.student_id = T2.student_id GROUP BY T1.student_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.student_details FROM students AS T1 JOIN student_course_registrations AS T2 ON T1.student_id = T2.student_id GROUP BY T1.student_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.student_id ,  count(*) FROM students AS T1 JOIN student_course_registrations AS T2 ON T1.student_id = T2.student_id GROUP BY T1.student_id;
+SELECT T1.student_id ,  count(*) FROM students AS T1 JOIN student_course_registrations AS T2 ON T1.student_id = T2.student_id GROUP BY T1.student_id;
+SELECT T3.course_name ,  count(*) FROM students AS T1 JOIN student_course_registrations AS T2 ON T1.student_id = T2.student_id JOIN courses AS T3 ON T2.course_id = T3.course_id GROUP BY T2.course_id;
+SELECT T3.course_name ,  count(*) FROM students AS T1 JOIN student_course_registrations AS T2 ON T1.student_id = T2.student_id JOIN courses AS T3 ON T2.course_id = T3.course_id GROUP BY T2.course_id;
+SELECT candidate_id FROM candidate_assessments WHERE asessment_outcome_code = 'Pass';
+SELECT candidate_id FROM candidate_assessments WHERE asessment_outcome_code = 'Pass';
+SELECT T3.cell_mobile_number FROM candidates AS T1 JOIN candidate_assessments AS T2 ON T1.candidate_id = T2.candidate_id JOIN people AS T3 ON T1.candidate_id = T3.person_id WHERE T2.asessment_outcome_code = 'Fail';
+SELECT T3.cell_mobile_number FROM candidates AS T1 JOIN candidate_assessments AS T2 ON T1.candidate_id = T2.candidate_id JOIN people AS T3 ON T1.candidate_id = T3.person_id WHERE T2.asessment_outcome_code = 'Fail';
+SELECT student_id FROM student_course_attendance WHERE course_id  =  301;
+SELECT student_id FROM student_course_attendance WHERE course_id  =  301;
+SELECT student_id FROM student_course_attendance WHERE course_id = 301 ORDER BY date_of_attendance DESC LIMIT 1;
+SELECT student_id FROM student_course_attendance WHERE course_id = 301 ORDER BY date_of_attendance DESC LIMIT 1;
+SELECT DISTINCT T1.city FROM addresses AS T1 JOIN people_addresses AS T2 ON T1.address_id = T2.address_id;
+SELECT DISTINCT T1.city FROM addresses AS T1 JOIN people_addresses AS T2 ON T1.address_id = T2.address_id;
+SELECT DISTINCT T1.city FROM addresses AS T1 JOIN people_addresses AS T2 ON T1.address_id = T2.address_id JOIN students AS T3 ON T2.person_id = T3.student_id;
+SELECT DISTINCT T1.city FROM addresses AS T1 JOIN people_addresses AS T2 ON T1.address_id = T2.address_id JOIN students AS T3 ON T2.person_id = T3.student_id;
+SELECT course_name FROM courses ORDER BY course_name;
+SELECT course_name FROM courses ORDER BY course_name;
+SELECT first_name FROM people ORDER BY first_name;
+SELECT first_name FROM people ORDER BY first_name;
+SELECT student_id FROM student_course_registrations UNION SELECT student_id FROM student_course_attendance;
+SELECT student_id FROM student_course_registrations UNION SELECT student_id FROM student_course_attendance;
+SELECT course_id FROM student_course_registrations WHERE student_id = 121 UNION SELECT course_id FROM student_course_attendance WHERE student_id = 121;
+SELECT course_id FROM student_course_registrations WHERE student_id = 121 UNION SELECT course_id FROM student_course_attendance WHERE student_id = 121;
+SELECT * FROM student_course_registrations WHERE student_id NOT IN (SELECT student_id FROM student_course_attendance);
+SELECT * FROM student_course_registrations WHERE student_id NOT IN (SELECT student_id FROM student_course_attendance);
+SELECT T2.student_id FROM courses AS T1 JOIN student_course_registrations AS T2 ON T1.course_id = T2.course_id WHERE T1.course_name = 'statistics' ORDER BY T2.registration_date;
+SELECT T2.student_id FROM courses AS T1 JOIN student_course_registrations AS T2 ON T1.course_id = T2.course_id WHERE T1.course_name = 'statistics' ORDER BY T2.registration_date;
+SELECT T2.student_id FROM courses AS T1 JOIN student_course_attendance AS T2 ON T1.course_id = T2.course_id WHERE T1.course_name = 'statistics' ORDER BY T2.date_of_attendance;
+SELECT T2.student_id FROM courses AS T1 JOIN student_course_attendance AS T2 ON T1.course_id = T2.course_id WHERE T1.course_name = 'statistics' ORDER BY T2.date_of_attendance;
+SELECT date FROM weather WHERE max_temperature_f  >  85;
+SELECT date FROM weather WHERE max_temperature_f  >  85;
+SELECT name FROM station WHERE lat  <  37.5;
+SELECT name FROM station WHERE lat  <  37.5;
+SELECT city ,  max(lat) FROM station GROUP BY city;
+SELECT city ,  max(lat) FROM station GROUP BY city;
+SELECT start_station_name ,  end_station_name FROM trip ORDER BY id LIMIT 3;
+SELECT start_station_name ,  end_station_name FROM trip ORDER BY id LIMIT 3;
+SELECT avg(lat) ,  avg(long) FROM station WHERE city  =  'San Jose';
+SELECT avg(lat) ,  avg(long) FROM station WHERE city  =  'San Jose';
+SELECT id FROM trip ORDER BY duration LIMIT 1;
+SELECT id FROM trip ORDER BY duration LIMIT 1;
+SELECT sum(duration) ,  max(duration) FROM trip WHERE bike_id  =  636;
+SELECT sum(duration) ,  max(duration) FROM trip WHERE bike_id  =  636;
+SELECT zip_code  ,  avg(mean_temperature_f) FROM weather WHERE date LIKE '8/%' GROUP BY zip_code;
+SELECT zip_code  ,  avg(mean_temperature_f) FROM weather WHERE date LIKE '8/%' GROUP BY zip_code;
+SELECT count(DISTINCT bike_id) FROM trip;
+SELECT count(DISTINCT bike_id) FROM trip;
+SELECT count(DISTINCT city) FROM station;
+SELECT count(DISTINCT city) FROM station;
+SELECT COUNT(*) FROM station WHERE city  =  'Mountain View';
+SELECT COUNT(*) FROM station WHERE city  =  'Mountain View';
+SELECT DISTINCT T1.name FROM station AS T1 JOIN status AS T2 ON T1.id  =  T2.station_id WHERE T2.bikes_available  =  7;
+SELECT DISTINCT T1.name FROM station AS T1 JOIN status AS T2 ON T1.id  =  T2.station_id WHERE T2.bikes_available  =  7;
+SELECT start_station_name ,  start_station_id FROM trip WHERE start_date LIKE '8/%' GROUP BY start_station_name ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT start_station_name ,  start_station_id FROM trip WHERE start_date LIKE '8/%' GROUP BY start_station_name ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT bike_id FROM trip WHERE zip_code  =  94002 GROUP BY bike_id ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT bike_id FROM trip WHERE zip_code  =  94002 GROUP BY bike_id ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT COUNT(*) FROM weather WHERE mean_humidity  >  50 AND mean_visibility_miles  >  8;
+SELECT COUNT(*) FROM weather WHERE mean_humidity  >  50 AND mean_visibility_miles  >  8;
+SELECT T1.lat ,  T1.long ,  T1.city FROM station AS T1 JOIN trip AS T2 ON T1.id  =  T2.start_station_id ORDER BY T2.duration LIMIT 1;
+SELECT T1.lat ,  T1.long ,  T1.city FROM station AS T1 JOIN trip AS T2 ON T1.id  =  T2.start_station_id ORDER BY T2.duration LIMIT 1;
+SELECT id FROM station WHERE city  =  'San Francisco' INTERSECT SELECT station_id FROM status GROUP BY station_id HAVING avg(bikes_available)  >  10;
+SELECT id FROM station WHERE city  =  'San Francisco' INTERSECT SELECT station_id FROM status GROUP BY station_id HAVING avg(bikes_available)  >  10;
+SELECT T1.name ,  T1.id FROM station AS T1 JOIN status AS T2 ON T1.id  =  T2.station_id GROUP BY T2.station_id HAVING avg(T2.bikes_available)  >  14 UNION SELECT name ,  id FROM station WHERE installation_date LIKE '12/%';
+SELECT T1.name ,  T1.id FROM station AS T1 JOIN status AS T2 ON T1.id  =  T2.station_id GROUP BY T2.station_id HAVING avg(T2.bikes_available)  >  14 UNION SELECT name ,  id FROM station WHERE installation_date LIKE '12/%';
+SELECT cloud_cover FROM weather WHERE zip_code  =  94107 GROUP BY cloud_cover ORDER BY COUNT (*) DESC LIMIT 3;
+SELECT cloud_cover FROM weather WHERE zip_code  =  94107 GROUP BY cloud_cover ORDER BY COUNT (*) DESC LIMIT 3;
+SELECT zip_code FROM weather GROUP BY zip_code ORDER BY avg(mean_sea_level_pressure_inches) LIMIT 1;
+SELECT zip_code FROM weather GROUP BY zip_code ORDER BY avg(mean_sea_level_pressure_inches) LIMIT 1;
+SELECT avg(bikes_available) FROM status WHERE station_id NOT IN (SELECT id FROM station WHERE city  =  'Palo Alto');
+SELECT avg(bikes_available) FROM status WHERE station_id NOT IN (SELECT id FROM station WHERE city  =  'Palo Alto');
+SELECT avg(long) FROM station WHERE id NOT IN (SELECT station_id FROM status GROUP BY station_id HAVING max(bikes_available)  >  10);
+SELECT avg(long) FROM station WHERE id NOT IN (SELECT station_id FROM status GROUP BY station_id HAVING max(bikes_available)  >  10);
+SELECT date ,  zip_code FROM weather WHERE max_temperature_f  >=  80;
+SELECT date ,  zip_code FROM weather WHERE max_temperature_f  >=  80;
+SELECT T1.id FROM trip AS T1 JOIN weather AS T2 ON T1.zip_code  =  T2.zip_code GROUP BY T2.zip_code HAVING avg(T2.mean_temperature_f)  >  60;
+SELECT T1.id FROM trip AS T1 JOIN weather AS T2 ON T1.zip_code  =  T2.zip_code GROUP BY T2.zip_code HAVING avg(T2.mean_temperature_f)  >  60;
+SELECT zip_code , count(*) FROM weather WHERE max_wind_Speed_mph  >=  25 GROUP BY zip_code;
+SELECT zip_code , count(*) FROM weather WHERE max_wind_Speed_mph  >=  25 GROUP BY zip_code;
+SELECT date ,  zip_code FROM weather WHERE min_dew_point_f  <  (SELECT min(min_dew_point_f) FROM weather WHERE zip_code  =  94107);
+SELECT date ,  zip_code FROM weather WHERE min_dew_point_f  <  (SELECT min(min_dew_point_f) FROM weather WHERE zip_code  =  94107);
+SELECT T1.id ,  T2.installation_date FROM trip AS T1 JOIN station AS T2 ON T1.end_station_id  =  T2.id;
+SELECT T1.id ,  T2.installation_date FROM trip AS T1 JOIN station AS T2 ON T1.end_station_id  =  T2.id;
+SELECT T1.id FROM trip AS T1 JOIN station AS T2 ON T1.start_station_id  =  T2.id ORDER BY T2.dock_count DESC LIMIT 1;
+SELECT T1.id FROM trip AS T1 JOIN station AS T2 ON T1.start_station_id  =  T2.id ORDER BY T2.dock_count DESC LIMIT 1;
+SELECT count(*) FROM trip AS T1 JOIN station AS T2 ON T1.end_station_id  =  T2.id WHERE T2.city !=  'San Francisco';
+SELECT count(*) FROM trip AS T1 JOIN station AS T2 ON T1.end_station_id  =  T2.id WHERE T2.city != 'San Francisco';
+SELECT date FROM weather WHERE zip_code  =  94107 AND EVENTS != 'Fog' AND EVENTS != 'Rain';
+SELECT date FROM weather WHERE zip_code  =  94107 AND EVENTS != 'Fog' AND EVENTS != 'Rain';
+SELECT id FROM station WHERE lat  >  37.4 EXCEPT SELECT station_id FROM status GROUP BY station_id HAVING min(bikes_available)  <  7;
+SELECT id FROM station WHERE lat  >  37.4 EXCEPT SELECT station_id FROM status GROUP BY station_id HAVING min(bikes_available)  <  7;
+SELECT T1.name FROM station AS T1 JOIN status AS T2 ON T1.id  =  T2.station_id GROUP BY T2.station_id HAVING avg(bikes_available)  >  10 EXCEPT SELECT name FROM station WHERE city  =  'San Jose';
+SELECT T1.name FROM station AS T1 JOIN status AS T2 ON T1.id  =  T2.station_id GROUP BY T2.station_id HAVING avg(bikes_available)  >  10 EXCEPT SELECT name FROM station WHERE city  =  'San Jose';
+SELECT name ,  lat ,  city FROM station ORDER BY lat LIMIT 1;
+SELECT name ,  lat ,  city FROM station ORDER BY lat LIMIT 1;
+SELECT date ,  mean_temperature_f ,  mean_humidity FROM weather ORDER BY max_gust_speed_mph DESC LIMIT 3;
+SELECT date ,  mean_temperature_f ,  mean_humidity FROM weather ORDER BY max_gust_speed_mph DESC LIMIT 3;
+SELECT city ,  COUNT(*) FROM station GROUP BY city HAVING COUNT(*)  >=  15;
+SELECT city ,  COUNT(*) FROM station GROUP BY city HAVING COUNT(*)  >=  15;
+SELECT start_station_id ,  start_station_name FROM trip GROUP BY start_station_name HAVING COUNT(*)  >=  200;
+SELECT start_station_id ,  start_station_name FROM trip GROUP BY start_station_name HAVING COUNT(*)  >=  200;
+SELECT zip_code FROM weather GROUP BY zip_code HAVING avg(mean_visibility_miles)  <  10;
+SELECT zip_code FROM weather GROUP BY zip_code HAVING avg(mean_visibility_miles)  <  10;
+SELECT city FROM station GROUP BY city ORDER BY max(lat) DESC;
+SELECT city FROM station GROUP BY city ORDER BY max(lat) DESC;
+SELECT date ,  cloud_cover FROM weather ORDER BY cloud_cover DESC LIMIT 5;
+SELECT date ,  cloud_cover FROM weather ORDER BY cloud_cover DESC LIMIT 5;
+SELECT id ,  duration FROM trip ORDER BY duration DESC LIMIT 3;
+SELECT id ,  duration FROM trip ORDER BY duration DESC LIMIT 3;
+SELECT T1.name ,  T1.long ,  avg(T2.duration) FROM station AS T1 JOIN trip AS T2 ON T1.id  =  T2.start_station_id GROUP BY T2.start_station_id;
+SELECT T1.name ,  T1.long ,  avg(T2.duration) FROM station AS T1 JOIN trip AS T2 ON T1.id  =  T2.start_station_id GROUP BY T2.start_station_id;
+SELECT T1.name ,  T1.lat ,  min(T2.duration) FROM station AS T1 JOIN trip AS T2 ON T1.id  =  T2.end_station_id GROUP BY T2.end_station_id;
+SELECT T1.name ,  T1.lat ,  min(T2.duration) FROM station AS T1 JOIN trip AS T2 ON T1.id  =  T2.end_station_id GROUP BY T2.end_station_id;
+SELECT DISTINCT start_station_name FROM trip WHERE duration  <  100;
+SELECT DISTINCT start_station_name FROM trip WHERE duration  <  100;
+SELECT DISTINCT zip_code FROM weather EXCEPT SELECT DISTINCT zip_code FROM weather WHERE max_dew_point_f  >=  70;
+SELECT DISTINCT zip_code FROM weather EXCEPT SELECT DISTINCT zip_code FROM weather WHERE max_dew_point_f  >=  70;
+SELECT id FROM trip WHERE duration  >=  (SELECT avg(duration) FROM trip WHERE zip_code  =  94103);
+SELECT id FROM trip WHERE duration  >=  (SELECT avg(duration) FROM trip WHERE zip_code  =  94103);
+SELECT date FROM weather WHERE mean_sea_level_pressure_inches BETWEEN 30.3 AND 31;
+SELECT date FROM weather WHERE mean_sea_level_pressure_inches BETWEEN 30.3 AND 31;
+SELECT date ,  max_temperature_f - min_temperature_f FROM weather ORDER BY max_temperature_f - min_temperature_f LIMIT 1;
+SELECT date ,  max_temperature_f - min_temperature_f FROM weather ORDER BY max_temperature_f - min_temperature_f LIMIT 1;
+SELECT DISTINCT T1.id ,  T1.name FROM station AS T1 JOIN status AS T2 ON T1.id  =  T2.station_id WHERE T2.bikes_available  >  12;
+SELECT DISTINCT T1.id ,  T1.name FROM station AS T1 JOIN status AS T2 ON T1.id  =  T2.station_id WHERE T2.bikes_available  >  12;
+SELECT zip_code FROM weather GROUP BY zip_code HAVING avg(mean_humidity)  <  70 INTERSECT SELECT zip_code FROM trip GROUP BY zip_code HAVING count(*)  >=  100;
+SELECT zip_code FROM weather GROUP BY zip_code HAVING avg(mean_humidity)  <  70 INTERSECT SELECT zip_code FROM trip GROUP BY zip_code HAVING count(*)  >=  100;
+SELECT name FROM station WHERE city  =  'Palo Alto' EXCEPT SELECT end_station_name FROM trip GROUP BY end_station_name HAVING count(*)  >  100;
+SELECT name FROM station WHERE city  =  'Palo Alto' EXCEPT SELECT end_station_name FROM trip GROUP BY end_station_name HAVING count(*)  >  100;
+SELECT count(*) FROM station AS T1 JOIN trip AS T2 JOIN station AS T3 JOIN trip AS T4 ON T1.id  =  T2.start_station_id AND T2.id  =  T4.id AND T3.id  =  T4.end_station_id WHERE T1.city  =  'Mountain View' AND T3.city  =  'Palo Alto';
+SELECT count(*) FROM station AS T1 JOIN trip AS T2 JOIN station AS T3 JOIN trip AS T4 ON T1.id  =  T2.start_station_id AND T2.id  =  T4.id AND T3.id  =  T4.end_station_id WHERE T1.city  =  'Mountain View' AND T3.city  =  'Palo Alto';
+SELECT avg(T1.lat) ,  avg(T1.long) FROM station AS T1 JOIN trip AS T2 ON T1.id  =  T2.start_station_id;
+SELECT avg(T1.lat) ,  avg(T1.long) FROM station AS T1 JOIN trip AS T2 ON T1.id  =  T2.start_station_id;
+SELECT count(*) FROM book;
+SELECT Writer FROM book ORDER BY Writer ASC;
+SELECT Title FROM book ORDER BY Issues ASC;
+SELECT Title FROM book WHERE Writer != 'Elaine Lee';
+SELECT Title ,  Issues FROM book;
+SELECT Publication_Date FROM publication ORDER BY Price DESC;
+SELECT DISTINCT Publisher FROM publication WHERE Price  >  5000000;
+SELECT Publisher FROM publication ORDER BY Price DESC LIMIT 1;
+SELECT Publication_Date FROM publication ORDER BY Price ASC LIMIT 3;
+SELECT T1.Title ,  T2.Publication_Date FROM book AS T1 JOIN publication AS T2 ON T1.Book_ID  =  T2.Book_ID;
+SELECT T1.Writer FROM book AS T1 JOIN publication AS T2 ON T1.Book_ID  =  T2.Book_ID WHERE T2.Price  >  4000000;
+SELECT T1.Title FROM book AS T1 JOIN publication AS T2 ON T1.Book_ID  =  T2.Book_ID ORDER BY T2.Price DESC;
+SELECT Publisher FROM publication GROUP BY Publisher HAVING COUNT(*)  >  1;
+SELECT Publisher ,  COUNT(*) FROM publication GROUP BY Publisher;
+SELECT Publication_Date FROM publication GROUP BY Publication_Date ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Writer FROM book GROUP BY Writer HAVING COUNT(*)  >  1;
+SELECT Title FROM book WHERE Book_ID NOT IN (SELECT Book_ID FROM publication);
+SELECT Publisher FROM publication WHERE Price  >  10000000 INTERSECT SELECT Publisher FROM publication WHERE Price  <  5000000;
+SELECT COUNT (DISTINCT Publication_Date) FROM publication;
+SELECT COUNT (DISTINCT Publication_Date) FROM publication;
+SELECT Price FROM publication WHERE Publisher  =  'Person' OR Publisher  =  'Wiley';
+SELECT count(*) FROM actor;
+SELECT count(*) FROM actor;
+SELECT Name FROM actor ORDER BY Name ASC;
+SELECT Name FROM actor ORDER BY Name ASC;
+SELECT Character ,  Duration FROM actor;
+SELECT Character ,  Duration FROM actor;
+SELECT Name FROM actor WHERE Age != 20;
+SELECT Name FROM actor WHERE Age != 20;
+SELECT Character FROM actor ORDER BY age DESC;
+SELECT Character FROM actor ORDER BY age DESC;
+SELECT Duration FROM actor ORDER BY Age DESC LIMIT 1;
+SELECT Duration FROM actor ORDER BY Age DESC LIMIT 1;
+SELECT Name FROM musical WHERE Nominee  =  'Bob Fosse';
+SELECT Name FROM musical WHERE Nominee  =  'Bob Fosse';
+SELECT DISTINCT Nominee FROM musical WHERE Award != 'Tony Award';
+SELECT DISTINCT Nominee FROM musical WHERE Award != 'Tony Award';
+SELECT T1.Name ,  T2.Name FROM actor AS T1 JOIN musical AS T2 ON T1.Musical_ID  =  T2.Musical_ID;
+SELECT T1.Name ,  T2.Name FROM actor AS T1 JOIN musical AS T2 ON T1.Musical_ID  =  T2.Musical_ID;
+SELECT T1.Name FROM actor AS T1 JOIN musical AS T2 ON T1.Musical_ID  =  T2.Musical_ID WHERE T2.Name  =  'The Phantom of the Opera';
+SELECT T1.Name FROM actor AS T1 JOIN musical AS T2 ON T1.Musical_ID  =  T2.Musical_ID WHERE T2.Name  =  'The Phantom of the Opera';
+SELECT T1.Name FROM actor AS T1 JOIN musical AS T2 ON T1.Musical_ID  =  T2.Musical_ID ORDER BY T2.Year DESC;
+SELECT T1.Name FROM actor AS T1 JOIN musical AS T2 ON T1.Musical_ID  =  T2.Musical_ID ORDER BY T2.Year DESC;
+SELECT T2.Name ,  COUNT(*) FROM actor AS T1 JOIN musical AS T2 ON T1.Musical_ID  =  T2.Musical_ID GROUP BY T1.Musical_ID;
+SELECT T2.Name ,  COUNT(*) FROM actor AS T1 JOIN musical AS T2 ON T1.Musical_ID  =  T2.Musical_ID GROUP BY T1.Musical_ID;
+SELECT T2.Name FROM actor AS T1 JOIN musical AS T2 ON T1.Musical_ID  =  T2.Musical_ID GROUP BY T1.Musical_ID HAVING COUNT(*)  >=  3;
+SELECT T2.Name FROM actor AS T1 JOIN musical AS T2 ON T1.Musical_ID  =  T2.Musical_ID GROUP BY T1.Musical_ID HAVING COUNT(*)  >=  3;
+SELECT Nominee ,  COUNT(*) FROM musical GROUP BY Nominee;
+SELECT Nominee ,  COUNT(*) FROM musical GROUP BY Nominee;
+SELECT Nominee FROM musical GROUP BY Nominee ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Nominee FROM musical GROUP BY Nominee ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT RESULT FROM musical GROUP BY RESULT ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT RESULT FROM musical GROUP BY RESULT ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Nominee FROM musical GROUP BY Nominee HAVING COUNT(*)  >  2;
+SELECT Nominee FROM musical GROUP BY Nominee HAVING COUNT(*)  >  2;
+SELECT Name FROM musical WHERE Musical_ID NOT IN (SELECT Musical_ID FROM actor);
+SELECT Name FROM musical WHERE Musical_ID NOT IN (SELECT Musical_ID FROM actor);
+SELECT Nominee FROM musical WHERE Award  =  'Tony Award' INTERSECT SELECT Nominee FROM musical WHERE Award  =  'Drama Desk Award';
+SELECT Nominee FROM musical WHERE Award  =  'Tony Award' INTERSECT SELECT Nominee FROM musical WHERE Award  =  'Drama Desk Award';
+SELECT Nominee FROM musical WHERE Award  =  'Tony Award' OR Award  =  'Cleavant Derricks';
+SELECT Nominee FROM musical WHERE Award  =  'Tony Award' OR Award  =  'Cleavant Derricks';
+SELECT email FROM user_profiles WHERE name  =  'Mary';
+SELECT partitionid FROM user_profiles WHERE name  =  'Iron Man';
+SELECT count(*) FROM user_profiles;
+SELECT count(*) FROM follows;
+SELECT count(*) FROM follows GROUP BY f1;
+SELECT count(*) FROM tweets;
+SELECT count(DISTINCT UID) FROM tweets;
+SELECT name ,  email FROM user_profiles WHERE name LIKE '%Swift%';
+SELECT name FROM user_profiles WHERE email LIKE '%superstar%' OR email LIKE '%edu%';
+SELECT text FROM tweets WHERE text LIKE '%intern%';
+SELECT name ,  email FROM user_profiles WHERE followers  >  1000;
+SELECT T1.name FROM user_profiles AS T1 JOIN follows AS T2 ON T1.uid  =  T2.f1 GROUP BY T2.f1 HAVING count(*)  >  (SELECT count(*) FROM user_profiles AS T1 JOIN follows AS T2 ON T1.uid  =  T2.f1 WHERE T1.name  =  'Tyler Swift');
+SELECT T1.name ,  T1.email FROM user_profiles AS T1 JOIN follows AS T2 ON T1.uid  =  T2.f1 GROUP BY T2.f1 HAVING count(*)  >  1;
+SELECT T1.name FROM user_profiles AS T1 JOIN tweets AS T2 ON T1.uid  =  T2.uid GROUP BY T2.uid HAVING count(*)  >  1;
+SELECT T2.f1 FROM user_profiles AS T1 JOIN follows AS T2 ON T1.uid  =  T2.f2 WHERE T1.name  =  'Mary' INTERSECT SELECT T2.f1 FROM user_profiles AS T1 JOIN follows AS T2 ON T1.uid  =  T2.f2 WHERE T1.name  =  'Susan';
+SELECT T2.f1 FROM user_profiles AS T1 JOIN follows AS T2 ON T1.uid  =  T2.f2 WHERE T1.name  =  'Mary' OR T1.name  =  'Susan';
+SELECT name FROM user_profiles ORDER BY followers DESC LIMIT 1;
+SELECT name ,  email FROM user_profiles ORDER BY followers LIMIT 1;
+SELECT name ,  followers FROM user_profiles ORDER BY followers DESC;
+SELECT name FROM user_profiles ORDER BY followers DESC LIMIT 5;
+SELECT text FROM tweets ORDER BY createdate;
+SELECT T1.name ,  count(*) FROM user_profiles AS T1 JOIN tweets AS T2 ON T1.uid  =  T2.uid GROUP BY T2.uid;
+SELECT T1.name ,  T1.partitionid FROM user_profiles AS T1 JOIN tweets AS T2 ON T1.uid  =  T2.uid GROUP BY T2.uid HAVING count(*)  <  2;
+SELECT T1.name ,  count(*) FROM user_profiles AS T1 JOIN tweets AS T2 ON T1.uid  =  T2.uid GROUP BY T2.uid HAVING count(*)  >  1;
+SELECT avg(followers) FROM user_profiles WHERE UID NOT IN (SELECT UID FROM tweets);
+SELECT avg(followers) FROM user_profiles WHERE UID IN (SELECT UID FROM tweets);
+SELECT max(followers) ,  sum(followers) FROM user_profiles;
+SELECT distinct(catalog_entry_name) FROM catalog_contents;
+SELECT distinct(catalog_entry_name) FROM catalog_contents;
+SELECT attribute_data_type FROM Attribute_Definitions GROUP BY attribute_data_type HAVING count(*)  >  3;
+SELECT attribute_data_type FROM Attribute_Definitions GROUP BY attribute_data_type HAVING count(*)  >  3;
+SELECT attribute_data_type FROM Attribute_Definitions WHERE attribute_name  =  'Green';
+SELECT attribute_data_type FROM Attribute_Definitions WHERE attribute_name  =  'Green';
+SELECT catalog_level_name ,  catalog_level_number FROM Catalog_Structure WHERE catalog_level_number BETWEEN 5 AND 10;
+SELECT catalog_level_name ,  catalog_level_number FROM Catalog_Structure WHERE catalog_level_number BETWEEN 5 AND 10;
+SELECT distinct(catalog_publisher) FROM catalogs WHERE catalog_publisher LIKE '%Murray%';
+SELECT distinct(catalog_publisher) FROM catalogs WHERE catalog_publisher LIKE '%Murray%';
+SELECT catalog_publisher FROM catalogs GROUP BY catalog_publisher ORDER BY count(*) DESC LIMIT 1;
+SELECT catalog_publisher FROM catalogs GROUP BY catalog_publisher ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.catalog_name ,  t1.date_of_publication FROM catalogs AS t1 JOIN catalog_structure AS t2 ON t1.catalog_id  =  t2.catalog_id WHERE catalog_level_number  >  5;
+SELECT t1.catalog_name ,  t1.date_of_publication FROM catalogs AS t1 JOIN catalog_structure AS t2 ON t1.catalog_id  =  t2.catalog_id WHERE catalog_level_number  >  5;
+SELECT t1.catalog_entry_name FROM Catalog_Contents AS t1 JOIN Catalog_Contents_Additional_Attributes AS t2 ON t1.catalog_entry_id  =  t2.catalog_entry_id WHERE t2.attribute_value  =  (SELECT attribute_value FROM Catalog_Contents_Additional_Attributes GROUP BY attribute_value ORDER BY count(*) DESC LIMIT 1);
+SELECT t1.catalog_entry_name FROM Catalog_Contents AS t1 JOIN Catalog_Contents_Additional_Attributes AS t2 ON t1.catalog_entry_id  =  t2.catalog_entry_id WHERE t2.attribute_value  =  (SELECT attribute_value FROM Catalog_Contents_Additional_Attributes GROUP BY attribute_value ORDER BY count(*) DESC LIMIT 1);
+SELECT catalog_entry_name FROM catalog_contents ORDER BY price_in_dollars DESC LIMIT 1;
+SELECT catalog_entry_name FROM catalog_contents ORDER BY price_in_dollars DESC LIMIT 1;
+SELECT t2.catalog_level_name FROM catalog_contents AS t1 JOIN catalog_structure AS t2 ON t1.catalog_level_number  =  t2.catalog_level_number ORDER BY t1.price_in_dollars LIMIT 1;
+SELECT t2.catalog_level_name FROM catalog_contents AS t1 JOIN catalog_structure AS t2 ON t1.catalog_level_number  =  t2.catalog_level_number ORDER BY t1.price_in_dollars LIMIT 1;
+SELECT avg(price_in_euros) ,  min(price_in_euros) FROM catalog_contents;
+SELECT avg(price_in_euros) ,  min(price_in_euros) FROM catalog_contents;
+SELECT catalog_entry_name FROM catalog_contents ORDER BY height DESC LIMIT 1;
+SELECT catalog_entry_name FROM catalog_contents ORDER BY height DESC LIMIT 1;
+SELECT catalog_entry_name FROM catalog_contents ORDER BY capacity ASC LIMIT 1;
+SELECT catalog_entry_name FROM catalog_contents ORDER BY capacity ASC LIMIT 1;
+SELECT catalog_entry_name FROM catalog_contents WHERE product_stock_number LIKE '2%';
+SELECT catalog_entry_name FROM catalog_contents WHERE product_stock_number LIKE '2%';
+SELECT t1.catalog_entry_name FROM Catalog_Contents AS t1 JOIN Catalog_Contents_Additional_Attributes AS t2 ON t1.catalog_entry_id  =  t2.catalog_entry_id WHERE t2.catalog_level_number  =  '8';
+SELECT t1.catalog_entry_name FROM Catalog_Contents AS t1 JOIN Catalog_Contents_Additional_Attributes AS t2 ON t1.catalog_entry_id  =  t2.catalog_entry_id WHERE t2.catalog_level_number  =  '8';
+SELECT catalog_entry_name FROM catalog_contents WHERE LENGTH  <  3 OR width  >  5;
+SELECT catalog_entry_name FROM catalog_contents WHERE LENGTH  <  3 OR width  >  5;
+SELECT t1.attribute_name ,  t1.attribute_id FROM Attribute_Definitions AS t1 JOIN Catalog_Contents_Additional_Attributes AS t2 ON t1.attribute_id  =  t2.attribute_id WHERE t2.attribute_value  =  0;
+SELECT t1.attribute_name ,  t1.attribute_id FROM Attribute_Definitions AS t1 JOIN Catalog_Contents_Additional_Attributes AS t2 ON t1.attribute_id  =  t2.attribute_id WHERE t2.attribute_value  =  0;
+SELECT catalog_entry_name ,  capacity FROM Catalog_Contents WHERE price_in_dollars  >  700;
+SELECT catalog_entry_name ,  capacity FROM Catalog_Contents WHERE price_in_dollars  >  700;
+SELECT date_of_latest_revision FROM Catalogs GROUP BY date_of_latest_revision HAVING count(*)  >  1;
+SELECT date_of_latest_revision FROM Catalogs GROUP BY date_of_latest_revision HAVING count(*)  >  1;
+SELECT count(*) FROM catalog_contents;
+SELECT count(*) FROM catalog_contents;
+SELECT catalog_entry_name FROM catalog_contents WHERE next_entry_id  >  8;
+SELECT catalog_entry_name FROM catalog_contents WHERE next_entry_id  >  8;
+SELECT count(*) FROM Aircraft;
+SELECT count(*) FROM Aircraft;
+SELECT name ,  distance FROM Aircraft;
+SELECT name ,  distance FROM Aircraft;
+SELECT aid FROM Aircraft WHERE distance  >  1000;
+SELECT aid FROM Aircraft WHERE distance  >  1000;
+SELECT count(*) FROM Aircraft WHERE distance BETWEEN 1000 AND 5000;
+SELECT count(*) FROM Aircraft WHERE distance BETWEEN 1000 AND 5000;
+SELECT name ,  distance FROM Aircraft WHERE aid  =  12;
+SELECT name ,  distance FROM Aircraft WHERE aid  =  12;
+SELECT min(distance) ,  avg(distance) ,  max(distance) FROM Aircraft;
+SELECT min(distance) ,  avg(distance) ,  max(distance) FROM Aircraft;
+SELECT aid ,  name FROM Aircraft ORDER BY distance DESC LIMIT 1;
+SELECT aid ,  name FROM Aircraft ORDER BY distance DESC LIMIT 1;
+SELECT name FROM Aircraft ORDER BY distance LIMIT 3;
+SELECT name FROM Aircraft ORDER BY distance LIMIT 3;
+SELECT name FROM Aircraft WHERE distance  >  (SELECT avg(distance) FROM Aircraft);
+SELECT name FROM Aircraft WHERE distance  >  (SELECT avg(distance) FROM Aircraft);
+SELECT count(*) FROM Employee;
+SELECT count(*) FROM Employee;
+SELECT name ,  salary FROM Employee ORDER BY salary;
+SELECT name ,  salary FROM Employee ORDER BY salary;
+SELECT eid FROM Employee WHERE salary  >  100000;
+SELECT eid FROM Employee WHERE salary  >  100000;
+SELECT count(*) FROM Employee WHERE salary BETWEEN 100000 AND 200000;
+SELECT count(*) FROM Employee WHERE salary BETWEEN 100000 AND 200000;
+SELECT name ,  salary FROM Employee WHERE eid  =  242518965;
+SELECT name ,  salary FROM Employee WHERE eid  =  242518965;
+SELECT avg(salary) ,  max(salary) FROM Employee;
+SELECT avg(salary) ,  max(salary) FROM Employee;
+SELECT eid ,  name FROM Employee ORDER BY salary DESC LIMIT 1;
+SELECT eid ,  name FROM Employee ORDER BY salary DESC LIMIT 1;
+SELECT name FROM Employee ORDER BY salary ASC LIMIT 3;
+SELECT name FROM Employee ORDER BY salary ASC LIMIT 3;
+SELECT name FROM Employee WHERE salary  >  (SELECT avg(salary) FROM Employee);
+SELECT name FROM Employee WHERE salary  >  (SELECT avg(salary) FROM Employee);
+SELECT eid ,  salary FROM Employee WHERE name  =  'Mark Young';
+SELECT eid ,  salary FROM Employee WHERE name  =  'Mark Young';
+SELECT count(*) FROM Flight;
+SELECT count(*) FROM Flight;
+SELECT flno ,  origin ,  destination FROM Flight ORDER BY origin;
+SELECT flno ,  origin ,  destination FROM Flight ORDER BY origin;
+SELECT flno FROM Flight WHERE origin  =  'Los Angeles';
+SELECT flno FROM Flight WHERE origin  =  'Los Angeles';
+SELECT origin FROM Flight WHERE destination  =  'Honolulu';
+SELECT origin FROM Flight WHERE destination  =  'Honolulu';
+SELECT departure_date ,  arrival_date FROM Flight WHERE origin  =  'Los Angeles' AND destination  =  'Honolulu';
+SELECT departure_date ,  arrival_date FROM Flight WHERE origin  =  'Los Angeles' AND destination  =  'Honolulu';
+SELECT flno FROM Flight WHERE distance  >  2000;
+SELECT flno FROM Flight WHERE distance  >  2000;
+SELECT avg(price) FROM Flight WHERE origin  =  'Los Angeles' AND destination  =  'Honolulu';
+SELECT avg(price) FROM Flight WHERE origin  =  'Los Angeles' AND destination  =  'Honolulu';
+SELECT origin ,  destination FROM Flight WHERE price  >  300;
+SELECT origin ,  destination FROM Flight WHERE price  >  300;
+SELECT flno ,  distance FROM Flight ORDER BY price DESC LIMIT 1;
+SELECT flno ,  distance FROM Flight ORDER BY price DESC LIMIT 1;
+SELECT flno FROM Flight ORDER BY distance ASC LIMIT 3;
+SELECT flno FROM Flight ORDER BY distance ASC LIMIT 3;
+SELECT avg(distance) ,  avg(price) FROM Flight WHERE origin  =  'Los Angeles';
+SELECT avg(distance) ,  avg(price) FROM Flight WHERE origin  =  'Los Angeles';
+SELECT origin ,  count(*) FROM Flight GROUP BY origin;
+SELECT origin ,  count(*) FROM Flight GROUP BY origin;
+SELECT destination ,  count(*) FROM Flight GROUP BY destination;
+SELECT destination ,  count(*) FROM Flight GROUP BY destination;
+SELECT origin FROM Flight GROUP BY origin ORDER BY count(*) DESC LIMIT 1;
+SELECT origin FROM Flight GROUP BY origin ORDER BY count(*) DESC LIMIT 1;
+SELECT destination FROM Flight GROUP BY destination ORDER BY count(*) LIMIT 1;
+SELECT destination FROM Flight GROUP BY destination ORDER BY count(*) LIMIT 1;
+SELECT T2.name FROM Flight AS T1 JOIN Aircraft AS T2 ON T1.aid  =  T2.aid WHERE T1.flno  =  99;
+SELECT T2.name FROM Flight AS T1 JOIN Aircraft AS T2 ON T1.aid  =  T2.aid WHERE T1.flno  =  99;
+SELECT T1.flno FROM Flight AS T1 JOIN Aircraft AS T2 ON T1.aid  =  T2.aid WHERE T2.name  =  'Airbus A340-300';
+SELECT T1.flno FROM Flight AS T1 JOIN Aircraft AS T2 ON T1.aid  =  T2.aid WHERE T2.name  =  'Airbus A340-300';
+SELECT T2.name ,  count(*) FROM Flight AS T1 JOIN Aircraft AS T2 ON T1.aid  =  T2.aid GROUP BY T1.aid;
+SELECT T2.name ,  count(*) FROM Flight AS T1 JOIN Aircraft AS T2 ON T1.aid  =  T2.aid GROUP BY T1.aid;
+SELECT T2.name FROM Flight AS T1 JOIN Aircraft AS T2 ON T1.aid  =  T2.aid GROUP BY T1.aid HAVING count(*)  >= 2;
+SELECT T2.name FROM Flight AS T1 JOIN Aircraft AS T2 ON T1.aid  =  T2.aid GROUP BY T1.aid HAVING count(*)  >= 2;
+SELECT count(DISTINCT eid) FROM Certificate;
+SELECT count(DISTINCT eid) FROM Certificate;
+SELECT eid FROM Employee EXCEPT SELECT eid FROM Certificate;
+SELECT eid FROM Employee EXCEPT SELECT eid FROM Certificate;
+SELECT T3.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid  =  T2.eid JOIN Aircraft AS T3 ON T3.aid  =  T2.aid WHERE T1.name  =  'John Williams';
+SELECT T3.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid  =  T2.eid JOIN Aircraft AS T3 ON T3.aid  =  T2.aid WHERE T1.name  =  'John Williams';
+SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid  =  T2.eid JOIN Aircraft AS T3 ON T3.aid  =  T2.aid WHERE T3.name  =  'Boeing 737-800';
+SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid  =  T2.eid JOIN Aircraft AS T3 ON T3.aid  =  T2.aid WHERE T3.name  =  'Boeing 737-800';
+SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid  =  T2.eid JOIN Aircraft AS T3 ON T3.aid  =  T2.aid WHERE T3.name  =  'Boeing 737-800' INTERSECT SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid  =  T2.eid JOIN Aircraft AS T3 ON T3.aid  =  T2.aid WHERE T3.name  =  'Airbus A340-300';
+SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid  =  T2.eid JOIN Aircraft AS T3 ON T3.aid  =  T2.aid WHERE T3.name  =  'Boeing 737-800' INTERSECT SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid  =  T2.eid JOIN Aircraft AS T3 ON T3.aid  =  T2.aid WHERE T3.name  =  'Airbus A340-300';
+SELECT name FROM Employee EXCEPT SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid  =  T2.eid JOIN Aircraft AS T3 ON T3.aid  =  T2.aid WHERE T3.name  =  'Boeing 737-800';
+SELECT name FROM Employee EXCEPT SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid  =  T2.eid JOIN Aircraft AS T3 ON T3.aid  =  T2.aid WHERE T3.name  =  'Boeing 737-800';
+SELECT T2.name FROM Certificate AS T1 JOIN Aircraft AS T2 ON T2.aid  =  T1.aid GROUP BY T1.aid ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.name FROM Certificate AS T1 JOIN Aircraft AS T2 ON T2.aid  =  T1.aid GROUP BY T1.aid ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.name FROM Certificate AS T1 JOIN Aircraft AS T2 ON T2.aid  =  T1.aid WHERE T2.distance  >  5000 GROUP BY T1.aid ORDER BY count(*)  >=  5;
+SELECT T2.name FROM Certificate AS T1 JOIN Aircraft AS T2 ON T2.aid  =  T1.aid WHERE T2.distance  >  5000 GROUP BY T1.aid ORDER BY count(*)  >=  5;
+SELECT T1.name ,  T1.salary FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid  =  T2.eid GROUP BY T1.eid ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name ,  T1.salary FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid  =  T2.eid GROUP BY T1.eid ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid  =  T2.eid JOIN Aircraft AS T3 ON T3.aid  =  T2.aid WHERE T3.distance  >  5000 GROUP BY T1.eid ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid  =  T2.eid JOIN Aircraft AS T3 ON T3.aid  =  T2.aid WHERE T3.distance  >  5000 GROUP BY T1.eid ORDER BY count(*) DESC LIMIT 1;
+SELECT count(DISTINCT allergy) FROM Allergy_type;
+SELECT count(DISTINCT allergy) FROM Allergy_type;
+SELECT count(DISTINCT allergytype) FROM Allergy_type;
+SELECT count(DISTINCT allergytype) FROM Allergy_type;
+SELECT DISTINCT allergytype FROM Allergy_type;
+SELECT DISTINCT allergytype FROM Allergy_type;
+SELECT allergy ,  allergytype FROM Allergy_type;
+SELECT allergy ,  allergytype FROM Allergy_type;
+SELECT DISTINCT allergy FROM Allergy_type WHERE allergytype  =  'food';
+SELECT DISTINCT allergy FROM Allergy_type WHERE allergytype  =  'food';
+SELECT allergytype FROM Allergy_type WHERE allergy  =  'Cat';
+SELECT allergytype FROM Allergy_type WHERE allergy  =  'Cat';
+SELECT count(*) FROM Allergy_type WHERE allergytype  =  'animal';
+SELECT count(*) FROM Allergy_type WHERE allergytype  =  'animal';
+SELECT allergytype ,  count(*) FROM Allergy_type GROUP BY allergytype;
+SELECT allergytype ,  count(*) FROM Allergy_type GROUP BY allergytype;
+SELECT allergytype FROM Allergy_type GROUP BY allergytype ORDER BY count(*) DESC LIMIT 1;
+SELECT allergytype FROM Allergy_type GROUP BY allergytype ORDER BY count(*) DESC LIMIT 1;
+SELECT allergytype FROM Allergy_type GROUP BY allergytype ORDER BY count(*) ASC LIMIT 1;
+SELECT allergytype FROM Allergy_type GROUP BY allergytype ORDER BY count(*) ASC LIMIT 1;
+SELECT count(*) FROM Student;
+SELECT count(*) FROM Student;
+SELECT Fname ,  Lname FROM Student;
+SELECT Fname ,  Lname FROM Student;
+SELECT count(DISTINCT advisor) FROM Student;
+SELECT count(DISTINCT advisor) FROM Student;
+SELECT DISTINCT Major FROM Student;
+SELECT DISTINCT Major FROM Student;
+SELECT DISTINCT city_code FROM Student;
+SELECT DISTINCT city_code FROM Student;
+SELECT Fname ,  Lname ,  Age FROM Student WHERE Sex  =  'F';
+SELECT Fname ,  Lname ,  Age FROM Student WHERE Sex  =  'F';
+SELECT StuID FROM Student WHERE Sex  =  'M';
+SELECT StuID FROM Student WHERE Sex  =  'M';
+SELECT count(*) FROM Student WHERE age  =  18;
+SELECT count(*) FROM Student WHERE age  =  18;
+SELECT StuID FROM Student WHERE age  >  20;
+SELECT StuID FROM Student WHERE age  >  20;
+SELECT city_code FROM Student WHERE LName  =  'Kim';
+SELECT city_code FROM Student WHERE LName  =  'Kim';
+SELECT Advisor FROM Student WHERE StuID  =  1004;
+SELECT Advisor FROM Student WHERE StuID  =  1004;
+SELECT count(*) FROM Student WHERE city_code  =  'HKG' OR city_code  =  'CHI';
+SELECT count(*) FROM Student WHERE city_code  =  'HKG' OR city_code  =  'CHI';
+SELECT min(age) ,  avg(age) ,  max(age) FROM Student;
+SELECT min(age) ,  avg(age) ,  max(age) FROM Student;
+SELECT LName FROM Student WHERE age  =  (SELECT min(age) FROM Student);
+SELECT LName FROM Student WHERE age  =  (SELECT min(age) FROM Student);
+SELECT StuID FROM Student WHERE age  =  (SELECT max(age) FROM Student);
+SELECT StuID FROM Student WHERE age  =  (SELECT max(age) FROM Student);
+SELECT major ,  count(*) FROM Student GROUP BY major;
+SELECT major ,  count(*) FROM Student GROUP BY major;
+SELECT major FROM Student GROUP BY major ORDER BY count(*) DESC LIMIT 1;
+SELECT major FROM Student GROUP BY major ORDER BY count(*) DESC LIMIT 1;
+SELECT age ,  count(*) FROM Student GROUP BY age;
+SELECT age ,  count(*) FROM Student GROUP BY age;
+SELECT avg(age) ,  sex FROM Student GROUP BY sex;
+SELECT avg(age) ,  sex FROM Student GROUP BY sex;
+SELECT city_code ,  count(*) FROM Student GROUP BY city_code;
+SELECT city_code ,  count(*) FROM Student GROUP BY city_code;
+SELECT advisor ,  count(*) FROM Student GROUP BY advisor;
+SELECT advisor ,  count(*) FROM Student GROUP BY advisor;
+SELECT advisor FROM Student GROUP BY advisor ORDER BY count(*) DESC LIMIT 1;
+SELECT advisor FROM Student GROUP BY advisor ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM Has_allergy WHERE Allergy  =  'Cat';
+SELECT count(*) FROM Has_allergy WHERE Allergy  =  'Cat';
+SELECT StuID FROM Has_allergy GROUP BY StuID HAVING count(*)  >=  2;
+SELECT StuID FROM Has_allergy GROUP BY StuID HAVING count(*)  >=  2;
+SELECT StuID FROM Student EXCEPT SELECT StuID FROM Has_allergy;
+SELECT StuID FROM Student EXCEPT SELECT StuID FROM Has_allergy;
+SELECT count(*) FROM has_allergy AS T1 JOIN Student AS T2 ON T1.StuID  =  T2.StuID WHERE T2.sex  =  'F' AND T1.allergy  =  'Milk' OR T1.allergy  =  'Eggs';
+SELECT count(*) FROM has_allergy AS T1 JOIN Student AS T2 ON T1.StuID  =  T2.StuID WHERE T2.sex  =  'F' AND T1.allergy  =  'Milk' OR T1.allergy  =  'Eggs';
+SELECT count(*) FROM Has_allergy AS T1 JOIN Allergy_type AS T2 ON T1.allergy  =  T2.allergy WHERE T2.allergytype  =  'food';
+SELECT count(*) FROM Has_allergy AS T1 JOIN Allergy_type AS T2 ON T1.allergy  =  T2.allergy WHERE T2.allergytype  =  'food';
+SELECT Allergy FROM Has_allergy GROUP BY Allergy ORDER BY count(*) DESC LIMIT 1;
+SELECT Allergy FROM Has_allergy GROUP BY Allergy ORDER BY count(*) DESC LIMIT 1;
+SELECT Allergy ,  count(*) FROM Has_allergy GROUP BY Allergy;
+SELECT Allergy ,  count(*) FROM Has_allergy GROUP BY Allergy;
+SELECT T2.allergytype ,  count(*) FROM Has_allergy AS T1 JOIN Allergy_type AS T2 ON T1.allergy  =  T2.allergy GROUP BY T2.allergytype;
+SELECT T2.allergytype ,  count(*) FROM Has_allergy AS T1 JOIN Allergy_type AS T2 ON T1.allergy  =  T2.allergy GROUP BY T2.allergytype;
+SELECT lname ,  age FROM Student WHERE StuID IN (SELECT StuID FROM Has_allergy WHERE Allergy  =  'Milk' INTERSECT SELECT StuID FROM Has_allergy WHERE Allergy  =  'Cat');
+SELECT lname ,  age FROM Student WHERE StuID IN (SELECT StuID FROM Has_allergy WHERE Allergy  =  'Milk' INTERSECT SELECT StuID FROM Has_allergy WHERE Allergy  =  'Cat');
+SELECT T1.Allergy ,  T1.AllergyType FROM Allergy_type AS T1 JOIN Has_allergy AS T2 ON T1.Allergy  =  T2.Allergy JOIN Student AS T3 ON T3.StuID  =  T2.StuID WHERE T3.Fname  =  'Lisa' ORDER BY T1.Allergy;
+SELECT T1.Allergy ,  T1.AllergyType FROM Allergy_type AS T1 JOIN Has_allergy AS T2 ON T1.Allergy  =  T2.Allergy JOIN Student AS T3 ON T3.StuID  =  T2.StuID WHERE T3.Fname  =  'Lisa' ORDER BY T1.Allergy;
+SELECT fname ,  sex FROM Student WHERE StuID IN (SELECT StuID FROM Has_allergy WHERE Allergy  =  'Milk' EXCEPT SELECT StuID FROM Has_allergy WHERE Allergy  =  'Cat');
+SELECT fname ,  sex FROM Student WHERE StuID IN (SELECT StuID FROM Has_allergy WHERE Allergy  =  'Milk' EXCEPT SELECT StuID FROM Has_allergy WHERE Allergy  =  'Cat');
+SELECT avg(age) FROM Student WHERE StuID IN ( SELECT T1.StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy  =  T2.Allergy WHERE T2.allergytype  =  'food' INTERSECT SELECT T1.StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy  =  T2.Allergy WHERE T2.allergytype  =  'animal');
+SELECT avg(age) FROM Student WHERE StuID IN ( SELECT T1.StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy  =  T2.Allergy WHERE T2.allergytype  =  'food' INTERSECT SELECT T1.StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy  =  T2.Allergy WHERE T2.allergytype  =  'animal');
+SELECT fname ,  lname FROM Student WHERE StuID NOT IN (SELECT T1.StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy  =  T2.Allergy WHERE T2.allergytype  =  'food');
+SELECT fname ,  lname FROM Student WHERE StuID NOT IN (SELECT T1.StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy  =  T2.Allergy WHERE T2.allergytype  =  'food');
+SELECT count(*) FROM Student WHERE sex  =  'M' AND StuID IN (SELECT StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy  =  T2.Allergy WHERE T2.allergytype  =  'food');
+SELECT count(*) FROM Student WHERE sex  =  'M' AND StuID IN (SELECT StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy  =  T2.Allergy WHERE T2.allergytype  =  'food');
+SELECT DISTINCT T1.fname ,  T1.city_code FROM Student AS T1 JOIN Has_Allergy AS T2 ON T1.stuid  =  T2.stuid WHERE T2.Allergy  =  'Milk' OR T2.Allergy  =  'Cat';
+SELECT DISTINCT T1.fname ,  T1.city_code FROM Student AS T1 JOIN Has_Allergy AS T2 ON T1.stuid  =  T2.stuid WHERE T2.Allergy  =  'Milk' OR T2.Allergy  =  'Cat';
+SELECT count(*) FROM Student WHERE age  >  18 AND StuID NOT IN ( SELECT StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy  =  T2.Allergy WHERE T2.allergytype  =  'food' OR T2.allergytype  =  'animal');
+SELECT count(*) FROM Student WHERE age  >  18 AND StuID NOT IN ( SELECT StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy  =  T2.Allergy WHERE T2.allergytype  =  'food' OR T2.allergytype  =  'animal');
+SELECT fname ,  major FROM Student WHERE StuID NOT IN (SELECT StuID FROM Has_allergy WHERE Allergy  =  'Soy');
+SELECT fname ,  major FROM Student WHERE StuID NOT IN (SELECT StuID FROM Has_allergy WHERE Allergy  =  'Soy');
+SELECT billing_country ,  COUNT(*) FROM invoices GROUP BY billing_country ORDER BY count(*) DESC LIMIT 5;
+SELECT billing_country ,  COUNT(*) FROM invoices GROUP BY billing_country ORDER BY count(*) DESC LIMIT 5;
+SELECT billing_country ,  SUM(total) FROM invoices GROUP BY billing_country ORDER BY SUM(total) DESC LIMIT 8;
+SELECT billing_country ,  SUM(total) FROM invoices GROUP BY billing_country ORDER BY SUM(total) DESC LIMIT 8;
+SELECT billing_country ,  AVG(total) FROM invoices GROUP BY billing_country ORDER BY AVG(total) DESC LIMIT 10;
+SELECT billing_country ,  AVG(total) FROM invoices GROUP BY billing_country ORDER BY AVG(total) DESC LIMIT 10;
+SELECT T1.first_name ,  T1.last_name FROM customers AS T1 JOIN invoices AS T2 ON T2.customer_id  =  T1.id ORDER BY T2.invoice_date DESC LIMIT 5;
+SELECT T1.first_name ,  T1.last_name FROM customers AS T1 JOIN invoices AS T2 ON T2.customer_id  =  T1.id ORDER BY T2.invoice_date DESC LIMIT 5;
+SELECT T1.first_name ,  T1.last_name ,  COUNT(*) FROM customers AS T1 JOIN invoices AS T2 ON T2.customer_id  =  T1.id GROUP BY T1.id ORDER BY COUNT(*) DESC LIMIT 10;
+SELECT T1.first_name ,  T1.last_name ,  COUNT(*) FROM customers AS T1 JOIN invoices AS T2 ON T2.customer_id  =  T1.id GROUP BY T1.id ORDER BY COUNT(*) DESC LIMIT 10;
+SELECT T1.first_name ,  T1.last_name ,  SUM(T2.total) FROM customers AS T1 JOIN invoices AS T2 ON T2.customer_id  =  T1.id GROUP BY T1.id ORDER BY SUM(T2.total) DESC LIMIT 10;
+SELECT T1.first_name ,  T1.last_name ,  SUM(T2.total) FROM customers AS T1 JOIN invoices AS T2 ON T2.customer_id  =  T1.id GROUP BY T1.id ORDER BY SUM(T2.total) DESC LIMIT 10;
+SELECT T1.name ,  COUNT(*) FROM genres AS T1 JOIN tracks AS T2 ON T2.genre_id  =  T1.id GROUP BY T1.id ORDER BY count(*) DESC LIMIT 5;
+SELECT T1.name ,  COUNT(*) FROM genres AS T1 JOIN tracks AS T2 ON T2.genre_id  =  T1.id GROUP BY T1.id ORDER BY count(*) DESC LIMIT 5;
+SELECT title FROM albums;
+SELECT title FROM albums;
+SELECT title FROM albums ORDER BY title;
+SELECT title FROM albums ORDER BY title;
+SELECT title FROM albums WHERE title LIKE 'A%' ORDER BY title;
+SELECT title FROM albums WHERE title LIKE 'A%' ORDER BY title;
+SELECT T1.first_name ,  T1.last_name FROM customers AS T1 JOIN invoices AS T2 ON T2.customer_id  =  T1.id ORDER BY total LIMIT 10;
+SELECT T1.first_name ,  T1.last_name FROM customers AS T1 JOIN invoices AS T2 ON T2.customer_id  =  T1.id ORDER BY total LIMIT 10;
+SELECT sum(total) FROM invoices WHERE billing_city  =  'Chicago' AND billing_state  =  'IL';
+SELECT sum(total) FROM invoices WHERE billing_city  =  'Chicago' AND billing_state  =  'IL';
+SELECT COUNT(*) FROM invoices WHERE billing_city  =  'Chicago' AND billing_state  =  'IL';
+SELECT COUNT(*) FROM invoices WHERE billing_city  =  'Chicago' AND billing_state  =  'IL';
+SELECT billing_state ,  COUNT(*) FROM invoices WHERE billing_country  =  'USA' GROUP BY billing_state;
+SELECT billing_state ,  COUNT(*) FROM invoices WHERE billing_country  =  'USA' GROUP BY billing_state;
+SELECT billing_state ,  COUNT(*) FROM invoices WHERE billing_country  =  'USA' GROUP BY billing_state ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT billing_state ,  COUNT(*) FROM invoices WHERE billing_country  =  'USA' GROUP BY billing_state ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT billing_state ,  COUNT(*) ,  SUM(total) FROM invoices WHERE billing_state  =  'CA';
+SELECT billing_state ,  COUNT(*) ,  SUM(total) FROM invoices WHERE billing_state  =  'CA';
+SELECT T1.title FROM albums AS T1 JOIN artists AS T2 ON  T1.artist_id = T2.id WHERE T2.name = 'Aerosmith';
+SELECT T1.title FROM albums AS T1 JOIN artists AS T2 ON  T1.artist_id = T2.id WHERE T2.name = 'Aerosmith';
+SELECT count(*) FROM albums AS T1 JOIN artists AS T2 ON  T1.artist_id = T2.id WHERE T2.name = 'Billy Cobham';
+SELECT count(*) FROM albums AS T1 JOIN artists AS T2 ON  T1.artist_id = T2.id WHERE T2.name = 'Billy Cobham';
+SELECT company FROM customers WHERE first_name = 'Eduardo' AND last_name = 'Martins';
+SELECT company FROM customers WHERE first_name = 'Eduardo' AND last_name = 'Martins';
+SELECT email ,  phone FROM customers WHERE first_name = 'Astrid' AND last_name = 'Gruber';
+SELECT email ,  phone FROM customers WHERE first_name = 'Astrid' AND last_name = 'Gruber';
+SELECT count(*) FROM customers WHERE city = 'Prague';
+SELECT count(*) FROM customers WHERE city = 'Prague';
+SELECT count(*) FROM customers WHERE state = 'CA';
+SELECT count(*) FROM customers WHERE state = 'CA';
+SELECT country FROM customers WHERE first_name = 'Roberto' AND last_name = 'Almeida';
+SELECT country FROM customers WHERE first_name = 'Roberto' AND last_name = 'Almeida';
+SELECT T2.title FROM artists AS T1 JOIN albums AS T2 ON T1.id  =  T2.artist_id WHERE T1.name LIKE '%Led%';
+SELECT T2.title FROM artists AS T1 JOIN albums AS T2 ON T1.id  =  T2.artist_id WHERE T1.name LIKE '%Led%';
+SELECT count(*) FROM employees AS T1 JOIN customers AS T2 ON T2.support_rep_id = T1.id WHERE T1.first_name = 'Steve' AND T1.last_name = 'Johnson';
+SELECT count(*) FROM employees AS T1 JOIN customers AS T2 ON T2.support_rep_id = T1.id WHERE T1.first_name = 'Steve' AND T1.last_name = 'Johnson';
+SELECT title ,  phone ,  hire_date FROM employees WHERE first_name = 'Nancy' AND last_name = 'Edwards';
+SELECT title ,  phone ,  hire_date FROM employees WHERE first_name = 'Nancy' AND last_name = 'Edwards';
+SELECT T2.first_name , T2.last_name FROM employees AS T1 JOIN employees AS T2 ON T1.id = T2.reports_to WHERE T1.first_name = 'Nancy' AND T1.last_name = 'Edwards';
+SELECT T2.first_name , T2.last_name FROM employees AS T1 JOIN employees AS T2 ON T1.id = T2.reports_to WHERE T1.first_name = 'Nancy' AND T1.last_name = 'Edwards';
+SELECT address FROM employees WHERE first_name = 'Nancy' AND last_name = 'Edwards';
+SELECT address FROM employees WHERE first_name = 'Nancy' AND last_name = 'Edwards';
+SELECT T1.first_name , T1.last_name FROM employees AS T1 JOIN customers AS T2 ON T1.id  =  T2.support_rep_id GROUP BY T1.id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.first_name , T1.last_name FROM employees AS T1 JOIN customers AS T2 ON T1.id  =  T2.support_rep_id GROUP BY T1.id ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM employees WHERE country = 'Canada';
+SELECT count(*) FROM employees WHERE country = 'Canada';
+SELECT phone FROM employees WHERE first_name = 'Nancy' AND last_name = 'Edwards';
+SELECT phone FROM employees WHERE first_name = 'Nancy' AND last_name = 'Edwards';
+SELECT first_name , last_name FROM employees ORDER BY birth_date DESC LIMIT 1;
+SELECT first_name , last_name FROM employees ORDER BY birth_date DESC LIMIT 1;
+SELECT first_name , last_name FROM employees ORDER BY hire_date ASC LIMIT 10;
+SELECT first_name , last_name FROM employees ORDER BY hire_date ASC LIMIT 10;
+SELECT count(*) ,  city FROM employees WHERE title  =  'IT Staff' GROUP BY city;
+SELECT count(*) ,  city FROM employees WHERE title  =  'IT Staff' GROUP BY city;
+SELECT T2.first_name , T2.last_name ,  count(T1.reports_to) FROM employees AS T1 JOIN employees AS T2 ON T1.reports_to = T2.id GROUP BY T1.reports_to ORDER BY count(T1.reports_to) DESC LIMIT 1;
+SELECT T2.first_name , T2.last_name ,  count(T1.reports_to) FROM employees AS T1 JOIN employees AS T2 ON T1.reports_to = T2.id GROUP BY T1.reports_to ORDER BY count(T1.reports_to) DESC LIMIT 1;
+SELECT count(*) FROM customers AS T1 JOIN invoices AS T2 ON T1.id = T2.customer_id WHERE T1.first_name = 'Lucas' AND T1.last_name = 'Mancini';
+SELECT count(*) FROM customers AS T1 JOIN invoices AS T2 ON T1.id = T2.customer_id WHERE T1.first_name = 'Lucas' AND T1.last_name = 'Mancini';
+SELECT sum(T2.total) FROM customers AS T1 JOIN invoices AS T2 ON T1.id = T2.customer_id WHERE T1.first_name = 'Lucas' AND T1.last_name = 'Mancini';
+SELECT sum(T2.total) FROM customers AS T1 JOIN invoices AS T2 ON T1.id = T2.customer_id WHERE T1.first_name = 'Lucas' AND T1.last_name = 'Mancini';
+SELECT name FROM media_types;
+SELECT name FROM media_types;
+SELECT DISTINCT name FROM genres;
+SELECT DISTINCT name FROM genres;
+SELECT name FROM playlists;
+SELECT name FROM playlists;
+SELECT composer FROM tracks WHERE name = 'Fast As a Shark';
+SELECT composer FROM tracks WHERE name = 'Fast As a Shark';
+SELECT milliseconds FROM tracks WHERE name = 'Fast As a Shark';
+SELECT milliseconds FROM tracks WHERE name = 'Fast As a Shark';
+SELECT T2.name FROM genres AS T1 JOIN tracks AS T2 ON T1.id = T2.genre_id WHERE T1.name = 'Rock';
+SELECT T2.name FROM genres AS T1 JOIN tracks AS T2 ON T1.id = T2.genre_id WHERE T1.name = 'Rock';
+SELECT T1.title FROM albums AS T1 JOIN tracks AS T2 ON T1.id = T2.genre_id WHERE T2.name = 'Balls to the Wall';
+SELECT T1.title FROM albums AS T1 JOIN tracks AS T2 ON T1.id = T2.genre_id WHERE T2.name = 'Balls to the Wall';
+SELECT T2.name FROM albums AS T1 JOIN tracks AS T2 ON T1.id = T2.genre_id WHERE T1.title = 'Balls to the Wall';
+SELECT T2.name FROM albums AS T1 JOIN tracks AS T2 ON T1.id = T2.genre_id WHERE T1.title = 'Balls to the Wall';
+SELECT T1.title FROM albums AS T1 JOIN tracks AS T2 ON T1.id = T2.album_id GROUP BY T1.id HAVING count(T1.id)  >  10;
+SELECT T1.title FROM albums AS T1 JOIN tracks AS T2 ON T1.id = T2.album_id GROUP BY T1.id HAVING count(T1.id)  >  10;
+SELECT T2.name FROM genres AS T1 JOIN tracks AS T2 ON T1.id = T2.genre_id JOIN media_types AS T3 ON T3.id = T2.media_type_id WHERE T1.name = 'Rock' AND T3.name = 'MPEG audio file';
+SELECT T2.name FROM genres AS T1 JOIN tracks AS T2 ON T1.id = T2.genre_id JOIN media_types AS T3 ON T3.id = T2.media_type_id WHERE T1.name = 'Rock' AND T3.name = 'MPEG audio file';
+SELECT T2.name FROM genres AS T1 JOIN tracks AS T2 ON T1.id = T2.genre_id JOIN media_types AS T3 ON T3.id = T2.media_type_id WHERE T1.name = 'Rock' OR T3.name = 'MPEG audio file';
+SELECT T2.name FROM genres AS T1 JOIN tracks AS T2 ON T1.id = T2.genre_id JOIN media_types AS T3 ON T3.id = T2.media_type_id WHERE T1.name = 'Rock' OR T3.name = 'MPEG audio file';
+SELECT T2.name FROM genres AS T1 JOIN tracks AS T2 ON T1.id = T2.genre_id WHERE T1.name = 'Rock' OR T1.name = 'Jazz';
+SELECT T2.name FROM genres AS T1 JOIN tracks AS T2 ON T1.id = T2.genre_id WHERE T1.name = 'Rock' OR T1.name = 'Jazz';
+SELECT T1.name FROM tracks AS T1 JOIN playlist_tracks AS T2 ON T1.id = T2.track_id JOIN playlists AS T3 ON T3.id = T2.playlist_id WHERE T3.name = 'Movies';
+SELECT T1.name FROM tracks AS T1 JOIN playlist_tracks AS T2 ON T1.id = T2.track_id JOIN playlists AS T3 ON T3.id = T2.playlist_id WHERE T3.name = 'Movies';
+SELECT T2.name FROM playlist_tracks AS T1 JOIN playlists AS T2 ON T2.id = T1.playlist_id GROUP BY T1.playlist_id HAVING count(T1.track_id)  >  100;
+SELECT T2.name FROM playlist_tracks AS T1 JOIN playlists AS T2 ON T2.id = T1.playlist_id GROUP BY T1.playlist_id HAVING count(T1.track_id)  >  100;
+SELECT T1.name FROM tracks AS T1 JOIN invoice_lines AS T2 ON T1.id = T2.track_id JOIN invoices AS T3 ON T3.id = T2.invoice_id JOIN customers AS T4 ON T4.id = T3.customer_id WHERE T4.first_name = 'Daan' AND T4.last_name = 'Peeters';
+SELECT T1.name FROM tracks AS T1 JOIN invoice_lines AS T2 ON T1.id = T2.track_id JOIN invoices AS T3 ON T3.id = T2.invoice_id JOIN customers AS T4 ON T4.id = T3.customer_id WHERE T4.first_name = 'Daan' AND T4.last_name = 'Peeters';
+SELECT unit_price FROM tracks WHERE name = 'Fast As a Shark';
+SELECT unit_price FROM tracks WHERE name = 'Fast As a Shark';
+SELECT T1.name FROM tracks AS T1 JOIN playlist_tracks AS T2 ON T1.id  =  T2.track_id JOIN playlists AS T3 ON T2.playlist_id  =  T3.id WHERE T3.name  =  'Movies' EXCEPT SELECT T1.name FROM tracks AS T1 JOIN playlist_tracks AS T2 ON T1.id  =  T2.track_id JOIN playlists AS T3 ON T2.playlist_id  =  T3.id WHERE T3.name  =  'Music';
+SELECT T1.name FROM tracks AS T1 JOIN playlist_tracks AS T2 ON T1.id  =  T2.track_id JOIN playlists AS T3 ON T2.playlist_id  =  T3.id WHERE T3.name  =  'Movies' EXCEPT SELECT T1.name FROM tracks AS T1 JOIN playlist_tracks AS T2 ON T1.id  =  T2.track_id JOIN playlists AS T3 ON T2.playlist_id  =  T3.id WHERE T3.name  =  'Music';
+SELECT T1.name FROM tracks AS T1 JOIN playlist_tracks AS T2 ON T1.id  =  T2.track_id JOIN playlists AS T3 ON T2.playlist_id  =  T3.id WHERE T3.name  =  'Movies' INTERSECT SELECT T1.name FROM tracks AS T1 JOIN playlist_tracks AS T2 ON T1.id  =  T2.track_id JOIN playlists AS T3 ON T2.playlist_id  =  T3.id WHERE T3.name  =  'Music';
+SELECT T1.name FROM tracks AS T1 JOIN playlist_tracks AS T2 ON T1.id  =  T2.track_id JOIN playlists AS T3 ON T2.playlist_id  =  T3.id WHERE T3.name  =  'Movies' INTERSECT SELECT T1.name FROM tracks AS T1 JOIN playlist_tracks AS T2 ON T1.id  =  T2.track_id JOIN playlists AS T3 ON T2.playlist_id  =  T3.id WHERE T3.name  =  'Music';
+SELECT count(*) ,  T1.name FROM genres AS T1 JOIN tracks AS T2 ON T1.id = T2.genre_id GROUP BY T1.name;
+SELECT count(*) ,  T1.name FROM genres AS T1 JOIN tracks AS T2 ON T1.id = T2.genre_id GROUP BY T1.name;
+SELECT count(*) FROM editor;
+SELECT Name FROM editor ORDER BY Age ASC;
+SELECT Name ,  Age FROM editor;
+SELECT Name FROM editor WHERE Age  >  25;
+SELECT Name FROM editor WHERE Age  =  24 OR Age  =  25;
+SELECT Name FROM editor ORDER BY Age ASC LIMIT 1;
+SELECT Age ,  COUNT(*) FROM editor GROUP BY Age;
+SELECT Age FROM editor GROUP BY Age ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT DISTINCT Theme FROM journal;
+SELECT T2.Name ,  T3.Theme FROM journal_committee AS T1 JOIN editor AS T2 ON T1.Editor_ID  =  T2.Editor_ID JOIN journal AS T3 ON T1.Journal_ID  =  T3.Journal_ID;
+SELECT T2.Name ,  T3.Theme FROM journal_committee AS T1 JOIN editor AS T2 ON T1.Editor_ID  =  T2.Editor_ID JOIN journal AS T3 ON T1.Journal_ID  =  T3.Journal_ID;
+SELECT T2.Name ,  T2.age ,  T3.Theme FROM journal_committee AS T1 JOIN editor AS T2 ON T1.Editor_ID  =  T2.Editor_ID JOIN journal AS T3 ON T1.Journal_ID  =  T3.Journal_ID ORDER BY T3.Theme ASC;
+SELECT T2.Name FROM journal_committee AS T1 JOIN editor AS T2 ON T1.Editor_ID  =  T2.Editor_ID JOIN journal AS T3 ON T1.Journal_ID  =  T3.Journal_ID WHERE T3.Sales  >  3000;
+SELECT T1.editor_id ,  T1.Name ,  COUNT(*) FROM editor AS T1 JOIN journal_committee AS T2 ON T1.Editor_ID  =  T2.Editor_ID GROUP BY T1.editor_id;
+SELECT T1.Name FROM editor AS T1 JOIN journal_committee AS T2 ON T1.Editor_ID  =  T2.Editor_ID GROUP BY T1.Name HAVING COUNT(*)  >=  2;
+SELECT Name FROM editor WHERE editor_id NOT IN (SELECT editor_id FROM journal_committee);
+SELECT date ,  theme ,  sales FROM journal EXCEPT SELECT T1.date ,  T1.theme ,  T1.sales FROM journal AS T1 JOIN journal_committee AS T2 ON T1.journal_ID  =  T2.journal_ID;
+SELECT avg(T1.sales) FROM journal AS T1 JOIN journal_committee AS T2 ON T1.journal_ID  =  T2.journal_ID WHERE T2.work_type  =  'Photo';
+SELECT count(*) FROM Accounts;
+SELECT count(*) FROM Accounts;
+SELECT account_id ,  customer_id ,  account_name FROM Accounts;
+SELECT account_id ,  customer_id ,  account_name FROM Accounts;
+SELECT other_account_details FROM Accounts WHERE account_name  =  '338';
+SELECT other_account_details FROM Accounts WHERE account_name  =  '338';
+SELECT T2.customer_first_name ,  T2.customer_last_name ,  T2.customer_phone FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T1.account_name  =  '162';
+SELECT T2.customer_first_name ,  T2.customer_last_name ,  T2.customer_phone FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T1.account_name  =  '162';
+SELECT count(*) FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.customer_first_name  =  'Art' AND T2.customer_last_name  =  'Turcotte';
+SELECT count(*) FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.customer_first_name  =  'Art' AND T2.customer_last_name  =  'Turcotte';
+SELECT customer_id ,  count(*) FROM Accounts GROUP BY customer_id;
+SELECT customer_id ,  count(*) FROM Accounts GROUP BY customer_id;
+SELECT customer_id ,  count(*) FROM Accounts GROUP BY customer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT customer_id ,  count(*) FROM Accounts GROUP BY customer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.customer_first_name ,  T2.customer_last_name ,  T1.customer_id FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id ORDER BY count(*) ASC LIMIT 1;
+SELECT T2.customer_first_name ,  T2.customer_last_name ,  T1.customer_id FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id ORDER BY count(*) ASC LIMIT 1;
+SELECT count(*) FROM Customers WHERE customer_id NOT IN (SELECT customer_id FROM Accounts);
+SELECT count(*) FROM Customers WHERE customer_id NOT IN (SELECT customer_id FROM Accounts);
+SELECT customer_first_name ,  customer_last_name FROM Customers EXCEPT SELECT T1.customer_first_name ,  T1.customer_last_name FROM Customers AS T1 JOIN Accounts AS T2 ON T1.customer_id  =  T2.customer_id;
+SELECT customer_first_name ,  customer_last_name FROM Customers EXCEPT SELECT T1.customer_first_name ,  T1.customer_last_name FROM Customers AS T1 JOIN Accounts AS T2 ON T1.customer_id  =  T2.customer_id;
+SELECT DISTINCT T1.customer_first_name ,  T1.customer_last_name FROM Customers AS T1 JOIN Accounts AS T2 ON T1.customer_id  =  T2.customer_id;
+SELECT DISTINCT T1.customer_first_name ,  T1.customer_last_name FROM Customers AS T1 JOIN Accounts AS T2 ON T1.customer_id  =  T2.customer_id;
+SELECT count(DISTINCT customer_id) FROM Accounts;
+SELECT count(DISTINCT customer_id) FROM Accounts;
+SELECT count(*) FROM Customers;
+SELECT count(*) FROM Customers;
+SELECT customer_id ,  customer_first_name ,  customer_last_name ,  customer_phone FROM Customers;
+SELECT customer_id ,  customer_first_name ,  customer_last_name ,  customer_phone FROM Customers;
+SELECT customer_phone ,  customer_email FROM Customers WHERE customer_first_name  =  'Aniyah' AND customer_last_name  =  'Feest';
+SELECT customer_phone ,  customer_email FROM Customers WHERE customer_first_name  =  'Aniyah' AND customer_last_name  =  'Feest';
+SELECT count(*) FROM Customers_cards;
+SELECT count(*) FROM Customers_cards;
+SELECT card_id ,  customer_id ,  card_type_code ,  card_number FROM Customers_cards;
+SELECT card_id ,  customer_id ,  card_type_code ,  card_number FROM Customers_cards;
+SELECT date_valid_from ,  date_valid_to FROM Customers_cards WHERE card_number  =  '4560596484842';
+SELECT date_valid_from ,  date_valid_to FROM Customers_cards WHERE card_number  =  '4560596484842';
+SELECT T2.customer_first_name ,  T2.customer_last_name ,  T2.customer_phone FROM Customers_cards AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T1.card_number  =  '4560596484842';
+SELECT T2.customer_first_name ,  T2.customer_last_name ,  T2.customer_phone FROM Customers_cards AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T1.card_number  =  '4560596484842';
+SELECT count(*) FROM Customers_cards AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.customer_first_name  =  'Art' AND T2.customer_last_name  =  'Turcotte';
+SELECT count(*) FROM Customers_cards AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.customer_first_name  =  'Art' AND T2.customer_last_name  =  'Turcotte';
+SELECT count(*) FROM Customers_cards WHERE card_type_code  =  'Debit';
+SELECT count(*) FROM Customers_cards WHERE card_type_code  =  'Debit';
+SELECT count(*) FROM Customers_cards AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.customer_first_name  =  'Blanche' AND T2.customer_last_name  =  'Huels' AND T1.card_type_code  =  'Credit';
+SELECT count(*) FROM Customers_cards AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.customer_first_name  =  'Blanche' AND T2.customer_last_name  =  'Huels' AND T1.card_type_code  =  'Credit';
+SELECT customer_id ,  count(*) FROM Customers_cards GROUP BY customer_id;
+SELECT customer_id ,  count(*) FROM Customers_cards GROUP BY customer_id;
+SELECT customer_id ,  count(*) FROM Customers_cards GROUP BY customer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT customer_id ,  count(*) FROM Customers_cards GROUP BY customer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.customer_id ,  T2.customer_first_name ,  T2.customer_last_name FROM Customers_cards AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id HAVING count(*)  >=  2;
+SELECT T1.customer_id ,  T2.customer_first_name ,  T2.customer_last_name FROM Customers_cards AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id HAVING count(*)  >=  2;
+SELECT T1.customer_id ,  T2.customer_first_name ,  T2.customer_last_name FROM Customers_cards AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id ORDER BY count(*) ASC LIMIT 1;
+SELECT T1.customer_id ,  T2.customer_first_name ,  T2.customer_last_name FROM Customers_cards AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id ORDER BY count(*) ASC LIMIT 1;
+SELECT card_type_code ,  count(*) FROM Customers_cards GROUP BY card_type_code;
+SELECT card_type_code ,  count(*) FROM Customers_cards GROUP BY card_type_code;
+SELECT card_type_code FROM Customers_cards GROUP BY card_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT card_type_code FROM Customers_cards GROUP BY card_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT card_type_code FROM Customers_cards GROUP BY card_type_code HAVING count(*)  >=  5;
+SELECT card_type_code FROM Customers_cards GROUP BY card_type_code HAVING count(*)  >=  5;
+SELECT card_type_code ,  count(DISTINCT customer_id) FROM Customers_cards GROUP BY card_type_code;
+SELECT card_type_code ,  count(DISTINCT customer_id) FROM Customers_cards GROUP BY card_type_code;
+SELECT customer_id ,  customer_first_name FROM Customers EXCEPT SELECT T1.customer_id ,  T2.customer_first_name FROM Customers_cards AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE card_type_code  =  'Credit';
+SELECT customer_id ,  customer_first_name FROM Customers EXCEPT SELECT T1.customer_id ,  T2.customer_first_name FROM Customers_cards AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE card_type_code  =  'Credit';
+SELECT DISTINCT card_type_code FROM Customers_Cards;
+SELECT DISTINCT card_type_code FROM Customers_Cards;
+SELECT count(DISTINCT card_type_code) FROM Customers_Cards;
+SELECT count(DISTINCT card_type_code) FROM Customers_Cards;
+SELECT DISTINCT transaction_type FROM Financial_Transactions;
+SELECT DISTINCT transaction_type FROM Financial_Transactions;
+SELECT count(DISTINCT transaction_type) FROM Financial_Transactions;
+SELECT count(DISTINCT transaction_type) FROM Financial_Transactions;
+SELECT avg(transaction_amount) ,  sum(transaction_amount) FROM Financial_transactions;
+SELECT avg(transaction_amount) ,  sum(transaction_amount) FROM Financial_transactions;
+SELECT T2.card_type_code ,  count(*) FROM Financial_transactions AS T1 JOIN Customers_cards AS T2 ON T1.card_id  =  T2.card_id GROUP BY T2.card_type_code;
+SELECT T2.card_type_code ,  count(*) FROM Financial_transactions AS T1 JOIN Customers_cards AS T2 ON T1.card_id  =  T2.card_id GROUP BY T2.card_type_code;
+SELECT transaction_type ,  count(*) FROM Financial_transactions GROUP BY transaction_type;
+SELECT transaction_type ,  count(*) FROM Financial_transactions GROUP BY transaction_type;
+SELECT transaction_type FROM Financial_transactions GROUP BY transaction_type ORDER BY sum(transaction_amount) DESC LIMIT 1;
+SELECT transaction_type FROM Financial_transactions GROUP BY transaction_type ORDER BY sum(transaction_amount) DESC LIMIT 1;
+SELECT account_id ,  count(*) FROM Financial_transactions GROUP BY account_id;
+SELECT account_id ,  count(*) FROM Financial_transactions GROUP BY account_id;
+SELECT count(*) FROM track;
+SELECT count(*) FROM track;
+SELECT name ,  LOCATION FROM track;
+SELECT name ,  LOCATION FROM track;
+SELECT name ,  seating FROM track WHERE year_opened  >  2000 ORDER BY seating;
+SELECT name ,  seating FROM track WHERE year_opened  >  2000 ORDER BY seating;
+SELECT name ,  LOCATION ,  seating FROM track ORDER BY year_opened DESC LIMIT 1;
+SELECT name ,  LOCATION ,  seating FROM track ORDER BY year_opened DESC LIMIT 1;
+SELECT min(seating) ,  max(seating) ,  avg(seating) FROM track;
+SELECT min(seating) ,  max(seating) ,  avg(seating) FROM track;
+SELECT name ,  LOCATION ,  year_opened FROM track WHERE seating  >  (SELECT avg(seating) FROM track);
+SELECT name ,  LOCATION ,  year_opened FROM track WHERE seating  >  (SELECT avg(seating) FROM track);
+SELECT DISTINCT LOCATION FROM track;
+SELECT DISTINCT LOCATION FROM track;
+SELECT count(*) FROM race;
+SELECT count(*) FROM race;
+SELECT DISTINCT CLASS FROM race;
+SELECT DISTINCT CLASS FROM race;
+SELECT name ,  CLASS ,  date FROM race;
+SELECT name ,  CLASS ,  date FROM race;
+SELECT CLASS ,  count(*) FROM race GROUP BY CLASS;
+SELECT CLASS ,  count(*) FROM race GROUP BY CLASS;
+SELECT CLASS FROM race GROUP BY CLASS ORDER BY count(*) DESC LIMIT 1;
+SELECT CLASS FROM race GROUP BY CLASS ORDER BY count(*) DESC LIMIT 1;
+SELECT CLASS FROM race GROUP BY CLASS HAVING count(*)  >=  2;
+SELECT CLASS FROM race GROUP BY CLASS HAVING count(*)  >=  2;
+SELECT name FROM track EXCEPT SELECT T2.name FROM race AS T1 JOIN track AS T2 ON T1.track_id  =  T2.track_id WHERE T1.class  =  'GT';
+SELECT name FROM track EXCEPT SELECT T2.name FROM race AS T1 JOIN track AS T2 ON T1.track_id  =  T2.track_id WHERE T1.class  =  'GT';
+SELECT name FROM track WHERE track_id NOT IN (SELECT track_id FROM race);
+SELECT name FROM track WHERE track_id NOT IN (SELECT track_id FROM race);
+SELECT year_opened FROM track WHERE seating BETWEEN 4000 AND 5000;
+SELECT year_opened FROM track WHERE seating BETWEEN 4000 AND 5000;
+SELECT T2.name ,  count(*) FROM race AS T1 JOIN track AS T2 ON T1.track_id  =  T2.track_id GROUP BY T1.track_id;
+SELECT T2.name ,  count(*) FROM race AS T1 JOIN track AS T2 ON T1.track_id  =  T2.track_id GROUP BY T1.track_id;
+SELECT T2.name FROM race AS T1 JOIN track AS T2 ON T1.track_id  =  T2.track_id GROUP BY T1.track_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.name FROM race AS T1 JOIN track AS T2 ON T1.track_id  =  T2.track_id GROUP BY T1.track_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name ,  T1.date ,  T2.name FROM race AS T1 JOIN track AS T2 ON T1.track_id  =  T2.track_id;
+SELECT T1.name ,  T1.date ,  T2.name FROM race AS T1 JOIN track AS T2 ON T1.track_id  =  T2.track_id;
+SELECT T2.name ,  T2.location FROM race AS T1 JOIN track AS T2 ON T1.track_id  =  T2.track_id GROUP BY T1.track_id HAVING count(*)  =  1;
+SELECT T2.name ,  T2.location FROM race AS T1 JOIN track AS T2 ON T1.track_id  =  T2.track_id GROUP BY T1.track_id HAVING count(*)  =  1;
+SELECT LOCATION FROM track WHERE seating  >  90000 INTERSECT SELECT LOCATION FROM track WHERE seating  <  70000;
+SELECT LOCATION FROM track WHERE seating  >  90000 INTERSECT SELECT LOCATION FROM track WHERE seating  <  70000;
+SELECT count(*) FROM member WHERE Membership_card  =  'Black';
+SELECT count(*) ,  address FROM member GROUP BY address;
+SELECT name FROM member WHERE address  =  'Harford' OR address  =  'Waterbury';
+SELECT name ,  member_id FROM member WHERE Membership_card  =  'Black' OR age  <  30;
+SELECT Time_of_purchase ,  age ,  address FROM member ORDER BY Time_of_purchase;
+SELECT Membership_card FROM member GROUP BY Membership_card HAVING count(*)  >  5;
+SELECT address FROM member WHERE age  <  30 INTERSECT SELECT address FROM member WHERE age  >  40;
+SELECT membership_card FROM member WHERE address  =  'Hartford' INTERSECT SELECT membership_card FROM member WHERE address  =  'Waterbury';
+SELECT count(*) FROM member WHERE address != 'Hartford';
+SELECT address FROM member EXCEPT SELECT address FROM member WHERE Membership_card  =  'Black';
+SELECT address FROM shop ORDER BY open_year;
+SELECT avg(num_of_staff) ,  avg(score) FROM shop;
+SELECT shop_id ,  address FROM shop WHERE score  <  (SELECT avg(score) FROM shop);
+SELECT address ,  num_of_staff FROM shop WHERE shop_id NOT IN (SELECT shop_id FROM happy_hour);
+SELECT t1.address ,  t1.shop_id FROM shop AS t1 JOIN happy_hour AS t2 ON t1.shop_id  =  t2.shop_id WHERE MONTH  =  'May';
+SELECT shop_id ,  count(*) FROM happy_hour GROUP BY shop_id ORDER BY count(*) DESC LIMIT 1;
+SELECT MONTH FROM happy_hour GROUP BY MONTH ORDER BY count(*) DESC LIMIT 1;
+SELECT MONTH FROM happy_hour GROUP BY MONTH HAVING count(*)  >  2;
+SELECT count(*) FROM ALBUM;
+SELECT count(*) FROM ALBUM;
+SELECT Name FROM GENRE;
+SELECT Name FROM GENRE;
+SELECT * FROM CUSTOMER WHERE State  =  'NY';
+SELECT * FROM CUSTOMER WHERE State  =  'NY';
+SELECT FirstName ,  LastName FROM EMPLOYEE WHERE City  =  'Calgary';
+SELECT FirstName ,  LastName FROM EMPLOYEE WHERE City  =  'Calgary';
+SELECT distinct(BillingCountry) FROM INVOICE;
+SELECT distinct(BillingCountry) FROM INVOICE;
+SELECT Name FROM ARTIST WHERE Name LIKE '%a%';
+SELECT Name FROM ARTIST WHERE Name LIKE '%a%';
+SELECT Title FROM ALBUM AS T1 JOIN ARTIST AS T2 ON T1.ArtistId  =  T2.ArtistId WHERE T2.Name  =  'AC/DC';
+SELECT Title FROM ALBUM AS T1 JOIN ARTIST AS T2 ON T1.ArtistId  =  T2.ArtistId WHERE T2.Name  =  'AC/DC';
+SELECT COUNT(*) FROM ALBUM AS T1 JOIN ARTIST AS T2 ON T1.ArtistId  =  T2.ArtistId WHERE T2.Name  =  'Metallica';
+SELECT COUNT(*) FROM ALBUM AS T1 JOIN ARTIST AS T2 ON T1.ArtistId  =  T2.ArtistId WHERE T2.Name  =  'Metallica';
+SELECT T2.Name FROM ALBUM AS T1 JOIN ARTIST AS T2 ON T1.ArtistId  =  T2.ArtistId WHERE T1.Title  =  'Balls to the Wall';
+SELECT T2.Name FROM ALBUM AS T1 JOIN ARTIST AS T2 ON T1.ArtistId  =  T2.ArtistId WHERE T1.Title  =  'Balls to the Wall';
+SELECT T2.Name FROM ALBUM AS T1 JOIN ARTIST AS T2 ON T1.ArtistId  =  T2.ArtistId GROUP BY T2.Name ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T2.Name FROM ALBUM AS T1 JOIN ARTIST AS T2 ON T1.ArtistId  =  T2.ArtistId GROUP BY T2.Name ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Name FROM TRACK WHERE Name LIKE '%you%';
+SELECT Name FROM TRACK WHERE Name LIKE '%you%';
+SELECT AVG(UnitPrice) FROM TRACK;
+SELECT AVG(UnitPrice) FROM TRACK;
+SELECT max(Milliseconds) ,  min(Milliseconds) FROM TRACK;
+SELECT max(Milliseconds) ,  min(Milliseconds) FROM TRACK;
+SELECT T1.Title ,  T2.AlbumID ,  COUNT(*) FROM ALBUM AS T1 JOIN TRACK AS T2 ON T1.AlbumId  =  T2.AlbumId GROUP BY T2.AlbumID;
+SELECT T1.Title ,  T2.AlbumID ,  COUNT(*) FROM ALBUM AS T1 JOIN TRACK AS T2 ON T1.AlbumId  =  T2.AlbumId GROUP BY T2.AlbumID;
+SELECT T1.Name FROM GENRE AS T1 JOIN TRACK AS T2 ON T1.GenreId  =  T2.GenreId GROUP BY T2.GenreId ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T1.Name FROM GENRE AS T1 JOIN TRACK AS T2 ON T1.GenreId  =  T2.GenreId GROUP BY T2.GenreId ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T1.Name FROM MEDIATYPE AS T1 JOIN TRACK AS T2 ON T1.MediaTypeId  =  T2.MediaTypeId GROUP BY T2.MediaTypeId ORDER BY COUNT(*) ASC LIMIT 1;
+SELECT T1.Name FROM MEDIATYPE AS T1 JOIN TRACK AS T2 ON T1.MediaTypeId  =  T2.MediaTypeId GROUP BY T2.MediaTypeId ORDER BY COUNT(*) ASC LIMIT 1;
+SELECT T1.Title ,  T2.AlbumID FROM ALBUM AS T1 JOIN TRACK AS T2 ON T1.AlbumId  =  T2.AlbumId WHERE T2.UnitPrice  >  1 GROUP BY T2.AlbumID;
+SELECT T1.Title ,  T2.AlbumID FROM ALBUM AS T1 JOIN TRACK AS T2 ON T1.AlbumId  =  T2.AlbumId WHERE T2.UnitPrice  >  1 GROUP BY T2.AlbumID;
+SELECT COUNT(*) FROM GENRE AS T1 JOIN TRACK AS T2 ON T1.GenreId  =  T2.GenreId WHERE T1.Name  =  'Rock';
+SELECT COUNT(*) FROM GENRE AS T1 JOIN TRACK AS T2 ON T1.GenreId  =  T2.GenreId WHERE T1.Name  =  'Rock';
+SELECT AVG(UnitPrice) FROM GENRE AS T1 JOIN TRACK AS T2 ON T1.GenreId  =  T2.GenreId WHERE T1.Name  =  'Jazz';
+SELECT AVG(UnitPrice) FROM GENRE AS T1 JOIN TRACK AS T2 ON T1.GenreId  =  T2.GenreId WHERE T1.Name  =  'Jazz';
+SELECT FirstName ,  LastName FROM CUSTOMER WHERE Email  =  'luisg@embraer.com.br';
+SELECT FirstName ,  LastName FROM CUSTOMER WHERE Email  =  'luisg@embraer.com.br';
+SELECT COUNT(*) FROM CUSTOMER WHERE Email LIKE '%gmail.com%';
+SELECT COUNT(*) FROM CUSTOMER WHERE Email LIKE '%gmail.com%';
+SELECT T2.FirstName ,  T2.LastName FROM CUSTOMER AS T1 JOIN EMPLOYEE AS T2 ON T1.SupportRepId  =  T2.EmployeeId WHERE T1.FirstName  =  'Leonie';
+SELECT T2.FirstName ,  T2.LastName FROM CUSTOMER AS T1 JOIN EMPLOYEE AS T2 ON T1.SupportRepId  =  T2.EmployeeId WHERE T1.FirstName  =  'Leonie';
+SELECT T2.City FROM CUSTOMER AS T1 JOIN EMPLOYEE AS T2 ON T1.SupportRepId  =  T2.EmployeeId WHERE T1.PostalCode  =  '70174';
+SELECT T2.City FROM CUSTOMER AS T1 JOIN EMPLOYEE AS T2 ON T1.SupportRepId  =  T2.EmployeeId WHERE T1.PostalCode  =  '70174';
+SELECT COUNT(DISTINCT city) FROM EMPLOYEE;
+SELECT COUNT(DISTINCT city) FROM EMPLOYEE;
+SELECT T2.InvoiceDate FROM CUSTOMER AS T1 JOIN INVOICE AS T2 ON T1.CustomerId  =  T2.CustomerId WHERE T1.FirstName  =  'Astrid' AND LastName  =  'Gruber';
+SELECT T2.InvoiceDate FROM CUSTOMER AS T1 JOIN INVOICE AS T2 ON T1.CustomerId  =  T2.CustomerId WHERE T1.FirstName  =  'Astrid' AND LastName  =  'Gruber';
+SELECT LastName FROM CUSTOMER EXCEPT SELECT T1.LastName FROM CUSTOMER AS T1 JOIN Invoice AS T2 ON T1.CustomerId  =  T2.CustomerId WHERE T2.total  >  20;
+SELECT LastName FROM CUSTOMER EXCEPT SELECT T1.LastName FROM CUSTOMER AS T1 JOIN Invoice AS T2 ON T1.CustomerId  =  T2.CustomerId WHERE T2.total  >  20;
+SELECT DISTINCT T1.FirstName FROM CUSTOMER AS T1 JOIN INVOICE AS T2 ON T1.CustomerId  =  T2.CustomerId WHERE T1.country  =  'Brazil';
+SELECT DISTINCT T1.FirstName FROM CUSTOMER AS T1 JOIN INVOICE AS T2 ON T1.CustomerId  =  T2.CustomerId WHERE T1.country  =  'Brazil';
+SELECT DISTINCT T1.Address FROM CUSTOMER AS T1 JOIN INVOICE AS T2 ON T1.CustomerId  =  T2.CustomerId WHERE T1.country  =  'Germany';
+SELECT DISTINCT T1.Address FROM CUSTOMER AS T1 JOIN INVOICE AS T2 ON T1.CustomerId  =  T2.CustomerId WHERE T1.country  =  'Germany';
+SELECT Phone FROM EMPLOYEE;
+SELECT Phone FROM EMPLOYEE;
+SELECT COUNT(*) FROM MEDIATYPE AS T1 JOIN TRACK AS T2 ON T1.MediaTypeId  =  T2.MediaTypeId WHERE T1.Name  =  'AAC audio file';
+SELECT COUNT(*) FROM MEDIATYPE AS T1 JOIN TRACK AS T2 ON T1.MediaTypeId  =  T2.MediaTypeId WHERE T1.Name  =  'AAC audio file';
+SELECT AVG(Milliseconds) FROM GENRE AS T1 JOIN TRACK AS T2 ON T1.GenreId  =  T2.GenreId WHERE T1.Name  =  'Latin' OR T1.Name  =  'Pop';
+SELECT AVG(Milliseconds) FROM GENRE AS T1 JOIN TRACK AS T2 ON T1.GenreId  =  T2.GenreId WHERE T1.Name  =  'Latin' OR T1.Name  =  'Pop';
+SELECT T1.FirstName ,  T1.SupportRepId FROM CUSTOMER AS T1 JOIN EMPLOYEE AS T2 ON T1.SupportRepId  =  T2.EmployeeId GROUP BY T1.SupportRepId HAVING COUNT(*)  >=  10;
+SELECT T1.FirstName ,  T1.SupportRepId FROM CUSTOMER AS T1 JOIN EMPLOYEE AS T2 ON T1.SupportRepId  =  T2.EmployeeId GROUP BY T1.SupportRepId HAVING COUNT(*)  >=  10;
+SELECT T1.LastName FROM CUSTOMER AS T1 JOIN EMPLOYEE AS T2 ON T1.SupportRepId  =  T2.EmployeeId GROUP BY T1.SupportRepId HAVING COUNT(*)  <=  20;
+SELECT T1.LastName FROM CUSTOMER AS T1 JOIN EMPLOYEE AS T2 ON T1.SupportRepId  =  T2.EmployeeId GROUP BY T1.SupportRepId HAVING COUNT(*)  <=  20;
+SELECT Title FROM ALBUM ORDER BY Title;
+SELECT Title FROM ALBUM ORDER BY Title;
+SELECT T2.Name ,  T1.ArtistId FROM ALBUM AS T1 JOIN ARTIST AS T2 ON T1.ArtistId  =  T2.ArtistID GROUP BY T1.ArtistId HAVING COUNT(*)  >=  3 ORDER BY T2.Name;
+SELECT T2.Name ,  T1.ArtistId FROM ALBUM AS T1 JOIN ARTIST AS T2 ON T1.ArtistId  =  T2.ArtistID GROUP BY T1.ArtistId HAVING COUNT(*)  >=  3 ORDER BY T2.Name;
+SELECT Name FROM ARTIST EXCEPT SELECT T2.Name FROM ALBUM AS T1 JOIN ARTIST AS T2 ON T1.ArtistId  =  T2.ArtistId;
+SELECT Name FROM ARTIST EXCEPT SELECT T2.Name FROM ALBUM AS T1 JOIN ARTIST AS T2 ON T1.ArtistId  =  T2.ArtistId;
+SELECT AVG(T2.UnitPrice) FROM GENRE AS T1 JOIN TRACK AS T2 ON T1.GenreId  =  T2.GenreId WHERE T1.Name  =  'Rock';
+SELECT AVG(T2.UnitPrice) FROM GENRE AS T1 JOIN TRACK AS T2 ON T1.GenreId  =  T2.GenreId WHERE T1.Name  =  'Rock';
+SELECT max(Milliseconds) ,  min(Milliseconds) FROM GENRE AS T1 JOIN TRACK AS T2 ON T1.GenreId  =  T2.GenreId WHERE T1.Name  =  'Pop';
+SELECT max(Milliseconds) ,  min(Milliseconds) FROM GENRE AS T1 JOIN TRACK AS T2 ON T1.GenreId  =  T2.GenreId WHERE T1.Name  =  'Pop';
+SELECT BirthDate FROM EMPLOYEE WHERE City  =  'Edmonton';
+SELECT BirthDate FROM EMPLOYEE WHERE City  =  'Edmonton';
+SELECT distinct(UnitPrice) FROM TRACK;
+SELECT distinct(UnitPrice) FROM TRACK;
+SELECT count(*) FROM ARTIST WHERE artistid NOT IN(SELECT artistid FROM ALBUM);
+SELECT count(*) FROM ARTIST WHERE artistid NOT IN(SELECT artistid FROM ALBUM);
+SELECT T1.Title FROM Album AS T1 JOIN Track AS T2 ON T1.AlbumId  =  T2.AlbumId JOIN Genre AS T3 ON T2.GenreID  =  T3.GenreID WHERE T3.Name  =  'Reggae' INTERSECT SELECT T1.Title FROM Album AS T1 JOIN Track AS T2 ON T1.AlbumId  =  T2.AlbumId JOIN Genre AS T3 ON T2.GenreID  =  T3.GenreID WHERE T3.Name  =  'Rock';
+SELECT T1.Title FROM Album AS T1 JOIN Track AS T2 ON T1.AlbumId  =  T2.AlbumId JOIN Genre AS T3 ON T2.GenreID  =  T3.GenreID WHERE T3.Name  =  'Reggae' INTERSECT SELECT T1.Title FROM Album AS T1 JOIN Track AS T2 ON T1.AlbumId  =  T2.AlbumId JOIN Genre AS T3 ON T2.GenreID  =  T3.GenreID WHERE T3.Name  =  'Rock';
+SELECT customer_phone FROM available_policies;
+SELECT customer_phone FROM available_policies;
+SELECT customer_phone FROM available_policies WHERE policy_type_code  =  'Life Insurance';
+SELECT customer_phone FROM available_policies WHERE policy_type_code  =  'Life Insurance';
+SELECT policy_type_code FROM available_policies GROUP BY policy_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT policy_type_code FROM available_policies GROUP BY policy_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT customer_phone FROM available_policies WHERE policy_type_code  =  (SELECT policy_type_code FROM available_policies GROUP BY policy_type_code ORDER BY count(*) DESC LIMIT 1);
+SELECT customer_phone FROM available_policies WHERE policy_type_code  =  (SELECT policy_type_code FROM available_policies GROUP BY policy_type_code ORDER BY count(*) DESC LIMIT 1);
+SELECT policy_type_code FROM available_policies GROUP BY policy_type_code HAVING count(*)  >  4;
+SELECT policy_type_code FROM available_policies GROUP BY policy_type_code HAVING count(*)  >  4;
+SELECT sum(settlement_amount) ,  avg(settlement_amount) FROM settlements;
+SELECT sum(settlement_amount) ,  avg(settlement_amount) FROM settlements;
+SELECT t2.service_name FROM first_notification_of_loss AS t1 JOIN services AS t2 ON t1.service_id  =  t2.service_id GROUP BY t1.service_id HAVING count(*)  >  2;
+SELECT t2.service_name FROM first_notification_of_loss AS t1 JOIN services AS t2 ON t1.service_id  =  t2.service_id GROUP BY t1.service_id HAVING count(*)  >  2;
+SELECT t1.Effective_Date FROM claims AS t1 JOIN settlements AS t2 ON t1.claim_id  =  t2.claim_id GROUP BY t1.claim_id ORDER BY sum(t2.settlement_amount) DESC LIMIT 1;
+SELECT t1.Effective_Date FROM claims AS t1 JOIN settlements AS t2 ON t1.claim_id  =  t2.claim_id GROUP BY t1.claim_id ORDER BY sum(t2.settlement_amount) DESC LIMIT 1;
+SELECT count(*) FROM customers AS t1 JOIN customers_policies AS t2 ON t1.customer_id  =  t2.customer_id WHERE t1.customer_name  =  'Dayana Robel';
+SELECT count(*) FROM customers AS t1 JOIN customers_policies AS t2 ON t1.customer_id  =  t2.customer_id WHERE t1.customer_name  =  'Dayana Robel';
+SELECT t1.customer_name FROM customers AS t1 JOIN customers_policies AS t2 ON t1.customer_id  =  t2.customer_id GROUP BY t1.customer_name ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.customer_name FROM customers AS t1 JOIN customers_policies AS t2 ON t1.customer_id  =  t2.customer_id GROUP BY t1.customer_name ORDER BY count(*) DESC LIMIT 1;
+SELECT DISTINCT t3.policy_type_code FROM customers AS t1 JOIN customers_policies AS t2 ON t1.customer_id  =  t2.customer_id JOIN available_policies AS t3 ON t2.policy_id  =  t3.policy_id WHERE t1.customer_name  =  'Dayana Robel';
+SELECT DISTINCT t3.policy_type_code FROM customers AS t1 JOIN customers_policies AS t2 ON t1.customer_id  =  t2.customer_id JOIN available_policies AS t3 ON t2.policy_id  =  t3.policy_id WHERE t1.customer_name  =  'Dayana Robel';
+SELECT DISTINCT t3.policy_type_code FROM customers AS t1 JOIN customers_policies AS t2 ON t1.customer_id  =  t2.customer_id JOIN available_policies AS t3 ON t2.policy_id  =  t3.policy_id WHERE t1.customer_name  =  (SELECT t1.customer_name FROM customers AS t1 JOIN customers_policies AS t2 ON t1.customer_id  =  t2.customer_id GROUP BY t1.customer_name ORDER BY count(*) DESC LIMIT 1);
+SELECT DISTINCT t3.policy_type_code FROM customers AS t1 JOIN customers_policies AS t2 ON t1.customer_id  =  t2.customer_id JOIN available_policies AS t3 ON t2.policy_id  =  t3.policy_id WHERE t1.customer_name  =  (SELECT t1.customer_name FROM customers AS t1 JOIN customers_policies AS t2 ON t1.customer_id  =  t2.customer_id GROUP BY t1.customer_name ORDER BY count(*) DESC LIMIT 1);
+SELECT service_name FROM services ORDER BY service_name;
+SELECT service_name FROM services ORDER BY service_name;
+SELECT count(*) FROM services;
+SELECT count(*) FROM services;
+SELECT customer_name FROM customers EXCEPT SELECT t1.customer_name FROM customers AS t1 JOIN first_notification_of_loss AS t2 ON t1.customer_id  =  t2.customer_id;
+SELECT customer_name FROM customers EXCEPT SELECT t1.customer_name FROM customers AS t1 JOIN first_notification_of_loss AS t2 ON t1.customer_id  =  t2.customer_id;
+SELECT t1.customer_name FROM customers AS t1 JOIN first_notification_of_loss AS t2 ON t1.customer_id  =  t2.customer_id JOIN services AS t3 ON t2.service_id  =  t3.service_id WHERE t3.service_name  =  'Close a policy' OR t3.service_name  =  'Upgrade a policy';
+SELECT t1.customer_name FROM customers AS t1 JOIN first_notification_of_loss AS t2 ON t1.customer_id  =  t2.customer_id JOIN services AS t3 ON t2.service_id  =  t3.service_id WHERE t3.service_name  =  'Close a policy' OR t3.service_name  =  'Upgrade a policy';
+SELECT t1.customer_name FROM customers AS t1 JOIN first_notification_of_loss AS t2 ON t1.customer_id  =  t2.customer_id JOIN services AS t3 ON t2.service_id  =  t3.service_id WHERE t3.service_name  =  'Close a policy' INTERSECT SELECT t1.customer_name FROM customers AS t1 JOIN first_notification_of_loss AS t2 ON t1.customer_id  =  t2.customer_id JOIN services AS t3 ON t2.service_id  =  t3.service_id WHERE t3.service_name  =  'New policy application';
+SELECT t1.customer_name FROM customers AS t1 JOIN first_notification_of_loss AS t2 ON t1.customer_id  =  t2.customer_id JOIN services AS t3 ON t2.service_id  =  t3.service_id WHERE t3.service_name  =  'Close a policy' INTERSECT SELECT t1.customer_name FROM customers AS t1 JOIN first_notification_of_loss AS t2 ON t1.customer_id  =  t2.customer_id JOIN services AS t3 ON t2.service_id  =  t3.service_id WHERE t3.service_name  =  'New policy application';
+SELECT customer_id FROM customers WHERE customer_name LIKE '%Diana%';
+SELECT customer_id FROM customers WHERE customer_name LIKE '%Diana%';
+SELECT max(settlement_amount) ,  min(settlement_amount) FROM settlements;
+SELECT max(settlement_amount) ,  min(settlement_amount) FROM settlements;
+SELECT customer_id ,  customer_name FROM customers ORDER BY customer_id ASC;
+SELECT customer_id ,  customer_name FROM customers ORDER BY customer_id ASC;
+SELECT t2.date_opened ,  t2.date_closed FROM customers AS t1 JOIN customers_policies AS t2 ON t1.customer_id  =  t2.customer_id WHERE t1.customer_name LIKE '%Diana%';
+SELECT t2.date_opened ,  t2.date_closed FROM customers AS t1 JOIN customers_policies AS t2 ON t1.customer_id  =  t2.customer_id WHERE t1.customer_name LIKE '%Diana%';
+SELECT count(*) FROM enzyme;
+SELECT count(*) FROM enzyme;
+SELECT name FROM enzyme ORDER BY name DESC;
+SELECT name FROM enzyme ORDER BY name DESC;
+SELECT name ,  LOCATION FROM enzyme;
+SELECT name ,  LOCATION FROM enzyme;
+SELECT max(OMIM) FROM enzyme;
+SELECT max(OMIM) FROM enzyme;
+SELECT product ,  chromosome ,  porphyria FROM enzyme WHERE LOCATION  =  'Cytosol';
+SELECT product ,  chromosome ,  porphyria FROM enzyme WHERE LOCATION  =  'Cytosol';
+SELECT name FROM enzyme WHERE product != 'Heme';
+SELECT name FROM enzyme WHERE product != 'Heme';
+SELECT name ,  trade_name FROM medicine WHERE FDA_approved  =  'Yes';
+SELECT name ,  trade_name FROM medicine WHERE FDA_approved  =  'Yes';
+SELECT T1.name FROM enzyme AS T1 JOIN medicine_enzyme_interaction AS T2 ON T1.id  =  T2.enzyme_id JOIN medicine AS T3 ON T2.medicine_id  =  T3.id WHERE T3.name  =  'Amisulpride' AND T2.interaction_type  =  'inhibitor';
+SELECT T1.name FROM enzyme AS T1 JOIN medicine_enzyme_interaction AS T2 ON T1.id  =  T2.enzyme_id JOIN medicine AS T3 ON T2.medicine_id  =  T3.id WHERE T3.name  =  'Amisulpride' AND T2.interaction_type  =  'inhibitor';
+SELECT T1.id ,  T1.Name FROM medicine AS T1 JOIN medicine_enzyme_interaction AS T2 ON T2.medicine_id  =  T1.id GROUP BY T1.id HAVING count(*)  >=  2;
+SELECT T1.id ,  T1.Name FROM medicine AS T1 JOIN medicine_enzyme_interaction AS T2 ON T2.medicine_id  =  T1.id GROUP BY T1.id HAVING count(*)  >=  2;
+SELECT T1.id ,  T1.Name ,  T1.FDA_approved FROM medicine AS T1 JOIN medicine_enzyme_interaction AS T2 ON T2.medicine_id  =  T1.id GROUP BY T1.id ORDER BY count(*) DESC;
+SELECT T1.id ,  T1.Name ,  T1.FDA_approved FROM medicine AS T1 JOIN medicine_enzyme_interaction AS T2 ON T2.medicine_id  =  T1.id GROUP BY T1.id ORDER BY count(*) DESC;
+SELECT T1.id ,  T1.name FROM enzyme AS T1 JOIN medicine_enzyme_interaction AS T2 ON T1.id  =  T2.enzyme_id WHERE T2.interaction_type  =  'activitor' GROUP BY T1.id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.id ,  T1.name FROM enzyme AS T1 JOIN medicine_enzyme_interaction AS T2 ON T1.id  =  T2.enzyme_id WHERE T2.interaction_type  =  'activitor' GROUP BY T1.id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.interaction_type FROM medicine_enzyme_interaction AS T1 JOIN medicine AS T2 ON T1.medicine_id  =  T2.id JOIN enzyme AS T3 ON T1.enzyme_id  =  T3.id WHERE T3.name  =  'ALA synthase' AND T2.name  =  'Aripiprazole';
+SELECT T1.interaction_type FROM medicine_enzyme_interaction AS T1 JOIN medicine AS T2 ON T1.medicine_id  =  T2.id JOIN enzyme AS T3 ON T1.enzyme_id  =  T3.id WHERE T3.name  =  'ALA synthase' AND T2.name  =  'Aripiprazole';
+SELECT interaction_type ,  count(*) FROM medicine_enzyme_interaction GROUP BY interaction_type ORDER BY count(*) DESC LIMIT 1;
+SELECT interaction_type ,  count(*) FROM medicine_enzyme_interaction GROUP BY interaction_type ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM medicine WHERE FDA_approved  =  'No';
+SELECT count(*) FROM medicine WHERE FDA_approved  =  'No';
+SELECT count(*) FROM enzyme WHERE id NOT IN ( SELECT enzyme_id FROM medicine_enzyme_interaction );
+SELECT count(*) FROM enzyme WHERE id NOT IN ( SELECT enzyme_id FROM medicine_enzyme_interaction );
+SELECT T1.id ,  T1.trade_name FROM medicine AS T1 JOIN medicine_enzyme_interaction AS T2 ON T2.medicine_id  =  T1.id GROUP BY T1.id HAVING COUNT(*)  >=  3;
+SELECT T1.id ,  T1.trade_name FROM medicine AS T1 JOIN medicine_enzyme_interaction AS T2 ON T2.medicine_id  =  T1.id GROUP BY T1.id HAVING COUNT(*)  >=  3;
+SELECT DISTINCT T1.name ,  T1.location ,  T1.product FROM enzyme AS T1 JOIN medicine_enzyme_interaction AS T2 ON T2.enzyme_id  =  T1.id WHERE T2.interaction_type  =  'inhibitor';
+SELECT DISTINCT T1.name ,  T1.location ,  T1.product FROM enzyme AS T1 JOIN medicine_enzyme_interaction AS T2 ON T2.enzyme_id  =  T1.id WHERE T2.interaction_type  =  'inhibitor';
+SELECT T1.name ,  T1.trade_name FROM medicine AS T1 JOIN medicine_enzyme_interaction AS T2 ON T2.medicine_id  =  T1.id WHERE interaction_type  =  'inhibitor' INTERSECT SELECT T1.name ,  T1.trade_name FROM medicine AS T1 JOIN medicine_enzyme_interaction AS T2 ON T2.medicine_id  =  T1.id WHERE interaction_type  =  'activitor';
+SELECT T1.name ,  T1.trade_name FROM medicine AS T1 JOIN medicine_enzyme_interaction AS T2 ON T2.medicine_id  =  T1.id WHERE interaction_type  =  'inhibitor' INTERSECT SELECT T1.name ,  T1.trade_name FROM medicine AS T1 JOIN medicine_enzyme_interaction AS T2 ON T2.medicine_id  =  T1.id WHERE interaction_type  =  'activitor';
+SELECT name ,  trade_name FROM medicine EXCEPT SELECT T1.name ,  T1.trade_name FROM medicine AS T1 JOIN medicine_enzyme_interaction AS T2 ON T2.medicine_id  =  T1.id JOIN enzyme AS T3 ON T3.id  =  T2.enzyme_id WHERE T3.product  =  'Protoporphyrinogen IX';
+SELECT name ,  trade_name FROM medicine EXCEPT SELECT T1.name ,  T1.trade_name FROM medicine AS T1 JOIN medicine_enzyme_interaction AS T2 ON T2.medicine_id  =  T1.id JOIN enzyme AS T3 ON T3.id  =  T2.enzyme_id WHERE T3.product  =  'Protoporphyrinogen IX';
+SELECT count(DISTINCT FDA_approved) FROM medicine;
+SELECT count(DISTINCT FDA_approved) FROM medicine;
+SELECT name FROM enzyme WHERE name LIKE '%ALA%';
+SELECT name FROM enzyme WHERE name LIKE '%ALA%';
+SELECT trade_name ,  count(*) FROM medicine GROUP BY trade_name;
+SELECT trade_name ,  count(*) FROM medicine GROUP BY trade_name;
+SELECT school ,  nickname FROM university ORDER BY founded;
+SELECT school ,  nickname FROM university ORDER BY founded;
+SELECT school ,  LOCATION FROM university WHERE affiliation  =  'Public';
+SELECT school ,  LOCATION FROM university WHERE affiliation  =  'Public';
+SELECT founded FROM university ORDER BY enrollment DESC LIMIT 1;
+SELECT founded FROM university ORDER BY enrollment DESC LIMIT 1;
+SELECT founded FROM university WHERE affiliation != 'Public' ORDER BY founded DESC LIMIT 1;
+SELECT founded FROM university WHERE affiliation != 'Public' ORDER BY founded DESC LIMIT 1;
+SELECT count(DISTINCT school_id) FROM basketball_match;
+SELECT count(DISTINCT school_id) FROM basketball_match;
+SELECT acc_percent FROM basketball_match ORDER BY acc_percent DESC LIMIT 1;
+SELECT acc_percent FROM basketball_match ORDER BY acc_percent DESC LIMIT 1;
+SELECT t1.Primary_conference FROM university AS t1 JOIN basketball_match AS t2 ON t1.school_id  =  t2.school_id ORDER BY t2.acc_percent LIMIT 1;
+SELECT t1.Primary_conference FROM university AS t1 JOIN basketball_match AS t2 ON t1.school_id  =  t2.school_id ORDER BY t2.acc_percent LIMIT 1;
+SELECT t2.team_name ,  t2.ACC_Regular_Season FROM university AS t1 JOIN basketball_match AS t2 ON t1.school_id  =  t2.school_id ORDER BY t1.founded LIMIT 1;
+SELECT t2.team_name ,  t2.ACC_Regular_Season FROM university AS t1 JOIN basketball_match AS t2 ON t1.school_id  =  t2.school_id ORDER BY t1.founded LIMIT 1;
+SELECT t2.All_Games ,  t1.location FROM university AS t1 JOIN basketball_match AS t2 ON t1.school_id  =  t2.school_id WHERE team_name  =  'Clemson';
+SELECT t2.All_Games ,  t1.location FROM university AS t1 JOIN basketball_match AS t2 ON t1.school_id  =  t2.school_id WHERE team_name  =  'Clemson';
+SELECT avg(enrollment) FROM university WHERE founded  <  1850;
+SELECT avg(enrollment) FROM university WHERE founded  <  1850;
+SELECT enrollment ,  primary_conference FROM university ORDER BY founded LIMIT 1;
+SELECT enrollment ,  primary_conference FROM university ORDER BY founded LIMIT 1;
+SELECT sum(enrollment) ,  min(enrollment) FROM university;
+SELECT sum(enrollment) ,  min(enrollment) FROM university;
+SELECT sum(enrollment) ,  affiliation FROM university GROUP BY affiliation;
+SELECT sum(enrollment) ,  affiliation FROM university GROUP BY affiliation;
+SELECT count(*) FROM university WHERE school_id NOT IN (SELECT school_id FROM basketball_match);
+SELECT count(*) FROM university WHERE school_id NOT IN (SELECT school_id FROM basketball_match);
+SELECT school FROM university WHERE founded  >  1850 OR affiliation  =  'Public';
+SELECT school FROM university WHERE founded  >  1850 OR affiliation  =  'Public';
+SELECT count(DISTINCT affiliation) FROM university;
+SELECT count(DISTINCT affiliation) FROM university;
+SELECT count(*) FROM university WHERE LOCATION LIKE '%NY%';
+SELECT count(*) FROM university WHERE LOCATION LIKE '%NY%';
+SELECT t2.team_name FROM university AS t1 JOIN basketball_match AS t2 ON t1.school_id  =  t2.school_id WHERE enrollment  <  (SELECT avg(enrollment) FROM university);
+SELECT t2.team_name FROM university AS t1 JOIN basketball_match AS t2 ON t1.school_id  =  t2.school_id WHERE enrollment  <  (SELECT avg(enrollment) FROM university);
+SELECT count(*) ,  affiliation FROM university WHERE enrollment  >  20000 GROUP BY affiliation;
+SELECT count(*) ,  affiliation FROM university WHERE enrollment  >  20000 GROUP BY affiliation;
+SELECT sum(Enrollment) ,  affiliation FROM university WHERE founded  >  1850 GROUP BY affiliation;
+SELECT sum(Enrollment) ,  affiliation FROM university WHERE founded  >  1850 GROUP BY affiliation;
+SELECT max(Enrollment) FROM university;
+SELECT max(Enrollment) FROM university;
+SELECT * FROM basketball_match;
+SELECT * FROM basketball_match;
+SELECT team_name FROM basketball_match ORDER BY All_Home DESC;
+SELECT team_name FROM basketball_match ORDER BY All_Home DESC;
+SELECT Model_name FROM chip_model WHERE Launch_year BETWEEN 2002 AND 2004;
+SELECT Model_name ,  RAM_MiB FROM chip_model ORDER BY RAM_MiB ASC LIMIT 1;
+SELECT chip_model ,  screen_mode FROM phone WHERE Hardware_Model_name = 'LG-P760';
+SELECT count(*) FROM phone WHERE Company_name = 'Nokia Corporation';
+SELECT max(T1.RAM_MiB) ,  min(T1.RAM_MiB) FROM chip_model AS T1 JOIN phone AS T2 ON T1.Model_name  =  T2.chip_model WHERE T2.Company_name = 'Nokia Corporation';
+SELECT avg(T1.ROM_MiB) FROM chip_model AS T1 JOIN phone AS T2 ON T1.Model_name  =  T2.chip_model WHERE T2.Company_name = 'Nokia Corporation';
+SELECT T2.Hardware_Model_name ,  T2.Company_name FROM chip_model AS T1 JOIN phone AS T2 ON T1.Model_name  =  T2.chip_model WHERE T1.Launch_year = 2002 OR T1.RAM_MiB  >  32;
+SELECT Hardware_Model_name ,  Company_name FROM phone WHERE Accreditation_type LIKE 'Full';
+SELECT T1.Char_cells ,  T1.Pixels ,  T1.Hardware_colours FROM screen_mode AS T1 JOIN phone AS T2 ON T1.Graphics_mode = T2.screen_mode WHERE T2.Hardware_Model_name = 'LG-P760';
+SELECT T2.Hardware_Model_name ,  T2.Company_name FROM screen_mode AS T1 JOIN phone AS T2 ON T1.Graphics_mode = T2.screen_mode WHERE T1.Type  =  'Graphics';
+SELECT Company_name ,  count(*) FROM phone GROUP BY Company_name ORDER BY count(*) ASC LIMIT 1;
+SELECT Company_name FROM phone GROUP BY Company_name HAVING count(*)  >  1;
+SELECT max(used_kb) , min(used_kb) , avg(used_kb) FROM screen_mode;
+SELECT T2.Hardware_Model_name FROM chip_model AS T1 JOIN phone AS T2 ON T1.Model_name  =  T2.chip_model WHERE T1.Launch_year = 2002 ORDER BY T1.RAM_MiB DESC LIMIT 1;
+SELECT T1.WiFi , T3.Type FROM chip_model AS T1 JOIN phone AS T2 ON T1.Model_name  =  T2.chip_model JOIN screen_mode AS T3 ON T2.screen_mode = T3.Graphics_mode WHERE T2.Hardware_Model_name = 'LG-P760';
+SELECT T2.Hardware_Model_name FROM chip_model AS T1 JOIN phone AS T2 ON T1.Model_name  =  T2.chip_model JOIN screen_mode AS T3 ON T2.screen_mode = T3.Graphics_mode WHERE T3.Type = 'Text' OR T1.RAM_MiB  >  32;
+SELECT DISTINCT T2.Hardware_Model_name FROM screen_mode AS T1 JOIN phone AS T2 ON T1.Graphics_mode = T2.screen_mode WHERE T1.Type  =  'Graphics' OR t2.Company_name  =  'Nokia Corporation';
+SELECT DISTINCT T2.Hardware_Model_name FROM screen_mode AS T1 JOIN phone AS T2 ON T1.Graphics_mode = T2.screen_mode WHERE t2.Company_name  =  'Nokia Corporation' AND T1.Type != 'Text';
+SELECT DISTINCT T2.Hardware_Model_name , T2.Company_name FROM screen_mode AS T1 JOIN phone AS T2 ON T1.Graphics_mode = T2.screen_mode WHERE T1.used_kb BETWEEN 10 AND 15;
+SELECT Accreditation_type ,  count(*) FROM phone GROUP BY Accreditation_type;
+SELECT Accreditation_type ,  count(*) FROM phone GROUP BY Accreditation_type;
+SELECT Accreditation_level FROM phone GROUP BY Accreditation_level HAVING count(*)  >  3;
+SELECT * FROM chip_model;
+SELECT count(*) FROM chip_model WHERE wifi  =  'No';
+SELECT count(*) FROM chip_model WHERE wifi  =  'No';
+SELECT model_name FROM chip_model ORDER BY launch_year;
+SELECT avg(RAM_MiB) FROM chip_model WHERE model_name NOT IN (SELECT chip_model FROM phone);
+SELECT model_name FROM chip_model EXCEPT SELECT chip_model FROM phone WHERE Accreditation_type  =  'Full';
+SELECT t1.pixels FROM screen_mode AS t1 JOIN phone AS t2 ON t1.Graphics_mode  =  t2.screen_mode WHERE t2.Accreditation_type  =  'Provisional' INTERSECT SELECT t1.pixels FROM screen_mode AS t1 JOIN phone AS t2 ON t1.Graphics_mode  =  t2.screen_mode WHERE t2.Accreditation_type  =  'Full';
+SELECT count(*) FROM country;
+SELECT count(*) FROM country;
+SELECT Country_name ,  Capital FROM country;
+SELECT Country_name ,  Capital FROM country;
+SELECT Official_native_language FROM country WHERE Official_native_language LIKE '%English%';
+SELECT Official_native_language FROM country WHERE Official_native_language LIKE '%English%';
+SELECT DISTINCT POSITION FROM match_season;
+SELECT DISTINCT POSITION FROM match_season;
+SELECT Player FROM match_season WHERE College  =  'UCLA';
+SELECT Player FROM match_season WHERE College  =  'UCLA';
+SELECT DISTINCT POSITION FROM match_season WHERE College  =  'UCLA' OR College  =  'Duke';
+SELECT DISTINCT POSITION FROM match_season WHERE College  =  'UCLA' OR College  =  'Duke';
+SELECT Draft_Pick_Number ,  Draft_Class FROM match_season WHERE POSITION  =  'Defender';
+SELECT Draft_Pick_Number ,  Draft_Class FROM match_season WHERE POSITION  =  'Defender';
+SELECT count(DISTINCT Team) FROM match_season;
+SELECT count(DISTINCT Team) FROM match_season;
+SELECT Player ,  Years_Played FROM player;
+SELECT Player ,  Years_Played FROM player;
+SELECT Name FROM Team;
+SELECT Name FROM Team;
+SELECT T2.Season ,  T2.Player ,  T1.Country_name FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id  =  T2.Country;
+SELECT T2.Season ,  T2.Player ,  T1.Country_name FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id  =  T2.Country;
+SELECT T2.Player FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id  =  T2.Country WHERE T1.Country_name  =  'Indonesia';
+SELECT T2.Player FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id  =  T2.Country WHERE T1.Country_name  =  'Indonesia';
+SELECT DISTINCT T2.Position FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id  =  T2.Country WHERE T1.Capital  =  'Dublin';
+SELECT DISTINCT T2.Position FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id  =  T2.Country WHERE T1.Capital  =  'Dublin';
+SELECT T1.Official_native_language FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id  =  T2.Country WHERE T2.College  =  'Maryland' OR T2.College  =  'Duke';
+SELECT T1.Official_native_language FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id  =  T2.Country WHERE T2.College  =  'Maryland' OR T2.College  =  'Duke';
+SELECT count(DISTINCT T1.Official_native_language) FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id  =  T2.Country WHERE T2.Position  =  'Defender';
+SELECT count(DISTINCT T1.Official_native_language) FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id  =  T2.Country WHERE T2.Position  =  'Defender';
+SELECT T1.Season ,  T1.Player ,  T2.Name FROM match_season AS T1 JOIN team AS T2 ON T1.Team  =  T2.Team_id;
+SELECT T1.Season ,  T1.Player ,  T2.Name FROM match_season AS T1 JOIN team AS T2 ON T1.Team  =  T2.Team_id;
+SELECT T1.Position FROM match_season AS T1 JOIN team AS T2 ON T1.Team  =  T2.Team_id WHERE T2.Name  =  'Ryley Goldner';
+SELECT T1.Position FROM match_season AS T1 JOIN team AS T2 ON T1.Team  =  T2.Team_id WHERE T2.Name  =  'Ryley Goldner';
+SELECT count(DISTINCT T1.College) FROM match_season AS T1 JOIN team AS T2 ON T1.Team  =  T2.Team_id WHERE T2.Name  =  'Columbus Crew';
+SELECT count(DISTINCT T1.College) FROM match_season AS T1 JOIN team AS T2 ON T1.Team  =  T2.Team_id WHERE T2.Name  =  'Columbus Crew';
+SELECT T1.Player , T1.Years_Played FROM player AS T1 JOIN team AS T2 ON T1.Team  =  T2.Team_id WHERE T2.Name  =  'Columbus Crew';
+SELECT T1.Player , T1.Years_Played FROM player AS T1 JOIN team AS T2 ON T1.Team  =  T2.Team_id WHERE T2.Name  =  'Columbus Crew';
+SELECT POSITION ,  COUNT(*) FROM match_season GROUP BY POSITION;
+SELECT POSITION ,  COUNT(*) FROM match_season GROUP BY POSITION;
+SELECT Country_name ,  COUNT(*) FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id  =  T2.Country GROUP BY T1.Country_name;
+SELECT Country_name ,  COUNT(*) FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id  =  T2.Country GROUP BY T1.Country_name;
+SELECT player FROM match_season ORDER BY College ASC;
+SELECT player FROM match_season ORDER BY College ASC;
+SELECT POSITION FROM match_season GROUP BY POSITION ORDER BY count(*) DESC LIMIT 1;
+SELECT POSITION FROM match_season GROUP BY POSITION ORDER BY count(*) DESC LIMIT 1;
+SELECT College FROM match_season GROUP BY College ORDER BY count(*) DESC LIMIT 3;
+SELECT College FROM match_season GROUP BY College ORDER BY count(*) DESC LIMIT 3;
+SELECT College FROM match_season GROUP BY College HAVING count(*)  >=  2;
+SELECT College FROM match_season GROUP BY College HAVING count(*)  >=  2;
+SELECT College FROM match_season GROUP BY College HAVING count(*)  >=  2 ORDER BY College DESC;
+SELECT College FROM match_season GROUP BY College HAVING count(*)  >=  2 ORDER BY College DESC;
+SELECT Name FROM team WHERE Team_id NOT IN (SELECT Team FROM match_season);
+SELECT Name FROM team WHERE Team_id NOT IN (SELECT Team FROM match_season);
+SELECT T1.Country_name FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id  =  T2.Country WHERE T2.Position  =  'Forward' INTERSECT SELECT T1.Country_name FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id  =  T2.Country WHERE T2.Position  =  'Defender';
+SELECT T1.Country_name FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id  =  T2.Country WHERE T2.Position  =  'Forward' INTERSECT SELECT T1.Country_name FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id  =  T2.Country WHERE T2.Position  =  'Defender';
+SELECT College FROM match_season WHERE POSITION  =  'Midfielder' INTERSECT SELECT College FROM match_season WHERE POSITION  =  'Defender';
+SELECT College FROM match_season WHERE POSITION  =  'Midfielder' INTERSECT SELECT College FROM match_season WHERE POSITION  =  'Defender';
+SELECT count(*) FROM climber;
+SELECT count(*) FROM climber;
+SELECT Name FROM climber ORDER BY Points DESC;
+SELECT Name FROM climber ORDER BY Points DESC;
+SELECT Name FROM climber WHERE Country != 'Switzerland';
+SELECT Name FROM climber WHERE Country != 'Switzerland';
+SELECT max(Points) FROM climber WHERE Country  =  'United Kingdom';
+SELECT max(Points) FROM climber WHERE Country  =  'United Kingdom';
+SELECT COUNT(DISTINCT Country) FROM climber;
+SELECT COUNT(DISTINCT Country) FROM climber;
+SELECT Name FROM mountain ORDER BY Name ASC;
+SELECT Name FROM mountain ORDER BY Name ASC;
+SELECT Country FROM mountain WHERE Height  >  5000;
+SELECT Country FROM mountain WHERE Height  >  5000;
+SELECT Name FROM mountain ORDER BY Height DESC LIMIT 1;
+SELECT Name FROM mountain ORDER BY Height DESC LIMIT 1;
+SELECT DISTINCT Range FROM mountain ORDER BY Prominence DESC LIMIT 3;
+SELECT DISTINCT Range FROM mountain ORDER BY Prominence DESC LIMIT 3;
+SELECT T1.Name ,  T2.Name FROM climber AS T1 JOIN mountain AS T2 ON T1.Mountain_ID  =  T2.Mountain_ID;
+SELECT T1.Name ,  T2.Name FROM climber AS T1 JOIN mountain AS T2 ON T1.Mountain_ID  =  T2.Mountain_ID;
+SELECT T1.Name ,  T2.Height FROM climber AS T1 JOIN mountain AS T2 ON T1.Mountain_ID  =  T2.Mountain_ID;
+SELECT T1.Name ,  T2.Height FROM climber AS T1 JOIN mountain AS T2 ON T1.Mountain_ID  =  T2.Mountain_ID;
+SELECT T2.Height FROM climber AS T1 JOIN mountain AS T2 ON T1.Mountain_ID  =  T2.Mountain_ID ORDER BY T1.Points DESC LIMIT 1;
+SELECT T2.Height FROM climber AS T1 JOIN mountain AS T2 ON T1.Mountain_ID  =  T2.Mountain_ID ORDER BY T1.Points DESC LIMIT 1;
+SELECT DISTINCT T2.Name FROM climber AS T1 JOIN mountain AS T2 ON T1.Mountain_ID  =  T2.Mountain_ID WHERE T1.Country  =  'West Germany';
+SELECT DISTINCT T2.Name FROM climber AS T1 JOIN mountain AS T2 ON T1.Mountain_ID  =  T2.Mountain_ID WHERE T1.Country  =  'West Germany';
+SELECT T1.Time FROM climber AS T1 JOIN mountain AS T2 ON T1.Mountain_ID  =  T2.Mountain_ID WHERE T2.Country  =  'Uganda';
+SELECT T1.Time FROM climber AS T1 JOIN mountain AS T2 ON T1.Mountain_ID  =  T2.Mountain_ID WHERE T2.Country  =  'Uganda';
+SELECT Country ,  COUNT(*) FROM climber GROUP BY Country;
+SELECT Country ,  COUNT(*) FROM climber GROUP BY Country;
+SELECT Country FROM mountain GROUP BY Country HAVING COUNT(*)  >  1;
+SELECT Country FROM mountain GROUP BY Country HAVING COUNT(*)  >  1;
+SELECT Name FROM mountain WHERE Mountain_ID NOT IN (SELECT Mountain_ID FROM climber);
+SELECT Name FROM mountain WHERE Mountain_ID NOT IN (SELECT Mountain_ID FROM climber);
+SELECT Country FROM mountain WHERE Height  >  5600 INTERSECT SELECT Country FROM mountain WHERE Height  <  5200;
+SELECT Country FROM mountain WHERE Height  >  5600 INTERSECT SELECT Country FROM mountain WHERE Height  <  5200;
+SELECT Range FROM mountain GROUP BY Range ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Range FROM mountain GROUP BY Range ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Name FROM mountain WHERE Height  >  5000 OR Prominence  >  1000;
+SELECT Name FROM mountain WHERE Height  >  5000 OR Prominence  >  1000;
+SELECT count(*) FROM body_builder;
+SELECT Total FROM body_builder ORDER BY Total ASC;
+SELECT Snatch ,  Clean_Jerk FROM body_builder ORDER BY Snatch ASC;
+SELECT avg(Snatch) FROM body_builder;
+SELECT Clean_Jerk FROM body_builder ORDER BY Total DESC LIMIT 1;
+SELECT Birth_Date FROM People ORDER BY Height ASC;
+SELECT T2.Name FROM body_builder AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID;
+SELECT T2.Name FROM body_builder AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID WHERE T1.Total  >  300;
+SELECT T2.Name FROM body_builder AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID ORDER BY T2.Weight DESC LIMIT 1;
+SELECT T2.Birth_Date ,  T2.Birth_Place FROM body_builder AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID ORDER BY T1.Total DESC LIMIT 1;
+SELECT T2.Height FROM body_builder AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID WHERE T1.Total  <  315;
+SELECT avg(T1.Total) FROM body_builder AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID WHERE T2.Height  >  200;
+SELECT T2.Name FROM body_builder AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID ORDER BY T1.Total DESC;
+SELECT Birth_Place ,  COUNT(*) FROM people GROUP BY Birth_Place;
+SELECT Birth_Place FROM people GROUP BY Birth_Place ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Birth_Place FROM people GROUP BY Birth_Place HAVING COUNT(*)  >=  2;
+SELECT Height ,  Weight FROM people ORDER BY Height DESC;
+SELECT * FROM body_builder;
+SELECT Name ,  birth_place FROM people EXCEPT SELECT T1.Name ,  T1.birth_place FROM people AS T1 JOIN body_builder AS T2 ON T1.people_id  =  T2.people_id;
+SELECT count(DISTINCT Birth_Place) FROM people;
+SELECT count(*) FROM people WHERE people_id NOT IN (SELECT People_ID FROM body_builder);
+SELECT T2.weight FROM body_builder AS T1 JOIN people AS T2 ON T1.people_id  =  T2.people_id WHERE T1.snatch  >  140 OR T2.height  >  200;
+SELECT T1.total FROM body_builder AS T1 JOIN people AS T2 ON T1.people_id  =  T2.people_id WHERE T2.Birth_Date LIKE '%January%';
+SELECT min(snatch) FROM body_builder;
+SELECT count(*) FROM election;
+SELECT Votes FROM election ORDER BY Votes DESC;
+SELECT Date ,  Vote_Percent FROM election;
+SELECT min(Vote_Percent) ,  max(Vote_Percent) FROM election;
+SELECT Name ,  Party FROM representative;
+SELECT Name FROM Representative WHERE Party != 'Republican';
+SELECT Lifespan FROM representative WHERE State  =  'New York' OR State  =  'Indiana';
+SELECT T2.Name ,  T1.Date FROM election AS T1 JOIN representative AS T2 ON T1.Representative_ID  =  T2.Representative_ID;
+SELECT T2.Name FROM election AS T1 JOIN representative AS T2 ON T1.Representative_ID  =  T2.Representative_ID WHERE Votes  >  10000;
+SELECT T2.Name FROM election AS T1 JOIN representative AS T2 ON T1.Representative_ID  =  T2.Representative_ID ORDER BY votes DESC;
+SELECT T2.Party FROM election AS T1 JOIN representative AS T2 ON T1.Representative_ID  =  T2.Representative_ID ORDER BY votes ASC LIMIT 1;
+SELECT T2.Lifespan FROM election AS T1 JOIN representative AS T2 ON T1.Representative_ID  =  T2.Representative_ID ORDER BY Vote_Percent DESC;
+SELECT avg(T1.Votes) FROM election AS T1 JOIN representative AS T2 ON T1.Representative_ID  =  T2.Representative_ID WHERE T2.Party  =  'Republican';
+SELECT Party ,  COUNT(*) FROM representative GROUP BY Party;
+SELECT Party ,  COUNT(*) FROM representative GROUP BY Party ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Party FROM representative GROUP BY Party HAVING COUNT(*)  >=  3;
+SELECT State FROM representative GROUP BY State HAVING COUNT(*)  >=  2;
+SELECT Name FROM representative WHERE Representative_ID NOT IN (SELECT Representative_ID FROM election);
+SELECT Party FROM representative WHERE State  =  'New York' INTERSECT SELECT Party FROM representative WHERE State  =  'Pennsylvania';
+SELECT count(DISTINCT Party) FROM representative;
+SELECT count(*) FROM Apartment_Bookings;
+SELECT count(*) FROM Apartment_Bookings;
+SELECT booking_start_date ,  booking_end_date FROM Apartment_Bookings;
+SELECT booking_start_date ,  booking_end_date FROM Apartment_Bookings;
+SELECT DISTINCT building_description FROM Apartment_Buildings;
+SELECT DISTINCT building_description FROM Apartment_Buildings;
+SELECT building_short_name FROM Apartment_Buildings WHERE building_manager	 =  'Emma';
+SELECT building_short_name FROM Apartment_Buildings WHERE building_manager	 =  'Emma';
+SELECT building_address ,  building_phone FROM Apartment_Buildings WHERE building_manager	 =  'Brenden';
+SELECT building_address ,  building_phone FROM Apartment_Buildings WHERE building_manager	 =  'Brenden';
+SELECT building_full_name FROM Apartment_Buildings WHERE building_full_name LIKE '%court%';
+SELECT building_full_name FROM Apartment_Buildings WHERE building_full_name LIKE '%court%';
+SELECT min(bathroom_count) ,  max(bathroom_count) FROM Apartments;
+SELECT min(bathroom_count) ,  max(bathroom_count) FROM Apartments;
+SELECT avg(bedroom_count) FROM Apartments;
+SELECT avg(bedroom_count) FROM Apartments;
+SELECT apt_number ,  room_count FROM Apartments;
+SELECT apt_number ,  room_count FROM Apartments;
+SELECT avg(room_count) FROM Apartments WHERE apt_type_code  =  'Studio';
+SELECT avg(room_count) FROM Apartments WHERE apt_type_code  =  'Studio';
+SELECT apt_number FROM Apartments WHERE apt_type_code  =  'Flat';
+SELECT apt_number FROM Apartments WHERE apt_type_code  =  'Flat';
+SELECT guest_first_name ,  guest_last_name FROM Guests;
+SELECT guest_first_name ,  guest_last_name FROM Guests;
+SELECT date_of_birth FROM Guests WHERE gender_code  =  'Male';
+SELECT date_of_birth FROM Guests WHERE gender_code  =  'Male';
+SELECT T2.apt_number ,  T1.booking_start_date ,  T1.booking_start_date FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id;
+SELECT T2.apt_number ,  T1.booking_start_date ,  T1.booking_start_date FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id;
+SELECT T1.booking_start_date ,  T1.booking_start_date FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id WHERE T2.apt_type_code  =  'Duplex';
+SELECT T1.booking_start_date ,  T1.booking_start_date FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id WHERE T2.apt_type_code  =  'Duplex';
+SELECT T1.booking_start_date ,  T1.booking_start_date FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id WHERE T2.bedroom_count  >  2;
+SELECT T1.booking_start_date ,  T1.booking_start_date FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id WHERE T2.bedroom_count  >  2;
+SELECT T1.booking_status_code FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id WHERE T2.apt_number  =  'Suite 634';
+SELECT T1.booking_status_code FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id WHERE T2.apt_number  =  'Suite 634';
+SELECT DISTINCT T2.apt_number FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id WHERE T1.booking_status_code  =  'Confirmed';
+SELECT DISTINCT T2.apt_number FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id WHERE T1.booking_status_code  =  'Confirmed';
+SELECT avg(room_count) FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id WHERE T1.booking_status_code  =  'Provisional';
+SELECT avg(room_count) FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id WHERE T1.booking_status_code  =  'Provisional';
+SELECT T2.guest_first_name ,  T1.booking_start_date ,  T1.booking_start_date FROM Apartment_Bookings AS T1 JOIN Guests AS T2 ON T1.guest_id  =  T2.guest_id;
+SELECT T2.guest_first_name ,  T1.booking_start_date ,  T1.booking_start_date FROM Apartment_Bookings AS T1 JOIN Guests AS T2 ON T1.guest_id  =  T2.guest_id;
+SELECT T1.booking_start_date ,  T1.booking_start_date FROM Apartment_Bookings AS T1 JOIN Guests AS T2 ON T1.guest_id  =  T2.guest_id WHERE T2.gender_code  =  'Female';
+SELECT T1.booking_start_date ,  T1.booking_start_date FROM Apartment_Bookings AS T1 JOIN Guests AS T2 ON T1.guest_id  =  T2.guest_id WHERE T2.gender_code  =  'Female';
+SELECT T2.guest_first_name ,  T2.guest_last_name FROM Apartment_Bookings AS T1 JOIN Guests AS T2 ON T1.guest_id  =  T2.guest_id WHERE T1.booking_status_code  =  'Confirmed';
+SELECT T2.guest_first_name ,  T2.guest_last_name FROM Apartment_Bookings AS T1 JOIN Guests AS T2 ON T1.guest_id  =  T2.guest_id WHERE T1.booking_status_code  =  'Confirmed';
+SELECT T1.facility_code FROM Apartment_Facilities AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id WHERE T2.bedroom_count  >  4;
+SELECT T1.facility_code FROM Apartment_Facilities AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id WHERE T2.bedroom_count  >  4;
+SELECT sum(T2.room_count) FROM Apartment_Facilities AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id WHERE T1.facility_code  =  'Gym';
+SELECT sum(T2.room_count) FROM Apartment_Facilities AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id WHERE T1.facility_code  =  'Gym';
+SELECT sum(T2.room_count) FROM Apartment_Buildings AS T1 JOIN Apartments AS T2 ON T1.building_id  =  T2.building_id WHERE T1.building_short_name  =  'Columbus Square';
+SELECT sum(T2.room_count) FROM Apartment_Buildings AS T1 JOIN Apartments AS T2 ON T1.building_id  =  T2.building_id WHERE T1.building_short_name  =  'Columbus Square';
+SELECT T1.building_address FROM Apartment_Buildings AS T1 JOIN Apartments AS T2 ON T1.building_id  =  T2.building_id WHERE T2.bathroom_count  >  2;
+SELECT T1.building_address FROM Apartment_Buildings AS T1 JOIN Apartments AS T2 ON T1.building_id  =  T2.building_id WHERE T2.bathroom_count  >  2;
+SELECT T2.apt_type_code ,  T2.apt_number FROM Apartment_Buildings AS T1 JOIN Apartments AS T2 ON T1.building_id  =  T2.building_id WHERE T1.building_manager  =  'Kyle';
+SELECT T2.apt_type_code ,  T2.apt_number FROM Apartment_Buildings AS T1 JOIN Apartments AS T2 ON T1.building_id  =  T2.building_id WHERE T1.building_manager  =  'Kyle';
+SELECT 	booking_status_code ,  COUNT(*) FROM Apartment_Bookings GROUP BY booking_status_code;
+SELECT 	booking_status_code ,  COUNT(*) FROM Apartment_Bookings GROUP BY booking_status_code;
+SELECT apt_number FROM Apartments ORDER BY room_count ASC;
+SELECT apt_number FROM Apartments ORDER BY room_count ASC;
+SELECT apt_number FROM Apartments ORDER BY bedroom_count DESC LIMIT 1;
+SELECT apt_number FROM Apartments ORDER BY bedroom_count DESC LIMIT 1;
+SELECT apt_type_code ,  COUNT(*) FROM Apartments GROUP BY apt_type_code ORDER BY COUNT(*) ASC;
+SELECT apt_type_code ,  COUNT(*) FROM Apartments GROUP BY apt_type_code ORDER BY COUNT(*) ASC;
+SELECT apt_type_code FROM Apartments GROUP BY apt_type_code ORDER BY avg(room_count) DESC LIMIT 3;
+SELECT apt_type_code FROM Apartments GROUP BY apt_type_code ORDER BY avg(room_count) DESC LIMIT 3;
+SELECT apt_type_code ,  bathroom_count ,  bedroom_count FROM Apartments GROUP BY apt_type_code ORDER BY sum(room_count) DESC LIMIT 1;
+SELECT apt_type_code ,  bathroom_count ,  bedroom_count FROM Apartments GROUP BY apt_type_code ORDER BY sum(room_count) DESC LIMIT 1;
+SELECT apt_type_code FROM Apartments GROUP BY apt_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT apt_type_code FROM Apartments GROUP BY apt_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT apt_type_code FROM Apartments WHERE bathroom_count  >  1 GROUP BY apt_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT apt_type_code FROM Apartments WHERE bathroom_count  >  1 GROUP BY apt_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT apt_type_code ,  max(room_count) ,  min(room_count) FROM Apartments GROUP BY apt_type_code;
+SELECT apt_type_code ,  max(room_count) ,  min(room_count) FROM Apartments GROUP BY apt_type_code;
+SELECT gender_code ,  COUNT(*) FROM Guests GROUP BY gender_code ORDER BY COUNT(*) DESC;
+SELECT gender_code ,  COUNT(*) FROM Guests GROUP BY gender_code ORDER BY COUNT(*) DESC;
+SELECT count(*) FROM Apartments WHERE apt_id NOT IN (SELECT apt_id FROM Apartment_Facilities);
+SELECT count(*) FROM Apartments WHERE apt_id NOT IN (SELECT apt_id FROM Apartment_Facilities);
+SELECT T2.apt_number FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id WHERE T1.booking_status_code  =  'Confirmed' INTERSECT SELECT T2.apt_number FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id WHERE T1.booking_status_code  =  'Provisional';
+SELECT T2.apt_number FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id WHERE T1.booking_status_code  =  'Confirmed' INTERSECT SELECT T2.apt_number FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id  =  T2.apt_id WHERE T1.booking_status_code  =  'Provisional';
+SELECT T1.apt_number FROM Apartments AS T1 JOIN View_Unit_Status AS T2 ON T1.apt_id  =  T2.apt_id WHERE T2.available_yn  =  0 INTERSECT SELECT T1.apt_number FROM Apartments AS T1 JOIN View_Unit_Status AS T2 ON T1.apt_id  =  T2.apt_id WHERE T2.available_yn  =  1;
+SELECT T1.apt_number FROM Apartments AS T1 JOIN View_Unit_Status AS T2 ON T1.apt_id  =  T2.apt_id WHERE T2.available_yn  =  0 INTERSECT SELECT T1.apt_number FROM Apartments AS T1 JOIN View_Unit_Status AS T2 ON T1.apt_id  =  T2.apt_id WHERE T2.available_yn  =  1;
+SELECT count(*) FROM game WHERE season  >  2007;
+SELECT Date FROM game ORDER BY home_team DESC;
+SELECT season ,  home_team ,  away_team FROM game;
+SELECT max(home_games) ,  min(home_games) ,  avg(home_games) FROM stadium;
+SELECT average_attendance FROM stadium WHERE capacity_percentage  >  100;
+SELECT player ,  number_of_matches ,  SOURCE FROM injury_accident WHERE injury != 'Knee problem';
+SELECT T1.season FROM game AS T1 JOIN injury_accident AS T2 ON T1.id  =  T2.game_id WHERE T2.player  =  'Walter Samuel';
+SELECT T1.id ,  T1.score ,  T1.date FROM game AS T1 JOIN injury_accident AS T2 ON T2.game_id  =  T1.id GROUP BY T1.id HAVING count(*)  >=  2;
+SELECT T1.id ,  T1.name FROM stadium AS T1 JOIN game AS T2 ON T1.id  =  T2.stadium_id JOIN injury_accident AS T3 ON T2.id  =  T3.game_id GROUP BY T1.id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.id ,  T1.name FROM stadium AS T1 JOIN game AS T2 ON T1.id  =  T2.stadium_id JOIN injury_accident AS T3 ON T2.id  =  T3.game_id GROUP BY T1.id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.season ,  T2.name FROM game AS T1 JOIN stadium AS T2 ON T1.stadium_id  =  T2.id JOIN injury_accident AS T3 ON T1.id  =  T3.game_id WHERE T3.injury  =  'Foot injury' OR T3.injury  =  'Knee problem';
+SELECT count(DISTINCT SOURCE) FROM injury_accident;
+SELECT count(*) FROM game WHERE id NOT IN ( SELECT game_id FROM injury_accident );
+SELECT count(DISTINCT T1.injury) FROM injury_accident AS T1 JOIN game AS T2 ON T1.game_id  =  T2.id WHERE T2.season  >  2010;
+SELECT T2.name FROM game AS T1 JOIN stadium AS T2 ON T1.stadium_id  =  T2.id JOIN injury_accident AS T3 ON T1.id  =  T3.game_id WHERE T3.player  =  'Walter Samuel' INTERSECT SELECT T2.name FROM game AS T1 JOIN stadium AS T2 ON T1.stadium_id  =  T2.id JOIN injury_accident AS T3 ON T1.id  =  T3.game_id WHERE T3.player  =  'Thiago Motta';
+SELECT name ,  average_attendance ,  total_attendance FROM stadium EXCEPT SELECT T2.name ,  T2.average_attendance ,  T2.total_attendance FROM game AS T1 JOIN stadium AS T2 ON T1.stadium_id  =  T2.id JOIN injury_accident AS T3 ON T1.id  =  T3.game_id;
+SELECT name FROM stadium WHERE name LIKE '%Bank%';
+SELECT T1.id ,  count(*) FROM stadium AS T1 JOIN game AS T2 ON T1.id  =  T2.stadium_id GROUP BY T1.id;
+SELECT T1.date ,  T2.player FROM game AS T1 JOIN injury_accident AS T2 ON T1.id  =  T2.game_id ORDER BY T1.season DESC;
+SELECT T1.name ,  T2.name FROM Country AS T1 JOIN League AS T2 ON T1.id  =  T2.country_id;
+SELECT count(*) FROM Country AS T1 JOIN League AS T2 ON T1.id  =  T2.country_id WHERE T1.name  =  'England';
+SELECT avg(weight) FROM Player;
+SELECT max(weight) ,  min(weight) FROM Player;
+SELECT DISTINCT T1.player_name FROM Player AS T1 JOIN Player_Attributes AS T2 ON T1.player_api_id = T2.player_api_id WHERE T2.overall_rating  >  ( SELECT avg(overall_rating) FROM Player_Attributes );
+SELECT DISTINCT T1.player_name FROM Player AS T1 JOIN Player_Attributes AS T2 ON T1.player_api_id = T2.player_api_id WHERE T2.dribbling  =  ( SELECT max(overall_rating) FROM Player_Attributes);
+SELECT DISTINCT T1.player_name FROM Player AS T1 JOIN Player_Attributes AS T2 ON T1.player_api_id = T2.player_api_id WHERE T2.crossing  >  90 AND T2.preferred_foot  =  'right';
+SELECT DISTINCT T1.player_name FROM Player AS T1 JOIN Player_Attributes AS T2 ON T1.player_api_id = T2.player_api_id WHERE T2.preferred_foot  =  'left' AND T2.overall_rating  >=  85 AND T2.overall_rating  <=  90;
+SELECT preferred_foot ,  avg(overall_rating) FROM Player_Attributes GROUP BY preferred_foot;
+SELECT preferred_foot ,  count(*) FROM Player_Attributes WHERE overall_rating  >  80 GROUP BY preferred_foot;
+SELECT player_api_id FROM Player WHERE height  >=  180 INTERSECT SELECT player_api_id FROM Player_Attributes WHERE overall_rating  >  85;
+SELECT player_api_id FROM Player WHERE height  >=  180 AND height  <=  190 INTERSECT SELECT player_api_id FROM Player_Attributes WHERE preferred_foot  =  'left';
+SELECT DISTINCT T1.player_name FROM Player AS T1 JOIN Player_Attributes AS T2 ON T1.player_api_id  =  T2.player_api_id ORDER BY overall_rating DESC LIMIT 3;
+SELECT DISTINCT T1.player_name ,  T1.birthday FROM Player AS T1 JOIN Player_Attributes AS T2 ON T1.player_api_id  =  T2.player_api_id ORDER BY potential DESC LIMIT 5;
+SELECT count(*) FROM performance;
+SELECT HOST FROM performance ORDER BY Attendance ASC;
+SELECT Date ,  LOCATION FROM performance;
+SELECT Attendance FROM performance WHERE LOCATION  =  'TD Garden' OR LOCATION  =  'Bell Centre';
+SELECT avg(Attendance) FROM performance;
+SELECT Date FROM performance ORDER BY Attendance DESC LIMIT 1;
+SELECT LOCATION ,  COUNT(*) FROM performance GROUP BY LOCATION;
+SELECT LOCATION FROM performance GROUP BY LOCATION ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT LOCATION FROM performance GROUP BY LOCATION HAVING COUNT(*)  >=  2;
+SELECT LOCATION FROM performance WHERE Attendance  >  2000 INTERSECT SELECT LOCATION FROM performance WHERE Attendance  <  1000;
+SELECT T2.Name ,  T3.Location FROM member_attendance AS T1 JOIN member AS T2 ON T1.Member_ID  =  T2.Member_ID JOIN performance AS T3 ON T1.Performance_ID  =  T3.Performance_ID;
+SELECT T2.Name ,  T3.Location FROM member_attendance AS T1 JOIN member AS T2 ON T1.Member_ID  =  T2.Member_ID JOIN performance AS T3 ON T1.Performance_ID  =  T3.Performance_ID ORDER BY T2.Name ASC;
+SELECT T3.Date FROM member_attendance AS T1 JOIN member AS T2 ON T1.Member_ID  =  T2.Member_ID JOIN performance AS T3 ON T1.Performance_ID  =  T3.Performance_ID WHERE T2.Role  =  'Violin';
+SELECT T2.Name ,  T3.Date FROM member_attendance AS T1 JOIN member AS T2 ON T1.Member_ID  =  T2.Member_ID JOIN performance AS T3 ON T1.Performance_ID  =  T3.Performance_ID ORDER BY T3.Attendance DESC;
+SELECT Name FROM member WHERE Member_ID NOT IN (SELECT Member_ID FROM member_attendance);
+SELECT DISTINCT building FROM classroom WHERE capacity  >  50;
+SELECT DISTINCT building FROM classroom WHERE capacity  >  50;
+SELECT count(*) FROM classroom WHERE building != 'Lamberton';
+SELECT count(*) FROM classroom WHERE building != 'Lamberton';
+SELECT dept_name ,  building FROM department WHERE budget  >  (SELECT avg(budget) FROM department);
+SELECT dept_name ,  building FROM department WHERE budget  >  (SELECT avg(budget) FROM department);
+SELECT building ,  room_number FROM classroom WHERE capacity BETWEEN 50 AND 100;
+SELECT building ,  room_number FROM classroom WHERE capacity BETWEEN 50 AND 100;
+SELECT dept_name ,  building FROM department ORDER BY budget DESC LIMIT 1;
+SELECT dept_name ,  building FROM department ORDER BY budget DESC LIMIT 1;
+SELECT name FROM student WHERE dept_name  =  'History' ORDER BY tot_cred DESC LIMIT 1;
+SELECT name FROM student WHERE dept_name  =  'History' ORDER BY tot_cred DESC LIMIT 1;
+SELECT count(*) FROM classroom WHERE building  =  'Lamberton';
+SELECT count(*) FROM classroom WHERE building  =  'Lamberton';
+SELECT count(DISTINCT s_id) FROM advisor;
+SELECT count(DISTINCT s_id) FROM advisor;
+SELECT count(DISTINCT dept_name) FROM course;
+SELECT count(DISTINCT dept_name) FROM course;
+SELECT count(DISTINCT course_id) FROM course WHERE dept_name  =  'Physics';
+SELECT count(DISTINCT course_id) FROM course WHERE dept_name  =  'Physics';
+SELECT T1.title FROM course AS T1 JOIN prereq AS T2 ON T1.course_id  =  T2.course_id GROUP BY T2.course_id HAVING count(*)  =  2;
+SELECT T1.title FROM course AS T1 JOIN prereq AS T2 ON T1.course_id  =  T2.course_id GROUP BY T2.course_id HAVING count(*)  =  2;
+SELECT T1.title ,  T1.credits , T1.dept_name FROM course AS T1 JOIN prereq AS T2 ON T1.course_id  =  T2.course_id GROUP BY T2.course_id HAVING count(*)  >  1;
+SELECT T1.title ,  T1.credits , T1.dept_name FROM course AS T1 JOIN prereq AS T2 ON T1.course_id  =  T2.course_id GROUP BY T2.course_id HAVING count(*)  >  1;
+SELECT count(*) FROM course WHERE course_id NOT IN (SELECT course_id FROM prereq);
+SELECT count(*) FROM course WHERE course_id NOT IN (SELECT course_id FROM prereq);
+SELECT title FROM course WHERE course_id NOT IN (SELECT course_id FROM prereq);
+SELECT title FROM course WHERE course_id NOT IN (SELECT course_id FROM prereq);
+SELECT COUNT (DISTINCT id) FROM teaches;
+SELECT COUNT (DISTINCT id) FROM teaches;
+SELECT sum(budget) FROM department WHERE dept_name  =  'Marketing' OR dept_name  =  'Finance';
+SELECT sum(budget) FROM department WHERE dept_name  =  'Marketing' OR dept_name  =  'Finance';
+SELECT dept_name FROM instructor WHERE name LIKE '%Soisalon%';
+SELECT dept_name FROM instructor WHERE name LIKE '%Soisalon%';
+SELECT count(*) FROM classroom WHERE building  =  'Lamberton' AND capacity  <  50;
+SELECT count(*) FROM classroom WHERE building  =  'Lamberton' AND capacity  <  50;
+SELECT dept_name ,  budget FROM department WHERE budget  >  (SELECT avg(budget) FROM department);
+SELECT dept_name ,  budget FROM department WHERE budget  >  (SELECT avg(budget) FROM department);
+SELECT name FROM instructor WHERE dept_name  =  'Statistics' ORDER BY salary LIMIT 1;
+SELECT name FROM instructor WHERE dept_name  =  'Statistics' ORDER BY salary LIMIT 1;
+SELECT title FROM course WHERE dept_name  =  'Statistics' INTERSECT SELECT title FROM course WHERE dept_name  =  'Psychology';
+SELECT title FROM course WHERE dept_name  =  'Statistics' INTERSECT SELECT title FROM course WHERE dept_name  =  'Psychology';
+SELECT title FROM course WHERE dept_name  =  'Statistics' EXCEPT SELECT title FROM course WHERE dept_name  =  'Psychology';
+SELECT title FROM course WHERE dept_name  =  'Statistics' EXCEPT SELECT title FROM course WHERE dept_name  =  'Psychology';
+SELECT id FROM teaches WHERE semester  =  'Fall' AND YEAR  =  2009 EXCEPT SELECT id FROM teaches WHERE semester  =  'Spring' AND YEAR  =  2010;
+SELECT id FROM teaches WHERE semester  =  'Fall' AND YEAR  =  2009 EXCEPT SELECT id FROM teaches WHERE semester  =  'Spring' AND YEAR  =  2010;
+SELECT DISTINCT T1.name FROM student AS T1 JOIN takes AS T2 ON T1.id  =  T2.id WHERE YEAR  =  2009 OR YEAR  =  2010;
+SELECT DISTINCT T1.name FROM student AS T1 JOIN takes AS T2 ON T1.id  =  T2.id WHERE YEAR  =  2009 OR YEAR  =  2010;
+SELECT dept_name FROM course GROUP BY dept_name ORDER BY count(*) DESC LIMIT 3;
+SELECT dept_name FROM course GROUP BY dept_name ORDER BY count(*) DESC LIMIT 3;
+SELECT dept_name FROM course GROUP BY dept_name ORDER BY sum(credits) DESC LIMIT 1;
+SELECT dept_name FROM course GROUP BY dept_name ORDER BY sum(credits) DESC LIMIT 1;
+SELECT title FROM course ORDER BY title ,  credits;
+SELECT title FROM course ORDER BY title ,  credits;
+SELECT dept_name FROM department ORDER BY budget LIMIT 1;
+SELECT dept_name FROM department ORDER BY budget LIMIT 1;
+SELECT dept_name ,  building FROM department ORDER BY budget DESC;
+SELECT dept_name ,  building FROM department ORDER BY budget DESC;
+SELECT name FROM instructor ORDER BY salary DESC LIMIT 1;
+SELECT name FROM instructor ORDER BY salary DESC LIMIT 1;
+SELECT * FROM instructor ORDER BY salary;
+SELECT * FROM instructor ORDER BY salary;
+SELECT name ,  dept_name FROM student ORDER BY tot_cred;
+SELECT name ,  dept_name FROM student ORDER BY tot_cred;
+SELECT T1.title ,  T3.name FROM course AS T1 JOIN teaches AS T2 ON T1.course_id  =  T2.course_id JOIN instructor AS T3 ON T2.id  =  T3.id WHERE YEAR  =  2008 ORDER BY T1.title;
+SELECT T1.title ,  T3.name FROM course AS T1 JOIN teaches AS T2 ON T1.course_id  =  T2.course_id JOIN instructor AS T3 ON T2.id  =  T3.id WHERE YEAR  =  2008 ORDER BY T1.title;
+SELECT T1.name FROM instructor AS T1 JOIN advisor AS T2 ON T1.id  =  T2.i_id GROUP BY T2.i_id HAVING count(*)  >  1;
+SELECT T1.name FROM instructor AS T1 JOIN advisor AS T2 ON T1.id  =  T2.i_id GROUP BY T2.i_id HAVING count(*)  >  1;
+SELECT T1.name FROM student AS T1 JOIN advisor AS T2 ON T1.id  =  T2.s_id GROUP BY T2.s_id HAVING count(*)  >  1;
+SELECT T1.name FROM student AS T1 JOIN advisor AS T2 ON T1.id  =  T2.s_id GROUP BY T2.s_id HAVING count(*)  >  1;
+SELECT count(*) ,  building FROM classroom WHERE capacity  >  50 GROUP BY building;
+SELECT count(*) ,  building FROM classroom WHERE capacity  >  50 GROUP BY building;
+SELECT max(capacity) ,  avg(capacity) ,  building FROM classroom GROUP BY building;
+SELECT max(capacity) ,  avg(capacity) ,  building FROM classroom GROUP BY building;
+SELECT title FROM course GROUP BY title HAVING count(*)  >  1;
+SELECT title FROM course GROUP BY title HAVING count(*)  >  1;
+SELECT sum(credits) ,  dept_name FROM course GROUP BY dept_name;
+SELECT sum(credits) ,  dept_name FROM course GROUP BY dept_name;
+SELECT min(salary) ,  dept_name FROM instructor GROUP BY dept_name HAVING avg(salary)  >  (SELECT avg(salary) FROM instructor);
+SELECT min(salary) ,  dept_name FROM instructor GROUP BY dept_name HAVING avg(salary)  >  (SELECT avg(salary) FROM instructor);
+SELECT count(*) ,  semester ,  YEAR FROM SECTION GROUP BY semester ,  YEAR;
+SELECT count(*) ,  semester ,  YEAR FROM SECTION GROUP BY semester ,  YEAR;
+SELECT YEAR FROM SECTION GROUP BY YEAR ORDER BY count(*) DESC LIMIT 1;
+SELECT YEAR FROM SECTION GROUP BY YEAR ORDER BY count(*) DESC LIMIT 1;
+SELECT semester ,  YEAR FROM SECTION GROUP BY semester ,  YEAR ORDER BY count(*) DESC LIMIT 1;
+SELECT semester ,  YEAR FROM SECTION GROUP BY semester ,  YEAR ORDER BY count(*) DESC LIMIT 1;
+SELECT dept_name FROM student GROUP BY dept_name ORDER BY count(*) DESC LIMIT 1;
+SELECT dept_name FROM student GROUP BY dept_name ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) ,  dept_name FROM student GROUP BY dept_name;
+SELECT count(*) ,  dept_name FROM student GROUP BY dept_name;
+SELECT semester ,  YEAR FROM takes GROUP BY semester ,  YEAR ORDER BY count(*) LIMIT 1;
+SELECT semester ,  YEAR FROM takes GROUP BY semester ,  YEAR ORDER BY count(*) LIMIT 1;
+SELECT i_id FROM advisor AS T1 JOIN student AS T2 ON T1.s_id  =  T2.id WHERE T2.dept_name  =  'History';
+SELECT i_id FROM advisor AS T1 JOIN student AS T2 ON T1.s_id  =  T2.id WHERE T2.dept_name  =  'History';
+SELECT T2.name ,  T2.salary FROM advisor AS T1 JOIN instructor AS T2 ON T1.i_id  =  T2.id JOIN student AS T3 ON T1.s_id  =  T3.id WHERE T3.dept_name  =  'History';
+SELECT T2.name ,  T2.salary FROM advisor AS T1 JOIN instructor AS T2 ON T1.i_id  =  T2.id JOIN student AS T3 ON T1.s_id  =  T3.id WHERE T3.dept_name  =  'History';
+SELECT course_id FROM course EXCEPT SELECT course_id FROM prereq;
+SELECT course_id FROM course EXCEPT SELECT course_id FROM prereq;
+SELECT title FROM course WHERE course_id NOT IN (SELECT course_id FROM prereq);
+SELECT title FROM course WHERE course_id NOT IN (SELECT course_id FROM prereq);
+SELECT title FROM course WHERE course_id IN (SELECT T1.prereq_id FROM prereq AS T1 JOIN course AS T2 ON T1.course_id  =  T2.course_id WHERE T2.title  =  'International Finance');
+SELECT title FROM course WHERE course_id IN (SELECT T1.prereq_id FROM prereq AS T1 JOIN course AS T2 ON T1.course_id  =  T2.course_id WHERE T2.title  =  'International Finance');
+SELECT title FROM course WHERE course_id IN (SELECT T1.course_id FROM prereq AS T1 JOIN course AS T2 ON T1.prereq_id  =  T2.course_id WHERE T2.title  =  'Differential Geometry');
+SELECT title FROM course WHERE course_id IN (SELECT T1.course_id FROM prereq AS T1 JOIN course AS T2 ON T1.prereq_id  =  T2.course_id WHERE T2.title  =  'Differential Geometry');
+SELECT name FROM student WHERE id IN (SELECT id FROM takes WHERE semester  =  'Fall' AND YEAR  =  2003);
+SELECT name FROM student WHERE id IN (SELECT id FROM takes WHERE semester  =  'Fall' AND YEAR  =  2003);
+SELECT T1.title FROM course AS T1 JOIN SECTION AS T2 ON T1.course_id  =  T2.course_id WHERE building  =  'Chandler' AND semester  =  'Fall' AND YEAR  =  2010;
+SELECT T1.title FROM course AS T1 JOIN SECTION AS T2 ON T1.course_id  =  T2.course_id WHERE building  =  'Chandler' AND semester  =  'Fall' AND YEAR  =  2010;
+SELECT T1.name FROM instructor AS T1 JOIN teaches AS T2 ON T1.id  =  T2.id JOIN course AS T3 ON T2.course_id  =  T3.course_id WHERE T3.title  =  'C Programming';
+SELECT T1.name FROM instructor AS T1 JOIN teaches AS T2 ON T1.id  =  T2.id JOIN course AS T3 ON T2.course_id  =  T3.course_id WHERE T3.title  =  'C Programming';
+SELECT T2.name ,  T2.salary FROM advisor AS T1 JOIN instructor AS T2 ON T1.i_id  =  T2.id JOIN student AS T3 ON T1.s_id  =  T3.id WHERE T3.dept_name  =  'Math';
+SELECT T2.name ,  T2.salary FROM advisor AS T1 JOIN instructor AS T2 ON T1.i_id  =  T2.id JOIN student AS T3 ON T1.s_id  =  T3.id WHERE T3.dept_name  =  'Math';
+SELECT T2.name FROM advisor AS T1 JOIN instructor AS T2 ON T1.i_id  =  T2.id JOIN student AS T3 ON T1.s_id  =  T3.id WHERE T3.dept_name  =  'Math' ORDER BY T3.tot_cred;
+SELECT T2.name FROM advisor AS T1 JOIN instructor AS T2 ON T1.i_id  =  T2.id JOIN student AS T3 ON T1.s_id  =  T3.id WHERE T3.dept_name  =  'Math' ORDER BY T3.tot_cred;
+SELECT title FROM course WHERE course_id IN (SELECT T1.prereq_id FROM prereq AS T1 JOIN course AS T2 ON T1.course_id  =  T2.course_id WHERE T2.title  =  'Mobile Computing');
+SELECT title FROM course WHERE course_id IN (SELECT T1.prereq_id FROM prereq AS T1 JOIN course AS T2 ON T1.course_id  =  T2.course_id WHERE T2.title  =  'Mobile Computing');
+SELECT T2.name FROM advisor AS T1 JOIN instructor AS T2 ON T1.i_id  =  T2.id JOIN student AS T3 ON T1.s_id  =  T3.id ORDER BY T3.tot_cred DESC LIMIT 1;
+SELECT T2.name FROM advisor AS T1 JOIN instructor AS T2 ON T1.i_id  =  T2.id JOIN student AS T3 ON T1.s_id  =  T3.id ORDER BY T3.tot_cred DESC LIMIT 1;
+SELECT name FROM instructor WHERE id NOT IN (SELECT id FROM teaches);
+SELECT name FROM instructor WHERE id NOT IN (SELECT id FROM teaches);
+SELECT id FROM instructor EXCEPT SELECT id FROM teaches;
+SELECT id FROM instructor EXCEPT SELECT id FROM teaches;
+SELECT name FROM instructor WHERE id NOT IN (SELECT id FROM teaches WHERE semester  =  'Spring');
+SELECT name FROM instructor WHERE id NOT IN (SELECT id FROM teaches WHERE semester  =  'Spring');
+SELECT dept_name FROM instructor GROUP BY dept_name ORDER BY avg(salary) DESC LIMIT 1;
+SELECT dept_name FROM instructor GROUP BY dept_name ORDER BY avg(salary) DESC LIMIT 1;
+SELECT avg(T1.salary) ,  count(*) FROM instructor AS T1 JOIN department AS T2 ON T1.dept_name  =  T2.dept_name ORDER BY T2.budget DESC LIMIT 1;
+SELECT avg(T1.salary) ,  count(*) FROM instructor AS T1 JOIN department AS T2 ON T1.dept_name  =  T2.dept_name ORDER BY T2.budget DESC LIMIT 1;
+SELECT T3.title ,  T3.credits FROM classroom AS T1 JOIN SECTION AS T2 ON T1.building  =  T2.building AND T1.room_number  =  T2.room_number JOIN course AS T3 ON T2.course_id  =  T3.course_id WHERE T1.capacity  =  (SELECT max(capacity) FROM classroom);
+SELECT T3.title ,  T3.credits FROM classroom AS T1 JOIN SECTION AS T2 ON T1.building  =  T2.building AND T1.room_number  =  T2.room_number JOIN course AS T3 ON T2.course_id  =  T3.course_id WHERE T1.capacity  =  (SELECT max(capacity) FROM classroom);
+SELECT name FROM student WHERE id NOT IN (SELECT T1.id FROM takes AS T1 JOIN course AS T2 ON T1.course_id  =  T2.course_id WHERE T2.dept_name  =  'Biology');
+SELECT name FROM student WHERE id NOT IN (SELECT T1.id FROM takes AS T1 JOIN course AS T2 ON T1.course_id  =  T2.course_id WHERE T2.dept_name  =  'Biology');
+SELECT count(DISTINCT T2.id) ,  count(DISTINCT T3.id) ,  T3.dept_name FROM department AS T1 JOIN student AS T2 ON T1.dept_name  =  T2.dept_name JOIN instructor AS T3 ON T1.dept_name  =  T3.dept_name GROUP BY T3.dept_name;
+SELECT count(DISTINCT T2.id) ,  count(DISTINCT T3.id) ,  T3.dept_name FROM department AS T1 JOIN student AS T2 ON T1.dept_name  =  T2.dept_name JOIN instructor AS T3 ON T1.dept_name  =  T3.dept_name GROUP BY T3.dept_name;
+SELECT T1.name FROM student AS T1 JOIN takes AS T2 ON T1.id  =  T2.id WHERE T2.course_id IN (SELECT T4.prereq_id FROM course AS T3 JOIN prereq AS T4 ON T3.course_id  =  T4.course_id WHERE T3.title  =  'International Finance');
+SELECT T1.name FROM student AS T1 JOIN takes AS T2 ON T1.id  =  T2.id WHERE T2.course_id IN (SELECT T4.prereq_id FROM course AS T3 JOIN prereq AS T4 ON T3.course_id  =  T4.course_id WHERE T3.title  =  'International Finance');
+SELECT name ,  salary FROM instructor WHERE salary  <  (SELECT avg(salary) FROM instructor WHERE dept_name  =  'Physics');
+SELECT name ,  salary FROM instructor WHERE salary  <  (SELECT avg(salary) FROM instructor WHERE dept_name  =  'Physics');
+SELECT T3.name FROM course AS T1 JOIN takes AS T2 ON T1.course_id  =  T2.course_id JOIN student AS T3 ON T2.id  =  T3.id WHERE T1.dept_name  =  'Statistics';
+SELECT T3.name FROM course AS T1 JOIN takes AS T2 ON T1.course_id  =  T2.course_id JOIN student AS T3 ON T2.id  =  T3.id WHERE T1.dept_name  =  'Statistics';
+SELECT T2.building ,  T2.room_number ,  T2.semester ,  T2.year FROM course AS T1 JOIN SECTION AS T2 ON T1.course_id  =  T2.course_id WHERE T1.dept_name  =  'Psychology' ORDER BY T1.title;
+SELECT T2.building ,  T2.room_number ,  T2.semester ,  T2.year FROM course AS T1 JOIN SECTION AS T2 ON T1.course_id  =  T2.course_id WHERE T1.dept_name  =  'Psychology' ORDER BY T1.title;
+SELECT name FROM instructor WHERE dept_name  =  'Comp. Sci.';
+SELECT name FROM instructor WHERE dept_name  =  'Comp. Sci.';
+SELECT name FROM instructor WHERE dept_name  =  'Comp. Sci.'  AND salary  >  80000;
+SELECT name FROM instructor WHERE dept_name  =  'Comp. Sci.'  AND salary  >  80000;
+SELECT name ,  course_id FROM instructor AS T1 JOIN teaches AS T2 ON T1.ID  =  T2.ID;
+SELECT name ,  course_id FROM instructor AS T1 JOIN teaches AS T2 ON T1.ID  =  T2.ID;
+SELECT name ,  course_id FROM instructor AS T1 JOIN teaches AS T2 ON T1.ID  =  T2.ID WHERE T1.dept_name  =  'Art';
+SELECT name ,  course_id FROM instructor AS T1 JOIN teaches AS T2 ON T1.ID  =  T2.ID WHERE T1.dept_name  =  'Art';
+SELECT name FROM instructor WHERE name LIKE '%dar%';
+SELECT name FROM instructor WHERE name LIKE '%dar%';
+SELECT DISTINCT name FROM  instructor ORDER BY name;
+SELECT DISTINCT name FROM  instructor ORDER BY name;
+SELECT course_id FROM SECTION WHERE semester  =  'Fall' AND YEAR  =  2009 UNION SELECT course_id FROM SECTION WHERE semester  =  'Spring' AND YEAR  =  2010;
+SELECT course_id FROM SECTION WHERE semester  =  'Fall' AND YEAR  =  2009 UNION SELECT course_id FROM SECTION WHERE semester  =  'Spring' AND YEAR  =  2010;
+SELECT course_id FROM SECTION WHERE semester  =  'Fall' AND YEAR  =  2009 INTERSECT SELECT course_id FROM SECTION WHERE semester  =  'Spring' AND YEAR  =  2010;
+SELECT course_id FROM SECTION WHERE semester  =  'Fall' AND YEAR  =  2009 INTERSECT SELECT course_id FROM SECTION WHERE semester  =  'Spring' AND YEAR  =  2010;
+SELECT course_id FROM SECTION WHERE semester  =  'Fall' AND YEAR  =  2009 EXCEPT SELECT course_id FROM SECTION WHERE semester  =  'Spring' AND YEAR  =  2010;
+SELECT course_id FROM SECTION WHERE semester  =  'Fall' AND YEAR  =  2009 EXCEPT SELECT course_id FROM SECTION WHERE semester  =  'Spring' AND YEAR  =  2010;
+SELECT DISTINCT salary FROM instructor WHERE salary  <  (SELECT max(salary) FROM instructor);
+SELECT DISTINCT salary FROM instructor WHERE salary  <  (SELECT max(salary) FROM instructor);
+SELECT COUNT (DISTINCT ID) FROM teaches WHERE semester  =  'Spring' AND YEAR  =  2010;
+SELECT COUNT (DISTINCT ID) FROM teaches WHERE semester  =  'Spring' AND YEAR  =  2010;
+SELECT dept_name ,  AVG (salary) FROM instructor GROUP BY dept_name HAVING AVG (salary)  >  42000;
+SELECT dept_name ,  AVG (salary) FROM instructor GROUP BY dept_name HAVING AVG (salary)  >  42000;
+SELECT name FROM instructor WHERE salary  >  (SELECT min(salary) FROM instructor WHERE dept_name  =  'Biology');
+SELECT name FROM instructor WHERE salary  >  (SELECT min(salary) FROM instructor WHERE dept_name  =  'Biology');
+SELECT name FROM instructor WHERE salary  >  (SELECT max(salary) FROM instructor WHERE dept_name  =  'Biology');
+SELECT name FROM instructor WHERE salary  >  (SELECT max(salary) FROM instructor WHERE dept_name  =  'Biology');
+SELECT count(*) FROM debate;
+SELECT Venue FROM debate ORDER BY Num_of_Audience ASC;
+SELECT Date ,  Venue FROM debate;
+SELECT Date FROM debate WHERE Num_of_Audience  >  150;
+SELECT Name FROM  people WHERE Age  =  35 OR Age  =  36;
+SELECT Party FROM people ORDER BY Age ASC LIMIT 1;
+SELECT Party ,  COUNT(*) FROM people GROUP BY Party;
+SELECT Party FROM people GROUP BY Party ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT DISTINCT Venue FROM debate;
+SELECT T3.Name ,  T2.Date ,  T2.Venue FROM debate_people AS T1 JOIN debate AS T2 ON T1.Debate_ID  =  T2.Debate_ID JOIN people AS T3 ON T1.Affirmative  =  T3.People_ID;
+SELECT T3.Name ,  T2.Date ,  T2.Venue FROM debate_people AS T1 JOIN debate AS T2 ON T1.Debate_ID  =  T2.Debate_ID JOIN people AS T3 ON T1.Negative  =  T3.People_ID ORDER BY T3.Name ASC;
+SELECT T3.Name FROM debate_people AS T1 JOIN debate AS T2 ON T1.Debate_ID  =  T2.Debate_ID JOIN people AS T3 ON T1.Affirmative  =  T3.People_ID WHERE T2.Num_of_Audience  >  200;
+SELECT T2.Name ,  COUNT(*) FROM debate_people AS T1 JOIN people AS T2 ON T1.Affirmative  =  T2.People_ID GROUP BY T2.Name;
+SELECT T2.Name FROM debate_people AS T1 JOIN people AS T2 ON T1.Negative  =  T2.People_ID GROUP BY T2.Name HAVING COUNT(*)  >=  2;
+SELECT Name FROM people WHERE People_id NOT IN (SELECT Affirmative FROM debate_people);
+SELECT customer_details FROM customers ORDER BY customer_details;
+SELECT customer_details FROM customers ORDER BY customer_details;
+SELECT policy_type_code FROM policies AS t1 JOIN customers AS t2 ON t1.customer_id  =  t2.customer_id WHERE t2.customer_details  =  'Dayana Robel';
+SELECT policy_type_code FROM policies AS t1 JOIN customers AS t2 ON t1.customer_id  =  t2.customer_id WHERE t2.customer_details  =  'Dayana Robel';
+SELECT policy_type_code FROM policies GROUP BY policy_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT policy_type_code FROM policies GROUP BY policy_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT policy_type_code FROM policies GROUP BY policy_type_code HAVING count(*)  >  2;
+SELECT policy_type_code FROM policies GROUP BY policy_type_code HAVING count(*)  >  2;
+SELECT sum(amount_piad) ,  avg(amount_piad) FROM claim_headers;
+SELECT sum(amount_piad) ,  avg(amount_piad) FROM claim_headers;
+SELECT sum(t1.amount_claimed) FROM claim_headers AS t1 JOIN claims_documents AS t2 ON t1.claim_header_id  =  t2.claim_id WHERE t2.created_date  =  (SELECT created_date FROM claims_documents ORDER BY created_date LIMIT 1);
+SELECT sum(t1.amount_claimed) FROM claim_headers AS t1 JOIN claims_documents AS t2 ON t1.claim_header_id  =  t2.claim_id WHERE t2.created_date  =  (SELECT created_date FROM claims_documents ORDER BY created_date LIMIT 1);
+SELECT t3.customer_details FROM claim_headers AS t1 JOIN policies AS t2 ON t1.policy_id  =  t2.policy_id JOIN customers AS t3 ON t2.customer_id  =  t3.customer_id WHERE t1.amount_claimed  =  (SELECT max(amount_claimed) FROM claim_headers);
+SELECT t3.customer_details FROM claim_headers AS t1 JOIN policies AS t2 ON t1.policy_id  =  t2.policy_id JOIN customers AS t3 ON t2.customer_id  =  t3.customer_id WHERE t1.amount_claimed  =  (SELECT max(amount_claimed) FROM claim_headers);
+SELECT t3.customer_details FROM claim_headers AS t1 JOIN policies AS t2 ON t1.policy_id  =  t2.policy_id JOIN customers AS t3 ON t2.customer_id  =  t3.customer_id WHERE t1.amount_piad  =  (SELECT min(amount_piad) FROM claim_headers);
+SELECT t3.customer_details FROM claim_headers AS t1 JOIN policies AS t2 ON t1.policy_id  =  t2.policy_id JOIN customers AS t3 ON t2.customer_id  =  t3.customer_id WHERE t1.amount_piad  =  (SELECT min(amount_piad) FROM claim_headers);
+SELECT customer_details FROM customers EXCEPT SELECT t2.customer_details FROM policies AS t1 JOIN customers AS t2 ON t1.customer_id  =  t2.customer_id;
+SELECT customer_details FROM customers EXCEPT SELECT t2.customer_details FROM policies AS t1 JOIN customers AS t2 ON t1.customer_id  =  t2.customer_id;
+SELECT count(*) FROM claims_processing_stages;
+SELECT count(*) FROM claims_processing_stages;
+SELECT t2.claim_status_name FROM claims_processing AS t1 JOIN claims_processing_stages AS t2 ON t1.claim_stage_id  =  t2.claim_stage_id GROUP BY t1.claim_stage_id ORDER BY count(*) DESC LIMIT 1;
+SELECT t2.claim_status_name FROM claims_processing AS t1 JOIN claims_processing_stages AS t2 ON t1.claim_stage_id  =  t2.claim_stage_id GROUP BY t1.claim_stage_id ORDER BY count(*) DESC LIMIT 1;
+SELECT customer_details FROM customers WHERE customer_details LIKE '%Diana%';
+SELECT customer_details FROM customers WHERE customer_details LIKE '%Diana%';
+SELECT DISTINCT t2.customer_details FROM policies AS t1 JOIN customers AS t2 ON t1.customer_id  =  t2.customer_id WHERE t1.policy_type_code  =  'Deputy';
+SELECT DISTINCT t2.customer_details FROM policies AS t1 JOIN customers AS t2 ON t1.customer_id  =  t2.customer_id WHERE t1.policy_type_code  =  'Deputy';
+SELECT DISTINCT t2.customer_details FROM policies AS t1 JOIN customers AS t2 ON t1.customer_id  =  t2.customer_id WHERE t1.policy_type_code  =  'Deputy' OR t1.policy_type_code  =  'Uniform';
+SELECT DISTINCT t2.customer_details FROM policies AS t1 JOIN customers AS t2 ON t1.customer_id  =  t2.customer_id WHERE t1.policy_type_code  =  'Deputy' OR t1.policy_type_code  =  'Uniform';
+SELECT customer_details FROM customers UNION SELECT staff_details FROM staff;
+SELECT customer_details FROM customers UNION SELECT staff_details FROM staff;
+SELECT policy_type_code ,  count(*) FROM policies GROUP BY policy_type_code;
+SELECT policy_type_code ,  count(*) FROM policies GROUP BY policy_type_code;
+SELECT t2.customer_details FROM policies AS t1 JOIN customers AS t2 ON t1.customer_id  =  t2.customer_id GROUP BY t2.customer_details ORDER BY count(*) DESC LIMIT 1;
+SELECT t2.customer_details FROM policies AS t1 JOIN customers AS t2 ON t1.customer_id  =  t2.customer_id GROUP BY t2.customer_details ORDER BY count(*) DESC LIMIT 1;
+SELECT claim_status_description FROM claims_processing_stages WHERE claim_status_name  =  'Open';
+SELECT claim_status_description FROM claims_processing_stages WHERE claim_status_name  =  'Open';
+SELECT count(DISTINCT claim_outcome_code) FROM claims_processing;
+SELECT count(DISTINCT claim_outcome_code) FROM claims_processing;
+SELECT t2.customer_details FROM policies AS t1 JOIN customers AS t2 ON t1.customer_id  =  t2.customer_id WHERE t1.start_date  =  (SELECT max(start_date) FROM policies);
+SELECT t2.customer_details FROM policies AS t1 JOIN customers AS t2 ON t1.customer_id  =  t2.customer_id WHERE t1.start_date  =  (SELECT max(start_date) FROM policies);
+SELECT count(*) FROM Accounts;
+SELECT count(*) FROM Accounts;
+SELECT count(DISTINCT customer_id) FROM Accounts;
+SELECT count(DISTINCT customer_id) FROM Accounts;
+SELECT account_id ,  date_account_opened ,  account_name ,  other_account_details FROM Accounts;
+SELECT account_id ,  date_account_opened ,  account_name ,  other_account_details FROM Accounts;
+SELECT T1.account_id ,  T1.date_account_opened ,  T1.account_name ,  T1.other_account_details FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.customer_first_name  =  'Meaghan';
+SELECT T1.account_id ,  T1.date_account_opened ,  T1.account_name ,  T1.other_account_details FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.customer_first_name  =  'Meaghan';
+SELECT T1.account_name ,  T1.other_account_details FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.customer_first_name  =  'Meaghan' AND T2.customer_last_name  =  'Keeling';
+SELECT T1.account_name ,  T1.other_account_details FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.customer_first_name  =  'Meaghan' AND T2.customer_last_name  =  'Keeling';
+SELECT T2.customer_first_name ,  T2.customer_last_name FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T1.account_name  =  '900';
+SELECT T2.customer_first_name ,  T2.customer_last_name FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T1.account_name  =  '900';
+SELECT count(*) FROM Customers WHERE customer_id NOT IN (SELECT customer_id FROM Accounts);
+SELECT count(*) FROM Customers WHERE customer_id NOT IN (SELECT customer_id FROM Accounts);
+SELECT DISTINCT T1.customer_first_name ,  T1.customer_last_name ,  T1.phone_number FROM Customers AS T1 JOIN Accounts AS T2 ON T1.customer_id  =  T2.customer_id;
+SELECT DISTINCT T1.customer_first_name ,  T1.customer_last_name ,  T1.phone_number FROM Customers AS T1 JOIN Accounts AS T2 ON T1.customer_id  =  T2.customer_id;
+SELECT customer_id FROM Customers EXCEPT SELECT customer_id FROM Accounts;
+SELECT customer_id FROM Customers EXCEPT SELECT customer_id FROM Accounts;
+SELECT count(*) ,  customer_id FROM Accounts GROUP BY customer_id;
+SELECT count(*) ,  customer_id FROM Accounts GROUP BY customer_id;
+SELECT T1.customer_id ,  T2.customer_first_name ,  T2.customer_last_name FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.customer_id ,  T2.customer_first_name ,  T2.customer_last_name FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.customer_id ,  T2.customer_first_name ,  T2.customer_last_name ,  count(*) FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id;
+SELECT T1.customer_id ,  T2.customer_first_name ,  T2.customer_last_name ,  count(*) FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id;
+SELECT T2.customer_first_name ,  T1.customer_id FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id HAVING count(*)  >=  2;
+SELECT T2.customer_first_name ,  T1.customer_id FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id HAVING count(*)  >=  2;
+SELECT count(*) FROM Customers;
+SELECT count(*) FROM Customers;
+SELECT gender ,  count(*) FROM Customers GROUP BY gender;
+SELECT gender ,  count(*) FROM Customers GROUP BY gender;
+SELECT count(*) FROM Financial_transactions;
+SELECT count(*) FROM Financial_transactions;
+SELECT count(*) ,  account_id FROM Financial_transactions;
+SELECT count(*) ,  account_id FROM Financial_transactions;
+SELECT count(*) FROM Financial_transactions AS T1 JOIN Accounts AS T2 ON T1.account_id  =  T2.account_id WHERE T2.account_name  =  '337';
+SELECT count(*) FROM Financial_transactions AS T1 JOIN Accounts AS T2 ON T1.account_id  =  T2.account_id WHERE T2.account_name  =  '337';
+SELECT avg(transaction_amount) ,  min(transaction_amount) ,  max(transaction_amount) ,   sum(transaction_amount) FROM Financial_transactions;
+SELECT avg(transaction_amount) ,  min(transaction_amount) ,  max(transaction_amount) ,   sum(transaction_amount) FROM Financial_transactions;
+SELECT transaction_id FROM Financial_transactions WHERE transaction_amount  >  (SELECT avg(transaction_amount) FROM Financial_transactions);
+SELECT transaction_id FROM Financial_transactions WHERE transaction_amount  >  (SELECT avg(transaction_amount) FROM Financial_transactions);
+SELECT transaction_type ,  sum(transaction_amount) FROM Financial_transactions GROUP BY transaction_type;
+SELECT transaction_type ,  sum(transaction_amount) FROM Financial_transactions GROUP BY transaction_type;
+SELECT T2.account_name ,  T1.account_id ,  count(*) FROM Financial_transactions AS T1 JOIN Accounts AS T2 ON T1.account_id  =  T2.account_id GROUP BY T1.account_id;
+SELECT T2.account_name ,  T1.account_id ,  count(*) FROM Financial_transactions AS T1 JOIN Accounts AS T2 ON T1.account_id  =  T2.account_id GROUP BY T1.account_id;
+SELECT account_id FROM Financial_transactions GROUP BY account_id ORDER BY count(*) DESC LIMIT 1;
+SELECT account_id FROM Financial_transactions GROUP BY account_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.account_id ,  T2.account_name FROM Financial_transactions AS T1 JOIN Accounts AS T2 ON T1.account_id  =  T2.account_id GROUP BY T1.account_id HAVING count(*)  >=  4;
+SELECT T1.account_id ,  T2.account_name FROM Financial_transactions AS T1 JOIN Accounts AS T2 ON T1.account_id  =  T2.account_id GROUP BY T1.account_id HAVING count(*)  >=  4;
+SELECT DISTINCT product_size FROM Products;
+SELECT DISTINCT product_size FROM Products;
+SELECT DISTINCT product_color FROM Products;
+SELECT DISTINCT product_color FROM Products;
+SELECT invoice_number ,  count(*) FROM Financial_transactions GROUP BY invoice_number;
+SELECT invoice_number ,  count(*) FROM Financial_transactions GROUP BY invoice_number;
+SELECT T2.invoice_number ,  T2.invoice_date FROM Financial_transactions AS T1 JOIN Invoices AS T2 ON T1.invoice_number  =  T2.invoice_number GROUP BY T1.invoice_number ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.invoice_number ,  T2.invoice_date FROM Financial_transactions AS T1 JOIN Invoices AS T2 ON T1.invoice_number  =  T2.invoice_number GROUP BY T1.invoice_number ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM Invoices;
+SELECT count(*) FROM Invoices;
+SELECT T1.invoice_date ,  T1.order_id ,  T2.order_details FROM Invoices AS T1 JOIN Orders AS T2 ON T1.order_id  =  T2.order_id;
+SELECT T1.invoice_date ,  T1.order_id ,  T2.order_details FROM Invoices AS T1 JOIN Orders AS T2 ON T1.order_id  =  T2.order_id;
+SELECT order_id ,  count(*) FROM Invoices GROUP BY order_id;
+SELECT order_id ,  count(*) FROM Invoices GROUP BY order_id;
+SELECT T2.order_id ,  T2.order_details FROM Invoices AS T1 JOIN Orders AS T2 ON T1.order_id  =  T2.order_id GROUP BY T2.order_id HAVING count(*)  >  2;
+SELECT T2.order_id ,  T2.order_details FROM Invoices AS T1 JOIN Orders AS T2 ON T1.order_id  =  T2.order_id GROUP BY T2.order_id HAVING count(*)  >  2;
+SELECT T2.customer_last_name ,  T1.customer_id ,  T2.phone_number FROM Orders AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.customer_last_name ,  T1.customer_id ,  T2.phone_number FROM Orders AS T1 JOIN Customers AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT product_name FROM Products EXCEPT SELECT T1.product_name FROM Products AS T1 JOIN Order_items AS T2 ON T1.product_id  =  T2.product_id;
+SELECT product_name FROM Products EXCEPT SELECT T1.product_name FROM Products AS T1 JOIN Order_items AS T2 ON T1.product_id  =  T2.product_id;
+SELECT T2.product_name ,  sum(T1.product_quantity) FROM Order_items AS T1 JOIN Products AS T2 ON T1.product_id  =  T2.product_id GROUP BY T2.product_name;
+SELECT T2.product_name ,  sum(T1.product_quantity) FROM Order_items AS T1 JOIN Products AS T2 ON T1.product_id  =  T2.product_id GROUP BY T2.product_name;
+SELECT order_id ,  count(*) FROM Order_items GROUP BY order_id;
+SELECT order_id ,  count(*) FROM Order_items GROUP BY order_id;
+SELECT product_id ,  count(DISTINCT order_id) FROM Order_items GROUP BY product_id;
+SELECT product_id ,  count(DISTINCT order_id) FROM Order_items GROUP BY product_id;
+SELECT T2.product_name ,  count(*) FROM Order_items AS T1 JOIN Products AS T2 ON T1.product_id  =  T2.product_id JOIN Orders AS T3 ON T3.order_id  =  T1.order_id GROUP BY T2.product_name;
+SELECT T2.product_name ,  count(*) FROM Order_items AS T1 JOIN Products AS T2 ON T1.product_id  =  T2.product_id JOIN Orders AS T3 ON T3.order_id  =  T1.order_id GROUP BY T2.product_name;
+SELECT order_id ,  count(DISTINCT product_id) FROM Order_items GROUP BY order_id;
+SELECT order_id ,  count(DISTINCT product_id) FROM Order_items GROUP BY order_id;
+SELECT order_id ,  sum(product_quantity) FROM Order_items GROUP BY order_id;
+SELECT order_id ,  sum(product_quantity) FROM Order_items GROUP BY order_id;
+SELECT count(*) FROM products WHERE product_id NOT IN ( SELECT product_id FROM Order_items );
+SELECT count(*) FROM products WHERE product_id NOT IN ( SELECT product_id FROM Order_items );
+SELECT count(*) FROM Church WHERE Open_Date  <  1850;
+SELECT name ,  open_date ,  organized_by FROM Church;
+SELECT name FROM church ORDER BY open_date DESC;
+SELECT open_date FROM church GROUP BY open_date HAVING count(*)  >=  2;
+SELECT organized_by ,  name FROM church WHERE open_date BETWEEN 1830 AND 1840;
+SELECT open_date ,  count(*) FROM church GROUP BY open_date;
+SELECT name ,  open_date FROM church ORDER BY open_date DESC LIMIT 3;
+SELECT count(*) FROM people WHERE is_male  =  'F' AND age  >  30;
+SELECT country FROM people WHERE age  <  25 INTERSECT SELECT country FROM people WHERE age  >  30;
+SELECT min(age) ,  max(age) ,  avg(age) FROM people;
+SELECT name ,  country FROM people WHERE age  <  (SELECT avg(age) FROM people);
+SELECT T2.name ,  T3.name FROM wedding AS T1 JOIN people AS T2 ON T1.male_id  =  T2.people_id JOIN people AS T3 ON T1.female_id  =  T3.people_id WHERE T1.year  >  2014;
+SELECT name ,  age FROM people WHERE is_male  =  'T' AND people_id NOT IN (SELECT male_id FROM wedding);
+SELECT name FROM church EXCEPT SELECT T1.name FROM church AS T1 JOIN wedding AS T2 ON T1.church_id  =  T2.church_id WHERE T2.year  =  2015;
+SELECT T1.name FROM church AS T1 JOIN wedding AS T2 ON T1.church_id  =  T2.church_id GROUP BY T1.church_id HAVING count(*)  >=  2;
+SELECT T2.name FROM wedding AS T1 JOIN people AS T2 ON T1.female_id  =  T2.people_id WHERE T1.year  =  2016 AND T2.is_male  =  'F' AND T2.country  =  'Canada';
+SELECT count(*) FROM wedding WHERE YEAR  =  2016;
+SELECT T4.name FROM wedding AS T1 JOIN people AS T2 ON T1.male_id  =  T2.people_id JOIN people AS T3 ON T1.female_id  =  T3.people_id JOIN church AS T4 ON T4.church_id  =  T1.church_id WHERE T2.age  >  30 OR T3.age  >  30;
+SELECT country ,  count(*) FROM people GROUP BY country;
+SELECT COUNT (DISTINCT church_id) FROM wedding WHERE YEAR  =  2016;
+SELECT count(*) FROM artist;
+SELECT count(*) FROM artist;
+SELECT name ,  age ,  country FROM artist ORDER BY Year_Join;
+SELECT name ,  age ,  country FROM artist ORDER BY Year_Join;
+SELECT DISTINCT country FROM artist;
+SELECT DISTINCT country FROM artist;
+SELECT name ,  year_join FROM artist WHERE country != 'United States';
+SELECT name ,  year_join FROM artist WHERE country != 'United States';
+SELECT count(*) FROM artist WHERE age  >  46 AND year_join  >  1990;
+SELECT count(*) FROM artist WHERE age  >  46 AND year_join  >  1990;
+SELECT avg(age) ,  min(age) FROM artist WHERE country  =  'United States';
+SELECT avg(age) ,  min(age) FROM artist WHERE country  =  'United States';
+SELECT name FROM artist ORDER BY year_join DESC LIMIT 1;
+SELECT name FROM artist ORDER BY year_join DESC LIMIT 1;
+SELECT count(*) FROM exhibition WHERE YEAR  >=  2005;
+SELECT count(*) FROM exhibition WHERE YEAR  >=  2005;
+SELECT theme ,  YEAR FROM exhibition WHERE ticket_price  <  15;
+SELECT theme ,  YEAR FROM exhibition WHERE ticket_price  <  15;
+SELECT T2.name ,  count(*) FROM exhibition AS T1 JOIN artist AS T2 ON T1.artist_id  =  T2.artist_id GROUP BY T1.artist_id;
+SELECT T2.name ,  count(*) FROM exhibition AS T1 JOIN artist AS T2 ON T1.artist_id  =  T2.artist_id GROUP BY T1.artist_id;
+SELECT T2.name , T2.country FROM exhibition AS T1 JOIN artist AS T2 ON T1.artist_id  =  T2.artist_id GROUP BY T1.artist_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.name , T2.country FROM exhibition AS T1 JOIN artist AS T2 ON T1.artist_id  =  T2.artist_id GROUP BY T1.artist_id ORDER BY count(*) DESC LIMIT 1;
+SELECT name FROM artist WHERE artist_id NOT IN (SELECT artist_id FROM exhibition);
+SELECT name FROM artist WHERE artist_id NOT IN (SELECT artist_id FROM exhibition);
+SELECT T1.theme ,  T2.name FROM exhibition AS T1 JOIN artist AS T2 ON T1.artist_id  =  T2.artist_id WHERE T1.ticket_price  >  (SELECT avg(ticket_price) FROM exhibition);
+SELECT T1.theme ,  T2.name FROM exhibition AS T1 JOIN artist AS T2 ON T1.artist_id  =  T2.artist_id WHERE T1.ticket_price  >  (SELECT avg(ticket_price) FROM exhibition);
+SELECT avg(ticket_price) ,  min(ticket_price) ,  max(ticket_price) FROM exhibition WHERE YEAR  <  2009;
+SELECT avg(ticket_price) ,  min(ticket_price) ,  max(ticket_price) FROM exhibition WHERE YEAR  <  2009;
+SELECT theme ,  YEAR FROM exhibition ORDER BY ticket_price DESC;
+SELECT theme ,  YEAR FROM exhibition ORDER BY ticket_price DESC;
+SELECT T2.theme ,  T1.date ,  T1.attendance FROM exhibition_record AS T1 JOIN exhibition AS T2 ON T1.exhibition_id  =  T2.exhibition_id WHERE T2.year  =  2004;
+SELECT T2.theme ,  T1.date ,  T1.attendance FROM exhibition_record AS T1 JOIN exhibition AS T2 ON T1.exhibition_id  =  T2.exhibition_id WHERE T2.year  =  2004;
+SELECT name FROM artist EXCEPT SELECT T2.name FROM exhibition AS T1 JOIN artist AS T2 ON T1.artist_id  =  T2.artist_id WHERE T1.year  =  2004;
+SELECT name FROM artist EXCEPT SELECT T2.name FROM exhibition AS T1 JOIN artist AS T2 ON T1.artist_id  =  T2.artist_id WHERE T1.year  =  2004;
+SELECT T2.theme FROM exhibition_record AS T1 JOIN exhibition AS T2 ON T1.exhibition_id  =  T2.exhibition_id WHERE T1.attendance  <  100 INTERSECT SELECT T2.theme FROM exhibition_record AS T1 JOIN exhibition AS T2 ON T1.exhibition_id  =  T2.exhibition_id WHERE T1.attendance  >  500;
+SELECT T2.theme FROM exhibition_record AS T1 JOIN exhibition AS T2 ON T1.exhibition_id  =  T2.exhibition_id WHERE T1.attendance  <  100 INTERSECT SELECT T2.theme FROM exhibition_record AS T1 JOIN exhibition AS T2 ON T1.exhibition_id  =  T2.exhibition_id WHERE T1.attendance  >  500;
+SELECT count(*) FROM exhibition_record AS T1 JOIN exhibition AS T2 ON T1.exhibition_id  =  T2.exhibition_id WHERE T1.attendance  >  100 OR T2.ticket_price  <  10;
+SELECT count(*) FROM exhibition_record AS T1 JOIN exhibition AS T2 ON T1.exhibition_id  =  T2.exhibition_id WHERE T1.attendance  >  100 OR T2.ticket_price  <  10;
+SELECT T3.name FROM exhibition_record AS T1 JOIN exhibition AS T2 ON T1.exhibition_id  =  T2.exhibition_id JOIN artist AS T3 ON T3.artist_id  =  T2.artist_id GROUP BY T3.artist_id HAVING avg(T1.attendance)  >  200;
+SELECT T3.name FROM exhibition_record AS T1 JOIN exhibition AS T2 ON T1.exhibition_id  =  T2.exhibition_id JOIN artist AS T3 ON T3.artist_id  =  T2.artist_id GROUP BY T3.artist_id HAVING avg(T1.attendance)  >  200;
+SELECT i_id FROM item WHERE title  =  'orange';
+SELECT * FROM item;
+SELECT count(*) FROM review;
+SELECT count(*) FROM useracct;
+SELECT avg(rating) ,  max(rating) FROM review;
+SELECT min(rank) FROM review;
+SELECT count(DISTINCT u_id) FROM review;
+SELECT count(DISTINCT i_id) FROM review;
+SELECT count(*) FROM item WHERE i_id NOT IN (SELECT i_id FROM review);
+SELECT name FROM useracct WHERE u_id NOT IN (SELECT u_id FROM review);
+SELECT T1.title FROM item AS T1 JOIN review AS T2 ON T1.i_id  =  T2.i_id WHERE T2.rating  =  10;
+SELECT T1.title FROM item AS T1 JOIN review AS T2 ON T1.i_id  =  T2.i_id WHERE T2.rating  >  (SELECT avg(rating) FROM review);
+SELECT T1.title FROM item AS T1 JOIN review AS T2 ON T1.i_id  =  T2.i_id WHERE T2.rating  <  5;
+SELECT T1.title FROM item AS T1 JOIN review AS T2 ON T1.i_id  =  T2.i_id WHERE T2.rating  >  8 INTERSECT SELECT T1.title FROM item AS T1 JOIN review AS T2 ON T1.i_id  =  T2.i_id WHERE T2.rating  <  5;
+SELECT T1.title FROM item AS T1 JOIN review AS T2 ON T1.i_id  =  T2.i_id WHERE T2.rank  >  3 INTERSECT SELECT T1.title FROM item AS T1 JOIN review AS T2 ON T1.i_id  =  T2.i_id GROUP BY T2.i_id HAVING avg(T2.rating)  >  5;
+SELECT T1.title FROM item AS T1 JOIN review AS T2 ON T1.i_id  =  T2.i_id GROUP BY T2.i_id ORDER BY avg(T2.rating) LIMIT 1;
+SELECT title FROM item ORDER BY title;
+SELECT T1.name FROM useracct AS T1 JOIN review AS T2 ON T1.u_id  =  T2.u_id GROUP BY T2.u_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.title ,  T1.i_id FROM item AS T1 JOIN review AS T2 ON T1.i_id  =  T2.i_id GROUP BY T2.i_id ORDER BY avg(T2.rating) DESC LIMIT 1;
+SELECT T1.title ,  T1.i_id FROM item AS T1 JOIN review AS T2 ON T1.i_id  =  T2.i_id GROUP BY T2.i_id ORDER BY avg(T2.rank) DESC LIMIT 1;
+SELECT T1.name ,  avg(T2.rating) FROM useracct AS T1 JOIN review AS T2 ON T1.u_id  =  T2.u_id GROUP BY T2.u_id;
+SELECT T1.name ,  count(*) FROM useracct AS T1 JOIN review AS T2 ON T1.u_id  =  T2.u_id GROUP BY T2.u_id;
+SELECT T1.name FROM useracct AS T1 JOIN review AS T2 ON T1.u_id  =  T2.u_id ORDER BY T2.rating DESC LIMIT 1;
+SELECT T1.name FROM useracct AS T1 JOIN trust AS T2 ON T1.u_id  =  T2.source_u_id GROUP BY T2.source_u_id ORDER BY avg(trust) DESC LIMIT 1;
+SELECT T1.name ,  avg(trust) FROM useracct AS T1 JOIN trust AS T2 ON T1.u_id  =  T2.target_u_id GROUP BY T2.target_u_id;
+SELECT T1.name FROM useracct AS T1 JOIN trust AS T2 ON T1.u_id  =  T2.target_u_id ORDER BY trust LIMIT 1;
+SELECT title FROM item WHERE i_id NOT IN (SELECT i_id FROM review);
+SELECT name FROM useracct WHERE u_id NOT IN (SELECT u_id FROM review);
+SELECT count(*) FROM useracct WHERE u_id NOT IN (SELECT u_id FROM review);
+SELECT count(*) FROM item WHERE i_id NOT IN (SELECT i_id FROM review);
+SELECT count(*) FROM player;
+SELECT Player_name FROM player ORDER BY Votes ASC;
+SELECT Gender ,  Occupation FROM player;
+SELECT Player_name ,  residence FROM player WHERE Occupation != 'Researcher';
+SELECT Sponsor_name FROM player WHERE Residence  =  'Brandon' OR Residence  =  'Birtle';
+SELECT Player_name FROM player ORDER BY Votes DESC LIMIT 1;
+SELECT Occupation ,  COUNT(*) FROM player GROUP BY Occupation;
+SELECT Occupation FROM player GROUP BY Occupation ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Residence FROM player GROUP BY Residence HAVING COUNT(*)  >=  2;
+SELECT T3.Player_name ,  T2.coach_name FROM player_coach AS T1 JOIN coach AS T2 ON T1.Coach_ID  =  T2.Coach_ID JOIN player AS T3 ON T1.Player_ID  =  T3.Player_ID;
+SELECT T3.Player_name FROM player_coach AS T1 JOIN coach AS T2 ON T1.Coach_ID  =  T2.Coach_ID JOIN player AS T3 ON T1.Player_ID  =  T3.Player_ID WHERE T2.Rank  =  1;
+SELECT T3.Player_name ,  T3.gender FROM player_coach AS T1 JOIN coach AS T2 ON T1.Coach_ID  =  T2.Coach_ID JOIN player AS T3 ON T1.Player_ID  =  T3.Player_ID WHERE T1.Starting_year  >  2011;
+SELECT T3.Player_name ,  T2.coach_name FROM player_coach AS T1 JOIN coach AS T2 ON T1.Coach_ID  =  T2.Coach_ID JOIN player AS T3 ON T1.Player_ID  =  T3.Player_ID ORDER BY T3.Votes DESC;
+SELECT Player_name FROM player WHERE Player_ID NOT IN (SELECT Player_ID FROM player_coach);
+SELECT Residence FROM player WHERE gender  =  'M' INTERSECT SELECT Residence FROM player WHERE gender  =  'F';
+SELECT T1.club_id ,  T1.club_name, count(*) FROM club AS T1 JOIN coach AS T2 ON T1.club_id  =  T2.club_id GROUP BY T1.club_id;
+SELECT T1.club_id ,  T1.gold FROM match_result AS T1 JOIN coach AS T2 ON T1.club_id  =  T2.club_id GROUP BY T1.club_id ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM gymnast;
+SELECT count(*) FROM gymnast;
+SELECT Total_Points FROM gymnast ORDER BY Total_Points DESC;
+SELECT Total_Points FROM gymnast ORDER BY Total_Points DESC;
+SELECT Total_Points FROM gymnast ORDER BY Floor_Exercise_Points DESC;
+SELECT Total_Points FROM gymnast ORDER BY Floor_Exercise_Points DESC;
+SELECT avg(Horizontal_Bar_Points) FROM gymnast;
+SELECT avg(Horizontal_Bar_Points) FROM gymnast;
+SELECT Name FROM People ORDER BY Name ASC;
+SELECT Name FROM People ORDER BY Name ASC;
+SELECT T2.Name FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID;
+SELECT T2.Name FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID;
+SELECT T2.Name FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID WHERE T2.Hometown != 'Santo Domingo';
+SELECT T2.Name FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID WHERE T2.Hometown != 'Santo Domingo';
+SELECT Age FROM people ORDER BY Height DESC LIMIT 1;
+SELECT Age FROM people ORDER BY Height DESC LIMIT 1;
+SELECT Name FROM People ORDER BY Age DESC LIMIT 5;
+SELECT Name FROM People ORDER BY Age DESC LIMIT 5;
+SELECT T1.Total_Points FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID ORDER BY T2.Age ASC LIMIT 1;
+SELECT T1.Total_Points FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID ORDER BY T2.Age ASC LIMIT 1;
+SELECT avg(T2.Age) FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID;
+SELECT avg(T2.Age) FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID;
+SELECT DISTINCT T2.Hometown FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID WHERE T1.Total_Points  >  57.5;
+SELECT DISTINCT T2.Hometown FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID WHERE T1.Total_Points  >  57.5;
+SELECT T2.Hometown ,  COUNT(*) FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID GROUP BY T2.Hometown;
+SELECT T2.Hometown ,  COUNT(*) FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID GROUP BY T2.Hometown;
+SELECT T2.Hometown FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID GROUP BY T2.Hometown ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T2.Hometown FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID GROUP BY T2.Hometown ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T2.Hometown FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID GROUP BY T2.Hometown HAVING COUNT(*)  >=  2;
+SELECT T2.Hometown FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID GROUP BY T2.Hometown HAVING COUNT(*)  >=  2;
+SELECT T2.Name FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID ORDER BY T2.Height ASC;
+SELECT T2.Name FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID ORDER BY T2.Height ASC;
+SELECT DISTINCT Hometown FROM people EXCEPT SELECT DISTINCT T2.Hometown FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID;
+SELECT DISTINCT Hometown FROM people EXCEPT SELECT DISTINCT T2.Hometown FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID;
+SELECT Hometown FROM people WHERE Age  >  23 INTERSECT SELECT Hometown FROM people WHERE Age  <  20;
+SELECT Hometown FROM people WHERE Age  >  23 INTERSECT SELECT Hometown FROM people WHERE Age  <  20;
+SELECT count(DISTINCT Hometown) FROM people;
+SELECT count(DISTINCT Hometown) FROM people;
+SELECT T2.Age FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID ORDER BY T1.Total_Points DESC;
+SELECT T2.Age FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID  =  T2.People_ID ORDER BY T1.Total_Points DESC;
+SELECT sum(T2.balance) FROM accounts AS T1 JOIN savings AS T2 ON T1.custid  =  T2.custid WHERE T1.name != 'Brown';
+SELECT sum(T2.balance) FROM accounts AS T1 JOIN savings AS T2 ON T1.custid  =  T2.custid WHERE T1.name != 'Brown';
+SELECT count(*) FROM accounts;
+SELECT count(*) FROM accounts;
+SELECT sum(balance) FROM checking;
+SELECT sum(balance) FROM checking;
+SELECT avg(balance) FROM checking;
+SELECT avg(balance) FROM checking;
+SELECT count(*) FROM savings WHERE balance  >  (SELECT avg(balance) FROM savings);
+SELECT count(*) FROM savings WHERE balance  >  (SELECT avg(balance) FROM savings);
+SELECT T1.custid ,  T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid WHERE T2.balance  <  (SELECT max(balance) FROM checking);
+SELECT T1.custid ,  T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid WHERE T2.balance  <  (SELECT max(balance) FROM checking);
+SELECT T2.balance FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid WHERE T1.name LIKE '%ee%';
+SELECT T2.balance FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid WHERE T1.name LIKE '%ee%';
+SELECT T2.balance ,  T3.balance FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid WHERE T1.name  =  'Brown';
+SELECT T2.balance ,  T3.balance FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid WHERE T1.name  =  'Brown';
+SELECT T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid WHERE T2.balance  >  (SELECT avg(balance) FROM checking) INTERSECT SELECT T1.name FROM accounts AS T1 JOIN savings AS T2 ON T1.custid  =  T2.custid WHERE T2.balance  <  (SELECT avg(balance) FROM savings);
+SELECT T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid WHERE T2.balance  >  (SELECT avg(balance) FROM checking) INTERSECT SELECT T1.name FROM accounts AS T1 JOIN savings AS T2 ON T1.custid  =  T2.custid WHERE T2.balance  <  (SELECT avg(balance) FROM savings);
+SELECT T2.balance FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid WHERE T1.name IN (SELECT T1.name FROM accounts AS T1 JOIN savings AS T2 ON T1.custid  =  T2.custid WHERE T2.balance  >  (SELECT avg(balance) FROM savings));
+SELECT T2.balance FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid WHERE T1.name IN (SELECT T1.name FROM accounts AS T1 JOIN savings AS T2 ON T1.custid  =  T2.custid WHERE T2.balance  >  (SELECT avg(balance) FROM savings));
+SELECT name FROM accounts ORDER BY name;
+SELECT name FROM accounts ORDER BY name;
+SELECT T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid ORDER BY T2.balance + T3.balance LIMIT 1;
+SELECT T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid ORDER BY T2.balance + T3.balance LIMIT 1;
+SELECT T1.name ,  T2.balance + T3.balance FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid WHERE T3.balance  >  (SELECT avg(balance) FROM savings);
+SELECT T1.name ,  T2.balance + T3.balance FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid WHERE T3.balance  >  (SELECT avg(balance) FROM savings);
+SELECT T1.name ,  T2.balance FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid ORDER BY T3.balance LIMIT 1;
+SELECT T1.name ,  T2.balance FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid ORDER BY T3.balance LIMIT 1;
+SELECT count(*) ,  T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid GROUP BY T1.name;
+SELECT count(*) ,  T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid GROUP BY T1.name;
+SELECT sum(T2.balance) ,  T1.name FROM accounts AS T1 JOIN savings AS T2 ON T1.custid  =  T2.custid GROUP BY T1.name;
+SELECT sum(T2.balance) ,  T1.name FROM accounts AS T1 JOIN savings AS T2 ON T1.custid  =  T2.custid GROUP BY T1.name;
+SELECT T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid WHERE T2.balance  <  (SELECT avg(balance) FROM checking);
+SELECT T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid WHERE T2.balance  <  (SELECT avg(balance) FROM checking);
+SELECT T3.balance FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid ORDER BY T2.balance DESC LIMIT 1;
+SELECT T3.balance FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid ORDER BY T2.balance DESC LIMIT 1;
+SELECT T1.balance + T2.balance FROM checking AS T1 JOIN savings AS T2 ON T1.custid  =  T2.custid ORDER BY T1.balance + T2.balance;
+SELECT T1.balance + T2.balance FROM checking AS T1 JOIN savings AS T2 ON T1.custid  =  T2.custid ORDER BY T1.balance + T2.balance;
+SELECT T2.balance ,  T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid ORDER BY T3.balance LIMIT 1;
+SELECT T2.balance ,  T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid ORDER BY T3.balance LIMIT 1;
+SELECT T2.balance ,  T3.balance ,  T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid;
+SELECT T2.balance ,  T3.balance ,  T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid;
+SELECT T2.balance ,  T3.balance ,  T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid ORDER BY T2.balance + T3.balance DESC;
+SELECT T2.balance ,  T3.balance ,  T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid ORDER BY T2.balance + T3.balance DESC;
+SELECT T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid WHERE T2.balance  >  T3.balance;
+SELECT T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid WHERE T2.balance  >  T3.balance;
+SELECT T1.name ,  T3.balance + T2.balance FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid WHERE T3.balance  <  T2.balance;
+SELECT T1.name ,  T3.balance + T2.balance FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid WHERE T3.balance  <  T2.balance;
+SELECT T1.name ,  T2.balance FROM accounts AS T1 JOIN savings AS T2 ON T1.custid  =  T2.custid ORDER BY T2.balance DESC LIMIT 3;
+SELECT T1.name ,  T2.balance FROM accounts AS T1 JOIN savings AS T2 ON T1.custid  =  T2.custid ORDER BY T2.balance DESC LIMIT 3;
+SELECT count(*) FROM browser WHERE market_share  >=  5;
+SELECT name FROM browser ORDER BY market_share DESC;
+SELECT id ,  name ,  market_share FROM browser;
+SELECT max(market_share) ,  min(market_share) ,  avg(market_share) FROM browser;
+SELECT id ,  market_share FROM browser WHERE name  =  'Safari';
+SELECT name ,  operating_system FROM web_client_accelerator WHERE CONNECTION != 'Broadband';
+SELECT T1.name FROM browser AS T1 JOIN accelerator_compatible_browser AS T2 ON T1.id  =  T2.browser_id JOIN web_client_accelerator AS T3 ON T2.accelerator_id  =  T3.id WHERE T3.name  =  'CProxy' AND T2.compatible_since_year  >  1998;
+SELECT T1.id ,  T1.Name FROM web_client_accelerator AS T1 JOIN accelerator_compatible_browser AS T2 ON T2.accelerator_id  =  T1.id GROUP BY T1.id HAVING count(*)  >=  2;
+SELECT T1.id ,  T1.name FROM browser AS T1 JOIN accelerator_compatible_browser AS T2 ON T1.id  =  T2.browser_id GROUP BY T1.id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.compatible_since_year FROM accelerator_compatible_browser AS T1 JOIN browser AS T2 ON T1.browser_id  =  T2.id JOIN web_client_accelerator AS T3 ON T1.accelerator_id  =  T3.id WHERE T3.name  =  'CACHEbox' AND T2.name  =  'Internet Explorer';
+SELECT count(DISTINCT client) FROM web_client_accelerator;
+SELECT count(*) FROM web_client_accelerator WHERE id NOT IN ( SELECT accelerator_id FROM accelerator_compatible_browser );
+SELECT DISTINCT T1.name FROM web_client_accelerator AS T1 JOIN accelerator_compatible_browser AS T2 ON T2.accelerator_id  =  T1.id JOIN browser AS T3 ON T2.browser_id  =  T3.id WHERE T3.market_share  >  15;
+SELECT T3.name FROM web_client_accelerator AS T1 JOIN accelerator_compatible_browser AS T2 ON T2.accelerator_id  =  T1.id JOIN browser AS T3 ON T2.browser_id  =  T3.id WHERE T1.name  =  'CACHEbox' INTERSECT SELECT T3.name FROM web_client_accelerator AS T1 JOIN accelerator_compatible_browser AS T2 ON T2.accelerator_id  =  T1.id JOIN browser AS T3 ON T2.browser_id  =  T3.id WHERE T1.name  =  'Fasterfox';
+SELECT name ,  operating_system FROM web_client_accelerator EXCEPT SELECT T1.name ,  T1.operating_system FROM web_client_accelerator AS T1 JOIN accelerator_compatible_browser AS T2 ON T2.accelerator_id  =  T1.id JOIN browser AS T3 ON T2.browser_id  =  T3.id WHERE T3.name  =  'Opera';
+SELECT name FROM web_client_accelerator WHERE name LIKE '%Opera%';
+SELECT Operating_system ,  count(*) FROM web_client_accelerator GROUP BY Operating_system;
+SELECT T2.name ,  T3.name FROM accelerator_compatible_browser AS T1 JOIN browser AS T2 ON T1.browser_id  =  T2.id JOIN web_client_accelerator AS T3 ON T1.accelerator_id  =  T3.id ORDER BY T1.compatible_since_year DESC;
+SELECT count(*) FROM wrestler;
+SELECT count(*) FROM wrestler;
+SELECT Name FROM wrestler ORDER BY Days_held DESC;
+SELECT Name FROM wrestler ORDER BY Days_held DESC;
+SELECT Name FROM wrestler ORDER BY Days_held ASC LIMIT 1;
+SELECT Name FROM wrestler ORDER BY Days_held ASC LIMIT 1;
+SELECT DISTINCT Reign FROM wrestler WHERE LOCATION != 'Tokyo , Japan';
+SELECT DISTINCT Reign FROM wrestler WHERE LOCATION != 'Tokyo , Japan';
+SELECT Name ,  LOCATION FROM wrestler;
+SELECT Name ,  LOCATION FROM wrestler;
+SELECT Elimination_Move FROM Elimination WHERE Team  =  'Team Orton';
+SELECT Elimination_Move FROM Elimination WHERE Team  =  'Team Orton';
+SELECT T2.Name ,  T1.Elimination_Move FROM elimination AS T1 JOIN wrestler AS T2 ON T1.Wrestler_ID  =  T2.Wrestler_ID;
+SELECT T2.Name ,  T1.Elimination_Move FROM elimination AS T1 JOIN wrestler AS T2 ON T1.Wrestler_ID  =  T2.Wrestler_ID;
+SELECT T2.Name ,  T1.Team FROM elimination AS T1 JOIN wrestler AS T2 ON T1.Wrestler_ID  =  T2.Wrestler_ID ORDER BY T2.Days_held DESC;
+SELECT T2.Name ,  T1.Team FROM elimination AS T1 JOIN wrestler AS T2 ON T1.Wrestler_ID  =  T2.Wrestler_ID ORDER BY T2.Days_held DESC;
+SELECT T1.Time FROM elimination AS T1 JOIN wrestler AS T2 ON T1.Wrestler_ID  =  T2.Wrestler_ID ORDER BY T2.Days_held DESC LIMIT 1;
+SELECT T1.Time FROM elimination AS T1 JOIN wrestler AS T2 ON T1.Wrestler_ID  =  T2.Wrestler_ID ORDER BY T2.Days_held DESC LIMIT 1;
+SELECT T1.Time FROM elimination AS T1 JOIN wrestler AS T2 ON T1.Wrestler_ID  =  T2.Wrestler_ID WHERE T2.Days_held  >  50;
+SELECT T1.Time FROM elimination AS T1 JOIN wrestler AS T2 ON T1.Wrestler_ID  =  T2.Wrestler_ID WHERE T2.Days_held  >  50;
+SELECT Team ,  COUNT(*) FROM elimination GROUP BY Team;
+SELECT Team ,  COUNT(*) FROM elimination GROUP BY Team;
+SELECT Team FROM elimination GROUP BY Team HAVING COUNT(*)  >  3;
+SELECT Team FROM elimination GROUP BY Team HAVING COUNT(*)  >  3;
+SELECT Reign ,  Days_held FROM wrestler;
+SELECT Reign ,  Days_held FROM wrestler;
+SELECT Name FROM wrestler WHERE Days_held  <  100;
+SELECT Name FROM wrestler WHERE Days_held  <  100;
+SELECT Reign FROM wrestler GROUP BY Reign ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Reign FROM wrestler GROUP BY Reign ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT LOCATION FROM wrestler GROUP BY LOCATION HAVING COUNT(*)  >  2;
+SELECT LOCATION FROM wrestler GROUP BY LOCATION HAVING COUNT(*)  >  2;
+SELECT Name FROM wrestler WHERE Wrestler_ID NOT IN (SELECT Wrestler_ID FROM elimination);
+SELECT Name FROM wrestler WHERE Wrestler_ID NOT IN (SELECT Wrestler_ID FROM elimination);
+SELECT Team FROM Elimination WHERE Eliminated_By  =  'Orton' INTERSECT SELECT Team FROM Elimination WHERE Eliminated_By  =  'Benjamin';
+SELECT Team FROM Elimination WHERE Eliminated_By  =  'Orton' INTERSECT SELECT Team FROM Elimination WHERE Eliminated_By  =  'Benjamin';
+SELECT COUNT (DISTINCT team) FROM elimination;
+SELECT COUNT (DISTINCT team) FROM elimination;
+SELECT TIME FROM elimination WHERE Eliminated_By  =  'Punk' OR Eliminated_By  =  'Orton';
+SELECT TIME FROM elimination WHERE Eliminated_By  =  'Punk' OR Eliminated_By  =  'Orton';
+SELECT count(*) FROM school;
+SELECT count(*) FROM school;
+SELECT school_name FROM school ORDER BY school_name;
+SELECT school_name ,  LOCATION ,  mascot FROM school;
+SELECT sum(enrollment) ,  avg(enrollment) FROM school;
+SELECT mascot FROM school WHERE enrollment  >  (SELECT avg(enrollment) FROM school);
+SELECT school_name FROM school ORDER BY enrollment LIMIT 1;
+SELECT avg(enrollment) ,  max(enrollment) ,  min(enrollment) FROM school;
+SELECT county ,  count(*) ,  sum(enrollment) FROM school GROUP BY county;
+SELECT count(DISTINCT T1.donator_name) FROM endowment AS T1 JOIN school AS T2 ON T1.school_id  =  T2.school_id WHERE T2.school_name  =  'Glenn';
+SELECT donator_name ,  sum(amount) FROM endowment GROUP BY donator_name ORDER BY sum(amount) DESC;
+SELECT school_name FROM school WHERE school_id NOT IN (SELECT school_id FROM endowment);
+SELECT T2.school_name FROM endowment AS T1 JOIN school AS T2 ON T1.school_id  =  T2.school_id GROUP BY T1.school_id HAVING sum(T1.amount)  <=  10;
+SELECT T1.donator_name FROM endowment AS T1 JOIN school AS T2 ON T1.school_id  =  T2.school_id WHERE T2.school_name  =  'Glenn' INTERSECT SELECT T1.donator_name FROM endowment AS T1 JOIN school AS T2 ON T1.school_id  =  T2.school_id WHERE T2.school_name  =  'Triton';
+SELECT donator_name FROM endowment EXCEPT SELECT donator_name FROM endowment WHERE amount  <  9;
+SELECT amount ,  donator_name FROM endowment ORDER BY amount DESC LIMIT 1;
+SELECT count(*) FROM budget WHERE budgeted  >  3000 AND YEAR  <=  2001;
+SELECT count(*) FROM budget WHERE budgeted  >  3000 AND YEAR  <=  2001;
+SELECT T2.school_name ,  T1.budgeted ,  T1.invested FROM budget AS T1 JOIN school AS T2 ON T1.school_id  =  T2.school_id WHERE T1.year  >=  2002;
+SELECT DISTINCT donator_name FROM endowment;
+SELECT count(*) FROM budget WHERE budgeted  <  invested;
+SELECT sum(T1.budgeted) FROM budget AS T1 JOIN school AS T2 ON T1.school_id  =  T2.school_id WHERE T2.school_name  =  'Glenn';
+SELECT T2.school_name FROM budget AS T1 JOIN school AS T2 ON T1.school_id  =  T2.school_id JOIN endowment AS T3 ON T2.school_id  =  T3.school_id GROUP BY T2.school_name HAVING sum(T1.budgeted)  >  100 OR sum(T3.amount)  >  10;
+SELECT T2.School_name FROM endowment AS T1 JOIN school AS T2 ON T1.school_id  =  T2.school_id WHERE T1.amount  >  8.5 GROUP BY T1.school_id HAVING count(*)  >  1;
+SELECT count(*) FROM (SELECT * FROM endowment WHERE amount  >  8.5 GROUP BY school_id HAVING count(*)  >  1);
+SELECT T1.School_name ,  T1.Mascot ,  T1.IHSAA_Football_Class FROM school AS T1 JOIN budget AS T2 ON T1.school_id  =  T2.school_id WHERE Budgeted  >  6000 OR YEAR  <  2003 ORDER BY T2.total_budget_percent_invested ,  T2.total_budget_percent_budgeted;
+SELECT count(*) FROM building;
+SELECT name ,  street_address ,  floors FROM building ORDER BY floors;
+SELECT name FROM building ORDER BY height_feet DESC LIMIT 1;
+SELECT avg(floors) ,  max(floors) ,  min(floors) FROM building;
+SELECT count(*) FROM building WHERE height_feet  >  (SELECT avg(height_feet) FROM building) OR floors  >  (SELECT avg(floors) FROM building);
+SELECT name FROM building WHERE height_feet  >=  200 AND floors  >=  20;
+SELECT institution ,  LOCATION FROM institution WHERE founded  >  1990 AND TYPE  =  'Private';
+SELECT TYPE ,  count(*) ,  sum(enrollment) FROM institution GROUP BY TYPE;
+SELECT TYPE FROM institution GROUP BY TYPE ORDER BY count(*) DESC LIMIT 1;
+SELECT TYPE FROM institution WHERE founded  >  1990 AND enrollment  >=  1000;
+SELECT name FROM building WHERE building_id NOT IN (SELECT building_id FROM institution);
+SELECT name FROM building EXCEPT SELECT T1.name FROM building AS T1 JOIN institution AS T2 ON T1.building_id  =  T2.building_id WHERE T2.founded  =  2003;
+SELECT T1.name ,  count(*) FROM building AS T1 JOIN institution AS T2 ON T1.building_id  =  T2.building_id GROUP BY T1.building_id;
+SELECT T1.name ,  T1.height_feet FROM building AS T1 JOIN institution AS T2 ON T1.building_id  =  T2.building_id WHERE T2.founded  >  1880 GROUP BY T1.building_id HAVING count(*)  >=  2;
+SELECT DISTINCT TYPE FROM institution;
+SELECT T1.institution ,  count(*) FROM institution AS T1 JOIN protein AS T2 ON T1.institution_id  =  T2.institution_id GROUP BY T1.institution_id;
+SELECT count(*) FROM institution AS T1 JOIN protein AS T2 ON T1.institution_id  =  T2.institution_id WHERE T1.founded  >  1880 OR T1.type  =  'Private';
+SELECT T2.protein_name ,  T1.institution FROM institution AS T1 JOIN protein AS T2 ON T1.institution_id  =  T2.institution_id;
+SELECT count(*) FROM institution AS T1 JOIN protein AS T2 ON T1.institution_id  =  T2.institution_id JOIN building AS T3 ON T3.building_id  =  T1.building_id WHERE T3.floors  >=  20;
+SELECT count(*) FROM institution WHERE institution_id NOT IN (SELECT institution_id FROM protein);
+SELECT LOCATION FROM cinema EXCEPT SELECT LOCATION FROM cinema WHERE capacity  >  800;
+SELECT LOCATION FROM cinema WHERE openning_year  =  2010 INTERSECT SELECT LOCATION FROM cinema WHERE openning_year  =  2011;
+SELECT count(*) FROM cinema;
+SELECT count(*) FROM cinema;
+SELECT name ,  openning_year ,  capacity FROM cinema;
+SELECT name ,  LOCATION FROM cinema WHERE capacity  >  (SELECT avg(capacity) FROM cinema);
+SELECT DISTINCT LOCATION FROM cinema;
+SELECT DISTINCT LOCATION FROM cinema;
+SELECT name ,  openning_year FROM cinema ORDER BY openning_year DESC;
+SELECT name ,  LOCATION FROM cinema ORDER BY capacity DESC LIMIT 1;
+SELECT avg(capacity) ,  min(capacity) ,  max(capacity) FROM cinema WHERE openning_year  >=  2011;
+SELECT LOCATION ,  count(*) FROM cinema GROUP BY LOCATION;
+SELECT LOCATION FROM cinema WHERE openning_year  >=  2010 GROUP BY LOCATION ORDER BY count(*) DESC LIMIT 1;
+SELECT LOCATION FROM cinema WHERE capacity  >  300 GROUP BY LOCATION HAVING count(*)  >=  2;
+SELECT LOCATION FROM cinema WHERE capacity  >  300 GROUP BY LOCATION HAVING count(*)  >=  2;
+SELECT title ,  directed_by FROM film;
+SELECT title ,  directed_by FROM film;
+SELECT DISTINCT directed_by FROM film;
+SELECT DISTINCT directed_by FROM film;
+SELECT directed_by ,  count(*) FROM film GROUP BY directed_by;
+SELECT T2.name ,  sum(T1.show_times_per_day) FROM schedule AS T1 JOIN cinema AS T2 ON T1.cinema_id  =  T2.cinema_id GROUP BY T1.cinema_id;
+SELECT T2.title ,  max(T1.price) FROM schedule AS T1 JOIN film AS T2 ON T1.film_id  =  T2.film_id GROUP BY T1.film_id;
+SELECT T2.title ,  max(T1.price) FROM schedule AS T1 JOIN film AS T2 ON T1.film_id  =  T2.film_id GROUP BY T1.film_id;
+SELECT T3.name ,  T2.title ,  T1.date ,  T1.price FROM schedule AS T1 JOIN film AS T2 ON T1.film_id  =  T2.film_id JOIN cinema AS T3 ON T1.cinema_id  =  T3.cinema_id;
+SELECT title ,  directed_by FROM film WHERE film_id NOT IN (SELECT film_id FROM schedule);
+SELECT T2.directed_by FROM schedule AS T1 JOIN film AS T2 ON T1.film_id  =  T2.film_id GROUP BY T2.directed_by ORDER BY sum(T1.show_times_per_day) DESC LIMIT 1;
+SELECT LOCATION FROM cinema WHERE capacity  >  300 GROUP BY LOCATION HAVING count(*)  >  1;
+SELECT LOCATION FROM cinema WHERE capacity  >  300 GROUP BY LOCATION HAVING count(*)  >  1;
+SELECT count(*) FROM film WHERE title LIKE '%Dummy%';
+SELECT count(*) FROM film WHERE title LIKE '%Dummy%';
+SELECT T1.good_or_bad_customer FROM customers AS T1 JOIN discount_coupons AS T2 ON T1.coupon_id  =  T2.coupon_id WHERE T2.coupon_amount  =  500;
+SELECT T1.customer_id ,  T1.first_name ,  count(*) FROM Customers AS T1 JOIN bookings AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id;
+SELECT customer_id ,  sum(amount_paid) FROM Payments GROUP BY customer_id ORDER BY sum(amount_paid) DESC LIMIT 1;
+SELECT T1.booking_id ,  T1.amount_of_refund FROM Bookings AS T1 JOIN Payments AS T2 ON T1.booking_id  =  T2.booking_id GROUP BY T1.booking_id ORDER BY count(*) DESC LIMIT 1;
+SELECT product_id FROM products_booked GROUP BY product_id HAVING count(*)  =  3;
+SELECT T2.product_description FROM products_booked AS T1 JOIN products_for_hire AS T2 ON T1.product_id  =  T2.product_id WHERE T1.booked_amount  =  102.76;
+SELECT T3.booking_start_date ,   T3.booking_end_date FROM Products_for_hire AS T1 JOIN products_booked AS T2 ON T1.product_id  =  T2.product_id JOIN bookings AS T3 ON T2.booking_id  =  T3.booking_id WHERE T1.product_name  =  'Book collection A';
+SELECT T2.product_name FROM view_product_availability AS T1 JOIN products_for_hire AS T2 ON T1.product_id  =  T2.product_id WHERE T1.available_yn  =  1;
+SELECT count(DISTINCT product_type_code) FROM products_for_hire;
+SELECT first_name ,  last_name ,  gender_mf FROM customers WHERE good_or_bad_customer  =  'good' ORDER BY last_name;
+SELECT avg(amount_due) FROM payments;
+SELECT max(booked_count) ,  min(booked_count) ,  avg(booked_count) FROM products_booked;
+SELECT DISTINCT payment_type_code FROM payments;
+SELECT daily_hire_cost FROM Products_for_hire WHERE product_name LIKE '%Book%';
+SELECT count(*) FROM Products_for_hire WHERE product_id NOT IN ( SELECT product_id FROM products_booked WHERE booked_amount  >  200 );
+SELECT T1.coupon_amount FROM Discount_Coupons AS T1 JOIN customers AS T2 ON T1.coupon_id  =  T2.coupon_id WHERE T2.good_or_bad_customer  =  'good' INTERSECT SELECT T1.coupon_amount FROM Discount_Coupons AS T1 JOIN customers AS T2 ON T1.coupon_id  =  T2.coupon_id WHERE T2.good_or_bad_customer  =  'bad';
+SELECT payment_date FROM payments WHERE amount_paid  >  300 OR payment_type_code  =  'Check';
+SELECT product_name ,  product_description FROM products_for_hire WHERE product_type_code  =  'Cutlery' AND daily_hire_cost  <  20;
+SELECT count(*) FROM phone;
+SELECT Name FROM phone ORDER BY Price ASC;
+SELECT Memory_in_G ,  Carrier FROM phone;
+SELECT DISTINCT Carrier FROM phone WHERE Memory_in_G  >  32;
+SELECT Name FROM phone WHERE Carrier  =  'Sprint' OR Carrier  =  'TMobile';
+SELECT Carrier FROM phone ORDER BY Price DESC LIMIT 1;
+SELECT Carrier ,  COUNT(*) FROM phone GROUP BY Carrier;
+SELECT Carrier FROM phone GROUP BY Carrier ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Carrier FROM phone WHERE Memory_in_G  <  32 INTERSECT SELECT Carrier FROM phone WHERE Memory_in_G  >  64;
+SELECT T3.Name ,  T2.District FROM phone_market AS T1 JOIN market AS T2 ON T1.Market_ID  =  T2.Market_ID JOIN phone AS T3 ON T1.Phone_ID  =  T3.Phone_ID;
+SELECT T3.Name ,  T2.District FROM phone_market AS T1 JOIN market AS T2 ON T1.Market_ID  =  T2.Market_ID JOIN phone AS T3 ON T1.Phone_ID  =  T3.Phone_ID ORDER BY T2.Ranking;
+SELECT T3.Name FROM phone_market AS T1 JOIN market AS T2 ON T1.Market_ID  =  T2.Market_ID JOIN phone AS T3 ON T1.Phone_ID  =  T3.Phone_ID WHERE T2.Num_of_shops  >  50;
+SELECT T2.Name ,  sum(T1.Num_of_stock) FROM phone_market AS T1 JOIN phone AS T2 ON T1.Phone_ID  =  T2.Phone_ID GROUP BY T2.Name;
+SELECT T2.Name FROM phone_market AS T1 JOIN phone AS T2 ON T1.Phone_ID  =  T2.Phone_ID GROUP BY T2.Name HAVING sum(T1.Num_of_stock)  >=  2000 ORDER BY sum(T1.Num_of_stock) DESC;
+SELECT Name FROM phone WHERE Phone_id NOT IN (SELECT Phone_ID FROM phone_market);
+SELECT count(*) FROM company;
+SELECT count(*) FROM company;
+SELECT company ,  rank FROM company ORDER BY Sales_billion DESC;
+SELECT company ,  rank FROM company ORDER BY Sales_billion DESC;
+SELECT company ,  main_industry FROM company WHERE headquarters != 'USA';
+SELECT company ,  main_industry FROM company WHERE headquarters != 'USA';
+SELECT company ,  headquarters FROM company ORDER BY market_value DESC;
+SELECT company ,  headquarters FROM company ORDER BY market_value DESC;
+SELECT min(market_value) ,  max(market_value) ,  avg(market_value) FROM company;
+SELECT min(market_value) ,  max(market_value) ,  avg(market_value) FROM company;
+SELECT DISTINCT main_industry FROM company;
+SELECT DISTINCT main_industry FROM company;
+SELECT headquarters ,  count(*) FROM company GROUP BY headquarters;
+SELECT headquarters ,  count(*) FROM company GROUP BY headquarters;
+SELECT main_industry ,  sum(market_value) FROM company GROUP BY main_industry;
+SELECT main_industry ,  sum(market_value) FROM company GROUP BY main_industry;
+SELECT main_industry ,  count(*) FROM company GROUP BY main_industry ORDER BY sum(market_value) DESC LIMIT 1;
+SELECT main_industry ,  count(*) FROM company GROUP BY main_industry ORDER BY sum(market_value) DESC LIMIT 1;
+SELECT headquarters FROM company WHERE main_industry  =  'Banking' GROUP BY headquarters HAVING count(*)  >=  2;
+SELECT headquarters FROM company WHERE main_industry  =  'Banking' GROUP BY headquarters HAVING count(*)  >=  2;
+SELECT station_id ,  LOCATION ,  manager_name FROM gas_station ORDER BY open_year;
+SELECT station_id ,  LOCATION ,  manager_name FROM gas_station ORDER BY open_year;
+SELECT count(*) FROM gas_station WHERE open_year BETWEEN 2000 AND 2005;
+SELECT count(*) FROM gas_station WHERE open_year BETWEEN 2000 AND 2005;
+SELECT LOCATION ,  count(*) FROM gas_station GROUP BY LOCATION ORDER BY count(*);
+SELECT LOCATION ,  count(*) FROM gas_station GROUP BY LOCATION ORDER BY count(*);
+SELECT headquarters FROM company WHERE main_industry  =  'Banking' INTERSECT SELECT headquarters FROM company WHERE main_industry  =  'Oil and gas';
+SELECT headquarters FROM company WHERE main_industry  =  'Banking' INTERSECT SELECT headquarters FROM company WHERE main_industry  =  'Oil and gas';
+SELECT headquarters FROM company EXCEPT SELECT headquarters FROM company WHERE main_industry  =  'Banking';
+SELECT headquarters FROM company EXCEPT SELECT headquarters FROM company WHERE main_industry  =  'Banking';
+SELECT T2.company ,  count(*) FROM station_company AS T1 JOIN company AS T2 ON T1.company_id  =  T2.company_id GROUP BY T1.company_id;
+SELECT T2.company ,  count(*) FROM station_company AS T1 JOIN company AS T2 ON T1.company_id  =  T2.company_id GROUP BY T1.company_id;
+SELECT company ,  main_industry FROM company WHERE company_id NOT IN (SELECT company_id FROM station_company);
+SELECT company ,  main_industry FROM company WHERE company_id NOT IN (SELECT company_id FROM station_company);
+SELECT T3.manager_name FROM station_company AS T1 JOIN company AS T2 ON T1.company_id  =  T2.company_id JOIN gas_station AS T3 ON T1.station_id  =  T3.station_id WHERE T2.company  =  'ExxonMobil';
+SELECT T3.manager_name FROM station_company AS T1 JOIN company AS T2 ON T1.company_id  =  T2.company_id JOIN gas_station AS T3 ON T1.station_id  =  T3.station_id WHERE T2.company  =  'ExxonMobil';
+SELECT T3.location FROM station_company AS T1 JOIN company AS T2 ON T1.company_id  =  T2.company_id JOIN gas_station AS T3 ON T1.station_id  =  T3.station_id WHERE T2.market_value  >  100;
+SELECT T3.location FROM station_company AS T1 JOIN company AS T2 ON T1.company_id  =  T2.company_id JOIN gas_station AS T3 ON T1.station_id  =  T3.station_id WHERE T2.market_value  >  100;
+SELECT manager_name FROM gas_station WHERE open_year  >  2000 GROUP BY manager_name ORDER BY count(*) DESC LIMIT 1;
+SELECT manager_name FROM gas_station WHERE open_year  >  2000 GROUP BY manager_name ORDER BY count(*) DESC LIMIT 1;
+SELECT LOCATION FROM gas_station ORDER BY open_year;
+SELECT LOCATION FROM gas_station ORDER BY open_year;
+SELECT rank ,  company ,  market_value FROM company WHERE main_industry  =  'Banking' ORDER BY sales_billion ,  profits_billion;
+SELECT rank ,  company ,  market_value FROM company WHERE main_industry  =  'Banking' ORDER BY sales_billion ,  profits_billion;
+SELECT T3.location ,  T3.Representative_Name FROM station_company AS T1 JOIN company AS T2 ON T1.company_id  =  T2.company_id JOIN gas_station AS T3 ON T1.station_id  =  T3.station_id ORDER BY T2.Assets_billion DESC LIMIT 3;
+SELECT T3.location ,  T3.Representative_Name FROM station_company AS T1 JOIN company AS T2 ON T1.company_id  =  T2.company_id JOIN gas_station AS T3 ON T1.station_id  =  T3.station_id ORDER BY T2.Assets_billion DESC LIMIT 3;
+SELECT count(*) FROM region;
+SELECT count(*) FROM region;
+SELECT DISTINCT region_name FROM region ORDER BY Label;
+SELECT DISTINCT region_name FROM region ORDER BY Label;
+SELECT count(DISTINCT party_name) FROM party;
+SELECT count(DISTINCT party_name) FROM party;
+SELECT minister ,  took_office ,  left_office FROM party ORDER BY left_office;
+SELECT minister ,  took_office ,  left_office FROM party ORDER BY left_office;
+SELECT minister FROM party WHERE took_office  >  1961 OR took_office  <  1959;
+SELECT minister FROM party WHERE took_office  >  1961 OR took_office  <  1959;
+SELECT minister FROM party WHERE party_name != 'Progress Party';
+SELECT minister FROM party WHERE party_name != 'Progress Party';
+SELECT minister ,  party_name FROM party ORDER BY took_office DESC;
+SELECT minister ,  party_name FROM party ORDER BY took_office DESC;
+SELECT minister FROM party ORDER BY left_office DESC LIMIT 1;
+SELECT minister FROM party ORDER BY left_office DESC LIMIT 1;
+SELECT T1.member_name ,  T2.party_name FROM Member AS T1 JOIN party AS T2 ON T1.party_id  =  T2.party_id;
+SELECT T1.member_name ,  T2.party_name FROM Member AS T1 JOIN party AS T2 ON T1.party_id  =  T2.party_id;
+SELECT T2.party_name ,  count(*) FROM Member AS T1 JOIN party AS T2 ON T1.party_id  =  T2.party_id GROUP BY T1.party_id;
+SELECT T2.party_name ,  count(*) FROM Member AS T1 JOIN party AS T2 ON T1.party_id  =  T2.party_id GROUP BY T1.party_id;
+SELECT T2.party_name FROM Member AS T1 JOIN party AS T2 ON T1.party_id  =  T2.party_id GROUP BY T1.party_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.party_name FROM Member AS T1 JOIN party AS T2 ON T1.party_id  =  T2.party_id GROUP BY T1.party_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.party_name ,  T2.region_name FROM party AS T1 JOIN region AS T2 ON T1.region_id  =  T2.region_id;
+SELECT T1.party_name ,  T2.region_name FROM party AS T1 JOIN region AS T2 ON T1.region_id  =  T2.region_id;
+SELECT party_name FROM party WHERE party_id NOT IN (SELECT party_id FROM Member);
+SELECT party_name FROM party WHERE party_id NOT IN (SELECT party_id FROM Member);
+SELECT member_name FROM member WHERE party_id  =  3 INTERSECT SELECT member_name FROM member WHERE party_id  =  1;
+SELECT member_name FROM member WHERE party_id  =  3 INTERSECT SELECT member_name FROM member WHERE party_id  =  1;
+SELECT T1.member_name FROM Member AS T1 JOIN party AS T2 ON T1.party_id  =  T2.party_id WHERE T2.Party_name != 'Progress Party';
+SELECT T1.member_name FROM Member AS T1 JOIN party AS T2 ON T1.party_id  =  T2.party_id WHERE T2.Party_name != 'Progress Party';
+SELECT count(*) FROM party_events;
+SELECT count(*) FROM party_events;
+SELECT T2.party_name ,  count(*) FROM party_events AS T1 JOIN party AS T2 ON T1.party_id  =  T2.party_id GROUP BY T1.party_id;
+SELECT T2.party_name ,  count(*) FROM party_events AS T1 JOIN party AS T2 ON T1.party_id  =  T2.party_id GROUP BY T1.party_id;
+SELECT member_name FROM member EXCEPT SELECT T1.member_name FROM member AS T1 JOIN party_events AS T2 ON T1.member_id  =  T2.member_in_charge_id;
+SELECT member_name FROM member EXCEPT SELECT T1.member_name FROM member AS T1 JOIN party_events AS T2 ON T1.member_id  =  T2.member_in_charge_id;
+SELECT T2.party_name FROM party_events AS T1 JOIN party AS T2 ON T1.party_id  =  T2.party_id GROUP BY T1.party_id HAVING count(*)  >=  2;
+SELECT T2.party_name FROM party_events AS T1 JOIN party AS T2 ON T1.party_id  =  T2.party_id GROUP BY T1.party_id HAVING count(*)  >=  2;
+SELECT T1.member_name FROM member AS T1 JOIN party_events AS T2 ON T1.member_id  =  T2.member_in_charge_id GROUP BY T2.member_in_charge_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.member_name FROM member AS T1 JOIN party_events AS T2 ON T1.member_id  =  T2.member_in_charge_id GROUP BY T2.member_in_charge_id ORDER BY count(*) DESC LIMIT 1;
+SELECT event_name FROM party_events GROUP BY event_name HAVING count(*)  >  2;
+SELECT event_name FROM party_events GROUP BY event_name HAVING count(*)  >  2;
+SELECT count(*) FROM region AS t1 JOIN party AS t2 ON t1.region_id  =  t2.region_id JOIN party_events AS t3 ON t2.party_id  =  t3.party_id WHERE t1.region_name  =  'United Kingdom' AND t3.Event_Name  =  'Annaual Meeting';
+SELECT count(*) FROM region AS t1 JOIN party AS t2 ON t1.region_id  =  t2.region_id JOIN party_events AS t3 ON t2.party_id  =  t3.party_id WHERE t1.region_name  =  'United Kingdom' AND t3.Event_Name  =  'Annaual Meeting';
+SELECT count(*) FROM pilot;
+SELECT Pilot_name FROM pilot ORDER BY Rank ASC;
+SELECT POSITION ,  Team FROM pilot;
+SELECT DISTINCT POSITION FROM pilot WHERE Age  >  30;
+SELECT Pilot_name FROM pilot WHERE Team  =  'Bradley' OR Team  =  'Fordham';
+SELECT Join_Year FROM pilot ORDER BY Rank ASC LIMIT 1;
+SELECT Nationality ,  COUNT(*) FROM pilot GROUP BY Nationality;
+SELECT Nationality FROM pilot GROUP BY Nationality ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT POSITION FROM pilot WHERE Join_Year	 <  2000 INTERSECT SELECT POSITION FROM pilot WHERE Join_Year	 >  2005;
+SELECT T3.Pilot_name ,  T2.Model FROM pilot_record AS T1 JOIN aircraft AS T2 ON T1.Aircraft_ID  =  T2.Aircraft_ID JOIN pilot AS T3 ON T1.Pilot_ID  =  T3.Pilot_ID;
+SELECT T3.Pilot_name ,  T2.Fleet_Series FROM pilot_record AS T1 JOIN aircraft AS T2 ON T1.Aircraft_ID  =  T2.Aircraft_ID JOIN pilot AS T3 ON T1.Pilot_ID  =  T3.Pilot_ID ORDER BY T3.Rank;
+SELECT T2.Fleet_Series FROM pilot_record AS T1 JOIN aircraft AS T2 ON T1.Aircraft_ID  =  T2.Aircraft_ID JOIN pilot AS T3 ON T1.Pilot_ID  =  T3.Pilot_ID WHERE T3.Age  <  34;
+SELECT T2.Pilot_name ,  COUNT(*) FROM pilot_record AS T1 JOIN pilot AS T2 ON T1.pilot_ID  =  T2.pilot_ID GROUP BY T2.Pilot_name;
+SELECT T2.Pilot_name ,  COUNT(*) FROM pilot_record AS T1 JOIN pilot AS T2 ON T1.pilot_ID  =  T2.pilot_ID GROUP BY T2.Pilot_name HAVING COUNT(*)  >  1;
+SELECT Pilot_name FROM pilot WHERE Pilot_ID NOT IN (SELECT Pilot_ID FROM pilot_record);
+SELECT document_status_code FROM Ref_Document_Status;
+SELECT document_status_description FROM Ref_Document_Status WHERE document_status_code = 'working';
+SELECT document_type_code FROM Ref_Document_Types;
+SELECT document_type_description FROM Ref_Document_Types WHERE document_type_code = 'Paper';
+SELECT shipping_agent_name FROM Ref_Shipping_Agents;
+SELECT shipping_agent_code FROM Ref_Shipping_Agents WHERE shipping_agent_name = 'UPS';
+SELECT role_code FROM ROLES;
+SELECT role_description FROM ROLES WHERE role_code = 'ED';
+SELECT count(*) FROM Employees;
+SELECT T1.role_description FROM ROLES AS T1 JOIN Employees AS T2 ON T1.role_code = T2.role_code WHERE T2.employee_name = 'Koby';
+SELECT document_id ,  receipt_date FROM Documents;
+SELECT T1.role_description ,  T2.role_code ,  count(*) FROM ROLES AS T1 JOIN Employees AS T2 ON T1.role_code = T2.role_code GROUP BY T2.role_code;
+SELECT Roles.role_description , count(Employees.employee_id) FROM ROLES JOIN Employees ON Employees.role_code = Roles.role_code GROUP BY Employees.role_code HAVING count(Employees.employee_id)  >  1;
+SELECT Ref_Document_Status.document_status_description FROM Ref_Document_Status JOIN Documents ON Documents.document_status_code = Ref_Document_Status.document_status_code WHERE Documents.document_id = 1;
+SELECT count(*) FROM Documents WHERE document_status_code = 'done';
+SELECT document_type_code FROM Documents WHERE document_id = 2;
+SELECT document_id FROM Documents WHERE document_status_code = 'done' AND document_type_code = 'Paper';
+SELECT Ref_Shipping_Agents.shipping_agent_name FROM Ref_Shipping_Agents JOIN Documents ON Documents.shipping_agent_code = Ref_Shipping_Agents.shipping_agent_code WHERE Documents.document_id = 2;
+SELECT count(*) FROM Ref_Shipping_Agents JOIN Documents ON Documents.shipping_agent_code = Ref_Shipping_Agents.shipping_agent_code WHERE Ref_Shipping_Agents.shipping_agent_name = 'USPS';
+SELECT Ref_Shipping_Agents.shipping_agent_name , count(Documents.document_id) FROM Ref_Shipping_Agents JOIN Documents ON Documents.shipping_agent_code = Ref_Shipping_Agents.shipping_agent_code GROUP BY Ref_Shipping_Agents.shipping_agent_code ORDER BY count(Documents.document_id) DESC LIMIT 1;
+SELECT receipt_date FROM Documents WHERE document_id = 3;
+SELECT Addresses.address_details FROM Addresses JOIN Documents_Mailed ON Documents_Mailed.mailed_to_address_id = Addresses.address_id WHERE document_id = 4;
+SELECT mailing_date FROM Documents_Mailed WHERE document_id = 7;
+SELECT document_id FROM Documents WHERE document_status_code  =  'done' AND document_type_code = 'Paper' EXCEPT SELECT document_id FROM Documents JOIN Ref_Shipping_Agents ON Documents.shipping_agent_code = Ref_Shipping_Agents.shipping_agent_code WHERE Ref_Shipping_Agents.shipping_agent_name = 'USPS';
+SELECT document_id FROM Documents WHERE document_status_code  =  'done' AND document_type_code = 'Paper' INTERSECT SELECT document_id FROM Documents JOIN Ref_Shipping_Agents ON Documents.shipping_agent_code = Ref_Shipping_Agents.shipping_agent_code WHERE Ref_Shipping_Agents.shipping_agent_name = 'USPS';
+SELECT draft_details FROM Document_Drafts WHERE document_id = 7;
+SELECT count(*) FROM Draft_Copies WHERE document_id = 2;
+SELECT document_id , count(copy_number) FROM Draft_Copies GROUP BY document_id ORDER BY count(copy_number) DESC LIMIT 1;
+SELECT document_id , count(*) FROM Draft_Copies GROUP BY document_id HAVING count(*)  >  1;
+SELECT Employees.employee_name FROM Employees JOIN Circulation_History ON Circulation_History.employee_id = Employees.employee_id WHERE Circulation_History.document_id = 1;
+SELECT employee_name FROM Employees EXCEPT SELECT Employees.employee_name FROM Employees JOIN Circulation_History ON Circulation_History.employee_id = Employees.employee_id;
+SELECT Employees.employee_name , count(*) FROM Employees JOIN Circulation_History ON Circulation_History.employee_id = Employees.employee_id GROUP BY Circulation_History.document_id , Circulation_History.draft_number , Circulation_History.copy_number ORDER BY count(*) DESC LIMIT 1;
+SELECT document_id ,  count(DISTINCT employee_id) FROM Circulation_History GROUP BY document_id;
+SELECT dname FROM department ORDER BY mgr_start_date;
+SELECT Dependent_name FROM dependent WHERE relationship  =  'Spouse';
+SELECT count(*) FROM dependent WHERE sex  =  'F';
+SELECT t1.dname FROM department AS t1 JOIN dept_locations AS t2 ON t1.dnumber  =  t2.dnumber WHERE t2.dlocation  =  'Houston';
+SELECT fname ,  lname FROM employee WHERE salary  >  30000;
+SELECT count(*) ,  sex FROM employee WHERE salary  <  50000 GROUP BY sex;
+SELECT fname ,  lname ,  address FROM employee ORDER BY Bdate;
+SELECT T1.event_details FROM EVENTS AS T1 JOIN Services AS T2 ON T1.Service_ID  =  T2.Service_ID WHERE T2.Service_Type_Code  =  'Marriage';
+SELECT T1.event_id ,  T1.event_details FROM EVENTS AS T1 JOIN Participants_in_Events AS T2 ON T1.Event_ID  =  T2.Event_ID GROUP BY T1.Event_ID HAVING count(*)  >  1;
+SELECT T1.Participant_ID ,  T1.Participant_Type_Code ,  count(*) FROM Participants AS T1 JOIN Participants_in_Events AS T2 ON T1.Participant_ID  =  T2.Participant_ID GROUP BY T1.Participant_ID;
+SELECT Participant_ID ,  Participant_Type_Code ,  Participant_Details FROM  Participants;
+SELECT count(*) FROM participants WHERE participant_type_code  =  'Organizer';
+SELECT service_type_code FROM services ORDER BY service_type_code;
+SELECT service_id ,  event_details FROM EVENTS;
+SELECT count(*) FROM participants AS T1 JOIN Participants_in_Events AS T2 ON T1.Participant_ID  =  T2.Participant_ID WHERE T1.participant_details LIKE '%Dr.%';
+SELECT participant_type_code FROM participants GROUP BY participant_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT T3.service_id ,  T4.Service_Type_Code FROM participants AS T1 JOIN Participants_in_Events AS T2 ON T1.Participant_ID  =  T2.Participant_ID JOIN EVENTS AS T3 ON T2.Event_ID   =  T3.Event_ID JOIN services AS T4 ON T3.service_id  =  T4.service_id GROUP BY T3.service_id ORDER BY count(*) ASC LIMIT 1;
+SELECT Event_ID FROM Participants_in_Events GROUP BY Event_ID ORDER BY count(*) DESC LIMIT 1;
+SELECT event_id FROM EVENTS EXCEPT SELECT T1.event_id FROM Participants_in_Events AS T1 JOIN Participants AS T2 ON T1.Participant_ID  =  T2.Participant_ID WHERE Participant_Details  =  'Kenyatta Kuhn';
+SELECT T1.service_type_code FROM services AS T1 JOIN EVENTS AS T2 ON T1.service_id  =  T2.service_id WHERE T2.event_details  =  'Success' INTERSECT SELECT T1.service_type_code FROM services AS T1 JOIN EVENTS AS T2 ON T1.service_id  =  T2.service_id WHERE T2.event_details  =  'Fail';
+SELECT count(*) FROM EVENTS WHERE event_id NOT IN (SELECT event_id FROM Participants_in_Events);
+SELECT count(DISTINCT participant_id) FROM participants_in_Events;
+SELECT name FROM races ORDER BY date DESC LIMIT 1;
+SELECT name FROM races ORDER BY date DESC LIMIT 1;
+SELECT name ,  date FROM races ORDER BY date DESC LIMIT 1;
+SELECT name ,  date FROM races ORDER BY date DESC LIMIT 1;
+SELECT name FROM races WHERE YEAR = 2017;
+SELECT name FROM races WHERE YEAR = 2017;
+SELECT DISTINCT name FROM races WHERE YEAR BETWEEN 2014 AND 2017;
+SELECT DISTINCT name FROM races WHERE YEAR BETWEEN 2014 AND 2017;
+SELECT DISTINCT T1.forename ,  T1.surname FROM drivers AS T1 JOIN laptimes AS T2 ON T1.driverid = T2.driverid WHERE T2.milliseconds < 93000;
+SELECT DISTINCT T1.forename ,  T1.surname FROM drivers AS T1 JOIN laptimes AS T2 ON T1.driverid = T2.driverid WHERE T2.milliseconds < 93000;
+SELECT DISTINCT T1.driverid ,  T1.nationality FROM drivers AS T1 JOIN laptimes AS T2 ON T1.driverid = T2.driverid WHERE T2.milliseconds >  100000;
+SELECT DISTINCT T1.driverid ,  T1.nationality FROM drivers AS T1 JOIN laptimes AS T2 ON T1.driverid = T2.driverid WHERE T2.milliseconds >  100000;
+SELECT T1.forename ,  T1.surname FROM drivers AS T1 JOIN laptimes AS T2 ON T1.driverid = T2.driverid ORDER BY T2.milliseconds LIMIT 1;
+SELECT T1.forename ,  T1.surname FROM drivers AS T1 JOIN laptimes AS T2 ON T1.driverid = T2.driverid ORDER BY T2.milliseconds LIMIT 1;
+SELECT T1.driverid ,  T1.surname FROM drivers AS T1 JOIN laptimes AS T2 ON T1.driverid = T2.driverid ORDER BY T2.milliseconds DESC LIMIT 1;
+SELECT T1.driverid ,  T1.surname FROM drivers AS T1 JOIN laptimes AS T2 ON T1.driverid = T2.driverid ORDER BY T2.milliseconds DESC LIMIT 1;
+SELECT T1.driverid ,  T1.forename ,  T1.surname FROM drivers AS T1 JOIN laptimes AS T2 ON T1.driverid = T2.driverid WHERE POSITION  =  '1' GROUP BY T1.driverid HAVING count(*)  >=  2;
+SELECT T1.driverid ,  T1.forename ,  T1.surname FROM drivers AS T1 JOIN laptimes AS T2 ON T1.driverid = T2.driverid WHERE POSITION  =  '1' GROUP BY T1.driverid HAVING count(*)  >=  2;
+SELECT count(*) FROM results AS T1 JOIN races AS T2 ON T1.raceid = T2.raceid WHERE T2.name = 'Australian Grand Prix' AND YEAR = 2009;
+SELECT count(*) FROM results AS T1 JOIN races AS T2 ON T1.raceid = T2.raceid WHERE T2.name = 'Australian Grand Prix' AND YEAR = 2009;
+SELECT count(DISTINCT driverId) FROM results WHERE raceId NOT IN( SELECT raceId FROM races WHERE YEAR != 2009 );
+SELECT count(DISTINCT driverId) FROM results WHERE raceId NOT IN( SELECT raceId FROM races WHERE YEAR != 2009 );
+SELECT T2.name ,  T2.year FROM results AS T1 JOIN races AS T2 ON T1.raceid = T2.raceid JOIN drivers AS T3 ON T1.driverid = T3.driverid WHERE T3.forename = 'Lewis';
+SELECT T2.name ,  T2.year FROM results AS T1 JOIN races AS T2 ON T1.raceid = T2.raceid JOIN drivers AS T3 ON T1.driverid = T3.driverid WHERE T3.forename = 'Lewis';
+SELECT forename ,  surname FROM drivers WHERE nationality = 'German';
+SELECT forename ,  surname FROM drivers WHERE nationality = 'German';
+SELECT T2.driverid ,  T3.forename FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid JOIN drivers AS T3 ON T2.driverid  =  T3.driverid WHERE T1.name = 'Australian Grand Prix' INTERSECT SELECT T2.driverid ,  T3.forename FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid JOIN drivers AS T3 ON T2.driverid  =  T3.driverid WHERE T1.name = 'Chinese Grand Prix';
+SELECT T2.driverid ,  T3.forename FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid JOIN drivers AS T3 ON T2.driverid  =  T3.driverid WHERE T1.name = 'Australian Grand Prix' INTERSECT SELECT T2.driverid ,  T3.forename FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid JOIN drivers AS T3 ON T2.driverid  =  T3.driverid WHERE T1.name = 'Chinese Grand Prix';
+SELECT T3.forename ,  T3.surname FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid JOIN drivers AS T3 ON T2.driverid = T3.driverid WHERE T1.name = 'Australian Grand Prix' EXCEPT SELECT T3.forename ,  T3.surname FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid JOIN drivers AS T3 ON T2.driverid = T3.driverid WHERE T1.name = 'Chinese Grand Prix';
+SELECT T3.forename ,  T3.surname FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid JOIN drivers AS T3 ON T2.driverid = T3.driverid WHERE T1.name = 'Australian Grand Prix' EXCEPT SELECT T3.forename ,  T3.surname FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid JOIN drivers AS T3 ON T2.driverid = T3.driverid WHERE T1.name = 'Chinese Grand Prix';
+SELECT DISTINCT T1.forename FROM drivers AS T1 JOIN driverstandings AS T2 ON T1.driverid = T2.driverid WHERE T2.position = 1 AND T2.wins = 1;
+SELECT DISTINCT T1.forename FROM drivers AS T1 JOIN driverstandings AS T2 ON T1.driverid = T2.driverid WHERE T2.position = 1 AND T2.wins = 1;
+SELECT DISTINCT T1.forename FROM drivers AS T1 JOIN driverstandings AS T2 ON T1.driverid = T2.driverid WHERE T2.position = 1 AND T2.wins = 1 AND T2.points > 20;
+SELECT DISTINCT T1.forename FROM drivers AS T1 JOIN driverstandings AS T2 ON T1.driverid = T2.driverid WHERE T2.position = 1 AND T2.wins = 1 AND T2.points > 20;
+SELECT count(*) ,  nationality FROM constructors GROUP BY nationality;
+SELECT count(*) ,  nationality FROM constructors GROUP BY nationality;
+SELECT count(*) ,  constructorid FROM constructorStandings GROUP BY constructorid;
+SELECT count(*) ,  constructorid FROM constructorStandings GROUP BY constructorid;
+SELECT T1.name FROM races AS T1 JOIN circuits AS T2 ON T1.circuitid = T2.circuitid WHERE T2.country = 'Spain' AND T1.year > 2017;
+SELECT T1.name FROM races AS T1 JOIN circuits AS T2 ON T1.circuitid = T2.circuitid WHERE T2.country = 'Spain' AND T1.year > 2017;
+SELECT DISTINCT T1.name FROM races AS T1 JOIN circuits AS T2 ON T1.circuitid = T2.circuitid WHERE T2.country = 'Spain' AND T1.year > 2000;
+SELECT DISTINCT T1.name FROM races AS T1 JOIN circuits AS T2 ON T1.circuitid = T2.circuitid WHERE T2.country = 'Spain' AND T1.year > 2000;
+SELECT DISTINCT driverid ,  STOP FROM pitstops WHERE duration  <  (SELECT max(duration) FROM pitstops WHERE raceid  =  841);
+SELECT DISTINCT driverid ,  STOP FROM pitstops WHERE duration  <  (SELECT max(duration) FROM pitstops WHERE raceid  =  841);
+SELECT DISTINCT driverid ,  STOP FROM pitstops WHERE duration  >  (SELECT min(duration) FROM pitstops WHERE raceid  =  841);
+SELECT DISTINCT driverid ,  STOP FROM pitstops WHERE duration  >  (SELECT min(duration) FROM pitstops WHERE raceid  =  841);
+SELECT DISTINCT forename FROM drivers ORDER BY forename ASC;
+SELECT DISTINCT forename FROM drivers ORDER BY forename ASC;
+SELECT DISTINCT name FROM races ORDER BY name DESC;
+SELECT DISTINCT name FROM races ORDER BY name DESC;
+SELECT name FROM races WHERE YEAR BETWEEN 2009 AND 2011;
+SELECT name FROM races WHERE YEAR BETWEEN 2009 AND 2011;
+SELECT name FROM races WHERE TIME > '12:00:00' OR TIME < '09:00:00';
+SELECT name FROM races WHERE TIME > '12:00:00' OR TIME < '09:00:00';
+SELECT T1.forename ,  T1.surname ,  T1.driverid FROM drivers AS T1 JOIN pitstops AS T2 ON T1.driverid  =  T2.driverid GROUP BY T1.driverid HAVING count(*)  >  8 UNION SELECT T1.forename ,  T1.surname ,  T1.driverid FROM drivers AS T1 JOIN results AS T2 ON T1.driverid  =  T2.driverid GROUP BY T1.driverid HAVING count(*)  >  5;
+SELECT T1.forename ,  T1.surname ,  T1.driverid FROM drivers AS T1 JOIN pitstops AS T2 ON T1.driverid  =  T2.driverid GROUP BY T1.driverid HAVING count(*)  >  8 UNION SELECT T1.forename ,  T1.surname ,  T1.driverid FROM drivers AS T1 JOIN results AS T2 ON T1.driverid  =  T2.driverid GROUP BY T1.driverid HAVING count(*)  >  5;
+SELECT T1.surname ,  T1.driverid FROM drivers AS T1 JOIN pitstops AS T2 ON T1.driverid  =  T2.driverid GROUP BY T1.driverid HAVING count(*)  =  11 INTERSECT SELECT T1.surname ,  T1.driverid FROM drivers AS T1 JOIN results AS T2 ON T1.driverid  =  T2.driverid GROUP BY T1.driverid HAVING count(*)  >  5;
+SELECT T1.surname ,  T1.driverid FROM drivers AS T1 JOIN pitstops AS T2 ON T1.driverid  =  T2.driverid GROUP BY T1.driverid HAVING count(*)  =  11 INTERSECT SELECT T1.surname ,  T1.driverid FROM drivers AS T1 JOIN results AS T2 ON T1.driverid  =  T2.driverid GROUP BY T1.driverid HAVING count(*)  >  5;
+SELECT T1.driverid ,  T1.surname FROM drivers AS T1 JOIN results AS T2 ON T1.driverid  =  T2.driverid JOIN races AS T3 ON T2.raceid = T3.raceid WHERE T3.year > 2010 GROUP BY T1.driverid ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.driverid ,  T1.surname FROM drivers AS T1 JOIN results AS T2 ON T1.driverid  =  T2.driverid JOIN races AS T3 ON T2.raceid = T3.raceid WHERE T3.year > 2010 GROUP BY T1.driverid ORDER BY count(*) DESC LIMIT 1;
+SELECT name FROM circuits WHERE country = 'UK' OR country = 'Malaysia';
+SELECT name FROM circuits WHERE country = 'UK' OR country = 'Malaysia';
+SELECT circuitid ,  LOCATION FROM circuits WHERE country = 'France' OR country = 'Belgium';
+SELECT circuitid ,  LOCATION FROM circuits WHERE country = 'France' OR country = 'Belgium';
+SELECT T1.name FROM constructors AS T1 JOIN constructorstandings AS T2 ON T1.constructorid = T2.constructorid WHERE T1.nationality = 'Japanese' AND T2.points > 5;
+SELECT T1.name FROM constructors AS T1 JOIN constructorstandings AS T2 ON T1.constructorid = T2.constructorid WHERE T1.nationality = 'Japanese' AND T2.points > 5;
+SELECT avg(T2.fastestlapspeed) FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid WHERE T1.year = 2008 AND T1.name = 'Monaco Grand Prix';
+SELECT avg(T2.fastestlapspeed) FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid WHERE T1.year = 2008 AND T1.name = 'Monaco Grand Prix';
+SELECT max(T2.fastestlapspeed) FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid WHERE T1.year = 2008 AND T1.name = 'Monaco Grand Prix';
+SELECT max(T2.fastestlapspeed) FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid WHERE T1.year = 2008 AND T1.name = 'Monaco Grand Prix';
+SELECT max(T2.fastestlapspeed) ,  T1.name ,  T1.year FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid WHERE T1.year > 2014 GROUP BY T1.name ORDER BY T1.year;
+SELECT max(T2.fastestlapspeed) ,  T1.name ,  T1.year FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid WHERE T1.year > 2014 GROUP BY T1.name ORDER BY T1.year;
+SELECT avg(T2.fastestlapspeed) ,  T1.name ,  T1.year FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid WHERE T1.year > 2014 GROUP BY T1.name ORDER BY T1.year;
+SELECT avg(T2.fastestlapspeed) ,  T1.name ,  T1.year FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid WHERE T1.year > 2014 GROUP BY T1.name ORDER BY T1.year;
+SELECT T1.driverid ,  T1.forename ,  count(*) FROM drivers AS T1 JOIN results AS T2 ON T1.driverid = T2.driverid JOIN races AS T3 ON T2.raceid = T3.raceid GROUP BY T1.driverid HAVING count(*)  >=  2;
+SELECT T1.driverid ,  T1.forename ,  count(*) FROM drivers AS T1 JOIN results AS T2 ON T1.driverid = T2.driverid JOIN races AS T3 ON T2.raceid = T3.raceid GROUP BY T1.driverid HAVING count(*)  >=  2;
+SELECT T1.driverid ,  count(*) FROM drivers AS T1 JOIN results AS T2 ON T1.driverid = T2.driverid JOIN races AS T3 ON T2.raceid = T3.raceid GROUP BY T1.driverid HAVING count(*)  <=  30;
+SELECT T1.driverid ,  count(*) FROM drivers AS T1 JOIN results AS T2 ON T1.driverid = T2.driverid JOIN races AS T3 ON T2.raceid = T3.raceid GROUP BY T1.driverid HAVING count(*)  <=  30;
+SELECT T1.driverid ,  T1.surname FROM drivers AS T1 JOIN results AS T2 ON T1.driverid = T2.driverid JOIN races AS T3 ON T2.raceid = T3.raceid GROUP BY T1.driverid ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.driverid ,  T1.surname FROM drivers AS T1 JOIN results AS T2 ON T1.driverid = T2.driverid JOIN races AS T3 ON T2.raceid = T3.raceid GROUP BY T1.driverid ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM technician;
+SELECT count(*) FROM technician;
+SELECT Name FROM technician ORDER BY Age ASC;
+SELECT Name FROM technician ORDER BY Age ASC;
+SELECT Team ,  Starting_Year FROM technician;
+SELECT Team ,  Starting_Year FROM technician;
+SELECT Name FROM technician WHERE Team != 'NYY';
+SELECT Name FROM technician WHERE Team != 'NYY';
+SELECT Name FROM technician WHERE Age  =  36 OR Age  =  37;
+SELECT Name FROM technician WHERE Age  =  36 OR Age  =  37;
+SELECT Starting_Year FROM technician ORDER BY Age DESC LIMIT 1;
+SELECT Starting_Year FROM technician ORDER BY Age DESC LIMIT 1;
+SELECT Team ,  COUNT(*) FROM technician GROUP BY Team;
+SELECT Team ,  COUNT(*) FROM technician GROUP BY Team;
+SELECT Team FROM technician GROUP BY Team ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Team FROM technician GROUP BY Team ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Team FROM technician GROUP BY Team HAVING COUNT(*)  >=  2;
+SELECT Team FROM technician GROUP BY Team HAVING COUNT(*)  >=  2;
+SELECT T3.Name ,  T2.Machine_series FROM repair_assignment AS T1 JOIN machine AS T2 ON T1.machine_id  =  T2.machine_id JOIN technician AS T3 ON T1.technician_ID  =  T3.technician_ID;
+SELECT T3.Name ,  T2.Machine_series FROM repair_assignment AS T1 JOIN machine AS T2 ON T1.machine_id  =  T2.machine_id JOIN technician AS T3 ON T1.technician_ID  =  T3.technician_ID;
+SELECT T3.Name FROM repair_assignment AS T1 JOIN machine AS T2 ON T1.machine_id  =  T2.machine_id JOIN technician AS T3 ON T1.technician_ID  =  T3.technician_ID ORDER BY T2.quality_rank;
+SELECT T3.Name FROM repair_assignment AS T1 JOIN machine AS T2 ON T1.machine_id  =  T2.machine_id JOIN technician AS T3 ON T1.technician_ID  =  T3.technician_ID ORDER BY T2.quality_rank;
+SELECT T3.Name FROM repair_assignment AS T1 JOIN machine AS T2 ON T1.machine_id  =  T2.machine_id JOIN technician AS T3 ON T1.technician_ID  =  T3.technician_ID WHERE T2.value_points  >  70;
+SELECT T3.Name FROM repair_assignment AS T1 JOIN machine AS T2 ON T1.machine_id  =  T2.machine_id JOIN technician AS T3 ON T1.technician_ID  =  T3.technician_ID WHERE T2.value_points  >  70;
+SELECT T2.Name ,  COUNT(*) FROM repair_assignment AS T1 JOIN technician AS T2 ON T1.technician_ID  =  T2.technician_ID GROUP BY T2.Name;
+SELECT T2.Name ,  COUNT(*) FROM repair_assignment AS T1 JOIN technician AS T2 ON T1.technician_ID  =  T2.technician_ID GROUP BY T2.Name;
+SELECT Name FROM technician WHERE technician_id NOT IN (SELECT technician_id FROM repair_assignment);
+SELECT Name FROM technician WHERE technician_id NOT IN (SELECT technician_id FROM repair_assignment);
+SELECT Starting_Year FROM technician WHERE Team  =  'CLE' INTERSECT SELECT Starting_Year FROM technician WHERE Team  =  'CWS';
+SELECT Starting_Year FROM technician WHERE Team  =  'CLE' INTERSECT SELECT Starting_Year FROM technician WHERE Team  =  'CWS';
+SELECT count(*) FROM entrepreneur;
+SELECT count(*) FROM entrepreneur;
+SELECT Company FROM entrepreneur ORDER BY Money_Requested DESC;
+SELECT Company FROM entrepreneur ORDER BY Money_Requested DESC;
+SELECT Company ,  Investor FROM entrepreneur;
+SELECT Company ,  Investor FROM entrepreneur;
+SELECT avg(Money_Requested) FROM entrepreneur;
+SELECT avg(Money_Requested) FROM entrepreneur;
+SELECT Name FROM People ORDER BY Weight ASC;
+SELECT Name FROM People ORDER BY Weight ASC;
+SELECT T2.Name FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID;
+SELECT T2.Name FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID;
+SELECT T2.Name FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID WHERE T1.Investor != 'Rachel Elnaugh';
+SELECT T2.Name FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID WHERE T1.Investor != 'Rachel Elnaugh';
+SELECT Weight FROM people ORDER BY Height ASC LIMIT 1;
+SELECT Weight FROM people ORDER BY Height ASC LIMIT 1;
+SELECT T2.Name FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID ORDER BY T2.Weight DESC LIMIT 1;
+SELECT T2.Name FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID ORDER BY T2.Weight DESC LIMIT 1;
+SELECT sum(T1.Money_Requested) FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID WHERE T2.Height  >  1.85;
+SELECT sum(T1.Money_Requested) FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID WHERE T2.Height  >  1.85;
+SELECT T2.Date_of_Birth FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID WHERE T1.Investor  =  'Simon Woodroffe' OR T1.Investor  =  'Peter Jones';
+SELECT T2.Date_of_Birth FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID WHERE T1.Investor  =  'Simon Woodroffe' OR T1.Investor  =  'Peter Jones';
+SELECT T2.Weight FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID ORDER BY T1.Money_Requested DESC;
+SELECT T2.Weight FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID ORDER BY T1.Money_Requested DESC;
+SELECT Investor ,  COUNT(*) FROM entrepreneur GROUP BY Investor;
+SELECT Investor ,  COUNT(*) FROM entrepreneur GROUP BY Investor;
+SELECT Investor FROM entrepreneur GROUP BY Investor ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Investor FROM entrepreneur GROUP BY Investor ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Investor FROM entrepreneur GROUP BY Investor HAVING COUNT(*)  >=  2;
+SELECT Investor FROM entrepreneur GROUP BY Investor HAVING COUNT(*)  >=  2;
+SELECT T2.Name ,  T1.Company FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID ORDER BY T1.Money_Requested;
+SELECT T2.Name ,  T1.Company FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID ORDER BY T1.Money_Requested;
+SELECT Name FROM people WHERE People_ID NOT IN (SELECT People_ID FROM entrepreneur);
+SELECT Name FROM people WHERE People_ID NOT IN (SELECT People_ID FROM entrepreneur);
+SELECT Investor FROM entrepreneur WHERE Money_Requested  >  140000 INTERSECT SELECT Investor FROM entrepreneur WHERE Money_Requested  <  120000;
+SELECT Investor FROM entrepreneur WHERE Money_Requested  >  140000 INTERSECT SELECT Investor FROM entrepreneur WHERE Money_Requested  <  120000;
+SELECT count(DISTINCT Company) FROM entrepreneur;
+SELECT count(DISTINCT Company) FROM entrepreneur;
+SELECT T1.Company FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID ORDER BY T2.Height DESC LIMIT 1;
+SELECT T1.Company FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID ORDER BY T2.Height DESC LIMIT 1;
+SELECT count(*) FROM perpetrator;
+SELECT Date FROM perpetrator ORDER BY Killed DESC;
+SELECT Injured FROM perpetrator ORDER BY Injured ASC;
+SELECT avg(Injured) FROM perpetrator;
+SELECT LOCATION FROM perpetrator ORDER BY Killed DESC LIMIT 1;
+SELECT Name FROM People ORDER BY Height ASC;
+SELECT T1.Name FROM people AS T1 JOIN perpetrator AS T2 ON T1.People_ID  =  T2.People_ID;
+SELECT T1.Name FROM people AS T1 JOIN perpetrator AS T2 ON T1.People_ID  =  T2.People_ID WHERE T2.Country != 'China';
+SELECT T1.Name FROM people AS T1 JOIN perpetrator AS T2 ON T1.People_ID  =  T2.People_ID ORDER BY T1.Weight DESC LIMIT 1;
+SELECT sum(T2.Killed) FROM people AS T1 JOIN perpetrator AS T2 ON T1.People_ID  =  T2.People_ID WHERE T1.Height  >  1.84;
+SELECT T1.Name FROM people AS T1 JOIN perpetrator AS T2 ON T1.People_ID  =  T2.People_ID WHERE T2.Country  =  'China' OR T2.Country  =  'Japan';
+SELECT T1.Height FROM people AS T1 JOIN perpetrator AS T2 ON T1.People_ID  =  T2.People_ID ORDER BY T2.Injured DESC;
+SELECT Country ,  COUNT(*) FROM perpetrator GROUP BY Country;
+SELECT Country ,  COUNT(*) FROM perpetrator GROUP BY Country ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Country ,  COUNT(*) FROM perpetrator GROUP BY Country HAVING COUNT(*)  >=  2;
+SELECT T1.Name FROM people AS T1 JOIN perpetrator AS T2 ON T1.People_ID  =  T2.People_ID ORDER BY T2.Year DESC;
+SELECT Name FROM people WHERE People_ID NOT IN (SELECT People_ID FROM perpetrator);
+SELECT Country FROM perpetrator WHERE Injured  >  50 INTERSECT SELECT Country FROM perpetrator WHERE Injured  <  20;
+SELECT count(DISTINCT LOCATION) FROM perpetrator;
+SELECT T2.Date FROM people AS T1 JOIN perpetrator AS T2 ON T1.People_ID  =  T2.People_ID ORDER BY T1.Height DESC LIMIT 1;
+SELECT max(YEAR) FROM perpetrator;
+SELECT campus FROM campuses WHERE county  =  'Los Angeles';
+SELECT campus FROM campuses WHERE county  =  'Los Angeles';
+SELECT campus FROM campuses WHERE LOCATION  =  'Chico';
+SELECT campus FROM campuses WHERE LOCATION  =  'Chico';
+SELECT campus FROM campuses WHERE YEAR  =  1958;
+SELECT campus FROM campuses WHERE YEAR  =  1958;
+SELECT campus FROM campuses WHERE YEAR  <  1800;
+SELECT campus FROM campuses WHERE YEAR  <  1800;
+SELECT campus FROM campuses WHERE YEAR  >=  1935 AND YEAR  <=  1939;
+SELECT campus FROM campuses WHERE YEAR  >=  1935 AND YEAR  <=  1939;
+SELECT campus FROM campuses WHERE LOCATION  =  'Northridge' AND county  =  'Los Angeles' UNION SELECT campus FROM campuses WHERE LOCATION  =  'San Francisco' AND county  =  'San Francisco';
+SELECT campus FROM campuses WHERE LOCATION  =  'Northridge' AND county  =  'Los Angeles' UNION SELECT campus FROM campuses WHERE LOCATION  =  'San Francisco' AND county  =  'San Francisco';
+SELECT campusfee FROM campuses AS T1 JOIN csu_fees AS T2 ON T1.id  =  t2.campus WHERE t1.campus  =  'San Jose State University' AND T2.year  =  1996;
+SELECT campusfee FROM campuses AS T1 JOIN csu_fees AS T2 ON T1.id  =  t2.campus WHERE t1.campus  =  'San Jose State University' AND T2.year  =  1996;
+SELECT campusfee FROM campuses AS T1 JOIN csu_fees AS T2 ON T1.id  =  t2.campus WHERE t1.campus  =  'San Francisco State University' AND T2.year  =  1996;
+SELECT campusfee FROM campuses AS T1 JOIN csu_fees AS T2 ON T1.id  =  t2.campus WHERE t1.campus  =  'San Francisco State University' AND T2.year  =  1996;
+SELECT count(*) FROM csu_fees WHERE campusfee  >  (SELECT avg(campusfee) FROM csu_fees);
+SELECT count(*) FROM csu_fees WHERE campusfee  >  (SELECT avg(campusfee) FROM csu_fees);
+SELECT count(*) FROM csu_fees WHERE campusfee  >  (SELECT avg(campusfee) FROM csu_fees);
+SELECT count(*) FROM csu_fees WHERE campusfee  >  (SELECT avg(campusfee) FROM csu_fees);
+SELECT campus FROM campuses WHERE county  =  'Los Angeles' AND YEAR  >  1950;
+SELECT campus FROM campuses WHERE county  =  'Los Angeles' AND YEAR  >  1950;
+SELECT YEAR FROM degrees GROUP BY YEAR ORDER BY sum(degrees) DESC LIMIT 1;
+SELECT YEAR FROM degrees GROUP BY YEAR ORDER BY sum(degrees) DESC LIMIT 1;
+SELECT campus FROM degrees GROUP BY campus ORDER BY sum(degrees) DESC LIMIT 1;
+SELECT campus FROM degrees GROUP BY campus ORDER BY sum(degrees) DESC LIMIT 1;
+SELECT T1.campus FROM campuses AS T1 JOIN faculty AS T2 ON T1.id  =  T2.campus WHERE T2.year  =  2003 ORDER BY T2.faculty DESC LIMIT 1;
+SELECT T1.campus FROM campuses AS T1 JOIN faculty AS T2 ON T1.id  =  T2.campus WHERE T2.year  =  2003 ORDER BY T2.faculty DESC LIMIT 1;
+SELECT avg(campusfee) FROM csu_fees WHERE YEAR  =  1996;
+SELECT avg(campusfee) FROM csu_fees WHERE YEAR  =  1996;
+SELECT avg(campusfee) FROM csu_fees WHERE YEAR  =  2005;
+SELECT avg(campusfee) FROM csu_fees WHERE YEAR  =  2005;
+SELECT T1.campus ,  sum(T2.degrees) FROM campuses AS T1 JOIN degrees AS T2 ON T1.id  =  T2.campus WHERE T2.year  >=  1998 AND T2.year  <=  2002 GROUP BY T1.campus;
+SELECT T1.campus ,  sum(T2.degrees) FROM campuses AS T1 JOIN degrees AS T2 ON T1.id  =  T2.campus WHERE T2.year  >=  1998 AND T2.year  <=  2002 GROUP BY T1.campus;
+SELECT T1.campus ,  sum(T2.degrees) FROM campuses AS T1 JOIN degrees AS T2 ON T1.id  =  T2.campus WHERE T1.county  =  'Orange' AND T2.year  >=  2000 GROUP BY T1.campus;
+SELECT T1.campus ,  sum(T2.degrees) FROM campuses AS T1 JOIN degrees AS T2 ON T1.id  =  T2.campus WHERE T1.county  =  'Orange' AND T2.year  >=  2000 GROUP BY T1.campus;
+SELECT T1.campus FROM campuses AS T1 JOIN faculty AS T2 ON T1.id  =  T2.campus WHERE T2.year  =  2002 AND faculty  >  (SELECT max(faculty) FROM campuses AS T1 JOIN faculty AS T2 ON T1.id  =  T2.campus WHERE T2.year  =  2002 AND T1.county  =  'Orange');
+SELECT T1.campus FROM campuses AS T1 JOIN faculty AS T2 ON T1.id  =  T2.campus WHERE T2.year  =  2002 AND faculty  >  (SELECT max(faculty) FROM campuses AS T1 JOIN faculty AS T2 ON T1.id  =  T2.campus WHERE T2.year  =  2002 AND T1.county  =  'Orange');
+SELECT T1.campus FROM campuses AS t1 JOIN enrollments AS t2 ON t1.id  =  t2.campus WHERE t2.year  =  1956 AND totalenrollment_ay  >  400 AND FTE_AY  >  200;
+SELECT T1.campus FROM campuses AS t1 JOIN enrollments AS t2 ON t1.id  =  t2.campus WHERE t2.year  =  1956 AND totalenrollment_ay  >  400 AND FTE_AY  >  200;
+SELECT count(*) FROM campuses WHERE county  =  'Los Angeles';
+SELECT count(*) FROM campuses WHERE county  =  'Los Angeles';
+SELECT campus FROM campuses WHERE county  =  'Los Angeles';
+SELECT campus FROM campuses WHERE county  =  'Los Angeles';
+SELECT degrees FROM campuses AS T1 JOIN degrees AS T2 ON t1.id  =  t2.campus WHERE t1.campus  =  'San Jose State University' AND t2.year  =  2000;
+SELECT degrees FROM campuses AS T1 JOIN degrees AS T2 ON t1.id  =  t2.campus WHERE t1.campus  =  'San Jose State University' AND t2.year  =  2000;
+SELECT degrees FROM campuses AS T1 JOIN degrees AS T2 ON t1.id  =  t2.campus WHERE t1.campus  =  'San Francisco State University' AND t2.year  =  2001;
+SELECT degrees FROM campuses AS T1 JOIN degrees AS T2 ON t1.id  =  t2.campus WHERE t1.campus  =  'San Francisco State University' AND t2.year  =  2001;
+SELECT sum(faculty) FROM faculty WHERE YEAR  =  2002;
+SELECT sum(faculty) FROM faculty WHERE YEAR  =  2002;
+SELECT faculty FROM faculty AS T1 JOIN campuses AS T2 ON T1.campus  =  T2.id WHERE T1.year  =  2002 AND T2.campus  =  'Long Beach State University';
+SELECT faculty FROM faculty AS T1 JOIN campuses AS T2 ON T1.campus  =  T2.id WHERE T1.year  =  2002 AND T2.campus  =  'Long Beach State University';
+SELECT faculty FROM faculty AS T1 JOIN campuses AS T2 ON T1.campus  =  T2.id WHERE T1.year  =  2004 AND T2.campus  =  'San Francisco State University';
+SELECT faculty FROM faculty AS T1 JOIN campuses AS T2 ON T1.campus  =  T2.id WHERE T1.year  =  2004 AND T2.campus  =  'San Francisco State University';
+SELECT T1.campus FROM campuses AS t1 JOIN faculty AS t2 ON t1.id  =  t2.campus WHERE t2.faculty  >=  600 AND t2.faculty  <=  1000 AND T1.year  =  2004;
+SELECT T1.campus FROM campuses AS t1 JOIN faculty AS t2 ON t1.id  =  t2.campus WHERE t2.faculty  >=  600 AND t2.faculty  <=  1000 AND T1.year  =  2004;
+SELECT T2.faculty FROM campuses AS T1 JOIN faculty AS T2 ON T1.id  =  t2.campus JOIN degrees AS T3 ON T1.id  =  t3.campus AND t2.year  =  t3.year WHERE t2.year  =  2002 ORDER BY t3.degrees DESC LIMIT 1;
+SELECT T2.faculty FROM campuses AS T1 JOIN faculty AS T2 ON T1.id  =  t2.campus JOIN degrees AS T3 ON T1.id  =  t3.campus AND t2.year  =  t3.year WHERE t2.year  =  2002 ORDER BY t3.degrees DESC LIMIT 1;
+SELECT T2.faculty FROM campuses AS T1 JOIN faculty AS T2 ON T1.id  =  t2.campus JOIN degrees AS T3 ON T1.id  =  t3.campus AND t2.year  =  t3.year WHERE t2.year  =  2001 ORDER BY t3.degrees LIMIT 1;
+SELECT T2.faculty FROM campuses AS T1 JOIN faculty AS T2 ON T1.id  =  t2.campus JOIN degrees AS T3 ON T1.id  =  t3.campus AND t2.year  =  t3.year WHERE t2.year  =  2001 ORDER BY t3.degrees LIMIT 1;
+SELECT sum(t1.undergraduate) FROM discipline_enrollments AS t1 JOIN campuses AS t2 ON t1.campus  =  t2.id WHERE t1.year  =  2004 AND t2.campus  =  'San Jose State University';
+SELECT sum(t1.undergraduate) FROM discipline_enrollments AS t1 JOIN campuses AS t2 ON t1.campus  =  t2.id WHERE t1.year  =  2004 AND t2.campus  =  'San Jose State University';
+SELECT sum(t1.graduate) FROM discipline_enrollments AS t1 JOIN campuses AS t2 ON t1.campus  =  t2.id WHERE t1.year  =  2004 AND t2.campus  =  'San Francisco State University';
+SELECT sum(t1.graduate) FROM discipline_enrollments AS t1 JOIN campuses AS t2 ON t1.campus  =  t2.id WHERE t1.year  =  2004 AND t2.campus  =  'San Francisco State University';
+SELECT t1.campusfee FROM csu_fees AS t1 JOIN campuses AS t2 ON t1.campus  =  t2.id WHERE t2.campus  =  'San Francisco State University' AND t1.year  =  2000;
+SELECT t1.campusfee FROM csu_fees AS t1 JOIN campuses AS t2 ON t1.campus  =  t2.id WHERE t2.campus  =  'San Francisco State University' AND t1.year  =  2000;
+SELECT t1.campusfee FROM csu_fees AS t1 JOIN campuses AS t2 ON t1.campus  =  t2.id WHERE t2.campus  =  'San Jose State University' AND t1.year  =  2000;
+SELECT t1.campusfee FROM csu_fees AS t1 JOIN campuses AS t2 ON t1.campus  =  t2.id WHERE t2.campus  =  'San Jose State University' AND t1.year  =  2000;
+SELECT count(*) FROM campuses;
+SELECT count(*) FROM campuses;
+SELECT count(*) FROM candidate;
+SELECT count(*) FROM candidate;
+SELECT poll_source FROM candidate GROUP BY poll_source ORDER BY count(*) DESC LIMIT 1;
+SELECT poll_source FROM candidate GROUP BY poll_source ORDER BY count(*) DESC LIMIT 1;
+SELECT support_rate FROM candidate ORDER BY support_rate DESC LIMIT 3;
+SELECT support_rate FROM candidate ORDER BY support_rate DESC LIMIT 3;
+SELECT Candidate_ID FROM candidate ORDER BY oppose_rate LIMIT 1;
+SELECT Candidate_ID FROM candidate ORDER BY oppose_rate LIMIT 1;
+SELECT Support_rate ,  Consider_rate ,  Oppose_rate FROM candidate ORDER BY unsure_rate;
+SELECT Support_rate ,  Consider_rate ,  Oppose_rate FROM candidate ORDER BY unsure_rate;
+SELECT poll_source FROM candidate ORDER BY oppose_rate DESC LIMIT 1;
+SELECT poll_source FROM candidate ORDER BY oppose_rate DESC LIMIT 1;
+SELECT name FROM people ORDER BY date_of_birth;
+SELECT name FROM people ORDER BY date_of_birth;
+SELECT avg(height) ,  avg(weight) FROM people WHERE sex  =  'M';
+SELECT avg(height) ,  avg(weight) FROM people WHERE sex  =  'M';
+SELECT name FROM people WHERE height  >  200 OR height  <  190;
+SELECT name FROM people WHERE height  >  200 OR height  <  190;
+SELECT avg(weight) ,  min(weight) ,  sex FROM people GROUP BY sex;
+SELECT avg(weight) ,  min(weight) ,  sex FROM people GROUP BY sex;
+SELECT t1.name ,  t1.sex FROM people AS t1 JOIN candidate AS t2 ON t1.people_id  =  t2.people_id ORDER BY t2.support_rate DESC LIMIT 1;
+SELECT t1.name ,  t1.sex FROM people AS t1 JOIN candidate AS t2 ON t1.people_id  =  t2.people_id ORDER BY t2.support_rate DESC LIMIT 1;
+SELECT t1.name ,  t1.sex ,  min(oppose_rate) FROM people AS t1 JOIN candidate AS t2 ON t1.people_id  =  t2.people_id GROUP BY t1.sex;
+SELECT t1.name ,  t1.sex ,  min(oppose_rate) FROM people AS t1 JOIN candidate AS t2 ON t1.people_id  =  t2.people_id GROUP BY t1.sex;
+SELECT t1.sex FROM people AS t1 JOIN candidate AS t2 ON t1.people_id  =  t2.people_id GROUP BY t1.sex ORDER BY avg(t2.unsure_rate) DESC LIMIT 1;
+SELECT t1.sex FROM people AS t1 JOIN candidate AS t2 ON t1.people_id  =  t2.people_id GROUP BY t1.sex ORDER BY avg(t2.unsure_rate) DESC LIMIT 1;
+SELECT name FROM people WHERE people_id NOT IN (SELECT people_id FROM candidate);
+SELECT name FROM people WHERE people_id NOT IN (SELECT people_id FROM candidate);
+SELECT t1.name FROM people AS t1 JOIN candidate AS t2 ON t1.people_id  =  t2.people_id WHERE t2.support_rate  <  t2.oppose_rate;
+SELECT t1.name FROM people AS t1 JOIN candidate AS t2 ON t1.people_id  =  t2.people_id WHERE t2.support_rate  <  t2.oppose_rate;
+SELECT count(*) ,  sex FROM people WHERE weight  >  85 GROUP BY sex;
+SELECT count(*) ,  sex FROM people WHERE weight  >  85 GROUP BY sex;
+SELECT max(support_rate) ,  min(consider_rate) ,  min(oppose_rate) FROM candidate;
+SELECT max(support_rate) ,  min(consider_rate) ,  min(oppose_rate) FROM candidate;
+SELECT t1.name FROM people AS t1 JOIN candidate AS t2 ON t1.people_id  =  t2.people_id WHERE t1.sex  =  'F' ORDER BY t1.name;
+SELECT t1.name FROM people AS t1 JOIN candidate AS t2 ON t1.people_id  =  t2.people_id WHERE t1.sex  =  'F' ORDER BY t1.name;
+SELECT name FROM people WHERE height  <  (SELECT avg(height) FROM people);
+SELECT name FROM people WHERE height  <  (SELECT avg(height) FROM people);
+SELECT * FROM people;
+SELECT * FROM people;
+SELECT title FROM Movie WHERE director = 'Steven Spielberg';
+SELECT title FROM Movie WHERE director = 'Steven Spielberg';
+SELECT title FROM Movie WHERE director = 'James Cameron' AND YEAR  >  2000;
+SELECT title FROM Movie WHERE director = 'James Cameron' AND YEAR  >  2000;
+SELECT count(*) FROM Movie WHERE YEAR  <  2000;
+SELECT count(*) FROM Movie WHERE YEAR  <  2000;
+SELECT director FROM Movie WHERE title  = 'Avatar';
+SELECT director FROM Movie WHERE title  = 'Avatar';
+SELECT count(*) FROM Reviewer;
+SELECT count(*) FROM Reviewer;
+SELECT rID FROM Reviewer WHERE name LIKE '%Mike%';
+SELECT rID FROM Reviewer WHERE name LIKE '%Mike%';
+SELECT rID FROM Reviewer WHERE name  =  'Daniel Lewis';
+SELECT rID FROM Reviewer WHERE name  =  'Daniel Lewis';
+SELECT count(*) FROM Rating WHERE stars  >  3;
+SELECT count(*) FROM Rating WHERE stars  >  3;
+SELECT max(stars) ,  min(stars) FROM Rating;
+SELECT max(stars) ,  min(stars) FROM Rating;
+SELECT DISTINCT YEAR FROM Movie AS T1 JOIN Rating AS T2 ON T1.mID  =  T2.mID WHERE T2.stars  >=  4 ORDER BY T1.year;
+SELECT DISTINCT YEAR FROM Movie AS T1 JOIN Rating AS T2 ON T1.mID  =  T2.mID WHERE T2.stars  >=  4 ORDER BY T1.year;
+SELECT T1.director ,  T1.title FROM Movie AS T1 JOIN Rating AS T2 ON T1.mID  =  T2.mID WHERE T2.stars  =  5;
+SELECT T1.director ,  T1.title FROM Movie AS T1 JOIN Rating AS T2 ON T1.mID  =  T2.mID WHERE T2.stars  =  5;
+SELECT T2.name ,  avg(T1.stars) FROM Rating AS T1 JOIN Reviewer AS T2 ON T1.rID  =  T2.rID GROUP BY T2.name;
+SELECT T2.name ,  avg(T1.stars) FROM Rating AS T1 JOIN Reviewer AS T2 ON T1.rID  =  T2.rID GROUP BY T2.name;
+SELECT title FROM Movie WHERE mID NOT IN (SELECT mID FROM Rating);
+SELECT title FROM Movie WHERE mID NOT IN (SELECT mID FROM Rating);
+SELECT DISTINCT name FROM Reviewer AS T1 JOIN Rating AS T2 ON T1.rID  =  T2.rID WHERE ratingDate  =  'null';
+SELECT DISTINCT name FROM Reviewer AS T1 JOIN Rating AS T2 ON T1.rID  =  T2.rID WHERE ratingDate  =  'null';
+SELECT avg(T1.stars) ,  T2.title FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID WHERE T2.year  =  (SELECT min(YEAR) FROM Movie);
+SELECT avg(T1.stars) ,  T2.title FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID WHERE T2.year  =  (SELECT min(YEAR) FROM Movie);
+SELECT title FROM Movie WHERE YEAR  =  (SELECT max(YEAR) FROM Movie);
+SELECT title FROM Movie WHERE YEAR  =  (SELECT max(YEAR) FROM Movie);
+SELECT max(T1.stars) ,  T2.year FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID WHERE T2.year  =  (SELECT max(YEAR) FROM Movie);
+SELECT max(T1.stars) ,  T2.year FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID WHERE T2.year  =  (SELECT max(YEAR) FROM Movie);
+SELECT title FROM Movie WHERE YEAR  >  (SELECT max(YEAR) FROM Movie WHERE director  =  'Steven Spielberg');
+SELECT title FROM Movie WHERE YEAR  >  (SELECT max(YEAR) FROM Movie WHERE director  =  'Steven Spielberg');
+SELECT T2.title ,  T2.director FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID WHERE T1.stars  >  (SELECT avg(T1.stars) FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID WHERE T2.director  =  'James Cameron');
+SELECT T2.title ,  T2.director FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID WHERE T1.stars  >  (SELECT avg(T1.stars) FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID WHERE T2.director  =  'James Cameron');
+SELECT T3.name ,  T2.title ,  T1.stars ,  T1.ratingDate FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID JOIN Reviewer AS T3 ON T1.rID  =  T3.rID ORDER BY T3.name ,  T2.title ,  T1.stars;
+SELECT T3.name ,  T2.title ,  T1.stars ,  T1.ratingDate FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID JOIN Reviewer AS T3 ON T1.rID  =  T3.rID ORDER BY T3.name ,  T2.title ,  T1.stars;
+SELECT T2.name FROM Rating AS T1 JOIN Reviewer AS T2 ON T1.rID  =  T2.rID GROUP BY T1.rID HAVING COUNT(*)  >=  3;
+SELECT T2.name FROM Rating AS T1 JOIN Reviewer AS T2 ON T1.rID  =  T2.rID GROUP BY T1.rID HAVING COUNT(*)  >=  3;
+SELECT DISTINCT T3.name FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID JOIN Reviewer AS T3 ON T1.rID  =  T3.rID WHERE T2.title  =  'Gone with the Wind';
+SELECT DISTINCT T3.name FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID JOIN Reviewer AS T3 ON T1.rID  =  T3.rID WHERE T2.title  =  'Gone with the Wind';
+SELECT DISTINCT T2.director FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID JOIN Reviewer AS T3 ON T1.rID  =  T3.rID WHERE T3.name  =  'Sarah Martinez';
+SELECT DISTINCT T2.director FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID JOIN Reviewer AS T3 ON T1.rID  =  T3.rID WHERE T3.name  =  'Sarah Martinez';
+SELECT DISTINCT T3.name ,  T2.title ,  T1.stars FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID JOIN Reviewer AS T3 ON T1.rID  =  T3.rID WHERE T2.director  =  T3.name;
+SELECT DISTINCT T3.name ,  T2.title ,  T1.stars FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID JOIN Reviewer AS T3 ON T1.rID  =  T3.rID WHERE T2.director  =  T3.name;
+SELECT name FROM Reviewer UNION SELECT title FROM Movie;
+SELECT name FROM Reviewer UNION SELECT title FROM Movie;
+SELECT DISTINCT title FROM Movie EXCEPT SELECT T2.title FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID JOIN Reviewer AS T3 ON T1.rID  =  T3.rID WHERE T3.name  =  'Chris Jackson';
+SELECT DISTINCT title FROM Movie EXCEPT SELECT T2.title FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID JOIN Reviewer AS T3 ON T1.rID  =  T3.rID WHERE T3.name  =  'Chris Jackson';
+SELECT T1.title ,  T1.director FROM Movie AS T1 JOIN Movie AS T2 ON T1.director  =  T2.director WHERE T1.title != T2.title ORDER BY T1.director ,  T1.title;
+SELECT T1.title ,  T1.director FROM Movie AS T1 JOIN Movie AS T2 ON T1.director  =  T2.director WHERE T1.title != T2.title ORDER BY T1.director ,  T1.title;
+SELECT T1.title ,  T1.year FROM Movie AS T1 JOIN Movie AS T2 ON T1.director  =  T2.director WHERE T1.title != T2.title;
+SELECT T1.title ,  T1.year FROM Movie AS T1 JOIN Movie AS T2 ON T1.director  =  T2.director WHERE T1.title != T2.title;
+SELECT director FROM Movie GROUP BY director HAVING count(*)  =  1;
+SELECT director FROM Movie GROUP BY director HAVING count(*)  =  1;
+SELECT director FROM Movie WHERE director != 'null' GROUP BY director HAVING count(*)  =  1;
+SELECT director FROM Movie WHERE director != 'null' GROUP BY director HAVING count(*)  =  1;
+SELECT count(*) ,  T1.director FROM Movie AS T1 JOIN Rating AS T2 ON T1.mID  =  T2.mID GROUP BY T1.director;
+SELECT count(*) ,  T1.director FROM Movie AS T1 JOIN Rating AS T2 ON T1.mID  =  T2.mID GROUP BY T1.director;
+SELECT T2.title ,  avg(T1.stars) FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID GROUP BY T1.mID ORDER BY avg(T1.stars) DESC LIMIT 1;
+SELECT T2.title ,  avg(T1.stars) FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID GROUP BY T1.mID ORDER BY avg(T1.stars) DESC LIMIT 1;
+SELECT T2.title ,  avg(T1.stars) FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID GROUP BY T1.mID ORDER BY avg(T1.stars) LIMIT 1;
+SELECT T2.title ,  avg(T1.stars) FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID GROUP BY T1.mID ORDER BY avg(T1.stars) LIMIT 1;
+SELECT T2.title ,  T2.year FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID ORDER BY T1.stars DESC LIMIT 3;
+SELECT T2.title ,  T2.year FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID ORDER BY T1.stars DESC LIMIT 3;
+SELECT T2.title ,  T1.stars ,  T2.director ,  max(T1.stars) FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID WHERE director != 'null' GROUP BY director;
+SELECT T2.title ,  T1.stars ,  T2.director ,  max(T1.stars) FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID WHERE director != 'null' GROUP BY director;
+SELECT T2.title ,  T1.rID ,  T1.stars ,  min(T1.stars) FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID GROUP BY T1.rID;
+SELECT T2.title ,  T1.rID ,  T1.stars ,  min(T1.stars) FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID GROUP BY T1.rID;
+SELECT T2.title ,  T1.stars ,  T2.director ,  min(T1.stars) FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID GROUP BY T2.director;
+SELECT T2.title ,  T1.stars ,  T2.director ,  min(T1.stars) FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID GROUP BY T2.director;
+SELECT T2.title ,  T1.mID FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID GROUP BY T1.mID ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.title ,  T1.mID FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID GROUP BY T1.mID ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.title FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID WHERE T1.stars BETWEEN 3 AND 5;
+SELECT T2.title FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID WHERE T1.stars BETWEEN 3 AND 5;
+SELECT T2.name FROM Rating AS T1 JOIN Reviewer AS T2 ON T1.rID  =  T2.rID WHERE T1.stars  >  3;
+SELECT T2.name FROM Rating AS T1 JOIN Reviewer AS T2 ON T1.rID  =  T2.rID WHERE T1.stars  >  3;
+SELECT mID ,  avg(stars) FROM Rating WHERE mID NOT IN (SELECT T1.mID FROM Rating AS T1 JOIN Reviewer AS T2 ON T1.rID  =  T2.rID WHERE T2.name  =  'Brittany Harris') GROUP BY mID;
+SELECT mID ,  avg(stars) FROM Rating WHERE mID NOT IN (SELECT T1.mID FROM Rating AS T1 JOIN Reviewer AS T2 ON T1.rID  =  T2.rID WHERE T2.name  =  'Brittany Harris') GROUP BY mID;
+SELECT mID FROM Rating EXCEPT SELECT T1.mID FROM Rating AS T1 JOIN Reviewer AS T2 ON T1.rID  =  T2.rID WHERE T2.name  =  'Brittany Harris';
+SELECT mID FROM Rating EXCEPT SELECT T1.mID FROM Rating AS T1 JOIN Reviewer AS T2 ON T1.rID  =  T2.rID WHERE T2.name  =  'Brittany Harris';
+SELECT mID ,  avg(stars) FROM Rating GROUP BY mID HAVING count(*)  >=  2;
+SELECT mID ,  avg(stars) FROM Rating GROUP BY mID HAVING count(*)  >=  2;
+SELECT rID FROM Rating EXCEPT SELECT rID FROM Rating WHERE stars  =  4;
+SELECT rID FROM Rating EXCEPT SELECT rID FROM Rating WHERE stars  =  4;
+SELECT rID FROM Rating WHERE stars != 4;
+SELECT rID FROM Rating WHERE stars != 4;
+SELECT DISTINCT T2.title FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID JOIN Reviewer AS T3 ON T1.rID  =  T3.rID WHERE T3.name  =  'Brittany Harris' OR T2.year  >  2000;
+SELECT DISTINCT T2.title FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID JOIN Reviewer AS T3 ON T1.rID  =  T3.rID WHERE T3.name  =  'Brittany Harris' OR T2.year  >  2000;
+SELECT title FROM Movie WHERE director  =  'James Cameron' OR YEAR   <  1980;
+SELECT title FROM Movie WHERE director  =  'James Cameron' OR YEAR   <  1980;
+SELECT T2.name FROM Rating AS T1 JOIN Reviewer AS T2 ON T1.rID  =  T2.rID WHERE T1.stars  =  3 INTERSECT SELECT T2.name FROM Rating AS T1 JOIN Reviewer AS T2 ON T1.rID  =  T2.rID WHERE T1.stars  =  4;
+SELECT T2.name FROM Rating AS T1 JOIN Reviewer AS T2 ON T1.rID  =  T2.rID WHERE T1.stars  =  3 INTERSECT SELECT T2.name FROM Rating AS T1 JOIN Reviewer AS T2 ON T1.rID  =  T2.rID WHERE T1.stars  =  4;
+SELECT T2.title FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID WHERE T1.stars  =  3 INTERSECT SELECT T2.title FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID WHERE T1.stars  =  4;
+SELECT T2.title FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID WHERE T1.stars  =  3 INTERSECT SELECT T2.title FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID  =  T2.mID WHERE T1.stars  =  4;
+SELECT count(*) FROM county_public_safety;
+SELECT count(*) FROM county_public_safety;
+SELECT Name FROM county_public_safety ORDER BY Population DESC;
+SELECT Name FROM county_public_safety ORDER BY Population DESC;
+SELECT DISTINCT Police_force FROM county_public_safety WHERE LOCATION != 'East';
+SELECT DISTINCT Police_force FROM county_public_safety WHERE LOCATION != 'East';
+SELECT min(Crime_rate) ,  max(Crime_rate) FROM county_public_safety;
+SELECT min(Crime_rate) ,  max(Crime_rate) FROM county_public_safety;
+SELECT Crime_rate FROM county_public_safety ORDER BY Police_officers ASC;
+SELECT Crime_rate FROM county_public_safety ORDER BY Police_officers ASC;
+SELECT Name FROM city ORDER BY Name ASC;
+SELECT Name FROM city ORDER BY Name ASC;
+SELECT Hispanic FROM city WHERE Black  >  10;
+SELECT Hispanic FROM city WHERE Black  >  10;
+SELECT Name FROM county_public_safety ORDER BY Population DESC LIMIT 1;
+SELECT Name FROM county_public_safety ORDER BY Population DESC LIMIT 1;
+SELECT Name FROM city ORDER BY White DESC LIMIT 5;
+SELECT Name FROM city ORDER BY White DESC LIMIT 5;
+SELECT T1.Name ,  T2.Name FROM city AS T1 JOIN county_public_safety AS T2 ON T1.County_ID  =  T2.County_ID;
+SELECT T1.Name ,  T2.Name FROM city AS T1 JOIN county_public_safety AS T2 ON T1.County_ID  =  T2.County_ID;
+SELECT T1.White ,  T2.Crime_rate FROM city AS T1 JOIN county_public_safety AS T2 ON T1.County_ID  =  T2.County_ID;
+SELECT T1.White ,  T2.Crime_rate FROM city AS T1 JOIN county_public_safety AS T2 ON T1.County_ID  =  T2.County_ID;
+SELECT name FROM city WHERE county_ID  =  (SELECT county_ID FROM county_public_safety ORDER BY Police_officers DESC LIMIT 1);
+SELECT name FROM city WHERE county_ID  =  (SELECT county_ID FROM county_public_safety ORDER BY Police_officers DESC LIMIT 1);
+SELECT count(*) FROM city WHERE county_ID IN (SELECT county_ID FROM county_public_safety WHERE population  >  20000);
+SELECT count(*) FROM city WHERE county_ID IN (SELECT county_ID FROM county_public_safety WHERE population  >  20000);
+SELECT T2.Crime_rate FROM city AS T1 JOIN county_public_safety AS T2 ON T1.County_ID  =  T2.County_ID WHERE T1.White  >  90;
+SELECT T2.Crime_rate FROM city AS T1 JOIN county_public_safety AS T2 ON T1.County_ID  =  T2.County_ID WHERE T1.White  >  90;
+SELECT Police_force ,  COUNT(*) FROM county_public_safety GROUP BY Police_force;
+SELECT Police_force ,  COUNT(*) FROM county_public_safety GROUP BY Police_force;
+SELECT LOCATION FROM county_public_safety GROUP BY LOCATION ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT LOCATION FROM county_public_safety GROUP BY LOCATION ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Name FROM county_public_safety WHERE County_ID NOT IN (SELECT County_ID FROM city);
+SELECT Name FROM county_public_safety WHERE County_ID NOT IN (SELECT County_ID FROM city);
+SELECT Police_force FROM county_public_safety WHERE LOCATION  =  'East' INTERSECT SELECT Police_force FROM county_public_safety WHERE LOCATION  =  'West';
+SELECT Police_force FROM county_public_safety WHERE LOCATION  =  'East' INTERSECT SELECT Police_force FROM county_public_safety WHERE LOCATION  =  'West';
+SELECT name FROM city WHERE county_id IN (SELECT county_id FROM county_public_safety WHERE Crime_rate  <  100);
+SELECT name FROM city WHERE county_id IN (SELECT county_id FROM county_public_safety WHERE Crime_rate  <  100);
+SELECT Case_burden FROM county_public_safety ORDER BY Population DESC;
+SELECT Case_burden FROM county_public_safety ORDER BY Population DESC;
+SELECT roomName FROM Rooms WHERE basePrice  <  160 AND beds =  2 AND decor  =  'modern';
+SELECT roomName FROM Rooms WHERE basePrice  <  160 AND beds =  2 AND decor  =  'modern';
+SELECT roomName ,  RoomId FROM Rooms WHERE basePrice  >  160 AND maxOccupancy  >  2;
+SELECT roomName ,  RoomId FROM Rooms WHERE basePrice  >  160 AND maxOccupancy  >  2;
+SELECT T2.roomName FROM Reservations AS T1 JOIN Rooms AS T2 ON T1.Room  =  T2.RoomId GROUP BY T1.Room ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.roomName FROM Reservations AS T1 JOIN Rooms AS T2 ON T1.Room  =  T2.RoomId GROUP BY T1.Room ORDER BY count(*) DESC LIMIT 1;
+SELECT kids FROM Reservations WHERE FirstName = 'ROY' AND LastName  =  'SWEAZY';
+SELECT kids FROM Reservations WHERE FirstName = 'ROY' AND LastName  =  'SWEAZY';
+SELECT count(*) FROM Reservations WHERE FirstName = 'ROY' AND LastName  =  'SWEAZY';
+SELECT count(*) FROM Reservations WHERE FirstName = 'ROY' AND LastName  =  'SWEAZY';
+SELECT T2.roomName ,  T1.Rate ,  T1.CheckIn ,  T1.CheckOut FROM Reservations AS T1 JOIN Rooms AS T2 ON T1.Room  =  T2.RoomId GROUP BY T1.Room ORDER BY T1.Rate DESC LIMIT 1;
+SELECT T2.roomName ,  T1.Rate ,  T1.CheckIn ,  T1.CheckOut FROM Reservations AS T1 JOIN Rooms AS T2 ON T1.Room  =  T2.RoomId GROUP BY T1.Room ORDER BY T1.Rate DESC LIMIT 1;
+SELECT Adults FROM Reservations WHERE CheckIn  =  '2010-10-23' AND FirstName  =  'CONRAD' AND LastName  =  'SELBIG';
+SELECT Adults FROM Reservations WHERE CheckIn  =  '2010-10-23' AND FirstName  =  'CONRAD' AND LastName  =  'SELBIG';
+SELECT Kids FROM Reservations WHERE CheckIn  =  '2010-09-21' AND FirstName  =  'DAMIEN' AND LastName  =  'TRACHSEL';
+SELECT Kids FROM Reservations WHERE CheckIn  =  '2010-09-21' AND FirstName  =  'DAMIEN' AND LastName  =  'TRACHSEL';
+SELECT sum(beds) FROM Rooms WHERE bedtype  =  'King';
+SELECT sum(beds) FROM Rooms WHERE bedtype  =  'King';
+SELECT roomName ,  decor FROM Rooms WHERE bedtype  =  'King' ORDER BY basePrice;
+SELECT roomName ,  decor FROM Rooms WHERE bedtype  =  'King' ORDER BY basePrice;
+SELECT roomName ,  basePrice FROM Rooms ORDER BY basePrice ASC LIMIT 1;
+SELECT roomName ,  basePrice FROM Rooms ORDER BY basePrice ASC LIMIT 1;
+SELECT decor FROM Rooms WHERE roomName  =  'Recluse and defiance';
+SELECT decor FROM Rooms WHERE roomName  =  'Recluse and defiance';
+SELECT bedType ,  avg(basePrice) FROM Rooms GROUP BY bedType;
+SELECT bedType ,  avg(basePrice) FROM Rooms GROUP BY bedType;
+SELECT sum(maxOccupancy) FROM Rooms WHERE decor  =  'modern';
+SELECT sum(maxOccupancy) FROM Rooms WHERE decor  =  'modern';
+SELECT T2.decor FROM Reservations AS T1 JOIN Rooms AS T2 ON T1.Room  =  T2.RoomId GROUP BY T2.decor ORDER BY count(T2.decor) ASC LIMIT 1;
+SELECT T2.decor FROM Reservations AS T1 JOIN Rooms AS T2 ON T1.Room  =  T2.RoomId GROUP BY T2.decor ORDER BY count(T2.decor) ASC LIMIT 1;
+SELECT count(*) FROM Reservations AS T1 JOIN Rooms AS T2 ON T1.Room  =  T2.RoomId WHERE T2.maxOccupancy  =  T1.Adults + T1.Kids;
+SELECT count(*) FROM Reservations AS T1 JOIN Rooms AS T2 ON T1.Room  =  T2.RoomId WHERE T2.maxOccupancy  =  T1.Adults + T1.Kids;
+SELECT T1.firstname ,  T1.lastname FROM Reservations AS T1 JOIN Rooms AS T2 ON T1.Room  =  T2.RoomId WHERE T1.Rate - T2.basePrice  >  0;
+SELECT T1.firstname ,  T1.lastname FROM Reservations AS T1 JOIN Rooms AS T2 ON T1.Room  =  T2.RoomId WHERE T1.Rate - T2.basePrice  >  0;
+SELECT count(*) FROM Rooms;
+SELECT count(*) FROM Rooms;
+SELECT count(*) FROM Rooms WHERE bedType  =  'King';
+SELECT count(*) FROM Rooms WHERE bedType  =  'King';
+SELECT bedType ,  count(*) FROM Rooms GROUP BY bedType;
+SELECT bedType ,  count(*) FROM Rooms GROUP BY bedType;
+SELECT roomName FROM Rooms ORDER BY maxOccupancy DESC LIMIT 1;
+SELECT roomName FROM Rooms ORDER BY maxOccupancy DESC LIMIT 1;
+SELECT RoomId ,  roomName FROM Rooms ORDER BY basePrice DESC LIMIT 1;
+SELECT RoomId ,  roomName FROM Rooms ORDER BY basePrice DESC LIMIT 1;
+SELECT roomName ,  bedType FROM Rooms WHERE decor = 'traditional';
+SELECT roomName ,  bedType FROM Rooms WHERE decor = 'traditional';
+SELECT decor ,  count(*) FROM Rooms WHERE bedType = 'King' GROUP BY decor;
+SELECT decor ,  count(*) FROM Rooms WHERE bedType = 'King' GROUP BY decor;
+SELECT decor , avg(basePrice) ,  min(basePrice) FROM Rooms GROUP BY decor;
+SELECT decor , avg(basePrice) ,  min(basePrice) FROM Rooms GROUP BY decor;
+SELECT roomName FROM Rooms ORDER BY basePrice;
+SELECT roomName FROM Rooms ORDER BY basePrice;
+SELECT decor , count(*) FROM Rooms WHERE basePrice  >  120 GROUP BY decor;
+SELECT decor , count(*) FROM Rooms WHERE basePrice  >  120 GROUP BY decor;
+SELECT bedType ,  avg(basePrice) FROM Rooms GROUP BY bedType;
+SELECT bedType ,  avg(basePrice) FROM Rooms GROUP BY bedType;
+SELECT roomName FROM Rooms WHERE bedType  =  'King' OR bedType  =  'Queen';
+SELECT roomName FROM Rooms WHERE bedType  =  'King' OR bedType  =  'Queen';
+SELECT count(DISTINCT bedType) FROM Rooms;
+SELECT count(DISTINCT bedType) FROM Rooms;
+SELECT RoomId ,  roomName FROM Rooms ORDER BY basePrice DESC LIMIT 3;
+SELECT RoomId ,  roomName FROM Rooms ORDER BY basePrice DESC LIMIT 3;
+SELECT roomName FROM Rooms WHERE basePrice  >  ( SELECT avg(basePrice) FROM Rooms );
+SELECT roomName FROM Rooms WHERE basePrice  >  ( SELECT avg(basePrice) FROM Rooms );
+SELECT count(*) FROM rooms WHERE roomid NOT IN (SELECT DISTINCT room FROM reservations);
+SELECT count(*) FROM rooms WHERE roomid NOT IN (SELECT DISTINCT room FROM reservations);
+SELECT T2.roomName ,  count(*) ,  T1.Room FROM Reservations AS T1 JOIN Rooms AS T2 ON T1.Room  =  T2.RoomId GROUP BY T1.Room;
+SELECT T2.roomName ,  count(*) ,  T1.Room FROM Reservations AS T1 JOIN Rooms AS T2 ON T1.Room  =  T2.RoomId GROUP BY T1.Room;
+SELECT T2.roomName FROM Reservations AS T1 JOIN Rooms AS T2 ON T1.Room  =  T2.RoomId GROUP BY T1.Room HAVING count(*)  >  60;
+SELECT T2.roomName FROM Reservations AS T1 JOIN Rooms AS T2 ON T1.Room  =  T2.RoomId GROUP BY T1.Room HAVING count(*)  >  60;
+SELECT roomname FROM rooms WHERE baseprice BETWEEN 120 AND 150;
+SELECT roomname FROM rooms WHERE baseprice BETWEEN 120 AND 150;
+SELECT T2.roomName FROM Reservations AS T1 JOIN Rooms AS T2 ON T1.Room  =  T2.RoomId WHERE firstname LIKE '%ROY%';
+SELECT T2.roomName FROM Reservations AS T1 JOIN Rooms AS T2 ON T1.Room  =  T2.RoomId WHERE firstname LIKE '%ROY%';
+SELECT T1.cmi_details FROM Customer_Master_Index AS T1 JOIN CMI_Cross_References AS T2 ON T1.master_customer_id  =  T2.master_customer_id WHERE T2.source_system_code  =  'Tax';
+SELECT T1.cmi_cross_ref_id ,  T1.source_system_code FROM CMI_Cross_References AS T1 JOIN Council_Tax AS T2 ON T1.cmi_cross_ref_id  =  T2.cmi_cross_ref_id GROUP BY T1.cmi_cross_ref_id HAVING count(*)  >=  1;
+SELECT T2.cmi_cross_ref_id ,  T2.master_customer_id ,  count(*) FROM Business_Rates AS T1 JOIN CMI_Cross_References AS T2 ON T1.cmi_cross_ref_id  =  T2.cmi_cross_ref_id GROUP BY T2.cmi_cross_ref_id;
+SELECT T1.source_system_code ,  T2.council_tax_id FROM CMI_Cross_References AS T1 JOIN Benefits_Overpayments AS T2 ON T1.cmi_cross_ref_id  =  T2.cmi_cross_ref_id ORDER BY T2.council_tax_id;
+SELECT T1.source_system_code ,  T1.master_customer_id ,  T2.council_tax_id FROM CMI_Cross_References AS T1 JOIN Parking_Fines AS T2 ON T1.cmi_cross_ref_id  =  T2.cmi_cross_ref_id;
+SELECT T1.council_tax_id FROM Rent_Arrears AS T1 JOIN CMI_Cross_References AS T2 ON T1.cmi_cross_ref_id  =  T2.cmi_cross_ref_id JOIN Customer_Master_Index AS T3 ON T3.master_customer_id  =  T2.master_customer_id WHERE T3.cmi_details != 'Schmidt ,  Kertzmann and Lubowitz';
+SELECT T1.electoral_register_id FROM Electoral_Register AS T1 JOIN CMI_Cross_References AS T2 ON T1.cmi_cross_ref_id  =  T2.cmi_cross_ref_id WHERE T2.source_system_code  =  'Electoral' OR T2.source_system_code  =  'Tax';
+SELECT count(DISTINCT source_system_code) FROM CMI_cross_references;
+SELECT * FROM customer_master_index ORDER BY cmi_details DESC;
+SELECT council_tax_id ,  cmi_cross_ref_id FROM parking_fines;
+SELECT count(*) FROM rent_arrears;
+SELECT DISTINCT T2.source_system_code FROM customer_master_index AS T1 JOIN cmi_cross_references AS T2 ON T1.master_customer_id  =  T2.master_customer_id WHERE T1.cmi_details  =  'Gottlieb ,  Becker and Wyman';
+SELECT cmi_cross_ref_id FROM cmi_cross_references EXCEPT SELECT cmi_cross_ref_id FROM parking_fines;
+SELECT DISTINCT source_system_code FROM cmi_cross_references WHERE source_system_code LIKE '%en%';
+SELECT count(*) FROM party;
+SELECT count(*) FROM party;
+SELECT Party_Theme FROM party ORDER BY Number_of_hosts ASC;
+SELECT Party_Theme FROM party ORDER BY Number_of_hosts ASC;
+SELECT Party_Theme ,  LOCATION FROM party;
+SELECT Party_Theme ,  LOCATION FROM party;
+SELECT First_year ,  Last_year FROM party WHERE Party_Theme  =  'Spring' OR Party_Theme  =  'Teqnology';
+SELECT First_year ,  Last_year FROM party WHERE Party_Theme  =  'Spring' OR Party_Theme  =  'Teqnology';
+SELECT avg(Number_of_hosts) FROM party;
+SELECT avg(Number_of_hosts) FROM party;
+SELECT LOCATION FROM party ORDER BY Number_of_hosts DESC LIMIT 1;
+SELECT LOCATION FROM party ORDER BY Number_of_hosts DESC LIMIT 1;
+SELECT Nationality ,  COUNT(*) FROM HOST GROUP BY Nationality;
+SELECT Nationality ,  COUNT(*) FROM HOST GROUP BY Nationality;
+SELECT Nationality FROM HOST GROUP BY Nationality ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Nationality FROM HOST GROUP BY Nationality ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Nationality FROM HOST WHERE Age  >  45 INTERSECT SELECT Nationality FROM HOST WHERE Age  <  35;
+SELECT Nationality FROM HOST WHERE Age  >  45 INTERSECT SELECT Nationality FROM HOST WHERE Age  <  35;
+SELECT T3.Party_Theme ,  T2.Name FROM party_host AS T1 JOIN HOST AS T2 ON T1.Host_ID  =  T2.Host_ID JOIN party AS T3 ON T1.Party_ID  =  T3.Party_ID;
+SELECT T3.Party_Theme ,  T2.Name FROM party_host AS T1 JOIN HOST AS T2 ON T1.Host_ID  =  T2.Host_ID JOIN party AS T3 ON T1.Party_ID  =  T3.Party_ID;
+SELECT T3.Location ,  T2.Name FROM party_host AS T1 JOIN HOST AS T2 ON T1.Host_ID  =  T2.Host_ID JOIN party AS T3 ON T1.Party_ID  =  T3.Party_ID ORDER BY T2.Age;
+SELECT T3.Location ,  T2.Name FROM party_host AS T1 JOIN HOST AS T2 ON T1.Host_ID  =  T2.Host_ID JOIN party AS T3 ON T1.Party_ID  =  T3.Party_ID ORDER BY T2.Age;
+SELECT T3.Location FROM party_host AS T1 JOIN HOST AS T2 ON T1.Host_ID  =  T2.Host_ID JOIN party AS T3 ON T1.Party_ID  =  T3.Party_ID WHERE T2.Age  >  50;
+SELECT T3.Location FROM party_host AS T1 JOIN HOST AS T2 ON T1.Host_ID  =  T2.Host_ID JOIN party AS T3 ON T1.Party_ID  =  T3.Party_ID WHERE T2.Age  >  50;
+SELECT T2.Name FROM party_host AS T1 JOIN HOST AS T2 ON T1.Host_ID  =  T2.Host_ID JOIN party AS T3 ON T1.Party_ID  =  T3.Party_ID WHERE T3.Number_of_hosts  >  20;
+SELECT T2.Name FROM party_host AS T1 JOIN HOST AS T2 ON T1.Host_ID  =  T2.Host_ID JOIN party AS T3 ON T1.Party_ID  =  T3.Party_ID WHERE T3.Number_of_hosts  >  20;
+SELECT Name ,  Nationality FROM HOST ORDER BY Age DESC LIMIT 1;
+SELECT Name ,  Nationality FROM HOST ORDER BY Age DESC LIMIT 1;
+SELECT Name FROM HOST WHERE Host_ID NOT IN (SELECT Host_ID FROM party_host);
+SELECT Name FROM HOST WHERE Host_ID NOT IN (SELECT Host_ID FROM party_host);
+SELECT count(*) FROM region;
+SELECT count(*) FROM region;
+SELECT region_code ,  region_name FROM region ORDER BY region_code;
+SELECT region_code ,  region_name FROM region ORDER BY region_code;
+SELECT region_name FROM region ORDER BY region_name;
+SELECT region_name FROM region ORDER BY region_name;
+SELECT region_name FROM region WHERE region_name != 'Denmark';
+SELECT region_name FROM region WHERE region_name != 'Denmark';
+SELECT count(*) FROM storm WHERE Number_Deaths  >  0;
+SELECT count(*) FROM storm WHERE Number_Deaths  >  0;
+SELECT name ,  dates_active ,  number_deaths FROM storm WHERE number_deaths  >=  1;
+SELECT name ,  dates_active ,  number_deaths FROM storm WHERE number_deaths  >=  1;
+SELECT avg(damage_millions_USD) ,  max(damage_millions_USD) FROM storm WHERE max_speed  >  1000;
+SELECT avg(damage_millions_USD) ,  max(damage_millions_USD) FROM storm WHERE max_speed  >  1000;
+SELECT sum(number_deaths) ,  sum(damage_millions_USD) FROM storm WHERE max_speed  >  (SELECT avg(max_speed) FROM storm);
+SELECT sum(number_deaths) ,  sum(damage_millions_USD) FROM storm WHERE max_speed  >  (SELECT avg(max_speed) FROM storm);
+SELECT name ,  damage_millions_USD FROM storm ORDER BY max_speed DESC;
+SELECT name ,  damage_millions_USD FROM storm ORDER BY max_speed DESC;
+SELECT count(DISTINCT region_id) FROM affected_region;
+SELECT count(DISTINCT region_id) FROM affected_region;
+SELECT region_name FROM region WHERE region_id NOT IN (SELECT region_id FROM affected_region);
+SELECT region_name FROM region WHERE region_id NOT IN (SELECT region_id FROM affected_region);
+SELECT T1.region_name ,  count(*) FROM region AS T1 JOIN affected_region AS T2 ON T1.region_id = T2.region_id GROUP BY T1.region_id;
+SELECT T1.region_name ,  count(*) FROM region AS T1 JOIN affected_region AS T2 ON T1.region_id = T2.region_id GROUP BY T1.region_id;
+SELECT T1.name ,  count(*) FROM storm AS T1 JOIN affected_region AS T2 ON T1.storm_id  =  T2.storm_id GROUP BY T1.storm_id;
+SELECT T1.name ,  count(*) FROM storm AS T1 JOIN affected_region AS T2 ON T1.storm_id  =  T2.storm_id GROUP BY T1.storm_id;
+SELECT T1.name ,  T1.max_speed FROM storm AS T1 JOIN affected_region AS T2 ON T1.storm_id  =  T2.storm_id GROUP BY T1.storm_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name ,  T1.max_speed FROM storm AS T1 JOIN affected_region AS T2 ON T1.storm_id  =  T2.storm_id GROUP BY T1.storm_id ORDER BY count(*) DESC LIMIT 1;
+SELECT name FROM storm WHERE storm_id NOT IN (SELECT storm_id FROM affected_region);
+SELECT name FROM storm WHERE storm_id NOT IN (SELECT storm_id FROM affected_region);
+SELECT T1.name FROM storm AS T1 JOIN affected_region AS T2 ON T1.storm_id  =  T2.storm_id GROUP BY T1.storm_id HAVING count(*)  >=  2 INTERSECT SELECT T1.name FROM storm AS T1 JOIN affected_region AS T2 ON T1.storm_id  =  T2.storm_id GROUP BY T1.storm_id HAVING sum(T2.number_city_affected)  >=  10;
+SELECT T1.name FROM storm AS T1 JOIN affected_region AS T2 ON T1.storm_id  =  T2.storm_id GROUP BY T1.storm_id HAVING count(*)  >=  2 INTERSECT SELECT T1.name FROM storm AS T1 JOIN affected_region AS T2 ON T1.storm_id  =  T2.storm_id GROUP BY T1.storm_id HAVING sum(T2.number_city_affected)  >=  10;
+SELECT name FROM storm EXCEPT SELECT T1.name FROM storm AS T1 JOIN affected_region AS T2 ON T1.storm_id  =  T2.storm_id GROUP BY T1.storm_id HAVING count(*)  >=  2;
+SELECT name FROM storm EXCEPT SELECT T1.name FROM storm AS T1 JOIN affected_region AS T2 ON T1.storm_id  =  T2.storm_id GROUP BY T1.storm_id HAVING count(*)  >=  2;
+SELECT T2.region_name FROM affected_region AS T1 JOIN region AS T2 ON T1.region_id  =  T2.region_id JOIN storm AS T3 ON T1.storm_id  =  T3.storm_id WHERE T3.number_deaths  >=  10;
+SELECT T2.region_name FROM affected_region AS T1 JOIN region AS T2 ON T1.region_id  =  T2.region_id JOIN storm AS T3 ON T1.storm_id  =  T3.storm_id WHERE T3.number_deaths  >=  10;
+SELECT T3.name FROM affected_region AS T1 JOIN region AS T2 ON T1.region_id  =  T2.region_id JOIN storm AS T3 ON T1.storm_id  =  T3.storm_id WHERE T2.region_name  =  'Denmark';
+SELECT T3.name FROM affected_region AS T1 JOIN region AS T2 ON T1.region_id  =  T2.region_id JOIN storm AS T3 ON T1.storm_id  =  T3.storm_id WHERE T2.region_name  =  'Denmark';
+SELECT T1.region_name FROM region AS T1 JOIN affected_region AS T2 ON T1.region_id = T2.region_id GROUP BY T1.region_id HAVING count(*)  >=  2;
+SELECT T1.region_name FROM region AS T1 JOIN affected_region AS T2 ON T1.region_id = T2.region_id GROUP BY T1.region_id HAVING count(*)  >=  2;
+SELECT T2.region_name FROM affected_region AS T1 JOIN region AS T2 ON T1.region_id  =  T2.region_id JOIN storm AS T3 ON T1.storm_id  =  T3.storm_id ORDER BY T3.Number_Deaths DESC LIMIT 1;
+SELECT T2.region_name FROM affected_region AS T1 JOIN region AS T2 ON T1.region_id  =  T2.region_id JOIN storm AS T3 ON T1.storm_id  =  T3.storm_id ORDER BY T3.Number_Deaths DESC LIMIT 1;
+SELECT T3.Name FROM affected_region AS T1 JOIN region AS T2 ON T1.region_id  =  T2.region_id JOIN storm AS T3 ON T1.storm_id  =  T3.storm_id WHERE T2.Region_name  =  'Afghanistan' INTERSECT SELECT T3.Name FROM affected_region AS T1 JOIN region AS T2 ON T1.region_id  =  T2.region_id JOIN storm AS T3 ON T1.storm_id  =  T3.storm_id WHERE T2.Region_name  =  'Albania';
+SELECT T3.Name FROM affected_region AS T1 JOIN region AS T2 ON T1.region_id  =  T2.region_id JOIN storm AS T3 ON T1.storm_id  =  T3.storm_id WHERE T2.Region_name  =  'Afghanistan' INTERSECT SELECT T3.Name FROM affected_region AS T1 JOIN region AS T2 ON T1.region_id  =  T2.region_id JOIN storm AS T3 ON T1.storm_id  =  T3.storm_id WHERE T2.Region_name  =  'Albania';
+SELECT count(*) FROM county;
+SELECT count(*) FROM county;
+SELECT County_name ,  Population FROM county;
+SELECT County_name ,  Population FROM county;
+SELECT avg(Population) FROM county;
+SELECT avg(Population) FROM county;
+SELECT max(Population) ,  min(Population) FROM county;
+SELECT max(Population) ,  min(Population) FROM county;
+SELECT DISTINCT District FROM election;
+SELECT DISTINCT District FROM election;
+SELECT Zip_code FROM county WHERE County_name  =  'Howard';
+SELECT Zip_code FROM county WHERE County_name  =  'Howard';
+SELECT Delegate FROM election WHERE District  =  1;
+SELECT Delegate FROM election WHERE District  =  1;
+SELECT Delegate ,  Committee FROM election;
+SELECT Delegate ,  Committee FROM election;
+SELECT count(DISTINCT Governor) FROM party;
+SELECT count(DISTINCT Governor) FROM party;
+SELECT Lieutenant_Governor ,  Comptroller FROM party WHERE Party  =  'Democratic';
+SELECT Lieutenant_Governor ,  Comptroller FROM party WHERE Party  =  'Democratic';
+SELECT DISTINCT YEAR FROM party WHERE Governor  =  'Eliot Spitzer';
+SELECT DISTINCT YEAR FROM party WHERE Governor  =  'Eliot Spitzer';
+SELECT * FROM election;
+SELECT * FROM election;
+SELECT T2.Delegate ,  T1.County_name FROM county AS T1 JOIN election AS T2 ON T1.County_id  =  T2.District;
+SELECT T2.Delegate ,  T1.County_name FROM county AS T1 JOIN election AS T2 ON T1.County_id  =  T2.District;
+SELECT T2.Delegate FROM county AS T1 JOIN election AS T2 ON T1.County_id  =  T2.District WHERE T1.Population  <  100000;
+SELECT T2.Delegate FROM county AS T1 JOIN election AS T2 ON T1.County_id  =  T2.District WHERE T1.Population  <  100000;
+SELECT count(DISTINCT T2.Delegate) FROM county AS T1 JOIN election AS T2 ON T1.County_id  =  T2.District WHERE T1.Population  >  50000;
+SELECT count(DISTINCT T2.Delegate) FROM county AS T1 JOIN election AS T2 ON T1.County_id  =  T2.District WHERE T1.Population  >  50000;
+SELECT T1.County_name FROM county AS T1 JOIN election AS T2 ON T1.County_id  =  T2.District WHERE T2.Committee  =  'Appropriations';
+SELECT T1.County_name FROM county AS T1 JOIN election AS T2 ON T1.County_id  =  T2.District WHERE T2.Committee  =  'Appropriations';
+SELECT T1.Delegate ,  T2.Party FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID;
+SELECT T1.Delegate ,  T2.Party FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID;
+SELECT T2.Governor FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID WHERE T1.District  =  1;
+SELECT T2.Governor FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID WHERE T1.District  =  1;
+SELECT T2.Comptroller FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID WHERE T1.District  =  1 OR T1.District  =  2;
+SELECT T2.Comptroller FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID WHERE T1.District  =  1 OR T1.District  =  2;
+SELECT T1.Committee FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID WHERE T2.Party  =  'Democratic';
+SELECT T1.Committee FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID WHERE T2.Party  =  'Democratic';
+SELECT T1.County_name ,  COUNT(*) FROM county AS T1 JOIN election AS T2 ON T1.County_id  =  T2.District GROUP BY T1.County_id;
+SELECT T1.County_name ,  COUNT(*) FROM county AS T1 JOIN election AS T2 ON T1.County_id  =  T2.District GROUP BY T1.County_id;
+SELECT T2.Party ,  COUNT(*) FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID GROUP BY T1.Party;
+SELECT T2.Party ,  COUNT(*) FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID GROUP BY T1.Party;
+SELECT County_name FROM county ORDER BY Population ASC;
+SELECT County_name FROM county ORDER BY Population ASC;
+SELECT County_name FROM county ORDER BY County_name DESC;
+SELECT County_name FROM county ORDER BY County_name DESC;
+SELECT County_name FROM county ORDER BY Population DESC LIMIT 1;
+SELECT County_name FROM county ORDER BY Population DESC LIMIT 1;
+SELECT County_name FROM county ORDER BY Population ASC LIMIT 3;
+SELECT County_name FROM county ORDER BY Population ASC LIMIT 3;
+SELECT T1.County_name FROM county AS T1 JOIN election AS T2 ON T1.County_id  =  T2.District GROUP BY T1.County_id HAVING COUNT(*)  >=  2;
+SELECT T1.County_name FROM county AS T1 JOIN election AS T2 ON T1.County_id  =  T2.District GROUP BY T1.County_id HAVING COUNT(*)  >=  2;
+SELECT Party FROM party GROUP BY Party HAVING COUNT(*)  >=  2;
+SELECT Party FROM party GROUP BY Party HAVING COUNT(*)  >=  2;
+SELECT T2.Party FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID GROUP BY T1.Party ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T2.Party FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID GROUP BY T1.Party ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Governor FROM party GROUP BY Governor ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Governor FROM party GROUP BY Governor ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Comptroller ,  COUNT(*) FROM party GROUP BY Comptroller ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Comptroller ,  COUNT(*) FROM party GROUP BY Comptroller ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Party FROM party WHERE Party_ID NOT IN (SELECT Party FROM election);
+SELECT Party FROM party WHERE Party_ID NOT IN (SELECT Party FROM election);
+SELECT T2.Party FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID WHERE T1.Committee  =  'Appropriations' INTERSECT SELECT T2.Party FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID WHERE T1.Committee  =  'Economic Matters';
+SELECT T2.Party FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID WHERE T1.Committee  =  'Appropriations' INTERSECT SELECT T2.Party FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID WHERE T1.Committee  =  'Economic Matters';
+SELECT T1.Committee FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID WHERE T2.Party  =  'Democratic' INTERSECT SELECT T1.Committee FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID WHERE T2.Party  =  'Liberal';
+SELECT T1.Committee FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID WHERE T2.Party  =  'Democratic' INTERSECT SELECT T1.Committee FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID WHERE T2.Party  =  'Liberal';
+SELECT count(*) FROM journalist;
+SELECT Name FROM journalist ORDER BY Years_working ASC;
+SELECT Nationality ,  Age FROM journalist;
+SELECT Name FROM journalist WHERE Nationality  =  'England' OR Nationality  =  'Wales';
+SELECT avg(Years_working) FROM journalist;
+SELECT Nationality FROM journalist ORDER BY Years_working DESC LIMIT 1;
+SELECT Nationality ,  COUNT(*) FROM journalist GROUP BY Nationality;
+SELECT Nationality FROM journalist GROUP BY Nationality ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Nationality FROM journalist WHERE Years_working  >  10 INTERSECT SELECT Nationality FROM journalist WHERE Years_working  <  3;
+SELECT Date ,  Name ,  venue FROM event ORDER BY Event_Attendance DESC;
+SELECT T3.Name ,  T2.Date FROM news_report AS T1 JOIN event AS T2 ON T1.Event_ID  =  T2.Event_ID JOIN journalist AS T3 ON T1.journalist_ID  =  T3.journalist_ID;
+SELECT T3.Name ,  T2.Name FROM news_report AS T1 JOIN event AS T2 ON T1.Event_ID  =  T2.Event_ID JOIN journalist AS T3 ON T1.journalist_ID  =  T3.journalist_ID ORDER BY T2.Event_Attendance ASC;
+SELECT T3.Name ,  COUNT(*) FROM news_report AS T1 JOIN event AS T2 ON T1.Event_ID  =  T2.Event_ID JOIN journalist AS T3 ON T1.journalist_ID  =  T3.journalist_ID GROUP BY T3.Name;
+SELECT T3.Name FROM news_report AS T1 JOIN event AS T2 ON T1.Event_ID  =  T2.Event_ID JOIN journalist AS T3 ON T1.journalist_ID  =  T3.journalist_ID GROUP BY T3.Name HAVING COUNT(*)  >  1;
+SELECT Name FROM journalist WHERE journalist_ID NOT IN (SELECT journalist_ID FROM news_report);
+SELECT avg(Event_Attendance) ,  max(Event_Attendance) FROM event;
+SELECT avg(t1.age) ,  avg(Years_working) ,  t2.work_type FROM journalist AS t1 JOIN news_report AS t2 ON t1.journalist_id  =  t2.journalist_id GROUP BY t2.work_type;
+SELECT venue ,  name FROM event ORDER BY Event_Attendance DESC LIMIT 2;
+SELECT ResName FROM Restaurant;
+SELECT Address FROM Restaurant WHERE ResName = 'Subway';
+SELECT Rating FROM Restaurant WHERE ResName = 'Subway';
+SELECT ResTypeName FROM Restaurant_Type;
+SELECT ResTypeDescription FROM Restaurant_Type WHERE ResTypeName = 'Sandwich';
+SELECT ResName , Rating FROM Restaurant ORDER BY Rating DESC LIMIT 1;
+SELECT Age FROM Student WHERE Fname = 'Linda' AND Lname = 'Smith';
+SELECT Sex FROM Student WHERE Fname = 'Linda' AND Lname = 'Smith';
+SELECT Fname , Lname FROM Student WHERE Major  =  600;
+SELECT city_code FROM Student WHERE Fname = 'Linda' AND Lname = 'Smith';
+SELECT count(*) FROM Student WHERE Advisor =  1121;
+SELECT Advisor ,  count(*) FROM Student GROUP BY Advisor ORDER BY count(Advisor) DESC LIMIT 1;
+SELECT Major ,  count(*) FROM Student GROUP BY Major ORDER BY count(Major) ASC LIMIT 1;
+SELECT Major ,  count(*) FROM Student GROUP BY Major HAVING count(Major) BETWEEN 2 AND 30;
+SELECT Fname , Lname FROM Student WHERE Age  >  18 AND Major = 600;
+SELECT Fname , Lname FROM Student WHERE Age  >  18 AND Major != 600 AND Sex = 'F';
+SELECT count(*) FROM Restaurant JOIN Type_Of_Restaurant ON Restaurant.ResID =  Type_Of_Restaurant.ResID JOIN Restaurant_Type ON Type_Of_Restaurant.ResTypeID = Restaurant_Type.ResTypeID GROUP BY Type_Of_Restaurant.ResTypeID HAVING Restaurant_Type.ResTypeName = 'Sandwich';
+SELECT sum(Spent) FROM Student JOIN Visits_Restaurant ON Student.StuID = Visits_Restaurant.StuID WHERE Student.Fname = 'Linda' AND Student.Lname = 'Smith';
+SELECT count(*) FROM Student JOIN Visits_Restaurant ON Student.StuID = Visits_Restaurant.StuID JOIN Restaurant ON Visits_Restaurant.ResID = Restaurant.ResID WHERE Student.Fname = 'Linda' AND Student.Lname = 'Smith' AND Restaurant.ResName = 'Subway';
+SELECT TIME FROM Student JOIN Visits_Restaurant ON Student.StuID = Visits_Restaurant.StuID JOIN Restaurant ON Visits_Restaurant.ResID = Restaurant.ResID WHERE Student.Fname = 'Linda' AND Student.Lname = 'Smith' AND Restaurant.ResName = 'Subway';
+SELECT Restaurant.ResName ,  sum(Visits_Restaurant.Spent) FROM Visits_Restaurant JOIN Restaurant ON Visits_Restaurant.ResID = Restaurant.ResID GROUP BY Restaurant.ResID ORDER BY sum(Visits_Restaurant.Spent) ASC LIMIT 1;
+SELECT Student.Fname , Student.Lname FROM Student JOIN Visits_Restaurant ON Student.StuID = Visits_Restaurant.StuID GROUP BY Student.StuID ORDER BY count(*) DESC LIMIT 1;
+SELECT actual_order_id FROM actual_orders WHERE order_status_code  =  'Success';
+SELECT t1.product_name ,   t1.product_price FROM products AS t1 JOIN regular_order_products AS t2 ON t1.product_id  =  t2.product_id GROUP BY t2.product_id ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM customers;
+SELECT count(DISTINCT payment_method) FROM customers;
+SELECT truck_details FROM trucks ORDER BY truck_licence_number;
+SELECT product_name FROM products ORDER BY product_price DESC LIMIT 1;
+SELECT customer_name FROM customers EXCEPT SELECT t1.customer_name FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id  =  t2.customer_id JOIN addresses AS t3 ON t2.address_id  =  t3.address_id WHERE t3.state_province_county  =  'California';
+SELECT customer_email ,  customer_name FROM customers WHERE payment_method  =  'Visa';
+SELECT t1.customer_name ,  t1.customer_phone FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id  =  t2.customer_id JOIN addresses AS t3 ON t2.address_id  =  t3.address_id WHERE t3.state_province_county  =  'California';
+SELECT state_province_county FROM addresses WHERE address_id NOT IN (SELECT employee_address_id FROM Employees);
+SELECT customer_name ,  customer_phone ,  customer_email FROM Customers ORDER BY date_became_customer;
+SELECT customer_name FROM Customers ORDER BY date_became_customer LIMIT 5;
+SELECT payment_method FROM Customers GROUP BY payment_method ORDER BY count(*) DESC LIMIT 1;
+SELECT route_name FROM Delivery_Routes ORDER BY route_name;
+SELECT t1.route_name FROM Delivery_Routes AS t1 JOIN Delivery_Route_Locations AS t2 ON t1.route_id  =  t2.route_id GROUP BY t1.route_id ORDER BY count(*) DESC LIMIT 1;
+SELECT t2.state_province_county ,  count(*) FROM customer_addresses AS t1 JOIN addresses AS t2 ON t1.address_id  =  t2.address_id GROUP BY t2.state_province_county;
+SELECT count(*) FROM authors;
+SELECT count(*) FROM authors;
+SELECT count(*) FROM inst;
+SELECT count(*) FROM inst;
+SELECT count(*) FROM papers;
+SELECT count(*) FROM papers;
+SELECT t3.title FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid WHERE  t1.fname  =  'Jeremy' AND t1.lname  =  'Gibbons';
+SELECT t3.title FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid WHERE  t1.fname  =  'Jeremy' AND t1.lname  =  'Gibbons';
+SELECT t3.title FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid WHERE  t1.fname  =  'Aaron' AND t1.lname  =  'Turon';
+SELECT t3.title FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid WHERE  t1.fname  =  'Aaron' AND t1.lname  =  'Turon';
+SELECT count(*) FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid WHERE  t1.fname  =  'Atsushi' AND t1.lname  =  'Ohori';
+SELECT count(*) FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid WHERE  t1.fname  =  'Atsushi' AND t1.lname  =  'Ohori';
+SELECT DISTINCT t3.name FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN inst AS t3 ON t2.instid  =  t3.instid WHERE t1.fname  =  'Matthias' AND t1.lname  =  'Blume';
+SELECT DISTINCT t3.name FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN inst AS t3 ON t2.instid  =  t3.instid WHERE t1.fname  =  'Matthias' AND t1.lname  =  'Blume';
+SELECT DISTINCT t3.name FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN inst AS t3 ON t2.instid  =  t3.instid WHERE t1.fname  =  'Katsuhiro' AND t1.lname  =  'Ueno';
+SELECT DISTINCT t3.name FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN inst AS t3 ON t2.instid  =  t3.instid WHERE t1.fname  =  'Katsuhiro' AND t1.lname  =  'Ueno';
+SELECT DISTINCT t1.fname ,  t1.lname FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN inst AS t3 ON t2.instid  =  t3.instid WHERE t3.name  =  'University of Oxford';
+SELECT DISTINCT t1.fname ,  t1.lname FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN inst AS t3 ON t2.instid  =  t3.instid WHERE t3.name  =  'University of Oxford';
+SELECT DISTINCT t1.fname ,  t1.lname FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN inst AS t3 ON t2.instid  =  t3.instid WHERE t3.name  =  'Google';
+SELECT DISTINCT t1.fname ,  t1.lname FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN inst AS t3 ON t2.instid  =  t3.instid WHERE t3.name  =  'Google';
+SELECT t1.lname FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid WHERE t3.title  =  'Binders Unbound';
+SELECT t1.lname FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid WHERE t3.title  =  'Binders Unbound';
+SELECT t1.fname ,  t1.lname FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid WHERE t3.title  =  'Nameless ,  Painless';
+SELECT t1.fname ,  t1.lname FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid WHERE t3.title  =  'Nameless ,  Painless';
+SELECT DISTINCT t1.title FROM papers AS t1 JOIN authorship AS t2 ON t1.paperid  =  t2.paperid JOIN inst AS t3 ON t2.instid  =  t3.instid WHERE t3.name  =  'Indiana University';
+SELECT DISTINCT t1.title FROM papers AS t1 JOIN authorship AS t2 ON t1.paperid  =  t2.paperid JOIN inst AS t3 ON t2.instid  =  t3.instid WHERE t3.name  =  'Indiana University';
+SELECT DISTINCT t1.title FROM papers AS t1 JOIN authorship AS t2 ON t1.paperid  =  t2.paperid JOIN inst AS t3 ON t2.instid  =  t3.instid WHERE t3.name  =  'Google';
+SELECT DISTINCT t1.title FROM papers AS t1 JOIN authorship AS t2 ON t1.paperid  =  t2.paperid JOIN inst AS t3 ON t2.instid  =  t3.instid WHERE t3.name  =  'Google';
+SELECT count(DISTINCT t1.title) FROM papers AS t1 JOIN authorship AS t2 ON t1.paperid  =  t2.paperid JOIN inst AS t3 ON t2.instid  =  t3.instid WHERE t3.name  =  'Tokohu University';
+SELECT count(DISTINCT t1.title) FROM papers AS t1 JOIN authorship AS t2 ON t1.paperid  =  t2.paperid JOIN inst AS t3 ON t2.instid  =  t3.instid WHERE t3.name  =  'Tokohu University';
+SELECT count(DISTINCT t1.title) FROM papers AS t1 JOIN authorship AS t2 ON t1.paperid  =  t2.paperid JOIN inst AS t3 ON t2.instid  =  t3.instid WHERE t3.name  =  'University of Pennsylvania';
+SELECT count(DISTINCT t1.title) FROM papers AS t1 JOIN authorship AS t2 ON t1.paperid  =  t2.paperid JOIN inst AS t3 ON t2.instid  =  t3.instid WHERE t3.name  =  'University of Pennsylvania';
+SELECT t3.title FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid WHERE t1.fname  =  'Olin' AND t1.lname  =  'Shivers';
+SELECT t3.title FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid WHERE t1.fname  =  'Olin' AND t1.lname  =  'Shivers';
+SELECT t3.title FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid WHERE t1.fname  =  'Stephanie' AND t1.lname  =  'Weirich';
+SELECT t3.title FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid WHERE t1.fname  =  'Stephanie' AND t1.lname  =  'Weirich';
+SELECT t3.title FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid JOIN inst AS t4 ON t2.instid  =  t4.instid WHERE t4.country  =  'USA' AND t2.authorder  =  2 AND t1.lname  =  'Turon';
+SELECT t3.title FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid JOIN inst AS t4 ON t2.instid  =  t4.instid WHERE t4.country  =  'USA' AND t2.authorder  =  2 AND t1.lname  =  'Turon';
+SELECT t3.title FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid JOIN inst AS t4 ON t2.instid  =  t4.instid WHERE t4.country  =  'Japan' AND t2.authorder  =  1 AND t1.lname  =  'Ohori';
+SELECT t3.title FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid JOIN inst AS t4 ON t2.instid  =  t4.instid WHERE t4.country  =  'Japan' AND t2.authorder  =  1 AND t1.lname  =  'Ohori';
+SELECT t1.lname FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid GROUP BY t1.fname ,  t1.lname ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.lname FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid GROUP BY t1.fname ,  t1.lname ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.country FROM inst AS t1 JOIN authorship AS t2 ON t1.instid  =  t2.instid JOIN papers AS t3 ON t2.paperid  =  t3.paperid GROUP BY t1.country ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.country FROM inst AS t1 JOIN authorship AS t2 ON t1.instid  =  t2.instid JOIN papers AS t3 ON t2.paperid  =  t3.paperid GROUP BY t1.country ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.name FROM inst AS t1 JOIN authorship AS t2 ON t1.instid  =  t2.instid JOIN papers AS t3 ON t2.paperid  =  t3.paperid GROUP BY t1.name ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.name FROM inst AS t1 JOIN authorship AS t2 ON t1.instid  =  t2.instid JOIN papers AS t3 ON t2.paperid  =  t3.paperid GROUP BY t1.name ORDER BY count(*) DESC LIMIT 1;
+SELECT title FROM papers WHERE title LIKE '%ML%';
+SELECT title FROM papers WHERE title LIKE '%ML%';
+SELECT title FROM papers WHERE title LIKE '%Database%';
+SELECT title FROM papers WHERE title LIKE '%Database%';
+SELECT t1.fname FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid WHERE t3.title LIKE '%Functional%';
+SELECT t1.fname FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid WHERE t3.title LIKE '%Functional%';
+SELECT t1.lname FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid WHERE t3.title LIKE '%Monadic%';
+SELECT t1.lname FROM authors AS t1 JOIN authorship AS t2 ON t1.authid  =  t2.authid JOIN papers AS t3 ON t2.paperid  =  t3.paperid WHERE t3.title LIKE '%Monadic%';
+SELECT t2.title FROM authorship AS t1 JOIN papers AS t2 ON t1.paperid  =  t2.paperid WHERE t1.authorder  =  (SELECT max(authorder) FROM authorship);
+SELECT t2.title FROM authorship AS t1 JOIN papers AS t2 ON t1.paperid  =  t2.paperid WHERE t1.authorder  =  (SELECT max(authorder) FROM authorship);
+SELECT fname FROM authors WHERE lname  =  'Ueno';
+SELECT fname FROM authors WHERE lname  =  'Ueno';
+SELECT lname FROM authors WHERE fname  =  'Amal';
+SELECT lname FROM authors WHERE fname  =  'Amal';
+SELECT fname FROM authors ORDER BY fname;
+SELECT fname FROM authors ORDER BY fname;
+SELECT lname FROM authors ORDER BY lname;
+SELECT lname FROM authors ORDER BY lname;
+SELECT fname ,  lname FROM authors ORDER BY lname;
+SELECT fname ,  lname FROM authors ORDER BY lname;
+SELECT count(DISTINCT last_name) FROM actor;
+SELECT count(DISTINCT last_name) FROM actor;
+SELECT first_name FROM actor GROUP BY first_name ORDER BY count(*) DESC LIMIT 1;
+SELECT first_name FROM actor GROUP BY first_name ORDER BY count(*) DESC LIMIT 1;
+SELECT first_name ,  last_name FROM actor GROUP BY first_name ,  last_name ORDER BY count(*) DESC LIMIT 1;
+SELECT first_name ,  last_name FROM actor GROUP BY first_name ,  last_name ORDER BY count(*) DESC LIMIT 1;
+SELECT district FROM address GROUP BY district HAVING count(*)  >=  2;
+SELECT district FROM address GROUP BY district HAVING count(*)  >=  2;
+SELECT phone ,  postal_code FROM address WHERE address  =  '1031 Daugavpils Parkway';
+SELECT phone ,  postal_code FROM address WHERE address  =  '1031 Daugavpils Parkway';
+SELECT T2.city ,  count(*) ,  T1.city_id FROM address AS T1 JOIN city AS T2 ON T1.city_id  =  T2.city_id GROUP BY T1.city_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.city ,  count(*) ,  T1.city_id FROM address AS T1 JOIN city AS T2 ON T1.city_id  =  T2.city_id GROUP BY T1.city_id ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM address WHERE district  =  'California';
+SELECT count(*) FROM address WHERE district  =  'California';
+SELECT title ,  film_id FROM film WHERE rental_rate  =  0.99 INTERSECT SELECT T1.title ,  T1.film_id FROM film AS T1 JOIN inventory AS T2 ON T1.film_id  =  T2.film_id GROUP BY T1.film_id HAVING count(*)  <  3;
+SELECT title ,  film_id FROM film WHERE rental_rate  =  0.99 INTERSECT SELECT T1.title ,  T1.film_id FROM film AS T1 JOIN inventory AS T2 ON T1.film_id  =  T2.film_id GROUP BY T1.film_id HAVING count(*)  <  3;
+SELECT count(*) FROM city AS T1 JOIN country AS T2 ON T1.country_id  =  T2.country_id WHERE T2.country  =  'Australia';
+SELECT count(*) FROM city AS T1 JOIN country AS T2 ON T1.country_id  =  T2.country_id WHERE T2.country  =  'Australia';
+SELECT T2.country FROM city AS T1 JOIN country AS T2 ON T1.country_id  =  T2.country_id GROUP BY T2.country_id HAVING count(*)  >=  3;
+SELECT T2.country FROM city AS T1 JOIN country AS T2 ON T1.country_id  =  T2.country_id GROUP BY T2.country_id HAVING count(*)  >=  3;
+SELECT payment_date FROM payment WHERE amount  >  10 UNION SELECT T1.payment_date FROM payment AS T1 JOIN staff AS T2 ON T1.staff_id  =  T2.staff_id WHERE T2.first_name  =  'Elsa';
+SELECT payment_date FROM payment WHERE amount  >  10 UNION SELECT T1.payment_date FROM payment AS T1 JOIN staff AS T2 ON T1.staff_id  =  T2.staff_id WHERE T2.first_name  =  'Elsa';
+SELECT count(*) FROM customer WHERE active = '1';
+SELECT count(*) FROM customer WHERE active = '1';
+SELECT title ,  rental_rate FROM film ORDER BY rental_rate DESC LIMIT 1;
+SELECT title ,  rental_rate FROM film ORDER BY rental_rate DESC LIMIT 1;
+SELECT T2.title ,  T2.film_id ,  T2.description FROM film_actor AS T1 JOIN film AS T2 ON T1.film_id  =  T2.film_id GROUP BY T2.film_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.title ,  T2.film_id ,  T2.description FROM film_actor AS T1 JOIN film AS T2 ON T1.film_id  =  T2.film_id GROUP BY T2.film_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.first_name ,  T2.last_name ,  T2.actor_id FROM film_actor AS T1 JOIN actor AS T2 ON T1.actor_id  =  T2.actor_id GROUP BY T2.actor_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.first_name ,  T2.last_name ,  T2.actor_id FROM film_actor AS T1 JOIN actor AS T2 ON T1.actor_id  =  T2.actor_id GROUP BY T2.actor_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.first_name ,  T2.last_name FROM film_actor AS T1 JOIN actor AS T2 ON T1.actor_id  =  T2.actor_id GROUP BY T2.actor_id HAVING count(*)  >  30;
+SELECT T2.first_name ,  T2.last_name FROM film_actor AS T1 JOIN actor AS T2 ON T1.actor_id  =  T2.actor_id GROUP BY T2.actor_id HAVING count(*)  >  30;
+SELECT store_id FROM inventory GROUP BY store_id ORDER BY count(*) DESC LIMIT 1;
+SELECT store_id FROM inventory GROUP BY store_id ORDER BY count(*) DESC LIMIT 1;
+SELECT sum(amount) FROM payment;
+SELECT sum(amount) FROM payment;
+SELECT T1.first_name ,  T1.last_name ,  T1.customer_id FROM customer AS T1 JOIN payment AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id ORDER BY sum(amount) ASC LIMIT 1;
+SELECT T1.first_name ,  T1.last_name ,  T1.customer_id FROM customer AS T1 JOIN payment AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id ORDER BY sum(amount) ASC LIMIT 1;
+SELECT T1.name FROM category AS T1 JOIN film_category AS T2 ON T1.category_id  =  T2.category_id JOIN film AS T3 ON T2.film_id  =  T3.film_id WHERE T3.title  =  'HUNGER ROOF';
+SELECT T1.name FROM category AS T1 JOIN film_category AS T2 ON T1.category_id  =  T2.category_id JOIN film AS T3 ON T2.film_id  =  T3.film_id WHERE T3.title  =  'HUNGER ROOF';
+SELECT T2.name ,  T1.category_id ,  count(*) FROM film_category AS T1 JOIN category AS T2 ON T1.category_id  =  T2.category_id GROUP BY T1.category_id;
+SELECT T2.name ,  T1.category_id ,  count(*) FROM film_category AS T1 JOIN category AS T2 ON T1.category_id  =  T2.category_id GROUP BY T1.category_id;
+SELECT T1.title ,  T1.film_id FROM film AS T1 JOIN inventory AS T2 ON T1.film_id  =  T2.film_id GROUP BY T1.film_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.title ,  T1.film_id FROM film AS T1 JOIN inventory AS T2 ON T1.film_id  =  T2.film_id GROUP BY T1.film_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.title ,  T2.inventory_id FROM film AS T1 JOIN inventory AS T2 ON T1.film_id  =  T2.film_id JOIN rental AS T3 ON T2.inventory_id  =  T3.inventory_id GROUP BY T2.inventory_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.title ,  T2.inventory_id FROM film AS T1 JOIN inventory AS T2 ON T1.film_id  =  T2.film_id JOIN rental AS T3 ON T2.inventory_id  =  T3.inventory_id GROUP BY T2.inventory_id ORDER BY count(*) DESC LIMIT 1;
+SELECT count(DISTINCT language_id) FROM film;
+SELECT count(DISTINCT language_id) FROM film;
+SELECT title FROM film WHERE rating  =  'R';
+SELECT title FROM film WHERE rating  =  'R';
+SELECT T2.address FROM store AS T1 JOIN address AS T2 ON T1.address_id  =  T2.address_id WHERE store_id  =  1;
+SELECT T2.address FROM store AS T1 JOIN address AS T2 ON T1.address_id  =  T2.address_id WHERE store_id  =  1;
+SELECT T1.first_name ,  T1.last_name ,  T1.staff_id FROM staff AS T1 JOIN payment AS T2 ON T1.staff_id  =  T2.staff_id GROUP BY T1.staff_id ORDER BY count(*) ASC LIMIT 1;
+SELECT T1.first_name ,  T1.last_name ,  T1.staff_id FROM staff AS T1 JOIN payment AS T2 ON T1.staff_id  =  T2.staff_id GROUP BY T1.staff_id ORDER BY count(*) ASC LIMIT 1;
+SELECT T2.name FROM film AS T1 JOIN LANGUAGE AS T2 ON T1.language_id  =  T2.language_id WHERE T1.title  =  'AIRPORT POLLOCK';
+SELECT T2.name FROM film AS T1 JOIN LANGUAGE AS T2 ON T1.language_id  =  T2.language_id WHERE T1.title  =  'AIRPORT POLLOCK';
+SELECT count(*) FROM store;
+SELECT count(*) FROM store;
+SELECT count(DISTINCT rating) FROM film;
+SELECT count(DISTINCT rating) FROM film;
+SELECT title FROM film WHERE special_features LIKE '%Deleted Scenes%';
+SELECT title FROM film WHERE special_features LIKE '%Deleted Scenes%';
+SELECT count(*) FROM inventory WHERE store_id  =  1;
+SELECT count(*) FROM inventory WHERE store_id  =  1;
+SELECT payment_date FROM payment ORDER BY payment_date ASC LIMIT 1;
+SELECT payment_date FROM payment ORDER BY payment_date ASC LIMIT 1;
+SELECT T2.address ,  T1.email FROM customer AS T1 JOIN address AS T2 ON T2.address_id  =  T1.address_id WHERE T1.first_name  =  'LINDA';
+SELECT T2.address ,  T1.email FROM customer AS T1 JOIN address AS T2 ON T2.address_id  =  T1.address_id WHERE T1.first_name  =  'LINDA';
+SELECT title FROM film WHERE LENGTH  >  100 OR rating  =  'PG' EXCEPT SELECT title FROM film WHERE replacement_cost  >  200;
+SELECT title FROM film WHERE LENGTH  >  100 OR rating  =  'PG' EXCEPT SELECT title FROM film WHERE replacement_cost  >  200;
+SELECT T1.first_name ,  T1.last_name FROM customer AS T1 JOIN rental AS T2 ON T1.customer_id  =  T2.customer_id ORDER BY T2.rental_date ASC LIMIT 1;
+SELECT T1.first_name ,  T1.last_name FROM customer AS T1 JOIN rental AS T2 ON T1.customer_id  =  T2.customer_id ORDER BY T2.rental_date ASC LIMIT 1;
+SELECT DISTINCT T1.first_name ,  T1.last_name FROM staff AS T1 JOIN rental AS T2 ON T1.staff_id  =  T2.staff_id JOIN customer AS T3 ON T2.customer_id  =  T3.customer_id WHERE T3.first_name  =  'APRIL' AND T3.last_name  =  'BURNS';
+SELECT DISTINCT T1.first_name ,  T1.last_name FROM staff AS T1 JOIN rental AS T2 ON T1.staff_id  =  T2.staff_id JOIN customer AS T3 ON T2.customer_id  =  T3.customer_id WHERE T3.first_name  =  'APRIL' AND T3.last_name  =  'BURNS';
+SELECT store_id FROM customer GROUP BY store_id ORDER BY count(*) DESC LIMIT 1;
+SELECT store_id FROM customer GROUP BY store_id ORDER BY count(*) DESC LIMIT 1;
+SELECT amount FROM payment ORDER BY amount DESC LIMIT 1;
+SELECT amount FROM payment ORDER BY amount DESC LIMIT 1;
+SELECT T2.address FROM staff AS T1 JOIN address AS T2 ON T1.address_id  =  T2.address_id WHERE T1.first_name  =  'Elsa';
+SELECT T2.address FROM staff AS T1 JOIN address AS T2 ON T1.address_id  =  T2.address_id WHERE T1.first_name  =  'Elsa';
+SELECT first_name FROM customer WHERE customer_id NOT IN( SELECT customer_id FROM rental WHERE rental_date  >  '2005-08-23 02:06:01' );
+SELECT first_name FROM customer WHERE customer_id NOT IN( SELECT customer_id FROM rental WHERE rental_date  >  '2005-08-23 02:06:01' );
+SELECT count(*) FROM bank;
+SELECT count(*) FROM bank;
+SELECT sum(no_of_customers) FROM bank;
+SELECT sum(no_of_customers) FROM bank;
+SELECT sum(no_of_customers) FROM bank WHERE city  =  'New York City';
+SELECT sum(no_of_customers) FROM bank WHERE city  =  'New York City';
+SELECT avg(no_of_customers) FROM bank WHERE state  =  'Utah';
+SELECT avg(no_of_customers) FROM bank WHERE state  =  'Utah';
+SELECT avg(no_of_customers) FROM bank;
+SELECT avg(no_of_customers) FROM bank;
+SELECT city ,  state FROM bank WHERE bname  =  'morningside';
+SELECT city ,  state FROM bank WHERE bname  =  'morningside';
+SELECT bname FROM bank WHERE state  =  'New York';
+SELECT bname FROM bank WHERE state  =  'New York';
+SELECT cust_name FROM customer ORDER BY acc_bal;
+SELECT cust_name FROM customer ORDER BY acc_bal;
+SELECT T1.cust_name FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id  =  T2.cust_id GROUP BY T1.cust_name ORDER BY sum(T2.amount);
+SELECT T1.cust_name FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id  =  T2.cust_id GROUP BY T1.cust_name ORDER BY sum(T2.amount);
+SELECT state ,  acc_type ,  credit_score FROM customer WHERE no_of_loans  =  0;
+SELECT state ,  acc_type ,  credit_score FROM customer WHERE no_of_loans  =  0;
+SELECT count(DISTINCT city) FROM bank;
+SELECT count(DISTINCT city) FROM bank;
+SELECT count(DISTINCT state) FROM bank;
+SELECT count(DISTINCT state) FROM bank;
+SELECT count(DISTINCT acc_type) FROM customer;
+SELECT count(DISTINCT acc_type) FROM customer;
+SELECT cust_name ,  acc_bal FROM customer WHERE cust_name LIKE '%a%';
+SELECT cust_name ,  acc_bal FROM customer WHERE cust_name LIKE '%a%';
+SELECT sum(acc_bal) FROM customer WHERE state  =  'Utah' OR state  =  'Texas';
+SELECT sum(acc_bal) FROM customer WHERE state  =  'Utah' OR state  =  'Texas';
+SELECT cust_name FROM customer WHERE acc_type  =  'saving' INTERSECT SELECT cust_name FROM customer WHERE acc_type  =  'checking';
+SELECT cust_name FROM customer WHERE acc_type  =  'saving' INTERSECT SELECT cust_name FROM customer WHERE acc_type  =  'checking';
+SELECT cust_name FROM customer EXCEPT SELECT cust_name FROM customer WHERE acc_type  =  'saving';
+SELECT cust_name FROM customer EXCEPT SELECT cust_name FROM customer WHERE acc_type  =  'saving';
+SELECT cust_name FROM customer EXCEPT SELECT T1.cust_name FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id  =  T2.cust_id WHERE T2.loan_type  =  'Mortgages';
+SELECT cust_name FROM customer EXCEPT SELECT T1.cust_name FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id  =  T2.cust_id WHERE T2.loan_type  =  'Mortgages';
+SELECT T1.cust_name FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id  =  T2.cust_id WHERE loan_type  =  'Mortgages' INTERSECT SELECT T1.cust_name FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id  =  T2.cust_id WHERE loan_type  =  'Auto';
+SELECT T1.cust_name FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id  =  T2.cust_id WHERE loan_type  =  'Mortgages' INTERSECT SELECT T1.cust_name FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id  =  T2.cust_id WHERE loan_type  =  'Auto';
+SELECT cust_name FROM customer WHERE credit_score  <  (SELECT avg(credit_score) FROM customer);
+SELECT cust_name FROM customer WHERE credit_score  <  (SELECT avg(credit_score) FROM customer);
+SELECT bname FROM bank ORDER BY no_of_customers DESC LIMIT 1;
+SELECT bname FROM bank ORDER BY no_of_customers DESC LIMIT 1;
+SELECT cust_name FROM customer ORDER BY credit_score LIMIT 1;
+SELECT cust_name FROM customer ORDER BY credit_score LIMIT 1;
+SELECT cust_name ,  acc_type ,  acc_bal FROM customer ORDER BY credit_score DESC LIMIT 1;
+SELECT cust_name ,  acc_type ,  acc_bal FROM customer ORDER BY credit_score DESC LIMIT 1;
+SELECT T1.cust_name FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id  =  T2.cust_id GROUP BY T1.cust_name ORDER BY sum(T2.amount) DESC LIMIT 1;
+SELECT T1.cust_name FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id  =  T2.cust_id GROUP BY T1.cust_name ORDER BY sum(T2.amount) DESC LIMIT 1;
+SELECT state FROM bank GROUP BY state ORDER BY sum(no_of_customers) DESC LIMIT 1;
+SELECT state FROM bank GROUP BY state ORDER BY sum(no_of_customers) DESC LIMIT 1;
+SELECT avg(acc_bal) ,  acc_type FROM customer WHERE credit_score  <  50 GROUP BY acc_type;
+SELECT avg(acc_bal) ,  acc_type FROM customer WHERE credit_score  <  50 GROUP BY acc_type;
+SELECT sum(acc_bal) ,  state FROM customer WHERE credit_score  >  100 GROUP BY state;
+SELECT sum(acc_bal) ,  state FROM customer WHERE credit_score  >  100 GROUP BY state;
+SELECT sum(amount) ,  T1.bname FROM bank AS T1 JOIN loan AS T2 ON T1.branch_id  =  T2.branch_id GROUP BY T1.bname;
+SELECT sum(amount) ,  T1.bname FROM bank AS T1 JOIN loan AS T2 ON T1.branch_id  =  T2.branch_id GROUP BY T1.bname;
+SELECT T1.cust_name FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id  =  T2.cust_id GROUP BY T1.cust_name HAVING count(*)  >  1;
+SELECT T1.cust_name FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id  =  T2.cust_id GROUP BY T1.cust_name HAVING count(*)  >  1;
+SELECT T1.cust_name ,  T1.acc_type FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id  =  T2.cust_id GROUP BY T1.cust_name HAVING sum(T2.amount)  >  5000;
+SELECT T1.cust_name ,  T1.acc_type FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id  =  T2.cust_id GROUP BY T1.cust_name HAVING sum(T2.amount)  >  5000;
+SELECT T1.bname FROM bank AS T1 JOIN loan AS T2 ON T1.branch_id  =  T2.branch_id GROUP BY T1.bname ORDER BY sum(T2.amount) DESC LIMIT 1;
+SELECT T1.bname FROM bank AS T1 JOIN loan AS T2 ON T1.branch_id  =  T2.branch_id GROUP BY T1.bname ORDER BY sum(T2.amount) DESC LIMIT 1;
+SELECT T2.bname FROM loan AS T1 JOIN bank AS T2 ON T1.branch_id  =  T2.branch_id JOIN customer AS T3 ON T1.cust_id  =  T3.cust_id WHERE T3.credit_score  <  100 GROUP BY T2.bname ORDER BY sum(T1.amount) DESC LIMIT 1;
+SELECT T2.bname FROM loan AS T1 JOIN bank AS T2 ON T1.branch_id  =  T2.branch_id JOIN customer AS T3 ON T1.cust_id  =  T3.cust_id WHERE T3.credit_score  <  100 GROUP BY T2.bname ORDER BY sum(T1.amount) DESC LIMIT 1;
+SELECT DISTINCT T1.bname FROM bank AS T1 JOIN loan AS T2 ON T1.branch_id  =  T2.branch_id;
+SELECT DISTINCT T1.bname FROM bank AS T1 JOIN loan AS T2 ON T1.branch_id  =  T2.branch_id;
+SELECT DISTINCT T1.cust_name ,  T1.credit_score FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id  =  T2.cust_id;
+SELECT DISTINCT T1.cust_name ,  T1.credit_score FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id  =  T2.cust_id;
+SELECT T1.cust_name FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id  =  T2.cust_id WHERE amount  >  3000;
+SELECT T1.cust_name FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id  =  T2.cust_id WHERE amount  >  3000;
+SELECT T1.bname ,  T1.city FROM bank AS T1 JOIN loan AS T2 ON T1.branch_id  =  T2.branch_id WHERE T2.loan_type  =  'Business';
+SELECT T1.bname ,  T1.city FROM bank AS T1 JOIN loan AS T2 ON T1.branch_id  =  T2.branch_id WHERE T2.loan_type  =  'Business';
+SELECT T2.bname FROM loan AS T1 JOIN bank AS T2 ON T1.branch_id  =  T2.branch_id JOIN customer AS T3 ON T1.cust_id  =  T3.cust_id WHERE T3.credit_score  <  100;
+SELECT T2.bname FROM loan AS T1 JOIN bank AS T2 ON T1.branch_id  =  T2.branch_id JOIN customer AS T3 ON T1.cust_id  =  T3.cust_id WHERE T3.credit_score  <  100;
+SELECT sum(T2.amount) FROM bank AS T1 JOIN loan AS T2 ON T1.branch_id  =  T2.branch_id WHERE T1.state  =  'New York';
+SELECT sum(T2.amount) FROM bank AS T1 JOIN loan AS T2 ON T1.branch_id  =  T2.branch_id WHERE T1.state  =  'New York';
+SELECT avg(credit_score) FROM customer WHERE cust_id IN (SELECT cust_id FROM loan);
+SELECT avg(credit_score) FROM customer WHERE cust_id IN (SELECT cust_id FROM loan);
+SELECT avg(credit_score) FROM customer WHERE cust_id NOT IN (SELECT cust_id FROM loan);
+SELECT avg(credit_score) FROM customer WHERE cust_id NOT IN (SELECT cust_id FROM loan);
+SELECT count(*) FROM ASSESSMENT_NOTES;
+SELECT date_of_notes FROM Assessment_Notes;
+SELECT count(*) FROM ADDRESSES WHERE zip_postcode  =  '197';
+SELECT count(DISTINCT incident_type_code) FROM Behavior_Incident;
+SELECT DISTINCT detention_type_code FROM Detention;
+SELECT date_incident_start ,  date_incident_end FROM Behavior_Incident WHERE incident_type_code  =  'NOISE';
+SELECT detention_summary FROM Detention;
+SELECT cell_mobile_number ,  email_address FROM STUDENTS;
+SELECT email_address FROM Students WHERE first_name  =  'Emma' AND last_name  =  'Rohan';
+SELECT count(DISTINCT student_id) FROM Students_in_Detention;
+SELECT gender FROM TEACHERS WHERE last_name  =  'Medhurst';
+SELECT incident_type_description FROM Ref_Incident_Type WHERE incident_type_code  =  'VIOLENCE';
+SELECT max(monthly_rental) ,  min(monthly_rental) FROM Student_Addresses;
+SELECT first_name FROM Teachers WHERE email_address LIKE '%man%';
+SELECT * FROM Assessment_Notes ORDER BY date_of_notes ASC;
+SELECT city FROM Addresses ORDER BY city;
+SELECT first_name ,  last_name FROM Teachers ORDER BY last_name;
+SELECT * FROM Student_Addresses ORDER BY monthly_rental DESC;
+SELECT T1.student_id ,  T2.first_name FROM Assessment_Notes AS T1 JOIN Students AS T2 ON T1.student_id  =  T2.student_id GROUP BY T1.student_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.teacher_id ,  T2.first_name FROM Assessment_Notes AS T1 JOIN Teachers AS T2 ON T1.teacher_id  =  T2.teacher_id GROUP BY T1.teacher_id ORDER BY count(*) DESC LIMIT 3;
+SELECT T1.student_id ,  T2.last_name FROM Behavior_Incident AS T1 JOIN Students AS T2 ON T1.student_id  =  T2.student_id GROUP BY T1.student_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.teacher_id ,  T2.last_name FROM Detention AS T1 JOIN Teachers AS T2 ON T1.teacher_id  =  T2.teacher_id WHERE T1.detention_type_code  =  'AFTER' GROUP BY T1.teacher_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.student_id ,  T2.first_name FROM Student_Addresses AS T1 JOIN Students AS T2 ON T1.student_id  =  T2.student_id GROUP BY T1.student_id ORDER BY AVG(monthly_rental) DESC LIMIT 1;
+SELECT T2.address_id ,  T1.city FROM Addresses AS T1 JOIN Student_Addresses AS T2 ON T1.address_id  =  T2.address_id GROUP BY T2.address_id ORDER BY AVG(monthly_rental) DESC LIMIT 1;
+SELECT T1.incident_type_code ,  T2.incident_type_description FROM Behavior_Incident AS T1 JOIN Ref_Incident_Type AS T2 ON T1.incident_type_code  =  T2.incident_type_code GROUP BY T1.incident_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.detention_type_code ,  T2.detention_type_description FROM Detention AS T1 JOIN Ref_Detention_Type AS T2 ON T1.detention_type_code  =  T2.detention_type_code GROUP BY T1.detention_type_code ORDER BY count(*) ASC LIMIT 1;
+SELECT T1.date_of_notes FROM Assessment_Notes AS T1 JOIN Students AS T2 ON T1.student_id  =  T2.student_id WHERE T2.first_name  =  'Fanny';
+SELECT T1.text_of_notes FROM Assessment_Notes AS T1 JOIN Teachers AS T2 ON T1.teacher_id  =  T2.teacher_id WHERE T2.last_name  =  'Schuster';
+SELECT T1.date_incident_start ,  date_incident_end FROM Behavior_Incident AS T1 JOIN Students AS T2 ON T1.student_id  =  T2.student_id WHERE T2.last_name  =  'Fahey';
+SELECT T1.datetime_detention_start ,  datetime_detention_end FROM Detention AS T1 JOIN Teachers AS T2 ON T1.teacher_id  =  T2.teacher_id WHERE T2.last_name  =  'Schultz';
+SELECT T2.address_id ,  T1.zip_postcode FROM Addresses AS T1 JOIN Student_Addresses AS T2 ON T1.address_id  =  T2.address_id ORDER BY monthly_rental DESC LIMIT 1;
+SELECT T2.cell_mobile_number FROM Student_Addresses AS T1 JOIN Students AS T2 ON T1.student_id  =  T2.student_id ORDER BY T1.monthly_rental ASC LIMIT 1;
+SELECT T2.monthly_rental FROM Addresses AS T1 JOIN Student_Addresses AS T2 ON T1.address_id  =  T2.address_id WHERE T1.state_province_county  =  'Texas';
+SELECT T2.first_name ,  T2.last_name FROM Addresses AS T1 JOIN Students AS T2 ON T1.address_id  =  T2.address_id WHERE T1.state_province_county  =  'Wisconsin';
+SELECT T1.line_1 ,  avg(T2.monthly_rental) FROM Addresses AS T1 JOIN Student_Addresses AS T2 ON T1.address_id  =  T2.address_id GROUP BY T2.address_id;
+SELECT T1.zip_postcode FROM Addresses AS T1 JOIN Teachers AS T2 ON T1.address_id  =  T2.address_id WHERE T2.first_name  =  'Lyla';
+SELECT T2.email_address FROM Addresses AS T1 JOIN Teachers AS T2 ON T1.address_id  =  T2.address_id WHERE T1.zip_postcode  =  '918';
+SELECT count(*) FROM STUDENTS WHERE student_id NOT IN ( SELECT student_id FROM Behavior_Incident );
+SELECT last_name FROM Teachers EXCEPT SELECT T1.last_name FROM Teachers AS T1 JOIN Detention AS T2 ON T1.teacher_id  =  T2.teacher_id;
+SELECT T1.line_1 FROM Addresses AS T1 JOIN Students AS T2 ON T1.address_id  =  T2.address_id INTERSECT SELECT T1.line_1 FROM Addresses AS T1 JOIN Teachers AS T2 ON T1.address_id  =  T2.address_id;
+SELECT T1.asset_id ,  T1.asset_details FROM Assets AS T1 JOIN Asset_Parts AS T2 ON T1.asset_id  =  T2.asset_id GROUP BY T1.asset_id HAVING count(*)  =  2 INTERSECT SELECT T1.asset_id ,  T1.asset_details FROM Assets AS T1 JOIN Fault_Log AS T2 ON T1.asset_id  =  T2.asset_id GROUP BY T1.asset_id HAVING count(*)  <  2;
+SELECT count(*) ,  T1.maintenance_contract_id FROM Maintenance_Contracts AS T1 JOIN Assets AS T2 ON T1.maintenance_contract_id  =  T2.maintenance_contract_id GROUP BY T1.maintenance_contract_id;
+SELECT count(*) ,  T1.company_id FROM Third_Party_Companies AS T1 JOIN Assets AS T2 ON T1.company_id  =  T2.supplier_company_id GROUP BY T1.company_id;
+SELECT T1.company_id ,  T1.company_name FROM Third_Party_Companies AS T1 JOIN Maintenance_Engineers AS T2 ON T1.company_id  =  T2.company_id GROUP BY T1.company_id HAVING count(*)  >=  2 UNION SELECT T3.company_id ,  T3.company_name FROM Third_Party_Companies AS T3 JOIN Maintenance_Contracts AS T4 ON T3.company_id  =  T4.maintenance_contract_company_id GROUP BY T3.company_id HAVING count(*)  >=  2;
+SELECT T1.staff_name ,  T1.staff_id FROM Staff AS T1 JOIN Fault_Log AS T2 ON T1.staff_id  =  T2.recorded_by_staff_id EXCEPT SELECT T3.staff_name ,  T3.staff_id FROM Staff AS T3 JOIN Engineer_Visits AS T4 ON T3.staff_id  =  T4.contact_staff_id;
+SELECT T1.engineer_id ,  T1.first_name ,  T1.last_name FROM Maintenance_Engineers AS T1 JOIN Engineer_Visits AS T2 GROUP BY T1.engineer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.part_name ,  T1.part_id FROM Parts AS T1 JOIN Part_Faults AS T2 ON T1.part_id  =  T2.part_id GROUP BY T1.part_id HAVING count(*)  >  2;
+SELECT T1.first_name ,  T1.last_name ,  T1.other_details ,  T3.skill_description FROM Maintenance_Engineers AS T1 JOIN Engineer_Skills AS T2 ON T1.engineer_id  =  T2.engineer_id JOIN Skills AS T3 ON T2.skill_id  =  T3.skill_id;
+SELECT T1.fault_short_name ,  T3.skill_description FROM Part_Faults AS T1 JOIN Skills_Required_To_Fix AS T2 ON T1.part_fault_id  =  T2.part_fault_id JOIN Skills AS T3 ON T2.skill_id  =  T3.skill_id;
+SELECT T1.part_name ,  count(*) FROM Parts AS T1 JOIN Asset_Parts AS T2 ON T1.part_id  =  T2.part_id GROUP BY T1.part_name;
+SELECT T1.fault_description ,  T2.fault_status FROM Fault_Log AS T1 JOIN Fault_Log_Parts AS T2 ON T1.fault_log_entry_id  =  T2.fault_log_entry_id;
+SELECT count(*) ,  T1.fault_log_entry_id FROM Fault_Log AS T1 JOIN Engineer_Visits AS T2 ON T1.fault_log_entry_id  =  T2.fault_log_entry_id GROUP BY T1.fault_log_entry_id ORDER BY count(*) DESC LIMIT 1;
+SELECT DISTINCT last_name FROM Maintenance_Engineers;
+SELECT DISTINCT fault_status FROM Fault_Log_Parts;
+SELECT first_name ,  last_name FROM Maintenance_Engineers WHERE engineer_id NOT IN (SELECT engineer_id FROM Engineer_Visits);
+SELECT asset_id ,  asset_details ,  asset_make ,  asset_model FROM Assets;
+SELECT asset_acquired_date FROM Assets ORDER BY asset_acquired_date ASC LIMIT 1;
+SELECT T1.part_id ,  T1.part_name FROM Parts AS T1 JOIN Part_Faults AS T2 ON T1.part_id  =  T2.part_id JOIN Skills_Required_To_Fix AS T3 ON T2.part_fault_id  =  T3.part_fault_id GROUP BY T1.part_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.part_name FROM Parts AS T1 JOIN Part_Faults AS T2 ON T1.part_id  =  T2.part_id GROUP BY T1.part_name ORDER BY count(*) ASC LIMIT 1;
+SELECT T1.engineer_id ,  T1.first_name ,  T1.last_name FROM Maintenance_Engineers AS T1 JOIN Engineer_Visits AS T2 ON T1.engineer_id  =  T2.engineer_id GROUP BY T1.engineer_id ORDER BY count(*) ASC LIMIT 1;
+SELECT T1.staff_name ,  T3.first_name ,  T3.last_name FROM Staff AS T1 JOIN Engineer_Visits AS T2 ON T1.staff_id  =  T2.contact_staff_id JOIN Maintenance_Engineers AS T3 ON T2.engineer_id  =  T3.engineer_id;
+SELECT T1.fault_log_entry_id ,  T1.fault_description ,  T1.fault_log_entry_datetime FROM Fault_Log AS T1 JOIN Fault_Log_Parts AS T2 ON T1.fault_log_entry_id  =  T2.fault_log_entry_id GROUP BY T1.fault_log_entry_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.skill_id ,  T1.skill_description FROM Skills AS T1 JOIN Skills_Required_To_Fix AS T2 ON T1.skill_id  =  T2.skill_id GROUP BY T1.skill_id ORDER BY count(*) DESC LIMIT 1;
+SELECT DISTINCT asset_model FROM Assets;
+SELECT asset_make ,  asset_model ,  asset_details FROM Assets ORDER BY asset_disposed_date ASC;
+SELECT part_id ,  chargeable_amount FROM Parts ORDER BY chargeable_amount ASC LIMIT 1;
+SELECT T1.company_name FROM Third_Party_Companies AS T1 JOIN Maintenance_Contracts AS T2 ON T1.company_id  =  T2.maintenance_contract_company_id ORDER BY T2.contract_start_date ASC LIMIT 1;
+SELECT T1.company_name FROM Third_Party_Companies AS T1 JOIN Maintenance_Contracts AS T2 ON T1.company_id  =  T2.maintenance_contract_company_id JOIN Ref_Company_Types AS T3 ON T1.company_type_code  =  T3.company_type_code ORDER BY T2.contract_end_date DESC LIMIT 1;
+SELECT gender FROM staff GROUP BY gender ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.staff_name ,  count(*) FROM Staff AS T1 JOIN Engineer_Visits AS T2 ON T1.staff_id  =  T2.contact_staff_id GROUP BY T1.staff_name;
+SELECT asset_model FROM Assets WHERE asset_id NOT IN (SELECT asset_id FROM Fault_Log);
+SELECT local_authority ,  services FROM station;
+SELECT train_number ,  name FROM train ORDER BY TIME;
+SELECT TIME ,  train_number FROM train WHERE destination  =  'Chennai' ORDER BY TIME;
+SELECT count(*) FROM train WHERE name LIKE '%Express%';
+SELECT train_number ,  TIME FROM train WHERE origin  =  'Chennai' AND destination  =  'Guruvayur';
+SELECT origin ,  count(*) FROM train GROUP BY origin;
+SELECT t1.name FROM train AS t1 JOIN route AS t2 ON t1.id  =  t2.train_id GROUP BY t2.train_id ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) ,  t1.network_name ,  t1.services FROM station AS t1 JOIN route AS t2 ON t1.id  =  t2.station_id GROUP BY t2.station_id;
+SELECT avg(high_temperature) ,  day_of_week FROM weekly_weather GROUP BY day_of_week;
+SELECT max(t1.low_temperature) ,  avg(t1.precipitation) FROM weekly_weather AS t1 JOIN station AS t2 ON t1.station_id  =  t2.id WHERE t2.network_name  =  'Amersham';
+SELECT t3.name ,  t3.time FROM station AS t1 JOIN route AS t2 ON t1.id  =  t2.station_id JOIN train AS t3 ON t2.train_id  =  t3.id WHERE t1.local_authority  =  'Chiltern';
+SELECT count(DISTINCT services) FROM station;
+SELECT t2.id ,  t2.local_authority FROM weekly_weather AS t1 JOIN station AS t2 ON t1.station_id  =  t2.id GROUP BY t1.station_id ORDER BY avg(high_temperature) DESC LIMIT 1;
+SELECT t2.id ,  t2.local_authority FROM weekly_weather AS t1 JOIN station AS t2 ON t1.station_id  =  t2.id GROUP BY t1.station_id HAVING max(t1.precipitation)  >  50;
+SELECT min(low_temperature) ,  max(wind_speed_mph) FROM weekly_weather;
+SELECT origin FROM train GROUP BY origin HAVING count(*)  >  1;
+SELECT count(*) FROM professor AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code WHERE DEPT_NAME  =  'Accounting';
+SELECT count(*) FROM professor AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code WHERE DEPT_NAME  =  'Accounting';
+SELECT count(DISTINCT PROF_NUM) FROM CLASS WHERE CRS_CODE  =  'ACCT-211';
+SELECT count(DISTINCT PROF_NUM) FROM CLASS WHERE CRS_CODE  =  'ACCT-211';
+SELECT T3.EMP_FNAME ,  T3.EMP_LNAME FROM professor AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code JOIN employee AS T3 ON T1.EMP_NUM  =  T3.EMP_NUM WHERE DEPT_NAME  =  'Biology';
+SELECT T3.EMP_FNAME ,  T3.EMP_LNAME FROM professor AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code JOIN employee AS T3 ON T1.EMP_NUM  =  T3.EMP_NUM WHERE DEPT_NAME  =  'Biology';
+SELECT DISTINCT T1.EMP_FNAME ,  T1.EMP_DOB FROM employee AS T1 JOIN CLASS AS T2 ON T1.EMP_NUM  =  T2.PROF_NUM WHERE CRS_CODE  =  'ACCT-211';
+SELECT DISTINCT T1.EMP_FNAME ,  T1.EMP_DOB FROM employee AS T1 JOIN CLASS AS T2 ON T1.EMP_NUM  =  T2.PROF_NUM WHERE CRS_CODE  =  'ACCT-211';
+SELECT count(*) FROM employee AS T1 JOIN CLASS AS T2 ON T1.EMP_NUM  =  T2.PROF_NUM WHERE T1.EMP_LNAME  =  'Graztevski';
+SELECT count(*) FROM employee AS T1 JOIN CLASS AS T2 ON T1.EMP_NUM  =  T2.PROF_NUM WHERE T1.EMP_LNAME  =  'Graztevski';
+SELECT school_code FROM department WHERE dept_name  =  'Accounting';
+SELECT school_code FROM department WHERE dept_name  =  'Accounting';
+SELECT crs_credit ,  crs_description FROM course WHERE crs_code  =  'CIS-220';
+SELECT crs_credit ,  crs_description FROM course WHERE crs_code  =  'CIS-220';
+SELECT dept_address FROM department WHERE dept_name  =  'History';
+SELECT dept_address FROM department WHERE dept_name  =  'History';
+SELECT count(DISTINCT dept_address) FROM department WHERE school_code  =  'BUS';
+SELECT count(DISTINCT dept_address) FROM department WHERE school_code  =  'BUS';
+SELECT count(DISTINCT dept_address) ,  school_code FROM department GROUP BY school_code;
+SELECT count(DISTINCT dept_address) ,  school_code FROM department GROUP BY school_code;
+SELECT crs_credit ,  crs_description FROM course WHERE crs_code  =  'QM-261';
+SELECT crs_credit ,  crs_description FROM course WHERE crs_code  =  'QM-261';
+SELECT count(DISTINCT dept_name) ,  school_code FROM department GROUP BY school_code;
+SELECT count(DISTINCT dept_name) ,  school_code FROM department GROUP BY school_code;
+SELECT count(DISTINCT dept_name) ,  school_code FROM department GROUP BY school_code HAVING count(DISTINCT dept_name)  <  5;
+SELECT count(DISTINCT dept_name) ,  school_code FROM department GROUP BY school_code HAVING count(DISTINCT dept_name)  <  5;
+SELECT count(*) ,  crs_code FROM CLASS GROUP BY crs_code;
+SELECT count(*) ,  crs_code FROM CLASS GROUP BY crs_code;
+SELECT sum(crs_credit) ,  dept_code FROM course GROUP BY dept_code;
+SELECT sum(crs_credit) ,  dept_code FROM course GROUP BY dept_code;
+SELECT count(*) ,  class_room FROM CLASS GROUP BY class_room HAVING count(*)  >=  2;
+SELECT count(*) ,  class_room FROM CLASS GROUP BY class_room HAVING count(*)  >=  2;
+SELECT count(*) ,  dept_code FROM CLASS AS T1 JOIN course AS T2 ON T1.crs_code  =  T2.crs_code GROUP BY dept_code;
+SELECT count(*) ,  dept_code FROM CLASS AS T1 JOIN course AS T2 ON T1.crs_code  =  T2.crs_code GROUP BY dept_code;
+SELECT count(*) ,  T3.school_code FROM CLASS AS T1 JOIN course AS T2 ON T1.crs_code  =  T2.crs_code JOIN department AS T3 ON T2.dept_code  =  T3.dept_code GROUP BY T3.school_code;
+SELECT count(*) ,  T3.school_code FROM CLASS AS T1 JOIN course AS T2 ON T1.crs_code  =  T2.crs_code JOIN department AS T3 ON T2.dept_code  =  T3.dept_code GROUP BY T3.school_code;
+SELECT count(*) ,  T1.school_code FROM department AS T1 JOIN professor AS T2 ON T1.dept_code  =  T2.dept_code GROUP BY T1.school_code;
+SELECT count(*) ,  T1.school_code FROM department AS T1 JOIN professor AS T2 ON T1.dept_code  =  T2.dept_code GROUP BY T1.school_code;
+SELECT emp_jobcode ,  count(*) FROM employee GROUP BY emp_jobcode ORDER BY count(*) DESC LIMIT 1;
+SELECT emp_jobcode ,  count(*) FROM employee GROUP BY emp_jobcode ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.school_code FROM department AS T1 JOIN professor AS T2 ON T1.dept_code  =  T2.dept_code GROUP BY T1.school_code ORDER BY count(*) LIMIT 1;
+SELECT T1.school_code FROM department AS T1 JOIN professor AS T2 ON T1.dept_code  =  T2.dept_code GROUP BY T1.school_code ORDER BY count(*) LIMIT 1;
+SELECT count(*) ,  dept_code FROM professor WHERE prof_high_degree  =  'Ph.D.' GROUP BY dept_code;
+SELECT count(*) ,  dept_code FROM professor WHERE prof_high_degree  =  'Ph.D.' GROUP BY dept_code;
+SELECT count(*) ,  dept_code FROM student GROUP BY dept_code;
+SELECT count(*) ,  dept_code FROM student GROUP BY dept_code;
+SELECT sum(stu_hrs) ,  dept_code FROM student GROUP BY dept_code;
+SELECT sum(stu_hrs) ,  dept_code FROM student GROUP BY dept_code;
+SELECT max(stu_gpa) ,  avg(stu_gpa) ,  min(stu_gpa) ,  dept_code FROM student GROUP BY dept_code;
+SELECT max(stu_gpa) ,  avg(stu_gpa) ,  min(stu_gpa) ,  dept_code FROM student GROUP BY dept_code;
+SELECT T2.dept_name ,  avg(T1.stu_gpa) FROM student AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code GROUP BY T1.dept_code ORDER BY avg(T1.stu_gpa) DESC LIMIT 1;
+SELECT T2.dept_name ,  avg(T1.stu_gpa) FROM student AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code GROUP BY T1.dept_code ORDER BY avg(T1.stu_gpa) DESC LIMIT 1;
+SELECT count(DISTINCT school_code) FROM department;
+SELECT count(DISTINCT school_code) FROM department;
+SELECT count(DISTINCT class_code) FROM CLASS;
+SELECT count(DISTINCT class_code) FROM CLASS;
+SELECT count(DISTINCT crs_code) FROM CLASS;
+SELECT count(DISTINCT crs_code) FROM CLASS;
+SELECT count(DISTINCT dept_name) FROM department;
+SELECT count(DISTINCT dept_name) FROM department;
+SELECT count(*) FROM department AS T1 JOIN course AS T2 ON T1.dept_code  =  T2.dept_code WHERE dept_name  =  'Computer Info. Systems';
+SELECT count(*) FROM department AS T1 JOIN course AS T2 ON T1.dept_code  =  T2.dept_code WHERE dept_name  =  'Computer Info. Systems';
+SELECT count(DISTINCT class_section) FROM CLASS WHERE crs_code  =  'ACCT-211';
+SELECT count(DISTINCT class_section) FROM CLASS WHERE crs_code  =  'ACCT-211';
+SELECT sum(T1.crs_credit) ,  T1.dept_code FROM course AS T1 JOIN CLASS AS T2 ON T1.crs_code  =  T2.crs_code GROUP BY T1.dept_code;
+SELECT sum(T1.crs_credit) ,  T1.dept_code FROM course AS T1 JOIN CLASS AS T2 ON T1.crs_code  =  T2.crs_code GROUP BY T1.dept_code;
+SELECT T3.dept_name FROM course AS T1 JOIN CLASS AS T2 ON T1.crs_code  =  T2.crs_code JOIN department AS T3 ON T1.dept_code  =  T3.dept_code GROUP BY T1.dept_code ORDER BY sum(T1.crs_credit) DESC LIMIT 1;
+SELECT T3.dept_name FROM course AS T1 JOIN CLASS AS T2 ON T1.crs_code  =  T2.crs_code JOIN department AS T3 ON T1.dept_code  =  T3.dept_code GROUP BY T1.dept_code ORDER BY sum(T1.crs_credit) DESC LIMIT 1;
+SELECT count(*) FROM CLASS AS T1 JOIN enroll AS T2 ON T1.class_code  =  T2.class_code WHERE T1.crs_code  =  'ACCT-211';
+SELECT count(*) FROM CLASS AS T1 JOIN enroll AS T2 ON T1.class_code  =  T2.class_code WHERE T1.crs_code  =  'ACCT-211';
+SELECT T3.stu_fname FROM CLASS AS T1 JOIN enroll AS T2 ON T1.class_code  =  T2.class_code JOIN student AS T3 ON T2.stu_num  =  T3.stu_num WHERE T1.crs_code  =  'ACCT-211';
+SELECT T3.stu_fname FROM CLASS AS T1 JOIN enroll AS T2 ON T1.class_code  =  T2.class_code JOIN student AS T3 ON T2.stu_num  =  T3.stu_num WHERE T1.crs_code  =  'ACCT-211';
+SELECT T3.stu_fname FROM CLASS AS T1 JOIN enroll AS T2 ON T1.class_code  =  T2.class_code JOIN student AS T3 ON T2.stu_num  =  T3.stu_num WHERE T1.crs_code  =  'ACCT-211' AND T2.enroll_grade  =  'C';
+SELECT T3.stu_fname FROM CLASS AS T1 JOIN enroll AS T2 ON T1.class_code  =  T2.class_code JOIN student AS T3 ON T2.stu_num  =  T3.stu_num WHERE T1.crs_code  =  'ACCT-211' AND T2.enroll_grade  =  'C';
+SELECT count(*) FROM employee;
+SELECT count(*) FROM employee;
+SELECT count(*) FROM professor WHERE prof_high_degree  =  'Ph.D.';
+SELECT count(*) FROM professor WHERE prof_high_degree  =  'Ph.D.';
+SELECT count(*) FROM CLASS AS T1 JOIN enroll AS T2 ON T1.class_code  =  T2.class_code JOIN course AS T3 ON T1.crs_code  =  T3.crs_code JOIN department AS T4 ON T3.dept_code  =  T4.dept_code WHERE T4.dept_name  =  'Accounting';
+SELECT count(*) FROM CLASS AS T1 JOIN enroll AS T2 ON T1.class_code  =  T2.class_code JOIN course AS T3 ON T1.crs_code  =  T3.crs_code JOIN department AS T4 ON T3.dept_code  =  T4.dept_code WHERE T4.dept_name  =  'Accounting';
+SELECT T4.dept_name FROM CLASS AS T1 JOIN enroll AS T2 ON T1.class_code  =  T2.class_code JOIN course AS T3 ON T1.crs_code  =  T3.crs_code JOIN department AS T4 ON T3.dept_code  =  T4.dept_code GROUP BY T3.dept_code ORDER BY count(*) DESC LIMIT 1;
+SELECT T4.dept_name FROM CLASS AS T1 JOIN enroll AS T2 ON T1.class_code  =  T2.class_code JOIN course AS T3 ON T1.crs_code  =  T3.crs_code JOIN department AS T4 ON T3.dept_code  =  T4.dept_code GROUP BY T3.dept_code ORDER BY count(*) DESC LIMIT 1;
+SELECT dept_name FROM department ORDER BY dept_name;
+SELECT dept_name FROM department ORDER BY dept_name;
+SELECT class_code FROM CLASS WHERE class_room  =  'KLR209';
+SELECT class_code FROM CLASS WHERE class_room  =  'KLR209';
+SELECT emp_fname FROM employee WHERE emp_jobcode  =  'PROF' ORDER BY emp_dob;
+SELECT emp_fname FROM employee WHERE emp_jobcode  =  'PROF' ORDER BY emp_dob;
+SELECT T2.emp_fname ,  T1.prof_office FROM professor AS T1 JOIN employee AS T2 ON T1.emp_num  =  T2.emp_num ORDER BY T2.emp_fname;
+SELECT T2.emp_fname ,  T1.prof_office FROM professor AS T1 JOIN employee AS T2 ON T1.emp_num  =  T2.emp_num ORDER BY T2.emp_fname;
+SELECT emp_fname ,  emp_lname FROM employee ORDER BY emp_dob LIMIT 1;
+SELECT emp_fname ,  emp_lname FROM employee ORDER BY emp_dob LIMIT 1;
+SELECT stu_fname ,  stu_lname ,  stu_gpa FROM student WHERE stu_gpa  >  3 ORDER BY stu_dob DESC LIMIT 1;
+SELECT stu_fname ,  stu_lname ,  stu_gpa FROM student WHERE stu_gpa  >  3 ORDER BY stu_dob DESC LIMIT 1;
+SELECT DISTINCT stu_fname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num  =  T2.stu_num WHERE enroll_grade  =  'C';
+SELECT DISTINCT stu_fname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num  =  T2.stu_num WHERE enroll_grade  =  'C';
+SELECT T2.dept_name FROM professor AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code GROUP BY T1.dept_code ORDER BY count(*) LIMIT 1;
+SELECT T2.dept_name FROM professor AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code GROUP BY T1.dept_code ORDER BY count(*) LIMIT 1;
+SELECT T2.dept_name ,  T1.dept_code FROM professor AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code WHERE T1.prof_high_degree  =  'Ph.D.' GROUP BY T1.dept_code ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.dept_name ,  T1.dept_code FROM professor AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code WHERE T1.prof_high_degree  =  'Ph.D.' GROUP BY T1.dept_code ORDER BY count(*) DESC LIMIT 1;
+SELECT emp_fname FROM employee WHERE emp_jobcode  =  'PROF' EXCEPT SELECT T1.emp_fname FROM employee AS T1 JOIN CLASS AS T2 ON T1.emp_num  =  T2.prof_num;
+SELECT emp_fname FROM employee WHERE emp_jobcode  =  'PROF' EXCEPT SELECT T1.emp_fname FROM employee AS T1 JOIN CLASS AS T2 ON T1.emp_num  =  T2.prof_num;
+SELECT T1.emp_fname FROM employee AS T1 JOIN professor AS T2 ON T1.emp_num  =  T2.emp_num JOIN department AS T3 ON T2.dept_code  =  T3.dept_code WHERE T3.dept_name  =  'History' EXCEPT SELECT T4.emp_fname FROM employee AS T4 JOIN CLASS AS T5 ON T4.emp_num  =  T5.prof_num;
+SELECT T1.emp_fname FROM employee AS T1 JOIN professor AS T2 ON T1.emp_num  =  T2.emp_num JOIN department AS T3 ON T2.dept_code  =  T3.dept_code WHERE T3.dept_name  =  'History' EXCEPT SELECT T4.emp_fname FROM employee AS T4 JOIN CLASS AS T5 ON T4.emp_num  =  T5.prof_num;
+SELECT T1.emp_lname ,  T2.prof_office FROM employee AS T1 JOIN professor AS T2 ON T1.emp_num  =  T2.emp_num JOIN department AS T3 ON T2.dept_code  =  T3.dept_code WHERE T3.dept_name  =  'History';
+SELECT T1.emp_lname ,  T2.prof_office FROM employee AS T1 JOIN professor AS T2 ON T1.emp_num  =  T2.emp_num JOIN department AS T3 ON T2.dept_code  =  T3.dept_code WHERE T3.dept_name  =  'History';
+SELECT T3.dept_name  ,  T2.prof_office FROM employee AS T1 JOIN professor AS T2 ON T1.emp_num  =  T2.emp_num JOIN department AS T3 ON T2.dept_code  =  T3.dept_code WHERE T1.emp_lname  =  'Heffington';
+SELECT T3.dept_name  ,  T2.prof_office FROM employee AS T1 JOIN professor AS T2 ON T1.emp_num  =  T2.emp_num JOIN department AS T3 ON T2.dept_code  =  T3.dept_code WHERE T1.emp_lname  =  'Heffington';
+SELECT T1.emp_lname ,  T1.emp_hiredate FROM employee AS T1 JOIN professor AS T2 ON T1.emp_num  =  T2.emp_num WHERE T2.prof_office  =  'DRE 102';
+SELECT T1.emp_lname ,  T1.emp_hiredate FROM employee AS T1 JOIN professor AS T2 ON T1.emp_num  =  T2.emp_num WHERE T2.prof_office  =  'DRE 102';
+SELECT T1.crs_code FROM CLASS AS T1 JOIN enroll AS T2 ON T1.class_code  =  T2.class_code JOIN student AS T3 ON T3.stu_num  =  T2.stu_num WHERE T3.stu_lname  =  'Smithson';
+SELECT T1.crs_code FROM CLASS AS T1 JOIN enroll AS T2 ON T1.class_code  =  T2.class_code JOIN student AS T3 ON T3.stu_num  =  T2.stu_num WHERE T3.stu_lname  =  'Smithson';
+SELECT T4.crs_description ,  T4.crs_credit FROM CLASS AS T1 JOIN enroll AS T2 ON T1.class_code  =  T2.class_code JOIN student AS T3 ON T3.stu_num  =  T2.stu_num JOIN course AS T4 ON T4.crs_code  =  T1.crs_code WHERE T3.stu_lname  =  'Smithson';
+SELECT T4.crs_description ,  T4.crs_credit FROM CLASS AS T1 JOIN enroll AS T2 ON T1.class_code  =  T2.class_code JOIN student AS T3 ON T3.stu_num  =  T2.stu_num JOIN course AS T4 ON T4.crs_code  =  T1.crs_code WHERE T3.stu_lname  =  'Smithson';
+SELECT count(*) FROM professor WHERE prof_high_degree  =  'Ph.D.' OR prof_high_degree  =  'MA';
+SELECT count(*) FROM professor WHERE prof_high_degree  =  'Ph.D.' OR prof_high_degree  =  'MA';
+SELECT count(*) FROM professor AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code WHERE T2.dept_name  =  'Accounting' OR T2.dept_name  =  'Biology';
+SELECT count(*) FROM professor AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code WHERE T2.dept_name  =  'Accounting' OR T2.dept_name  =  'Biology';
+SELECT T1.emp_fname FROM employee AS T1 JOIN CLASS AS T2 ON T1.emp_num  =  T2.prof_num WHERE crs_code  =  'CIS-220' INTERSECT SELECT T1.emp_fname FROM employee AS T1 JOIN CLASS AS T2 ON T1.emp_num  =  T2.prof_num WHERE crs_code  =  'QM-261';
+SELECT T1.emp_fname FROM employee AS T1 JOIN CLASS AS T2 ON T1.emp_num  =  T2.prof_num WHERE crs_code  =  'CIS-220' INTERSECT SELECT T1.emp_fname FROM employee AS T1 JOIN CLASS AS T2 ON T1.emp_num  =  T2.prof_num WHERE crs_code  =  'QM-261';
+SELECT T1.stu_fname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num  =  T2.stu_num JOIN CLASS AS T3 ON T2.class_code  =  T3.class_code JOIN course AS T4 ON T3.crs_code  =  T4.crs_code JOIN department AS T5 ON T5.dept_code  =  T4.dept_code WHERE T5.dept_name  =  'Accounting' INTERSECT SELECT T1.stu_fname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num  =  T2.stu_num JOIN CLASS AS T3 ON T2.class_code  =  T3.class_code JOIN course AS T4 ON T3.crs_code  =  T4.crs_code JOIN department AS T5 ON T5.dept_code  =  T4.dept_code WHERE T5.dept_name  =  'Computer Info. Systems';
+SELECT T1.stu_fname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num  =  T2.stu_num JOIN CLASS AS T3 ON T2.class_code  =  T3.class_code JOIN course AS T4 ON T3.crs_code  =  T4.crs_code JOIN department AS T5 ON T5.dept_code  =  T4.dept_code WHERE T5.dept_name  =  'Accounting' INTERSECT SELECT T1.stu_fname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num  =  T2.stu_num JOIN CLASS AS T3 ON T2.class_code  =  T3.class_code JOIN course AS T4 ON T3.crs_code  =  T4.crs_code JOIN department AS T5 ON T5.dept_code  =  T4.dept_code WHERE T5.dept_name  =  'Computer Info. Systems';
+SELECT avg(T2.stu_gpa) FROM enroll AS T1 JOIN student AS T2 ON T1.stu_num  =  T2.stu_num JOIN CLASS AS T3 ON T1.class_code  =  T3.class_code WHERE T3.crs_code  =  'ACCT-211';
+SELECT avg(T2.stu_gpa) FROM enroll AS T1 JOIN student AS T2 ON T1.stu_num  =  T2.stu_num JOIN CLASS AS T3 ON T1.class_code  =  T3.class_code WHERE T3.crs_code  =  'ACCT-211';
+SELECT stu_gpa ,  stu_phone ,  stu_fname FROM student ORDER BY stu_gpa DESC LIMIT 5;
+SELECT stu_gpa ,  stu_phone ,  stu_fname FROM student ORDER BY stu_gpa DESC LIMIT 5;
+SELECT T2.dept_name FROM student AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code ORDER BY stu_gpa LIMIT 1;
+SELECT T2.dept_name FROM student AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code ORDER BY stu_gpa LIMIT 1;
+SELECT stu_fname ,  stu_gpa FROM student WHERE stu_gpa  <  (SELECT avg(stu_gpa) FROM student);
+SELECT stu_fname ,  stu_gpa FROM student WHERE stu_gpa  <  (SELECT avg(stu_gpa) FROM student);
+SELECT T2.dept_name ,  T2.dept_address FROM student AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code GROUP BY T1.dept_code ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.dept_name ,  T2.dept_address FROM student AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code GROUP BY T1.dept_code ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.dept_name ,  T2.dept_address ,  count(*) FROM student AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code GROUP BY T1.dept_code ORDER BY count(*) DESC LIMIT 3;
+SELECT T2.dept_name ,  T2.dept_address ,  count(*) FROM student AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code GROUP BY T1.dept_code ORDER BY count(*) DESC LIMIT 3;
+SELECT T1.emp_fname ,  T2.prof_office FROM employee AS T1 JOIN professor AS T2 ON T1.emp_num  =  T2.emp_num JOIN department AS T3 ON T3.dept_code  =  T2.dept_code WHERE T3.dept_name  =  'History' AND T2.prof_high_degree  =  'Ph.D.';
+SELECT T1.emp_fname ,  T2.prof_office FROM employee AS T1 JOIN professor AS T2 ON T1.emp_num  =  T2.emp_num JOIN department AS T3 ON T3.dept_code  =  T2.dept_code WHERE T3.dept_name  =  'History' AND T2.prof_high_degree  =  'Ph.D.';
+SELECT T2.emp_fname ,  T1.crs_code FROM CLASS AS T1 JOIN employee AS T2 ON T1.prof_num  =  T2.emp_num;
+SELECT T2.emp_fname ,  T1.crs_code FROM CLASS AS T1 JOIN employee AS T2 ON T1.prof_num  =  T2.emp_num;
+SELECT T2.emp_fname ,  T3.crs_description FROM CLASS AS T1 JOIN employee AS T2 ON T1.prof_num  =  T2.emp_num JOIN course AS T3 ON T1.crs_code  =  T3.crs_code;
+SELECT T2.emp_fname ,  T3.crs_description FROM CLASS AS T1 JOIN employee AS T2 ON T1.prof_num  =  T2.emp_num JOIN course AS T3 ON T1.crs_code  =  T3.crs_code;
+SELECT T2.emp_fname ,  T4.prof_office ,  T3.crs_description FROM CLASS AS T1 JOIN employee AS T2 ON T1.prof_num  =  T2.emp_num JOIN course AS T3 ON T1.crs_code  =  T3.crs_code JOIN professor AS T4 ON T2.emp_num  =  T4.emp_num;
+SELECT T2.emp_fname ,  T4.prof_office ,  T3.crs_description FROM CLASS AS T1 JOIN employee AS T2 ON T1.prof_num  =  T2.emp_num JOIN course AS T3 ON T1.crs_code  =  T3.crs_code JOIN professor AS T4 ON T2.emp_num  =  T4.emp_num;
+SELECT T2.emp_fname ,  T4.prof_office ,  T3.crs_description ,  T5.dept_name FROM CLASS AS T1 JOIN employee AS T2 ON T1.prof_num  =  T2.emp_num JOIN course AS T3 ON T1.crs_code  =  T3.crs_code JOIN professor AS T4 ON T2.emp_num  =  T4.emp_num JOIN department AS T5 ON T4.dept_code  =  T5.dept_code;
+SELECT T2.emp_fname ,  T4.prof_office ,  T3.crs_description ,  T5.dept_name FROM CLASS AS T1 JOIN employee AS T2 ON T1.prof_num  =  T2.emp_num JOIN course AS T3 ON T1.crs_code  =  T3.crs_code JOIN professor AS T4 ON T2.emp_num  =  T4.emp_num JOIN department AS T5 ON T4.dept_code  =  T5.dept_code;
+SELECT T1.stu_fname ,  T1.stu_lname ,  T4.crs_description FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num  =  T2.stu_num JOIN CLASS AS T3 ON T2.class_code  =  T3.class_code JOIN course AS T4 ON T3.crs_code  =  T4.crs_code;
+SELECT T1.stu_fname ,  T1.stu_lname ,  T4.crs_description FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num  =  T2.stu_num JOIN CLASS AS T3 ON T2.class_code  =  T3.class_code JOIN course AS T4 ON T3.crs_code  =  T4.crs_code;
+SELECT T1.stu_fname ,  T1.stu_lname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num  =  T2.stu_num WHERE T2.enroll_grade  =  'C' OR T2.enroll_grade  =  'A';
+SELECT T1.stu_fname ,  T1.stu_lname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num  =  T2.stu_num WHERE T2.enroll_grade  =  'C' OR T2.enroll_grade  =  'A';
+SELECT T2.emp_fname ,  T1.class_room FROM CLASS AS T1 JOIN employee AS T2 ON T1.prof_num  =  T2.emp_num JOIN professor AS T3 ON T2.emp_num  =  T3.emp_num JOIN department AS T4 ON T4.dept_code  =  T3.dept_code WHERE T4.dept_name  =  'Accounting';
+SELECT T2.emp_fname ,  T1.class_room FROM CLASS AS T1 JOIN employee AS T2 ON T1.prof_num  =  T2.emp_num JOIN professor AS T3 ON T2.emp_num  =  T3.emp_num JOIN department AS T4 ON T4.dept_code  =  T3.dept_code WHERE T4.dept_name  =  'Accounting';
+SELECT DISTINCT T2.emp_fname ,  T3.prof_high_degree FROM CLASS AS T1 JOIN employee AS T2 ON T1.prof_num  =  T2.emp_num JOIN professor AS T3 ON T2.emp_num  =  T3.emp_num JOIN department AS T4 ON T4.dept_code  =  T3.dept_code WHERE T4.dept_name  =  'Computer Info. Systems';
+SELECT DISTINCT T2.emp_fname ,  T3.prof_high_degree FROM CLASS AS T1 JOIN employee AS T2 ON T1.prof_num  =  T2.emp_num JOIN professor AS T3 ON T2.emp_num  =  T3.emp_num JOIN department AS T4 ON T4.dept_code  =  T3.dept_code WHERE T4.dept_name  =  'Computer Info. Systems';
+SELECT T1.stu_lname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num  =  T2.stu_num WHERE T2.enroll_grade  =  'A' AND T2.class_code  =  10018;
+SELECT T1.stu_lname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num  =  T2.stu_num WHERE T2.enroll_grade  =  'A' AND T2.class_code  =  10018;
+SELECT T2.emp_fname ,  T1.prof_office FROM professor AS T1 JOIN employee AS T2 ON T1.emp_num  =  T2.emp_num JOIN department AS T3 ON T1.dept_code  =  T3.dept_code WHERE T3.dept_name  =  'History' AND T1.prof_high_degree != 'Ph.D.';
+SELECT T2.emp_fname ,  T1.prof_office FROM professor AS T1 JOIN employee AS T2 ON T1.emp_num  =  T2.emp_num JOIN department AS T3 ON T1.dept_code  =  T3.dept_code WHERE T3.dept_name  =  'History' AND T1.prof_high_degree != 'Ph.D.';
+SELECT T2.emp_fname FROM CLASS AS T1 JOIN employee AS T2 ON T1.prof_num  =  T2.emp_num GROUP BY T1.prof_num HAVING count(*)  >  1;
+SELECT T2.emp_fname FROM CLASS AS T1 JOIN employee AS T2 ON T1.prof_num  =  T2.emp_num GROUP BY T1.prof_num HAVING count(*)  >  1;
+SELECT T1.stu_fname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num  =  T2.stu_num GROUP BY T2.stu_num HAVING count(*)  =  1;
+SELECT T1.stu_fname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num  =  T2.stu_num GROUP BY T2.stu_num HAVING count(*)  =  1;
+SELECT T2.dept_name FROM course AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code WHERE T1.crs_description LIKE '%Statistics%';
+SELECT T2.dept_name FROM course AS T1 JOIN department AS T2 ON T1.dept_code  =  T2.dept_code WHERE T1.crs_description LIKE '%Statistics%';
+SELECT T1.stu_fname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num  =  T2.stu_num JOIN CLASS AS T3 ON T2.class_code  =  T3.class_code WHERE T3.crs_code  =  'ACCT-211' AND T1.stu_lname LIKE 'S%';
+SELECT T1.stu_fname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num  =  T2.stu_num JOIN CLASS AS T3 ON T2.class_code  =  T3.class_code WHERE T3.crs_code  =  'ACCT-211' AND T1.stu_lname LIKE 'S%';
+SELECT count(*) FROM club;
+SELECT count(*) FROM club;
+SELECT DISTINCT Region FROM club ORDER BY Region ASC;
+SELECT DISTINCT Region FROM club ORDER BY Region ASC;
+SELECT avg(Gold) FROM club_rank;
+SELECT avg(Gold) FROM club_rank;
+SELECT Competition_type ,  Country FROM competition;
+SELECT Competition_type ,  Country FROM competition;
+SELECT DISTINCT YEAR FROM competition WHERE Competition_type != 'Tournament';
+SELECT DISTINCT YEAR FROM competition WHERE Competition_type != 'Tournament';
+SELECT max(Silver) ,  min(Silver) FROM club_rank;
+SELECT max(Silver) ,  min(Silver) FROM club_rank;
+SELECT count(*) FROM club_rank WHERE Total  <  10;
+SELECT count(*) FROM club_rank WHERE Total  <  10;
+SELECT name FROM club ORDER BY Start_year ASC;
+SELECT name FROM club ORDER BY Start_year ASC;
+SELECT name FROM club ORDER BY name DESC;
+SELECT name FROM club ORDER BY name DESC;
+SELECT T1.name ,  T2.Player_id FROM club AS T1 JOIN player AS T2 ON T1.Club_ID  =  T2.Club_ID;
+SELECT T1.name ,  T2.Player_id FROM club AS T1 JOIN player AS T2 ON T1.Club_ID  =  T2.Club_ID;
+SELECT T1.name FROM club AS T1 JOIN player AS T2 ON T1.Club_ID  =  T2.Club_ID WHERE T2.Position  =  'Right Wing';
+SELECT T1.name FROM club AS T1 JOIN player AS T2 ON T1.Club_ID  =  T2.Club_ID WHERE T2.Position  =  'Right Wing';
+SELECT avg(T2.Points) FROM club AS T1 JOIN player AS T2 ON T1.Club_ID  =  T2.Club_ID WHERE T1.name  =  'AIB';
+SELECT avg(T2.Points) FROM club AS T1 JOIN player AS T2 ON T1.Club_ID  =  T2.Club_ID WHERE T1.name  =  'AIB';
+SELECT POSITION ,  avg(Points) FROM player GROUP BY POSITION;
+SELECT POSITION ,  avg(Points) FROM player GROUP BY POSITION;
+SELECT POSITION FROM player GROUP BY name HAVING avg(Points)  >=  20;
+SELECT POSITION FROM player GROUP BY name HAVING avg(Points)  >=  20;
+SELECT Competition_type ,  COUNT(*) FROM competition GROUP BY Competition_type;
+SELECT Competition_type ,  COUNT(*) FROM competition GROUP BY Competition_type;
+SELECT Competition_type FROM competition GROUP BY Competition_type ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Competition_type FROM competition GROUP BY Competition_type ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Competition_type FROM competition GROUP BY Competition_type HAVING COUNT(*)  <=  5;
+SELECT Competition_type FROM competition GROUP BY Competition_type HAVING COUNT(*)  <=  5;
+SELECT name FROM CLub WHERE Club_ID NOT IN (SELECT Club_ID FROM player);
+SELECT name FROM CLub WHERE Club_ID NOT IN (SELECT Club_ID FROM player);
+SELECT POSITION FROM player WHERE Points  >  20 INTERSECT SELECT POSITION FROM player WHERE Points  <  10;
+SELECT POSITION FROM player WHERE Points  >  20 INTERSECT SELECT POSITION FROM player WHERE Points  <  10;
+SELECT sum(Points) FROM player;
+SELECT sum(Points) FROM player;
+SELECT count(DISTINCT POSITION) FROM player;
+SELECT count(DISTINCT POSITION) FROM player;
+SELECT name FROM player WHERE points  >  (SELECT avg(points) FROM player);
+SELECT name FROM player WHERE points  >  (SELECT avg(points) FROM player);
+SELECT count(*) ,  POSITION FROM player WHERE points  <  30 GROUP BY POSITION;
+SELECT count(*) ,  POSITION FROM player WHERE points  <  30 GROUP BY POSITION;
+SELECT country FROM competition WHERE competition_type  =  'Tournament' GROUP BY country ORDER BY count(*) DESC LIMIT 1;
+SELECT country FROM competition WHERE competition_type  =  'Tournament' GROUP BY country ORDER BY count(*) DESC LIMIT 1;
+SELECT country FROM competition WHERE competition_type  =  'Friendly' INTERSECT SELECT country FROM competition WHERE competition_type  =  'Tournament';
+SELECT country FROM competition WHERE competition_type  =  'Friendly' INTERSECT SELECT country FROM competition WHERE competition_type  =  'Tournament';
+SELECT country FROM competition EXCEPT SELECT country FROM competition WHERE competition_type  =  'Friendly';
+SELECT country FROM competition EXCEPT SELECT country FROM competition WHERE competition_type  =  'Friendly';
+SELECT sum(num_of_component) FROM furniture;
+SELECT name ,  furniture_id FROM furniture ORDER BY market_rate DESC LIMIT 1;
+SELECT sum(market_rate) FROM furniture ORDER BY market_rate DESC LIMIT 2;
+SELECT Num_of_Component ,  name FROM furniture WHERE Num_of_Component  >  10;
+SELECT name ,  Num_of_Component FROM furniture ORDER BY market_rate LIMIT 1;
+SELECT t1.name FROM furniture AS t1 JOIN furniture_manufacte AS t2 ON t1.Furniture_ID  =  t2.Furniture_ID WHERE t2.Price_in_Dollar  <  (SELECT max(Price_in_Dollar) FROM furniture_manufacte);
+SELECT open_year ,  name FROM manufacturer ORDER BY num_of_shops DESC LIMIT 1;
+SELECT avg(Num_of_Factories) FROM manufacturer WHERE num_of_shops  >  20;
+SELECT name ,  manufacturer_id FROM manufacturer ORDER BY open_year;
+SELECT name ,  open_year FROM manufacturer WHERE num_of_shops  >  10 OR Num_of_Factories  <  10;
+SELECT max(num_of_shops) ,  avg(Num_of_Factories) FROM manufacturer WHERE open_year  <  1990;
+SELECT t1.manufacturer_id ,  t1.num_of_shops FROM manufacturer AS t1 JOIN furniture_manufacte AS t2 ON t1.manufacturer_id  =  t2.manufacturer_id ORDER BY t2.Price_in_Dollar DESC LIMIT 1;
+SELECT count(*) ,  t1.name FROM manufacturer AS t1 JOIN furniture_manufacte AS t2 ON t1.manufacturer_id  =  t2.manufacturer_id GROUP BY t1.manufacturer_id;
+SELECT t1.name ,  t2.price_in_dollar FROM furniture AS t1 JOIN furniture_manufacte AS t2 ON t1.Furniture_ID  =  t2.Furniture_ID;
+SELECT Market_Rate ,  name FROM furniture WHERE Furniture_ID NOT IN (SELECT Furniture_ID FROM furniture_manufacte);
+SELECT t3.name FROM furniture AS t1 JOIN furniture_manufacte AS t2 ON t1.Furniture_ID  =  t2.Furniture_ID JOIN manufacturer AS t3 ON t2.manufacturer_id  =  t3.manufacturer_id WHERE t1.num_of_component  <  6 INTERSECT SELECT t3.name FROM furniture AS t1 JOIN furniture_manufacte AS t2 ON t1.Furniture_ID  =  t2.Furniture_ID JOIN manufacturer AS t3 ON t2.manufacturer_id  =  t3.manufacturer_id WHERE t1.num_of_component  >  10;
+SELECT T1.first_name ,  T2.department_name FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id;
+SELECT T1.first_name ,  T2.department_name FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id;
+SELECT first_name ,  last_name ,  salary FROM employees WHERE salary  <  6000;
+SELECT first_name ,  last_name ,  salary FROM employees WHERE salary  <  6000;
+SELECT first_name ,  department_id FROM employees WHERE last_name  =  'McEwen';
+SELECT first_name ,  department_id FROM employees WHERE last_name  =  'McEwen';
+SELECT * FROM employees WHERE department_id  =  'null';
+SELECT * FROM employees WHERE department_id  =  'null';
+SELECT * FROM  departments WHERE department_name  =  'Marketing';
+SELECT * FROM  departments WHERE department_name  =  'Marketing';
+SELECT hire_date FROM employees WHERE first_name NOT LIKE '%M%';
+SELECT hire_date FROM employees WHERE first_name NOT LIKE '%M%';
+SELECT first_name ,  last_name ,  hire_date ,  salary ,  department_id FROM employees WHERE first_name NOT LIKE '%M%';
+SELECT first_name ,  last_name ,  hire_date ,  salary ,  department_id FROM employees WHERE first_name NOT LIKE '%M%';
+SELECT first_name ,  last_name ,  hire_date ,  salary ,  department_id FROM employees WHERE first_name NOT LIKE '%M%' ORDER BY department_id;
+SELECT first_name ,  last_name ,  hire_date ,  salary ,  department_id FROM employees WHERE first_name NOT LIKE '%M%' ORDER BY department_id;
+SELECT phone_number FROM employees WHERE salary BETWEEN 8000 AND 12000;
+SELECT phone_number FROM employees WHERE salary BETWEEN 8000 AND 12000;
+SELECT * FROM employees WHERE salary BETWEEN 8000 AND 12000 AND commission_pct != 'null' OR  department_id != 40;
+SELECT * FROM employees WHERE salary BETWEEN 8000 AND 12000 AND commission_pct != 'null' OR  department_id != 40;
+SELECT first_name ,  last_name ,  salary FROM  employees WHERE commission_pct  =  'null';
+SELECT first_name ,  last_name ,  salary FROM  employees WHERE commission_pct  =  'null';
+SELECT first_name ,  last_name ,  salary FROM employees WHERE first_name LIKE '%m';
+SELECT first_name ,  last_name ,  salary FROM employees WHERE first_name LIKE '%m';
+SELECT job_id ,  hire_date FROM employees WHERE hire_date BETWEEN '2007-11-05' AND '2009-07-05';
+SELECT job_id ,  hire_date FROM employees WHERE hire_date BETWEEN '2007-11-05' AND '2009-07-05';
+SELECT first_name ,  last_name FROM employees WHERE department_id  =  70 OR department_id  =  90;
+SELECT first_name ,  last_name FROM employees WHERE department_id  =  70 OR department_id  =  90;
+SELECT salary ,  manager_id FROM employees WHERE manager_id != 'null';
+SELECT salary ,  manager_id FROM employees WHERE manager_id != 'null';
+SELECT * FROM employees WHERE hire_date  <  '2002-06-21';
+SELECT * FROM employees WHERE hire_date  <  '2002-06-21';
+SELECT * FROM employees WHERE first_name LIKE '%D%' OR first_name LIKE '%S%' ORDER BY salary DESC;
+SELECT * FROM employees WHERE first_name LIKE '%D%' OR first_name LIKE '%S%' ORDER BY salary DESC;
+SELECT * FROM employees WHERE hire_date  >  '1987-09-07';
+SELECT * FROM employees WHERE hire_date  >  '1987-09-07';
+SELECT job_title FROM jobs WHERE min_salary  >  9000;
+SELECT job_title FROM jobs WHERE min_salary  >  9000;
+SELECT job_title ,  max_salary - min_salary FROM jobs WHERE max_salary BETWEEN 12000 AND 18000;
+SELECT job_title ,  max_salary - min_salary FROM jobs WHERE max_salary BETWEEN 12000 AND 18000;
+SELECT email FROM employees WHERE commission_pct  =  'null' AND salary BETWEEN 7000 AND 12000 AND department_id = 50;
+SELECT email FROM employees WHERE commission_pct  =  'null' AND salary BETWEEN 7000 AND 12000 AND department_id = 50;
+SELECT employee_id ,  MAX(end_date) FROM job_history GROUP BY employee_id;
+SELECT employee_id ,  MAX(end_date) FROM job_history GROUP BY employee_id;
+SELECT department_id FROM employees GROUP BY department_id HAVING COUNT(commission_pct) > 10;
+SELECT department_id FROM employees GROUP BY department_id HAVING COUNT(commission_pct) > 10;
+SELECT DISTINCT department_id FROM employees GROUP BY department_id ,  manager_id HAVING COUNT(employee_id)  >= 4;
+SELECT DISTINCT department_id FROM employees GROUP BY department_id ,  manager_id HAVING COUNT(employee_id)  >= 4;
+SELECT department_id ,  AVG(salary) FROM employees WHERE commission_pct != 'null' GROUP BY department_id;
+SELECT department_id ,  AVG(salary) FROM employees WHERE commission_pct != 'null' GROUP BY department_id;
+SELECT country_id ,  COUNT(*) FROM locations GROUP BY country_id;
+SELECT country_id ,  COUNT(*) FROM locations GROUP BY country_id;
+SELECT job_id FROM job_history WHERE end_date - start_date  > 300 GROUP BY job_id HAVING COUNT(*) >= 2;
+SELECT job_id FROM job_history WHERE end_date - start_date  > 300 GROUP BY job_id HAVING COUNT(*) >= 2;
+SELECT employee_id FROM job_history GROUP BY employee_id HAVING COUNT(*)  >= 2;
+SELECT employee_id FROM job_history GROUP BY employee_id HAVING COUNT(*)  >= 2;
+SELECT T1.employee_id ,  T4.country_name FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id JOIN locations AS T3 ON T2.location_id  =  T3.location_id JOIN countries AS T4 ON T3.country_id  =  T4.country_id;
+SELECT T1.employee_id ,  T4.country_name FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id JOIN locations AS T3 ON T2.location_id  =  T3.location_id JOIN countries AS T4 ON T3.country_id  =  T4.country_id;
+SELECT T2.department_name ,  COUNT(*) FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id GROUP BY T2.department_name;
+SELECT T2.department_name ,  COUNT(*) FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id GROUP BY T2.department_name;
+SELECT * FROM  job_history AS T1 JOIN employees AS T2 ON T1.employee_id  =  T2.employee_id WHERE T2.salary  >=  12000;
+SELECT * FROM  job_history AS T1 JOIN employees AS T2 ON T1.employee_id  =  T2.employee_id WHERE T2.salary  >=  12000;
+SELECT job_title ,  AVG(salary) FROM employees AS T1 JOIN jobs AS T2 ON T1.job_id  =  T2.job_id GROUP BY T2.job_title;
+SELECT job_title ,  AVG(salary) FROM employees AS T1 JOIN jobs AS T2 ON T1.job_id  =  T2.job_id GROUP BY T2.job_title;
+SELECT first_name ,  last_name FROM employees WHERE salary  >  (SELECT salary FROM employees WHERE employee_id = 163 );
+SELECT first_name ,  last_name FROM employees WHERE salary  >  (SELECT salary FROM employees WHERE employee_id = 163 );
+SELECT MIN(salary) ,  department_id FROM employees GROUP BY department_id;
+SELECT MIN(salary) ,  department_id FROM employees GROUP BY department_id;
+SELECT first_name ,  last_name ,  department_id FROM employees WHERE salary IN (SELECT MIN(salary) FROM employees GROUP BY department_id);
+SELECT first_name ,  last_name ,  department_id FROM employees WHERE salary IN (SELECT MIN(salary) FROM employees GROUP BY department_id);
+SELECT employee_id FROM employees WHERE salary  >  (SELECT AVG(salary) FROM employees);
+SELECT employee_id FROM employees WHERE salary  >  (SELECT AVG(salary) FROM employees);
+SELECT employee_id ,  salary FROM employees WHERE manager_id  =  (SELECT employee_id FROM employees WHERE first_name  =  'Payam' );
+SELECT employee_id ,  salary FROM employees WHERE manager_id  =  (SELECT employee_id FROM employees WHERE first_name  =  'Payam' );
+SELECT DISTINCT T2.department_name FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id;
+SELECT DISTINCT T2.department_name FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id;
+SELECT DISTINCT * FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id WHERE T1.employee_id  =  T2.manager_id;
+SELECT DISTINCT * FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id WHERE T1.employee_id  =  T2.manager_id;
+SELECT * FROM  departments WHERE department_name  =  'Marketing';
+SELECT * FROM  departments WHERE department_name  =  'Marketing';
+SELECT employee_id FROM job_history GROUP BY employee_id HAVING COUNT(*)  >= 2;
+SELECT employee_id FROM job_history GROUP BY employee_id HAVING COUNT(*)  >= 2;
+SELECT DISTINCT department_id FROM employees GROUP BY department_id ,  manager_id HAVING COUNT(employee_id)  >= 4;
+SELECT DISTINCT department_id FROM employees GROUP BY department_id ,  manager_id HAVING COUNT(employee_id)  >= 4;
+SELECT job_id FROM employees GROUP BY job_id HAVING AVG(salary) > 8000;
+SELECT job_id FROM employees GROUP BY job_id HAVING AVG(salary) > 8000;
+SELECT T1.employee_id ,  T2.job_title FROM employees AS T1 JOIN jobs AS T2 ON T1.job_id  =  T2.job_id WHERE T1.department_id = 80;
+SELECT T1.employee_id ,  T2.job_title FROM employees AS T1 JOIN jobs AS T2 ON T1.job_id  =  T2.job_id WHERE T1.department_id = 80;
+SELECT T1.first_name ,  T1.job_id FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id WHERE T2.department_name  =  'Finance';
+SELECT T1.first_name ,  T1.job_id FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id WHERE T2.department_name  =  'Finance';
+SELECT * FROM employees WHERE salary BETWEEN (SELECT MIN(salary) FROM employees) AND 2500;
+SELECT * FROM employees WHERE salary BETWEEN (SELECT MIN(salary) FROM employees) AND 2500;
+SELECT * FROM employees WHERE department_id NOT IN (SELECT department_id FROM departments WHERE manager_id BETWEEN 100 AND 200);
+SELECT * FROM employees WHERE department_id NOT IN (SELECT department_id FROM departments WHERE manager_id BETWEEN 100 AND 200);
+SELECT first_name ,  last_name ,  hire_date FROM employees WHERE department_id  =  (SELECT department_id FROM employees WHERE first_name  =  'Clara');
+SELECT first_name ,  last_name ,  hire_date FROM employees WHERE department_id  =  (SELECT department_id FROM employees WHERE first_name  =  'Clara');
+SELECT first_name ,  last_name ,  hire_date FROM employees WHERE department_id  =  ( SELECT department_id FROM employees WHERE first_name  =  'Clara') AND first_name != 'Clara';
+SELECT first_name ,  last_name ,  hire_date FROM employees WHERE department_id  =  ( SELECT department_id FROM employees WHERE first_name  =  'Clara') AND first_name != 'Clara';
+SELECT employee_id ,  first_name ,  last_name FROM employees WHERE department_id IN ( SELECT department_id FROM employees WHERE first_name LIKE '%T%' );
+SELECT employee_id ,  first_name ,  last_name FROM employees WHERE department_id IN ( SELECT department_id FROM employees WHERE first_name LIKE '%T%' );
+SELECT employee_id ,  first_name ,  last_name ,  salary FROM employees WHERE salary  >  ( SELECT AVG (salary) FROM employees ) AND department_id IN ( SELECT department_id FROM employees WHERE first_name LIKE '%J%');
+SELECT employee_id ,  first_name ,  last_name ,  salary FROM employees WHERE salary  >  ( SELECT AVG (salary) FROM employees ) AND department_id IN ( SELECT department_id FROM employees WHERE first_name LIKE '%J%');
+SELECT employee_id ,  job_id FROM employees WHERE salary  <  ( SELECT min(salary) FROM employees WHERE job_id  =  'MK_MAN' );
+SELECT employee_id ,  job_id FROM employees WHERE salary  <  ( SELECT min(salary) FROM employees WHERE job_id  =  'MK_MAN' );
+SELECT employee_id ,  first_name ,  last_name ,  job_id FROM employees WHERE salary  >  ( SELECT max(salary) FROM employees WHERE job_id  =  'PU_MAN' );
+SELECT employee_id ,  first_name ,  last_name ,  job_id FROM employees WHERE salary  >  ( SELECT max(salary) FROM employees WHERE job_id  =  'PU_MAN' );
+SELECT department_id ,  SUM(salary) FROM employees GROUP BY department_id HAVING count(*)  >=  2;
+SELECT department_id ,  SUM(salary) FROM employees GROUP BY department_id HAVING count(*)  >=  2;
+SELECT * FROM employees WHERE employee_id NOT IN (SELECT employee_id FROM job_history);
+SELECT * FROM employees WHERE employee_id NOT IN (SELECT employee_id FROM job_history);
+SELECT first_name ,   last_name ,   salary ,  department_id ,  MAX(salary) FROM employees GROUP BY department_id;
+SELECT first_name ,   last_name ,   salary ,  department_id ,  MAX(salary) FROM employees GROUP BY department_id;
+SELECT T1.first_name ,  T1.last_name ,  T2.department_name ,  T3.city ,  T3.state_province FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id JOIN locations AS T3 ON T2.location_id  =  T3.location_id;
+SELECT T1.first_name ,  T1.last_name ,  T2.department_name ,  T3.city ,  T3.state_province FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id JOIN locations AS T3 ON T2.location_id  =  T3.location_id;
+SELECT T1.first_name ,  T1.last_name ,  T3.city FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id JOIN locations AS T3 ON T2.location_id  =  T3.location_id WHERE T1.first_name LIKE '%z%';
+SELECT T1.first_name ,  T1.last_name ,  T3.city FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id JOIN locations AS T3 ON T2.location_id  =  T3.location_id WHERE T1.first_name LIKE '%z%';
+SELECT T1.department_name ,  T2.city ,  T2.state_province FROM departments AS T1 JOIN locations AS T2 ON T2.location_id  =  T1.location_id;
+SELECT T1.department_name ,  T2.city ,  T2.state_province FROM departments AS T1 JOIN locations AS T2 ON T2.location_id  =  T1.location_id;
+SELECT T1.first_name ,  T1.last_name , T1.employee_id ,  T4.country_name FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id JOIN locations AS T3 ON T2.location_id  =  T3.location_id JOIN countries AS T4 ON T3.country_id  =  T4.country_id;
+SELECT T1.first_name ,  T1.last_name , T1.employee_id ,  T4.country_name FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id JOIN locations AS T3 ON T2.location_id  =  T3.location_id JOIN countries AS T4 ON T3.country_id  =  T4.country_id;
+SELECT department_name ,  COUNT(*) FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id GROUP BY department_name;
+SELECT department_name ,  COUNT(*) FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id GROUP BY department_name;
+SELECT first_name ,  last_name ,  salary FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id JOIN  locations AS T3 ON T2.location_id  =  T3.location_id WHERE  T3.city  =  'London';
+SELECT first_name ,  last_name ,  salary FROM employees AS T1 JOIN departments AS T2 ON T1.department_id  =  T2.department_id JOIN  locations AS T3 ON T2.location_id  =  T3.location_id WHERE  T3.city  =  'London';
+SELECT song_name ,  releasedate FROM song ORDER BY releasedate DESC LIMIT 1;
+SELECT song_name ,  releasedate FROM song ORDER BY releasedate DESC LIMIT 1;
+SELECT f_id FROM files ORDER BY duration DESC LIMIT 1;
+SELECT f_id FROM files ORDER BY duration DESC LIMIT 1;
+SELECT song_name FROM song WHERE languages  =  'english';
+SELECT song_name FROM song WHERE languages  =  'english';
+SELECT f_id FROM files WHERE formats  =  'mp3';
+SELECT f_id FROM files WHERE formats  =  'mp3';
+SELECT DISTINCT T1.artist_name ,  T1.country FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T2.rating  >  9;
+SELECT DISTINCT T1.artist_name ,  T1.country FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T2.rating  >  9;
+SELECT DISTINCT T1.file_size ,  T1.formats FROM files AS T1 JOIN song AS T2 ON T1.f_id  =  T2.f_id WHERE T2.resolution  <  800;
+SELECT DISTINCT T1.file_size ,  T1.formats FROM files AS T1 JOIN song AS T2 ON T1.f_id  =  T2.f_id WHERE T2.resolution  <  800;
+SELECT T1.artist_name FROM song AS T1 JOIN files AS T2 ON T1.f_id  =  T2.f_id ORDER BY T2.duration LIMIT 1;
+SELECT T1.artist_name FROM song AS T1 JOIN files AS T2 ON T1.f_id  =  T2.f_id ORDER BY T2.duration LIMIT 1;
+SELECT T1.artist_name ,  T1.country FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name ORDER BY T2.rating DESC LIMIT 3;
+SELECT T1.artist_name ,  T1.country FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name ORDER BY T2.rating DESC LIMIT 3;
+SELECT count(*) FROM files WHERE duration LIKE '4:%';
+SELECT count(*) FROM files WHERE duration LIKE '4:%';
+SELECT count(*) FROM artist WHERE country  =  'Bangladesh';
+SELECT count(*) FROM artist WHERE country  =  'Bangladesh';
+SELECT avg(T2.rating) FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T1.gender  =  'Female';
+SELECT avg(T2.rating) FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T1.gender  =  'Female';
+SELECT formats FROM files GROUP BY formats ORDER BY COUNT (*) DESC LIMIT 1;
+SELECT formats FROM files GROUP BY formats ORDER BY COUNT (*) DESC LIMIT 1;
+SELECT artist_name FROM artist WHERE country  =  'UK' INTERSECT SELECT artist_name FROM song WHERE languages  =  'english';
+SELECT artist_name FROM artist WHERE country  =  'UK' INTERSECT SELECT artist_name FROM song WHERE languages  =  'english';
+SELECT f_id FROM files WHERE formats  =  'mp4' INTERSECT SELECT f_id FROM song WHERE resolution  <  1000;
+SELECT f_id FROM files WHERE formats  =  'mp4' INTERSECT SELECT f_id FROM song WHERE resolution  <  1000;
+SELECT T1.country FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T1.gender  =  'Female' AND T2.languages  =  'bangla';
+SELECT T1.country FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T1.gender  =  'Female' AND T2.languages  =  'bangla';
+SELECT avg(T1.duration) FROM files AS T1 JOIN song AS T2 ON T1.f_id  =  T2.f_id WHERE T1.formats  =  'mp3' AND T2.resolution   <  800;
+SELECT avg(T1.duration) FROM files AS T1 JOIN song AS T2 ON T1.f_id  =  T2.f_id WHERE T1.formats  =  'mp3' AND T2.resolution   <  800;
+SELECT count(*) ,  gender FROM artist GROUP BY gender;
+SELECT count(*) ,  gender FROM artist GROUP BY gender;
+SELECT avg(rating) ,  languages FROM song GROUP BY languages;
+SELECT avg(rating) ,  languages FROM song GROUP BY languages;
+SELECT T1.gender ,  T1.artist_name FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name ORDER BY T2.resolution LIMIT 1;
+SELECT T1.gender ,  T1.artist_name FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name ORDER BY T2.resolution LIMIT 1;
+SELECT count(*) ,  formats FROM files GROUP BY formats;
+SELECT count(*) ,  formats FROM files GROUP BY formats;
+SELECT DISTINCT song_name FROM song WHERE resolution  >  (SELECT min(resolution) FROM song WHERE languages  =  'english');
+SELECT DISTINCT song_name FROM song WHERE resolution  >  (SELECT min(resolution) FROM song WHERE languages  =  'english');
+SELECT song_name FROM song WHERE rating  <  (SELECT max(rating) FROM song WHERE genre_is  =  'blues');
+SELECT song_name FROM song WHERE rating  <  (SELECT max(rating) FROM song WHERE genre_is  =  'blues');
+SELECT T1.artist_name ,  T1.country FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T2.song_name LIKE '%love%';
+SELECT T1.artist_name ,  T1.country FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T2.song_name LIKE '%love%';
+SELECT T1.artist_name ,  T1.gender FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T2.releasedate LIKE '%Mar%';
+SELECT T1.artist_name ,  T1.gender FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T2.releasedate LIKE '%Mar%';
+SELECT g_name ,  rating FROM genre ORDER BY g_name;
+SELECT g_name ,  rating FROM genre ORDER BY g_name;
+SELECT song_name FROM song ORDER BY resolution;
+SELECT song_name FROM song ORDER BY resolution;
+SELECT f_id FROM files WHERE formats  =  'mp4' UNION SELECT f_id FROM song WHERE resolution  >  720;
+SELECT f_id FROM files WHERE formats  =  'mp4' UNION SELECT f_id FROM song WHERE resolution  >  720;
+SELECT T2.song_name FROM files AS T1 JOIN song AS T2 ON T1.f_id  =  T2.f_id WHERE T1.duration LIKE '4:%' UNION SELECT song_name FROM song WHERE languages  =  'english';
+SELECT T2.song_name FROM files AS T1 JOIN song AS T2 ON T1.f_id  =  T2.f_id WHERE T1.duration LIKE '4:%' UNION SELECT song_name FROM song WHERE languages  =  'english';
+SELECT languages FROM song GROUP BY languages ORDER BY count(*) DESC LIMIT 1;
+SELECT languages FROM song GROUP BY languages ORDER BY count(*) DESC LIMIT 1;
+SELECT artist_name FROM song WHERE resolution  >  500 GROUP BY languages ORDER BY count(*) DESC LIMIT 1;
+SELECT artist_name FROM song WHERE resolution  >  500 GROUP BY languages ORDER BY count(*) DESC LIMIT 1;
+SELECT artist_name FROM artist WHERE country  =  'UK' AND gender  =  'Male';
+SELECT artist_name FROM artist WHERE country  =  'UK' AND gender  =  'Male';
+SELECT song_name FROM song WHERE genre_is  =  'modern' OR languages  =  'english';
+SELECT song_name FROM song WHERE genre_is  =  'modern' OR languages  =  'english';
+SELECT T2.song_name FROM files AS T1 JOIN song AS T2 ON T1.f_id  =  T2.f_id WHERE T1.formats  =  'mp3' INTERSECT SELECT song_name FROM song WHERE resolution  <  1000;
+SELECT T2.song_name FROM files AS T1 JOIN song AS T2 ON T1.f_id  =  T2.f_id WHERE T1.formats  =  'mp3' INTERSECT SELECT song_name FROM song WHERE resolution  <  1000;
+SELECT artist_name FROM artist WHERE country  =  'UK' INTERSECT SELECT T1.artist_name FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T2.languages  =  'english';
+SELECT artist_name FROM artist WHERE country  =  'UK' INTERSECT SELECT T1.artist_name FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T2.languages  =  'english';
+SELECT avg(rating) ,  avg(resolution) FROM song WHERE languages  =  'bangla';
+SELECT avg(rating) ,  avg(resolution) FROM song WHERE languages  =  'bangla';
+SELECT max(T2.resolution) ,  min(T2.resolution) FROM files AS T1 JOIN song AS T2 ON T1.f_id  =  T2.f_id WHERE T1.duration LIKE '3:%';
+SELECT max(T2.resolution) ,  min(T2.resolution) FROM files AS T1 JOIN song AS T2 ON T1.f_id  =  T2.f_id WHERE T1.duration LIKE '3:%';
+SELECT max(T1.duration) ,  max(T2.resolution) ,  T2.languages FROM files AS T1 JOIN song AS T2 ON T1.f_id  =  T2.f_id GROUP BY T2.languages ORDER BY T2.languages;
+SELECT max(T1.duration) ,  max(T2.resolution) ,  T2.languages FROM files AS T1 JOIN song AS T2 ON T1.f_id  =  T2.f_id GROUP BY T2.languages ORDER BY T2.languages;
+SELECT min(T1.duration) ,  min(T2.rating) ,  T2.genre_is FROM files AS T1 JOIN song AS T2 ON T1.f_id  =  T2.f_id GROUP BY T2.genre_is ORDER BY T2.genre_is;
+SELECT min(T1.duration) ,  min(T2.rating) ,  T2.genre_is FROM files AS T1 JOIN song AS T2 ON T1.f_id  =  T2.f_id GROUP BY T2.genre_is ORDER BY T2.genre_is;
+SELECT T1.artist_name ,  count(*) FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T2.languages  =  'english' GROUP BY T2.artist_name HAVING count(*)  >=  1;
+SELECT T1.artist_name ,  count(*) FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T2.languages  =  'english' GROUP BY T2.artist_name HAVING count(*)  >=  1;
+SELECT T1.artist_name ,  T1.country FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T2.resolution  >  900 GROUP BY T2.artist_name HAVING count(*)  >=  1;
+SELECT T1.artist_name ,  T1.country FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T2.resolution  >  900 GROUP BY T2.artist_name HAVING count(*)  >=  1;
+SELECT T1.artist_name ,  count(*) FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name GROUP BY T2.artist_name ORDER BY count(*) DESC LIMIT 3;
+SELECT T1.artist_name ,  count(*) FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name GROUP BY T2.artist_name ORDER BY count(*) DESC LIMIT 3;
+SELECT T1.country FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name GROUP BY T2.artist_name ORDER BY count(*) LIMIT 1;
+SELECT T1.country FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name GROUP BY T2.artist_name ORDER BY count(*) LIMIT 1;
+SELECT song_name FROM song WHERE rating  <   (SELECT min(rating) FROM song WHERE languages  =  'english');
+SELECT song_name FROM song WHERE rating  <   (SELECT min(rating) FROM song WHERE languages  =  'english');
+SELECT f_id FROM song WHERE resolution  >  (SELECT max(resolution) FROM song WHERE rating  <  8);
+SELECT f_id FROM song WHERE resolution  >  (SELECT max(resolution) FROM song WHERE rating  <  8);
+SELECT f_id FROM song WHERE resolution  >  (SELECT avg(resolution) FROM song WHERE genre_is  =  'modern');
+SELECT f_id FROM song WHERE resolution  >  (SELECT avg(resolution) FROM song WHERE genre_is  =  'modern');
+SELECT T1.artist_name FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T2.languages  =  'bangla' GROUP BY T2.artist_name ORDER BY count(*) DESC LIMIT 3;
+SELECT T1.artist_name FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T2.languages  =  'bangla' GROUP BY T2.artist_name ORDER BY count(*) DESC LIMIT 3;
+SELECT f_id ,  genre_is ,  artist_name FROM song WHERE languages  =   'english' ORDER BY rating;
+SELECT f_id ,  genre_is ,  artist_name FROM song WHERE languages  =   'english' ORDER BY rating;
+SELECT T1.duration ,  T1.file_size ,  T1.formats FROM files AS T1 JOIN song AS T2 ON T1.f_id  =  T2.f_id WHERE T2.genre_is  =  'pop' ORDER BY T2.song_name;
+SELECT T1.duration ,  T1.file_size ,  T1.formats FROM files AS T1 JOIN song AS T2 ON T1.f_id  =  T2.f_id WHERE T2.genre_is  =  'pop' ORDER BY T2.song_name;
+SELECT DISTINCT artist_name FROM song WHERE languages  =  'english' EXCEPT SELECT DISTINCT artist_name FROM song WHERE rating  >  8;
+SELECT DISTINCT artist_name FROM song WHERE languages  =  'english' EXCEPT SELECT DISTINCT artist_name FROM song WHERE rating  >  8;
+SELECT DISTINCT artist_name FROM artist WHERE country  =  'Bangladesh' EXCEPT SELECT DISTINCT artist_name FROM song WHERE rating  >  7;
+SELECT DISTINCT artist_name FROM artist WHERE country  =  'Bangladesh' EXCEPT SELECT DISTINCT artist_name FROM song WHERE rating  >  7;
+SELECT T1.name_full ,  T1.college_id FROM college AS T1 JOIN player_college AS T2 ON T1.college_id  =  T2.college_id GROUP BY T1.college_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name_full ,  T1.college_id FROM college AS T1 JOIN player_college AS T2 ON T1.college_id  =  T2.college_id GROUP BY T1.college_id ORDER BY count(*) DESC LIMIT 1;
+SELECT avg(T1.salary) FROM salary AS T1 JOIN team AS T2 ON T1.team_id  =  T2.team_id_br WHERE T2.name  = 'Boston Red Stockings';
+SELECT avg(T1.salary) FROM salary AS T1 JOIN team AS T2 ON T1.team_id  =  T2.team_id_br WHERE T2.name  = 'Boston Red Stockings';
+SELECT name_first ,  name_last FROM player AS T1 JOIN all_star AS T2 ON T1.player_id  =  T2.player_id WHERE YEAR  =  1998;
+SELECT name_first ,  name_last FROM player AS T1 JOIN all_star AS T2 ON T1.player_id  =  T2.player_id WHERE YEAR  =  1998;
+SELECT T1.name_first , T1.name_last ,  T1.player_id ,   count(*) FROM player AS T1 JOIN all_star AS T2 ON T1.player_id  =  T2.player_id GROUP BY T1.player_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name_first , T1.name_last ,  T1.player_id ,   count(*) FROM player AS T1 JOIN all_star AS T2 ON T1.player_id  =  T2.player_id GROUP BY T1.player_id ORDER BY count(*) DESC LIMIT 1;
+SELECT yearid ,  count(*) FROM hall_of_fame GROUP BY yearid;
+SELECT yearid ,  count(*) FROM hall_of_fame GROUP BY yearid;
+SELECT YEAR ,  avg(attendance) FROM home_game GROUP BY YEAR;
+SELECT YEAR ,  avg(attendance) FROM home_game GROUP BY YEAR;
+SELECT T2.team_id ,  T2.rank FROM home_game AS T1 JOIN team AS T2 ON T1.team_id  =  T2.team_id WHERE T1.year  =  2014 GROUP BY T1.team_id ORDER BY avg(T1.attendance) DESC LIMIT 1;
+SELECT T2.team_id ,  T2.rank FROM home_game AS T1 JOIN team AS T2 ON T1.team_id  =  T2.team_id WHERE T1.year  =  2014 GROUP BY T1.team_id ORDER BY avg(T1.attendance) DESC LIMIT 1;
+SELECT T1.name_first ,  T1.name_last ,  T2.player_id FROM player AS T1 JOIN manager_award AS T2 ON T1.player_id  =  T2.player_id GROUP BY T2.player_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name_first ,  T1.name_last ,  T2.player_id FROM player AS T1 JOIN manager_award AS T2 ON T1.player_id  =  T2.player_id GROUP BY T2.player_id ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM park WHERE state  =  'NY';
+SELECT count(*) FROM park WHERE state  =  'NY';
+SELECT T1.name_first ,  T1.name_last ,  T1.player_id FROM player AS T1 JOIN player_award AS T2 ON T1.player_id  =  T2.player_id GROUP BY T1.player_id ORDER BY count(*) DESC LIMIT 3;
+SELECT T1.name_first ,  T1.name_last ,  T1.player_id FROM player AS T1 JOIN player_award AS T2 ON T1.player_id  =  T2.player_id GROUP BY T1.player_id ORDER BY count(*) DESC LIMIT 3;
+SELECT birth_country FROM player GROUP BY birth_country ORDER BY count(*) ASC LIMIT 3;
+SELECT birth_country FROM player GROUP BY birth_country ORDER BY count(*) ASC LIMIT 3;
+SELECT name_first ,  name_last FROM player WHERE death_year = '';
+SELECT name_first ,  name_last FROM player WHERE death_year = '';
+SELECT count(*) FROM player WHERE birth_country = 'USA' AND bats  =  'R';
+SELECT count(*) FROM player WHERE birth_country = 'USA' AND bats  =  'R';
+SELECT avg(T1.height) FROM player AS T1 JOIN player_college AS T2 ON T1.player_id  =  T2.player_id JOIN college AS T3 ON T3.college_id  =  T2.college_id WHERE T3.name_full  =  'Yale University';
+SELECT avg(T1.height) FROM player AS T1 JOIN player_college AS T2 ON T1.player_id  =  T2.player_id JOIN college AS T3 ON T3.college_id  =  T2.college_id WHERE T3.name_full  =  'Yale University';
+SELECT T1.name ,  T1.team_id ,  max(T2.salary) FROM team AS T1 JOIN salary AS T2 ON T1.team_id  =  T2.team_id GROUP BY T1.team_id;
+SELECT T1.name ,  T1.team_id ,  max(T2.salary) FROM team AS T1 JOIN salary AS T2 ON T1.team_id  =  T2.team_id GROUP BY T1.team_id;
+SELECT T1.name ,  T1.team_id FROM team AS T1 JOIN salary AS T2 ON T1.team_id = T2.team_id GROUP BY T1.team_id ORDER BY avg(T2.salary) ASC LIMIT 1;
+SELECT T1.name ,  T1.team_id FROM team AS T1 JOIN salary AS T2 ON T1.team_id = T2.team_id GROUP BY T1.team_id ORDER BY avg(T2.salary) ASC LIMIT 1;
+SELECT T1.name_first , T1.name_last FROM player AS T1 JOIN player_award AS T2 WHERE T2.year  =  1960 INTERSECT SELECT T1.name_first , T1.name_last FROM player AS T1 JOIN player_award AS T2 WHERE T2.year  =  1961;
+SELECT T1.name_first , T1.name_last FROM player AS T1 JOIN player_award AS T2 WHERE T2.year  =  1960 INTERSECT SELECT T1.name_first , T1.name_last FROM player AS T1 JOIN player_award AS T2 WHERE T2.year  =  1961;
+SELECT name_first ,  name_last FROM player WHERE weight  >  220 OR height  <  75;
+SELECT name_first ,  name_last FROM player WHERE weight  >  220 OR height  <  75;
+SELECT max(T1.wins) FROM postseason AS T1 JOIN team AS T2 ON T1.team_id_winner  =  T2.team_id_br WHERE T2.name  =  'Boston Red Stockings';
+SELECT max(T1.wins) FROM postseason AS T1 JOIN team AS T2 ON T1.team_id_winner  =  T2.team_id_br WHERE T2.name  =  'Boston Red Stockings';
+SELECT count(*) FROM postseason AS T1 JOIN team AS T2 ON T1.team_id_loser  =  T2.team_id_br WHERE T2.name  =  'Boston Red Stockings' AND T1.year = 2009;
+SELECT count(*) FROM postseason AS T1 JOIN team AS T2 ON T1.team_id_loser  =  T2.team_id_br WHERE T2.name  =  'Boston Red Stockings' AND T1.year = 2009;
+SELECT T2.name ,  T1.team_id_winner FROM postseason AS T1 JOIN team AS T2 ON T1.team_id_winner  =  T2.team_id_br WHERE T1.year  =  2008 GROUP BY T1.team_id_winner ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.name ,  T1.team_id_winner FROM postseason AS T1 JOIN team AS T2 ON T1.team_id_winner  =  T2.team_id_br WHERE T1.year  =  2008 GROUP BY T1.team_id_winner ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) ,  T1.year FROM postseason AS T1 JOIN team AS T2 ON T1.team_id_winner  =  T2.team_id_br WHERE T2.name  =  'Boston Red Stockings' GROUP BY T1.year;
+SELECT count(*) ,  T1.year FROM postseason AS T1 JOIN team AS T2 ON T1.team_id_winner  =  T2.team_id_br WHERE T2.name  =  'Boston Red Stockings' GROUP BY T1.year;
+SELECT count(*) FROM ( SELECT * FROM postseason AS T1 JOIN team AS T2 ON T1.team_id_winner  =  T2.team_id_br WHERE T2.name  =  'Boston Red Stockings' UNION SELECT * FROM postseason AS T1 JOIN team AS T2 ON T1.team_id_loser  =  T2.team_id_br WHERE T2.name  =  'Boston Red Stockings' );
+SELECT count(*) FROM ( SELECT * FROM postseason AS T1 JOIN team AS T2 ON T1.team_id_winner  =  T2.team_id_br WHERE T2.name  =  'Boston Red Stockings' UNION SELECT * FROM postseason AS T1 JOIN team AS T2 ON T1.team_id_loser  =  T2.team_id_br WHERE T2.name  =  'Boston Red Stockings' );
+SELECT count(*) FROM postseason WHERE YEAR  =  1885 AND ties  =  1;
+SELECT count(*) FROM postseason WHERE YEAR  =  1885 AND ties  =  1;
+SELECT sum(T1.salary) FROM salary AS T1 JOIN team AS T2 ON T1.team_id  =  T2.team_id_br WHERE T2.name  = 'Boston Red Stockings' AND T1.year  =  2010;
+SELECT sum(T1.salary) FROM salary AS T1 JOIN team AS T2 ON T1.team_id  =  T2.team_id_br WHERE T2.name  = 'Boston Red Stockings' AND T1.year  =  2010;
+SELECT count(*) FROM salary AS T1 JOIN team AS T2 ON T1.team_id  =  T2.team_id_br WHERE T2.name  = 'Boston Red Stockings' AND T1.year  =  2000;
+SELECT count(*) FROM salary AS T1 JOIN team AS T2 ON T1.team_id  =  T2.team_id_br WHERE T2.name  = 'Boston Red Stockings' AND T1.year  =  2000;
+SELECT salary FROM salary WHERE YEAR  =  2001 ORDER BY salary DESC LIMIT 3;
+SELECT salary FROM salary WHERE YEAR  =  2001 ORDER BY salary DESC LIMIT 3;
+SELECT salary FROM salary WHERE YEAR  =  2010 UNION SELECT salary FROM salary WHERE YEAR  =  2001;
+SELECT salary FROM salary WHERE YEAR  =  2010 UNION SELECT salary FROM salary WHERE YEAR  =  2001;
+SELECT yearid FROM hall_of_fame GROUP BY yearid ORDER BY count(*) ASC LIMIT 1;
+SELECT yearid FROM hall_of_fame GROUP BY yearid ORDER BY count(*) ASC LIMIT 1;
+SELECT count(*) FROM park WHERE city = 'Atlanta';
+SELECT count(*) FROM park WHERE city = 'Atlanta';
+SELECT count(*) FROM home_game AS T1 JOIN park AS T2 ON T1.park_id  =  T2.park_id WHERE T1.year = 1907 AND T2.park_name = 'Columbia Park';
+SELECT count(*) FROM home_game AS T1 JOIN park AS T2 ON T1.park_id  =  T2.park_id WHERE T1.year = 1907 AND T2.park_name = 'Columbia Park';
+SELECT count(*) FROM home_game AS T1 JOIN park AS T2 ON T1.park_id  =  T2.park_id WHERE T1.year  =  2000 AND T2.city  =  'Atlanta';
+SELECT count(*) FROM home_game AS T1 JOIN park AS T2 ON T1.park_id  =  T2.park_id WHERE T1.year  =  2000 AND T2.city  =  'Atlanta';
+SELECT sum(T1.attendance) FROM home_game AS T1 JOIN team AS T2 ON T1.team_id  =  T2.team_id_br WHERE T2.name  =  'Boston Red Stockings' AND T1.year BETWEEN 2000 AND 2010;
+SELECT sum(T1.attendance) FROM home_game AS T1 JOIN team AS T2 ON T1.team_id  =  T2.team_id_br WHERE T2.name  =  'Boston Red Stockings' AND T1.year BETWEEN 2000 AND 2010;
+SELECT sum(T1.salary) FROM salary AS T1 JOIN player AS T2 ON T1.player_id = T2.player_id WHERE T2.name_first  =  'Len' AND T2.name_last = 'Barker' AND T1.year BETWEEN 1985 AND 1990;
+SELECT sum(T1.salary) FROM salary AS T1 JOIN player AS T2 ON T1.player_id = T2.player_id WHERE T2.name_first  =  'Len' AND T2.name_last = 'Barker' AND T1.year BETWEEN 1985 AND 1990;
+SELECT T2.name_first ,  T2.name_last FROM salary AS T1 JOIN player AS T2 ON T1.player_id = T2.player_id JOIN team AS T3 ON T3.team_id_br = T1.team_id WHERE T1.year = 2005 AND T3.name = 'Washington Nationals' INTERSECT SELECT T2.name_first , T2.name_last FROM salary AS T1 JOIN player AS T2 ON T1.player_id = T2.player_id JOIN team AS T3 ON T3.team_id_br = T1.team_id WHERE T1.year = 2007 AND T3.name = 'Washington Nationals';
+SELECT T2.name_first ,  T2.name_last FROM salary AS T1 JOIN player AS T2 ON T1.player_id = T2.player_id JOIN team AS T3 ON T3.team_id_br = T1.team_id WHERE T1.year = 2005 AND T3.name = 'Washington Nationals' INTERSECT SELECT T2.name_first , T2.name_last FROM salary AS T1 JOIN player AS T2 ON T1.player_id = T2.player_id JOIN team AS T3 ON T3.team_id_br = T1.team_id WHERE T1.year = 2007 AND T3.name = 'Washington Nationals';
+SELECT sum(T1.games) FROM home_game AS T1 JOIN team AS T2 ON T1.team_id = T2.team_id_br WHERE T2.name = 'Boston Red Stockings' AND T1.year BETWEEN 1990 AND 2000;
+SELECT sum(T1.games) FROM home_game AS T1 JOIN team AS T2 ON T1.team_id = T2.team_id_br WHERE T2.name = 'Boston Red Stockings' AND T1.year BETWEEN 1990 AND 2000;
+SELECT T2.name FROM home_game AS T1 JOIN team AS T2 ON T1.team_id = T2.team_id_br WHERE T1.year = 1980 ORDER BY T1.attendance ASC LIMIT 1;
+SELECT T2.name FROM home_game AS T1 JOIN team AS T2 ON T1.team_id = T2.team_id_br WHERE T1.year = 1980 ORDER BY T1.attendance ASC LIMIT 1;
+SELECT state FROM park GROUP BY state HAVING count(*)  >  2;
+SELECT state FROM park GROUP BY state HAVING count(*)  >  2;
+SELECT count(*) FROM team_franchise WHERE active = 'Y';
+SELECT count(*) FROM team_franchise WHERE active = 'Y';
+SELECT city FROM park GROUP BY city HAVING count(*) BETWEEN 2 AND 4;
+SELECT city FROM park GROUP BY city HAVING count(*) BETWEEN 2 AND 4;
+SELECT T2.park_name FROM home_game AS T1 JOIN park AS T2 ON T1.park_id  =  T2.park_id WHERE T1.year = 2008 ORDER BY T1.attendance DESC LIMIT 1;
+SELECT T2.park_name FROM home_game AS T1 JOIN park AS T2 ON T1.park_id  =  T2.park_id WHERE T1.year = 2008 ORDER BY T1.attendance DESC LIMIT 1;
+SELECT count(*) FROM camera_lens WHERE focal_length_mm  >  15;
+SELECT brand ,  name FROM camera_lens ORDER BY max_aperture DESC;
+SELECT id ,  color ,  name FROM photos;
+SELECT max(height) ,  avg(height) FROM mountain;
+SELECT avg(prominence) FROM mountain WHERE country  =  'Morocco';
+SELECT name ,  height ,  prominence FROM mountain WHERE range != 'Aberdare Range';
+SELECT T1.id ,  T1.name FROM mountain AS T1 JOIN photos AS T2 ON T1.id  =  T2.mountain_id WHERE T1.height  >  4000;
+SELECT T1.id ,  T1.name FROM mountain AS T1 JOIN photos AS T2 ON T1.id  =  T2.mountain_id GROUP BY T1.id HAVING count(*)  >=  2;
+SELECT T2.name FROM photos AS T1 JOIN camera_lens AS T2 ON T1.camera_lens_id  =  T2.id GROUP BY T2.id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name FROM camera_lens AS T1 JOIN photos AS T2 ON T2.camera_lens_id  =  T1.id WHERE T1.brand  =  'Sigma' OR T1.brand  =  'Olympus';
+SELECT count(DISTINCT brand) FROM camera_lens;
+SELECT count(*) FROM camera_lens WHERE id NOT IN ( SELECT camera_lens_id FROM photos );
+SELECT count(DISTINCT T2.camera_lens_id) FROM mountain AS T1 JOIN photos AS T2 ON T1.id  =  T2.mountain_id WHERE T1.country  =  'Ethiopia';
+SELECT T3.brand FROM mountain AS T1 JOIN photos AS T2 ON T1.id  =  T2.mountain_id JOIN camera_lens AS T3 ON T2.camera_lens_id  =  T3.id WHERE T1.range  =  'Toubkal Atlas' INTERSECT SELECT T3.brand FROM mountain AS T1 JOIN photos AS T2 ON T1.id  =  T2.mountain_id JOIN camera_lens AS T3 ON T2.camera_lens_id  =  T3.id WHERE T1.range  =  'Lasta Massif';
+SELECT name ,  prominence FROM mountain EXCEPT SELECT T1.name ,  T1.prominence FROM mountain AS T1 JOIN photos AS T2 ON T1.id  =  T2.mountain_id JOIN camera_lens AS T3 ON T2.camera_lens_id  =  T3.id WHERE T3.brand  =  'Sigma';
+SELECT name FROM camera_lens WHERE name LIKE '%Digital%';
+SELECT T1.name ,  count(*) FROM camera_lens AS T1 JOIN photos AS T2 ON T1.id  =  T2.camera_lens_id GROUP BY T1.id ORDER BY count(*);
+SELECT name FROM channel WHERE OWNER != 'CCTV';
+SELECT name FROM channel WHERE OWNER != 'CCTV';
+SELECT name FROM channel ORDER BY rating_in_percent DESC;
+SELECT name FROM channel ORDER BY rating_in_percent DESC;
+SELECT OWNER FROM channel ORDER BY rating_in_percent DESC LIMIT 1;
+SELECT OWNER FROM channel ORDER BY rating_in_percent DESC LIMIT 1;
+SELECT count(*) FROM program;
+SELECT count(*) FROM program;
+SELECT name FROM program ORDER BY launch;
+SELECT name FROM program ORDER BY launch;
+SELECT name ,  origin ,  OWNER FROM program;
+SELECT name ,  origin ,  OWNER FROM program;
+SELECT name FROM program ORDER BY launch DESC LIMIT 1;
+SELECT name FROM program ORDER BY launch DESC LIMIT 1;
+SELECT sum(Share_in_percent) FROM channel WHERE OWNER  =  'CCTV';
+SELECT sum(Share_in_percent) FROM channel WHERE OWNER  =  'CCTV';
+SELECT t1.name FROM channel AS t1 JOIN broadcast AS t2 ON t1.channel_id  =  t2.channel_id WHERE t2.time_of_day  =  'Morning';
+SELECT t1.name FROM channel AS t1 JOIN broadcast AS t2 ON t1.channel_id  =  t2.channel_id WHERE t2.time_of_day  =  'Morning';
+SELECT t1.name FROM channel AS t1 JOIN broadcast AS t2 ON t1.channel_id  =  t2.channel_id WHERE t2.time_of_day  =  'Morning' INTERSECT SELECT t1.name FROM channel AS t1 JOIN broadcast AS t2 ON t1.channel_id  =  t2.channel_id WHERE t2.time_of_day  =  'Night';
+SELECT t1.name FROM channel AS t1 JOIN broadcast AS t2 ON t1.channel_id  =  t2.channel_id WHERE t2.time_of_day  =  'Morning' INTERSECT SELECT t1.name FROM channel AS t1 JOIN broadcast AS t2 ON t1.channel_id  =  t2.channel_id WHERE t2.time_of_day  =  'Night';
+SELECT count(*) ,  time_of_day FROM broadcast GROUP BY time_of_day;
+SELECT count(*) ,  time_of_day FROM broadcast GROUP BY time_of_day;
+SELECT count(DISTINCT program_id) FROM broadcast WHERE time_of_day  =  'Night';
+SELECT count(DISTINCT program_id) FROM broadcast WHERE time_of_day  =  'Night';
+SELECT name FROM program EXCEPT SELECT t1.name FROM program AS t1 JOIN broadcast AS t2 ON t1.program_id  =  t2.program_id WHERE t2.Time_of_day  =  'Morning';
+SELECT name FROM program EXCEPT SELECT t1.name FROM program AS t1 JOIN broadcast AS t2 ON t1.program_id  =  t2.program_id WHERE t2.Time_of_day  =  'Morning';
+SELECT t1.owner FROM program AS t1 JOIN broadcast AS t2 ON t1.program_id  =  t2.program_id WHERE t2.Time_of_day  =  'Morning' INTERSECT SELECT t1.owner FROM program AS t1 JOIN broadcast AS t2 ON t1.program_id  =  t2.program_id WHERE t2.Time_of_day  =  'Night';
+SELECT t1.owner FROM program AS t1 JOIN broadcast AS t2 ON t1.program_id  =  t2.program_id WHERE t2.Time_of_day  =  'Morning' INTERSECT SELECT t1.owner FROM program AS t1 JOIN broadcast AS t2 ON t1.program_id  =  t2.program_id WHERE t2.Time_of_day  =  'Night';
+SELECT origin FROM program ORDER BY origin;
+SELECT origin FROM program ORDER BY origin;
+SELECT count(DISTINCT OWNER) FROM channel;
+SELECT count(DISTINCT OWNER) FROM channel;
+SELECT name FROM program WHERE origin != 'Beijing';
+SELECT name FROM program WHERE origin != 'Beijing';
+SELECT name FROM channel WHERE OWNER  =  'CCTV' OR OWNER  =  'HBS';
+SELECT name FROM channel WHERE OWNER  =  'CCTV' OR OWNER  =  'HBS';
+SELECT sum(Rating_in_percent) ,  OWNER FROM channel GROUP BY OWNER;
+SELECT sum(Rating_in_percent) ,  OWNER FROM channel GROUP BY OWNER;
+SELECT t1.name FROM program AS t1 JOIN broadcast AS t2 ON t1.program_id  =  t2.program_id GROUP BY t2.program_id ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.name FROM program AS t1 JOIN broadcast AS t2 ON t1.program_id  =  t2.program_id GROUP BY t2.program_id ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM COURSES;
+SELECT count(*) FROM COURSES;
+SELECT course_description FROM COURSES WHERE course_name  =  'database';
+SELECT course_description FROM COURSES WHERE course_name  =  'database';
+SELECT address_line_1 FROM Course_Authors_and_Tutors WHERE personal_name	 =  'Cathrine';
+SELECT address_line_1 FROM Course_Authors_and_Tutors WHERE personal_name	 =  'Cathrine';
+SELECT address_line_1 FROM Course_Authors_and_Tutors;
+SELECT address_line_1 FROM Course_Authors_and_Tutors;
+SELECT login_name ,  family_name FROM Course_Authors_and_Tutors;
+SELECT login_name ,  family_name FROM Course_Authors_and_Tutors;
+SELECT date_of_enrolment ,  date_of_completion FROM Student_Course_Enrolment;
+SELECT date_of_enrolment ,  date_of_completion FROM Student_Course_Enrolment;
+SELECT count(DISTINCT student_id) FROM Student_Course_Enrolment;
+SELECT count(DISTINCT student_id) FROM Student_Course_Enrolment;
+SELECT count(course_id) FROM Student_Course_Enrolment;
+SELECT count(course_id) FROM Student_Course_Enrolment;
+SELECT date_test_taken FROM Student_Tests_Taken WHERE test_result  =  'Pass';
+SELECT date_test_taken FROM Student_Tests_Taken WHERE test_result  =  'Pass';
+SELECT count(*) FROM Student_Tests_Taken WHERE test_result  =  'Fail';
+SELECT count(*) FROM Student_Tests_Taken WHERE test_result  =  'Fail';
+SELECT login_name FROM Students WHERE family_name  =  'Ward';
+SELECT login_name FROM Students WHERE family_name  =  'Ward';
+SELECT date_of_latest_logon FROM Students WHERE family_name  =  'Jaskolski' OR family_name  =  'Langosh';
+SELECT date_of_latest_logon FROM Students WHERE family_name  =  'Jaskolski' OR family_name  =  'Langosh';
+SELECT COUNT(*) FROM Students WHERE personal_name LIKE '%son%';
+SELECT COUNT(*) FROM Students WHERE personal_name LIKE '%son%';
+SELECT subject_name FROM SUBJECTS;
+SELECT subject_name FROM SUBJECTS;
+SELECT * FROM Course_Authors_and_Tutors ORDER BY personal_name;
+SELECT * FROM Course_Authors_and_Tutors ORDER BY personal_name;
+SELECT personal_name ,  family_name FROM Students ORDER BY family_name;
+SELECT personal_name ,  family_name FROM Students ORDER BY family_name;
+SELECT test_result ,  COUNT(*) FROM Student_Tests_Taken GROUP BY test_result ORDER BY COUNT(*) DESC;
+SELECT test_result ,  COUNT(*) FROM Student_Tests_Taken GROUP BY test_result ORDER BY COUNT(*) DESC;
+SELECT T1.login_name FROM Course_Authors_and_Tutors AS T1 JOIN Courses AS T2 ON T1.author_id  =  T2.author_id WHERE T2.course_name  =  'advanced database';
+SELECT T1.login_name FROM Course_Authors_and_Tutors AS T1 JOIN Courses AS T2 ON T1.author_id  =  T2.author_id WHERE T2.course_name  =  'advanced database';
+SELECT T1.address_line_1 FROM Course_Authors_and_Tutors AS T1 JOIN Courses AS T2 ON T1.author_id  =  T2.author_id WHERE T2.course_name  =  'operating system' OR T2.course_name  =  'data structure';
+SELECT T1.address_line_1 FROM Course_Authors_and_Tutors AS T1 JOIN Courses AS T2 ON T1.author_id  =  T2.author_id WHERE T2.course_name  =  'operating system' OR T2.course_name  =  'data structure';
+SELECT T1.personal_name ,  T1.family_name ,  T2.author_id FROM Course_Authors_and_Tutors AS T1 JOIN Courses AS T2 ON T1.author_id  =  T2.author_id GROUP BY T2.author_id ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T1.personal_name ,  T1.family_name ,  T2.author_id FROM Course_Authors_and_Tutors AS T1 JOIN Courses AS T2 ON T1.author_id  =  T2.author_id GROUP BY T2.author_id ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T1.address_line_1 ,  T2.author_id FROM Course_Authors_and_Tutors AS T1 JOIN Courses AS T2 ON T1.author_id  =  T2.author_id GROUP BY T2.author_id HAVING Count(*)  >=  2;
+SELECT T1.address_line_1 ,  T2.author_id FROM Course_Authors_and_Tutors AS T1 JOIN Courses AS T2 ON T1.author_id  =  T2.author_id GROUP BY T2.author_id HAVING Count(*)  >=  2;
+SELECT T2.course_name FROM Course_Authors_and_Tutors AS T1 JOIN Courses AS T2 ON T1.author_id  =  T2.author_id WHERE T1.personal_name  =  'Julio';
+SELECT T2.course_name FROM Course_Authors_and_Tutors AS T1 JOIN Courses AS T2 ON T1.author_id  =  T2.author_id WHERE T1.personal_name  =  'Julio';
+SELECT T1.course_name ,  T1.course_description FROM Courses AS T1 JOIN Subjects AS T2 ON T1.subject_id  =  T2.subject_id WHERE T2.subject_name  =  'Computer Science';
+SELECT T1.course_name ,  T1.course_description FROM Courses AS T1 JOIN Subjects AS T2 ON T1.subject_id  =  T2.subject_id WHERE T2.subject_name  =  'Computer Science';
+SELECT T1.subject_id ,  T2.subject_name ,  COUNT(*) FROM Courses AS T1 JOIN Subjects AS T2 ON T1.subject_id  =  T2.subject_id GROUP BY T1.subject_id;
+SELECT T1.subject_id ,  T2.subject_name ,  COUNT(*) FROM Courses AS T1 JOIN Subjects AS T2 ON T1.subject_id  =  T2.subject_id GROUP BY T1.subject_id;
+SELECT T1.subject_id ,  T2.subject_name ,  COUNT(*) FROM Courses AS T1 JOIN Subjects AS T2 ON T1.subject_id  =  T2.subject_id GROUP BY T1.subject_id ORDER BY COUNT(*) ASC;
+SELECT T1.subject_id ,  T2.subject_name ,  COUNT(*) FROM Courses AS T1 JOIN Subjects AS T2 ON T1.subject_id  =  T2.subject_id GROUP BY T1.subject_id ORDER BY COUNT(*) ASC;
+SELECT T2.date_of_enrolment FROM Courses AS T1 JOIN Student_Course_Enrolment AS T2 ON T1.course_id  =  T2.course_id WHERE T1.course_name  =  'Spanish';
+SELECT T2.date_of_enrolment FROM Courses AS T1 JOIN Student_Course_Enrolment AS T2 ON T1.course_id  =  T2.course_id WHERE T1.course_name  =  'Spanish';
+SELECT T1.course_name FROM Courses AS T1 JOIN Student_Course_Enrolment AS T2 ON T1.course_id  =  T2.course_id GROUP BY T1.course_name ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T1.course_name FROM Courses AS T1 JOIN Student_Course_Enrolment AS T2 ON T1.course_id  =  T2.course_id GROUP BY T1.course_name ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T1.course_name FROM Courses AS T1 JOIN Student_Course_Enrolment AS T2 ON T1.course_id  =  T2.course_id GROUP BY T1.course_name HAVING COUNT(*)  =  1;
+SELECT T1.course_name FROM Courses AS T1 JOIN Student_Course_Enrolment AS T2 ON T1.course_id  =  T2.course_id GROUP BY T1.course_name HAVING COUNT(*)  =  1;
+SELECT T1.course_description ,  T1.course_name FROM Courses AS T1 JOIN Student_Course_Enrolment AS T2 ON T1.course_id  =  T2.course_id GROUP BY T1.course_name HAVING COUNT(*)  >  2;
+SELECT T1.course_description ,  T1.course_name FROM Courses AS T1 JOIN Student_Course_Enrolment AS T2 ON T1.course_id  =  T2.course_id GROUP BY T1.course_name HAVING COUNT(*)  >  2;
+SELECT T1.course_name ,  COUNT(*) FROM Courses AS T1 JOIN Student_Course_Enrolment AS T2 ON T1.course_id  =  T2.course_id GROUP BY T1.course_name;
+SELECT T1.course_name ,  COUNT(*) FROM Courses AS T1 JOIN Student_Course_Enrolment AS T2 ON T1.course_id  =  T2.course_id GROUP BY T1.course_name;
+SELECT T1.date_of_enrolment FROM Student_Course_Enrolment AS T1 JOIN Student_Tests_Taken AS T2 ON T1.registration_id  =  T2.registration_id WHERE T2.test_result  =  'Pass';
+SELECT T1.date_of_enrolment FROM Student_Course_Enrolment AS T1 JOIN Student_Tests_Taken AS T2 ON T1.registration_id  =  T2.registration_id WHERE T2.test_result  =  'Pass';
+SELECT T1.date_of_completion FROM Student_Course_Enrolment AS T1 JOIN Student_Tests_Taken AS T2 ON T1.registration_id  =  T2.registration_id WHERE T2.test_result  =  'Fail';
+SELECT T1.date_of_completion FROM Student_Course_Enrolment AS T1 JOIN Student_Tests_Taken AS T2 ON T1.registration_id  =  T2.registration_id WHERE T2.test_result  =  'Fail';
+SELECT T1.date_of_enrolment ,  T1.date_of_completion FROM Student_Course_Enrolment AS T1 JOIN Students AS T2 ON T1.student_id  =  T2.student_id WHERE T2.personal_name  =  'Karson';
+SELECT T1.date_of_enrolment ,  T1.date_of_completion FROM Student_Course_Enrolment AS T1 JOIN Students AS T2 ON T1.student_id  =  T2.student_id WHERE T2.personal_name  =  'Karson';
+SELECT T1.date_of_enrolment ,  T1.date_of_completion FROM Student_Course_Enrolment AS T1 JOIN Students AS T2 ON T1.student_id  =  T2.student_id WHERE T2.family_name  =  'Zieme' AND T2.personal_name  =  'Bernie';
+SELECT T1.date_of_enrolment ,  T1.date_of_completion FROM Student_Course_Enrolment AS T1 JOIN Students AS T2 ON T1.student_id  =  T2.student_id WHERE T2.family_name  =  'Zieme' AND T2.personal_name  =  'Bernie';
+SELECT T1.student_id ,  T2.login_name FROM Student_Course_Enrolment AS T1 JOIN Students AS T2 ON T1.student_id  =  T2.student_id GROUP BY T1.student_id ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T1.student_id ,  T2.login_name FROM Student_Course_Enrolment AS T1 JOIN Students AS T2 ON T1.student_id  =  T2.student_id GROUP BY T1.student_id ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T1.student_id ,  T2.personal_name FROM Student_Course_Enrolment AS T1 JOIN Students AS T2 ON T1.student_id  =  T2.student_id GROUP BY T1.student_id HAVING COUNT(*)  >=  2;
+SELECT T1.student_id ,  T2.personal_name FROM Student_Course_Enrolment AS T1 JOIN Students AS T2 ON T1.student_id  =  T2.student_id GROUP BY T1.student_id HAVING COUNT(*)  >=  2;
+SELECT T1.student_id ,  T2.middle_name FROM Student_Course_Enrolment AS T1 JOIN Students AS T2 ON T1.student_id  =  T2.student_id GROUP BY T1.student_id HAVING COUNT(*)  <=  2;
+SELECT T1.student_id ,  T2.middle_name FROM Student_Course_Enrolment AS T1 JOIN Students AS T2 ON T1.student_id  =  T2.student_id GROUP BY T1.student_id HAVING COUNT(*)  <=  2;
+SELECT personal_name FROM Students EXCEPT SELECT T1.personal_name FROM Students AS T1 JOIN Student_Course_Enrolment AS T2 ON T1.student_id  =  T2.student_id;
+SELECT personal_name FROM Students EXCEPT SELECT T1.personal_name FROM Students AS T1 JOIN Student_Course_Enrolment AS T2 ON T1.student_id  =  T2.student_id;
+SELECT count(*) FROM Students WHERE student_id NOT IN (SELECT student_id FROM Student_Course_Enrolment);
+SELECT count(*) FROM Students WHERE student_id NOT IN (SELECT student_id FROM Student_Course_Enrolment);
+SELECT login_name FROM Course_Authors_and_Tutors INTERSECT SELECT login_name FROM Students;
+SELECT login_name FROM Course_Authors_and_Tutors INTERSECT SELECT login_name FROM Students;
+SELECT personal_name FROM Course_Authors_and_Tutors INTERSECT SELECT personal_name FROM Students;
+SELECT personal_name FROM Course_Authors_and_Tutors INTERSECT SELECT personal_name FROM Students;
+SELECT T1.Date_Claim_Made ,  T1.Claim_id FROM Claims AS T1 JOIN Settlements AS T2 ON T1.Claim_id  =  T2.Claim_id GROUP BY T1.Claim_id HAVING count(*)  >  2 UNION SELECT T1.Date_Claim_Made ,  T1.Claim_id FROM Claims AS T1 JOIN Settlements AS T2 ON T1.Claim_id  =  T2.Claim_id WHERE T1.Amount_Claimed  =  ( SELECT max(Amount_Claimed) FROM Claims );
+SELECT T1.Date_Claim_Made ,  T1.Claim_id FROM Claims AS T1 JOIN Settlements AS T2 ON T1.Claim_id  =  T2.Claim_id GROUP BY T1.Claim_id HAVING count(*)  >  2 UNION SELECT T1.Date_Claim_Made ,  T1.Claim_id FROM Claims AS T1 JOIN Settlements AS T2 ON T1.Claim_id  =  T2.Claim_id WHERE T1.Amount_Claimed  =  ( SELECT max(Amount_Claimed) FROM Claims );
+SELECT T1.customer_details ,  T1.customer_id FROM Customers AS T1 JOIN Customer_Policies AS T2 ON T1.Customer_id  =  T2.customer_id GROUP BY T1.customer_id HAVING count(*)  >=  2 EXCEPT SELECT T1.customer_details ,  T1.customer_id FROM Customers AS T1 JOIN Customer_Policies AS T2 ON T1.Customer_id  =  T2.customer_id JOIN Claims AS T3 ON T2.policy_id  =  T3.policy_id;
+SELECT T1.customer_details ,  T1.customer_id FROM Customers AS T1 JOIN Customer_Policies AS T2 ON T1.Customer_id  =  T2.customer_id GROUP BY T1.customer_id HAVING count(*)  >=  2 EXCEPT SELECT T1.customer_details ,  T1.customer_id FROM Customers AS T1 JOIN Customer_Policies AS T2 ON T1.Customer_id  =  T2.customer_id JOIN Claims AS T3 ON T2.policy_id  =  T3.policy_id;
+SELECT Payment_Method_Code ,  Date_Payment_Made ,  Amount_Payment FROM Payments ORDER BY Date_Payment_Made ASC;
+SELECT Payment_Method_Code ,  Date_Payment_Made ,  Amount_Payment FROM Payments ORDER BY Date_Payment_Made ASC;
+SELECT Amount_Settled ,  Amount_Claimed FROM Claims ORDER BY Amount_Claimed DESC LIMIT 1;
+SELECT Amount_Settled ,  Amount_Claimed FROM Claims ORDER BY Amount_Claimed DESC LIMIT 1;
+SELECT Amount_Settled ,  Amount_Claimed FROM Claims ORDER BY Amount_Settled ASC LIMIT 1;
+SELECT Amount_Settled ,  Amount_Claimed FROM Claims ORDER BY Amount_Settled ASC LIMIT 1;
+SELECT Date_Claim_Made ,  Date_Claim_Settled FROM Claims WHERE Amount_Claimed  >  ( SELECT avg(Amount_Claimed) FROM Claims );
+SELECT Date_Claim_Made ,  Date_Claim_Settled FROM Claims WHERE Amount_Claimed  >  ( SELECT avg(Amount_Claimed) FROM Claims );
+SELECT Date_Claim_Made FROM Claims WHERE Amount_Settled  <=  ( SELECT avg(Amount_Settled) FROM Claims );
+SELECT Date_Claim_Made FROM Claims WHERE Amount_Settled  <=  ( SELECT avg(Amount_Settled) FROM Claims );
+SELECT T1.Claim_id ,  count(*) FROM Claims AS T1 JOIN Settlements AS T2 ON T1.claim_id  =  T2.claim_id GROUP BY T1.claim_id;
+SELECT T1.Claim_id ,  count(*) FROM Claims AS T1 JOIN Settlements AS T2 ON T1.claim_id  =  T2.claim_id GROUP BY T1.claim_id;
+SELECT T1.claim_id ,  T1.date_claim_made ,  count(*) FROM Claims AS T1 JOIN Settlements AS T2 ON T1.claim_id  =  T2.claim_id GROUP BY T1.claim_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.claim_id ,  T1.date_claim_made ,  count(*) FROM Claims AS T1 JOIN Settlements AS T2 ON T1.claim_id  =  T2.claim_id GROUP BY T1.claim_id ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) ,  T1.claim_id FROM Claims AS T1 JOIN Settlements AS T2 ON T1.claim_id  =  T2.claim_id GROUP BY T1.claim_id ORDER BY T1.Date_Claim_Settled DESC LIMIT 1;
+SELECT count(*) ,  T1.claim_id FROM Claims AS T1 JOIN Settlements AS T2 ON T1.claim_id  =  T2.claim_id GROUP BY T1.claim_id ORDER BY T1.Date_Claim_Settled DESC LIMIT 1;
+SELECT Date_Claim_Made FROM Claims ORDER BY Date_Claim_Made ASC LIMIT 1;
+SELECT Date_Claim_Made FROM Claims ORDER BY Date_Claim_Made ASC LIMIT 1;
+SELECT sum(Amount_Settled) FROM Settlements;
+SELECT sum(Amount_Settled) FROM Settlements;
+SELECT T1.customer_details ,  T1.customer_id FROM Customers AS T1 JOIN Customer_Policies AS T2 ON T1.Customer_id  =  T2.Customer_id GROUP BY T1.customer_id HAVING count(*)  >  1;
+SELECT T1.customer_details ,  T1.customer_id FROM Customers AS T1 JOIN Customer_Policies AS T2 ON T1.Customer_id  =  T2.Customer_id GROUP BY T1.customer_id HAVING count(*)  >  1;
+SELECT Date_Claim_Made ,  Date_Claim_Settled FROM Settlements;
+SELECT Date_Claim_Made ,  Date_Claim_Settled FROM Settlements;
+SELECT Payment_Method_Code FROM Payments GROUP BY Payment_Method_Code ORDER BY count(*) DESC LIMIT 1;
+SELECT Payment_Method_Code FROM Payments GROUP BY Payment_Method_Code ORDER BY count(*) DESC LIMIT 1;
+SELECT Payment_Method_Code FROM Payments GROUP BY Payment_Method_Code ORDER BY count(*) ASC LIMIT 1;
+SELECT Payment_Method_Code FROM Payments GROUP BY Payment_Method_Code ORDER BY count(*) ASC LIMIT 1;
+SELECT sum(Amount_Payment) FROM Payments;
+SELECT sum(Amount_Payment) FROM Payments;
+SELECT DISTINCT customer_details FROM Customers;
+SELECT DISTINCT customer_details FROM Customers;
+SELECT Policy_Type_Code FROM Customer_Policies GROUP BY Policy_Type_Code ORDER BY count(*) DESC LIMIT 1;
+SELECT Policy_Type_Code FROM Customer_Policies GROUP BY Policy_Type_Code ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM Settlements;
+SELECT count(*) FROM Settlements;
+SELECT Payment_ID ,  Date_Payment_Made ,  Amount_Payment FROM Payments WHERE Payment_Method_Code  =  'Visa';
+SELECT Payment_ID ,  Date_Payment_Made ,  Amount_Payment FROM Payments WHERE Payment_Method_Code  =  'Visa';
+SELECT customer_details FROM Customers EXCEPT SELECT T1.customer_details FROM Customers AS T1 JOIN Customer_Policies AS T2 ON T1.customer_id  =  T2.customer_id;
+SELECT customer_details FROM Customers EXCEPT SELECT T1.customer_details FROM Customers AS T1 JOIN Customer_Policies AS T2 ON T1.customer_id  =  T2.customer_id;
+SELECT T1.claim_id ,  T1.date_claim_made ,  T1.Date_Claim_Settled FROM Claims AS T1 JOIN Settlements AS T2 ON T1.Claim_id  =  T2.Claim_id GROUP BY T1.claim_id HAVING count(*)  =  1;
+SELECT T1.claim_id ,  T1.date_claim_made ,  T1.Date_Claim_Settled FROM Claims AS T1 JOIN Settlements AS T2 ON T1.Claim_id  =  T2.Claim_id GROUP BY T1.claim_id HAVING count(*)  =  1;
+SELECT sum(Amount_Claimed) FROM Claims;
+SELECT sum(Amount_Claimed) FROM Claims;
+SELECT name FROM department GROUP BY departmentID ORDER BY count(departmentID) DESC LIMIT 1;
+SELECT name FROM department GROUP BY departmentID ORDER BY count(departmentID) DESC LIMIT 1;
+SELECT head FROM department GROUP BY departmentID ORDER BY count(departmentID) LIMIT 1;
+SELECT head FROM department GROUP BY departmentID ORDER BY count(departmentID) LIMIT 1;
+SELECT T2.name ,  T2.position FROM department AS T1 JOIN physician AS T2 ON T1.head  =  T2.EmployeeID GROUP BY departmentID ORDER BY count(departmentID) LIMIT 1;
+SELECT T2.name ,  T2.position FROM department AS T1 JOIN physician AS T2 ON T1.head  =  T2.EmployeeID GROUP BY departmentID ORDER BY count(departmentID) LIMIT 1;
+SELECT name FROM appointment AS T1 JOIN patient AS T2 ON T1.patient  =  T2.ssn;
+SELECT name FROM appointment AS T1 JOIN patient AS T2 ON T1.patient  =  T2.ssn;
+SELECT name ,  phone FROM appointment AS T1 JOIN patient AS T2 ON T1.patient  =  T2.ssn GROUP BY T1.patient HAVING count(*)  >  1;
+SELECT name ,  phone FROM appointment AS T1 JOIN patient AS T2 ON T1.patient  =  T2.ssn GROUP BY T1.patient HAVING count(*)  >  1;
+SELECT appointmentid FROM appointment ORDER BY START DESC LIMIT 1;
+SELECT appointmentid FROM appointment ORDER BY START DESC LIMIT 1;
+SELECT T2.name FROM appointment AS T1 JOIN physician AS T2 ON T1.Physician  =  T2.EmployeeID;
+SELECT T2.name FROM appointment AS T1 JOIN physician AS T2 ON T1.Physician  =  T2.EmployeeID;
+SELECT name FROM physician EXCEPT SELECT T2.name FROM appointment AS T1 JOIN physician AS T2 ON T1.Physician  =  T2.EmployeeID;
+SELECT name FROM physician EXCEPT SELECT T2.name FROM appointment AS T1 JOIN physician AS T2 ON T1.Physician  =  T2.EmployeeID;
+SELECT T1.name ,  T3.name FROM physician AS T1 JOIN affiliated_with AS T2 ON T1.EmployeeID  =  T2.physician JOIN department AS T3 ON T2.department  =  T3.DepartmentID WHERE T2.PrimaryAffiliation  =  1;
+SELECT T1.name ,  T3.name FROM physician AS T1 JOIN affiliated_with AS T2 ON T1.EmployeeID  =  T2.physician JOIN department AS T3 ON T2.department  =  T3.DepartmentID WHERE T2.PrimaryAffiliation  =  1;
+SELECT T1.name FROM patient AS T1 JOIN appointment AS T2 ON T1.ssn = T2.patient ORDER BY T2.start DESC LIMIT 1;
+SELECT T1.name FROM patient AS T1 JOIN appointment AS T2 ON T1.ssn = T2.patient ORDER BY T2.start DESC LIMIT 1;
+SELECT count(patient) FROM stay WHERE room  =  112;
+SELECT count(patient) FROM stay WHERE room  =  112;
+SELECT count(T1.SSN) FROM patient AS T1 JOIN prescribes AS T2 ON T1.SSN  =  T2.patient JOIN physician AS T3 ON T2.physician  =  T3.employeeid WHERE T3.name = 'John Dorian';
+SELECT count(T1.SSN) FROM patient AS T1 JOIN prescribes AS T2 ON T1.SSN  =  T2.patient JOIN physician AS T3 ON T2.physician  =  T3.employeeid WHERE T3.name = 'John Dorian';
+SELECT T4.name FROM stay AS T1 JOIN patient AS T2 ON T1.Patient  =  T2.SSN JOIN Prescribes AS T3 ON T3.Patient  =  T2.SSN JOIN Medication AS T4 ON T3.Medication  =  T4.Code WHERE room  =  111;
+SELECT T4.name FROM stay AS T1 JOIN patient AS T2 ON T1.Patient  =  T2.SSN JOIN Prescribes AS T3 ON T3.Patient  =  T2.SSN JOIN Medication AS T4 ON T3.Medication  =  T4.Code WHERE room  =  111;
+SELECT patient FROM stay WHERE room  =  111 ORDER BY staystart DESC LIMIT 1;
+SELECT patient FROM stay WHERE room  =  111 ORDER BY staystart DESC LIMIT 1;
+SELECT T1.name FROM nurse AS T1 JOIN appointment AS T2 ON T1.employeeid  =  T2.prepnurse GROUP BY T1.employeeid ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name FROM nurse AS T1 JOIN appointment AS T2 ON T1.employeeid  =  T2.prepnurse GROUP BY T1.employeeid ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name ,  count(*) FROM physician AS T1 JOIN patient AS T2 ON T1.employeeid  =  T2.PCP GROUP BY T1.employeeid;
+SELECT T1.name ,  count(*) FROM physician AS T1 JOIN patient AS T2 ON T1.employeeid  =  T2.PCP GROUP BY T1.employeeid;
+SELECT T1.name FROM physician AS T1 JOIN patient AS T2 ON T1.employeeid  =  T2.PCP GROUP BY T1.employeeid HAVING count(*)  >  1;
+SELECT T1.name FROM physician AS T1 JOIN patient AS T2 ON T1.employeeid  =  T2.PCP GROUP BY T1.employeeid HAVING count(*)  >  1;
+SELECT count(*) ,  T1.blockfloor FROM BLOCK AS T1 JOIN room AS T2 ON T1.blockfloor  =  T2.blockfloor AND T1.blockcode  =  T2.blockcode GROUP BY T1.blockfloor;
+SELECT count(*) ,  T1.blockfloor FROM BLOCK AS T1 JOIN room AS T2 ON T1.blockfloor  =  T2.blockfloor AND T1.blockcode  =  T2.blockcode GROUP BY T1.blockfloor;
+SELECT count(*) ,  T1.blockcode FROM BLOCK AS T1 JOIN room AS T2 ON T1.blockfloor  =  T2.blockfloor AND T1.blockcode  =  T2.blockcode GROUP BY T1.blockcode;
+SELECT count(*) ,  T1.blockcode FROM BLOCK AS T1 JOIN room AS T2 ON T1.blockfloor  =  T2.blockfloor AND T1.blockcode  =  T2.blockcode GROUP BY T1.blockcode;
+SELECT DISTINCT blockcode FROM room WHERE unavailable  =  0;
+SELECT DISTINCT blockcode FROM room WHERE unavailable  =  0;
+SELECT count(DISTINCT roomtype) FROM room;
+SELECT count(DISTINCT roomtype) FROM room;
+SELECT DISTINCT T1.name FROM physician AS T1 JOIN prescribes AS T2 ON T1.employeeid = T2.physician JOIN medication AS T3 ON T3.code = T2.medication WHERE T3.name  =  'Thesisin';
+SELECT DISTINCT T1.name FROM physician AS T1 JOIN prescribes AS T2 ON T1.employeeid = T2.physician JOIN medication AS T3 ON T3.code = T2.medication WHERE T3.name  =  'Thesisin';
+SELECT DISTINCT T1.name ,  T1.position FROM physician AS T1 JOIN prescribes AS T2 ON T1.employeeid = T2.physician JOIN medication AS T3 ON T3.code = T2.medication WHERE T3.Brand  =  'X';
+SELECT DISTINCT T1.name ,  T1.position FROM physician AS T1 JOIN prescribes AS T2 ON T1.employeeid = T2.physician JOIN medication AS T3 ON T3.code = T2.medication WHERE T3.Brand  =  'X';
+SELECT count(*) ,  T1.name FROM medication AS T1 JOIN prescribes AS T2 ON T1.code = T2.medication GROUP BY T1.brand;
+SELECT count(*) ,  T1.name FROM medication AS T1 JOIN prescribes AS T2 ON T1.code = T2.medication GROUP BY T1.brand;
+SELECT name FROM physician WHERE POSITION LIKE '%senior%';
+SELECT name FROM physician WHERE POSITION LIKE '%senior%';
+SELECT patient FROM undergoes ORDER BY dateundergoes LIMIT 1;
+SELECT patient FROM undergoes ORDER BY dateundergoes LIMIT 1;
+SELECT DISTINCT T2.name FROM undergoes AS T1 JOIN patient AS T2 ON T1.patient = T2.SSN JOIN stay AS T3 ON T1.Stay  =  T3.StayID WHERE T3.room  =  111;
+SELECT DISTINCT T2.name FROM undergoes AS T1 JOIN patient AS T2 ON T1.patient = T2.SSN JOIN stay AS T3 ON T1.Stay  =  T3.StayID WHERE T3.room  =  111;
+SELECT DISTINCT name FROM nurse ORDER BY name;
+SELECT DISTINCT name FROM nurse ORDER BY name;
+SELECT DISTINCT T2.name FROM undergoes AS T1 JOIN nurse AS T2 ON T1.AssistingNurse  =  T2.EmployeeID;
+SELECT DISTINCT T2.name FROM undergoes AS T1 JOIN nurse AS T2 ON T1.AssistingNurse  =  T2.EmployeeID;
+SELECT DISTINCT name FROM medication ORDER BY name;
+SELECT DISTINCT name FROM medication ORDER BY name;
+SELECT T1.name FROM physician AS T1 JOIN prescribes AS T2 ON T1.employeeid = T2.physician ORDER BY T2.dose DESC LIMIT 1;
+SELECT T1.name FROM physician AS T1 JOIN prescribes AS T2 ON T1.employeeid = T2.physician ORDER BY T2.dose DESC LIMIT 1;
+SELECT physician ,  department FROM affiliated_with WHERE primaryaffiliation  =  1;
+SELECT physician ,  department FROM affiliated_with WHERE primaryaffiliation  =  1;
+SELECT DISTINCT T2.name FROM affiliated_with AS T1 JOIN department AS T2 ON T1.department = T2.departmentid WHERE PrimaryAffiliation  =  1;
+SELECT DISTINCT T2.name FROM affiliated_with AS T1 JOIN department AS T2 ON T1.department = T2.departmentid WHERE PrimaryAffiliation  =  1;
+SELECT nurse FROM on_call WHERE blockfloor  =  1 AND blockcode  =  1;
+SELECT nurse FROM on_call WHERE blockfloor  =  1 AND blockcode  =  1;
+SELECT MAX(cost) ,  MIN(cost) ,  AVG(cost) FROM procedures;
+SELECT MAX(cost) ,  MIN(cost) ,  AVG(cost) FROM procedures;
+SELECT name ,  cost FROM procedures ORDER BY cost DESC;
+SELECT name ,  cost FROM procedures ORDER BY cost DESC;
+SELECT name FROM procedures ORDER BY cost LIMIT 3;
+SELECT name FROM procedures ORDER BY cost LIMIT 3;
+SELECT T1.name FROM physician AS T1 JOIN trained_in AS T2 ON T1.employeeid  =  T2.physician JOIN procedures AS T3 ON T3.code  =  T2.treatment WHERE T3.cost  >  5000;
+SELECT T1.name FROM physician AS T1 JOIN trained_in AS T2 ON T1.employeeid  =  T2.physician JOIN procedures AS T3 ON T3.code  =  T2.treatment WHERE T3.cost  >  5000;
+SELECT T1.name FROM physician AS T1 JOIN trained_in AS T2 ON T1.employeeid  =  T2.physician JOIN procedures AS T3 ON T3.code  =  T2.treatment ORDER BY T3.cost DESC LIMIT 1;
+SELECT T1.name FROM physician AS T1 JOIN trained_in AS T2 ON T1.employeeid  =  T2.physician JOIN procedures AS T3 ON T3.code  =  T2.treatment ORDER BY T3.cost DESC LIMIT 1;
+SELECT avg(T3.cost) FROM physician AS T1 JOIN trained_in AS T2 ON T1.employeeid  =  T2.physician JOIN procedures AS T3 ON T3.code  =  T2.treatment WHERE T1.name  =  'John Wen';
+SELECT avg(T3.cost) FROM physician AS T1 JOIN trained_in AS T2 ON T1.employeeid  =  T2.physician JOIN procedures AS T3 ON T3.code  =  T2.treatment WHERE T1.name  =  'John Wen';
+SELECT T3.name FROM physician AS T1 JOIN trained_in AS T2 ON T1.employeeid  =  T2.physician JOIN procedures AS T3 ON T3.code  =  T2.treatment WHERE T1.name  =  'John Wen';
+SELECT T3.name FROM physician AS T1 JOIN trained_in AS T2 ON T1.employeeid  =  T2.physician JOIN procedures AS T3 ON T3.code  =  T2.treatment WHERE T1.name  =  'John Wen';
+SELECT name FROM procedures WHERE cost  >  1000 UNION SELECT T3.name FROM physician AS T1 JOIN trained_in AS T2 ON T1.employeeid  =  T2.physician JOIN procedures AS T3 ON T3.code  =  T2.treatment WHERE T1.name  =  'John Wen';
+SELECT name FROM procedures WHERE cost  >  1000 UNION SELECT T3.name FROM physician AS T1 JOIN trained_in AS T2 ON T1.employeeid  =  T2.physician JOIN procedures AS T3 ON T3.code  =  T2.treatment WHERE T1.name  =  'John Wen';
+SELECT name FROM procedures WHERE cost  >  1000 EXCEPT SELECT T3.name FROM physician AS T1 JOIN trained_in AS T2 ON T1.employeeid  =  T2.physician JOIN procedures AS T3 ON T3.code  =  T2.treatment WHERE T1.name  =  'John Wen';
+SELECT name FROM procedures WHERE cost  >  1000 EXCEPT SELECT T3.name FROM physician AS T1 JOIN trained_in AS T2 ON T1.employeeid  =  T2.physician JOIN procedures AS T3 ON T3.code  =  T2.treatment WHERE T1.name  =  'John Wen';
+SELECT name FROM procedures WHERE cost  <  5000 INTERSECT SELECT T3.name FROM physician AS T1 JOIN trained_in AS T2 ON T1.employeeid  =  T2.physician JOIN procedures AS T3 ON T3.code  =  T2.treatment WHERE T1.name  =  'John Wen';
+SELECT name FROM procedures WHERE cost  <  5000 INTERSECT SELECT T3.name FROM physician AS T1 JOIN trained_in AS T2 ON T1.employeeid  =  T2.physician JOIN procedures AS T3 ON T3.code  =  T2.treatment WHERE T1.name  =  'John Wen';
+SELECT T1.name FROM physician AS T1 JOIN affiliated_with AS T2 ON T1.EmployeeID  =  T2.physician JOIN department AS T3 ON T2.department  =  T3.DepartmentID WHERE T3.name  =  'Surgery' INTERSECT SELECT T1.name FROM physician AS T1 JOIN affiliated_with AS T2 ON T1.EmployeeID  =  T2.physician JOIN department AS T3 ON T2.department  =  T3.DepartmentID WHERE T3.name  =  'Psychiatry';
+SELECT T1.name FROM physician AS T1 JOIN affiliated_with AS T2 ON T1.EmployeeID  =  T2.physician JOIN department AS T3 ON T2.department  =  T3.DepartmentID WHERE T3.name  =  'Surgery' INTERSECT SELECT T1.name FROM physician AS T1 JOIN affiliated_with AS T2 ON T1.EmployeeID  =  T2.physician JOIN department AS T3 ON T2.department  =  T3.DepartmentID WHERE T3.name  =  'Psychiatry';
+SELECT T1.name FROM physician AS T1 JOIN affiliated_with AS T2 ON T1.EmployeeID  =  T2.physician JOIN department AS T3 ON T2.department  =  T3.DepartmentID WHERE T3.name  =  'Surgery' OR T3.name  =  'Psychiatry';
+SELECT T1.name FROM physician AS T1 JOIN affiliated_with AS T2 ON T1.EmployeeID  =  T2.physician JOIN department AS T3 ON T2.department  =  T3.DepartmentID WHERE T3.name  =  'Surgery' OR T3.name  =  'Psychiatry';
+SELECT name FROM patient EXCEPT SELECT T1.name FROM patient AS T1 JOIN Prescribes AS T2 ON T2.Patient  =  T1.SSN JOIN Medication AS T3 ON T2.Medication  =  T3.Code WHERE T3.name  =  'Procrastin-X';
+SELECT name FROM patient EXCEPT SELECT T1.name FROM patient AS T1 JOIN Prescribes AS T2 ON T2.Patient  =  T1.SSN JOIN Medication AS T3 ON T2.Medication  =  T3.Code WHERE T3.name  =  'Procrastin-X';
+SELECT count(*) FROM patient WHERE SSN NOT IN ( SELECT T1.patient FROM Prescribes AS T1 JOIN Medication AS T2 ON T1.Medication  =  T2.Code WHERE T2.name  =  'Procrastin-X' );
+SELECT count(*) FROM patient WHERE SSN NOT IN ( SELECT T1.patient FROM Prescribes AS T1 JOIN Medication AS T2 ON T1.Medication  =  T2.Code WHERE T2.name  =  'Procrastin-X' );
+SELECT count(*) FROM appointment;
+SELECT count(*) FROM appointment;
+SELECT DISTINCT T1.name FROM nurse AS T1 JOIN on_call AS T2 ON T1.EmployeeID  =  T2.nurse;
+SELECT DISTINCT T1.name FROM nurse AS T1 JOIN on_call AS T2 ON T1.EmployeeID  =  T2.nurse;
+SELECT count(*) FROM ship;
+SELECT count(*) FROM ship;
+SELECT Name FROM ship ORDER BY Tonnage ASC;
+SELECT Name FROM ship ORDER BY Tonnage ASC;
+SELECT TYPE ,  Nationality FROM ship;
+SELECT TYPE ,  Nationality FROM ship;
+SELECT Name FROM ship WHERE Nationality != 'United States';
+SELECT Name FROM ship WHERE Nationality != 'United States';
+SELECT Name FROM ship WHERE Nationality  =  'United States' OR Nationality  =  'United Kingdom';
+SELECT Name FROM ship WHERE Nationality  =  'United States' OR Nationality  =  'United Kingdom';
+SELECT Name FROM ship ORDER BY Tonnage DESC LIMIT 1;
+SELECT Name FROM ship ORDER BY Tonnage DESC LIMIT 1;
+SELECT TYPE ,  COUNT(*) FROM ship GROUP BY TYPE;
+SELECT TYPE ,  COUNT(*) FROM ship GROUP BY TYPE;
+SELECT TYPE FROM ship GROUP BY TYPE ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT TYPE FROM ship GROUP BY TYPE ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Nationality FROM ship GROUP BY Nationality HAVING COUNT(*)  >  2;
+SELECT Nationality FROM ship GROUP BY Nationality HAVING COUNT(*)  >  2;
+SELECT TYPE ,  avg(Tonnage) FROM ship GROUP BY TYPE;
+SELECT TYPE ,  avg(Tonnage) FROM ship GROUP BY TYPE;
+SELECT T1.Code ,  T1.Fate ,  T2.Name FROM mission AS T1 JOIN ship AS T2 ON T1.Ship_ID  =  T2.Ship_ID;
+SELECT T1.Code ,  T1.Fate ,  T2.Name FROM mission AS T1 JOIN ship AS T2 ON T1.Ship_ID  =  T2.Ship_ID;
+SELECT T2.Name FROM mission AS T1 JOIN ship AS T2 ON T1.Ship_ID  =  T2.Ship_ID WHERE T1.Launched_Year  >  1928;
+SELECT T2.Name FROM mission AS T1 JOIN ship AS T2 ON T1.Ship_ID  =  T2.Ship_ID WHERE T1.Launched_Year  >  1928;
+SELECT DISTINCT T1.Fate FROM mission AS T1 JOIN ship AS T2 ON T1.Ship_ID  =  T2.Ship_ID WHERE T2.Nationality  =  'United States';
+SELECT DISTINCT T1.Fate FROM mission AS T1 JOIN ship AS T2 ON T1.Ship_ID  =  T2.Ship_ID WHERE T2.Nationality  =  'United States';
+SELECT Name FROM ship WHERE Ship_ID NOT IN (SELECT Ship_ID FROM mission);
+SELECT Name FROM ship WHERE Ship_ID NOT IN (SELECT Ship_ID FROM mission);
+SELECT TYPE FROM ship WHERE Tonnage  >  6000 INTERSECT SELECT TYPE FROM ship WHERE Tonnage  <  4000;
+SELECT TYPE FROM ship WHERE Tonnage  >  6000 INTERSECT SELECT TYPE FROM ship WHERE Tonnage  <  4000;
+SELECT count(*) FROM list;
+SELECT count(*) FROM list;
+SELECT lastname FROM list WHERE classroom  =  111;
+SELECT lastname FROM list WHERE classroom  =  111;
+SELECT firstname FROM list WHERE classroom  =  108;
+SELECT firstname FROM list WHERE classroom  =  108;
+SELECT DISTINCT firstname FROM list WHERE classroom  =  107;
+SELECT DISTINCT firstname FROM list WHERE classroom  =  107;
+SELECT DISTINCT classroom ,  grade FROM list;
+SELECT DISTINCT classroom ,  grade FROM list;
+SELECT DISTINCT grade FROM list WHERE classroom  =  103;
+SELECT DISTINCT grade FROM list WHERE classroom  =  103;
+SELECT DISTINCT grade FROM list WHERE classroom  =  105;
+SELECT DISTINCT grade FROM list WHERE classroom  =  105;
+SELECT DISTINCT classroom FROM list WHERE grade  =  4;
+SELECT DISTINCT classroom FROM list WHERE grade  =  4;
+SELECT DISTINCT classroom FROM list WHERE grade  =  5;
+SELECT DISTINCT classroom FROM list WHERE grade  =  5;
+SELECT DISTINCT T2.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE grade  =  5;
+SELECT DISTINCT T2.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE grade  =  5;
+SELECT DISTINCT T2.firstname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE grade  =  1;
+SELECT DISTINCT T2.firstname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE grade  =  1;
+SELECT firstname FROM teachers WHERE classroom  =  110;
+SELECT firstname FROM teachers WHERE classroom  =  110;
+SELECT lastname FROM teachers WHERE classroom  =  109;
+SELECT lastname FROM teachers WHERE classroom  =  109;
+SELECT DISTINCT firstname ,  lastname FROM teachers;
+SELECT DISTINCT firstname ,  lastname FROM teachers;
+SELECT DISTINCT firstname ,  lastname FROM list;
+SELECT DISTINCT firstname ,  lastname FROM list;
+SELECT T1.firstname ,  T1.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T2.firstname  =  'OTHA' AND T2.lastname  =  'MOYER';
+SELECT T1.firstname ,  T1.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T2.firstname  =  'OTHA' AND T2.lastname  =  'MOYER';
+SELECT T1.firstname ,  T1.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T2.firstname  =  'MARROTTE' AND T2.lastname  =  'KIRK';
+SELECT T1.firstname ,  T1.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T2.firstname  =  'MARROTTE' AND T2.lastname  =  'KIRK';
+SELECT T2.firstname ,  T2.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T1.firstname  =  'EVELINA' AND T1.lastname  =  'BROMLEY';
+SELECT T2.firstname ,  T2.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T1.firstname  =  'EVELINA' AND T1.lastname  =  'BROMLEY';
+SELECT T2.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T1.firstname  =  'GELL' AND T1.lastname  =  'TAMI';
+SELECT T2.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T1.firstname  =  'GELL' AND T1.lastname  =  'TAMI';
+SELECT count(*) FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T2.firstname  =  'LORIA' AND T2.lastname  =  'ONDERSMA';
+SELECT count(*) FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T2.firstname  =  'LORIA' AND T2.lastname  =  'ONDERSMA';
+SELECT count(*) FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T2.firstname  =  'KAWA' AND T2.lastname  =  'GORDON';
+SELECT count(*) FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T2.firstname  =  'KAWA' AND T2.lastname  =  'GORDON';
+SELECT count(*) FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T2.firstname  =  'TARRING' AND T2.lastname  =  'LEIA';
+SELECT count(*) FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T2.firstname  =  'TARRING' AND T2.lastname  =  'LEIA';
+SELECT count(*) FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T1.firstname  =  'CHRISSY' AND T1.lastname  =  'NABOZNY';
+SELECT count(*) FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T1.firstname  =  'CHRISSY' AND T1.lastname  =  'NABOZNY';
+SELECT count(*) FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T1.firstname  =  'MADLOCK' AND T1.lastname  =  'RAY';
+SELECT count(*) FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T1.firstname  =  'MADLOCK' AND T1.lastname  =  'RAY';
+SELECT DISTINCT T1.firstname ,  T1.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T1.grade  =  1 EXCEPT SELECT T1.firstname ,  T1.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T2.firstname  =  'OTHA' AND T2.lastname  =  'MOYER';
+SELECT DISTINCT T1.firstname ,  T1.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T1.grade  =  1 EXCEPT SELECT T1.firstname ,  T1.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T2.firstname  =  'OTHA' AND T2.lastname  =  'MOYER';
+SELECT DISTINCT T1.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T1.grade  =  3 AND T2.firstname != 'COVIN' AND T2.lastname != 'JEROME';
+SELECT DISTINCT T1.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom WHERE T1.grade  =  3 AND T2.firstname != 'COVIN' AND T2.lastname != 'JEROME';
+SELECT grade ,  count(DISTINCT classroom) ,  count(*) FROM list GROUP BY grade;
+SELECT grade ,  count(DISTINCT classroom) ,  count(*) FROM list GROUP BY grade;
+SELECT classroom ,  count(DISTINCT grade) FROM list GROUP BY classroom;
+SELECT classroom ,  count(DISTINCT grade) FROM list GROUP BY classroom;
+SELECT classroom FROM list GROUP BY classroom ORDER BY count(*) DESC LIMIT 1;
+SELECT classroom FROM list GROUP BY classroom ORDER BY count(*) DESC LIMIT 1;
+SELECT classroom ,  count(*) FROM list GROUP BY classroom;
+SELECT classroom ,  count(*) FROM list GROUP BY classroom;
+SELECT classroom ,  count(*) FROM list WHERE grade  =  '0' GROUP BY classroom;
+SELECT classroom ,  count(*) FROM list WHERE grade  =  '0' GROUP BY classroom;
+SELECT classroom ,  count(*) FROM list WHERE grade  =  '4' GROUP BY classroom;
+SELECT classroom ,  count(*) FROM list WHERE grade  =  '4' GROUP BY classroom;
+SELECT T2.firstname ,  T2.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom GROUP BY T2.firstname ,  T2.lastname ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.firstname ,  T2.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom GROUP BY T2.firstname ,  T2.lastname ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) ,  classroom FROM list GROUP BY classroom;
+SELECT count(*) ,  classroom FROM list GROUP BY classroom;
+SELECT count(*) FROM company WHERE Headquarters  =  'USA';
+SELECT Name FROM company ORDER BY Sales_in_Billion ASC;
+SELECT Headquarters ,  Industry FROM company;
+SELECT Name FROM company WHERE Industry  =  'Banking' OR Industry  =  'Retailing';
+SELECT max(Market_Value_in_Billion) ,  min(Market_Value_in_Billion) FROM company;
+SELECT Headquarters FROM company ORDER BY Sales_in_Billion DESC LIMIT 1;
+SELECT Headquarters ,  COUNT(*) FROM company GROUP BY Headquarters;
+SELECT Headquarters FROM company GROUP BY Headquarters ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Headquarters FROM company GROUP BY Headquarters HAVING COUNT(*)  >=  2;
+SELECT Headquarters FROM company WHERE Industry  =  'Banking' INTERSECT SELECT Headquarters FROM company WHERE Industry  =  'Oil and gas';
+SELECT T3.Name ,  T2.Name FROM employment AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID JOIN company AS T3 ON T1.Company_ID  =  T3.Company_ID;
+SELECT T3.Name ,  T2.Name FROM employment AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID JOIN company AS T3 ON T1.Company_ID  =  T3.Company_ID ORDER BY T1.Year_working;
+SELECT T2.Name FROM employment AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID JOIN company AS T3 ON T1.Company_ID  =  T3.Company_ID WHERE T3.Sales_in_Billion  >  200;
+SELECT T3.Name ,  COUNT(*) FROM employment AS T1 JOIN people AS T2 ON T1.People_ID  =  T2.People_ID JOIN company AS T3 ON T1.Company_ID  =  T3.Company_ID GROUP BY T3.Name;
+SELECT Name FROM people WHERE People_ID NOT IN (SELECT People_ID FROM employment);
+SELECT name FROM company WHERE Sales_in_Billion  >  200 ORDER BY Sales_in_Billion ,  Profits_in_Billion DESC;
+SELECT count(*) FROM film;
+SELECT count(*) FROM film;
+SELECT DISTINCT Director FROM film;
+SELECT DISTINCT Director FROM film;
+SELECT avg(Gross_in_dollar) FROM film;
+SELECT avg(Gross_in_dollar) FROM film;
+SELECT Low_Estimate ,  High_Estimate FROM film_market_estimation;
+SELECT Low_Estimate ,  High_Estimate FROM film_market_estimation;
+SELECT TYPE FROM film_market_estimation WHERE YEAR  =  1995;
+SELECT TYPE FROM film_market_estimation WHERE YEAR  =  1995;
+SELECT max(Number_cities) ,  min(Number_cities) FROM market;
+SELECT max(Number_cities) ,  min(Number_cities) FROM market;
+SELECT count(*) FROM market WHERE Number_cities  <  300;
+SELECT count(*) FROM market WHERE Number_cities  <  300;
+SELECT Country FROM market ORDER BY Country ASC;
+SELECT Country FROM market ORDER BY Country ASC;
+SELECT Country FROM market ORDER BY Number_cities DESC;
+SELECT Country FROM market ORDER BY Number_cities DESC;
+SELECT T1.Title ,  T2.Type FROM film AS T1 JOIN film_market_estimation AS T2 ON T1.Film_ID  =  T2.Film_ID;
+SELECT T1.Title ,  T2.Type FROM film AS T1 JOIN film_market_estimation AS T2 ON T1.Film_ID  =  T2.Film_ID;
+SELECT DISTINCT T1.Director FROM film AS T1 JOIN film_market_estimation AS T2 ON T1.Film_ID  =  T2.Film_ID WHERE T2.Year  =  1995;
+SELECT DISTINCT T1.Director FROM film AS T1 JOIN film_market_estimation AS T2 ON T1.Film_ID  =  T2.Film_ID WHERE T2.Year  =  1995;
+SELECT avg(T2.Number_cities) FROM film_market_estimation AS T1 JOIN market AS T2 ON T1.Market_ID  =  T2.Market_ID WHERE T1.Low_Estimate  >  10000;
+SELECT avg(T2.Number_cities) FROM film_market_estimation AS T1 JOIN market AS T2 ON T1.Market_ID  =  T2.Market_ID WHERE T1.Low_Estimate  >  10000;
+SELECT T2.Country ,  T1.Year FROM film_market_estimation AS T1 JOIN market AS T2 ON T1.Market_ID  =  T2.Market_ID;
+SELECT T2.Country ,  T1.Year FROM film_market_estimation AS T1 JOIN market AS T2 ON T1.Market_ID  =  T2.Market_ID;
+SELECT T1.Year FROM film_market_estimation AS T1 JOIN market AS T2 ON T1.Market_ID  =  T2.Market_ID WHERE T2.Country  =  'Japan' ORDER BY T1.Year DESC;
+SELECT T1.Year FROM film_market_estimation AS T1 JOIN market AS T2 ON T1.Market_ID  =  T2.Market_ID WHERE T2.Country  =  'Japan' ORDER BY T1.Year DESC;
+SELECT Studio ,  COUNT(*) FROM film GROUP BY Studio;
+SELECT Studio ,  COUNT(*) FROM film GROUP BY Studio;
+SELECT Studio FROM film GROUP BY Studio ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Studio FROM film GROUP BY Studio ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Studio FROM film GROUP BY Studio HAVING COUNT(*)  >=  2;
+SELECT Studio FROM film GROUP BY Studio HAVING COUNT(*)  >=  2;
+SELECT Title FROM film WHERE Film_ID NOT IN (SELECT Film_ID FROM film_market_estimation);
+SELECT Title FROM film WHERE Film_ID NOT IN (SELECT Film_ID FROM film_market_estimation);
+SELECT Studio FROM film WHERE Director  =  'Nicholas Meyer' INTERSECT SELECT Studio FROM film WHERE Director  =  'Walter Hill';
+SELECT Studio FROM film WHERE Director  =  'Nicholas Meyer' INTERSECT SELECT Studio FROM film WHERE Director  =  'Walter Hill';
+SELECT title ,  Studio FROM film WHERE Studio LIKE '%Universal%';
+SELECT title ,  Studio FROM film WHERE Studio LIKE '%Universal%';
+SELECT Studio FROM film EXCEPT SELECT Studio FROM film WHERE Director  =  'Walter Hill';
+SELECT Studio FROM film EXCEPT SELECT Studio FROM film WHERE Director  =  'Walter Hill';
+SELECT Studio FROM film GROUP BY Studio HAVING avg(Gross_in_dollar)  >=  4500000;
+SELECT Studio FROM film GROUP BY Studio HAVING avg(Gross_in_dollar)  >=  4500000;
+SELECT t1.title FROM film AS T1 JOIN film_market_estimation AS T2  ON T1.Film_ID  =  T2.Film_ID ORDER BY high_estimate DESC LIMIT 1;
+SELECT t1.title FROM film AS T1 JOIN film_market_estimation AS T2  ON T1.Film_ID  =  T2.Film_ID ORDER BY high_estimate DESC LIMIT 1;
+SELECT title ,  director FROM film WHERE film_id NOT IN (SELECT film_id FROM film_market_estimation AS T1 JOIN market AS T2 ON T1.market_id  =  T2.Market_ID WHERE country  =  'China');
+SELECT title ,  director FROM film WHERE film_id NOT IN (SELECT film_id FROM film_market_estimation AS T1 JOIN market AS T2 ON T1.market_id  =  T2.Market_ID WHERE country  =  'China');
+SELECT count(*) FROM Ref_calendar;
+SELECT count(*) FROM Ref_calendar;
+SELECT calendar_date ,  day_Number FROM Ref_calendar;
+SELECT calendar_date ,  day_Number FROM Ref_calendar;
+SELECT count(*) FROM Ref_document_types;
+SELECT count(*) FROM Ref_document_types;
+SELECT document_type_code ,  document_type_name FROM Ref_document_types;
+SELECT document_type_code ,  document_type_name FROM Ref_document_types;
+SELECT document_type_name ,  document_type_description FROM Ref_document_types WHERE document_type_code  =  'RV';
+SELECT document_type_name ,  document_type_description FROM Ref_document_types WHERE document_type_code  =  'RV';
+SELECT document_type_code FROM Ref_document_types WHERE document_type_name  =  'Paper';
+SELECT document_type_code FROM Ref_document_types WHERE document_type_name  =  'Paper';
+SELECT count(*) FROM All_documents WHERE document_type_code  =  'CV' OR document_type_code  =  'BK';
+SELECT count(*) FROM All_documents WHERE document_type_code  =  'CV' OR document_type_code  =  'BK';
+SELECT date_stored FROM All_documents WHERE Document_name  =  'Marry CV';
+SELECT date_stored FROM All_documents WHERE Document_name  =  'Marry CV';
+SELECT T2.day_Number ,  T1.Date_Stored FROM All_documents AS T1 JOIN Ref_calendar AS T2 ON T1.date_stored  =  T2.calendar_date;
+SELECT T2.day_Number ,  T1.Date_Stored FROM All_documents AS T1 JOIN Ref_calendar AS T2 ON T1.date_stored  =  T2.calendar_date;
+SELECT T2.document_type_name FROM All_documents AS T1 JOIN Ref_document_types AS T2 ON T1.document_type_code  =  T2.document_type_code WHERE T1.document_name  =  'How to read a book';
+SELECT T2.document_type_name FROM All_documents AS T1 JOIN Ref_document_types AS T2 ON T1.document_type_code  =  T2.document_type_code WHERE T1.document_name  =  'How to read a book';
+SELECT count(*) FROM Ref_locations;
+SELECT count(*) FROM Ref_locations;
+SELECT location_code ,  location_name FROM Ref_locations;
+SELECT location_code ,  location_name FROM Ref_locations;
+SELECT location_name ,  location_description FROM Ref_locations WHERE location_code  =  'x';
+SELECT location_name ,  location_description FROM Ref_locations WHERE location_code  =  'x';
+SELECT location_code FROM Ref_locations WHERE location_name  =  'Canada';
+SELECT location_code FROM Ref_locations WHERE location_name  =  'Canada';
+SELECT count(*) FROM ROLES;
+SELECT count(*) FROM ROLES;
+SELECT role_code ,  role_name ,  role_description FROM ROLES;
+SELECT role_code ,  role_name ,  role_description FROM ROLES;
+SELECT role_name ,  role_description FROM ROLES WHERE role_code  =  'MG';
+SELECT role_name ,  role_description FROM ROLES WHERE role_code  =  'MG';
+SELECT role_description FROM ROLES WHERE role_name  =  'Proof Reader';
+SELECT role_description FROM ROLES WHERE role_name  =  'Proof Reader';
+SELECT count(*) FROM Employees;
+SELECT count(*) FROM Employees;
+SELECT employee_name ,  role_code ,  date_of_birth FROM Employees WHERE employee_Name  =  'Armani';
+SELECT employee_name ,  role_code ,  date_of_birth FROM Employees WHERE employee_Name  =  'Armani';
+SELECT employee_ID FROM Employees WHERE employee_name  =  'Ebba';
+SELECT employee_ID FROM Employees WHERE employee_name  =  'Ebba';
+SELECT employee_name FROM Employees WHERE role_code  =  'HR';
+SELECT employee_name FROM Employees WHERE role_code  =  'HR';
+SELECT role_code ,  count(*) FROM Employees GROUP BY role_code;
+SELECT role_code ,  count(*) FROM Employees GROUP BY role_code;
+SELECT role_code FROM Employees GROUP BY role_code ORDER BY count(*) DESC LIMIT 1;
+SELECT role_code FROM Employees GROUP BY role_code ORDER BY count(*) DESC LIMIT 1;
+SELECT role_code FROM Employees GROUP BY role_code HAVING count(*)  >=  3;
+SELECT role_code FROM Employees GROUP BY role_code HAVING count(*)  >=  3;
+SELECT role_code FROM Employees GROUP BY role_code ORDER BY count(*) ASC LIMIT 1;
+SELECT role_code FROM Employees GROUP BY role_code ORDER BY count(*) ASC LIMIT 1;
+SELECT T2.role_name ,  T2.role_description FROM Employees AS T1 JOIN ROLES AS T2 ON T1.role_code  =  T2.role_code WHERE T1.employee_name  =  'Ebba';
+SELECT T2.role_name ,  T2.role_description FROM Employees AS T1 JOIN ROLES AS T2 ON T1.role_code  =  T2.role_code WHERE T1.employee_name  =  'Ebba';
+SELECT T1.employee_name FROM Employees AS T1 JOIN ROLES AS T2 ON T1.role_code  =  T2.role_code WHERE T2.role_name  =  'Editor';
+SELECT T1.employee_name FROM Employees AS T1 JOIN ROLES AS T2 ON T1.role_code  =  T2.role_code WHERE T2.role_name  =  'Editor';
+SELECT T1.employee_id FROM Employees AS T1 JOIN ROLES AS T2 ON T1.role_code  =  T2.role_code WHERE T2.role_name  =  'Human Resource' OR T2.role_name  =  'Manager';
+SELECT T1.employee_id FROM Employees AS T1 JOIN ROLES AS T2 ON T1.role_code  =  T2.role_code WHERE T2.role_name  =  'Human Resource' OR T2.role_name  =  'Manager';
+SELECT DISTINCT location_code FROM Document_locations;
+SELECT DISTINCT location_code FROM Document_locations;
+SELECT T3.location_name FROM All_documents AS T1 JOIN Document_locations AS T2 ON T1.document_id  =  T2.document_id JOIN Ref_locations AS T3 ON T2.location_code  =  T3.location_code WHERE T1.document_name  =  'Robin CV';
+SELECT T3.location_name FROM All_documents AS T1 JOIN Document_locations AS T2 ON T1.document_id  =  T2.document_id JOIN Ref_locations AS T3 ON T2.location_code  =  T3.location_code WHERE T1.document_name  =  'Robin CV';
+SELECT location_code ,  date_in_location_from ,  date_in_locaton_to FROM Document_locations;
+SELECT location_code ,  date_in_location_from ,  date_in_locaton_to FROM Document_locations;
+SELECT T1.date_in_location_from ,  T1.date_in_locaton_to FROM Document_locations AS T1 JOIN All_documents AS T2 ON T1.document_id  =  T2.document_id WHERE T2.document_name  =  'Robin CV';
+SELECT T1.date_in_location_from ,  T1.date_in_locaton_to FROM Document_locations AS T1 JOIN All_documents AS T2 ON T1.document_id  =  T2.document_id WHERE T2.document_name  =  'Robin CV';
+SELECT location_code ,  count(*) FROM Document_locations GROUP BY location_code;
+SELECT location_code ,  count(*) FROM Document_locations GROUP BY location_code;
+SELECT location_code FROM Document_locations GROUP BY location_code ORDER BY count(*) DESC LIMIT 1;
+SELECT location_code FROM Document_locations GROUP BY location_code ORDER BY count(*) DESC LIMIT 1;
+SELECT location_code FROM Document_locations GROUP BY location_code HAVING count(*)  >=  3;
+SELECT location_code FROM Document_locations GROUP BY location_code HAVING count(*)  >=  3;
+SELECT T2.location_name ,  T1.location_code FROM Document_locations AS T1 JOIN Ref_locations AS T2 ON T1.location_code  =  T2.location_code GROUP BY T1.location_code ORDER BY count(*) ASC LIMIT 1;
+SELECT T2.location_name ,  T1.location_code FROM Document_locations AS T1 JOIN Ref_locations AS T2 ON T1.location_code  =  T2.location_code GROUP BY T1.location_code ORDER BY count(*) ASC LIMIT 1;
+SELECT T2.employee_name ,  T3.employee_name FROM Documents_to_be_destroyed AS T1 JOIN Employees AS T2 ON T1.Destruction_Authorised_by_Employee_ID = T2.employee_id JOIN Employees AS T3 ON T1.Destroyed_by_Employee_ID = T3.employee_id;
+SELECT T2.employee_name ,  T3.employee_name FROM Documents_to_be_destroyed AS T1 JOIN Employees AS T2 ON T1.Destruction_Authorised_by_Employee_ID = T2.employee_id JOIN Employees AS T3 ON T1.Destroyed_by_Employee_ID = T3.employee_id;
+SELECT Destruction_Authorised_by_Employee_ID ,  count(*) FROM Documents_to_be_destroyed GROUP BY Destruction_Authorised_by_Employee_ID;
+SELECT Destruction_Authorised_by_Employee_ID ,  count(*) FROM Documents_to_be_destroyed GROUP BY Destruction_Authorised_by_Employee_ID;
+SELECT Destroyed_by_Employee_ID ,  count(*) FROM Documents_to_be_destroyed GROUP BY Destroyed_by_Employee_ID;
+SELECT Destroyed_by_Employee_ID ,  count(*) FROM Documents_to_be_destroyed GROUP BY Destroyed_by_Employee_ID;
+SELECT employee_id FROM Employees EXCEPT SELECT Destruction_Authorised_by_Employee_ID FROM Documents_to_be_destroyed;
+SELECT employee_id FROM Employees EXCEPT SELECT Destruction_Authorised_by_Employee_ID FROM Documents_to_be_destroyed;
+SELECT DISTINCT Destruction_Authorised_by_Employee_ID FROM Documents_to_be_destroyed;
+SELECT DISTINCT Destruction_Authorised_by_Employee_ID FROM Documents_to_be_destroyed;
+SELECT DISTINCT Destroyed_by_Employee_ID FROM Documents_to_be_destroyed;
+SELECT DISTINCT Destroyed_by_Employee_ID FROM Documents_to_be_destroyed;
+SELECT employee_id FROM Employees EXCEPT SELECT Destroyed_by_Employee_ID FROM Documents_to_be_destroyed;
+SELECT employee_id FROM Employees EXCEPT SELECT Destroyed_by_Employee_ID FROM Documents_to_be_destroyed;
+SELECT Destroyed_by_Employee_ID FROM Documents_to_be_destroyed UNION SELECT Destruction_Authorised_by_Employee_ID FROM Documents_to_be_destroyed;
+SELECT Destroyed_by_Employee_ID FROM Documents_to_be_destroyed UNION SELECT Destruction_Authorised_by_Employee_ID FROM Documents_to_be_destroyed;
+SELECT count(*) FROM club;
+SELECT count(*) FROM club;
+SELECT clubname FROM club;
+SELECT clubname FROM club;
+SELECT count(*) FROM student;
+SELECT count(*) FROM student;
+SELECT DISTINCT fname FROM student;
+SELECT DISTINCT fname FROM student;
+SELECT t3.lname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Bootup Baltimore';
+SELECT t3.lname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Bootup Baltimore';
+SELECT t3.lname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Hopkins Student Enterprises';
+SELECT t3.lname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Hopkins Student Enterprises';
+SELECT count(*) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Tennis Club';
+SELECT count(*) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Tennis Club';
+SELECT count(*) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Pen and Paper Gaming';
+SELECT count(*) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Pen and Paper Gaming';
+SELECT count(*) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t3.fname  =  'Linda' AND t3.lname  =  'Smith';
+SELECT count(*) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t3.fname  =  'Linda' AND t3.lname  =  'Smith';
+SELECT count(*) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t3.fname  =  'Tracy' AND t3.lname  =  'Kim';
+SELECT count(*) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t3.fname  =  'Tracy' AND t3.lname  =  'Kim';
+SELECT t3.fname ,  t3.lname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Bootup Baltimore' AND t3.sex  =  'F';
+SELECT t3.fname ,  t3.lname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Bootup Baltimore' AND t3.sex  =  'F';
+SELECT t3.fname ,  t3.lname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Hopkins Student Enterprises' AND t3.sex  =  'M';
+SELECT t3.fname ,  t3.lname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Hopkins Student Enterprises' AND t3.sex  =  'M';
+SELECT t3.fname ,  t3.lname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Bootup Baltimore' AND t3.major  =  '600';
+SELECT t3.fname ,  t3.lname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Bootup Baltimore' AND t3.major  =  '600';
+SELECT t1.clubname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t3.major  =  '600' GROUP BY t1.clubname ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.clubname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t3.major  =  '600' GROUP BY t1.clubname ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.clubname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t3.sex  =  'F' GROUP BY t1.clubname ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.clubname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t3.sex  =  'F' GROUP BY t1.clubname ORDER BY count(*) DESC LIMIT 1;
+SELECT clubdesc FROM club WHERE clubname  =  'Tennis Club';
+SELECT clubdesc FROM club WHERE clubname  =  'Tennis Club';
+SELECT clubdesc FROM club WHERE clubname  =  'Pen and Paper Gaming';
+SELECT clubdesc FROM club WHERE clubname  =  'Pen and Paper Gaming';
+SELECT clublocation FROM club WHERE clubname  =  'Tennis Club';
+SELECT clublocation FROM club WHERE clubname  =  'Tennis Club';
+SELECT clublocation FROM club WHERE clubname  =  'Pen and Paper Gaming';
+SELECT clublocation FROM club WHERE clubname  =  'Pen and Paper Gaming';
+SELECT clublocation FROM club WHERE clubname  =  'Hopkins Student Enterprises';
+SELECT clublocation FROM club WHERE clubname  =  'Hopkins Student Enterprises';
+SELECT clubname FROM club WHERE clublocation  =  'AKW';
+SELECT clubname FROM club WHERE clublocation  =  'AKW';
+SELECT count(*) FROM club WHERE clublocation  =  'HHH';
+SELECT count(*) FROM club WHERE clublocation  =  'HHH';
+SELECT t3.fname ,  t3.lname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Bootup Baltimore' AND t2.position  =  'President';
+SELECT t3.fname ,  t3.lname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Bootup Baltimore' AND t2.position  =  'President';
+SELECT t3.fname ,  t3.lname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Hopkins Student Enterprises' AND t2.position  =  'CTO';
+SELECT t3.fname ,  t3.lname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Hopkins Student Enterprises' AND t2.position  =  'CTO';
+SELECT count(DISTINCT t2.position) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid WHERE t1.clubname  =  'Bootup Baltimore';
+SELECT count(DISTINCT t2.position) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid WHERE t1.clubname  =  'Bootup Baltimore';
+SELECT count(*) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Bootup Baltimore' AND t3.age  >  18;
+SELECT count(*) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Bootup Baltimore' AND t3.age  >  18;
+SELECT count(*) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Bootup Baltimore' AND t3.age  <  18;
+SELECT count(*) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Bootup Baltimore' AND t3.age  <  18;
+SELECT DISTINCT t1.clubname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t3.city_code  =  'BAL';
+SELECT DISTINCT t1.clubname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t3.city_code  =  'BAL';
+SELECT DISTINCT t1.clubname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t3.city_code  =  'HOU';
+SELECT DISTINCT t1.clubname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t3.city_code  =  'HOU';
+SELECT count(DISTINCT t1.clubname) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t3.fname  =  'Eric' AND t3.lname  =  'Tai';
+SELECT count(DISTINCT t1.clubname) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t3.fname  =  'Eric' AND t3.lname  =  'Tai';
+SELECT DISTINCT t1.clubname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t3.fname  =  'Davis' AND t3.lname  =  'Steven';
+SELECT DISTINCT t1.clubname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t3.fname  =  'Davis' AND t3.lname  =  'Steven';
+SELECT DISTINCT t1.clubname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t3.advisor  =  1121;
+SELECT DISTINCT t1.clubname FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t3.advisor  =  1121;
+SELECT avg(t3.age) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Bootup Baltimore';
+SELECT avg(t3.age) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Bootup Baltimore';
+SELECT avg(t3.age) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Hopkins Student Enterprises';
+SELECT avg(t3.age) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Hopkins Student Enterprises';
+SELECT avg(t3.age) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Tennis Club';
+SELECT avg(t3.age) FROM club AS t1 JOIN member_of_club AS t2 ON t1.clubid  =  t2.clubid JOIN student AS t3 ON t2.stuid  =  t3.stuid WHERE t1.clubname  =  'Tennis Club';
+SELECT T1.grant_amount FROM Grants AS T1 JOIN Documents AS T2 ON T1.grant_id  =  T2.grant_id WHERE T2.sent_date  <  '1986-08-26 20:49:27' INTERSECT SELECT grant_amount FROM grants WHERE grant_end_date  >  '1989-03-16 18:27:16';
+SELECT T1.grant_amount FROM Grants AS T1 JOIN Documents AS T2 ON T1.grant_id  =  T2.grant_id WHERE T2.sent_date  <  '1986-08-26 20:49:27' INTERSECT SELECT grant_amount FROM grants WHERE grant_end_date  >  '1989-03-16 18:27:16';
+SELECT T1.project_details FROM Projects AS T1 JOIN Project_outcomes AS T2 ON T1.project_id  =  T2.project_id WHERE T2.outcome_code  =  'Paper' INTERSECT SELECT T1.project_details FROM Projects AS T1 JOIN Project_outcomes AS T2 ON T1.project_id  =  T2.project_id WHERE T2.outcome_code  =  'Patent';
+SELECT T1.project_details FROM Projects AS T1 JOIN Project_outcomes AS T2 ON T1.project_id  =  T2.project_id WHERE T2.outcome_code  =  'Paper' INTERSECT SELECT T1.project_details FROM Projects AS T1 JOIN Project_outcomes AS T2 ON T1.project_id  =  T2.project_id WHERE T2.outcome_code  =  'Patent';
+SELECT sum(grant_amount) FROM Grants AS T1 JOIN Organisations AS T2 ON T1.organisation_id  =  T2.organisation_id JOIN organisation_Types AS T3 ON T2.organisation_type  =  T3.organisation_type WHERE T3.organisation_type_description  =  'Research';
+SELECT sum(grant_amount) FROM Grants AS T1 JOIN Organisations AS T2 ON T1.organisation_id  =  T2.organisation_id JOIN organisation_Types AS T3 ON T2.organisation_type  =  T3.organisation_type WHERE T3.organisation_type_description  =  'Research';
+SELECT date_from ,  date_to FROM Project_Staff WHERE project_id IN( SELECT project_id FROM Project_Staff GROUP BY project_id ORDER BY count(*) DESC LIMIT 1 ) UNION SELECT date_from ,  date_to FROM Project_Staff WHERE role_code  =  'leader';
+SELECT date_from ,  date_to FROM Project_Staff WHERE project_id IN( SELECT project_id FROM Project_Staff GROUP BY project_id ORDER BY count(*) DESC LIMIT 1 ) UNION SELECT date_from ,  date_to FROM Project_Staff WHERE role_code  =  'leader';
+SELECT T2.organisation_id ,  T2.organisation_details FROM Grants AS T1 JOIN Organisations AS T2 ON T1.organisation_id  =  T2.organisation_id GROUP BY T2.organisation_id HAVING sum(T1.grant_amount)  >  6000;
+SELECT T2.organisation_id ,  T2.organisation_details FROM Grants AS T1 JOIN Organisations AS T2 ON T1.organisation_id  =  T2.organisation_id GROUP BY T2.organisation_id HAVING sum(T1.grant_amount)  >  6000;
+SELECT T1.organisation_type ,  T1.organisation_id FROM Organisations AS T1 JOIN Research_Staff AS T2 ON T1.organisation_id  =  T2.employer_organisation_id GROUP BY T1.organisation_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.organisation_type ,  T1.organisation_id FROM Organisations AS T1 JOIN Research_Staff AS T2 ON T1.organisation_id  =  T2.employer_organisation_id GROUP BY T1.organisation_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.organisation_type FROM Organisations AS T1 JOIN Research_Staff AS T2 ON T1.organisation_id  =  T2.employer_organisation_id GROUP BY T1.organisation_type ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.organisation_type FROM Organisations AS T1 JOIN Research_Staff AS T2 ON T1.organisation_id  =  T2.employer_organisation_id GROUP BY T1.organisation_type ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.sent_date FROM documents AS T1 JOIN Grants AS T2 ON T1.grant_id  =  T2.grant_id JOIN Organisations AS T3 ON T2.organisation_id  =  T3.organisation_id JOIN organisation_Types AS T4 ON T3.organisation_type  =  T4.organisation_type WHERE T2.grant_amount  >  5000 AND T4.organisation_type_description  =  'Research';
+SELECT T1.sent_date FROM documents AS T1 JOIN Grants AS T2 ON T1.grant_id  =  T2.grant_id JOIN Organisations AS T3 ON T2.organisation_id  =  T3.organisation_id JOIN organisation_Types AS T4 ON T3.organisation_type  =  T4.organisation_type WHERE T2.grant_amount  >  5000 AND T4.organisation_type_description  =  'Research';
+SELECT T1.response_received_date FROM Documents AS T1 JOIN Document_Types AS T2 ON T1.document_type_code  =  T2.document_type_code JOIN Grants AS T3 ON T1.grant_id  =  T3.grant_id WHERE T2.document_description  =  'Regular' OR T3.grant_amount  >  100;
+SELECT T1.response_received_date FROM Documents AS T1 JOIN Document_Types AS T2 ON T1.document_type_code  =  T2.document_type_code JOIN Grants AS T3 ON T1.grant_id  =  T3.grant_id WHERE T2.document_description  =  'Regular' OR T3.grant_amount  >  100;
+SELECT project_details FROM Projects WHERE project_id NOT IN ( SELECT project_id FROM Project_Staff WHERE role_code  =  'researcher' );
+SELECT project_details FROM Projects WHERE project_id NOT IN ( SELECT project_id FROM Project_Staff WHERE role_code  =  'researcher' );
+SELECT T1.task_details ,  T1.task_id ,  T2.project_id FROM Tasks AS T1 JOIN Projects AS T2 ON T1.project_id  =  T2.project_id WHERE T2.project_details  =  'omnis' UNION SELECT T1.task_details ,  T1.task_id ,  T2.project_id FROM Tasks AS T1 JOIN Projects AS T2 ON T1.project_id  =  T2.project_id JOIN Project_outcomes AS T3 ON T2.project_id  =  T3.project_id GROUP BY T2.project_id HAVING count(*)  >  2;
+SELECT T1.task_details ,  T1.task_id ,  T2.project_id FROM Tasks AS T1 JOIN Projects AS T2 ON T1.project_id  =  T2.project_id WHERE T2.project_details  =  'omnis' UNION SELECT T1.task_details ,  T1.task_id ,  T2.project_id FROM Tasks AS T1 JOIN Projects AS T2 ON T1.project_id  =  T2.project_id JOIN Project_outcomes AS T3 ON T2.project_id  =  T3.project_id GROUP BY T2.project_id HAVING count(*)  >  2;
+SELECT date_from ,  date_to FROM Project_Staff WHERE role_code  =  'researcher';
+SELECT date_from ,  date_to FROM Project_Staff WHERE role_code  =  'researcher';
+SELECT count(DISTINCT role_code) FROM Project_Staff;
+SELECT count(DISTINCT role_code) FROM Project_Staff;
+SELECT sum(grant_amount) ,  organisation_id FROM Grants GROUP BY organisation_id;
+SELECT sum(grant_amount) ,  organisation_id FROM Grants GROUP BY organisation_id;
+SELECT T1.project_details FROM Projects AS T1 JOIN Project_outcomes AS T2 ON T1.project_id  =  T2.project_id JOIN Research_outcomes AS T3 ON T2.outcome_code  =  T3.outcome_code WHERE T3.outcome_description LIKE '%Published%';
+SELECT T1.project_details FROM Projects AS T1 JOIN Project_outcomes AS T2 ON T1.project_id  =  T2.project_id JOIN Research_outcomes AS T3 ON T2.outcome_code  =  T3.outcome_code WHERE T3.outcome_description LIKE '%Published%';
+SELECT T1.project_id ,  count(*) FROM Project_Staff AS T1 JOIN Projects AS T2 ON T1.project_id  =  T2.project_id GROUP BY T1.project_id ORDER BY count(*) ASC;
+SELECT T1.project_id ,  count(*) FROM Project_Staff AS T1 JOIN Projects AS T2 ON T1.project_id  =  T2.project_id GROUP BY T1.project_id ORDER BY count(*) ASC;
+SELECT role_description FROM Staff_Roles WHERE role_code  =  'researcher';
+SELECT role_description FROM Staff_Roles WHERE role_code  =  'researcher';
+SELECT date_from FROM Project_Staff ORDER BY date_from ASC LIMIT 1;
+SELECT date_from FROM Project_Staff ORDER BY date_from ASC LIMIT 1;
+SELECT T1.project_details ,  T1.project_id FROM Projects AS T1 JOIN Project_outcomes AS T2 ON T1.project_id  =  T2.project_id GROUP BY T1.project_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.project_details ,  T1.project_id FROM Projects AS T1 JOIN Project_outcomes AS T2 ON T1.project_id  =  T2.project_id GROUP BY T1.project_id ORDER BY count(*) DESC LIMIT 1;
+SELECT project_details FROM Projects WHERE project_id NOT IN ( SELECT project_id FROM Project_outcomes );
+SELECT project_details FROM Projects WHERE project_id NOT IN ( SELECT project_id FROM Project_outcomes );
+SELECT T1.organisation_id ,  T1.organisation_type ,  T1.organisation_details FROM Organisations AS T1 JOIN Research_Staff AS T2 ON T1.organisation_id  =  T2.employer_organisation_id GROUP BY T1.organisation_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.organisation_id ,  T1.organisation_type ,  T1.organisation_details FROM Organisations AS T1 JOIN Research_Staff AS T2 ON T1.organisation_id  =  T2.employer_organisation_id GROUP BY T1.organisation_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.role_description ,  T2.staff_id FROM Staff_Roles AS T1 JOIN Project_Staff AS T2 ON T1.role_code  =  T2.role_code JOIN Project_outcomes AS T3 ON T2.project_id  =  T3.project_id GROUP BY T2.staff_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.role_description ,  T2.staff_id FROM Staff_Roles AS T1 JOIN Project_Staff AS T2 ON T1.role_code  =  T2.role_code JOIN Project_outcomes AS T3 ON T2.project_id  =  T3.project_id GROUP BY T2.staff_id ORDER BY count(*) DESC LIMIT 1;
+SELECT document_type_code FROM Document_Types WHERE document_description LIKE 'Initial%';
+SELECT document_type_code FROM Document_Types WHERE document_description LIKE 'Initial%';
+SELECT T1.grant_start_date FROM Grants AS T1 JOIN Documents AS T2 ON T1.grant_id  =  T2.grant_id JOIN Document_Types AS T3 ON T2.document_type_code  =  T3.document_type_code WHERE T3.document_description  =  'Regular' INTERSECT SELECT T1.grant_start_date FROM Grants AS T1 JOIN Documents AS T2 ON T1.grant_id  =  T2.grant_id JOIN Document_Types AS T3 ON T2.document_type_code  =  T3.document_type_code WHERE T3.document_description  =  'Initial Application';
+SELECT T1.grant_start_date FROM Grants AS T1 JOIN Documents AS T2 ON T1.grant_id  =  T2.grant_id JOIN Document_Types AS T3 ON T2.document_type_code  =  T3.document_type_code WHERE T3.document_description  =  'Regular' INTERSECT SELECT T1.grant_start_date FROM Grants AS T1 JOIN Documents AS T2 ON T1.grant_id  =  T2.grant_id JOIN Document_Types AS T3 ON T2.document_type_code  =  T3.document_type_code WHERE T3.document_description  =  'Initial Application';
+SELECT grant_id ,  count(*) FROM Documents GROUP BY grant_id ORDER BY count(*) DESC LIMIT 1;
+SELECT grant_id ,  count(*) FROM Documents GROUP BY grant_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.organisation_type_description FROM organisation_Types AS T1 JOIN Organisations AS T2 ON T1.organisation_type  =  T2.organisation_type WHERE T2.organisation_details  =  'quo';
+SELECT T1.organisation_type_description FROM organisation_Types AS T1 JOIN Organisations AS T2 ON T1.organisation_type  =  T2.organisation_type WHERE T2.organisation_details  =  'quo';
+SELECT organisation_details FROM Organisations AS T1 JOIN organisation_Types AS T2 ON T1.organisation_type  =  T2.organisation_type WHERE T2.organisation_type_description  =  'Sponsor' ORDER BY organisation_details;
+SELECT organisation_details FROM Organisations AS T1 JOIN organisation_Types AS T2 ON T1.organisation_type  =  T2.organisation_type WHERE T2.organisation_type_description  =  'Sponsor' ORDER BY organisation_details;
+SELECT count(*) FROM Project_outcomes WHERE outcome_code  =  'Patent';
+SELECT count(*) FROM Project_outcomes WHERE outcome_code  =  'Patent';
+SELECT count(*) FROM Project_Staff WHERE role_code  =  'leader' OR date_from  <  '1989-04-24 23:51:54';
+SELECT count(*) FROM Project_Staff WHERE role_code  =  'leader' OR date_from  <  '1989-04-24 23:51:54';
+SELECT date_to FROM Project_Staff ORDER BY date_to DESC LIMIT 1;
+SELECT date_to FROM Project_Staff ORDER BY date_to DESC LIMIT 1;
+SELECT T1.outcome_description FROM Research_outcomes AS T1 JOIN Project_outcomes AS T2 ON T1.outcome_code  =  T2.outcome_code JOIN Projects AS T3 ON T2.project_id  =  T3.project_id WHERE T3.project_details  =  'sint';
+SELECT T1.outcome_description FROM Research_outcomes AS T1 JOIN Project_outcomes AS T2 ON T1.outcome_code  =  T2.outcome_code JOIN Projects AS T3 ON T2.project_id  =  T3.project_id WHERE T3.project_details  =  'sint';
+SELECT T1.organisation_id ,  count(*) FROM Projects AS T1 JOIN Project_outcomes AS T2 ON T1.project_id  =  T2.project_id GROUP BY T1.organisation_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.organisation_id ,  count(*) FROM Projects AS T1 JOIN Project_outcomes AS T2 ON T1.project_id  =  T2.project_id GROUP BY T1.organisation_id ORDER BY count(*) DESC LIMIT 1;
+SELECT project_details FROM Projects WHERE organisation_id IN ( SELECT organisation_id FROM Projects GROUP BY organisation_id ORDER BY count(*) DESC LIMIT 1 );
+SELECT project_details FROM Projects WHERE organisation_id IN ( SELECT organisation_id FROM Projects GROUP BY organisation_id ORDER BY count(*) DESC LIMIT 1 );
+SELECT staff_details FROM Research_Staff ORDER BY staff_details ASC;
+SELECT staff_details FROM Research_Staff ORDER BY staff_details ASC;
+SELECT count(*) FROM Tasks;
+SELECT count(*) FROM Tasks;
+SELECT count(*) ,  T1.project_details FROM Projects AS T1 JOIN Tasks AS T2 ON T1.project_id  =  T2.project_id GROUP BY T1.project_id;
+SELECT count(*) ,  T1.project_details FROM Projects AS T1 JOIN Tasks AS T2 ON T1.project_id  =  T2.project_id GROUP BY T1.project_id;
+SELECT role_code FROM Project_Staff WHERE date_from  >  '2003-04-19 15:06:20' AND date_to  <  '2016-03-15 00:33:18';
+SELECT role_code FROM Project_Staff WHERE date_from  >  '2003-04-19 15:06:20' AND date_to  <  '2016-03-15 00:33:18';
+SELECT T1.outcome_description FROM Research_outcomes AS T1 JOIN Project_outcomes AS T2 ON T1.outcome_code  =  T2.outcome_code;
+SELECT T1.outcome_description FROM Research_outcomes AS T1 JOIN Project_outcomes AS T2 ON T1.outcome_code  =  T2.outcome_code;
+SELECT role_code FROM Project_Staff GROUP BY role_code ORDER BY count(*) DESC LIMIT 1;
+SELECT role_code FROM Project_Staff GROUP BY role_code ORDER BY count(*) DESC LIMIT 1;
+SELECT count(T2.friend) FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T1.name  =  'Dan';
+SELECT count(T2.friend) FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T1.name  =  'Dan';
+SELECT count(*) FROM Person WHERE gender  =  'female';
+SELECT count(*) FROM Person WHERE gender  =  'female';
+SELECT avg(age) FROM Person;
+SELECT avg(age) FROM Person;
+SELECT count(DISTINCT city) FROM Person;
+SELECT count(DISTINCT city) FROM Person;
+SELECT count(DISTINCT job) FROM Person;
+SELECT count(DISTINCT job) FROM Person;
+SELECT name FROM Person WHERE age  =  (SELECT max(age) FROM person);
+SELECT name FROM Person WHERE age  =  (SELECT max(age) FROM person);
+SELECT name FROM Person WHERE job  =  'student' AND age  =  (SELECT max(age) FROM person WHERE job  =  'student' );
+SELECT name FROM Person WHERE job  =  'student' AND age  =  (SELECT max(age) FROM person WHERE job  =  'student' );
+SELECT name FROM Person WHERE gender  =  'male' AND age  =  (SELECT min(age) FROM person WHERE gender  =  'male' );
+SELECT name FROM Person WHERE gender  =  'male' AND age  =  (SELECT min(age) FROM person WHERE gender  =  'male' );
+SELECT age FROM Person WHERE job  =  'doctor' AND name  =  'Zach';
+SELECT age FROM Person WHERE job  =  'doctor' AND name  =  'Zach';
+SELECT name FROM Person WHERE age  <  30;
+SELECT name FROM Person WHERE age  <  30;
+SELECT count(*) FROM Person WHERE age  >  30 AND job  =  'engineer';
+SELECT count(*) FROM Person WHERE age  >  30 AND job  =  'engineer';
+SELECT avg(age) ,  gender FROM Person GROUP BY gender;
+SELECT avg(age) ,  gender FROM Person GROUP BY gender;
+SELECT avg(age) ,  job FROM Person GROUP BY job;
+SELECT avg(age) ,  job FROM Person GROUP BY job;
+SELECT avg(age) ,  job FROM Person WHERE gender  =  'male' GROUP BY job;
+SELECT avg(age) ,  job FROM Person WHERE gender  =  'male' GROUP BY job;
+SELECT min(age) ,  job FROM Person GROUP BY job;
+SELECT min(age) ,  job FROM Person GROUP BY job;
+SELECT count(*) ,  gender FROM Person WHERE age  <  40 GROUP BY gender;
+SELECT count(*) ,  gender FROM Person WHERE age  <  40 GROUP BY gender;
+SELECT name FROM Person WHERE age  >  (SELECT min(age) FROM person WHERE job  =  'engineer') ORDER BY age;
+SELECT name FROM Person WHERE age  >  (SELECT min(age) FROM person WHERE job  =  'engineer') ORDER BY age;
+SELECT count(*) FROM Person WHERE age  >  (SELECT max(age) FROM person WHERE job  =  'engineer');
+SELECT count(*) FROM Person WHERE age  >  (SELECT max(age) FROM person WHERE job  =  'engineer');
+SELECT name ,  job FROM Person ORDER BY name;
+SELECT name ,  job FROM Person ORDER BY name;
+SELECT name FROM Person ORDER BY age DESC;
+SELECT name FROM Person ORDER BY age DESC;
+SELECT name FROM Person WHERE gender  =  'male' ORDER BY age;
+SELECT name FROM Person WHERE gender  =  'male' ORDER BY age;
+SELECT T1.name ,  T1.age FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend  =  'Dan' INTERSECT SELECT T1.name ,   T1.age FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend  =  'Alice';
+SELECT T1.name ,  T1.age FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend  =  'Dan' INTERSECT SELECT T1.name ,   T1.age FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend  =  'Alice';
+SELECT DISTINCT T1.name ,  T1.age FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend  =  'Dan' OR T2.friend  =  'Alice';
+SELECT DISTINCT T1.name ,  T1.age FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend  =  'Dan' OR T2.friend  =  'Alice';
+SELECT T1.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend IN (SELECT name FROM Person WHERE age  >  40) INTERSECT SELECT T1.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend IN (SELECT name FROM Person WHERE age  <  30);
+SELECT T1.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend IN (SELECT name FROM Person WHERE age  >  40) INTERSECT SELECT T1.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend IN (SELECT name FROM Person WHERE age  <  30);
+SELECT T1.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend IN (SELECT name FROM Person WHERE age  >  40) EXCEPT SELECT T1.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend IN (SELECT name FROM Person WHERE age  <  30);
+SELECT T1.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend IN (SELECT name FROM Person WHERE age  >  40) EXCEPT SELECT T1.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend IN (SELECT name FROM Person WHERE age  <  30);
+SELECT name FROM person EXCEPT SELECT T2.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.friend WHERE T1.job  =  'student';
+SELECT name FROM person EXCEPT SELECT T2.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.friend WHERE T1.job  =  'student';
+SELECT name FROM PersonFriend GROUP BY name HAVING count(*)  =  1;
+SELECT name FROM PersonFriend GROUP BY name HAVING count(*)  =  1;
+SELECT T2.friend FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T1.name  =  'Bob';
+SELECT T2.friend FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T1.name  =  'Bob';
+SELECT T1.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend  =  'Bob';
+SELECT T1.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend  =  'Bob';
+SELECT T1.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend  =  'Zach' AND T1.gender  =  'female';
+SELECT T1.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend  =  'Zach' AND T1.gender  =  'female';
+SELECT T2.friend FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.friend WHERE T2.name  =  'Alice' AND T1.gender  =  'female';
+SELECT T2.friend FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.friend WHERE T2.name  =  'Alice' AND T1.gender  =  'female';
+SELECT T2.friend FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.friend WHERE T2.name  =  'Alice' AND T1.gender  =  'male' AND T1.job  =  'doctor';
+SELECT T2.friend FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.friend WHERE T2.name  =  'Alice' AND T1.gender  =  'male' AND T1.job  =  'doctor';
+SELECT T2.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.friend WHERE T1.city  =  'new york city';
+SELECT T2.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.friend WHERE T1.city  =  'new york city';
+SELECT DISTINCT T2.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.friend WHERE T1.age  <  (SELECT avg(age) FROM person);
+SELECT DISTINCT T2.name FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.friend WHERE T1.age  <  (SELECT avg(age) FROM person);
+SELECT DISTINCT T2.name ,  T2.friend ,  T1.age FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.friend WHERE T1.age  >  (SELECT avg(age) FROM person);
+SELECT DISTINCT T2.name ,  T2.friend ,  T1.age FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.friend WHERE T1.age  >  (SELECT avg(age) FROM person);
+SELECT friend FROM PersonFriend WHERE name =  'Zach' AND YEAR  =  (SELECT max(YEAR) FROM PersonFriend WHERE name =  'Zach');
+SELECT friend FROM PersonFriend WHERE name =  'Zach' AND YEAR  =  (SELECT max(YEAR) FROM PersonFriend WHERE name =  'Zach');
+SELECT T1.age FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.friend WHERE T2.name =  'Zach' AND T2.year  =  (SELECT max(YEAR) FROM PersonFriend WHERE name =  'Zach');
+SELECT T1.age FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.friend WHERE T2.name =  'Zach' AND T2.year  =  (SELECT max(YEAR) FROM PersonFriend WHERE name =  'Zach');
+SELECT name FROM PersonFriend WHERE friend =  'Alice' AND YEAR  =  (SELECT min(YEAR) FROM PersonFriend WHERE friend =  'Alice');
+SELECT name FROM PersonFriend WHERE friend =  'Alice' AND YEAR  =  (SELECT min(YEAR) FROM PersonFriend WHERE friend =  'Alice');
+SELECT T1.name ,  T1.age ,  T1.job FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend =  'Alice' AND T2.year  =  (SELECT max(YEAR) FROM PersonFriend WHERE friend =  'Alice');
+SELECT T1.name ,  T1.age ,  T1.job FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.name WHERE T2.friend =  'Alice' AND T2.year  =  (SELECT max(YEAR) FROM PersonFriend WHERE friend =  'Alice');
+SELECT name FROM person EXCEPT SELECT name FROM PersonFriend;
+SELECT name FROM person EXCEPT SELECT name FROM PersonFriend;
+SELECT T2.name ,  avg(T1.age) FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.friend GROUP BY T2.name ORDER BY avg(T1.age) DESC LIMIT 1;
+SELECT T2.name ,  avg(T1.age) FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.friend GROUP BY T2.name ORDER BY avg(T1.age) DESC LIMIT 1;
+SELECT count(DISTINCT name) FROM PersonFriend WHERE friend NOT IN (SELECT name FROM person WHERE city  =  'Austin');
+SELECT count(DISTINCT name) FROM PersonFriend WHERE friend NOT IN (SELECT name FROM person WHERE city  =  'Austin');
+SELECT DISTINCT T4.name FROM PersonFriend AS T1 JOIN Person AS T2 ON T1.name  =  T2.name JOIN PersonFriend AS T3 ON T1.friend  =  T3.name JOIN PersonFriend AS T4 ON T3.friend  =  T4.name WHERE T2.name  =  'Alice' AND T4.name != 'Alice';
+SELECT DISTINCT T4.name FROM PersonFriend AS T1 JOIN Person AS T2 ON T1.name  =  T2.name JOIN PersonFriend AS T3 ON T1.friend  =  T3.name JOIN PersonFriend AS T4 ON T3.friend  =  T4.name WHERE T2.name  =  'Alice' AND T4.name != 'Alice';
+SELECT count(*) FROM member;
+SELECT Name FROM member ORDER BY Name ASC;
+SELECT Name ,  Country FROM member;
+SELECT Name FROM member WHERE Country  =  'United States' OR Country  =  'Canada';
+SELECT Country ,  COUNT(*) FROM member GROUP BY Country;
+SELECT Country FROM member GROUP BY Country ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Country FROM member GROUP BY Country HAVING COUNT(*)  >  2;
+SELECT Leader_Name ,  College_Location FROM college;
+SELECT T2.Name ,  T1.Name FROM college AS T1 JOIN member AS T2 ON T1.College_ID  =  T2.College_ID;
+SELECT T2.Name ,  T1.College_Location FROM college AS T1 JOIN member AS T2 ON T1.College_ID  =  T2.College_ID ORDER BY T2.Name ASC;
+SELECT DISTINCT T1.Leader_Name FROM college AS T1 JOIN member AS T2 ON T1.College_ID  =  T2.College_ID WHERE T2.Country  =  'Canada';
+SELECT T1.Name ,  T2.Decoration_Theme FROM member AS T1 JOIN round AS T2 ON T1.Member_ID  =  T2.Member_ID;
+SELECT T1.Name FROM member AS T1 JOIN round AS T2 ON T1.Member_ID  =  T2.Member_ID WHERE T2.Rank_in_Round  >  3;
+SELECT T1.Name FROM member AS T1 JOIN round AS T2 ON T1.Member_ID  =  T2.Member_ID ORDER BY Rank_in_Round ASC;
+SELECT Name FROM member WHERE Member_ID NOT IN (SELECT Member_ID FROM round);
+SELECT document_name ,  access_count FROM documents ORDER BY document_name;
+SELECT document_name ,  access_count FROM documents ORDER BY document_name;
+SELECT document_name ,  access_count FROM documents ORDER BY access_count DESC LIMIT 1;
+SELECT document_name ,  access_count FROM documents ORDER BY access_count DESC LIMIT 1;
+SELECT document_type_code FROM documents GROUP BY document_type_code HAVING count(*)  >  4;
+SELECT document_type_code FROM documents GROUP BY document_type_code HAVING count(*)  >  4;
+SELECT sum(access_count) FROM documents GROUP BY document_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT sum(access_count) FROM documents GROUP BY document_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT avg(access_count) FROM documents;
+SELECT avg(access_count) FROM documents;
+SELECT t2.document_structure_description FROM documents AS t1 JOIN document_structures AS t2 ON t1.document_structure_code  =  t2.document_structure_code GROUP BY t1.document_structure_code ORDER BY count(*) DESC LIMIT 1;
+SELECT t2.document_structure_description FROM documents AS t1 JOIN document_structures AS t2 ON t1.document_structure_code  =  t2.document_structure_code GROUP BY t1.document_structure_code ORDER BY count(*) DESC LIMIT 1;
+SELECT document_type_code FROM documents WHERE document_name  =  'David CV';
+SELECT document_type_code FROM documents WHERE document_name  =  'David CV';
+SELECT document_name FROM documents GROUP BY document_type_code ORDER BY count(*) DESC LIMIT 3 INTERSECT SELECT document_name FROM documents GROUP BY document_structure_code ORDER BY count(*) DESC LIMIT 3;
+SELECT document_name FROM documents GROUP BY document_type_code ORDER BY count(*) DESC LIMIT 3 INTERSECT SELECT document_name FROM documents GROUP BY document_structure_code ORDER BY count(*) DESC LIMIT 3;
+SELECT document_type_code FROM documents GROUP BY document_type_code HAVING sum(access_count)  >  10000;
+SELECT document_type_code FROM documents GROUP BY document_type_code HAVING sum(access_count)  >  10000;
+SELECT t2.section_title FROM documents AS t1 JOIN document_sections AS t2 ON t1.document_code  =  t2.document_code WHERE t1.document_name  =  'David CV';
+SELECT t2.section_title FROM documents AS t1 JOIN document_sections AS t2 ON t1.document_code  =  t2.document_code WHERE t1.document_name  =  'David CV';
+SELECT document_name FROM documents WHERE document_code NOT IN (SELECT document_code FROM document_sections);
+SELECT document_name FROM documents WHERE document_code NOT IN (SELECT document_code FROM document_sections);
+SELECT user_name ,  password FROM users GROUP BY role_code ORDER BY count(*) DESC LIMIT 1;
+SELECT user_name ,  password FROM users GROUP BY role_code ORDER BY count(*) DESC LIMIT 1;
+SELECT avg(t1.access_count) FROM documents AS t1 JOIN document_functional_areas AS t2 ON t1.document_code  =  t2.document_code JOIN functional_areas AS t3 ON t2.functional_area_code  =  t3.functional_area_code WHERE t3.functional_area_description  =  'Acknowledgement';
+SELECT avg(t1.access_count) FROM documents AS t1 JOIN document_functional_areas AS t2 ON t1.document_code  =  t2.document_code JOIN functional_areas AS t3 ON t2.functional_area_code  =  t3.functional_area_code WHERE t3.functional_area_description  =  'Acknowledgement';
+SELECT document_name FROM documents EXCEPT SELECT t1.document_name FROM documents AS t1 JOIN document_sections AS t2 ON t1.document_code  =  t2.document_code JOIN document_sections_images AS t3 ON t2.section_id  =  t3.section_id;
+SELECT document_name FROM documents EXCEPT SELECT t1.document_name FROM documents AS t1 JOIN document_sections AS t2 ON t1.document_code  =  t2.document_code JOIN document_sections_images AS t3 ON t2.section_id  =  t3.section_id;
+SELECT t1.document_name FROM documents AS t1 JOIN document_sections AS t2 ON t1.document_code  =  t2.document_code GROUP BY t1.document_code ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.document_name FROM documents AS t1 JOIN document_sections AS t2 ON t1.document_code  =  t2.document_code GROUP BY t1.document_code ORDER BY count(*) DESC LIMIT 1;
+SELECT document_name FROM documents WHERE document_name LIKE '%CV%';
+SELECT document_name FROM documents WHERE document_name LIKE '%CV%';
+SELECT count(*) FROM users WHERE user_login  =  1;
+SELECT count(*) FROM users WHERE user_login  =  1;
+SELECT role_description FROM ROLES WHERE role_code  =  (SELECT role_code FROM users WHERE user_login  =  1 GROUP BY role_code ORDER BY count(*) DESC LIMIT 1);
+SELECT role_description FROM ROLES WHERE role_code  =  (SELECT role_code FROM users WHERE user_login  =  1 GROUP BY role_code ORDER BY count(*) DESC LIMIT 1);
+SELECT avg(access_count) FROM documents GROUP BY document_structure_code ORDER BY count(*) ASC LIMIT 1;
+SELECT avg(access_count) FROM documents GROUP BY document_structure_code ORDER BY count(*) ASC LIMIT 1;
+SELECT image_name ,  image_url FROM images ORDER BY image_name;
+SELECT image_name ,  image_url FROM images ORDER BY image_name;
+SELECT count(*) ,  role_code FROM users GROUP BY role_code;
+SELECT count(*) ,  role_code FROM users GROUP BY role_code;
+SELECT document_type_code FROM documents GROUP BY document_type_code HAVING count(*)  >  2;
+SELECT document_type_code FROM documents GROUP BY document_type_code HAVING count(*)  >  2;
+SELECT count(*) FROM Companies;
+SELECT count(*) FROM Companies;
+SELECT name FROM Companies ORDER BY Market_Value_billion DESC;
+SELECT name FROM Companies ORDER BY Market_Value_billion DESC;
+SELECT name FROM Companies WHERE Headquarters != 'USA';
+SELECT name FROM Companies WHERE Headquarters != 'USA';
+SELECT name ,  Assets_billion FROM Companies ORDER BY name ASC;
+SELECT name ,  Assets_billion FROM Companies ORDER BY name ASC;
+SELECT avg(Profits_billion) FROM Companies;
+SELECT avg(Profits_billion) FROM Companies;
+SELECT max(Sales_billion) ,  min(Sales_billion) FROM Companies WHERE Industry != 'Banking';
+SELECT max(Sales_billion) ,  min(Sales_billion) FROM Companies WHERE Industry != 'Banking';
+SELECT count(DISTINCT Industry) FROM Companies;
+SELECT count(DISTINCT Industry) FROM Companies;
+SELECT name FROM buildings ORDER BY Height DESC;
+SELECT name FROM buildings ORDER BY Height DESC;
+SELECT Stories FROM buildings ORDER BY Height DESC LIMIT 1;
+SELECT Stories FROM buildings ORDER BY Height DESC LIMIT 1;
+SELECT T3.name ,  T2.name FROM Office_locations AS T1 JOIN buildings AS T2 ON T1.building_id  =  T2.id JOIN Companies AS T3 ON T1.company_id  =  T3.id;
+SELECT T3.name ,  T2.name FROM Office_locations AS T1 JOIN buildings AS T2 ON T1.building_id  =  T2.id JOIN Companies AS T3 ON T1.company_id  =  T3.id;
+SELECT T2.name FROM Office_locations AS T1 JOIN buildings AS T2 ON T1.building_id  =  T2.id JOIN Companies AS T3 ON T1.company_id  =  T3.id GROUP BY T1.building_id HAVING COUNT(*)  >  1;
+SELECT T2.name FROM Office_locations AS T1 JOIN buildings AS T2 ON T1.building_id  =  T2.id JOIN Companies AS T3 ON T1.company_id  =  T3.id GROUP BY T1.building_id HAVING COUNT(*)  >  1;
+SELECT T2.name FROM Office_locations AS T1 JOIN buildings AS T2 ON T1.building_id  =  T2.id JOIN Companies AS T3 ON T1.company_id  =  T3.id GROUP BY T1.building_id ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T2.name FROM Office_locations AS T1 JOIN buildings AS T2 ON T1.building_id  =  T2.id JOIN Companies AS T3 ON T1.company_id  =  T3.id GROUP BY T1.building_id ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT name FROM buildings WHERE Status  =  'on-hold' ORDER BY Stories ASC;
+SELECT name FROM buildings WHERE Status  =  'on-hold' ORDER BY Stories ASC;
+SELECT Industry ,  COUNT(*) FROM Companies GROUP BY Industry;
+SELECT Industry ,  COUNT(*) FROM Companies GROUP BY Industry;
+SELECT Industry FROM Companies GROUP BY Industry ORDER BY COUNT(*) DESC;
+SELECT Industry FROM Companies GROUP BY Industry ORDER BY COUNT(*) DESC;
+SELECT Industry FROM Companies GROUP BY Industry ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Industry FROM Companies GROUP BY Industry ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT name FROM buildings WHERE id NOT IN (SELECT building_id FROM Office_locations);
+SELECT name FROM buildings WHERE id NOT IN (SELECT building_id FROM Office_locations);
+SELECT Industry FROM Companies WHERE Headquarters  =  'USA' INTERSECT SELECT Industry FROM Companies WHERE Headquarters  =  'China';
+SELECT Industry FROM Companies WHERE Headquarters  =  'USA' INTERSECT SELECT Industry FROM Companies WHERE Headquarters  =  'China';
+SELECT count(*) FROM Companies WHERE Industry  =  'Banking' OR Industry  =  'Conglomerate';
+SELECT count(*) FROM Companies WHERE Industry  =  'Banking' OR Industry  =  'Conglomerate';
+SELECT Headquarters FROM Companies GROUP BY Headquarters HAVING COUNT(*)  >  2;
+SELECT Headquarters FROM Companies GROUP BY Headquarters HAVING COUNT(*)  >  2;
+SELECT count(*) FROM Products;
+SELECT Product_Name FROM Products ORDER BY Product_Price ASC;
+SELECT Product_Name ,  Product_Type_Code FROM Products;
+SELECT Product_Price FROM Products WHERE Product_Name  =  'Dining' OR Product_Name  =  'Trading Policy';
+SELECT avg(Product_Price) FROM Products;
+SELECT Product_Name FROM Products ORDER BY Product_Price DESC LIMIT 1;
+SELECT Product_Type_Code ,  COUNT(*) FROM Products GROUP BY Product_Type_Code;
+SELECT Product_Type_Code FROM Products GROUP BY Product_Type_Code ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Product_Type_Code FROM Products GROUP BY Product_Type_Code HAVING COUNT(*)  >=  2;
+SELECT Product_Type_Code FROM Products WHERE Product_Price  >  4500 INTERSECT SELECT Product_Type_Code FROM Products WHERE Product_Price  <  3000;
+SELECT T1.Product_Name ,  COUNT(*) FROM Products AS T1 JOIN Products_in_Events AS T2 ON T1.Product_ID  =  T2.Product_ID GROUP BY T1.Product_Name;
+SELECT T1.Product_Name ,  COUNT(*) FROM Products AS T1 JOIN Products_in_Events AS T2 ON T1.Product_ID  =  T2.Product_ID GROUP BY T1.Product_Name ORDER BY COUNT(*) DESC;
+SELECT T1.Product_Name FROM Products AS T1 JOIN Products_in_Events AS T2 ON T1.Product_ID  =  T2.Product_ID GROUP BY T1.Product_Name HAVING COUNT(*)  >=  2;
+SELECT T1.Product_Name FROM Products AS T1 JOIN Products_in_Events AS T2 ON T1.Product_ID  =  T2.Product_ID GROUP BY T1.Product_Name HAVING COUNT(*)  >=  2 ORDER BY T1.Product_Name;
+SELECT Product_Name FROM Products WHERE Product_ID NOT IN (SELECT Product_ID FROM Products_in_Events);
+SELECT count(*) FROM artwork;
+SELECT Name FROM artwork ORDER BY Name ASC;
+SELECT Name FROM artwork WHERE TYPE != 'Program Talent Show';
+SELECT Festival_Name ,  LOCATION FROM festival_detail;
+SELECT Chair_Name FROM festival_detail ORDER BY YEAR ASC;
+SELECT LOCATION FROM festival_detail ORDER BY Num_of_Audience DESC LIMIT 1;
+SELECT Festival_Name FROM festival_detail WHERE YEAR  =  2007;
+SELECT avg(Num_of_Audience) FROM festival_detail;
+SELECT Festival_Name FROM festival_detail ORDER BY YEAR DESC LIMIT 3;
+SELECT T2.Name ,  T3.Festival_Name FROM nomination AS T1 JOIN artwork AS T2 ON T1.Artwork_ID  =  T2.Artwork_ID JOIN festival_detail AS T3 ON T1.Festival_ID  =  T3.Festival_ID;
+SELECT DISTINCT T2.Type FROM nomination AS T1 JOIN artwork AS T2 ON T1.Artwork_ID  =  T2.Artwork_ID JOIN festival_detail AS T3 ON T1.Festival_ID  =  T3.Festival_ID WHERE T3.Year  =  2007;
+SELECT T2.Name FROM nomination AS T1 JOIN artwork AS T2 ON T1.Artwork_ID  =  T2.Artwork_ID JOIN festival_detail AS T3 ON T1.Festival_ID  =  T3.Festival_ID ORDER BY T3.Year;
+SELECT T3.Festival_Name FROM nomination AS T1 JOIN artwork AS T2 ON T1.Artwork_ID  =  T2.Artwork_ID JOIN festival_detail AS T3 ON T1.Festival_ID  =  T3.Festival_ID WHERE T2.Type  =  'Program Talent Show';
+SELECT T1.Festival_ID ,  T3.Festival_Name FROM nomination AS T1 JOIN artwork AS T2 ON T1.Artwork_ID  =  T2.Artwork_ID JOIN festival_detail AS T3 ON T1.Festival_ID  =  T3.Festival_ID GROUP BY T1.Festival_ID HAVING COUNT(*)  >=  2;
+SELECT T1.Festival_ID ,  T3.Festival_Name ,  COUNT(*) FROM nomination AS T1 JOIN artwork AS T2 ON T1.Artwork_ID  =  T2.Artwork_ID JOIN festival_detail AS T3 ON T1.Festival_ID  =  T3.Festival_ID GROUP BY T1.Festival_ID;
+SELECT TYPE ,  COUNT(*) FROM artwork GROUP BY TYPE;
+SELECT TYPE FROM artwork GROUP BY TYPE ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT YEAR FROM festival_detail GROUP BY YEAR HAVING COUNT(*)  >  1;
+SELECT Name FROM Artwork WHERE Artwork_ID NOT IN (SELECT Artwork_ID FROM nomination);
+SELECT Num_of_Audience FROM festival_detail WHERE YEAR  =  2008 OR YEAR  =  2010;
+SELECT sum(Num_of_Audience) FROM festival_detail;
+SELECT YEAR FROM festival_detail WHERE LOCATION  =  'United States' INTERSECT SELECT YEAR FROM festival_detail WHERE LOCATION != 'United States';
+SELECT count(*) FROM premises;
+SELECT DISTINCT premises_type FROM premises;
+SELECT premises_type ,  premise_details FROM premises ORDER BY premises_type;
+SELECT premises_type ,  count(*) FROM premises GROUP BY premises_type;
+SELECT product_category ,  count(*) FROM mailshot_campaigns GROUP BY product_category;
+SELECT customer_name ,  customer_phone FROM customers WHERE customer_id NOT IN (SELECT customer_id FROM mailshot_customers);
+SELECT T1.customer_name ,  T1.customer_phone FROM customers AS T1 JOIN mailshot_customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.outcome_code  =  'No Response';
+SELECT outcome_code ,  count(*) FROM mailshot_customers GROUP BY outcome_code;
+SELECT T2.customer_name FROM mailshot_customers AS T1 JOIN customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE outcome_code  =  'Order' GROUP BY T1.customer_id HAVING count(*)  >=  2;
+SELECT T2.customer_name FROM mailshot_customers AS T1 JOIN customers AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.customer_name ,  T2.payment_method FROM mailshot_customers AS T1 JOIN customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T1.outcome_code  =  'Order' INTERSECT SELECT T2.customer_name ,  T2.payment_method FROM mailshot_customers AS T1 JOIN customers AS T2 ON T1.customer_id  =  T2.customer_id WHERE T1.outcome_code  =  'No Response';
+SELECT T2.premises_type ,  T1.address_type_code FROM customer_addresses AS T1 JOIN premises AS T2 ON T1.premise_id  =  T2.premise_id;
+SELECT DISTINCT address_type_code FROM customer_addresses;
+SELECT order_shipping_charges ,  customer_id FROM customer_orders WHERE order_status_code  =  'Cancelled' OR order_status_code  =  'Paid';
+SELECT T1.customer_name FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id WHERE shipping_method_code  =  'FedEx' AND order_status_code  =  'Paid';
+SELECT count(*) FROM COURSE;
+SELECT count(*) FROM COURSE;
+SELECT count(*) FROM COURSE WHERE Credits  >  2;
+SELECT count(*) FROM COURSE WHERE Credits  >  2;
+SELECT CName FROM COURSE WHERE Credits  =  1;
+SELECT CName FROM COURSE WHERE Credits  =  1;
+SELECT CName FROM COURSE WHERE Days  =  'MTW';
+SELECT CName FROM COURSE WHERE Days  =  'MTW';
+SELECT count(*) FROM DEPARTMENT WHERE Division  =  'AS';
+SELECT count(*) FROM DEPARTMENT WHERE Division  =  'AS';
+SELECT DPhone FROM DEPARTMENT WHERE Room  =  268;
+SELECT DPhone FROM DEPARTMENT WHERE Room  =  268;
+SELECT COUNT(DISTINCT StuID) FROM ENROLLED_IN WHERE Grade  =  'B';
+SELECT COUNT(DISTINCT StuID) FROM ENROLLED_IN WHERE Grade  =  'B';
+SELECT max(gradepoint) ,  min(gradepoint) FROM GRADECONVERSION;
+SELECT max(gradepoint) ,  min(gradepoint) FROM GRADECONVERSION;
+SELECT DISTINCT Fname FROM STUDENT WHERE Fname LIKE '%a%';
+SELECT DISTINCT Fname FROM STUDENT WHERE Fname LIKE '%a%';
+SELECT Fname ,  Lname FROM FACULTY WHERE sex  =  'M' AND Building  =  'NEB';
+SELECT Fname ,  Lname FROM FACULTY WHERE sex  =  'M' AND Building  =  'NEB';
+SELECT Room FROM FACULTY WHERE Rank  =  'Professor' AND Building  =  'NEB';
+SELECT Room FROM FACULTY WHERE Rank  =  'Professor' AND Building  =  'NEB';
+SELECT DName FROM DEPARTMENT WHERE Building  =  'Mergenthaler';
+SELECT DName FROM DEPARTMENT WHERE Building  =  'Mergenthaler';
+SELECT * FROM COURSE ORDER BY Credits;
+SELECT * FROM COURSE ORDER BY Credits;
+SELECT CName FROM COURSE ORDER BY Credits;
+SELECT CName FROM COURSE ORDER BY Credits;
+SELECT Fname FROM STUDENT ORDER BY Age DESC;
+SELECT Fname FROM STUDENT ORDER BY Age DESC;
+SELECT LName FROM STUDENT WHERE Sex  =  'F' ORDER BY Age DESC;
+SELECT LName FROM STUDENT WHERE Sex  =  'F' ORDER BY Age DESC;
+SELECT Lname FROM FACULTY WHERE Building  =  'Barton' ORDER BY Lname;
+SELECT Lname FROM FACULTY WHERE Building  =  'Barton' ORDER BY Lname;
+SELECT Fname FROM FACULTY WHERE Rank  =  'Professor' ORDER BY Fname;
+SELECT Fname FROM FACULTY WHERE Rank  =  'Professor' ORDER BY Fname;
+SELECT T1.DName FROM DEPARTMENT AS T1 JOIN MINOR_IN AS T2 ON T1.DNO  =  T2.DNO GROUP BY T2.DNO ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.DName FROM DEPARTMENT AS T1 JOIN MINOR_IN AS T2 ON T1.DNO  =  T2.DNO GROUP BY T2.DNO ORDER BY count(*) DESC LIMIT 1;
+SELECT DName FROM DEPARTMENT EXCEPT SELECT T1.DName FROM DEPARTMENT AS T1 JOIN MINOR_IN AS T2 ON T1.DNO  =  T2.DNO;
+SELECT DName FROM DEPARTMENT EXCEPT SELECT T1.DName FROM DEPARTMENT AS T1 JOIN MINOR_IN AS T2 ON T1.DNO  =  T2.DNO;
+SELECT T1.DName FROM DEPARTMENT AS T1 JOIN MEMBER_OF AS T2 ON T1.DNO  =  T2.DNO GROUP BY T2.DNO ORDER BY count(*) ASC LIMIT 1;
+SELECT T1.DName FROM DEPARTMENT AS T1 JOIN MEMBER_OF AS T2 ON T1.DNO  =  T2.DNO GROUP BY T2.DNO ORDER BY count(*) ASC LIMIT 1;
+SELECT Rank FROM FACULTY GROUP BY Rank ORDER BY count(*) ASC LIMIT 1;
+SELECT Rank FROM FACULTY GROUP BY Rank ORDER BY count(*) ASC LIMIT 1;
+SELECT T2.Fname ,  T2.Lname FROM COURSE AS T1 JOIN FACULTY AS T2 ON T1.Instructor  =  T2.FacID GROUP BY T1.Instructor ORDER BY count(*) DESC LIMIT 3;
+SELECT T2.Fname ,  T2.Lname FROM COURSE AS T1 JOIN FACULTY AS T2 ON T1.Instructor  =  T2.FacID GROUP BY T1.Instructor ORDER BY count(*) DESC LIMIT 3;
+SELECT T2.Building FROM COURSE AS T1 JOIN FACULTY AS T2 ON T1.Instructor  =  T2.FacID GROUP BY T1.Instructor ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.Building FROM COURSE AS T1 JOIN FACULTY AS T2 ON T1.Instructor  =  T2.FacID GROUP BY T1.Instructor ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.CName FROM COURSE AS T1 JOIN ENROLLED_IN AS T2 ON T1.CID  =  T2.CID GROUP BY T2.CID HAVING COUNT(*)  >=  5;
+SELECT T1.CName FROM COURSE AS T1 JOIN ENROLLED_IN AS T2 ON T1.CID  =  T2.CID GROUP BY T2.CID HAVING COUNT(*)  >=  5;
+SELECT T2.Fname ,  T2.Lname FROM COURSE AS T1 JOIN FACULTY AS T2 ON T1.Instructor  =  T2.FacID WHERE T1.CName  =  'COMPUTER LITERACY';
+SELECT T2.Fname ,  T2.Lname FROM COURSE AS T1 JOIN FACULTY AS T2 ON T1.Instructor  =  T2.FacID WHERE T1.CName  =  'COMPUTER LITERACY';
+SELECT T2.Dname ,  T2.Room FROM COURSE AS T1 JOIN DEPARTMENT AS T2 ON T1.DNO  =  T2.DNO WHERE T1.CName  =  'INTRODUCTION TO COMPUTER SCIENCE';
+SELECT T2.Dname ,  T2.Room FROM COURSE AS T1 JOIN DEPARTMENT AS T2 ON T1.DNO  =  T2.DNO WHERE T1.CName  =  'INTRODUCTION TO COMPUTER SCIENCE';
+SELECT T3.Fname ,  T3.LName ,  T2.gradepoint FROM ENROLLED_IN AS T1 JOIN GRADECONVERSION AS T2 JOIN STUDENT AS T3 ON T1.Grade  =  T2.lettergrade AND T1.StuID  =  T3.StuID;
+SELECT T3.Fname ,  T3.LName ,  T2.gradepoint FROM ENROLLED_IN AS T1 JOIN GRADECONVERSION AS T2 JOIN STUDENT AS T3 ON T1.Grade  =  T2.lettergrade AND T1.StuID  =  T3.StuID;
+SELECT DISTINCT T3.Fname FROM ENROLLED_IN AS T1 JOIN GRADECONVERSION AS T2 JOIN STUDENT AS T3 ON T1.Grade  =  T2.lettergrade AND T1.StuID  =  T3.StuID WHERE T2.gradepoint  >=  3.8;
+SELECT DISTINCT T3.Fname FROM ENROLLED_IN AS T1 JOIN GRADECONVERSION AS T2 JOIN STUDENT AS T3 ON T1.Grade  =  T2.lettergrade AND T1.StuID  =  T3.StuID WHERE T2.gradepoint  >=  3.8;
+SELECT T1.Fname ,  T1.Lname FROM FACULTY AS T1 JOIN MEMBER_OF AS T2 ON T1.FacID  =  T2.FacID WHERE T2.DNO  =  520;
+SELECT T1.Fname ,  T1.Lname FROM FACULTY AS T1 JOIN MEMBER_OF AS T2 ON T1.FacID  =  T2.FacID WHERE T2.DNO  =  520;
+SELECT T2.Fname ,  T2.Lname FROM MINOR_IN AS T1 JOIN STUDENT AS T2 ON T1.StuID  =  T2.StuID WHERE T1.DNO  =  140;
+SELECT T2.Fname ,  T2.Lname FROM MINOR_IN AS T1 JOIN STUDENT AS T2 ON T1.StuID  =  T2.StuID WHERE T1.DNO  =  140;
+SELECT T2.Lname FROM DEPARTMENT AS T1 JOIN FACULTY AS T2 ON T1.DNO  =  T3.DNO JOIN MEMBER_OF AS T3 ON T2.FacID  =  T3.FacID WHERE T1.DName  =  'Computer Science';
+SELECT T2.Lname FROM DEPARTMENT AS T1 JOIN FACULTY AS T2 ON T1.DNO  =  T3.DNO JOIN MEMBER_OF AS T3 ON T2.FacID  =  T3.FacID WHERE T1.DName  =  'Computer Science';
+SELECT avg(T2.gradepoint) FROM ENROLLED_IN AS T1 JOIN GRADECONVERSION AS T2 JOIN STUDENT AS T3 ON T1.Grade  =  T2.lettergrade AND T1.StuID  =  T3.StuID WHERE T3.LName  =  'Smith';
+SELECT avg(T2.gradepoint) FROM ENROLLED_IN AS T1 JOIN GRADECONVERSION AS T2 JOIN STUDENT AS T3 ON T1.Grade  =  T2.lettergrade AND T1.StuID  =  T3.StuID WHERE T3.LName  =  'Smith';
+SELECT max(T2.gradepoint) ,  min(T2.gradepoint) FROM ENROLLED_IN AS T1 JOIN GRADECONVERSION AS T2 JOIN STUDENT AS T3 ON T1.Grade  =  T2.lettergrade AND T1.StuID  =  T3.StuID WHERE T3.city_code  =  'NYC';
+SELECT max(T2.gradepoint) ,  min(T2.gradepoint) FROM ENROLLED_IN AS T1 JOIN GRADECONVERSION AS T2 JOIN STUDENT AS T3 ON T1.Grade  =  T2.lettergrade AND T1.StuID  =  T3.StuID WHERE T3.city_code  =  'NYC';
+SELECT CName FROM COURSE WHERE Credits  =  3 UNION SELECT CName FROM COURSE WHERE Credits  =  1 AND Hours  =  4;
+SELECT CName FROM COURSE WHERE Credits  =  3 UNION SELECT CName FROM COURSE WHERE Credits  =  1 AND Hours  =  4;
+SELECT DName FROM DEPARTMENT WHERE Division  =  'AS' UNION SELECT DName FROM DEPARTMENT WHERE Division  =  'EN' AND Building  =  'NEB';
+SELECT DName FROM DEPARTMENT WHERE Division  =  'AS' UNION SELECT DName FROM DEPARTMENT WHERE Division  =  'EN' AND Building  =  'NEB';
+SELECT Fname FROM STUDENT WHERE StuID NOT IN (SELECT StuID FROM ENROLLED_IN);
+SELECT Fname FROM STUDENT WHERE StuID NOT IN (SELECT StuID FROM ENROLLED_IN);
+SELECT product_id FROM product_suppliers ORDER BY total_amount_purchased DESC LIMIT 3;
+SELECT product_id FROM product_suppliers ORDER BY total_amount_purchased DESC LIMIT 3;
+SELECT product_id ,  product_type_code FROM products ORDER BY product_price LIMIT 1;
+SELECT product_id ,  product_type_code FROM products ORDER BY product_price LIMIT 1;
+SELECT count(DISTINCT product_type_code) FROM products;
+SELECT count(DISTINCT product_type_code) FROM products;
+SELECT T1.address_details FROM addresses AS T1 JOIN customer_addresses AS T2 ON T1.address_id  =  T2.address_id WHERE T2.customer_id  =  10;
+SELECT T1.address_details FROM addresses AS T1 JOIN customer_addresses AS T2 ON T1.address_id  =  T2.address_id WHERE T2.customer_id  =  10;
+SELECT T1.staff_id ,  T1.staff_gender FROM staff AS T1 JOIN staff_department_assignments AS T2 ON T1.staff_id  =  T2.staff_id WHERE T2.job_title_code  =  'Department Manager';
+SELECT T1.staff_id ,  T1.staff_gender FROM staff AS T1 JOIN staff_department_assignments AS T2 ON T1.staff_id  =  T2.staff_id WHERE T2.job_title_code  =  'Department Manager';
+SELECT payment_method_code ,  count(*) FROM customers GROUP BY payment_method_code;
+SELECT payment_method_code ,  count(*) FROM customers GROUP BY payment_method_code;
+SELECT product_id FROM order_items GROUP BY product_id ORDER BY count(*) DESC LIMIT 1;
+SELECT product_id FROM order_items GROUP BY product_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.customer_name ,  T1.customer_phone ,  T1.customer_email FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T2.customer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.customer_name ,  T1.customer_phone ,  T1.customer_email FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T2.customer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT product_type_code ,  avg(product_price) FROM products GROUP BY product_type_code;
+SELECT product_type_code ,  avg(product_price) FROM products GROUP BY product_type_code;
+SELECT count(*) FROM department_stores AS T1 JOIN department_store_chain AS T2 ON T1.dept_store_chain_id  =  T2.dept_store_chain_id WHERE T2.dept_store_chain_name  =  'South';
+SELECT count(*) FROM department_stores AS T1 JOIN department_store_chain AS T2 ON T1.dept_store_chain_id  =  T2.dept_store_chain_id WHERE T2.dept_store_chain_name  =  'South';
+SELECT T1.staff_name ,  T2.job_title_code FROM staff AS T1 JOIN staff_department_assignments AS T2 ON T1.staff_id  =  T2.staff_id ORDER BY T2.date_assigned_to DESC LIMIT 1;
+SELECT T1.staff_name ,  T2.job_title_code FROM staff AS T1 JOIN staff_department_assignments AS T2 ON T1.staff_id  =  T2.staff_id ORDER BY T2.date_assigned_to DESC LIMIT 1;
+SELECT T2.product_type_code ,  T2.product_name ,  T2.product_price FROM product_suppliers AS T1 JOIN products AS T2 ON T1.product_id  =  T2.product_id WHERE T1.supplier_id  =  3;
+SELECT T2.product_type_code ,  T2.product_name ,  T2.product_price FROM product_suppliers AS T1 JOIN products AS T2 ON T1.product_id  =  T2.product_id WHERE T1.supplier_id  =  3;
+SELECT DISTINCT T1.customer_name FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.order_status_code  =  'Pending' ORDER BY T2.customer_id;
+SELECT DISTINCT T1.customer_name FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.order_status_code  =  'Pending' ORDER BY T2.customer_id;
+SELECT T1.customer_name ,  T1.customer_address FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.order_status_code  =  'New' INTERSECT SELECT T1.customer_name ,  T1.customer_address FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.order_status_code  =  'Pending';
+SELECT T1.customer_name ,  T1.customer_address FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.order_status_code  =  'New' INTERSECT SELECT T1.customer_name ,  T1.customer_address FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.order_status_code  =  'Pending';
+SELECT T1.product_id FROM product_suppliers AS T1 JOIN products AS T2 ON T1.product_id  =  T2.product_id WHERE T1.supplier_id  =  2 AND T2.product_price  >  (SELECT avg(product_price) FROM products);
+SELECT T1.product_id FROM product_suppliers AS T1 JOIN products AS T2 ON T1.product_id  =  T2.product_id WHERE T1.supplier_id  =  2 AND T2.product_price  >  (SELECT avg(product_price) FROM products);
+SELECT T2.dept_store_id ,  T2.store_name FROM departments AS T1 JOIN department_stores AS T2 ON T1.dept_store_id  =  T2.dept_store_id WHERE T1.department_name  =  'marketing' INTERSECT SELECT T2.dept_store_id ,  T2.store_name FROM departments AS T1 JOIN department_stores AS T2 ON T1.dept_store_id  =  T2.dept_store_id WHERE T1.department_name  =  'managing';
+SELECT T2.dept_store_id ,  T2.store_name FROM departments AS T1 JOIN department_stores AS T2 ON T1.dept_store_id  =  T2.dept_store_id WHERE T1.department_name  =  'marketing' INTERSECT SELECT T2.dept_store_id ,  T2.store_name FROM departments AS T1 JOIN department_stores AS T2 ON T1.dept_store_id  =  T2.dept_store_id WHERE T1.department_name  =  'managing';
+SELECT dept_store_chain_id FROM department_stores GROUP BY dept_store_chain_id ORDER BY count(*) DESC LIMIT 2;
+SELECT dept_store_chain_id FROM department_stores GROUP BY dept_store_chain_id ORDER BY count(*) DESC LIMIT 2;
+SELECT department_id FROM staff_department_assignments GROUP BY department_id ORDER BY count(*) LIMIT 1;
+SELECT department_id FROM staff_department_assignments GROUP BY department_id ORDER BY count(*) LIMIT 1;
+SELECT product_type_code ,  max(product_price) ,  min(product_price) FROM products GROUP BY product_type_code;
+SELECT product_type_code ,  max(product_price) ,  min(product_price) FROM products GROUP BY product_type_code;
+SELECT product_type_code FROM products GROUP BY product_type_code HAVING avg(product_price)  >  (SELECT avg(product_price) FROM products);
+SELECT product_type_code FROM products GROUP BY product_type_code HAVING avg(product_price)  >  (SELECT avg(product_price) FROM products);
+SELECT T1.staff_id ,  T1.staff_name FROM staff AS T1 JOIN Staff_Department_Assignments AS T2 ON T1.staff_id  =  T2.staff_id ORDER BY date_assigned_to - date_assigned_from LIMIT 1;
+SELECT T1.staff_id ,  T1.staff_name FROM staff AS T1 JOIN Staff_Department_Assignments AS T2 ON T1.staff_id  =  T2.staff_id ORDER BY date_assigned_to - date_assigned_from LIMIT 1;
+SELECT product_name ,  product_id FROM products WHERE product_price BETWEEN 600 AND 700;
+SELECT product_name ,  product_id FROM products WHERE product_price BETWEEN 600 AND 700;
+SELECT DISTINCT customer_id FROM Customer_Orders WHERE order_date  >  (SELECT min(order_date) FROM Customer_Orders WHERE order_status_code  =  'Cancelled');
+SELECT DISTINCT customer_id FROM Customer_Orders WHERE order_date  >  (SELECT min(order_date) FROM Customer_Orders WHERE order_status_code  =  'Cancelled');
+SELECT staff_id FROM Staff_Department_Assignments WHERE date_assigned_to  <  (SELECT max(date_assigned_to) FROM Staff_Department_Assignments WHERE job_title_code  =  'Clerical Staff');
+SELECT staff_id FROM Staff_Department_Assignments WHERE date_assigned_to  <  (SELECT max(date_assigned_to) FROM Staff_Department_Assignments WHERE job_title_code  =  'Clerical Staff');
+SELECT customer_name ,  customer_id FROM customers WHERE customer_address LIKE '%TN%';
+SELECT customer_name ,  customer_id FROM customers WHERE customer_address LIKE '%TN%';
+SELECT T1.staff_name ,  T1.staff_gender FROM staff AS T1 JOIN staff_department_assignments AS T2 ON T1.staff_id  =  T2.staff_id WHERE T2.date_assigned_from LIKE '2016%';
+SELECT T1.staff_name ,  T1.staff_gender FROM staff AS T1 JOIN staff_department_assignments AS T2 ON T1.staff_id  =  T2.staff_id WHERE T2.date_assigned_from LIKE '2016%';
+SELECT T1.staff_name FROM staff AS T1 JOIN staff_department_assignments AS T2 ON T1.staff_id  =  T2.staff_id GROUP BY T2.staff_id HAVING COUNT (*)  >  1;
+SELECT T1.staff_name FROM staff AS T1 JOIN staff_department_assignments AS T2 ON T1.staff_id  =  T2.staff_id GROUP BY T2.staff_id HAVING COUNT (*)  >  1;
+SELECT T1.supplier_name ,  T1.supplier_phone FROM Suppliers AS T1 JOIN supplier_addresses AS T2 ON T1.supplier_id  =  T2.supplier_id JOIN addresses AS T3 ON T2.address_id  =  T3.address_id ORDER BY T3.address_details;
+SELECT T1.supplier_name ,  T1.supplier_phone FROM Suppliers AS T1 JOIN supplier_addresses AS T2 ON T1.supplier_id  =  T2.supplier_id JOIN addresses AS T3 ON T2.address_id  =  T3.address_id ORDER BY T3.address_details;
+SELECT customer_phone FROM customers UNION SELECT supplier_phone FROM suppliers;
+SELECT customer_phone FROM customers UNION SELECT supplier_phone FROM suppliers;
+SELECT product_id FROM Order_Items GROUP BY product_id HAVING count(*)  >  3 UNION SELECT product_id FROM Product_Suppliers GROUP BY product_id HAVING sum(total_amount_purchased)  >  80000;
+SELECT product_id FROM Order_Items GROUP BY product_id HAVING count(*)  >  3 UNION SELECT product_id FROM Product_Suppliers GROUP BY product_id HAVING sum(total_amount_purchased)  >  80000;
+SELECT product_id ,  product_name FROM products WHERE product_price  <  600 OR product_price  >  900;
+SELECT product_id ,  product_name FROM products WHERE product_price  <  600 OR product_price  >  900;
+SELECT supplier_id FROM Product_Suppliers GROUP BY supplier_id HAVING avg(total_amount_purchased)  >  50000 OR avg(total_amount_purchased)  <  30000;
+SELECT supplier_id FROM Product_Suppliers GROUP BY supplier_id HAVING avg(total_amount_purchased)  >  50000 OR avg(total_amount_purchased)  <  30000;
+SELECT avg(total_amount_purchased) ,  avg(total_value_purchased) FROM Product_Suppliers WHERE supplier_id  =  (SELECT supplier_id FROM Product_Suppliers GROUP BY supplier_id ORDER BY count(*) DESC LIMIT 1);
+SELECT avg(total_amount_purchased) ,  avg(total_value_purchased) FROM Product_Suppliers WHERE supplier_id  =  (SELECT supplier_id FROM Product_Suppliers GROUP BY supplier_id ORDER BY count(*) DESC LIMIT 1);
+SELECT max(customer_code) ,  min(customer_code) FROM Customers;
+SELECT max(customer_code) ,  min(customer_code) FROM Customers;
+SELECT DISTINCT T1.customer_name FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id JOIN order_items AS T3 ON T2.order_id  =  T3.order_id JOIN products AS T4 ON T3.product_id  =  T4.product_id WHERE T4.product_name  =  'keyboard';
+SELECT DISTINCT T1.customer_name FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id JOIN order_items AS T3 ON T2.order_id  =  T3.order_id JOIN products AS T4 ON T3.product_id  =  T4.product_id WHERE T4.product_name  =  'keyboard';
+SELECT DISTINCT T1.supplier_name ,  T1.supplier_phone FROM suppliers AS T1 JOIN product_suppliers AS T2 ON T1.supplier_id  =  T2.supplier_id JOIN products AS T3 ON T2.product_id  =  T3.product_id WHERE T3.product_name  =  'red jeans';
+SELECT DISTINCT T1.supplier_name ,  T1.supplier_phone FROM suppliers AS T1 JOIN product_suppliers AS T2 ON T1.supplier_id  =  T2.supplier_id JOIN products AS T3 ON T2.product_id  =  T3.product_id WHERE T3.product_name  =  'red jeans';
+SELECT max(product_price) ,  min(product_price) ,  product_type_code FROM products GROUP BY product_type_code ORDER BY product_type_code;
+SELECT max(product_price) ,  min(product_price) ,  product_type_code FROM products GROUP BY product_type_code ORDER BY product_type_code;
+SELECT order_id ,  customer_id FROM customer_orders WHERE order_status_code  =   'Cancelled' ORDER BY order_date;
+SELECT order_id ,  customer_id FROM customer_orders WHERE order_status_code  =   'Cancelled' ORDER BY order_date;
+SELECT DISTINCT T3.product_name FROM customer_orders AS T1 JOIN order_items AS T2 ON T1.order_id  =  T2.order_id JOIN products AS T3 ON T2.product_id  =  T3.product_id GROUP BY T3.product_id HAVING COUNT (DISTINCT T1.customer_id)  >=  2;
+SELECT DISTINCT T3.product_name FROM customer_orders AS T1 JOIN order_items AS T2 ON T1.order_id  =  T2.order_id JOIN products AS T3 ON T2.product_id  =  T3.product_id GROUP BY T3.product_id HAVING COUNT (DISTINCT T1.customer_id)  >=  2;
+SELECT DISTINCT T1.customer_name FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id JOIN order_items AS T3 ON T2.order_id  =  T3.order_id GROUP BY T1.customer_id HAVING COUNT (DISTINCT T3.product_id)  >=  3;
+SELECT DISTINCT T1.customer_name FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id JOIN order_items AS T3 ON T2.order_id  =  T3.order_id GROUP BY T1.customer_id HAVING COUNT (DISTINCT T3.product_id)  >=  3;
+SELECT T1.staff_name ,  T1.staff_gender FROM staff AS T1 JOIN Staff_Department_Assignments AS T2 ON T1.staff_id  =  T2.staff_id WHERE T2.job_title_code  =  'Sales Person' EXCEPT SELECT T1.staff_name ,  T1.staff_gender FROM staff AS T1 JOIN Staff_Department_Assignments AS T2 ON T1.staff_id  =  T2.staff_id WHERE T2.job_title_code  =  'Clerical Staff';
+SELECT T1.staff_name ,  T1.staff_gender FROM staff AS T1 JOIN Staff_Department_Assignments AS T2 ON T1.staff_id  =  T2.staff_id WHERE T2.job_title_code  =  'Sales Person' EXCEPT SELECT T1.staff_name ,  T1.staff_gender FROM staff AS T1 JOIN Staff_Department_Assignments AS T2 ON T1.staff_id  =  T2.staff_id WHERE T2.job_title_code  =  'Clerical Staff';
+SELECT customer_id ,  customer_name FROM customers WHERE customer_address LIKE '%WY%' AND payment_method_code != 'Credit Card';
+SELECT customer_id ,  customer_name FROM customers WHERE customer_address LIKE '%WY%' AND payment_method_code != 'Credit Card';
+SELECT avg(product_price) FROM products WHERE product_type_code  =  'Clothes';
+SELECT avg(product_price) FROM products WHERE product_type_code  =  'Clothes';
+SELECT product_name FROM products WHERE product_type_code  =  'Hardware' ORDER BY product_price DESC LIMIT 1;
+SELECT product_name FROM products WHERE product_type_code  =  'Hardware' ORDER BY product_price DESC LIMIT 1;
+SELECT count(*) FROM aircraft;
+SELECT count(*) FROM aircraft;
+SELECT Description FROM aircraft;
+SELECT Description FROM aircraft;
+SELECT avg(International_Passengers) FROM airport;
+SELECT avg(International_Passengers) FROM airport;
+SELECT International_Passengers ,  Domestic_Passengers FROM airport WHERE Airport_Name  =  'London Heathrow';
+SELECT International_Passengers ,  Domestic_Passengers FROM airport WHERE Airport_Name  =  'London Heathrow';
+SELECT sum(Domestic_Passengers) FROM airport WHERE Airport_Name LIKE '%London%';
+SELECT sum(Domestic_Passengers) FROM airport WHERE Airport_Name LIKE '%London%';
+SELECT max(Transit_Passengers) ,  min(Transit_Passengers) FROM airport;
+SELECT max(Transit_Passengers) ,  min(Transit_Passengers) FROM airport;
+SELECT Name FROM pilot WHERE Age  >=  25;
+SELECT Name FROM pilot WHERE Age  >=  25;
+SELECT Name FROM pilot ORDER BY Name ASC;
+SELECT Name FROM pilot ORDER BY Name ASC;
+SELECT Name FROM pilot WHERE Age  <=  30 ORDER BY Name DESC;
+SELECT Name FROM pilot WHERE Age  <=  30 ORDER BY Name DESC;
+SELECT T1.Aircraft FROM aircraft AS T1 JOIN airport_aircraft AS T2 ON T1.Aircraft_ID  =  T2.Aircraft_ID JOIN airport AS T3 ON T2.Airport_ID  =  T3.Airport_ID WHERE T3.Airport_Name  =  'London Gatwick';
+SELECT T1.Aircraft FROM aircraft AS T1 JOIN airport_aircraft AS T2 ON T1.Aircraft_ID  =  T2.Aircraft_ID JOIN airport AS T3 ON T2.Airport_ID  =  T3.Airport_ID WHERE T3.Airport_Name  =  'London Gatwick';
+SELECT T1.Aircraft ,  T1.Description FROM aircraft AS T1 JOIN airport_aircraft AS T2 ON T1.Aircraft_ID  =  T2.Aircraft_ID JOIN airport AS T3 ON T2.Airport_ID  =  T3.Airport_ID WHERE T3.Total_Passengers  >  10000000;
+SELECT T1.Aircraft ,  T1.Description FROM aircraft AS T1 JOIN airport_aircraft AS T2 ON T1.Aircraft_ID  =  T2.Aircraft_ID JOIN airport AS T3 ON T2.Airport_ID  =  T3.Airport_ID WHERE T3.Total_Passengers  >  10000000;
+SELECT avg(T3.Total_Passengers) FROM aircraft AS T1 JOIN airport_aircraft AS T2 ON T1.Aircraft_ID  =  T2.Aircraft_ID JOIN airport AS T3 ON T2.Airport_ID  =  T3.Airport_ID WHERE T1.Aircraft  =  'Robinson R-22';
+SELECT avg(T3.Total_Passengers) FROM aircraft AS T1 JOIN airport_aircraft AS T2 ON T1.Aircraft_ID  =  T2.Aircraft_ID JOIN airport AS T3 ON T2.Airport_ID  =  T3.Airport_ID WHERE T1.Aircraft  =  'Robinson R-22';
+SELECT T2.Location ,  T1.Aircraft FROM aircraft AS T1 JOIN MATCH AS T2 ON T1.Aircraft_ID  =  T2.Winning_Aircraft;
+SELECT T2.Location ,  T1.Aircraft FROM aircraft AS T1 JOIN MATCH AS T2 ON T1.Aircraft_ID  =  T2.Winning_Aircraft;
+SELECT T1.Aircraft FROM aircraft AS T1 JOIN MATCH AS T2 ON T1.Aircraft_ID  =  T2.Winning_Aircraft GROUP BY T2.Winning_Aircraft ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T1.Aircraft FROM aircraft AS T1 JOIN MATCH AS T2 ON T1.Aircraft_ID  =  T2.Winning_Aircraft GROUP BY T2.Winning_Aircraft ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T1.Aircraft ,  COUNT(*) FROM aircraft AS T1 JOIN MATCH AS T2 ON T1.Aircraft_ID  =  T2.Winning_Aircraft GROUP BY T2.Winning_Aircraft;
+SELECT T1.Aircraft ,  COUNT(*) FROM aircraft AS T1 JOIN MATCH AS T2 ON T1.Aircraft_ID  =  T2.Winning_Aircraft GROUP BY T2.Winning_Aircraft;
+SELECT Name FROM pilot ORDER BY Age DESC;
+SELECT Name FROM pilot ORDER BY Age DESC;
+SELECT T1.Aircraft FROM aircraft AS T1 JOIN MATCH AS T2 ON T1.Aircraft_ID  =  T2.Winning_Aircraft GROUP BY T2.Winning_Aircraft HAVING COUNT(*)  >=  2;
+SELECT T1.Aircraft FROM aircraft AS T1 JOIN MATCH AS T2 ON T1.Aircraft_ID  =  T2.Winning_Aircraft GROUP BY T2.Winning_Aircraft HAVING COUNT(*)  >=  2;
+SELECT Aircraft FROM aircraft WHERE Aircraft_ID NOT IN (SELECT Winning_Aircraft FROM MATCH);
+SELECT Aircraft FROM aircraft WHERE Aircraft_ID NOT IN (SELECT Winning_Aircraft FROM MATCH);
+SELECT T1.Aircraft FROM aircraft AS T1 JOIN airport_aircraft AS T2 ON T1.Aircraft_ID  =  T2.Aircraft_ID JOIN airport AS T3 ON T2.Airport_ID  =  T3.Airport_ID WHERE T3.Airport_Name  =  'London Heathrow' INTERSECT SELECT T1.Aircraft FROM aircraft AS T1 JOIN airport_aircraft AS T2 ON T1.Aircraft_ID  =  T2.Aircraft_ID JOIN airport AS T3 ON T2.Airport_ID  =  T3.Airport_ID WHERE T3.Airport_Name  =  'London Gatwick';
+SELECT T1.Aircraft FROM aircraft AS T1 JOIN airport_aircraft AS T2 ON T1.Aircraft_ID  =  T2.Aircraft_ID JOIN airport AS T3 ON T2.Airport_ID  =  T3.Airport_ID WHERE T3.Airport_Name  =  'London Heathrow' INTERSECT SELECT T1.Aircraft FROM aircraft AS T1 JOIN airport_aircraft AS T2 ON T1.Aircraft_ID  =  T2.Aircraft_ID JOIN airport AS T3 ON T2.Airport_ID  =  T3.Airport_ID WHERE T3.Airport_Name  =  'London Gatwick';
+SELECT * FROM airport ORDER BY International_Passengers DESC LIMIT 1;
+SELECT * FROM airport ORDER BY International_Passengers DESC LIMIT 1;
+SELECT t1.name ,  t1.age FROM pilot AS t1 JOIN MATCH AS t2 ON t1.pilot_id  =  t2.winning_pilot WHERE t1.age  <  30 GROUP BY t2.winning_pilot ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.name ,  t1.age FROM pilot AS t1 JOIN MATCH AS t2 ON t1.pilot_id  =  t2.winning_pilot WHERE t1.age  <  30 GROUP BY t2.winning_pilot ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.name ,  t1.age FROM pilot AS t1 JOIN MATCH AS t2 ON t1.pilot_id  =  t2.winning_pilot ORDER BY t1.age LIMIT 1;
+SELECT t1.name ,  t1.age FROM pilot AS t1 JOIN MATCH AS t2 ON t1.pilot_id  =  t2.winning_pilot ORDER BY t1.age LIMIT 1;
+SELECT name FROM pilot WHERE pilot_id NOT IN (SELECT Winning_Pilot  FROM MATCH WHERE country  =  'Australia');
+SELECT name FROM pilot WHERE pilot_id NOT IN (SELECT Winning_Pilot  FROM MATCH WHERE country  =  'Australia');
+SELECT T1.property_id ,  count(*) FROM properties AS T1 JOIN residents AS T2 ON T1.property_id  =  T2.property_id GROUP BY T1.property_id;
+SELECT DISTINCT T1.service_type_code FROM services AS T1 JOIN organizations AS T2 ON T1.organization_id  =  T2.organization_id WHERE T2.organization_details  =  'Denesik and Sons Party';
+SELECT T1.resident_id ,  T1.other_details ,  count(*) FROM Residents AS T1 JOIN Residents_Services AS T2 ON T1.resident_id  =  T2.resident_id GROUP BY T1.resident_id ORDER BY count(*) DESC;
+SELECT T1.service_id ,  T1.service_details ,  count(*) FROM Services AS T1 JOIN Residents_Services AS T2 ON T1.service_id  =  T2.service_id GROUP BY T1.service_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.thing_id ,  T1.type_of_Thing_Code ,  T2.organization_details FROM Things AS T1 JOIN Organizations AS T2 ON T1.organization_id  =  T2.organization_id;
+SELECT T1.customer_id ,  T1.customer_details FROM Customers AS T1 JOIN Customer_Events AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id HAVING count(*)  >=  3;
+SELECT T2.date_moved_in ,  T1.customer_id ,  T1.customer_details FROM Customers AS T1 JOIN Customer_Events AS T2 ON T1.customer_id  =  T2.customer_id;
+SELECT T1.Customer_Event_ID  ,  T1.property_id FROM Customer_Events AS T1 JOIN Customer_Event_Notes AS T2 ON T1.Customer_Event_ID  =  T2.Customer_Event_ID GROUP BY T1.customer_event_id HAVING count(*) BETWEEN 1 AND 3;
+SELECT DISTINCT T2.thing_id ,  T2.Type_of_Thing_Code FROM Timed_Status_of_Things AS T1 JOIN Things AS T2 ON T1.thing_id  =  T2.thing_id WHERE T1.Status_of_Thing_Code  =  'Close' OR T1.Date_and_Date  <  '2017-06-19 02:59:21';
+SELECT count(DISTINCT T2.Location_Code) FROM Things AS T1 JOIN Timed_Locations_of_Things AS T2 ON T1.thing_id  =  T2.thing_id WHERE T1.service_details  =  'Unsatisfied';
+SELECT count(DISTINCT Status_of_Thing_Code) FROM Timed_Status_of_Things;
+SELECT organization_id FROM organizations EXCEPT SELECT parent_organization_id FROM organizations;
+SELECT max(date_moved_in) FROM Residents;
+SELECT other_details FROM Residents WHERE other_details LIKE '%Miss%';
+SELECT customer_event_id ,  date_moved_in ,  property_id FROM customer_events;
+SELECT count(*) FROM customers WHERE customer_id NOT IN ( SELECT customer_id FROM customer_events );
+SELECT DISTINCT date_moved_in FROM residents;
+SELECT count(*) FROM school;
+SELECT count(*) FROM school;
+SELECT LOCATION FROM school ORDER BY Enrollment ASC;
+SELECT LOCATION FROM school ORDER BY Enrollment ASC;
+SELECT LOCATION FROM school ORDER BY Founded DESC;
+SELECT LOCATION FROM school ORDER BY Founded DESC;
+SELECT Enrollment FROM school WHERE Denomination != 'Catholic';
+SELECT Enrollment FROM school WHERE Denomination != 'Catholic';
+SELECT avg(Enrollment) FROM school;
+SELECT avg(Enrollment) FROM school;
+SELECT Team FROM player ORDER BY Team ASC;
+SELECT Team FROM player ORDER BY Team ASC;
+SELECT count(DISTINCT POSITION) FROM player;
+SELECT count(DISTINCT POSITION) FROM player;
+SELECT Team FROM player ORDER BY Age DESC LIMIT 1;
+SELECT Team FROM player ORDER BY Age DESC LIMIT 1;
+SELECT Team FROM player ORDER BY Age DESC LIMIT 5;
+SELECT Team FROM player ORDER BY Age DESC LIMIT 5;
+SELECT T1.Team ,  T2.Location FROM player AS T1 JOIN school AS T2 ON T1.School_ID  =  T2.School_ID;
+SELECT T1.Team ,  T2.Location FROM player AS T1 JOIN school AS T2 ON T1.School_ID  =  T2.School_ID;
+SELECT T2.Location FROM player AS T1 JOIN school AS T2 ON T1.School_ID  =  T2.School_ID GROUP BY T1.School_ID HAVING COUNT(*)  >  1;
+SELECT T2.Location FROM player AS T1 JOIN school AS T2 ON T1.School_ID  =  T2.School_ID GROUP BY T1.School_ID HAVING COUNT(*)  >  1;
+SELECT T2.Denomination FROM player AS T1 JOIN school AS T2 ON T1.School_ID  =  T2.School_ID GROUP BY T1.School_ID ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T2.Denomination FROM player AS T1 JOIN school AS T2 ON T1.School_ID  =  T2.School_ID GROUP BY T1.School_ID ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T1.Location ,  T2.Nickname FROM school AS T1 JOIN school_details AS T2 ON T1.School_ID  =  T2.School_ID;
+SELECT T1.Location ,  T2.Nickname FROM school AS T1 JOIN school_details AS T2 ON T1.School_ID  =  T2.School_ID;
+SELECT Denomination ,  COUNT(*) FROM school GROUP BY Denomination;
+SELECT Denomination ,  COUNT(*) FROM school GROUP BY Denomination;
+SELECT Denomination ,  COUNT(*) FROM school GROUP BY Denomination ORDER BY COUNT(*) DESC;
+SELECT Denomination ,  COUNT(*) FROM school GROUP BY Denomination ORDER BY COUNT(*) DESC;
+SELECT School_Colors FROM school ORDER BY Enrollment DESC LIMIT 1;
+SELECT School_Colors FROM school ORDER BY Enrollment DESC LIMIT 1;
+SELECT LOCATION FROM school WHERE School_ID NOT IN (SELECT School_ID FROM Player);
+SELECT LOCATION FROM school WHERE School_ID NOT IN (SELECT School_ID FROM Player);
+SELECT Denomination FROM school WHERE Founded  <  1890 INTERSECT SELECT Denomination FROM school WHERE Founded  >  1900;
+SELECT Denomination FROM school WHERE Founded  <  1890 INTERSECT SELECT Denomination FROM school WHERE Founded  >  1900;
+SELECT Nickname FROM school_details WHERE Division != 'Division 1';
+SELECT Nickname FROM school_details WHERE Division != 'Division 1';
+SELECT Denomination FROM school GROUP BY Denomination HAVING COUNT(*)  >  1;
+SELECT Denomination FROM school GROUP BY Denomination HAVING COUNT(*)  >  1;
+SELECT DISTINCT District_name FROM district ORDER BY city_area DESC;
+SELECT DISTINCT District_name FROM district ORDER BY city_area DESC;
+SELECT max_page_size FROM product GROUP BY max_page_size HAVING count(*)  >  3;
+SELECT max_page_size FROM product GROUP BY max_page_size HAVING count(*)  >  3;
+SELECT District_name ,  City_Population FROM district WHERE City_Population BETWEEN 200000 AND 2000000;
+SELECT District_name ,  City_Population FROM district WHERE City_Population BETWEEN 200000 AND 2000000;
+SELECT district_name FROM district WHERE city_area  >  10 OR City_Population  >  100000;
+SELECT district_name FROM district WHERE city_area  >  10 OR City_Population  >  100000;
+SELECT district_name FROM district ORDER BY city_population DESC LIMIT 1;
+SELECT district_name FROM district ORDER BY city_population DESC LIMIT 1;
+SELECT district_name FROM district ORDER BY city_area ASC LIMIT 1;
+SELECT district_name FROM district ORDER BY city_area ASC LIMIT 1;
+SELECT sum(city_population) FROM district ORDER BY city_area DESC LIMIT 3;
+SELECT sum(city_population) FROM district ORDER BY city_area DESC LIMIT 3;
+SELECT TYPE ,  count(*) FROM store GROUP BY TYPE;
+SELECT TYPE ,  count(*) FROM store GROUP BY TYPE;
+SELECT t1.store_name FROM store AS t1 JOIN store_district AS t2 ON t1.store_id  =  t2.store_id JOIN district AS t3 ON t2.district_id  =  t3.district_id WHERE t3.district_name  =  'Khanewal District';
+SELECT t1.store_name FROM store AS t1 JOIN store_district AS t2 ON t1.store_id  =  t2.store_id JOIN district AS t3 ON t2.district_id  =  t3.district_id WHERE t3.district_name  =  'Khanewal District';
+SELECT t1.store_name FROM store AS t1 JOIN store_district AS t2 ON t1.store_id  =  t2.store_id WHERE district_id  =  (SELECT district_id FROM district ORDER BY city_population DESC LIMIT 1);
+SELECT t1.store_name FROM store AS t1 JOIN store_district AS t2 ON t1.store_id  =  t2.store_id WHERE district_id  =  (SELECT district_id FROM district ORDER BY city_population DESC LIMIT 1);
+SELECT t3.headquartered_city FROM store AS t1 JOIN store_district AS t2 ON t1.store_id  =  t2.store_id JOIN district AS t3 ON t2.district_id  =  t3.district_id WHERE t1.store_name  =  'Blackville';
+SELECT t3.headquartered_city FROM store AS t1 JOIN store_district AS t2 ON t1.store_id  =  t2.store_id JOIN district AS t3 ON t2.district_id  =  t3.district_id WHERE t1.store_name  =  'Blackville';
+SELECT t3.headquartered_city ,  count(*) FROM store AS t1 JOIN store_district AS t2 ON t1.store_id  =  t2.store_id JOIN district AS t3 ON t2.district_id  =  t3.district_id GROUP BY t3.headquartered_city;
+SELECT t3.headquartered_city ,  count(*) FROM store AS t1 JOIN store_district AS t2 ON t1.store_id  =  t2.store_id JOIN district AS t3 ON t2.district_id  =  t3.district_id GROUP BY t3.headquartered_city;
+SELECT t3.headquartered_city FROM store AS t1 JOIN store_district AS t2 ON t1.store_id  =  t2.store_id JOIN district AS t3 ON t2.district_id  =  t3.district_id GROUP BY t3.headquartered_city ORDER BY count(*) DESC LIMIT 1;
+SELECT t3.headquartered_city FROM store AS t1 JOIN store_district AS t2 ON t1.store_id  =  t2.store_id JOIN district AS t3 ON t2.district_id  =  t3.district_id GROUP BY t3.headquartered_city ORDER BY count(*) DESC LIMIT 1;
+SELECT avg(pages_per_minute_color) FROM product;
+SELECT avg(pages_per_minute_color) FROM product;
+SELECT t1.product FROM product AS t1 JOIN store_product AS t2 ON t1.product_id  =  t2.product_id JOIN store AS t3 ON t2.store_id  =  t3.store_id WHERE t3.store_name  =  'Miramichi';
+SELECT t1.product FROM product AS t1 JOIN store_product AS t2 ON t1.product_id  =  t2.product_id JOIN store AS t3 ON t2.store_id  =  t3.store_id WHERE t3.store_name  =  'Miramichi';
+SELECT product FROM product WHERE max_page_size  =  'A4' AND pages_per_minute_color  <  5;
+SELECT product FROM product WHERE max_page_size  =  'A4' AND pages_per_minute_color  <  5;
+SELECT product FROM product WHERE max_page_size  =  'A4' OR pages_per_minute_color  <  5;
+SELECT product FROM product WHERE max_page_size  =  'A4' OR pages_per_minute_color  <  5;
+SELECT product FROM product WHERE product LIKE '%Scanner%';
+SELECT product FROM product WHERE product LIKE '%Scanner%';
+SELECT max_page_size FROM product GROUP BY max_page_size ORDER BY count(*) DESC LIMIT 1;
+SELECT max_page_size FROM product GROUP BY max_page_size ORDER BY count(*) DESC LIMIT 1;
+SELECT product FROM product WHERE product != (SELECT max_page_size FROM product GROUP BY max_page_size ORDER BY count(*) DESC LIMIT 1);
+SELECT product FROM product WHERE product != (SELECT max_page_size FROM product GROUP BY max_page_size ORDER BY count(*) DESC LIMIT 1);
+SELECT sum(city_population) FROM district WHERE city_area  >  (SELECT avg(city_area) FROM district);
+SELECT sum(city_population) FROM district WHERE city_area  >  (SELECT avg(city_area) FROM district);
+SELECT t3.District_name FROM store AS t1 JOIN store_district AS t2 ON t1.store_id  =  t2.store_id JOIN district AS t3 ON t2.district_id  =  t3.district_id WHERE t1.Type  =  'City Mall' INTERSECT SELECT t3.District_name FROM store AS t1 JOIN store_district AS t2 ON t1.store_id  =  t2.store_id JOIN district AS t3 ON t2.district_id  =  t3.district_id WHERE t1.Type  =  'Village Store';
+SELECT t3.District_name FROM store AS t1 JOIN store_district AS t2 ON t1.store_id  =  t2.store_id JOIN district AS t3 ON t2.district_id  =  t3.district_id WHERE t1.Type  =  'City Mall' INTERSECT SELECT t3.District_name FROM store AS t1 JOIN store_district AS t2 ON t1.store_id  =  t2.store_id JOIN district AS t3 ON t2.district_id  =  t3.district_id WHERE t1.Type  =  'Village Store';
+SELECT sum(enr) FROM College;
+SELECT sum(enr) FROM College;
+SELECT avg(enr) FROM College;
+SELECT avg(enr) FROM College;
+SELECT count(*) FROM College;
+SELECT count(*) FROM College;
+SELECT count(*) FROM Player WHERE HS  >  1000;
+SELECT count(*) FROM Player WHERE HS  >  1000;
+SELECT count(*) FROM College WHERE enr  >  15000;
+SELECT count(*) FROM College WHERE enr  >  15000;
+SELECT avg(HS) FROM Player;
+SELECT avg(HS) FROM Player;
+SELECT pName ,  HS FROM Player WHERE HS  <  1500;
+SELECT pName ,  HS FROM Player WHERE HS  <  1500;
+SELECT count(DISTINCT cName) FROM tryout;
+SELECT count(DISTINCT cName) FROM tryout;
+SELECT count(DISTINCT pPos) FROM tryout;
+SELECT count(DISTINCT pPos) FROM tryout;
+SELECT count(*) FROM tryout WHERE decision  =  'yes';
+SELECT count(*) FROM tryout WHERE decision  =  'yes';
+SELECT count(*) FROM tryout WHERE pPos  =  'goalie';
+SELECT count(*) FROM tryout WHERE pPos  =  'goalie';
+SELECT avg(HS) ,  max(HS) ,  min(HS) FROM Player;
+SELECT avg(HS) ,  max(HS) ,  min(HS) FROM Player;
+SELECT avg(enr) FROM College WHERE state  =  'FL';
+SELECT avg(enr) FROM College WHERE state  =  'FL';
+SELECT pName FROM Player WHERE HS BETWEEN 500 AND 1500;
+SELECT pName FROM Player WHERE HS BETWEEN 500 AND 1500;
+SELECT DISTINCT pName FROM Player WHERE pName LIKE '%a%';
+SELECT DISTINCT pName FROM Player WHERE pName LIKE '%a%';
+SELECT cName ,  enr FROM College WHERE enr  >  10000 AND state = 'LA';
+SELECT cName ,  enr FROM College WHERE enr  >  10000 AND state = 'LA';
+SELECT * FROM College ORDER BY enr;
+SELECT * FROM College ORDER BY enr;
+SELECT cName FROM College WHERE enr  >  18000 ORDER BY cName;
+SELECT cName FROM College WHERE enr  >  18000 ORDER BY cName;
+SELECT pName FROM Player WHERE yCard  =  'yes' ORDER BY HS DESC;
+SELECT pName FROM Player WHERE yCard  =  'yes' ORDER BY HS DESC;
+SELECT DISTINCT cName FROM tryout ORDER BY cName;
+SELECT DISTINCT cName FROM tryout ORDER BY cName;
+SELECT pPos FROM tryout GROUP BY pPos ORDER BY count(*) DESC LIMIT 1;
+SELECT pPos FROM tryout GROUP BY pPos ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) ,  cName FROM tryout GROUP BY cName ORDER BY count(*) DESC;
+SELECT count(*) ,  cName FROM tryout GROUP BY cName ORDER BY count(*) DESC;
+SELECT min(T2.HS) ,   T1.pPos FROM tryout AS T1 JOIN player AS T2 ON T1.pID  =  T2.pID GROUP BY T1.pPos;
+SELECT min(T2.HS) ,   T1.pPos FROM tryout AS T1 JOIN player AS T2 ON T1.pID  =  T2.pID GROUP BY T1.pPos;
+SELECT cName FROM college ORDER BY enr DESC LIMIT 3;
+SELECT cName FROM college ORDER BY enr DESC LIMIT 3;
+SELECT cName ,  state ,  min(enr) FROM college GROUP BY state;
+SELECT cName ,  state ,  min(enr) FROM college GROUP BY state;
+SELECT DISTINCT state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName;
+SELECT DISTINCT state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName;
+SELECT DISTINCT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName WHERE T2.decision  =  'yes';
+SELECT DISTINCT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName WHERE T2.decision  =  'yes';
+SELECT T1.pName ,  T2.cName FROM player AS T1 JOIN tryout AS T2 ON T1.pID  =  T2.pID WHERE T2.decision  =  'yes';
+SELECT T1.pName ,  T2.cName FROM player AS T1 JOIN tryout AS T2 ON T1.pID  =  T2.pID WHERE T2.decision  =  'yes';
+SELECT T1.pName FROM player AS T1 JOIN tryout AS T2 ON T1.pID  =  T2.pID ORDER BY T1.pName;
+SELECT T1.pName FROM player AS T1 JOIN tryout AS T2 ON T1.pID  =  T2.pID ORDER BY T1.pName;
+SELECT T1.pName ,  T1.HS FROM player AS T1 JOIN tryout AS T2 ON T1.pID  =  T2.pID WHERE T2.decision  =  'yes';
+SELECT T1.pName ,  T1.HS FROM player AS T1 JOIN tryout AS T2 ON T1.pID  =  T2.pID WHERE T2.decision  =  'yes';
+SELECT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName WHERE T2.pPos  =  'striker';
+SELECT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName WHERE T2.pPos  =  'striker';
+SELECT T1.pName FROM player AS T1 JOIN tryout AS T2 ON T1.pID  =  T2.pID WHERE T2.decision  =  'yes' AND T2.pPos  =  'striker';
+SELECT T1.pName FROM player AS T1 JOIN tryout AS T2 ON T1.pID  =  T2.pID WHERE T2.decision  =  'yes' AND T2.pPos  =  'striker';
+SELECT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName JOIN player AS T3 ON T2.pID  =  T3.pID WHERE T3.pName  =  'Charles';
+SELECT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName JOIN player AS T3 ON T2.pID  =  T3.pID WHERE T3.pName  =  'Charles';
+SELECT avg(T1.HS) ,  max(T1.HS) FROM player AS T1 JOIN tryout AS T2 ON T1.pID  =  T2.pID WHERE T2.decision  =  'yes';
+SELECT avg(T1.HS) ,  max(T1.HS) FROM player AS T1 JOIN tryout AS T2 ON T1.pID  =  T2.pID WHERE T2.decision  =  'yes';
+SELECT avg(T1.HS) FROM player AS T1 JOIN tryout AS T2 ON T1.pID  =  T2.pID WHERE T2.decision  =  'no';
+SELECT avg(T1.HS) FROM player AS T1 JOIN tryout AS T2 ON T1.pID  =  T2.pID WHERE T2.decision  =  'no';
+SELECT max(T1.HS) ,  pPos FROM player AS T1 JOIN tryout AS T2 ON T1.pID  =  T2.pID WHERE T1.HS  >  1000 GROUP BY T2.pPos;
+SELECT max(T1.HS) ,  pPos FROM player AS T1 JOIN tryout AS T2 ON T1.pID  =  T2.pID WHERE T1.HS  >  1000 GROUP BY T2.pPos;
+SELECT T1.cName FROM  tryout AS T1 JOIN player AS T2 ON T1.pID  =  T2.pID WHERE T2.pName LIKE 'D%';
+SELECT T1.cName FROM  tryout AS T1 JOIN player AS T2 ON T1.pID  =  T2.pID WHERE T2.pName LIKE 'D%';
+SELECT cName FROM  tryout WHERE decision  =  'yes' AND pPos  =  'goalie';
+SELECT cName FROM  tryout WHERE decision  =  'yes' AND pPos  =  'goalie';
+SELECT T2.pName FROM  tryout AS T1 JOIN player AS T2 ON T1.pID  =  T2.pID WHERE T1.cName  =  (SELECT cName FROM college ORDER BY enr DESC LIMIT 1);
+SELECT T2.pName FROM  tryout AS T1 JOIN player AS T2 ON T1.pID  =  T2.pID WHERE T1.cName  =  (SELECT cName FROM college ORDER BY enr DESC LIMIT 1);
+SELECT DISTINCT T1.state ,  T1.enr FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName WHERE T2.decision  =  'yes';
+SELECT DISTINCT T1.state ,  T1.enr FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName WHERE T2.decision  =  'yes';
+SELECT cName FROM College WHERE enr  <  13000 AND state = 'AZ' UNION SELECT cName FROM College WHERE enr  >  15000 AND state = 'LA';
+SELECT cName FROM College WHERE enr  <  13000 AND state = 'AZ' UNION SELECT cName FROM College WHERE enr  >  15000 AND state = 'LA';
+SELECT cName FROM  tryout WHERE pPos  =  'goalie' INTERSECT SELECT cName FROM  tryout WHERE pPos  =  'mid';
+SELECT cName FROM  tryout WHERE pPos  =  'goalie' INTERSECT SELECT cName FROM  tryout WHERE pPos  =  'mid';
+SELECT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName WHERE T2.pPos  =  'goalie' INTERSECT SELECT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName WHERE T2.pPos  =  'mid';
+SELECT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName WHERE T2.pPos  =  'goalie' INTERSECT SELECT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName WHERE T2.pPos  =  'mid';
+SELECT COUNT(*) FROM (SELECT cName FROM  tryout WHERE pPos  =  'goalie' INTERSECT SELECT cName FROM  tryout WHERE pPos  =  'mid');
+SELECT COUNT(*) FROM (SELECT cName FROM  tryout WHERE pPos  =  'goalie' INTERSECT SELECT cName FROM  tryout WHERE pPos  =  'mid');
+SELECT cName FROM  tryout WHERE pPos  =  'mid' EXCEPT SELECT cName FROM  tryout WHERE pPos  =  'goalie';
+SELECT cName FROM  tryout WHERE pPos  =  'mid' EXCEPT SELECT cName FROM  tryout WHERE pPos  =  'goalie';
+SELECT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName WHERE T2.pPos  =  'mid' EXCEPT SELECT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName WHERE T2.pPos  =  'goalie';
+SELECT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName WHERE T2.pPos  =  'mid' EXCEPT SELECT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName WHERE T2.pPos  =  'goalie';
+SELECT COUNT(*) FROM (SELECT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName WHERE T2.pPos  =  'mid' EXCEPT SELECT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName WHERE T2.pPos  =  'goalie');
+SELECT COUNT(*) FROM (SELECT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName WHERE T2.pPos  =  'mid' EXCEPT SELECT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName  =  T2.cName WHERE T2.pPos  =  'goalie');
+SELECT DISTINCT state FROM college WHERE enr  <  (SELECT max(enr) FROM college);
+SELECT DISTINCT state FROM college WHERE enr  <  (SELECT max(enr) FROM college);
+SELECT DISTINCT cName FROM college WHERE enr  >  (SELECT min(enr) FROM college WHERE state  =  'FL');
+SELECT DISTINCT cName FROM college WHERE enr  >  (SELECT min(enr) FROM college WHERE state  =  'FL');
+SELECT cName FROM college WHERE enr  >  (SELECT max(enr) FROM college WHERE state  =  'FL');
+SELECT cName FROM college WHERE enr  >  (SELECT max(enr) FROM college WHERE state  =  'FL');
+SELECT sum(enr) FROM college WHERE cName NOT IN (SELECT cName FROM tryout WHERE pPos  =  'goalie');
+SELECT sum(enr) FROM college WHERE cName NOT IN (SELECT cName FROM tryout WHERE pPos  =  'goalie');
+SELECT count(DISTINCT state) FROM college WHERE enr  >  (SELECT avg(enr) FROM college);
+SELECT count(DISTINCT state) FROM college WHERE enr  >  (SELECT avg(enr) FROM college);
+SELECT count(DISTINCT state) FROM college WHERE enr  <  (SELECT avg(enr) FROM college);
+SELECT count(DISTINCT state) FROM college WHERE enr  <  (SELECT avg(enr) FROM college);
+SELECT count(*) FROM device;
+SELECT count(*) FROM device;
+SELECT Carrier FROM device ORDER BY Carrier ASC;
+SELECT Carrier FROM device ORDER BY Carrier ASC;
+SELECT Carrier FROM device WHERE Software_Platform != 'Android';
+SELECT Carrier FROM device WHERE Software_Platform != 'Android';
+SELECT Shop_Name FROM shop ORDER BY Open_Year ASC;
+SELECT Shop_Name FROM shop ORDER BY Open_Year ASC;
+SELECT avg(Quantity) FROM stock;
+SELECT avg(Quantity) FROM stock;
+SELECT Shop_Name ,  LOCATION FROM shop ORDER BY Shop_Name ASC;
+SELECT Shop_Name ,  LOCATION FROM shop ORDER BY Shop_Name ASC;
+SELECT count(DISTINCT Software_Platform) FROM device;
+SELECT count(DISTINCT Software_Platform) FROM device;
+SELECT Open_Date ,  Open_Year FROM shop WHERE Shop_Name  =  'Apple';
+SELECT Open_Date ,  Open_Year FROM shop WHERE Shop_Name  =  'Apple';
+SELECT Shop_Name FROM shop ORDER BY Open_Year DESC LIMIT 1;
+SELECT Shop_Name FROM shop ORDER BY Open_Year DESC LIMIT 1;
+SELECT T3.Shop_Name ,  T2.Carrier FROM stock AS T1 JOIN device AS T2 ON T1.Device_ID  =  T2.Device_ID JOIN shop AS T3 ON T1.Shop_ID  =  T3.Shop_ID;
+SELECT T3.Shop_Name ,  T2.Carrier FROM stock AS T1 JOIN device AS T2 ON T1.Device_ID  =  T2.Device_ID JOIN shop AS T3 ON T1.Shop_ID  =  T3.Shop_ID;
+SELECT T2.Shop_Name FROM stock AS T1 JOIN shop AS T2 ON T1.Shop_ID  =  T2.Shop_ID GROUP BY T1.Shop_ID HAVING COUNT(*)  >  1;
+SELECT T2.Shop_Name FROM stock AS T1 JOIN shop AS T2 ON T1.Shop_ID  =  T2.Shop_ID GROUP BY T1.Shop_ID HAVING COUNT(*)  >  1;
+SELECT T2.Shop_Name FROM stock AS T1 JOIN shop AS T2 ON T1.Shop_ID  =  T2.Shop_ID GROUP BY T1.Shop_ID ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T2.Shop_Name FROM stock AS T1 JOIN shop AS T2 ON T1.Shop_ID  =  T2.Shop_ID GROUP BY T1.Shop_ID ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T2.Shop_Name FROM stock AS T1 JOIN shop AS T2 ON T1.Shop_ID  =  T2.Shop_ID GROUP BY T1.Shop_ID ORDER BY SUM(T1.quantity) DESC LIMIT 1;
+SELECT T2.Shop_Name FROM stock AS T1 JOIN shop AS T2 ON T1.Shop_ID  =  T2.Shop_ID GROUP BY T1.Shop_ID ORDER BY SUM(T1.quantity) DESC LIMIT 1;
+SELECT Software_Platform ,  COUNT(*) FROM device GROUP BY Software_Platform;
+SELECT Software_Platform ,  COUNT(*) FROM device GROUP BY Software_Platform;
+SELECT Software_Platform FROM device GROUP BY Software_Platform ORDER BY COUNT(*) DESC;
+SELECT Software_Platform FROM device GROUP BY Software_Platform ORDER BY COUNT(*) DESC;
+SELECT Software_Platform FROM device GROUP BY Software_Platform ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Software_Platform FROM device GROUP BY Software_Platform ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Shop_Name FROM shop WHERE Shop_ID NOT IN (SELECT Shop_ID FROM stock);
+SELECT Shop_Name FROM shop WHERE Shop_ID NOT IN (SELECT Shop_ID FROM stock);
+SELECT LOCATION FROM shop WHERE Open_Year  >  2012 INTERSECT SELECT LOCATION FROM shop WHERE Open_Year  <  2008;
+SELECT LOCATION FROM shop WHERE Open_Year  >  2012 INTERSECT SELECT LOCATION FROM shop WHERE Open_Year  <  2008;
+SELECT Carrier FROM device WHERE Device_ID NOT IN (SELECT Device_ID FROM stock);
+SELECT Carrier FROM device WHERE Device_ID NOT IN (SELECT Device_ID FROM stock);
+SELECT T2.Carrier FROM stock AS T1 JOIN device AS T2 ON T1.Device_ID  =  T2.Device_ID GROUP BY T1.Device_ID HAVING COUNT(*)  >  1;
+SELECT T2.Carrier FROM stock AS T1 JOIN device AS T2 ON T1.Device_ID  =  T2.Device_ID GROUP BY T1.Device_ID HAVING COUNT(*)  >  1;
+SELECT count(*) FROM BOOKINGS;
+SELECT count(*) FROM BOOKINGS;
+SELECT Order_Date FROM BOOKINGS;
+SELECT Order_Date FROM BOOKINGS;
+SELECT Planned_Delivery_Date ,  Actual_Delivery_Date FROM BOOKINGS;
+SELECT Planned_Delivery_Date ,  Actual_Delivery_Date FROM BOOKINGS;
+SELECT count(*) FROM CUSTOMERS;
+SELECT count(*) FROM CUSTOMERS;
+SELECT Customer_Phone ,  Customer_Email_Address FROM CUSTOMERS WHERE Customer_Name  =  'Harold';
+SELECT Customer_Phone ,  Customer_Email_Address FROM CUSTOMERS WHERE Customer_Name  =  'Harold';
+SELECT Store_Name FROM Drama_Workshop_Groups;
+SELECT Store_Name FROM Drama_Workshop_Groups;
+SELECT min(Order_Quantity) ,  avg(Order_Quantity) ,  max(Order_Quantity) FROM INVOICES;
+SELECT min(Order_Quantity) ,  avg(Order_Quantity) ,  max(Order_Quantity) FROM INVOICES;
+SELECT DISTINCT payment_method_code FROM INVOICES;
+SELECT DISTINCT payment_method_code FROM INVOICES;
+SELECT Marketing_Region_Descriptrion FROM Marketing_Regions WHERE Marketing_Region_Name  =  'China';
+SELECT Marketing_Region_Descriptrion FROM Marketing_Regions WHERE Marketing_Region_Name  =  'China';
+SELECT DISTINCT Product_Name FROM PRODUCTS WHERE Product_Price  >  (SELECT avg(Product_Price) FROM PRODUCTS);
+SELECT DISTINCT Product_Name FROM PRODUCTS WHERE Product_Price  >  (SELECT avg(Product_Price) FROM PRODUCTS);
+SELECT Product_Name FROM PRODUCTS ORDER BY Product_Price DESC LIMIT 1;
+SELECT Product_Name FROM PRODUCTS ORDER BY Product_Price DESC LIMIT 1;
+SELECT Product_Name FROM Products ORDER BY Product_Price ASC;
+SELECT Product_Name FROM Products ORDER BY Product_Price ASC;
+SELECT Customer_Phone FROM PERFORMERS WHERE Customer_Name  =  'Ashley';
+SELECT Customer_Phone FROM PERFORMERS WHERE Customer_Name  =  'Ashley';
+SELECT payment_method_code ,  count(*) FROM INVOICES GROUP BY payment_method_code;
+SELECT payment_method_code ,  count(*) FROM INVOICES GROUP BY payment_method_code;
+SELECT payment_method_code FROM INVOICES GROUP BY payment_method_code ORDER BY count(*) DESC LIMIT 1;
+SELECT payment_method_code FROM INVOICES GROUP BY payment_method_code ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.City_Town FROM Addresses AS T1 JOIN Stores AS T2 ON T1.Address_ID  =  T2.Address_ID WHERE T2.Store_Name  =  'FJA Filming';
+SELECT T1.City_Town FROM Addresses AS T1 JOIN Stores AS T2 ON T1.Address_ID  =  T2.Address_ID WHERE T2.Store_Name  =  'FJA Filming';
+SELECT T1.State_County FROM Addresses AS T1 JOIN Stores AS T2 ON T1.Address_ID  =  T2.Address_ID WHERE T2.Marketing_Region_Code  =  'CA';
+SELECT T1.State_County FROM Addresses AS T1 JOIN Stores AS T2 ON T1.Address_ID  =  T2.Address_ID WHERE T2.Marketing_Region_Code  =  'CA';
+SELECT T1.Marketing_Region_Name FROM Marketing_Regions AS T1 JOIN Stores AS T2 ON T1.Marketing_Region_Code  =  T2.Marketing_Region_Code WHERE T2.Store_Name  =  'Rob Dinning';
+SELECT T1.Marketing_Region_Name FROM Marketing_Regions AS T1 JOIN Stores AS T2 ON T1.Marketing_Region_Code  =  T2.Marketing_Region_Code WHERE T2.Store_Name  =  'Rob Dinning';
+SELECT T1.Service_Type_Description FROM Ref_Service_Types AS T1 JOIN Services AS T2 ON T1.Service_Type_Code  =  T2.Service_Type_Code WHERE T2.Product_Price  >  100;
+SELECT T1.Service_Type_Description FROM Ref_Service_Types AS T1 JOIN Services AS T2 ON T1.Service_Type_Code  =  T2.Service_Type_Code WHERE T2.Product_Price  >  100;
+SELECT T1.Service_Type_Description ,  T2.Service_Type_Code ,  COUNT(*) FROM Ref_Service_Types AS T1 JOIN Services AS T2 ON T1.Service_Type_Code  =  T2.Service_Type_Code GROUP BY T2.Service_Type_Code;
+SELECT T1.Service_Type_Description ,  T2.Service_Type_Code ,  COUNT(*) FROM Ref_Service_Types AS T1 JOIN Services AS T2 ON T1.Service_Type_Code  =  T2.Service_Type_Code GROUP BY T2.Service_Type_Code;
+SELECT T1.Service_Type_Description , T1.Service_Type_Code FROM Ref_Service_Types AS T1 JOIN Services AS T2 ON T1.Service_Type_Code  =  T2.Service_Type_Code GROUP BY T1.Service_Type_Code ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T1.Service_Type_Description , T1.Service_Type_Code FROM Ref_Service_Types AS T1 JOIN Services AS T2 ON T1.Service_Type_Code  =  T2.Service_Type_Code GROUP BY T1.Service_Type_Code ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T1.Store_Phone ,  T1.Store_Email_Address FROM Drama_Workshop_Groups AS T1 JOIN Services AS T2 ON T1.Workshop_Group_ID  =  T2.Workshop_Group_ID;
+SELECT T1.Store_Phone ,  T1.Store_Email_Address FROM Drama_Workshop_Groups AS T1 JOIN Services AS T2 ON T1.Workshop_Group_ID  =  T2.Workshop_Group_ID;
+SELECT T1.Store_Phone ,  T1.Store_Email_Address FROM Drama_Workshop_Groups AS T1 JOIN Services AS T2 ON T1.Workshop_Group_ID  =  T2.Workshop_Group_ID WHERE T2.Product_Name  =  'film';
+SELECT T1.Store_Phone ,  T1.Store_Email_Address FROM Drama_Workshop_Groups AS T1 JOIN Services AS T2 ON T1.Workshop_Group_ID  =  T2.Workshop_Group_ID WHERE T2.Product_Name  =  'film';
+SELECT Product_Name ,  avg(Product_Price) FROM PRODUCTS GROUP BY Product_Name;
+SELECT Product_Name ,  avg(Product_Price) FROM PRODUCTS GROUP BY Product_Name;
+SELECT Product_Name FROM PRODUCTS GROUP BY Product_Name HAVING avg(Product_Price)  <  1000000;
+SELECT Product_Name FROM PRODUCTS GROUP BY Product_Name HAVING avg(Product_Price)  <  1000000;
+SELECT sum(T1.Order_Quantity) FROM ORDER_ITEMS AS T1 JOIN Products AS T2 ON T1.Product_ID  =  T2.Product_ID WHERE T2.Product_Name  =  'photo';
+SELECT sum(T1.Order_Quantity) FROM ORDER_ITEMS AS T1 JOIN Products AS T2 ON T1.Product_ID  =  T2.Product_ID WHERE T2.Product_Name  =  'photo';
+SELECT T1.Other_Item_Details FROM ORDER_ITEMS AS T1 JOIN Products AS T2 ON T1.Product_ID  =  T2.Product_ID WHERE T2.Product_price  >  2000;
+SELECT T1.Other_Item_Details FROM ORDER_ITEMS AS T1 JOIN Products AS T2 ON T1.Product_ID  =  T2.Product_ID WHERE T2.Product_price  >  2000;
+SELECT T1.Actual_Delivery_Date FROM Customer_Orders AS T1 JOIN ORDER_ITEMS AS T2 ON T1.Order_ID  =  T2.Order_ID WHERE T2.Order_Quantity  =  1;
+SELECT T1.Actual_Delivery_Date FROM Customer_Orders AS T1 JOIN ORDER_ITEMS AS T2 ON T1.Order_ID  =  T2.Order_ID WHERE T2.Order_Quantity  =  1;
+SELECT T1.Order_Date FROM Customer_Orders AS T1 JOIN ORDER_ITEMS AS T2 ON T1.Order_ID  =  T2.Order_ID JOIN Products AS T3 ON T2.Product_ID  =  T3.Product_ID WHERE T3.Product_price  >  1000;
+SELECT T1.Order_Date FROM Customer_Orders AS T1 JOIN ORDER_ITEMS AS T2 ON T1.Order_ID  =  T2.Order_ID JOIN Products AS T3 ON T2.Product_ID  =  T3.Product_ID WHERE T3.Product_price  >  1000;
+SELECT count(DISTINCT Currency_Code) FROM Drama_Workshop_Groups;
+SELECT count(DISTINCT Currency_Code) FROM Drama_Workshop_Groups;
+SELECT T2.Store_Name FROM Addresses AS T1 JOIN Drama_Workshop_Groups AS T2 ON T1.Address_ID  =  T2.Address_ID WHERE T1.City_Town  =  'Feliciaberg';
+SELECT T2.Store_Name FROM Addresses AS T1 JOIN Drama_Workshop_Groups AS T2 ON T1.Address_ID  =  T2.Address_ID WHERE T1.City_Town  =  'Feliciaberg';
+SELECT T2.Store_Email_Address FROM Addresses AS T1 JOIN Drama_Workshop_Groups AS T2 ON T1.Address_ID  =  T2.Address_ID WHERE T1.State_County  =  'Alaska';
+SELECT T2.Store_Email_Address FROM Addresses AS T1 JOIN Drama_Workshop_Groups AS T2 ON T1.Address_ID  =  T2.Address_ID WHERE T1.State_County  =  'Alaska';
+SELECT T1.City_Town ,  count(*) FROM Addresses AS T1 JOIN Drama_Workshop_Groups AS T2 ON T1.Address_ID  =  T2.Address_ID GROUP BY T1.City_Town;
+SELECT T1.City_Town ,  count(*) FROM Addresses AS T1 JOIN Drama_Workshop_Groups AS T2 ON T1.Address_ID  =  T2.Address_ID GROUP BY T1.City_Town;
+SELECT Marketing_Region_Code FROM Drama_Workshop_Groups GROUP BY Marketing_Region_Code ORDER BY count(*) DESC LIMIT 1;
+SELECT Marketing_Region_Code FROM Drama_Workshop_Groups GROUP BY Marketing_Region_Code ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.City_Town FROM Addresses AS T1 JOIN Customers AS T2 ON T1.Address_ID  =  T2.Address_ID EXCEPT SELECT T1.City_Town FROM Addresses AS T1 JOIN Performers AS T2 ON T1.Address_ID  =  T2.Address_ID;
+SELECT T1.City_Town FROM Addresses AS T1 JOIN Customers AS T2 ON T1.Address_ID  =  T2.Address_ID EXCEPT SELECT T1.City_Town FROM Addresses AS T1 JOIN Performers AS T2 ON T1.Address_ID  =  T2.Address_ID;
+SELECT Status_Code FROM BOOKINGS GROUP BY Status_Code ORDER BY count(*) DESC LIMIT 1;
+SELECT Status_Code FROM BOOKINGS GROUP BY Status_Code ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.Store_Name FROM Bookings AS T1 JOIN Drama_Workshop_Groups AS T2 ON T1.Workshop_Group_ID  =  T2.Workshop_Group_ID WHERE T1.Status_Code  =  'stop';
+SELECT T2.Store_Name FROM Bookings AS T1 JOIN Drama_Workshop_Groups AS T2 ON T1.Workshop_Group_ID  =  T2.Workshop_Group_ID WHERE T1.Status_Code  =  'stop';
+SELECT Customer_Name FROM Clients EXCEPT SELECT T2.Customer_Name FROM Bookings AS T1 JOIN Clients AS T2 ON T1.Customer_ID  =  T2.Client_ID;
+SELECT Customer_Name FROM Clients EXCEPT SELECT T2.Customer_Name FROM Bookings AS T1 JOIN Clients AS T2 ON T1.Customer_ID  =  T2.Client_ID;
+SELECT avg(Order_Quantity) FROM Invoices WHERE payment_method_code  =  'MasterCard';
+SELECT avg(Order_Quantity) FROM Invoices WHERE payment_method_code  =  'MasterCard';
+SELECT Product_ID FROM INVOICES GROUP BY Product_ID ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Product_ID FROM INVOICES GROUP BY Product_ID ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T1.Service_Type_Description FROM Ref_Service_Types AS T1 JOIN Services AS T2 ON T1.Service_Type_Code  =  T2.Service_Type_Code WHERE T2.Product_Name  =  'photo' INTERSECT SELECT T1.Service_Type_Description FROM Ref_Service_Types AS T1 JOIN Services AS T2 ON T1.Service_Type_Code  =  T2.Service_Type_Code WHERE T2.Product_Name  =  'film';
+SELECT T1.Service_Type_Description FROM Ref_Service_Types AS T1 JOIN Services AS T2 ON T1.Service_Type_Code  =  T2.Service_Type_Code WHERE T2.Product_Name  =  'photo' INTERSECT SELECT T1.Service_Type_Description FROM Ref_Service_Types AS T1 JOIN Services AS T2 ON T1.Service_Type_Code  =  T2.Service_Type_Code WHERE T2.Product_Name  =  'film';
+SELECT count(*) FROM Band;
+SELECT count(*) FROM Band;
+SELECT DISTINCT label FROM Albums;
+SELECT DISTINCT label FROM Albums;
+SELECT * FROM Albums WHERE YEAR  =  2012;
+SELECT * FROM Albums WHERE YEAR  =  2012;
+SELECT DISTINCT T1.stageposition FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id WHERE Firstname  =  'Solveig';
+SELECT DISTINCT T1.stageposition FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id WHERE Firstname  =  'Solveig';
+SELECT count(*) FROM Songs;
+SELECT count(*) FROM Songs;
+SELECT T3.Title FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId WHERE T2.Lastname  =  'Heilo';
+SELECT T3.Title FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId WHERE T2.Lastname  =  'Heilo';
+SELECT count(*) FROM performance AS T1 JOIN band AS T2 ON T1.bandmate  =  T2.id JOIN songs AS T3 ON T3.songid  =  T1.songid WHERE T3.Title  =  'Flash';
+SELECT count(*) FROM performance AS T1 JOIN band AS T2 ON T1.bandmate  =  T2.id JOIN songs AS T3 ON T3.songid  =  T1.songid WHERE T3.Title  =  'Flash';
+SELECT T3.Title FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId WHERE T2.firstname  =  'Marianne';
+SELECT T3.Title FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId WHERE T2.firstname  =  'Marianne';
+SELECT T2.firstname ,  T2.lastname FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId WHERE T3.Title  =  'Badlands';
+SELECT T2.firstname ,  T2.lastname FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId WHERE T3.Title  =  'Badlands';
+SELECT T2.firstname ,  T2.lastname FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId WHERE T3.Title  =  'Badlands' AND T1.StagePosition  =  'back';
+SELECT T2.firstname ,  T2.lastname FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId WHERE T3.Title  =  'Badlands' AND T1.StagePosition  =  'back';
+SELECT count(DISTINCT label) FROM albums;
+SELECT count(DISTINCT label) FROM albums;
+SELECT label FROM albums GROUP BY label ORDER BY count(*) DESC LIMIT 1;
+SELECT label FROM albums GROUP BY label ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.lastname FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId GROUP BY lastname ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.lastname FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId GROUP BY lastname ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.lastname FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id WHERE stageposition  =  'back' GROUP BY lastname ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.lastname FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id WHERE stageposition  =  'back' GROUP BY lastname ORDER BY count(*) DESC LIMIT 1;
+SELECT title FROM songs WHERE title LIKE '% the %';
+SELECT title FROM songs WHERE title LIKE '% the %';
+SELECT DISTINCT instrument FROM Instruments;
+SELECT DISTINCT instrument FROM Instruments;
+SELECT T4.instrument FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId JOIN Instruments AS T4 ON T4.songid  =  T3.songid AND T4.bandmateid  =  T2.id WHERE T2.lastname  =  'Heilo' AND T3.title  =  'Le Pop';
+SELECT T4.instrument FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId JOIN Instruments AS T4 ON T4.songid  =  T3.songid AND T4.bandmateid  =  T2.id WHERE T2.lastname  =  'Heilo' AND T3.title  =  'Le Pop';
+SELECT instrument FROM instruments GROUP BY instrument ORDER BY count(*) DESC LIMIT 1;
+SELECT instrument FROM instruments GROUP BY instrument ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM instruments WHERE instrument  =  'drums';
+SELECT count(*) FROM instruments WHERE instrument  =  'drums';
+SELECT instrument FROM instruments AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid WHERE title  =  'Le Pop';
+SELECT instrument FROM instruments AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid WHERE title  =  'Le Pop';
+SELECT count(DISTINCT instrument) FROM instruments AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid WHERE title  =  'Le Pop';
+SELECT count(DISTINCT instrument) FROM instruments AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid WHERE title  =  'Le Pop';
+SELECT count(DISTINCT instrument) FROM instruments AS T1 JOIN Band AS T2 ON T1.bandmateid  =  T2.id WHERE T2.lastname  =  'Heilo';
+SELECT count(DISTINCT instrument) FROM instruments AS T1 JOIN Band AS T2 ON T1.bandmateid  =  T2.id WHERE T2.lastname  =  'Heilo';
+SELECT instrument FROM instruments AS T1 JOIN Band AS T2 ON T1.bandmateid  =  T2.id WHERE T2.lastname  =  'Heilo';
+SELECT instrument FROM instruments AS T1 JOIN Band AS T2 ON T1.bandmateid  =  T2.id WHERE T2.lastname  =  'Heilo';
+SELECT title FROM vocals AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid GROUP BY T1.songid ORDER BY count(*) DESC LIMIT 1;
+SELECT title FROM vocals AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid GROUP BY T1.songid ORDER BY count(*) DESC LIMIT 1;
+SELECT TYPE FROM vocals GROUP BY TYPE ORDER BY count(*) DESC LIMIT 1;
+SELECT TYPE FROM vocals GROUP BY TYPE ORDER BY count(*) DESC LIMIT 1;
+SELECT TYPE FROM vocals AS T1 JOIN band AS T2 ON T1.bandmate  =  T2.id WHERE lastname  =  'Heilo' GROUP BY TYPE ORDER BY count(*) DESC LIMIT 1;
+SELECT TYPE FROM vocals AS T1 JOIN band AS T2 ON T1.bandmate  =  T2.id WHERE lastname  =  'Heilo' GROUP BY TYPE ORDER BY count(*) DESC LIMIT 1;
+SELECT TYPE FROM vocals AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid WHERE title  =  'Le Pop';
+SELECT TYPE FROM vocals AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid WHERE title  =  'Le Pop';
+SELECT count(*) FROM vocals AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid WHERE title  =  'Demon Kitty Rag';
+SELECT count(*) FROM vocals AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid WHERE title  =  'Demon Kitty Rag';
+SELECT count(DISTINCT title) FROM vocals AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid WHERE TYPE  =  'lead';
+SELECT count(DISTINCT title) FROM vocals AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid WHERE TYPE  =  'lead';
+SELECT TYPE FROM vocals AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid JOIN band AS T3 ON T1.bandmate  =  T3.id WHERE T3.firstname  =  'Solveig' AND T2.title  =  'A Bar In Amsterdam';
+SELECT TYPE FROM vocals AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid JOIN band AS T3 ON T1.bandmate  =  T3.id WHERE T3.firstname  =  'Solveig' AND T2.title  =  'A Bar In Amsterdam';
+SELECT DISTINCT title FROM vocals AS t1 JOIN songs AS t2 ON t1.songid  =  t2.songid EXCEPT SELECT t2.title FROM vocals AS t1 JOIN songs AS t2 ON t1.songid  =  t2.songid WHERE TYPE  =  'lead';
+SELECT DISTINCT title FROM vocals AS t1 JOIN songs AS t2 ON t1.songid  =  t2.songid EXCEPT SELECT t2.title FROM vocals AS t1 JOIN songs AS t2 ON t1.songid  =  t2.songid WHERE TYPE  =  'lead';
+SELECT DISTINCT TYPE FROM vocals;
+SELECT DISTINCT TYPE FROM vocals;
+SELECT * FROM Albums WHERE YEAR  =  2010;
+SELECT * FROM Albums WHERE YEAR  =  2010;
+SELECT T2.firstname ,  T2.lastname FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId WHERE T3.Title  =  'Le Pop';
+SELECT T2.firstname ,  T2.lastname FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId WHERE T3.Title  =  'Le Pop';
+SELECT T2.lastname FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId GROUP BY lastname ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.lastname FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId GROUP BY lastname ORDER BY count(*) DESC LIMIT 1;
+SELECT T4.instrument FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId JOIN Instruments AS T4 ON T4.songid  =  T3.songid AND T4.bandmateid  =  T2.id WHERE T2.lastname  =  'Heilo' AND T3.title  =  'Badlands';
+SELECT T4.instrument FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId JOIN Instruments AS T4 ON T4.songid  =  T3.songid AND T4.bandmateid  =  T2.id WHERE T2.lastname  =  'Heilo' AND T3.title  =  'Badlands';
+SELECT count(DISTINCT instrument) FROM instruments AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid WHERE title  =  'Badlands';
+SELECT count(DISTINCT instrument) FROM instruments AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid WHERE title  =  'Badlands';
+SELECT TYPE FROM vocals AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid WHERE title  =  'Badlands';
+SELECT TYPE FROM vocals AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid WHERE title  =  'Badlands';
+SELECT count(*) FROM vocals AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid WHERE title  =  'Le Pop';
+SELECT count(*) FROM vocals AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid WHERE title  =  'Le Pop';
+SELECT count(DISTINCT title) FROM vocals AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid WHERE TYPE  =  'shared';
+SELECT count(DISTINCT title) FROM vocals AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid WHERE TYPE  =  'shared';
+SELECT DISTINCT title FROM vocals AS t1 JOIN songs AS t2 ON t1.songid  =  t2.songid EXCEPT SELECT t2.title FROM vocals AS t1 JOIN songs AS t2 ON t1.songid  =  t2.songid WHERE TYPE  =  'back';
+SELECT DISTINCT title FROM vocals AS t1 JOIN songs AS t2 ON t1.songid  =  t2.songid EXCEPT SELECT t2.title FROM vocals AS t1 JOIN songs AS t2 ON t1.songid  =  t2.songid WHERE TYPE  =  'back';
+SELECT TYPE FROM vocals AS T1 JOIN band AS T2 ON T1.bandmate  =  T2.id WHERE firstname  =  'Solveig' GROUP BY TYPE ORDER BY count(*) DESC LIMIT 1;
+SELECT TYPE FROM vocals AS T1 JOIN band AS T2 ON T1.bandmate  =  T2.id WHERE firstname  =  'Solveig' GROUP BY TYPE ORDER BY count(*) DESC LIMIT 1;
+SELECT TYPE FROM vocals AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid JOIN band AS T3 ON T1.bandmate  =  T3.id WHERE T3.lastname  =  'Heilo' AND T2.title  =  'Der Kapitan';
+SELECT TYPE FROM vocals AS T1 JOIN songs AS T2 ON T1.songid  =  T2.songid JOIN band AS T3 ON T1.bandmate  =  T3.id WHERE T3.lastname  =  'Heilo' AND T2.title  =  'Der Kapitan';
+SELECT t2.firstname FROM Performance AS t1 JOIN Band AS t2 ON t1.bandmate  =  t2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId GROUP BY firstname ORDER BY count(*) DESC LIMIT 1;
+SELECT t2.firstname FROM Performance AS t1 JOIN Band AS t2 ON t1.bandmate  =  t2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId GROUP BY firstname ORDER BY count(*) DESC LIMIT 1;
+SELECT TYPE FROM vocals AS T1 JOIN band AS T2 ON T1.bandmate  =  T2.id WHERE firstname  =  'Marianne' GROUP BY TYPE ORDER BY count(*) DESC LIMIT 1;
+SELECT TYPE FROM vocals AS T1 JOIN band AS T2 ON T1.bandmate  =  T2.id WHERE firstname  =  'Marianne' GROUP BY TYPE ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.firstname ,  T2.lastname FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId WHERE T3.Title  =  'Der Kapitan' AND T1.StagePosition  =  'back';
+SELECT T2.firstname ,  T2.lastname FROM Performance AS T1 JOIN Band AS T2 ON T1.bandmate  =  T2.id JOIN Songs AS T3 ON T3.SongId  =  T1.SongId WHERE T3.Title  =  'Der Kapitan' AND T1.StagePosition  =  'back';
+SELECT DISTINCT title FROM vocals AS t1 JOIN songs AS t2 ON t1.songid  =  t2.songid EXCEPT SELECT t2.title FROM vocals AS t1 JOIN songs AS t2 ON t1.songid  =  t2.songid WHERE TYPE  =  'back';
+SELECT DISTINCT title FROM vocals AS t1 JOIN songs AS t2 ON t1.songid  =  t2.songid EXCEPT SELECT t2.title FROM vocals AS t1 JOIN songs AS t2 ON t1.songid  =  t2.songid WHERE TYPE  =  'back';
+SELECT T3.title FROM albums AS T1 JOIN tracklists AS T2 ON T1.aid  =  T2.albumid JOIN songs AS T3 ON T2.songid  =  T3.songid WHERE T1.title  =  'A Kiss Before You Go: Live in Hamburg';
+SELECT T3.title FROM albums AS T1 JOIN tracklists AS T2 ON T1.aid  =  T2.albumid JOIN songs AS T3 ON T2.songid  =  T3.songid WHERE T1.title  =  'A Kiss Before You Go: Live in Hamburg';
+SELECT T3.title FROM albums AS T1 JOIN tracklists AS T2 ON T1.aid  =  T2.albumid JOIN songs AS T3 ON T2.songid  =  T3.songid WHERE t1.label  =  'Universal Music Group';
+SELECT T3.title FROM albums AS T1 JOIN tracklists AS T2 ON T1.aid  =  T2.albumid JOIN songs AS T3 ON T2.songid  =  T3.songid WHERE t1.label  =  'Universal Music Group';
+SELECT count(DISTINCT T3.title) FROM albums AS T1 JOIN tracklists AS T2 ON T1.aid  =  T2.albumid JOIN songs AS T3 ON T2.songid  =  T3.songid WHERE t1.type  =  'Studio';
+SELECT count(DISTINCT T3.title) FROM albums AS T1 JOIN tracklists AS T2 ON T1.aid  =  T2.albumid JOIN songs AS T3 ON T2.songid  =  T3.songid WHERE t1.type  =  'Studio';
+SELECT founder FROM manufacturers WHERE name  =  'Sony';
+SELECT founder FROM manufacturers WHERE name  =  'Sony';
+SELECT headquarter FROM manufacturers WHERE founder  =  'James';
+SELECT headquarter FROM manufacturers WHERE founder  =  'James';
+SELECT name ,  headquarter FROM manufacturers ORDER BY revenue DESC;
+SELECT name ,  headquarter FROM manufacturers ORDER BY revenue DESC;
+SELECT avg(revenue) ,  max(revenue) ,  sum(revenue) FROM manufacturers;
+SELECT avg(revenue) ,  max(revenue) ,  sum(revenue) FROM manufacturers;
+SELECT count(*) FROM manufacturers WHERE founder  =  'Andy';
+SELECT count(*) FROM manufacturers WHERE founder  =  'Andy';
+SELECT sum(revenue) FROM manufacturers WHERE headquarter  =  'Austin';
+SELECT sum(revenue) FROM manufacturers WHERE headquarter  =  'Austin';
+SELECT DISTINCT headquarter FROM manufacturers;
+SELECT DISTINCT headquarter FROM manufacturers;
+SELECT count(*) FROM manufacturers WHERE headquarter  =  'Tokyo' OR headquarter  =  'Beijing';
+SELECT count(*) FROM manufacturers WHERE headquarter  =  'Tokyo' OR headquarter  =  'Beijing';
+SELECT founder FROM manufacturers WHERE name LIKE 'S%';
+SELECT founder FROM manufacturers WHERE name LIKE 'S%';
+SELECT name FROM manufacturers WHERE revenue BETWEEN 100 AND 150;
+SELECT name FROM manufacturers WHERE revenue BETWEEN 100 AND 150;
+SELECT sum(revenue) FROM manufacturers WHERE Headquarter  =  'Tokyo' OR Headquarter  =  'Taiwan';
+SELECT sum(revenue) FROM manufacturers WHERE Headquarter  =  'Tokyo' OR Headquarter  =  'Taiwan';
+SELECT T1.name FROM products AS T1 JOIN manufacturers AS T2 ON T1.Manufacturer  =  T2.code WHERE T2.name  =  'Creative Labs' INTERSECT SELECT T1.name FROM products AS T1 JOIN manufacturers AS T2 ON T1.Manufacturer  =  T2.code WHERE T2.name  =  'Sony';
+SELECT T1.name FROM products AS T1 JOIN manufacturers AS T2 ON T1.Manufacturer  =  T2.code WHERE T2.name  =  'Creative Labs' INTERSECT SELECT T1.name FROM products AS T1 JOIN manufacturers AS T2 ON T1.Manufacturer  =  T2.code WHERE T2.name  =  'Sony';
+SELECT name ,  headquarter ,  founder FROM manufacturers ORDER BY revenue DESC LIMIT 1;
+SELECT name ,  headquarter ,  founder FROM manufacturers ORDER BY revenue DESC LIMIT 1;
+SELECT name ,  headquarter ,  revenue FROM manufacturers ORDER BY revenue DESC;
+SELECT name ,  headquarter ,  revenue FROM manufacturers ORDER BY revenue DESC;
+SELECT name FROM manufacturers WHERE revenue  >  (SELECT avg(revenue) FROM manufacturers);
+SELECT name FROM manufacturers WHERE revenue  >  (SELECT avg(revenue) FROM manufacturers);
+SELECT name FROM manufacturers WHERE revenue  <  (SELECT min(revenue) FROM manufacturers WHERE headquarter  =  'Austin');
+SELECT name FROM manufacturers WHERE revenue  <  (SELECT min(revenue) FROM manufacturers WHERE headquarter  =  'Austin');
+SELECT sum(revenue) FROM manufacturers WHERE revenue  >  (SELECT min(revenue) FROM manufacturers WHERE headquarter  =  'Austin');
+SELECT sum(revenue) FROM manufacturers WHERE revenue  >  (SELECT min(revenue) FROM manufacturers WHERE headquarter  =  'Austin');
+SELECT sum(revenue) ,  founder FROM manufacturers GROUP BY founder;
+SELECT sum(revenue) ,  founder FROM manufacturers GROUP BY founder;
+SELECT name ,  max(revenue) ,  Headquarter FROM manufacturers GROUP BY Headquarter;
+SELECT name ,  max(revenue) ,  Headquarter FROM manufacturers GROUP BY Headquarter;
+SELECT sum(revenue) ,  name FROM manufacturers GROUP BY name;
+SELECT sum(revenue) ,  name FROM manufacturers GROUP BY name;
+SELECT avg(T1.price) ,  T2.name FROM products AS T1 JOIN manufacturers AS T2 ON T1.Manufacturer  =  T2.code GROUP BY T2.name;
+SELECT avg(T1.price) ,  T2.name FROM products AS T1 JOIN manufacturers AS T2 ON T1.Manufacturer  =  T2.code GROUP BY T2.name;
+SELECT count(DISTINCT T1.name) ,  T2.Headquarter FROM products AS T1 JOIN manufacturers AS T2 ON T1.Manufacturer  =  T2.code GROUP BY T2.Headquarter;
+SELECT count(DISTINCT T1.name) ,  T2.Headquarter FROM products AS T1 JOIN manufacturers AS T2 ON T1.Manufacturer  =  T2.code GROUP BY T2.Headquarter;
+SELECT count(DISTINCT name) FROM products WHERE name NOT IN (SELECT T1.name FROM products AS T1 JOIN manufacturers AS T2 ON T1.Manufacturer  =  T2.code WHERE T2.name  =  'Sony');
+SELECT count(DISTINCT name) FROM products WHERE name NOT IN (SELECT T1.name FROM products AS T1 JOIN manufacturers AS T2 ON T1.Manufacturer  =  T2.code WHERE T2.name  =  'Sony');
+SELECT name FROM manufacturers EXCEPT SELECT T2.name FROM products AS T1 JOIN manufacturers AS T2 ON T1.Manufacturer  =  T2.code WHERE T1.name  =  'DVD drive';
+SELECT name FROM manufacturers EXCEPT SELECT T2.name FROM products AS T1 JOIN manufacturers AS T2 ON T1.Manufacturer  =  T2.code WHERE T1.name  =  'DVD drive';
+SELECT count(*) ,  T2.name FROM products AS T1 JOIN manufacturers AS T2 ON T1.Manufacturer  =  T2.code GROUP BY T2.name;
+SELECT count(*) ,  T2.name FROM products AS T1 JOIN manufacturers AS T2 ON T1.Manufacturer  =  T2.code GROUP BY T2.name;
+SELECT Name FROM Products;
+SELECT Name FROM Products;
+SELECT name ,  price FROM products;
+SELECT name ,  price FROM products;
+SELECT name FROM products WHERE price  <=  200;
+SELECT name FROM products WHERE price  <=  200;
+SELECT * FROM products WHERE price BETWEEN 60 AND 120;
+SELECT * FROM products WHERE price BETWEEN 60 AND 120;
+SELECT avg(price) FROM products;
+SELECT avg(price) FROM products;
+SELECT avg(price) FROM products WHERE Manufacturer  =  2;
+SELECT avg(price) FROM products WHERE Manufacturer  =  2;
+SELECT count(*) FROM products WHERE price >= 180;
+SELECT count(*) FROM products WHERE price >= 180;
+SELECT name ,  price FROM products WHERE price  >=  180 ORDER BY price DESC ,  name ASC;
+SELECT name ,  price FROM products WHERE price  >=  180 ORDER BY price DESC ,  name ASC;
+SELECT * FROM products AS T1 JOIN Manufacturers AS T2 ON T1.manufacturer  =  T2.code;
+SELECT * FROM products AS T1 JOIN Manufacturers AS T2 ON T1.manufacturer  =  T2.code;
+SELECT AVG(Price) ,  Manufacturer FROM Products GROUP BY Manufacturer;
+SELECT AVG(Price) ,  Manufacturer FROM Products GROUP BY Manufacturer;
+SELECT avg(T1.Price) ,  T2.name FROM products AS T1 JOIN Manufacturers AS T2 ON T1.manufacturer  =  T2.code GROUP BY T2.name;
+SELECT avg(T1.Price) ,  T2.name FROM products AS T1 JOIN Manufacturers AS T2 ON T1.manufacturer  =  T2.code GROUP BY T2.name;
+SELECT avg(T1.Price) ,  T2.name FROM products AS T1 JOIN Manufacturers AS T2 ON T1.manufacturer  =  T2.code GROUP BY T2.name HAVING avg(T1.price) >= 150;
+SELECT avg(T1.Price) ,  T2.name FROM products AS T1 JOIN Manufacturers AS T2 ON T1.manufacturer  =  T2.code GROUP BY T2.name HAVING avg(T1.price) >= 150;
+SELECT name ,  price FROM Products ORDER BY price ASC LIMIT 1;
+SELECT name ,  price FROM Products ORDER BY price ASC LIMIT 1;
+SELECT T1.Name ,  max(T1.Price) ,  T2.name FROM products AS T1 JOIN Manufacturers AS T2 ON T1.manufacturer  =  T2.code GROUP BY T2.name;
+SELECT T1.Name ,  max(T1.Price) ,  T2.name FROM products AS T1 JOIN Manufacturers AS T2 ON T1.manufacturer  =  T2.code GROUP BY T2.name;
+SELECT code ,  name ,  min(price) FROM products GROUP BY name;
+SELECT code ,  name ,  min(price) FROM products GROUP BY name;
+SELECT problem_log_id FROM problem_log ORDER BY log_entry_date DESC LIMIT 1;
+SELECT problem_log_id FROM problem_log ORDER BY log_entry_date DESC LIMIT 1;
+SELECT problem_log_id ,  problem_id FROM problem_log ORDER BY log_entry_date LIMIT 1;
+SELECT problem_log_id ,  problem_id FROM problem_log ORDER BY log_entry_date LIMIT 1;
+SELECT problem_log_id ,  log_entry_date FROM problem_log WHERE problem_id = 10;
+SELECT problem_log_id ,  log_entry_date FROM problem_log WHERE problem_id = 10;
+SELECT problem_log_id ,  log_entry_description FROM problem_log;
+SELECT problem_log_id ,  log_entry_description FROM problem_log;
+SELECT DISTINCT staff_first_name ,  staff_last_name FROM staff AS T1 JOIN problem_log AS T2 ON T1.staff_id = T2.assigned_to_staff_id WHERE T2.problem_id = 1;
+SELECT DISTINCT staff_first_name ,  staff_last_name FROM staff AS T1 JOIN problem_log AS T2 ON T1.staff_id = T2.assigned_to_staff_id WHERE T2.problem_id = 1;
+SELECT DISTINCT T2.problem_id ,  T2.problem_log_id FROM staff AS T1 JOIN problem_log AS T2 ON T1.staff_id = T2.assigned_to_staff_id WHERE T1.staff_first_name = 'Rylan' AND T1.staff_last_name = 'Homenick';
+SELECT DISTINCT T2.problem_id ,  T2.problem_log_id FROM staff AS T1 JOIN problem_log AS T2 ON T1.staff_id = T2.assigned_to_staff_id WHERE T1.staff_first_name = 'Rylan' AND T1.staff_last_name = 'Homenick';
+SELECT count(*) FROM product AS T1 JOIN problems AS T2 ON T1.product_id = T2.product_id WHERE T1.product_name = 'voluptatem';
+SELECT count(*) FROM product AS T1 JOIN problems AS T2 ON T1.product_id = T2.product_id WHERE T1.product_name = 'voluptatem';
+SELECT count(*) ,  T1.product_name FROM product AS T1 JOIN problems AS T2 ON T1.product_id = T2.product_id GROUP BY T1.product_name ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) ,  T1.product_name FROM product AS T1 JOIN problems AS T2 ON T1.product_id = T2.product_id GROUP BY T1.product_name ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.problem_description FROM problems AS T1 JOIN staff AS T2 ON T1.reported_by_staff_id = T2.staff_id WHERE T2.staff_first_name = 'Christop';
+SELECT T1.problem_description FROM problems AS T1 JOIN staff AS T2 ON T1.reported_by_staff_id = T2.staff_id WHERE T2.staff_first_name = 'Christop';
+SELECT T1.problem_id FROM problems AS T1 JOIN staff AS T2 ON T1.reported_by_staff_id = T2.staff_id WHERE T2.staff_last_name = 'Bosco';
+SELECT T1.problem_id FROM problems AS T1 JOIN staff AS T2 ON T1.reported_by_staff_id = T2.staff_id WHERE T2.staff_last_name = 'Bosco';
+SELECT problem_id FROM problems WHERE date_problem_reported > '1978-06-26';
+SELECT problem_id FROM problems WHERE date_problem_reported > '1978-06-26';
+SELECT problem_id FROM problems WHERE date_problem_reported < '1978-06-26';
+SELECT problem_id FROM problems WHERE date_problem_reported < '1978-06-26';
+SELECT count(*) ,  T2.product_id FROM problems AS T1 JOIN product AS T2 ON T1.product_id = T2.product_id GROUP BY T2.product_id;
+SELECT count(*) ,  T2.product_id FROM problems AS T1 JOIN product AS T2 ON T1.product_id = T2.product_id GROUP BY T2.product_id;
+SELECT count(*) ,  T2.product_id FROM problems AS T1 JOIN product AS T2 ON T1.product_id = T2.product_id WHERE T1.date_problem_reported > '1986-11-13' GROUP BY T2.product_id;
+SELECT count(*) ,  T2.product_id FROM problems AS T1 JOIN product AS T2 ON T1.product_id = T2.product_id WHERE T1.date_problem_reported > '1986-11-13' GROUP BY T2.product_id;
+SELECT DISTINCT product_name FROM product ORDER BY product_name;
+SELECT DISTINCT product_name FROM product ORDER BY product_name;
+SELECT DISTINCT product_name FROM product ORDER BY product_id;
+SELECT DISTINCT product_name FROM product ORDER BY product_id;
+SELECT product_id FROM problems AS T1 JOIN staff AS T2 ON T1.reported_by_staff_id = T2.staff_id WHERE T2.staff_first_name = 'Dameon' AND T2.staff_last_name = 'Frami' UNION SELECT product_id FROM problems AS T1 JOIN staff AS T2 ON T1.reported_by_staff_id = T2.staff_id WHERE T2.staff_first_name = 'Jolie' AND T2.staff_last_name = 'Weber';
+SELECT product_id FROM problems AS T1 JOIN staff AS T2 ON T1.reported_by_staff_id = T2.staff_id WHERE T2.staff_first_name = 'Dameon' AND T2.staff_last_name = 'Frami' UNION SELECT product_id FROM problems AS T1 JOIN staff AS T2 ON T1.reported_by_staff_id = T2.staff_id WHERE T2.staff_first_name = 'Jolie' AND T2.staff_last_name = 'Weber';
+SELECT product_id FROM problems AS T1 JOIN staff AS T2 ON T1.reported_by_staff_id = T2.staff_id WHERE T2.staff_first_name = 'Christop' AND T2.staff_last_name = 'Berge' INTERSECT SELECT product_id FROM problems AS T1 JOIN staff AS T2 ON T1.closure_authorised_by_staff_id = T2.staff_id WHERE T2.staff_first_name = 'Ashley' AND T2.staff_last_name = 'Medhurst';
+SELECT product_id FROM problems AS T1 JOIN staff AS T2 ON T1.reported_by_staff_id = T2.staff_id WHERE T2.staff_first_name = 'Christop' AND T2.staff_last_name = 'Berge' INTERSECT SELECT product_id FROM problems AS T1 JOIN staff AS T2 ON T1.closure_authorised_by_staff_id = T2.staff_id WHERE T2.staff_first_name = 'Ashley' AND T2.staff_last_name = 'Medhurst';
+SELECT T1.problem_id FROM problems AS T1 JOIN staff AS T2 ON T1.reported_by_staff_id = T2.staff_id WHERE date_problem_reported  <  ( SELECT min(date_problem_reported) FROM problems AS T3 JOIN staff AS T4 ON T3.reported_by_staff_id = T4.staff_id WHERE T4.staff_first_name = 'Lysanne' AND T4.staff_last_name = 'Turcotte' );
+SELECT T1.problem_id FROM problems AS T1 JOIN staff AS T2 ON T1.reported_by_staff_id = T2.staff_id WHERE date_problem_reported  <  ( SELECT min(date_problem_reported) FROM problems AS T3 JOIN staff AS T4 ON T3.reported_by_staff_id = T4.staff_id WHERE T4.staff_first_name = 'Lysanne' AND T4.staff_last_name = 'Turcotte' );
+SELECT T1.problem_id FROM problems AS T1 JOIN staff AS T2 ON T1.reported_by_staff_id = T2.staff_id WHERE date_problem_reported  >  ( SELECT max(date_problem_reported) FROM problems AS T3 JOIN staff AS T4 ON T3.reported_by_staff_id = T4.staff_id WHERE T4.staff_first_name = 'Rylan' AND T4.staff_last_name = 'Homenick' );
+SELECT T1.problem_id FROM problems AS T1 JOIN staff AS T2 ON T1.reported_by_staff_id = T2.staff_id WHERE date_problem_reported  >  ( SELECT max(date_problem_reported) FROM problems AS T3 JOIN staff AS T4 ON T3.reported_by_staff_id = T4.staff_id WHERE T4.staff_first_name = 'Rylan' AND T4.staff_last_name = 'Homenick' );
+SELECT T2.product_name FROM problems AS T1 JOIN product AS T2 ON T1.product_id = T2.product_id GROUP BY T2.product_name ORDER BY count(*) DESC LIMIT 3;
+SELECT T2.product_name FROM problems AS T1 JOIN product AS T2 ON T1.product_id = T2.product_id GROUP BY T2.product_name ORDER BY count(*) DESC LIMIT 3;
+SELECT T1.problem_id FROM problems AS T1 JOIN product AS T2 ON T1.product_id = T2.product_id WHERE T2.product_name = 'voluptatem' AND T1.date_problem_reported > '1995';
+SELECT T1.problem_id FROM problems AS T1 JOIN product AS T2 ON T1.product_id = T2.product_id WHERE T2.product_name = 'voluptatem' AND T1.date_problem_reported > '1995';
+SELECT T3.staff_first_name ,  T3.staff_last_name FROM problems AS T1 JOIN product AS T2 JOIN staff AS T3 ON T1.product_id = T2.product_id AND T1.reported_by_staff_id = T3.staff_id WHERE T2.product_name = 'rem' EXCEPT SELECT T3.staff_first_name ,  T3.staff_last_name FROM problems AS T1 JOIN product AS T2 JOIN staff AS T3 ON T1.product_id = T2.product_id AND T1.reported_by_staff_id = T3.staff_id WHERE T2.product_name = 'aut';
+SELECT T3.staff_first_name ,  T3.staff_last_name FROM problems AS T1 JOIN product AS T2 JOIN staff AS T3 ON T1.product_id = T2.product_id AND T1.reported_by_staff_id = T3.staff_id WHERE T2.product_name = 'rem' EXCEPT SELECT T3.staff_first_name ,  T3.staff_last_name FROM problems AS T1 JOIN product AS T2 JOIN staff AS T3 ON T1.product_id = T2.product_id AND T1.reported_by_staff_id = T3.staff_id WHERE T2.product_name = 'aut';
+SELECT T2.product_name FROM problems AS T1 JOIN product AS T2 JOIN staff AS T3 ON T1.product_id = T2.product_id AND T1.reported_by_staff_id = T3.staff_id WHERE T3.staff_first_name = 'Lacey' AND T3.staff_last_name = 'Bosco' INTERSECT SELECT T2.product_name FROM problems AS T1 JOIN product AS T2 JOIN staff AS T3 ON T1.product_id = T2.product_id AND T1.reported_by_staff_id = T3.staff_id WHERE T3.staff_first_name = 'Kenton' AND T3.staff_last_name = 'Champlin';
+SELECT T2.product_name FROM problems AS T1 JOIN product AS T2 JOIN staff AS T3 ON T1.product_id = T2.product_id AND T1.reported_by_staff_id = T3.staff_id WHERE T3.staff_first_name = 'Lacey' AND T3.staff_last_name = 'Bosco' INTERSECT SELECT T2.product_name FROM problems AS T1 JOIN product AS T2 JOIN staff AS T3 ON T1.product_id = T2.product_id AND T1.reported_by_staff_id = T3.staff_id WHERE T3.staff_first_name = 'Kenton' AND T3.staff_last_name = 'Champlin';
+SELECT count(*) FROM branch WHERE membership_amount  >  (SELECT avg(membership_amount) FROM branch);
+SELECT count(*) FROM branch WHERE membership_amount  >  (SELECT avg(membership_amount) FROM branch);
+SELECT name ,  address_road ,  city FROM branch ORDER BY open_year;
+SELECT name ,  address_road ,  city FROM branch ORDER BY open_year;
+SELECT name FROM branch ORDER BY membership_amount DESC LIMIT 3;
+SELECT name FROM branch ORDER BY membership_amount DESC LIMIT 3;
+SELECT DISTINCT city FROM branch WHERE membership_amount  >=  100;
+SELECT DISTINCT city FROM branch WHERE membership_amount  >=  100;
+SELECT open_year FROM branch GROUP BY open_year HAVING count(*)  >=  2;
+SELECT open_year FROM branch GROUP BY open_year HAVING count(*)  >=  2;
+SELECT min(membership_amount) ,  max(membership_amount) FROM branch WHERE open_year  =  2011 OR city  =  'London';
+SELECT min(membership_amount) ,  max(membership_amount) FROM branch WHERE open_year  =  2011 OR city  =  'London';
+SELECT city ,  count(*) FROM branch WHERE open_year  <  2010 GROUP BY city;
+SELECT city ,  count(*) FROM branch WHERE open_year  <  2010 GROUP BY city;
+SELECT count(DISTINCT LEVEL) FROM member;
+SELECT count(DISTINCT LEVEL) FROM member;
+SELECT card_number ,  name ,  hometown FROM member ORDER BY LEVEL DESC;
+SELECT card_number ,  name ,  hometown FROM member ORDER BY LEVEL DESC;
+SELECT LEVEL FROM member GROUP BY LEVEL ORDER BY count(*) DESC LIMIT 1;
+SELECT LEVEL FROM member GROUP BY LEVEL ORDER BY count(*) DESC LIMIT 1;
+SELECT T3.name ,  T2.name FROM membership_register_branch AS T1 JOIN branch AS T2 ON T1.branch_id  =  T2.branch_id JOIN member AS T3 ON T1.member_id  =  T3.member_id ORDER BY T1.register_year;
+SELECT T3.name ,  T2.name FROM membership_register_branch AS T1 JOIN branch AS T2 ON T1.branch_id  =  T2.branch_id JOIN member AS T3 ON T1.member_id  =  T3.member_id ORDER BY T1.register_year;
+SELECT T2.name ,  count(*) FROM membership_register_branch AS T1 JOIN branch AS T2 ON T1.branch_id  =  T2.branch_id WHERE T1.register_year  >  2015 GROUP BY T2.branch_id;
+SELECT T2.name ,  count(*) FROM membership_register_branch AS T1 JOIN branch AS T2 ON T1.branch_id  =  T2.branch_id WHERE T1.register_year  >  2015 GROUP BY T2.branch_id;
+SELECT name FROM member WHERE member_id NOT IN (SELECT member_id FROM membership_register_branch);
+SELECT name FROM member WHERE member_id NOT IN (SELECT member_id FROM membership_register_branch);
+SELECT name ,  city FROM branch WHERE branch_id NOT IN (SELECT branch_id FROM membership_register_branch);
+SELECT name ,  city FROM branch WHERE branch_id NOT IN (SELECT branch_id FROM membership_register_branch);
+SELECT T2.name ,  T2.open_year FROM membership_register_branch AS T1 JOIN branch AS T2 ON T1.branch_id  =  T2.branch_id WHERE T1.register_year  =  2016 GROUP BY T2.branch_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.name ,  T2.open_year FROM membership_register_branch AS T1 JOIN branch AS T2 ON T1.branch_id  =  T2.branch_id WHERE T1.register_year  =  2016 GROUP BY T2.branch_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.name ,  T2.hometown FROM membership_register_branch AS T1 JOIN member AS T2 ON T1.member_id  =  T2.member_id WHERE T1.register_year  =  2016;
+SELECT T2.name ,  T2.hometown FROM membership_register_branch AS T1 JOIN member AS T2 ON T1.member_id  =  T2.member_id WHERE T1.register_year  =  2016;
+SELECT city FROM branch WHERE open_year  =  2001 AND membership_amount  >  100;
+SELECT city FROM branch WHERE open_year  =  2001 AND membership_amount  >  100;
+SELECT city FROM branch EXCEPT SELECT city FROM branch WHERE membership_amount  >  100;
+SELECT city FROM branch EXCEPT SELECT city FROM branch WHERE membership_amount  >  100;
+SELECT sum(total_pounds) FROM purchase AS T1 JOIN branch AS T2 ON T1.branch_id  =  T2.branch_id WHERE T2.city  =  'London' AND T1.year  =  2018;
+SELECT sum(total_pounds) FROM purchase AS T1 JOIN branch AS T2 ON T1.branch_id  =  T2.branch_id WHERE T2.city  =  'London' AND T1.year  =  2018;
+SELECT count(*) FROM purchase AS T1 JOIN member AS T2 ON T1.member_id  =  T2.member_id WHERE T2.level  =  6;
+SELECT count(*) FROM purchase AS T1 JOIN member AS T2 ON T1.member_id  =  T2.member_id WHERE T2.level  =  6;
+SELECT T2.name FROM membership_register_branch AS T1 JOIN branch AS T2 ON T1.branch_id  =  T2.branch_id JOIN member AS T3 ON T1.member_id  =  T3.member_id WHERE T3.Hometown  =  'Louisville ,  Kentucky' INTERSECT SELECT T2.name FROM membership_register_branch AS T1 JOIN branch AS T2 ON T1.branch_id  =  T2.branch_id JOIN member AS T3 ON T1.member_id  =  T3.member_id WHERE T3.Hometown  =  'Hiram ,  Georgia';
+SELECT T2.name FROM membership_register_branch AS T1 JOIN branch AS T2 ON T1.branch_id  =  T2.branch_id JOIN member AS T3 ON T1.member_id  =  T3.member_id WHERE T3.Hometown  =  'Louisville ,  Kentucky' INTERSECT SELECT T2.name FROM membership_register_branch AS T1 JOIN branch AS T2 ON T1.branch_id  =  T2.branch_id JOIN member AS T3 ON T1.member_id  =  T3.member_id WHERE T3.Hometown  =  'Hiram ,  Georgia';
+SELECT card_number FROM member WHERE Hometown LIKE '%Kentucky%';
+SELECT card_number FROM member WHERE Hometown LIKE '%Kentucky%';
+SELECT count(*) FROM STUDENT;
+SELECT count(*) FROM STUDENT;
+SELECT count(*) FROM VOTING_RECORD;
+SELECT count(*) FROM VOTING_RECORD;
+SELECT count(DISTINCT President_Vote) FROM VOTING_RECORD;
+SELECT count(DISTINCT President_Vote) FROM VOTING_RECORD;
+SELECT max(Age) FROM STUDENT;
+SELECT max(Age) FROM STUDENT;
+SELECT LName FROM STUDENT WHERE Major  =  50;
+SELECT LName FROM STUDENT WHERE Major  =  50;
+SELECT Fname FROM STUDENT WHERE Age  >  22;
+SELECT Fname FROM STUDENT WHERE Age  >  22;
+SELECT Major FROM STUDENT WHERE Sex  =  'M';
+SELECT Major FROM STUDENT WHERE Sex  =  'M';
+SELECT avg(Age) FROM STUDENT WHERE Sex  =  'F';
+SELECT avg(Age) FROM STUDENT WHERE Sex  =  'F';
+SELECT max(Age) ,  min(Age) FROM STUDENT WHERE Major  =  600;
+SELECT max(Age) ,  min(Age) FROM STUDENT WHERE Major  =  600;
+SELECT Advisor FROM STUDENT WHERE city_code  =  'BAL';
+SELECT Advisor FROM STUDENT WHERE city_code  =  'BAL';
+SELECT DISTINCT Secretary_Vote FROM VOTING_RECORD WHERE ELECTION_CYCLE  =  'Fall';
+SELECT DISTINCT Secretary_Vote FROM VOTING_RECORD WHERE ELECTION_CYCLE  =  'Fall';
+SELECT DISTINCT PRESIDENT_Vote FROM VOTING_RECORD WHERE Registration_Date  =  '08/30/2015';
+SELECT DISTINCT PRESIDENT_Vote FROM VOTING_RECORD WHERE Registration_Date  =  '08/30/2015';
+SELECT DISTINCT Registration_Date ,  Election_Cycle FROM VOTING_RECORD;
+SELECT DISTINCT Registration_Date ,  Election_Cycle FROM VOTING_RECORD;
+SELECT DISTINCT President_Vote ,  VICE_President_Vote FROM VOTING_RECORD;
+SELECT DISTINCT President_Vote ,  VICE_President_Vote FROM VOTING_RECORD;
+SELECT DISTINCT T1.LName FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  T2.CLASS_President_VOTE;
+SELECT DISTINCT T1.LName FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  T2.CLASS_President_VOTE;
+SELECT DISTINCT T1.Fname FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  T2.CLASS_Senator_VOTE;
+SELECT DISTINCT T1.Fname FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  T2.CLASS_Senator_VOTE;
+SELECT DISTINCT T1.Age FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  T2.Secretary_Vote WHERE T2.Election_Cycle  =  'Fall';
+SELECT DISTINCT T1.Age FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  T2.Secretary_Vote WHERE T2.Election_Cycle  =  'Fall';
+SELECT DISTINCT T1.Advisor FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  T2.Treasurer_Vote WHERE T2.Election_Cycle  =  'Spring';
+SELECT DISTINCT T1.Advisor FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  T2.Treasurer_Vote WHERE T2.Election_Cycle  =  'Spring';
+SELECT DISTINCT T1.Major FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  T2.Treasurer_Vote;
+SELECT DISTINCT T1.Major FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  T2.Treasurer_Vote;
+SELECT DISTINCT T1.Fname ,  T1.LName FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  T2.President_VOTE WHERE T1.sex  =  'F';
+SELECT DISTINCT T1.Fname ,  T1.LName FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  T2.President_VOTE WHERE T1.sex  =  'F';
+SELECT DISTINCT T1.Fname ,  T1.LName FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  T2.VICE_President_VOTE WHERE T1.age  =  18;
+SELECT DISTINCT T1.Fname ,  T1.LName FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  T2.VICE_President_VOTE WHERE T1.age  =  18;
+SELECT count(*) FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  Class_Senator_Vote WHERE T1.Sex  =  'M' AND T2.Election_Cycle  =  'Fall';
+SELECT count(*) FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  Class_Senator_Vote WHERE T1.Sex  =  'M' AND T2.Election_Cycle  =  'Fall';
+SELECT count(*) FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  Class_Senator_Vote WHERE T1.city_code  =  'NYC' AND T2.Election_Cycle  =  'Spring';
+SELECT count(*) FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  Class_Senator_Vote WHERE T1.city_code  =  'NYC' AND T2.Election_Cycle  =  'Spring';
+SELECT avg(T1.Age) FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  SECRETARY_Vote WHERE T1.city_code  =  'NYC' AND T2.Election_Cycle  =  'Spring';
+SELECT avg(T1.Age) FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  SECRETARY_Vote WHERE T1.city_code  =  'NYC' AND T2.Election_Cycle  =  'Spring';
+SELECT avg(T1.Age) FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  SECRETARY_Vote WHERE T1.Sex  =  'F' AND T2.Election_Cycle  =  'Spring';
+SELECT avg(T1.Age) FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  SECRETARY_Vote WHERE T1.Sex  =  'F' AND T2.Election_Cycle  =  'Spring';
+SELECT DISTINCT T1.Fname FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  T2.VICE_PRESIDENT_Vote EXCEPT SELECT DISTINCT Fname FROM STUDENT WHERE city_code  =  'PIT';
+SELECT DISTINCT T1.Fname FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  T2.VICE_PRESIDENT_Vote EXCEPT SELECT DISTINCT Fname FROM STUDENT WHERE city_code  =  'PIT';
+SELECT DISTINCT T1.LName FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  PRESIDENT_Vote EXCEPT SELECT DISTINCT LName FROM STUDENT WHERE Advisor  =  '2192';
+SELECT DISTINCT T1.LName FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  PRESIDENT_Vote EXCEPT SELECT DISTINCT LName FROM STUDENT WHERE Advisor  =  '2192';
+SELECT DISTINCT T1.LName FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  PRESIDENT_Vote INTERSECT SELECT DISTINCT LName FROM STUDENT WHERE Advisor  =  '8741';
+SELECT DISTINCT T1.LName FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  PRESIDENT_Vote INTERSECT SELECT DISTINCT LName FROM STUDENT WHERE Advisor  =  '8741';
+SELECT Advisor ,  count(*) FROM STUDENT GROUP BY Advisor;
+SELECT Advisor ,  count(*) FROM STUDENT GROUP BY Advisor;
+SELECT Advisor FROM STUDENT GROUP BY Advisor HAVING COUNT(*)  >  2;
+SELECT Advisor FROM STUDENT GROUP BY Advisor HAVING COUNT(*)  >  2;
+SELECT Major FROM STUDENT GROUP BY Major HAVING COUNT(*)  <  3;
+SELECT Major FROM STUDENT GROUP BY Major HAVING COUNT(*)  <  3;
+SELECT Election_Cycle ,  count(*) FROM VOTING_RECORD GROUP BY Election_Cycle;
+SELECT Election_Cycle ,  count(*) FROM VOTING_RECORD GROUP BY Election_Cycle;
+SELECT Major FROM STUDENT GROUP BY major ORDER BY count(*) DESC LIMIT 1;
+SELECT Major FROM STUDENT GROUP BY major ORDER BY count(*) DESC LIMIT 1;
+SELECT Major FROM STUDENT WHERE Sex  =  'F' GROUP BY major ORDER BY count(*) DESC LIMIT 1;
+SELECT Major FROM STUDENT WHERE Sex  =  'F' GROUP BY major ORDER BY count(*) DESC LIMIT 1;
+SELECT city_code FROM STUDENT GROUP BY city_code ORDER BY count(*) DESC LIMIT 1;
+SELECT city_code FROM STUDENT GROUP BY city_code ORDER BY count(*) DESC LIMIT 1;
+SELECT Advisor FROM STUDENT GROUP BY Advisor HAVING count(*)  >  2;
+SELECT Advisor FROM STUDENT GROUP BY Advisor HAVING count(*)  >  2;
+SELECT count(*) FROM products;
+SELECT count(*) FROM products;
+SELECT count(*) FROM ref_colors;
+SELECT count(*) FROM ref_colors;
+SELECT count(*) FROM CHARACTERISTICS;
+SELECT count(*) FROM CHARACTERISTICS;
+SELECT product_name ,  typical_buying_price FROM products;
+SELECT product_name ,  typical_buying_price FROM products;
+SELECT color_description FROM ref_colors;
+SELECT color_description FROM ref_colors;
+SELECT DISTINCT characteristic_name FROM CHARACTERISTICS;
+SELECT DISTINCT characteristic_name FROM CHARACTERISTICS;
+SELECT product_name FROM products WHERE product_category_code  =  'Spices';
+SELECT product_name FROM products WHERE product_category_code  =  'Spices';
+SELECT T1.product_name ,  T2.color_description ,  T1.product_description FROM products AS T1 JOIN Ref_colors AS T2 ON T1.color_code  =  T2.color_code WHERE product_category_code  =  'Herbs';
+SELECT T1.product_name ,  T2.color_description ,  T1.product_description FROM products AS T1 JOIN Ref_colors AS T2 ON T1.color_code  =  T2.color_code WHERE product_category_code  =  'Herbs';
+SELECT count(*) FROM products WHERE product_category_code  =  'Seeds';
+SELECT count(*) FROM products WHERE product_category_code  =  'Seeds';
+SELECT count(*) FROM products WHERE product_category_code  =  'Spices' AND typical_buying_price  >  1000;
+SELECT count(*) FROM products WHERE product_category_code  =  'Spices' AND typical_buying_price  >  1000;
+SELECT product_category_code ,  typical_buying_price FROM products WHERE product_name  =  'cumin';
+SELECT product_category_code ,  typical_buying_price FROM products WHERE product_name  =  'cumin';
+SELECT product_category_code FROM products WHERE product_name  =  'flax';
+SELECT product_category_code FROM products WHERE product_name  =  'flax';
+SELECT T1.product_name FROM products AS T1 JOIN ref_colors AS T2 ON T1.color_code  =  T2.color_code WHERE T2.color_description  =  'yellow';
+SELECT T1.product_name FROM products AS T1 JOIN ref_colors AS T2 ON T1.color_code  =  T2.color_code WHERE T2.color_description  =  'yellow';
+SELECT T1.product_category_description FROM ref_product_categories AS T1 JOIN products AS T2 ON T1.product_category_code  =  T2.product_category_code WHERE T2.product_description LIKE '%t%';
+SELECT T1.product_category_description FROM ref_product_categories AS T1 JOIN products AS T2 ON T1.product_category_code  =  T2.product_category_code WHERE T2.product_description LIKE '%t%';
+SELECT t2.color_description FROM products AS t1 JOIN ref_colors AS t2 ON t1.color_code  =  t2.color_code WHERE t1.product_name  =  'catnip';
+SELECT t2.color_description FROM products AS t1 JOIN ref_colors AS t2 ON t1.color_code  =  t2.color_code WHERE t1.product_name  =  'catnip';
+SELECT t1.color_code ,  t2.color_description FROM products AS t1 JOIN ref_colors AS t2 ON t1.color_code  =  t2.color_code WHERE t1.product_name  =  'chervil';
+SELECT t1.color_code ,  t2.color_description FROM products AS t1 JOIN ref_colors AS t2 ON t1.color_code  =  t2.color_code WHERE t1.product_name  =  'chervil';
+SELECT t1.product_id ,  t2.color_description FROM products AS t1 JOIN ref_colors AS t2 ON t1.color_code  =  t2.color_code JOIN product_characteristics AS t3 ON t1.product_id  =  t3.product_id GROUP BY t1.product_id HAVING count(*)  >=  2;
+SELECT t1.product_id ,  t2.color_description FROM products AS t1 JOIN ref_colors AS t2 ON t1.color_code  =  t2.color_code JOIN product_characteristics AS t3 ON t1.product_id  =  t3.product_id GROUP BY t1.product_id HAVING count(*)  >=  2;
+SELECT t1.product_name FROM products AS t1 JOIN ref_colors AS t2 ON t1.color_code  =  t2.color_code WHERE t2.color_description  =  'white';
+SELECT t1.product_name FROM products AS t1 JOIN ref_colors AS t2 ON t1.color_code  =  t2.color_code WHERE t2.color_description  =  'white';
+SELECT t1.product_name ,  t1.typical_buying_price ,  t1.typical_selling_price FROM products AS t1 JOIN ref_colors AS t2 ON t1.color_code  =  t2.color_code WHERE t2.color_description  =  'yellow';
+SELECT t1.product_name ,  t1.typical_buying_price ,  t1.typical_selling_price FROM products AS t1 JOIN ref_colors AS t2 ON t1.color_code  =  t2.color_code WHERE t2.color_description  =  'yellow';
+SELECT count(*) FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id WHERE t1.product_name  =  'sesame';
+SELECT count(*) FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id WHERE t1.product_name  =  'sesame';
+SELECT count(DISTINCT t3.characteristic_name) FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id WHERE t1.product_name  =  'sesame';
+SELECT count(DISTINCT t3.characteristic_name) FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id WHERE t1.product_name  =  'sesame';
+SELECT t3.characteristic_name FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id WHERE t1.product_name  =  'sesame';
+SELECT t3.characteristic_name FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id WHERE t1.product_name  =  'sesame';
+SELECT t3.characteristic_name ,  t3.characteristic_data_type FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id WHERE t1.product_name  =  'cumin';
+SELECT t3.characteristic_name ,  t3.characteristic_data_type FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id WHERE t1.product_name  =  'cumin';
+SELECT t3.characteristic_name FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id WHERE t1.product_name  =  'sesame' AND t3.characteristic_type_code  =  'Grade';
+SELECT t3.characteristic_name FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id WHERE t1.product_name  =  'sesame' AND t3.characteristic_type_code  =  'Grade';
+SELECT count(*) FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id WHERE t1.product_name  =  'laurel';
+SELECT count(*) FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id WHERE t1.product_name  =  'laurel';
+SELECT count(*) FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id WHERE t1.product_name  =  'flax';
+SELECT count(*) FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id WHERE t1.product_name  =  'flax';
+SELECT product_name FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id JOIN ref_colors AS t4 ON t1.color_code  =  t4.color_code WHERE t4.color_description  =  'red' AND t3.characteristic_name  =  'fast';
+SELECT product_name FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id JOIN ref_colors AS t4 ON t1.color_code  =  t4.color_code WHERE t4.color_description  =  'red' AND t3.characteristic_name  =  'fast';
+SELECT count(*) FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id WHERE t3.characteristic_name  =  'hot';
+SELECT count(*) FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id WHERE t3.characteristic_name  =  'hot';
+SELECT DISTINCT t1.product_name FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id WHERE t3.characteristic_name  =  'warm';
+SELECT DISTINCT t1.product_name FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id WHERE t3.characteristic_name  =  'warm';
+SELECT count(*) FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id JOIN ref_colors AS t4 ON t1.color_code  =  t4.color_code WHERE t4.color_description  =  'red' AND t3.characteristic_name  =  'slow';
+SELECT count(*) FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id JOIN ref_colors AS t4 ON t1.color_code  =  t4.color_code WHERE t4.color_description  =  'red' AND t3.characteristic_name  =  'slow';
+SELECT count(*) FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id JOIN ref_colors AS t4 ON t1.color_code  =  t4.color_code WHERE t4.color_description  =  'white' OR t3.characteristic_name  =  'hot';
+SELECT count(*) FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id JOIN ref_colors AS t4 ON t1.color_code  =  t4.color_code WHERE t4.color_description  =  'white' OR t3.characteristic_name  =  'hot';
+SELECT unit_of_measure FROM ref_product_categories WHERE product_category_code  =  'Herbs';
+SELECT unit_of_measure FROM ref_product_categories WHERE product_category_code  =  'Herbs';
+SELECT product_category_description FROM ref_product_categories WHERE product_category_code  =  'Spices';
+SELECT product_category_description FROM ref_product_categories WHERE product_category_code  =  'Spices';
+SELECT product_category_description ,  unit_of_measure FROM ref_product_categories WHERE product_category_code  =  'Herbs';
+SELECT product_category_description ,  unit_of_measure FROM ref_product_categories WHERE product_category_code  =  'Herbs';
+SELECT t2.unit_of_measure FROM products AS t1 JOIN ref_product_categories AS t2 ON t1.product_category_code  =  t2.product_category_code WHERE t1.product_name  =  'cumin';
+SELECT t2.unit_of_measure FROM products AS t1 JOIN ref_product_categories AS t2 ON t1.product_category_code  =  t2.product_category_code WHERE t1.product_name  =  'cumin';
+SELECT t2.unit_of_measure ,  t2.product_category_code FROM products AS t1 JOIN ref_product_categories AS t2 ON t1.product_category_code  =  t2.product_category_code WHERE t1.product_name  =  'chervil';
+SELECT t2.unit_of_measure ,  t2.product_category_code FROM products AS t1 JOIN ref_product_categories AS t2 ON t1.product_category_code  =  t2.product_category_code WHERE t1.product_name  =  'chervil';
+SELECT t1.product_name FROM products AS t1 JOIN ref_product_categories AS t2 ON t1.product_category_code  =  t2.product_category_code JOIN ref_colors AS t3 ON t1.color_code  =  t3.color_code WHERE t3.color_description  =  'white' AND t2.unit_of_measure != 'Handful';
+SELECT t1.product_name FROM products AS t1 JOIN ref_product_categories AS t2 ON t1.product_category_code  =  t2.product_category_code JOIN ref_colors AS t3 ON t1.color_code  =  t3.color_code WHERE t3.color_description  =  'white' AND t2.unit_of_measure != 'Handful';
+SELECT t2.color_description FROM products AS t1 JOIN ref_colors AS t2 ON t1.color_code  =  t2.color_code GROUP BY t2.color_description ORDER BY count(*) DESC LIMIT 1;
+SELECT t2.color_description FROM products AS t1 JOIN ref_colors AS t2 ON t1.color_code  =  t2.color_code GROUP BY t2.color_description ORDER BY count(*) DESC LIMIT 1;
+SELECT t2.color_description FROM products AS t1 JOIN ref_colors AS t2 ON t1.color_code  =  t2.color_code GROUP BY t2.color_description ORDER BY count(*) ASC LIMIT 1;
+SELECT t2.color_description FROM products AS t1 JOIN ref_colors AS t2 ON t1.color_code  =  t2.color_code GROUP BY t2.color_description ORDER BY count(*) ASC LIMIT 1;
+SELECT t3.characteristic_name FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id GROUP BY t3.characteristic_name ORDER BY count(*) DESC LIMIT 1;
+SELECT t3.characteristic_name FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id GROUP BY t3.characteristic_name ORDER BY count(*) DESC LIMIT 1;
+SELECT characteristic_name ,  other_characteristic_details ,  characteristic_data_type FROM CHARACTERISTICS EXCEPT SELECT t1.characteristic_name ,  t1.other_characteristic_details ,  t1.characteristic_data_type FROM CHARACTERISTICS AS t1 JOIN product_characteristics AS t2 ON t1.characteristic_id  =  t2.characteristic_id;
+SELECT characteristic_name ,  other_characteristic_details ,  characteristic_data_type FROM CHARACTERISTICS EXCEPT SELECT t1.characteristic_name ,  t1.other_characteristic_details ,  t1.characteristic_data_type FROM CHARACTERISTICS AS t1 JOIN product_characteristics AS t2 ON t1.characteristic_id  =  t2.characteristic_id;
+SELECT t3.characteristic_name FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id GROUP BY t3.characteristic_name HAVING count(*)  >= 2;
+SELECT t3.characteristic_name FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id  =  t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id  =  t3.characteristic_id GROUP BY t3.characteristic_name HAVING count(*)  >= 2;
+SELECT count(*) FROM Ref_colors WHERE color_code NOT IN ( SELECT color_code FROM products );
+SELECT count(*) FROM Ref_colors WHERE color_code NOT IN ( SELECT color_code FROM products );
+SELECT count(*) FROM event;
+SELECT name FROM event ORDER BY YEAR DESC;
+SELECT name FROM event ORDER BY YEAR DESC LIMIT 1;
+SELECT count(*) FROM stadium;
+SELECT name FROM stadium ORDER BY capacity DESC LIMIT 1;
+SELECT name FROM stadium WHERE capacity  <  (SELECT avg(capacity) FROM stadium);
+SELECT country FROM stadium GROUP BY country ORDER BY count(*) DESC LIMIT 1;
+SELECT country FROM stadium GROUP BY country HAVING count(*)  <=  3;
+SELECT country FROM stadium WHERE capacity  >  60000 INTERSECT SELECT country FROM stadium WHERE capacity  <  50000;
+SELECT count(DISTINCT city) FROM stadium WHERE opening_year  <  2006;
+SELECT country ,  count(*) FROM stadium GROUP BY country;
+SELECT country FROM stadium EXCEPT SELECT country FROM stadium WHERE opening_year  >  2006;
+SELECT count(*) FROM stadium WHERE country != 'Russia';
+SELECT name FROM swimmer ORDER BY meter_100;
+SELECT count(DISTINCT nationality) FROM swimmer;
+SELECT nationality ,  count(*) FROM swimmer GROUP BY nationality HAVING count(*)  >  1;
+SELECT meter_200 ,  meter_300 FROM swimmer WHERE nationality  =  'Australia';
+SELECT t1.name FROM swimmer AS t1 JOIN record AS t2 ON t1.id  =  t2.swimmer_id WHERE RESULT  =  'Win';
+SELECT t1.name FROM stadium AS t1 JOIN event AS t2 ON t1.id  =  t2.stadium_id GROUP BY t2.stadium_id ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.name ,  t1.capacity FROM stadium AS t1 JOIN event AS t2 ON t1.id  =  t2.stadium_id WHERE t2.name  =  'World Junior';
+SELECT name FROM stadium WHERE id NOT IN (SELECT stadium_id FROM event);
+SELECT t1.name FROM swimmer AS t1 JOIN record AS t2 ON t1.id  =  t2.swimmer_id GROUP BY t2.swimmer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.name FROM swimmer AS t1 JOIN record AS t2 ON t1.id  =  t2.swimmer_id GROUP BY t2.swimmer_id HAVING count(*)  >=  2;
+SELECT t1.name ,  t1.nationality FROM swimmer AS t1 JOIN record AS t2 ON t1.id  =  t2.swimmer_id WHERE RESULT  =  'Win' GROUP BY t2.swimmer_id HAVING count(*)  >  1;
+SELECT name FROM swimmer WHERE id NOT IN (SELECT swimmer_id FROM record);
+SELECT t1.name FROM swimmer AS t1 JOIN record AS t2 ON t1.id  =  t2.swimmer_id WHERE RESULT  =  'Win' INTERSECT SELECT t1.name FROM swimmer AS t1 JOIN record AS t2 ON t1.id  =  t2.swimmer_id WHERE RESULT  =  'Loss';
+SELECT t4.name FROM swimmer AS t1 JOIN record AS t2 ON t1.id  =  t2.swimmer_id JOIN event AS t3 ON t2.event_id  =  t3.id JOIN stadium AS t4 ON t4.id  =  t3.stadium_id WHERE t1.nationality  =  'Australia';
+SELECT t3.name FROM record AS t1 JOIN event AS t2 ON t1.event_id  =  t2.id JOIN stadium AS t3 ON t3.id  =  t2.stadium_id GROUP BY t2.stadium_id ORDER BY count(*) DESC LIMIT 1;
+SELECT * FROM swimmer;
+SELECT avg(capacity) FROM stadium WHERE opening_year  =  2005;
+SELECT count(*) FROM railway;
+SELECT Builder FROM railway ORDER BY Builder ASC;
+SELECT Wheels ,  LOCATION FROM railway;
+SELECT max(LEVEL) FROM manager WHERE Country != 'Australia	';
+SELECT avg(Age) FROM manager;
+SELECT Name FROM manager ORDER BY LEVEL ASC;
+SELECT Name ,  Arrival FROM train;
+SELECT Name FROM manager ORDER BY Age DESC LIMIT 1;
+SELECT T2.Name ,  T1.Location FROM railway AS T1 JOIN train AS T2 ON T1.Railway_ID  =  T2.Railway_ID;
+SELECT T1.Builder FROM railway AS T1 JOIN train AS T2 ON T1.Railway_ID  =  T2.Railway_ID WHERE T2.Name  =  'Andaman Exp';
+SELECT T2.Railway_ID ,  T1.Location FROM railway AS T1 JOIN train AS T2 ON T1.Railway_ID  =  T2.Railway_ID GROUP BY T2.Railway_ID HAVING COUNT(*)  >  1;
+SELECT T2.Railway_ID ,  T1.Builder FROM railway AS T1 JOIN train AS T2 ON T1.Railway_ID  =  T2.Railway_ID GROUP BY T2.Railway_ID ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Builder ,  COUNT(*) FROM railway GROUP BY Builder;
+SELECT Builder FROM railway GROUP BY Builder ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT LOCATION ,  COUNT(*) FROM railway GROUP BY LOCATION;
+SELECT LOCATION FROM railway GROUP BY LOCATION HAVING COUNT(*)  >  1;
+SELECT ObjectNumber FROM railway WHERE Railway_ID NOT IN (SELECT Railway_ID FROM train);
+SELECT Country FROM manager WHERE Age  >  50 INTERSECT SELECT Country FROM manager WHERE Age  <  46;
+SELECT DISTINCT Country FROM manager;
+SELECT Working_year_starts FROM manager ORDER BY LEVEL DESC;
+SELECT Country FROM manager WHERE Age  >  50 OR Age  <  46;
+SELECT count(*) FROM addresses WHERE country  =  'USA';
+SELECT DISTINCT city FROM addresses;
+SELECT state_province_county ,  count(*) FROM addresses GROUP BY state_province_county;
+SELECT customer_name ,  customer_phone FROM customers WHERE customer_id NOT IN (SELECT customer_id FROM customer_address_history);
+SELECT T1.customer_name FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT product_type_code FROM products GROUP BY product_type_code HAVING count(*)  >=  2;
+SELECT T1.customer_name FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.order_status_code  =  'Completed' INTERSECT SELECT T1.customer_name FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.order_status_code  =  'Part';
+SELECT customer_name ,  customer_phone ,  payment_method_code FROM customers ORDER BY customer_number DESC;
+SELECT T1.product_name ,  sum(T2.order_quantity) FROM products AS T1 JOIN order_items AS T2 ON T1.product_id  =  T2.product_id GROUP BY T1.product_id;
+SELECT min(product_price) ,  max(product_price) ,  avg(product_price) FROM products;
+SELECT count(*) FROM products WHERE product_price  >  (SELECT avg(product_price) FROM products);
+SELECT T2.customer_name ,  T3.city ,  T1.date_from ,  T1.date_to FROM customer_address_history AS T1 JOIN customers AS T2 ON T1.customer_id  =  T2.customer_id JOIN addresses AS T3 ON T1.address_id  =  T3.address_id;
+SELECT T1.customer_name FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id WHERE T1.payment_method_code  =  'Credit Card' GROUP BY T1.customer_id HAVING count(*)  >  2;
+SELECT  T1.customer_name ,  T1.customer_phone FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id JOIN order_items AS T3 ON T3.order_id  =  T2.order_id GROUP BY T1.customer_id ORDER BY sum(T3.order_quantity) DESC LIMIT 1;
+SELECT product_type_code ,  product_name FROM products WHERE product_price  >  1000 OR product_price  <  500;
+SELECT dorm_name FROM dorm WHERE gender  =  'F';
+SELECT dorm_name FROM dorm WHERE gender  =  'F';
+SELECT dorm_name FROM dorm WHERE student_capacity  >  300;
+SELECT dorm_name FROM dorm WHERE student_capacity  >  300;
+SELECT count(*) FROM student WHERE sex  =  'F' AND age  <  25;
+SELECT count(*) FROM student WHERE sex  =  'F' AND age  <  25;
+SELECT fname FROM student WHERE age  >  20;
+SELECT fname FROM student WHERE age  >  20;
+SELECT fname FROM student WHERE city_code  =  'PHL' AND age BETWEEN 20 AND 25;
+SELECT fname FROM student WHERE city_code  =  'PHL' AND age BETWEEN 20 AND 25;
+SELECT count(*) FROM dorm;
+SELECT count(*) FROM dorm;
+SELECT count(*) FROM dorm_amenity;
+SELECT count(*) FROM dorm_amenity;
+SELECT sum(student_capacity) FROM dorm;
+SELECT sum(student_capacity) FROM dorm;
+SELECT count(*) FROM student;
+SELECT count(*) FROM student;
+SELECT avg(age) ,  city_code FROM student GROUP BY city_code;
+SELECT avg(age) ,  city_code FROM student GROUP BY city_code;
+SELECT avg(student_capacity) ,  sum(student_capacity) FROM dorm WHERE gender  =  'X';
+SELECT avg(student_capacity) ,  sum(student_capacity) FROM dorm WHERE gender  =  'X';
+SELECT count(DISTINCT dormid) FROM has_amenity;
+SELECT count(DISTINCT dormid) FROM has_amenity;
+SELECT dorm_name FROM dorm WHERE dormid NOT IN (SELECT dormid FROM has_amenity);
+SELECT dorm_name FROM dorm WHERE dormid NOT IN (SELECT dormid FROM has_amenity);
+SELECT count(DISTINCT gender) FROM dorm;
+SELECT count(DISTINCT gender) FROM dorm;
+SELECT student_capacity ,  gender FROM dorm WHERE dorm_name LIKE '%Donor%';
+SELECT student_capacity ,  gender FROM dorm WHERE dorm_name LIKE '%Donor%';
+SELECT dorm_name ,  gender FROM dorm WHERE student_capacity  >  300 OR student_capacity  <  100;
+SELECT dorm_name ,  gender FROM dorm WHERE student_capacity  >  300 OR student_capacity  <  100;
+SELECT count(DISTINCT major) ,  count(DISTINCT city_code) FROM student;
+SELECT count(DISTINCT major) ,  count(DISTINCT city_code) FROM student;
+SELECT T1.dorm_name FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid WHERE T3.amenity_name  =  'TV Lounge' INTERSECT SELECT T1.dorm_name FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid WHERE T3.amenity_name  =  'Study Room';
+SELECT T1.dorm_name FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid WHERE T3.amenity_name  =  'TV Lounge' INTERSECT SELECT T1.dorm_name FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid WHERE T3.amenity_name  =  'Study Room';
+SELECT T1.dorm_name FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid WHERE T3.amenity_name  =  'TV Lounge' EXCEPT SELECT T1.dorm_name FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid WHERE T3.amenity_name  =  'Study Room';
+SELECT T1.dorm_name FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid WHERE T3.amenity_name  =  'TV Lounge' EXCEPT SELECT T1.dorm_name FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid WHERE T3.amenity_name  =  'Study Room';
+SELECT lname FROM student WHERE sex  =  'F' AND city_code  =  'BAL' UNION SELECT lname FROM student WHERE sex  =  'M' AND age  <  20;
+SELECT lname FROM student WHERE sex  =  'F' AND city_code  =  'BAL' UNION SELECT lname FROM student WHERE sex  =  'M' AND age  <  20;
+SELECT dorm_name FROM dorm ORDER BY student_capacity DESC LIMIT 1;
+SELECT dorm_name FROM dorm ORDER BY student_capacity DESC LIMIT 1;
+SELECT amenity_name FROM dorm_amenity ORDER BY amenity_name;
+SELECT amenity_name FROM dorm_amenity ORDER BY amenity_name;
+SELECT city_code FROM student GROUP BY city_code ORDER BY count(*) DESC LIMIT 1;
+SELECT city_code FROM student GROUP BY city_code ORDER BY count(*) DESC LIMIT 1;
+SELECT fname ,  lname FROM student WHERE age  <  (SELECT avg(age) FROM student);
+SELECT fname ,  lname FROM student WHERE age  <  (SELECT avg(age) FROM student);
+SELECT fname ,  lname FROM student WHERE city_code != 'HKG' ORDER BY age;
+SELECT fname ,  lname FROM student WHERE city_code != 'HKG' ORDER BY age;
+SELECT T1.amenity_name FROM dorm_amenity AS T1 JOIN has_amenity AS T2 ON T2.amenid  =  T1.amenid JOIN dorm AS T3 ON T2.dormid  =  T3.dormid WHERE T3.dorm_name  =  'Anonymous Donor Hall' ORDER BY T1.amenity_name;
+SELECT T1.amenity_name FROM dorm_amenity AS T1 JOIN has_amenity AS T2 ON T2.amenid  =  T1.amenid JOIN dorm AS T3 ON T2.dormid  =  T3.dormid WHERE T3.dorm_name  =  'Anonymous Donor Hall' ORDER BY T1.amenity_name;
+SELECT count(*) ,  sum(student_capacity) ,  gender FROM dorm GROUP BY gender;
+SELECT count(*) ,  sum(student_capacity) ,  gender FROM dorm GROUP BY gender;
+SELECT avg(age) ,  max(age) ,  sex FROM student GROUP BY sex;
+SELECT avg(age) ,  max(age) ,  sex FROM student GROUP BY sex;
+SELECT count(*) ,  major FROM student GROUP BY major;
+SELECT count(*) ,  major FROM student GROUP BY major;
+SELECT count(*) ,  avg(age) ,  city_code FROM student GROUP BY city_code;
+SELECT count(*) ,  avg(age) ,  city_code FROM student GROUP BY city_code;
+SELECT count(*) ,  avg(age) ,  city_code FROM student WHERE sex  =  'M' GROUP BY city_code;
+SELECT count(*) ,  avg(age) ,  city_code FROM student WHERE sex  =  'M' GROUP BY city_code;
+SELECT count(*) ,  city_code FROM student GROUP BY city_code HAVING count(*)  >  1;
+SELECT count(*) ,  city_code FROM student GROUP BY city_code HAVING count(*)  >  1;
+SELECT fname ,  lname FROM student WHERE major != (SELECT major FROM student GROUP BY major ORDER BY count(*) DESC LIMIT 1);
+SELECT fname ,  lname FROM student WHERE major != (SELECT major FROM student GROUP BY major ORDER BY count(*) DESC LIMIT 1);
+SELECT count(*) ,  sex FROM student WHERE age  >  (SELECT avg(age) FROM student) GROUP BY sex;
+SELECT count(*) ,  sex FROM student WHERE age  >  (SELECT avg(age) FROM student) GROUP BY sex;
+SELECT avg(T1.age) ,  T3.dorm_name FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid JOIN dorm AS T3 ON T3.dormid  =  T2.dormid GROUP BY T3.dorm_name;
+SELECT avg(T1.age) ,  T3.dorm_name FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid JOIN dorm AS T3 ON T3.dormid  =  T2.dormid GROUP BY T3.dorm_name;
+SELECT count(*) ,  T1.dormid FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid WHERE T1.student_capacity  >  100 GROUP BY T1.dormid;
+SELECT count(*) ,  T1.dormid FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid WHERE T1.student_capacity  >  100 GROUP BY T1.dormid;
+SELECT count(*) ,  T3.dorm_name FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid JOIN dorm AS T3 ON T3.dormid  =  T2.dormid WHERE T1.age  >  20 GROUP BY T3.dorm_name;
+SELECT count(*) ,  T3.dorm_name FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid JOIN dorm AS T3 ON T3.dormid  =  T2.dormid WHERE T1.age  >  20 GROUP BY T3.dorm_name;
+SELECT T1.fname FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid JOIN dorm AS T3 ON T3.dormid  =  T2.dormid WHERE T3.dorm_name  =  'Smith Hall';
+SELECT T1.fname FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid JOIN dorm AS T3 ON T3.dormid  =  T2.dormid WHERE T3.dorm_name  =  'Smith Hall';
+SELECT avg(T1.age) FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid JOIN dorm AS T3 ON T3.dormid  =  T2.dormid WHERE T3.student_capacity  =  (SELECT max(student_capacity) FROM dorm);
+SELECT avg(T1.age) FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid JOIN dorm AS T3 ON T3.dormid  =  T2.dormid WHERE T3.student_capacity  =  (SELECT max(student_capacity) FROM dorm);
+SELECT count(*) FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid JOIN dorm AS T3 ON T3.dormid  =  T2.dormid WHERE T3.gender  =  'M';
+SELECT count(*) FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid JOIN dorm AS T3 ON T3.dormid  =  T2.dormid WHERE T3.gender  =  'M';
+SELECT count(*) FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid JOIN dorm AS T3 ON T3.dormid  =  T2.dormid WHERE T3.dorm_name  =  'Smith Hall' AND T1.sex  =  'F';
+SELECT count(*) FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid JOIN dorm AS T3 ON T3.dormid  =  T2.dormid WHERE T3.dorm_name  =  'Smith Hall' AND T1.sex  =  'F';
+SELECT T3.amenity_name FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid WHERE T1.dorm_name  =  'Smith Hall';
+SELECT T3.amenity_name FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid WHERE T1.dorm_name  =  'Smith Hall';
+SELECT T3.amenity_name FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid WHERE T1.dorm_name  =  'Smith Hall' ORDER BY T3.amenity_name;
+SELECT T3.amenity_name FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid WHERE T1.dorm_name  =  'Smith Hall' ORDER BY T3.amenity_name;
+SELECT T1.amenity_name FROM dorm_amenity AS T1 JOIN has_amenity AS T2 ON T1.amenid  =  T2.amenid GROUP BY T2.amenid ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.amenity_name FROM dorm_amenity AS T1 JOIN has_amenity AS T2 ON T1.amenid  =  T2.amenid GROUP BY T2.amenid ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.fname FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid WHERE T2.dormid IN (SELECT T2.dormid FROM dorm AS T3 JOIN has_amenity AS T4 ON T3.dormid  =  T4.dormid JOIN dorm_amenity AS T5 ON T4.amenid  =  T5.amenid GROUP BY T3.dormid ORDER BY count(*) DESC LIMIT 1);
+SELECT T1.fname FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid WHERE T2.dormid IN (SELECT T2.dormid FROM dorm AS T3 JOIN has_amenity AS T4 ON T3.dormid  =  T4.dormid JOIN dorm_amenity AS T5 ON T4.amenid  =  T5.amenid GROUP BY T3.dormid ORDER BY count(*) DESC LIMIT 1);
+SELECT T1.dorm_name ,  T1.student_capacity FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid GROUP BY T2.dormid ORDER BY count(*) LIMIT 1;
+SELECT T1.dorm_name ,  T1.student_capacity FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid GROUP BY T2.dormid ORDER BY count(*) LIMIT 1;
+SELECT dorm_name FROM dorm EXCEPT SELECT T1.dorm_name FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid WHERE T3.amenity_name  =  'TV Lounge';
+SELECT dorm_name FROM dorm EXCEPT SELECT T1.dorm_name FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid WHERE T3.amenity_name  =  'TV Lounge';
+SELECT T1.fname ,  T1.lname FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid WHERE T2.dormid IN (SELECT T3.dormid FROM has_amenity AS T3 JOIN dorm_amenity AS T4 ON T3.amenid  =  T4.amenid WHERE T4.amenity_name  =  'TV Lounge');
+SELECT T1.fname ,  T1.lname FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid WHERE T2.dormid IN (SELECT T3.dormid FROM has_amenity AS T3 JOIN dorm_amenity AS T4 ON T3.amenid  =  T4.amenid WHERE T4.amenity_name  =  'TV Lounge');
+SELECT T1.fname ,  T1.age FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid WHERE T2.dormid NOT IN (SELECT T3.dormid FROM has_amenity AS T3 JOIN dorm_amenity AS T4 ON T3.amenid  =  T4.amenid WHERE T4.amenity_name  =  'TV Lounge');
+SELECT T1.fname ,  T1.age FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid WHERE T2.dormid NOT IN (SELECT T3.dormid FROM has_amenity AS T3 JOIN dorm_amenity AS T4 ON T3.amenid  =  T4.amenid WHERE T4.amenity_name  =  'TV Lounge');
+SELECT T3.amenity_name FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid JOIN lives_in AS T4 ON T4.dormid  =  T1.dormid JOIN student AS T5 ON T5.stuid  =  T4.stuid WHERE T5.lname  =  'Smith';
+SELECT T3.amenity_name FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid JOIN lives_in AS T4 ON T4.dormid  =  T1.dormid JOIN student AS T5 ON T5.stuid  =  T4.stuid WHERE T5.lname  =  'Smith';
+SELECT count(*) FROM customers;
+SELECT count(*) FROM customers;
+SELECT email_address ,  phone_number FROM customers ORDER BY email_address ,  phone_number;
+SELECT email_address ,  phone_number FROM customers ORDER BY email_address ,  phone_number;
+SELECT town_city FROM customers WHERE customer_type_code  =  'Good Credit Rating' GROUP BY town_city ORDER BY count(*) LIMIT 1;
+SELECT town_city FROM customers WHERE customer_type_code  =  'Good Credit Rating' GROUP BY town_city ORDER BY count(*) LIMIT 1;
+SELECT t1.product_name ,  count(*) FROM products AS t1 JOIN complaints AS t2 ON t1.product_id  =  t2.product_id GROUP BY t1.product_name;
+SELECT t1.product_name ,  count(*) FROM products AS t1 JOIN complaints AS t2 ON t1.product_id  =  t2.product_id GROUP BY t1.product_name;
+SELECT t1.email_address FROM customers AS t1 JOIN complaints AS t2 ON t1.customer_id  =  t2.customer_id GROUP BY t1.customer_id ORDER BY count(*) LIMIT 1;
+SELECT t1.email_address FROM customers AS t1 JOIN complaints AS t2 ON t1.customer_id  =  t2.customer_id GROUP BY t1.customer_id ORDER BY count(*) LIMIT 1;
+SELECT DISTINCT t1.product_name FROM products AS t1 JOIN complaints AS t2 ON t1.product_id  =  t2.product_id JOIN customers AS t3 GROUP BY t3.customer_id ORDER BY count(*) LIMIT 1;
+SELECT DISTINCT t1.product_name FROM products AS t1 JOIN complaints AS t2 ON t1.product_id  =  t2.product_id JOIN customers AS t3 GROUP BY t3.customer_id ORDER BY count(*) LIMIT 1;
+SELECT t1.phone_number FROM customers AS t1 JOIN complaints AS t2 ON t1.customer_id  =  t2.customer_id ORDER BY t2.date_complaint_raised DESC LIMIT 1;
+SELECT t1.phone_number FROM customers AS t1 JOIN complaints AS t2 ON t1.customer_id  =  t2.customer_id ORDER BY t2.date_complaint_raised DESC LIMIT 1;
+SELECT email_address ,  phone_number FROM customers WHERE customer_id NOT IN (SELECT customer_id FROM complaints);
+SELECT email_address ,  phone_number FROM customers WHERE customer_id NOT IN (SELECT customer_id FROM complaints);
+SELECT phone_number FROM customers UNION SELECT phone_number FROM staff;
+SELECT phone_number FROM customers UNION SELECT phone_number FROM staff;
+SELECT product_description FROM products WHERE product_name  =  'Chocolate';
+SELECT product_description FROM products WHERE product_name  =  'Chocolate';
+SELECT product_name ,  product_category_code FROM products ORDER BY product_price DESC LIMIT 1;
+SELECT product_name ,  product_category_code FROM products ORDER BY product_price DESC LIMIT 1;
+SELECT product_price FROM products WHERE product_id NOT IN (SELECT product_id FROM complaints);
+SELECT product_price FROM products WHERE product_id NOT IN (SELECT product_id FROM complaints);
+SELECT avg(product_price) ,  product_category_code FROM products GROUP BY product_category_code;
+SELECT avg(product_price) ,  product_category_code FROM products GROUP BY product_category_code;
+SELECT t1.last_name FROM staff AS t1 JOIN complaints AS t2 ON t1.staff_id  =  t2.staff_id JOIN products AS t3 ON t2.product_id  =  t3.product_id ORDER BY t3.product_price LIMIT 1;
+SELECT t1.last_name FROM staff AS t1 JOIN complaints AS t2 ON t1.staff_id  =  t2.staff_id JOIN products AS t3 ON t2.product_id  =  t3.product_id ORDER BY t3.product_price LIMIT 1;
+SELECT complaint_status_code FROM complaints GROUP BY complaint_status_code HAVING count(*)  >  3;
+SELECT complaint_status_code FROM complaints GROUP BY complaint_status_code HAVING count(*)  >  3;
+SELECT last_name FROM staff WHERE email_address LIKE '%wrau%';
+SELECT last_name FROM staff WHERE email_address LIKE '%wrau%';
+SELECT count(*) FROM customers GROUP BY customer_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM customers GROUP BY customer_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.last_name FROM staff AS t1 JOIN complaints AS t2 ON t1.staff_id  =  t2.staff_id ORDER BY t2.date_complaint_raised LIMIT 1;
+SELECT t1.last_name FROM staff AS t1 JOIN complaints AS t2 ON t1.staff_id  =  t2.staff_id ORDER BY t2.date_complaint_raised LIMIT 1;
+SELECT count(DISTINCT complaint_type_code) FROM complaints;
+SELECT count(DISTINCT complaint_type_code) FROM complaints;
+SELECT address_line_1 ,  address_line_2 FROM customers WHERE email_address  =  'vbogisich@example.org';
+SELECT address_line_1 ,  address_line_2 FROM customers WHERE email_address  =  'vbogisich@example.org';
+SELECT complaint_status_code ,  count(*) FROM complaints WHERE complaint_type_code  =  'Product Failure' GROUP BY complaint_status_code;
+SELECT complaint_status_code ,  count(*) FROM complaints WHERE complaint_type_code  =  'Product Failure' GROUP BY complaint_status_code;
+SELECT t1.first_name FROM staff AS t1 JOIN complaints AS t2 ON t1.staff_id  =  t2.staff_id GROUP BY t2.staff_id ORDER BY count(*) LIMIT 5;
+SELECT t1.first_name FROM staff AS t1 JOIN complaints AS t2 ON t1.staff_id  =  t2.staff_id GROUP BY t2.staff_id ORDER BY count(*) LIMIT 5;
+SELECT state FROM customers GROUP BY state ORDER BY count(*) LIMIT 1;
+SELECT state FROM customers GROUP BY state ORDER BY count(*) LIMIT 1;
+SELECT count(*) FROM submission;
+SELECT count(*) FROM submission;
+SELECT Author FROM submission ORDER BY Scores ASC;
+SELECT Author FROM submission ORDER BY Scores ASC;
+SELECT Author ,  College FROM submission;
+SELECT Author ,  College FROM submission;
+SELECT Author FROM submission WHERE College  =  'Florida' OR College  =  'Temple';
+SELECT Author FROM submission WHERE College  =  'Florida' OR College  =  'Temple';
+SELECT avg(Scores) FROM submission;
+SELECT avg(Scores) FROM submission;
+SELECT Author FROM submission ORDER BY Scores DESC LIMIT 1;
+SELECT Author FROM submission ORDER BY Scores DESC LIMIT 1;
+SELECT College ,  COUNT(*) FROM submission GROUP BY College;
+SELECT College ,  COUNT(*) FROM submission GROUP BY College;
+SELECT College FROM submission GROUP BY College ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT College FROM submission GROUP BY College ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT College FROM submission WHERE Scores  >  90 INTERSECT SELECT College FROM submission WHERE Scores  <  80;
+SELECT College FROM submission WHERE Scores  >  90 INTERSECT SELECT College FROM submission WHERE Scores  <  80;
+SELECT T2.Author ,  T1.Result FROM acceptance AS T1 JOIN submission AS T2 ON T1.Submission_ID  =  T2.Submission_ID;
+SELECT T2.Author ,  T1.Result FROM acceptance AS T1 JOIN submission AS T2 ON T1.Submission_ID  =  T2.Submission_ID;
+SELECT T1.Result FROM acceptance AS T1 JOIN submission AS T2 ON T1.Submission_ID  =  T2.Submission_ID ORDER BY T2.Scores DESC LIMIT 1;
+SELECT T1.Result FROM acceptance AS T1 JOIN submission AS T2 ON T1.Submission_ID  =  T2.Submission_ID ORDER BY T2.Scores DESC LIMIT 1;
+SELECT T2.Author ,  COUNT(DISTINCT T1.workshop_id) FROM acceptance AS T1 JOIN submission AS T2 ON T1.Submission_ID  =  T2.Submission_ID GROUP BY T2.Author;
+SELECT T2.Author ,  COUNT(DISTINCT T1.workshop_id) FROM acceptance AS T1 JOIN submission AS T2 ON T1.Submission_ID  =  T2.Submission_ID GROUP BY T2.Author;
+SELECT T2.Author FROM acceptance AS T1 JOIN submission AS T2 ON T1.Submission_ID  =  T2.Submission_ID GROUP BY T2.Author HAVING COUNT(DISTINCT T1.workshop_id)  >  1;
+SELECT T2.Author FROM acceptance AS T1 JOIN submission AS T2 ON T1.Submission_ID  =  T2.Submission_ID GROUP BY T2.Author HAVING COUNT(DISTINCT T1.workshop_id)  >  1;
+SELECT Date ,  Venue FROM workshop ORDER BY Venue;
+SELECT Date ,  Venue FROM workshop ORDER BY Venue;
+SELECT Author FROM submission WHERE Submission_ID NOT IN (SELECT Submission_ID FROM acceptance);
+SELECT Author FROM submission WHERE Submission_ID NOT IN (SELECT Submission_ID FROM acceptance);
+SELECT count(*) FROM INVESTORS;
+SELECT Investor_details FROM INVESTORS;
+SELECT DISTINCT lot_details FROM LOTS;
+SELECT max(amount_of_transaction) FROM TRANSACTIONS;
+SELECT date_of_transaction ,  share_count FROM TRANSACTIONS;
+SELECT sum(share_count) FROM TRANSACTIONS;
+SELECT transaction_id FROM TRANSACTIONS WHERE transaction_type_code  =  'PUR';
+SELECT date_of_transaction FROM TRANSACTIONS WHERE transaction_type_code  =  'SALE';
+SELECT avg(amount_of_transaction) FROM TRANSACTIONS WHERE transaction_type_code  =  'SALE';
+SELECT transaction_type_description FROM Ref_Transaction_Types WHERE transaction_type_code	  =  'PUR';
+SELECT min(amount_of_transaction) FROM TRANSACTIONS WHERE transaction_type_code  =  'PUR' AND share_count  >  50;
+SELECT max(share_count) FROM TRANSACTIONS WHERE amount_of_transaction  <  10000;
+SELECT date_of_transaction FROM TRANSACTIONS WHERE share_count  >  100 OR amount_of_transaction  >  1000;
+SELECT T1.transaction_type_description ,  T2.date_of_transaction FROM Ref_Transaction_Types AS T1 JOIN TRANSACTIONS AS T2 ON T1.transaction_type_code  =  T2.transaction_type_code WHERE T2.share_count  <  10;
+SELECT T1.Investor_details FROM  INVESTORS AS T1 JOIN TRANSACTIONS AS T2 ON T1.investor_id  =  T2.investor_id WHERE T2.share_count  >  100;
+SELECT COUNT(DISTINCT transaction_type_code) FROM TRANSACTIONS;
+SELECT lot_details ,  investor_id FROM LOTS;
+SELECT T2.lot_details FROM INVESTORS AS T1 JOIN LOTS AS T2 ON  T1.investor_id  =  T2.investor_id WHERE T1.Investor_details  =  'l';
+SELECT T1.purchase_details FROM PURCHASES AS T1 JOIN TRANSACTIONS AS T2 ON T1.purchase_transaction_id  =  T2.transaction_id WHERE T2.amount_of_transaction  >  10000;
+SELECT T1.sales_details ,  T2.date_of_transaction FROM SALES AS T1 JOIN TRANSACTIONS AS T2 ON T1.sales_transaction_id  =  T2.transaction_id WHERE T2.amount_of_transaction  <  3000;
+SELECT T1.lot_details FROM LOTS AS T1 JOIN TRANSACTIONS_LOTS AS T2 ON T1.lot_id  =  T2.transaction_id JOIN TRANSACTIONS AS T3 ON T2.transaction_id  =  T3.transaction_id WHERE T3.share_count  <  50;
+SELECT T1.lot_details FROM LOTS AS T1 JOIN TRANSACTIONS_LOTS AS T2 ON  T1.lot_id  =  T2.transaction_id JOIN TRANSACTIONS AS T3 ON T2.transaction_id  =  T3.transaction_id WHERE T3.share_count  >  100 AND T3.transaction_type_code  =  'PUR';
+SELECT transaction_type_code ,  avg(amount_of_transaction) FROM TRANSACTIONS GROUP BY transaction_type_code;
+SELECT transaction_type_code ,  max(share_count) ,  min(share_count) FROM TRANSACTIONS GROUP BY transaction_type_code;
+SELECT investor_id ,  avg(share_count) FROM TRANSACTIONS GROUP BY investor_id;
+SELECT investor_id ,  avg(share_count) FROM TRANSACTIONS GROUP BY investor_id ORDER BY avg(share_count);
+SELECT investor_id ,  avg(amount_of_transaction) FROM TRANSACTIONS GROUP BY investor_id;
+SELECT T2.lot_id ,  avg(amount_of_transaction) FROM TRANSACTIONS AS T1 JOIN Transactions_Lots AS T2 ON T1.transaction_id  =  T2.transaction_id GROUP BY T2.lot_id;
+SELECT T2.lot_id ,  avg(amount_of_transaction) FROM TRANSACTIONS AS T1 JOIN Transactions_Lots AS T2 ON T1.transaction_id  =  T2.transaction_id GROUP BY T2.lot_id ORDER BY avg(amount_of_transaction);
+SELECT investor_id ,  COUNT(*) FROM TRANSACTIONS WHERE transaction_type_code  =  'SALE' GROUP BY investor_id;
+SELECT investor_id ,  COUNT(*) FROM TRANSACTIONS GROUP BY investor_id;
+SELECT transaction_type_code FROM TRANSACTIONS GROUP BY transaction_type_code ORDER BY COUNT(*) ASC LIMIT 1;
+SELECT transaction_type_code FROM TRANSACTIONS GROUP BY transaction_type_code ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T1.transaction_type_description FROM Ref_Transaction_Types AS T1 JOIN TRANSACTIONS AS T2 ON T1.transaction_type_code  =  T2.transaction_type_code GROUP BY T1.transaction_type_code ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T2.investor_id ,  T1.Investor_details FROM INVESTORS AS T1 JOIN TRANSACTIONS AS T2 ON T1.investor_id  =  T2.investor_id GROUP BY T2.investor_id ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T2.investor_id ,  T1.Investor_details FROM INVESTORS AS T1 JOIN TRANSACTIONS AS T2 ON T1.investor_id  =  T2.investor_id GROUP BY T2.investor_id ORDER BY COUNT(*) DESC LIMIT 3;
+SELECT T2.investor_id FROM INVESTORS AS T1 JOIN TRANSACTIONS AS T2 ON T1.investor_id  =  T2.investor_id GROUP BY T2.investor_id HAVING COUNT(*)  >=  2;
+SELECT T2.investor_id ,  T1.Investor_details FROM INVESTORS AS T1 JOIN TRANSACTIONS AS T2 ON T1.investor_id  =  T2.investor_id WHERE T2.transaction_type_code  =  'SALE' GROUP BY T2.investor_id HAVING COUNT(*)  >=  2;
+SELECT date_of_transaction FROM TRANSACTIONS WHERE share_count  >=  100 OR amount_of_transaction  >=  100;
+SELECT sales_details FROM sales UNION SELECT purchase_details FROM purchases;
+SELECT lot_details FROM Lots EXCEPT SELECT T1.lot_details FROM Lots AS T1 JOIN transactions_lots AS T2 ON T1.lot_id  =  T2.lot_id;
+SELECT count(*) FROM HOTELS;
+SELECT count(*) FROM HOTELS;
+SELECT price_range FROM HOTELS;
+SELECT price_range FROM HOTELS;
+SELECT DISTINCT Location_Name FROM LOCATIONS;
+SELECT DISTINCT Location_Name FROM LOCATIONS;
+SELECT Name ,  Other_Details FROM Staff;
+SELECT Name ,  Other_Details FROM Staff;
+SELECT Tourist_Details FROM VISITORS;
+SELECT Tourist_Details FROM VISITORS;
+SELECT price_range FROM HOTELS WHERE star_rating_code  =  '5';
+SELECT price_range FROM HOTELS WHERE star_rating_code  =  '5';
+SELECT avg(price_range) FROM HOTELS WHERE star_rating_code  =  '5' AND pets_allowed_yn  =  1;
+SELECT avg(price_range) FROM HOTELS WHERE star_rating_code  =  '5' AND pets_allowed_yn  =  1;
+SELECT Address FROM LOCATIONS WHERE Location_Name  =  'UK Gallery';
+SELECT Address FROM LOCATIONS WHERE Location_Name  =  'UK Gallery';
+SELECT Other_Details FROM LOCATIONS WHERE Location_Name  =  'UK Gallery';
+SELECT Other_Details FROM LOCATIONS WHERE Location_Name  =  'UK Gallery';
+SELECT Location_Name FROM LOCATIONS WHERE Location_Name LIKE '%film%';
+SELECT Location_Name FROM LOCATIONS WHERE Location_Name LIKE '%film%';
+SELECT count(DISTINCT Name) FROM PHOTOS;
+SELECT count(DISTINCT Name) FROM PHOTOS;
+SELECT DISTINCT Visit_Date FROM VISITS;
+SELECT DISTINCT Visit_Date FROM VISITS;
+SELECT Name FROM TOURIST_ATTRACTIONS WHERE How_to_Get_There  =  'bus';
+SELECT Name FROM TOURIST_ATTRACTIONS WHERE How_to_Get_There  =  'bus';
+SELECT Name ,  Opening_Hours FROM TOURIST_ATTRACTIONS WHERE How_to_Get_There  =  'bus' OR How_to_Get_There  =  'walk';
+SELECT Name ,  Opening_Hours FROM TOURIST_ATTRACTIONS WHERE How_to_Get_There  =  'bus' OR How_to_Get_There  =  'walk';
+SELECT T2.star_rating_description FROM HOTELS AS T1 JOIN Ref_Hotel_Star_Ratings AS T2 ON T1.star_rating_code  =  T2.star_rating_code WHERE T1.price_range  >  10000;
+SELECT T2.star_rating_description FROM HOTELS AS T1 JOIN Ref_Hotel_Star_Ratings AS T2 ON T1.star_rating_code  =  T2.star_rating_code WHERE T1.price_range  >  10000;
+SELECT T1.Museum_Details ,  T2.Opening_Hours FROM MUSEUMS AS T1 JOIN TOURIST_ATTRACTIONS AS T2 ON T1.Museum_ID  =  T2.Tourist_Attraction_ID;
+SELECT T1.Museum_Details ,  T2.Opening_Hours FROM MUSEUMS AS T1 JOIN TOURIST_ATTRACTIONS AS T2 ON T1.Museum_ID  =  T2.Tourist_Attraction_ID;
+SELECT T2.Name FROM PHOTOS AS T1 JOIN TOURIST_ATTRACTIONS AS T2 ON T1.Tourist_Attraction_ID  =  T2.Tourist_Attraction_ID WHERE T1.Name  =  'game1';
+SELECT T2.Name FROM PHOTOS AS T1 JOIN TOURIST_ATTRACTIONS AS T2 ON T1.Tourist_Attraction_ID  =  T2.Tourist_Attraction_ID WHERE T1.Name  =  'game1';
+SELECT T1.Name ,  T1.Description FROM PHOTOS AS T1 JOIN TOURIST_ATTRACTIONS AS T2 ON T1.Tourist_Attraction_ID  =  T2.Tourist_Attraction_ID WHERE T2.Name  =  'film festival';
+SELECT T1.Name ,  T1.Description FROM PHOTOS AS T1 JOIN TOURIST_ATTRACTIONS AS T2 ON T1.Tourist_Attraction_ID  =  T2.Tourist_Attraction_ID WHERE T2.Name  =  'film festival';
+SELECT T1.Royal_Family_Details ,  T2.How_to_Get_There FROM ROYAL_FAMILY AS T1 JOIN TOURIST_ATTRACTIONS AS T2 ON T1.Royal_Family_ID  =  T2.Tourist_Attraction_ID;
+SELECT T1.Royal_Family_Details ,  T2.How_to_Get_There FROM ROYAL_FAMILY AS T1 JOIN TOURIST_ATTRACTIONS AS T2 ON T1.Royal_Family_ID  =  T2.Tourist_Attraction_ID;
+SELECT T1.Shop_Details FROM SHOPS AS T1 JOIN TOURIST_ATTRACTIONS AS T2 ON T1.Shop_ID  =  T2.Tourist_Attraction_ID WHERE T2.How_to_Get_There  =  'walk';
+SELECT T1.Shop_Details FROM SHOPS AS T1 JOIN TOURIST_ATTRACTIONS AS T2 ON T1.Shop_ID  =  T2.Tourist_Attraction_ID WHERE T2.How_to_Get_There  =  'walk';
+SELECT T1.Name FROM STAFF AS T1 JOIN TOURIST_ATTRACTIONS AS T2 ON T1.Tourist_Attraction_ID  =  T2.Tourist_Attraction_ID WHERE T2.Name  =  'US museum';
+SELECT T1.Name FROM STAFF AS T1 JOIN TOURIST_ATTRACTIONS AS T2 ON T1.Tourist_Attraction_ID  =  T2.Tourist_Attraction_ID WHERE T2.Name  =  'US museum';
+SELECT T1.Market_Details FROM Street_Markets AS T1 JOIN TOURIST_ATTRACTIONS AS T2 ON T1.Market_ID  =  T2.Tourist_Attraction_ID WHERE T2.How_to_Get_There  =  'walk' OR  T2.How_to_Get_There  =  'bus';
+SELECT T1.Market_Details FROM Street_Markets AS T1 JOIN TOURIST_ATTRACTIONS AS T2 ON T1.Market_ID  =  T2.Tourist_Attraction_ID WHERE T2.How_to_Get_There  =  'walk' OR  T2.How_to_Get_There  =  'bus';
+SELECT T2.Visit_Date ,  T2.Visit_Details FROM VISITORS AS T1 JOIN VISITS AS T2 ON T1.Tourist_ID  =  T2.Tourist_ID WHERE T1.Tourist_Details  =  'Vincent';
+SELECT T2.Visit_Date ,  T2.Visit_Details FROM VISITORS AS T1 JOIN VISITS AS T2 ON T1.Tourist_ID  =  T2.Tourist_ID WHERE T1.Tourist_Details  =  'Vincent';
+SELECT T1.Name FROM Tourist_Attractions AS T1 JOIN VISITS AS T2 ON T1.Tourist_Attraction_ID  =  T2.Tourist_Attraction_ID JOIN VISITORS AS T3 ON T2.Tourist_ID  =  T3.Tourist_ID WHERE T3.Tourist_Details  =  'Vincent';
+SELECT T1.Name FROM Tourist_Attractions AS T1 JOIN VISITS AS T2 ON T1.Tourist_Attraction_ID  =  T2.Tourist_Attraction_ID JOIN VISITORS AS T3 ON T2.Tourist_ID  =  T3.Tourist_ID WHERE T3.Tourist_Details  =  'Vincent';
+SELECT T1.Name ,  T3.Visit_Date FROM Tourist_Attractions AS T1 JOIN VISITORS AS T2 JOIN VISITS AS T3 ON T1.Tourist_Attraction_ID  =  T3.Tourist_Attraction_ID AND T2.Tourist_ID  =  T3.Tourist_ID WHERE T2.Tourist_Details  =  'Vincent' OR T2.Tourist_Details  =  'Vivian';
+SELECT T1.Name ,  T3.Visit_Date FROM Tourist_Attractions AS T1 JOIN VISITORS AS T2 JOIN VISITS AS T3 ON T1.Tourist_Attraction_ID  =  T3.Tourist_Attraction_ID AND T2.Tourist_ID  =  T3.Tourist_ID WHERE T2.Tourist_Details  =  'Vincent' OR T2.Tourist_Details  =  'Vivian';
+SELECT star_rating_code ,  avg(price_range) FROM HOTELS GROUP BY star_rating_code;
+SELECT star_rating_code ,  avg(price_range) FROM HOTELS GROUP BY star_rating_code;
+SELECT pets_allowed_yn ,  avg(price_range) FROM HOTELS GROUP BY pets_allowed_yn;
+SELECT pets_allowed_yn ,  avg(price_range) FROM HOTELS GROUP BY pets_allowed_yn;
+SELECT hotel_id ,  star_rating_code FROM HOTELS ORDER BY price_range ASC;
+SELECT hotel_id ,  star_rating_code FROM HOTELS ORDER BY price_range ASC;
+SELECT other_hotel_details FROM HOTELS ORDER BY price_range DESC LIMIT 3;
+SELECT other_hotel_details FROM HOTELS ORDER BY price_range DESC LIMIT 3;
+SELECT other_hotel_details ,  star_rating_code FROM HOTELS ORDER BY price_range ASC LIMIT 3;
+SELECT other_hotel_details ,  star_rating_code FROM HOTELS ORDER BY price_range ASC LIMIT 3;
+SELECT How_to_Get_There FROM Tourist_Attractions GROUP BY How_to_Get_There ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT How_to_Get_There FROM Tourist_Attractions GROUP BY How_to_Get_There ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T1.Attraction_Type_Description ,  T2.Attraction_Type_Code FROM Ref_Attraction_Types AS T1 JOIN Tourist_Attractions AS T2 ON T1.Attraction_Type_Code  =  T2.Attraction_Type_Code GROUP BY T2.Attraction_Type_Code ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT T1.Attraction_Type_Description ,  T2.Attraction_Type_Code FROM Ref_Attraction_Types AS T1 JOIN Tourist_Attractions AS T2 ON T1.Attraction_Type_Code  =  T2.Attraction_Type_Code GROUP BY T2.Attraction_Type_Code ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT How_to_Get_There ,  COUNT(*) FROM Tourist_Attractions GROUP BY How_to_Get_There;
+SELECT How_to_Get_There ,  COUNT(*) FROM Tourist_Attractions GROUP BY How_to_Get_There;
+SELECT T1.Name ,  T2.Tourist_Attraction_ID ,   COUNT(*) FROM Tourist_Attractions AS T1 JOIN VISITS AS T2 ON T1.Tourist_Attraction_ID  =  T2.Tourist_Attraction_ID GROUP BY T2.Tourist_Attraction_ID;
+SELECT T1.Name ,  T2.Tourist_Attraction_ID ,   COUNT(*) FROM Tourist_Attractions AS T1 JOIN VISITS AS T2 ON T1.Tourist_Attraction_ID  =  T2.Tourist_Attraction_ID GROUP BY T2.Tourist_Attraction_ID;
+SELECT T1.Name ,  T2.Tourist_Attraction_ID FROM Tourist_Attractions AS T1 JOIN VISITS AS T2 ON T1.Tourist_Attraction_ID  =  T2.Tourist_Attraction_ID GROUP BY T2.Tourist_Attraction_ID HAVING count(*)  >=  2;
+SELECT T1.Name ,  T2.Tourist_Attraction_ID FROM Tourist_Attractions AS T1 JOIN VISITS AS T2 ON T1.Tourist_Attraction_ID  =  T2.Tourist_Attraction_ID GROUP BY T2.Tourist_Attraction_ID HAVING count(*)  >=  2;
+SELECT T1.Name ,  T1.Tourist_Attraction_ID FROM Tourist_Attractions AS T1 JOIN VISITS AS T2 ON T1.Tourist_Attraction_ID  =  T2.Tourist_Attraction_ID GROUP BY T2.Tourist_Attraction_ID HAVING count(*)  <=  1;
+SELECT T1.Name ,  T1.Tourist_Attraction_ID FROM Tourist_Attractions AS T1 JOIN VISITS AS T2 ON T1.Tourist_Attraction_ID  =  T2.Tourist_Attraction_ID GROUP BY T2.Tourist_Attraction_ID HAVING count(*)  <=  1;
+SELECT T2.Name FROM Locations AS T1 JOIN Tourist_Attractions AS T2 ON T1.Location_ID  =  T2.Location_ID WHERE T1.Address  =  '660 Shea Crescent' OR T2.How_to_Get_There  =  'walk';
+SELECT T2.Name FROM Locations AS T1 JOIN Tourist_Attractions AS T2 ON T1.Location_ID  =  T2.Location_ID WHERE T1.Address  =  '660 Shea Crescent' OR T2.How_to_Get_There  =  'walk';
+SELECT T1.Name FROM Tourist_Attractions AS T1 JOIN Tourist_Attraction_Features AS T2 ON T1.tourist_attraction_id  =  T2.tourist_attraction_id JOIN Features AS T3 ON T2.Feature_ID  =  T3.Feature_ID WHERE T3.feature_Details  =  'park' UNION SELECT T1.Name FROM Tourist_Attractions AS T1 JOIN Tourist_Attraction_Features AS T2 ON T1.tourist_attraction_id  =  T2.tourist_attraction_id JOIN Features AS T3 ON T2.Feature_ID  =  T3.Feature_ID WHERE T3.feature_Details  =  'shopping';
+SELECT T1.Name FROM Tourist_Attractions AS T1 JOIN Tourist_Attraction_Features AS T2 ON T1.tourist_attraction_id  =  T2.tourist_attraction_id JOIN Features AS T3 ON T2.Feature_ID  =  T3.Feature_ID WHERE T3.feature_Details  =  'park' UNION SELECT T1.Name FROM Tourist_Attractions AS T1 JOIN Tourist_Attraction_Features AS T2 ON T1.tourist_attraction_id  =  T2.tourist_attraction_id JOIN Features AS T3 ON T2.Feature_ID  =  T3.Feature_ID WHERE T3.feature_Details  =  'shopping';
+SELECT T2.Name FROM Locations AS T1 JOIN Tourist_Attractions AS T2 ON T1.Location_ID  =  T2.Location_ID WHERE T1.Address  =  '254 Ottilie Junction' OR T2.How_to_Get_There  =  'bus';
+SELECT T2.Name FROM Locations AS T1 JOIN Tourist_Attractions AS T2 ON T1.Location_ID  =  T2.Location_ID WHERE T1.Address  =  '254 Ottilie Junction' OR T2.How_to_Get_There  =  'bus';
+SELECT T1.Name FROM Tourist_Attractions AS T1 JOIN VISITORS AS T2 JOIN VISITS AS T3 ON T1.Tourist_Attraction_ID  =  T3.Tourist_Attraction_ID AND T2.Tourist_ID  =  T3.Tourist_ID WHERE T2.Tourist_Details  =  'Vincent' INTERSECT SELECT T1.Name FROM Tourist_Attractions AS T1 JOIN VISITORS AS T2 JOIN VISITS AS T3 ON T1.Tourist_Attraction_ID  =  T3.Tourist_Attraction_ID AND T2.Tourist_ID  =  T3.Tourist_ID WHERE T2.Tourist_Details  =  'Marcelle';
+SELECT T1.Name FROM Tourist_Attractions AS T1 JOIN VISITORS AS T2 JOIN VISITS AS T3 ON T1.Tourist_Attraction_ID  =  T3.Tourist_Attraction_ID AND T2.Tourist_ID  =  T3.Tourist_ID WHERE T2.Tourist_Details  =  'Vincent' INTERSECT SELECT T1.Name FROM Tourist_Attractions AS T1 JOIN VISITORS AS T2 JOIN VISITS AS T3 ON T1.Tourist_Attraction_ID  =  T3.Tourist_Attraction_ID AND T2.Tourist_ID  =  T3.Tourist_ID WHERE T2.Tourist_Details  =  'Marcelle';
+SELECT T1.Name FROM Tourist_Attractions AS T1 JOIN VISITORS AS T2 JOIN VISITS AS T3 ON T1.Tourist_Attraction_ID  =  T3.Tourist_Attraction_ID AND T2.Tourist_ID  =  T3.Tourist_ID WHERE T2.Tourist_Details  =  'Alison' EXCEPT SELECT T1.Name FROM Tourist_Attractions AS T1 JOIN VISITORS AS T2 JOIN VISITS AS T3 ON T1.Tourist_Attraction_ID  =  T3.Tourist_Attraction_ID AND T2.Tourist_ID  =  T3.Tourist_ID WHERE T2.Tourist_Details  =  'Rosalind';
+SELECT T1.Name FROM Tourist_Attractions AS T1 JOIN VISITORS AS T2 JOIN VISITS AS T3 ON T1.Tourist_Attraction_ID  =  T3.Tourist_Attraction_ID AND T2.Tourist_ID  =  T3.Tourist_ID WHERE T2.Tourist_Details  =  'Alison' EXCEPT SELECT T1.Name FROM Tourist_Attractions AS T1 JOIN VISITORS AS T2 JOIN VISITS AS T3 ON T1.Tourist_Attraction_ID  =  T3.Tourist_Attraction_ID AND T2.Tourist_ID  =  T3.Tourist_ID WHERE T2.Tourist_Details  =  'Rosalind';
+SELECT count(*) FROM Visitors WHERE Tourist_ID NOT IN ( SELECT Tourist_ID FROM Visits );
+SELECT count(*) FROM Visitors WHERE Tourist_ID NOT IN ( SELECT Tourist_ID FROM Visits );
+SELECT count(*) FROM Video_games;
+SELECT count(*) FROM Video_games;
+SELECT count(DISTINCT gtype) FROM Video_games;
+SELECT count(DISTINCT gtype) FROM Video_games;
+SELECT DISTINCT gtype FROM Video_games;
+SELECT DISTINCT gtype FROM Video_games;
+SELECT gname ,  gtype FROM Video_games ORDER BY gname;
+SELECT gname ,  gtype FROM Video_games ORDER BY gname;
+SELECT gname FROM Video_games WHERE gtype  =  'Collectible card game';
+SELECT gname FROM Video_games WHERE gtype  =  'Collectible card game';
+SELECT gtype FROM Video_games WHERE gname  =  'Call of Destiny';
+SELECT gtype FROM Video_games WHERE gname  =  'Call of Destiny';
+SELECT count(*) FROM Video_games WHERE gtype  =  'Massively multiplayer online game';
+SELECT count(*) FROM Video_games WHERE gtype  =  'Massively multiplayer online game';
+SELECT gtype ,  count(*) FROM Video_games GROUP BY gtype;
+SELECT gtype ,  count(*) FROM Video_games GROUP BY gtype;
+SELECT gtype FROM Video_games GROUP BY gtype ORDER BY count(*) DESC LIMIT 1;
+SELECT gtype FROM Video_games GROUP BY gtype ORDER BY count(*) DESC LIMIT 1;
+SELECT gtype FROM Video_games GROUP BY gtype ORDER BY count(*) LIMIT 1;
+SELECT gtype FROM Video_games GROUP BY gtype ORDER BY count(*) LIMIT 1;
+SELECT StuID FROM Student WHERE city_code  =  'CHI';
+SELECT StuID FROM Student WHERE city_code  =  'CHI';
+SELECT StuID FROM Student WHERE Advisor  =  1121;
+SELECT StuID FROM Student WHERE Advisor  =  1121;
+SELECT Fname FROM Student WHERE Major  =  600;
+SELECT Fname FROM Student WHERE Major  =  600;
+SELECT major ,  avg(age) ,  min(age) ,  max(age) FROM Student GROUP BY major;
+SELECT major ,  avg(age) ,  min(age) ,  max(age) FROM Student GROUP BY major;
+SELECT advisor FROM Student GROUP BY advisor HAVING count(*)  >=  2;
+SELECT advisor FROM Student GROUP BY advisor HAVING count(*)  >=  2;
+SELECT count(DISTINCT sportname) FROM Sportsinfo;
+SELECT count(DISTINCT sportname) FROM Sportsinfo;
+SELECT count(DISTINCT StuID) FROM Sportsinfo;
+SELECT count(DISTINCT StuID) FROM Sportsinfo;
+SELECT StuID FROM Sportsinfo WHERE onscholarship  =  'Y';
+SELECT StuID FROM Sportsinfo WHERE onscholarship  =  'Y';
+SELECT T2.Lname FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID  =  T2.StuID WHERE T1.onscholarship  =  'Y';
+SELECT T2.Lname FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID  =  T2.StuID WHERE T1.onscholarship  =  'Y';
+SELECT sum(gamesplayed) FROM Sportsinfo;
+SELECT sum(gamesplayed) FROM Sportsinfo;
+SELECT sum(gamesplayed) FROM Sportsinfo WHERE sportname  =  'Football' AND onscholarship  =  'Y';
+SELECT sum(gamesplayed) FROM Sportsinfo WHERE sportname  =  'Football' AND onscholarship  =  'Y';
+SELECT sportname ,  count(*) FROM Sportsinfo GROUP BY sportname;
+SELECT sportname ,  count(*) FROM Sportsinfo GROUP BY sportname;
+SELECT StuID ,  count(*) ,  sum(gamesplayed) FROM Sportsinfo GROUP BY StuID;
+SELECT StuID ,  count(*) ,  sum(gamesplayed) FROM Sportsinfo GROUP BY StuID;
+SELECT StuID FROM Sportsinfo GROUP BY StuID HAVING sum(hoursperweek)  >  10;
+SELECT StuID FROM Sportsinfo GROUP BY StuID HAVING sum(hoursperweek)  >  10;
+SELECT T2.Fname ,  T2.Lname FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID  =  T2.StuID GROUP BY T1.StuID ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.Fname ,  T2.Lname FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID  =  T2.StuID GROUP BY T1.StuID ORDER BY count(*) DESC LIMIT 1;
+SELECT sportname FROM Sportsinfo WHERE onscholarship  =  'Y' GROUP BY sportname ORDER BY count(*) DESC LIMIT 1;
+SELECT sportname FROM Sportsinfo WHERE onscholarship  =  'Y' GROUP BY sportname ORDER BY count(*) DESC LIMIT 1;
+SELECT StuID FROM Student EXCEPT SELECT StuID FROM Sportsinfo;
+SELECT StuID FROM Student EXCEPT SELECT StuID FROM Sportsinfo;
+SELECT StuID FROM Student WHERE major  =  600 INTERSECT SELECT StuID FROM Sportsinfo WHERE onscholarship  =  'Y';
+SELECT StuID FROM Student WHERE major  =  600 INTERSECT SELECT StuID FROM Sportsinfo WHERE onscholarship  =  'Y';
+SELECT StuID FROM Student WHERE sex  =  'F' INTERSECT SELECT StuID FROM Sportsinfo WHERE sportname  =  'Football';
+SELECT StuID FROM Student WHERE sex  =  'F' INTERSECT SELECT StuID FROM Sportsinfo WHERE sportname  =  'Football';
+SELECT StuID FROM Student WHERE sex  =  'M' EXCEPT SELECT StuID FROM Sportsinfo WHERE sportname  =  'Football';
+SELECT StuID FROM Student WHERE sex  =  'M' EXCEPT SELECT StuID FROM Sportsinfo WHERE sportname  =  'Football';
+SELECT sum(hoursperweek) ,  sum(gamesplayed) FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID  =  T2.StuID WHERE T2.Fname  =  'David' AND T2.Lname  =  'Shieber';
+SELECT sum(hoursperweek) ,  sum(gamesplayed) FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID  =  T2.StuID WHERE T2.Fname  =  'David' AND T2.Lname  =  'Shieber';
+SELECT sum(hoursperweek) ,  sum(gamesplayed) FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID  =  T2.StuID WHERE T2.age  <  20;
+SELECT sum(hoursperweek) ,  sum(gamesplayed) FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID  =  T2.StuID WHERE T2.age  <  20;
+SELECT count(DISTINCT StuID) FROM Plays_games;
+SELECT count(DISTINCT StuID) FROM Plays_games;
+SELECT StuID FROM Student EXCEPT SELECT StuID FROM Plays_games;
+SELECT StuID FROM Student EXCEPT SELECT StuID FROM Plays_games;
+SELECT StuID FROM Sportsinfo INTERSECT SELECT StuID FROM Plays_games;
+SELECT StuID FROM Sportsinfo INTERSECT SELECT StuID FROM Plays_games;
+SELECT gameid ,  sum(hours_played) FROM Plays_games GROUP BY gameid;
+SELECT gameid ,  sum(hours_played) FROM Plays_games GROUP BY gameid;
+SELECT Stuid ,  sum(hours_played) FROM Plays_games GROUP BY Stuid;
+SELECT Stuid ,  sum(hours_played) FROM Plays_games GROUP BY Stuid;
+SELECT gname FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.gameid  =  T2.gameid GROUP BY T1.gameid ORDER BY sum(hours_played) DESC LIMIT 1;
+SELECT gname FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.gameid  =  T2.gameid GROUP BY T1.gameid ORDER BY sum(hours_played) DESC LIMIT 1;
+SELECT gname FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.gameid  =  T2.gameid GROUP BY T1.gameid HAVING sum(hours_played)  >=  1000;
+SELECT gname FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.gameid  =  T2.gameid GROUP BY T1.gameid HAVING sum(hours_played)  >=  1000;
+SELECT Gname FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.gameid  =  T2.gameid JOIN Student AS T3 ON T3.Stuid  =  T1.Stuid WHERE T3.Lname  =  'Smith' AND T3.Fname  =  'Linda';
+SELECT Gname FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.gameid  =  T2.gameid JOIN Student AS T3 ON T3.Stuid  =  T1.Stuid WHERE T3.Lname  =  'Smith' AND T3.Fname  =  'Linda';
+SELECT T2.lname ,  T2.fname FROM SportsInfo AS T1 JOIN Student AS T2 ON T1.StuID  =  T2.StuID WHERE T1.SportName  =  'Football' OR T1.SportName  =  'Lacrosse';
+SELECT T2.lname ,  T2.fname FROM SportsInfo AS T1 JOIN Student AS T2 ON T1.StuID  =  T2.StuID WHERE T1.SportName  =  'Football' OR T1.SportName  =  'Lacrosse';
+SELECT fname ,  age FROM Student WHERE StuID IN (SELECT StuID FROM Sportsinfo WHERE SportName  =  'Football' INTERSECT SELECT StuID FROM Sportsinfo WHERE SportName  =  'Lacrosse');
+SELECT fname ,  age FROM Student WHERE StuID IN (SELECT StuID FROM Sportsinfo WHERE SportName  =  'Football' INTERSECT SELECT StuID FROM Sportsinfo WHERE SportName  =  'Lacrosse');
+SELECT lname ,  sex FROM Student WHERE StuID IN (SELECT T1.StuID FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.GameID  =  T2.GameID WHERE T2.Gname  =  'Call of Destiny' INTERSECT SELECT T1.StuID FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.GameID  =  T2.GameID WHERE T2.Gname  =  'Works of Widenius');
+SELECT lname ,  sex FROM Student WHERE StuID IN (SELECT T1.StuID FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.GameID  =  T2.GameID WHERE T2.Gname  =  'Call of Destiny' INTERSECT SELECT T1.StuID FROM Plays_games AS T1 JOIN Video_games AS T2 ON T1.GameID  =  T2.GameID WHERE T2.Gname  =  'Works of Widenius');
+SELECT customer_name FROM customers;
+SELECT customer_name FROM customers;
+SELECT count(*) FROM customers;
+SELECT count(*) FROM customers;
+SELECT avg(order_quantity) FROM order_items;
+SELECT avg(order_quantity) FROM order_items;
+SELECT customer_name FROM customers WHERE payment_method  =  'Cash';
+SELECT customer_name FROM customers WHERE payment_method  =  'Cash';
+SELECT date_became_customer FROM customers WHERE customer_id BETWEEN 10 AND 20;
+SELECT date_became_customer FROM customers WHERE customer_id BETWEEN 10 AND 20;
+SELECT payment_method FROM customers GROUP BY payment_method ORDER BY count(*) DESC LIMIT 1;
+SELECT payment_method FROM customers GROUP BY payment_method ORDER BY count(*) DESC LIMIT 1;
+SELECT customer_name FROM customers WHERE payment_method  =  (SELECT payment_method FROM customers GROUP BY payment_method ORDER BY count(*) DESC LIMIT 1);
+SELECT customer_name FROM customers WHERE payment_method  =  (SELECT payment_method FROM customers GROUP BY payment_method ORDER BY count(*) DESC LIMIT 1);
+SELECT DISTINCT payment_method FROM customers;
+SELECT DISTINCT payment_method FROM customers;
+SELECT DISTINCT product_details FROM products;
+SELECT DISTINCT product_details FROM products;
+SELECT customer_name FROM customers WHERE customer_name LIKE '%Alex%';
+SELECT customer_name FROM customers WHERE customer_name LIKE '%Alex%';
+SELECT product_details FROM products WHERE product_details LIKE '%Latte%' OR product_details LIKE '%Americano%';
+SELECT product_details FROM products WHERE product_details LIKE '%Latte%' OR product_details LIKE '%Americano%';
+SELECT t3.address_content FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id  =  t2.customer_id JOIN addresses AS t3 ON t2.address_id  =  t3.address_id WHERE t1.customer_name  =  'Maudie Kertzmann';
+SELECT t3.address_content FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id  =  t2.customer_id JOIN addresses AS t3 ON t2.address_id  =  t3.address_id WHERE t1.customer_name  =  'Maudie Kertzmann';
+SELECT count(*) FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id  =  t2.customer_id JOIN addresses AS t3 ON t2.address_id  =  t3.address_id WHERE t3.city  =  'Lake Geovannyton';
+SELECT count(*) FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id  =  t2.customer_id JOIN addresses AS t3 ON t2.address_id  =  t3.address_id WHERE t3.city  =  'Lake Geovannyton';
+SELECT t1.customer_name FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id  =  t2.customer_id JOIN addresses AS t3 ON t2.address_id  =  t3.address_id WHERE t3.state_province_county  =  'Colorado';
+SELECT t1.customer_name FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id  =  t2.customer_id JOIN addresses AS t3 ON t2.address_id  =  t3.address_id WHERE t3.state_province_county  =  'Colorado';
+SELECT city FROM addresses WHERE city NOT IN ( SELECT DISTINCT t3.city FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id  =  t2.customer_id JOIN addresses AS t3 ON t2.address_id  =  t3.address_id);
+SELECT city FROM addresses WHERE city NOT IN ( SELECT DISTINCT t3.city FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id  =  t2.customer_id JOIN addresses AS t3 ON t2.address_id  =  t3.address_id);
+SELECT t3.city FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id  =  t2.customer_id JOIN addresses AS t3 ON t2.address_id  =  t3.address_id GROUP BY t3.city ORDER BY count(*) DESC LIMIT 1;
+SELECT t3.city FROM customers AS t1 JOIN customer_addresses AS t2 ON t1.customer_id  =  t2.customer_id JOIN addresses AS t3 ON t2.address_id  =  t3.address_id GROUP BY t3.city ORDER BY count(*) DESC LIMIT 1;
+SELECT DISTINCT city FROM addresses;
+SELECT DISTINCT city FROM addresses;
+SELECT city FROM addresses WHERE zip_postcode  =  255;
+SELECT city FROM addresses WHERE zip_postcode  =  255;
+SELECT state_province_county ,  country FROM addresses WHERE zip_postcode LIKE '4%';
+SELECT state_province_county ,  country FROM addresses WHERE zip_postcode LIKE '4%';
+SELECT country FROM addresses GROUP BY country HAVING count(address_id)  >  4;
+SELECT country FROM addresses GROUP BY country HAVING count(address_id)  >  4;
+SELECT channel_code FROM customer_contact_channels GROUP BY channel_code HAVING count(customer_id)  <  5;
+SELECT channel_code FROM customer_contact_channels GROUP BY channel_code HAVING count(customer_id)  <  5;
+SELECT DISTINCT channel_code FROM customers AS t1 JOIN customer_contact_channels AS t2 ON t1.customer_id  =  t2.customer_id WHERE t1.customer_name  =  'Tillman Ernser';
+SELECT DISTINCT channel_code FROM customers AS t1 JOIN customer_contact_channels AS t2 ON t1.customer_id  =  t2.customer_id WHERE t1.customer_name  =  'Tillman Ernser';
+SELECT max(t2.active_to_date) FROM customers AS t1 JOIN customer_contact_channels AS t2 ON t1.customer_id  =  t2.customer_id WHERE t1.customer_name  =  'Tillman Ernser';
+SELECT max(t2.active_to_date) FROM customers AS t1 JOIN customer_contact_channels AS t2 ON t1.customer_id  =  t2.customer_id WHERE t1.customer_name  =  'Tillman Ernser';
+SELECT avg(active_to_date - active_from_date) FROM customer_contact_channels;
+SELECT avg(active_to_date - active_from_date) FROM customer_contact_channels;
+SELECT channel_code ,  contact_number FROM customer_contact_channels WHERE active_to_date - active_from_date  =  (SELECT active_to_date - active_from_date FROM customer_contact_channels ORDER BY (active_to_date - active_from_date) DESC LIMIT 1);
+SELECT channel_code ,  contact_number FROM customer_contact_channels WHERE active_to_date - active_from_date  =  (SELECT active_to_date - active_from_date FROM customer_contact_channels ORDER BY (active_to_date - active_from_date) DESC LIMIT 1);
+SELECT t1.customer_name ,  t2.active_from_date FROM customers AS t1 JOIN customer_contact_channels AS t2 ON t1.customer_id  =  t2.customer_id WHERE t2.channel_code  =  'Email';
+SELECT t1.customer_name ,  t2.active_from_date FROM customers AS t1 JOIN customer_contact_channels AS t2 ON t1.customer_id  =  t2.customer_id WHERE t2.channel_code  =  'Email';
+SELECT t1.customer_name FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id JOIN order_items AS t3 ON t2.order_id  =  t3.order_id WHERE t3.order_quantity  =  ( SELECT max(order_quantity) FROM order_items);
+SELECT t1.customer_name FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id JOIN order_items AS t3 ON t2.order_id  =  t3.order_id WHERE t3.order_quantity  =  ( SELECT max(order_quantity) FROM order_items);
+SELECT t1.customer_name FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id JOIN order_items AS t3 ON t2.order_id  =  t3.order_id GROUP BY t1.customer_name ORDER BY sum(t3.order_quantity) DESC LIMIT 1;
+SELECT t1.customer_name FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id JOIN order_items AS t3 ON t2.order_id  =  t3.order_id GROUP BY t1.customer_name ORDER BY sum(t3.order_quantity) DESC LIMIT 1;
+SELECT t1.payment_method FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id JOIN order_items AS t3 ON t2.order_id  =  t3.order_id GROUP BY t1.customer_name ORDER BY sum(t3.order_quantity) LIMIT 1;
+SELECT t1.payment_method FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id JOIN order_items AS t3 ON t2.order_id  =  t3.order_id GROUP BY t1.customer_name ORDER BY sum(t3.order_quantity) LIMIT 1;
+SELECT count(DISTINCT t3.product_id) FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id JOIN order_items AS t3 ON t2.order_id  =  t3.order_id WHERE t1.customer_name  =  'Rodrick Heaney';
+SELECT count(DISTINCT t3.product_id) FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id JOIN order_items AS t3 ON t2.order_id  =  t3.order_id WHERE t1.customer_name  =  'Rodrick Heaney';
+SELECT sum(t3.order_quantity) FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id JOIN order_items AS t3 ON t2.order_id  =  t3.order_id WHERE t1.customer_name  =  'Rodrick Heaney';
+SELECT sum(t3.order_quantity) FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id JOIN order_items AS t3 ON t2.order_id  =  t3.order_id WHERE t1.customer_name  =  'Rodrick Heaney';
+SELECT count(DISTINCT customer_id) FROM customer_orders WHERE order_status  =  'Cancelled';
+SELECT count(DISTINCT customer_id) FROM customer_orders WHERE order_status  =  'Cancelled';
+SELECT count(*) FROM customer_orders WHERE order_details  =  'Second time';
+SELECT count(*) FROM customer_orders WHERE order_details  =  'Second time';
+SELECT t1.customer_name ,  t2.order_date FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id WHERE order_status  =  'Delivered';
+SELECT t1.customer_name ,  t2.order_date FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id WHERE order_status  =  'Delivered';
+SELECT sum(t2.order_quantity) FROM customer_orders AS t1 JOIN order_items AS t2 ON t1.order_id  =  t2.order_id WHERE t1.order_status  =  'Cancelled';
+SELECT sum(t2.order_quantity) FROM customer_orders AS t1 JOIN order_items AS t2 ON t1.order_id  =  t2.order_id WHERE t1.order_status  =  'Cancelled';
+SELECT sum(t2.order_quantity) FROM customer_orders AS t1 JOIN order_items AS t2 ON t1.order_id  =  t2.order_id WHERE t1.order_date  <  '2018-03-17 07:13:53';
+SELECT sum(t2.order_quantity) FROM customer_orders AS t1 JOIN order_items AS t2 ON t1.order_id  =  t2.order_id WHERE t1.order_date  <  '2018-03-17 07:13:53';
+SELECT t1.customer_name FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id ORDER BY t2.order_date DESC LIMIT 1;
+SELECT t1.customer_name FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id ORDER BY t2.order_date DESC LIMIT 1;
+SELECT t2.product_details FROM order_items AS t1 JOIN products AS t2 ON t1.product_id  =  t2.product_id GROUP BY t1.product_id ORDER BY count(*) DESC LIMIT 1;
+SELECT t2.product_details FROM order_items AS t1 JOIN products AS t2 ON t1.product_id  =  t2.product_id GROUP BY t1.product_id ORDER BY count(*) DESC LIMIT 1;
+SELECT t2.product_details ,  t2.product_id FROM order_items AS t1 JOIN products AS t2 ON t1.product_id  =  t2.product_id GROUP BY t1.product_id ORDER BY sum(t1.order_quantity) LIMIT 1;
+SELECT t2.product_details ,  t2.product_id FROM order_items AS t1 JOIN products AS t2 ON t1.product_id  =  t2.product_id GROUP BY t1.product_id ORDER BY sum(t1.order_quantity) LIMIT 1;
+SELECT address_content FROM addresses WHERE city  =  'East Julianaside' AND state_province_county  =  'Texas' UNION SELECT address_content FROM addresses WHERE city  =  'Gleasonmouth' AND state_province_county  =  'Arizona';
+SELECT address_content FROM addresses WHERE city  =  'East Julianaside' AND state_province_county  =  'Texas' UNION SELECT address_content FROM addresses WHERE city  =  'Gleasonmouth' AND state_province_county  =  'Arizona';
+SELECT customer_name FROM customers WHERE payment_method != 'Cash';
+SELECT customer_name FROM customers WHERE payment_method != 'Cash';
+SELECT customer_name FROM customers EXCEPT SELECT t1.customer_name FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id JOIN order_items AS t3 ON t2.order_id  =  t3.order_id JOIN products AS t4 ON t3.product_id  =  t4.product_id WHERE t4.product_details  =  'Latte';
+SELECT customer_name FROM customers EXCEPT SELECT t1.customer_name FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id JOIN order_items AS t3 ON t2.order_id  =  t3.order_id JOIN products AS t4 ON t3.product_id  =  t4.product_id WHERE t4.product_details  =  'Latte';
+SELECT customer_name FROM customers EXCEPT SELECT t1.customer_name FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id;
+SELECT customer_name FROM customers EXCEPT SELECT t1.customer_name FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id;
+SELECT t1.customer_name FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id JOIN order_items AS t3 ON t2.order_id  =  t3.order_id JOIN products AS t4 ON t3.product_id  =  t4.product_id WHERE t4.product_details  =  'Latte' INTERSECT SELECT t1.customer_name FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id JOIN order_items AS t3 ON t2.order_id  =  t3.order_id JOIN products AS t4 ON t3.product_id  =  t4.product_id WHERE t4.product_details  =  'Americano';
+SELECT t1.customer_name FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id JOIN order_items AS t3 ON t2.order_id  =  t3.order_id JOIN products AS t4 ON t3.product_id  =  t4.product_id WHERE t4.product_details  =  'Latte' INTERSECT SELECT t1.customer_name FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id  =  t2.customer_id JOIN order_items AS t3 ON t2.order_id  =  t3.order_id JOIN products AS t4 ON t3.product_id  =  t4.product_id WHERE t4.product_details  =  'Americano';
+SELECT count(*) FROM artist;
+SELECT count(*) FROM artist;
+SELECT Age FROM artist;
+SELECT Age FROM artist;
+SELECT avg(Age) FROM artist;
+SELECT avg(Age) FROM artist;
+SELECT Famous_Title FROM artist WHERE Artist  =  'Triumfall';
+SELECT Famous_Title FROM artist WHERE Artist  =  'Triumfall';
+SELECT distinct(Famous_Release_date) FROM artist;
+SELECT distinct(Famous_Release_date) FROM artist;
+SELECT Date_of_ceremony ,  RESULT FROM music_festival;
+SELECT Date_of_ceremony ,  RESULT FROM music_festival;
+SELECT Category FROM music_festival WHERE RESULT  =  'Awarded';
+SELECT Category FROM music_festival WHERE RESULT  =  'Awarded';
+SELECT max(Weeks_on_Top) ,  min(Weeks_on_Top) FROM volume;
+SELECT max(Weeks_on_Top) ,  min(Weeks_on_Top) FROM volume;
+SELECT Song FROM volume WHERE Weeks_on_Top  >  1;
+SELECT Song FROM volume WHERE Weeks_on_Top  >  1;
+SELECT Song FROM volume ORDER BY Song;
+SELECT Song FROM volume ORDER BY Song;
+SELECT COUNT(DISTINCT Artist_ID) FROM volume;
+SELECT COUNT(DISTINCT Artist_ID) FROM volume;
+SELECT T1.Date_of_ceremony FROM music_festival AS T1 JOIN volume AS T2 ON T1.Volume  =  T2.Volume_ID WHERE T2.Weeks_on_Top  >  2;
+SELECT T1.Date_of_ceremony FROM music_festival AS T1 JOIN volume AS T2 ON T1.Volume  =  T2.Volume_ID WHERE T2.Weeks_on_Top  >  2;
+SELECT T2.Song FROM music_festival AS T1 JOIN volume AS T2 ON T1.Volume  =  T2.Volume_ID WHERE T1.Result  =  'Nominated';
+SELECT T2.Song FROM music_festival AS T1 JOIN volume AS T2 ON T1.Volume  =  T2.Volume_ID WHERE T1.Result  =  'Nominated';
+SELECT T2.Issue_Date FROM artist AS T1 JOIN volume AS T2 ON T1.Artist_ID  =  T2.Artist_ID WHERE T1.Artist  =  'Gorgoroth';
+SELECT T2.Issue_Date FROM artist AS T1 JOIN volume AS T2 ON T1.Artist_ID  =  T2.Artist_ID WHERE T1.Artist  =  'Gorgoroth';
+SELECT T2.Song FROM artist AS T1 JOIN volume AS T2 ON T1.Artist_ID  =  T2.Artist_ID WHERE T1.age  >=  32;
+SELECT T2.Song FROM artist AS T1 JOIN volume AS T2 ON T1.Artist_ID  =  T2.Artist_ID WHERE T1.age  >=  32;
+SELECT avg(T2.Weeks_on_Top) FROM artist AS T1 JOIN volume AS T2 ON T1.Artist_ID  =  T2.Artist_ID WHERE T1.age  <=  25;
+SELECT avg(T2.Weeks_on_Top) FROM artist AS T1 JOIN volume AS T2 ON T1.Artist_ID  =  T2.Artist_ID WHERE T1.age  <=  25;
+SELECT T1.Famous_Title FROM artist AS T1 JOIN volume AS T2 ON T1.Artist_ID  =  T2.Artist_ID WHERE T2.Weeks_on_Top  >  2;
+SELECT T1.Famous_Title FROM artist AS T1 JOIN volume AS T2 ON T1.Artist_ID  =  T2.Artist_ID WHERE T2.Weeks_on_Top  >  2;
+SELECT Famous_Title ,  Age FROM artist ORDER BY Age DESC;
+SELECT Famous_Title ,  Age FROM artist ORDER BY Age DESC;
+SELECT Famous_Release_date FROM artist ORDER BY Age DESC LIMIT 1;
+SELECT Famous_Release_date FROM artist ORDER BY Age DESC LIMIT 1;
+SELECT Category ,  COUNT(*) FROM music_festival GROUP BY Category;
+SELECT Category ,  COUNT(*) FROM music_festival GROUP BY Category;
+SELECT RESULT FROM music_festival GROUP BY RESULT ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT RESULT FROM music_festival GROUP BY RESULT ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Category FROM music_festival GROUP BY Category HAVING COUNT(*)  >  1;
+SELECT Category FROM music_festival GROUP BY Category HAVING COUNT(*)  >  1;
+SELECT Song FROM volume ORDER BY Weeks_on_Top DESC LIMIT 1;
+SELECT Song FROM volume ORDER BY Weeks_on_Top DESC LIMIT 1;
+SELECT Famous_Title FROM artist WHERE Artist_ID NOT IN(SELECT Artist_ID FROM volume);
+SELECT Famous_Title FROM artist WHERE Artist_ID NOT IN(SELECT Artist_ID FROM volume);
+SELECT T1.Famous_Title FROM artist AS T1 JOIN volume AS T2 ON T1.Artist_ID  =  T2.Artist_ID WHERE T2.Weeks_on_Top  >  2 INTERSECT SELECT T1.Famous_Title FROM artist AS T1 JOIN volume AS T2 ON T1.Artist_ID  =  T2.Artist_ID WHERE T2.Weeks_on_Top  <  2;
+SELECT T1.Famous_Title FROM artist AS T1 JOIN volume AS T2 ON T1.Artist_ID  =  T2.Artist_ID WHERE T2.Weeks_on_Top  >  2 INTERSECT SELECT T1.Famous_Title FROM artist AS T1 JOIN volume AS T2 ON T1.Artist_ID  =  T2.Artist_ID WHERE T2.Weeks_on_Top  <  2;
+SELECT Date_of_ceremony FROM music_festival WHERE Category  =  'Best Song' AND RESULT  =  'Awarded';
+SELECT Date_of_ceremony FROM music_festival WHERE Category  =  'Best Song' AND RESULT  =  'Awarded';
+SELECT Issue_Date FROM volume ORDER BY Weeks_on_Top ASC LIMIT 1;
+SELECT Issue_Date FROM volume ORDER BY Weeks_on_Top ASC LIMIT 1;
+SELECT COUNT(DISTINCT Artist_ID) FROM volume;
+SELECT COUNT(DISTINCT Artist_ID) FROM volume;
+SELECT RESULT ,  COUNT(*) FROM music_festival GROUP BY RESULT ORDER BY COUNT(*) DESC;
+SELECT RESULT ,  COUNT(*) FROM music_festival GROUP BY RESULT ORDER BY COUNT(*) DESC;
+SELECT Issue_Date FROM artist AS T1 JOIN volume AS T2 ON T1.Artist_ID  =  T2.Artist_ID WHERE T1.age  <=  23;
+SELECT Issue_Date FROM artist AS T1 JOIN volume AS T2 ON T1.Artist_ID  =  T2.Artist_ID WHERE T1.age  <=  23;
+SELECT count(*) FROM roller_coaster;
+SELECT Name FROM roller_coaster ORDER BY LENGTH ASC;
+SELECT LENGTH ,  Height FROM roller_coaster;
+SELECT Name FROM country WHERE Languages != 'German';
+SELECT Status FROM roller_coaster WHERE LENGTH  >  3300 OR Height  >  100;
+SELECT Speed FROM roller_coaster ORDER BY LENGTH DESC LIMIT 1;
+SELECT avg(Speed) FROM roller_coaster;
+SELECT Status ,  COUNT(*) FROM roller_coaster GROUP BY Status;
+SELECT Status FROM roller_coaster GROUP BY Status ORDER BY COUNT(*) DESC LIMIT 1;
+SELECT Status FROM roller_coaster GROUP BY Status HAVING COUNT(*)  >  2;
+SELECT Park FROM roller_coaster ORDER BY Speed DESC LIMIT 1;
+SELECT T2.Name ,  T1.Name FROM country AS T1 JOIN roller_coaster AS T2 ON T1.Country_ID  =  T2.Country_ID;
+SELECT T1.Name FROM country AS T1 JOIN roller_coaster AS T2 ON T1.Country_ID  =  T2.Country_ID GROUP BY T1.Name HAVING COUNT(*)  >  1;
+SELECT T1.Name ,  T1.population FROM country AS T1 JOIN roller_coaster AS T2 ON T1.Country_ID  =  T2.Country_ID ORDER BY T2.Height DESC LIMIT 1;
+SELECT T1.Name ,  avg(T2.Speed) FROM country AS T1 JOIN roller_coaster AS T2 ON T1.Country_ID  =  T2.Country_ID GROUP BY T1.Name;
+SELECT count(*) FROM country WHERE country_id NOT IN ( SELECT country_id FROM roller_coaster WHERE LENGTH  >  3000 );
+SELECT T1.name ,  T1.area ,  T1.population FROM country AS T1 JOIN roller_coaster AS T2 ON T1.Country_ID  =  T2.Country_ID WHERE T2.speed  >  60 INTERSECT SELECT T1.name ,  T1.area ,  T1.population FROM country AS T1 JOIN roller_coaster AS T2 ON T1.Country_ID  =  T2.Country_ID WHERE T2.speed  <  55;
+SELECT count(DISTINCT rank) FROM captain;
+SELECT count(DISTINCT rank) FROM captain;
+SELECT count(*) ,  rank FROM captain GROUP BY rank;
+SELECT count(*) ,  rank FROM captain GROUP BY rank;
+SELECT count(*) ,  rank FROM captain WHERE age  <  50 GROUP BY rank;
+SELECT count(*) ,  rank FROM captain WHERE age  <  50 GROUP BY rank;
+SELECT name FROM captain ORDER BY age DESC;
+SELECT name FROM captain ORDER BY age DESC;
+SELECT name ,  CLASS ,  rank FROM captain;
+SELECT name ,  CLASS ,  rank FROM captain;
+SELECT rank FROM captain GROUP BY rank ORDER BY count(*) DESC LIMIT 1;
+SELECT rank FROM captain GROUP BY rank ORDER BY count(*) DESC LIMIT 1;
+SELECT CLASS FROM captain GROUP BY CLASS HAVING count(*)  >  2;
+SELECT CLASS FROM captain GROUP BY CLASS HAVING count(*)  >  2;
+SELECT name FROM captain WHERE rank  =  'Midshipman' OR rank  =  'Lieutenant';
+SELECT name FROM captain WHERE rank  =  'Midshipman' OR rank  =  'Lieutenant';
+SELECT avg(age) ,  min(age) ,  CLASS FROM captain GROUP BY CLASS;
+SELECT avg(age) ,  min(age) ,  CLASS FROM captain GROUP BY CLASS;
+SELECT rank FROM captain WHERE CLASS  =  'Cutter' INTERSECT SELECT rank FROM captain WHERE CLASS  =  'Armed schooner';
+SELECT rank FROM captain WHERE CLASS  =  'Cutter' INTERSECT SELECT rank FROM captain WHERE CLASS  =  'Armed schooner';
+SELECT rank FROM captain EXCEPT SELECT rank FROM captain WHERE CLASS  =  'Third-rate ship of the line';
+SELECT rank FROM captain EXCEPT SELECT rank FROM captain WHERE CLASS  =  'Third-rate ship of the line';
+SELECT name FROM captain ORDER BY age LIMIT 1;
+SELECT name FROM captain ORDER BY age LIMIT 1;
+SELECT count(*) FROM ship;
+SELECT count(*) FROM ship;
+SELECT name ,  TYPE ,  flag FROM ship ORDER BY built_year DESC LIMIT 1;
+SELECT name ,  TYPE ,  flag FROM ship ORDER BY built_year DESC LIMIT 1;
+SELECT count(*) ,  flag FROM ship GROUP BY flag;
+SELECT count(*) ,  flag FROM ship GROUP BY flag;
+SELECT flag FROM ship GROUP BY flag ORDER BY count(*) DESC LIMIT 1;
+SELECT flag FROM ship GROUP BY flag ORDER BY count(*) DESC LIMIT 1;
+SELECT name FROM ship ORDER BY built_year ,  CLASS;
+SELECT name FROM ship ORDER BY built_year ,  CLASS;
+SELECT TYPE FROM ship WHERE flag  =  'Panama' INTERSECT SELECT TYPE FROM ship WHERE flag  =  'Malta';
+SELECT TYPE FROM ship WHERE flag  =  'Panama' INTERSECT SELECT TYPE FROM ship WHERE flag  =  'Malta';
+SELECT built_year FROM ship GROUP BY built_year ORDER BY count(*) DESC LIMIT 1;
+SELECT built_year FROM ship GROUP BY built_year ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.name FROM ship AS t1 JOIN captain AS t2 ON t1.ship_id  =  t2.ship_id GROUP BY t2.ship_id HAVING count(*)  >  1;
+SELECT t1.name FROM ship AS t1 JOIN captain AS t2 ON t1.ship_id  =  t2.ship_id GROUP BY t2.ship_id HAVING count(*)  >  1;
+SELECT name ,  CLASS FROM ship WHERE ship_id NOT IN (SELECT ship_id FROM captain);
+SELECT name ,  CLASS FROM ship WHERE ship_id NOT IN (SELECT ship_id FROM captain);
+SELECT t1.name FROM ship AS t1 JOIN captain AS t2 ON t1.ship_id  =  t2.ship_id ORDER BY t2.age LIMIT 1;
+SELECT t1.name FROM ship AS t1 JOIN captain AS t2 ON t1.ship_id  =  t2.ship_id ORDER BY t2.age LIMIT 1;
+SELECT name ,  flag FROM ship WHERE ship_id NOT IN (SELECT ship_id FROM captain WHERE rank  =  'Midshipman');
+SELECT name ,  flag FROM ship WHERE ship_id NOT IN (SELECT ship_id FROM captain WHERE rank  =  'Midshipman');
+SELECT t1.name FROM ship AS t1 JOIN captain AS t2 ON t1.ship_id  =  t2.ship_id WHERE t2.rank  =  'Midshipman' INTERSECT SELECT t1.name FROM ship AS t1 JOIN captain AS t2 ON t1.ship_id  =  t2.ship_id WHERE t2.rank  =  'Lieutenant';
+SELECT t1.name FROM ship AS t1 JOIN captain AS t2 ON t1.ship_id  =  t2.ship_id WHERE t2.rank  =  'Midshipman' INTERSECT SELECT t1.name FROM ship AS t1 JOIN captain AS t2 ON t1.ship_id  =  t2.ship_id WHERE t2.rank  =  'Lieutenant';
+SELECT host_city FROM hosting_city ORDER BY YEAR DESC LIMIT 1;
+SELECT host_city FROM hosting_city ORDER BY YEAR DESC LIMIT 1;
+SELECT match_id FROM MATCH WHERE competition = '1994 FIFA World Cup qualification';
+SELECT match_id FROM MATCH WHERE competition = '1994 FIFA World Cup qualification';
+SELECT T1.city FROM city AS T1 JOIN hosting_city AS T2 ON T1.city_id = T2.host_city WHERE T2.year  >  2010;
+SELECT T1.city FROM city AS T1 JOIN hosting_city AS T2 ON T1.city_id = T2.host_city WHERE T2.year  >  2010;
+SELECT T1.city FROM city AS T1 JOIN hosting_city AS T2 ON T1.city_id  =  T2.host_city GROUP BY T2.host_city ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.city FROM city AS T1 JOIN hosting_city AS T2 ON T1.city_id  =  T2.host_city GROUP BY T2.host_city ORDER BY count(*) DESC LIMIT 1;
+SELECT T3.venue FROM city AS T1 JOIN hosting_city AS T2 ON T1.city_id = T2.host_city JOIN MATCH AS T3 ON T2.match_id = T3.match_id WHERE T1.city = 'Nanjing ( Jiangsu )' AND T3.competition = '1994 FIFA World Cup qualification';
+SELECT T3.venue FROM city AS T1 JOIN hosting_city AS T2 ON T1.city_id = T2.host_city JOIN MATCH AS T3 ON T2.match_id = T3.match_id WHERE T1.city = 'Nanjing ( Jiangsu )' AND T3.competition = '1994 FIFA World Cup qualification';
+SELECT T2.Jan FROM city AS T1 JOIN temperature AS T2 ON T1.city_id = T2.city_id WHERE T1.city = 'Shanghai';
+SELECT T2.Jan FROM city AS T1 JOIN temperature AS T2 ON T1.city_id = T2.city_id WHERE T1.city = 'Shanghai';
+SELECT T2.year FROM city AS T1 JOIN hosting_city AS T2 ON T1.city_id  =  T2.host_city WHERE T1.city  =  'Taizhou ( Zhejiang )';
+SELECT T2.year FROM city AS T1 JOIN hosting_city AS T2 ON T1.city_id  =  T2.host_city WHERE T1.city  =  'Taizhou ( Zhejiang )';
+SELECT city FROM city ORDER BY regional_population DESC LIMIT 3;
+SELECT city FROM city ORDER BY regional_population DESC LIMIT 3;
+SELECT city ,  GDP FROM city ORDER BY GDP LIMIT 1;
+SELECT city ,  GDP FROM city ORDER BY GDP LIMIT 1;
+SELECT T1.city FROM city AS T1 JOIN temperature AS T2 ON T1.city_id  =  T2.city_id ORDER BY T2.Feb DESC LIMIT 1;
+SELECT T1.city FROM city AS T1 JOIN temperature AS T2 ON T1.city_id  =  T2.city_id ORDER BY T2.Feb DESC LIMIT 1;
+SELECT T1.city FROM city AS T1 JOIN temperature AS T2 ON T1.city_id  =  T2.city_id WHERE T2.Mar  <  T2.Jul OR T2.Mar  >  T2.Oct;
+SELECT T1.city FROM city AS T1 JOIN temperature AS T2 ON T1.city_id  =  T2.city_id WHERE T2.Mar  <  T2.Jul OR T2.Mar  >  T2.Oct;
+SELECT T1.city FROM city AS T1 JOIN temperature AS T2 ON T1.city_id  =  T2.city_id WHERE T2.Mar  <  T2.Jul INTERSECT SELECT T3.city FROM city AS T3 JOIN hosting_city AS T4 ON T3.city_id  =  T4.host_city;
+SELECT T1.city FROM city AS T1 JOIN temperature AS T2 ON T1.city_id  =  T2.city_id WHERE T2.Mar  <  T2.Jul INTERSECT SELECT T3.city FROM city AS T3 JOIN hosting_city AS T4 ON T3.city_id  =  T4.host_city;
+SELECT T1.city FROM city AS T1 JOIN temperature AS T2 ON T1.city_id  =  T2.city_id WHERE T2.Mar  <  T2.Dec EXCEPT SELECT T3.city FROM city AS T3 JOIN hosting_city AS T4 ON T3.city_id  =  T4.host_city;
+SELECT T1.city FROM city AS T1 JOIN temperature AS T2 ON T1.city_id  =  T2.city_id WHERE T2.Mar  <  T2.Dec EXCEPT SELECT T3.city FROM city AS T3 JOIN hosting_city AS T4 ON T3.city_id  =  T4.host_city;
+SELECT T1.city FROM city AS T1 JOIN temperature AS T2 ON T1.city_id  =  T2.city_id WHERE T2.Feb  >  T2.Jun UNION SELECT T3.city FROM city AS T3 JOIN hosting_city AS T4 ON T3.city_id  =  T4.host_city;
+SELECT T1.city FROM city AS T1 JOIN temperature AS T2 ON T1.city_id  =  T2.city_id WHERE T2.Feb  >  T2.Jun UNION SELECT T3.city FROM city AS T3 JOIN hosting_city AS T4 ON T3.city_id  =  T4.host_city;
+SELECT city FROM city WHERE regional_population  >  10000000;
+SELECT city FROM city WHERE regional_population  >  10000000;
+SELECT city FROM city WHERE regional_population  >  10000000 UNION SELECT city FROM city WHERE regional_population  <  5000000;
+SELECT city FROM city WHERE regional_population  >  10000000 UNION SELECT city FROM city WHERE regional_population  <  5000000;
+SELECT count(*) ,  Competition FROM MATCH GROUP BY Competition;
+SELECT count(*) ,  Competition FROM MATCH GROUP BY Competition;
+SELECT venue FROM MATCH ORDER BY date DESC;
+SELECT venue FROM MATCH ORDER BY date DESC;
+SELECT gdp FROM city ORDER BY Regional_Population DESC LIMIT 1;
+SELECT gdp FROM city ORDER BY Regional_Population DESC LIMIT 1;
+SELECT t1.gdp ,  t1.Regional_Population FROM city AS T1 JOIN hosting_city AS T2 ON T1.city_id  =  T2.host_city GROUP BY t2.Host_City HAVING count(*)  >  1;
+SELECT t1.gdp ,  t1.Regional_Population FROM city AS T1 JOIN hosting_city AS T2 ON T1.city_id  =  T2.host_city GROUP BY t2.Host_City HAVING count(*)  >  1;
+SELECT individual_first_name ,  individual_middle_name ,  individual_last_name FROM individuals ORDER BY individual_last_name;
+SELECT individual_first_name ,  individual_middle_name ,  individual_last_name FROM individuals ORDER BY individual_last_name;
+SELECT DISTINCT form_type_code FROM forms;
+SELECT DISTINCT form_type_code FROM forms;
+SELECT t1.form_name FROM forms AS t1 JOIN party_forms AS t2 ON t1.form_id  =  t2.form_id GROUP BY t2.form_id ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.form_name FROM forms AS t1 JOIN party_forms AS t2 ON t1.form_id  =  t2.form_id GROUP BY t2.form_id ORDER BY count(*) DESC LIMIT 1;
+SELECT payment_method_code ,  party_phone FROM parties WHERE party_email  =  'enrico09@example.com';
+SELECT payment_method_code ,  party_phone FROM parties WHERE party_email  =  'enrico09@example.com';
+SELECT t1.party_email FROM parties AS t1 JOIN party_forms AS t2 ON t1.party_id  =  t2.party_id WHERE t2.form_id  =  (SELECT form_id FROM party_forms GROUP BY form_id ORDER BY count(*) DESC LIMIT 1);
+SELECT t1.party_email FROM parties AS t1 JOIN party_forms AS t2 ON t1.party_id  =  t2.party_id WHERE t2.form_id  =  (SELECT form_id FROM party_forms GROUP BY form_id ORDER BY count(*) DESC LIMIT 1);
+SELECT organization_name FROM organizations ORDER BY date_formed ASC;
+SELECT organization_name FROM organizations ORDER BY date_formed ASC;
+SELECT organization_name FROM organizations ORDER BY date_formed DESC LIMIT 1;
+SELECT organization_name FROM organizations ORDER BY date_formed DESC LIMIT 1;
+SELECT t3.individual_last_name FROM organizations AS t1 JOIN organization_contact_individuals AS t2 ON t1.organization_id  =  t2.organization_id JOIN individuals AS t3 ON t2.individual_id  =  t3.individual_id WHERE t1.organization_name  =  'Labour Party' ORDER BY t2.date_contact_to DESC LIMIT 1;
+SELECT t3.individual_last_name FROM organizations AS t1 JOIN organization_contact_individuals AS t2 ON t1.organization_id  =  t2.organization_id JOIN individuals AS t3 ON t2.individual_id  =  t3.individual_id WHERE t1.organization_name  =  'Labour Party' ORDER BY t2.date_contact_to DESC LIMIT 1;
+SELECT t3.individual_last_name FROM organizations AS t1 JOIN organization_contact_individuals AS t2 ON t1.organization_id  =  t2.organization_id JOIN individuals AS t3 ON t2.individual_id  =  t3.individual_id WHERE t1.uk_vat_number  =  (SELECT max(uk_vat_number) FROM organizations) ORDER BY t2.date_contact_to ASC LIMIT 1;
+SELECT t3.individual_last_name FROM organizations AS t1 JOIN organization_contact_individuals AS t2 ON t1.organization_id  =  t2.organization_id JOIN individuals AS t3 ON t2.individual_id  =  t3.individual_id WHERE t1.uk_vat_number  =  (SELECT max(uk_vat_number) FROM organizations) ORDER BY t2.date_contact_to ASC LIMIT 1;
+SELECT count(*) FROM services;
+SELECT count(*) FROM services;
+SELECT service_name FROM services EXCEPT SELECT t1.service_name FROM services AS t1 JOIN party_services AS t2 ON t1.service_id  =  t2.service_id;
+SELECT service_name FROM services EXCEPT SELECT t1.service_name FROM services AS t1 JOIN party_services AS t2 ON t1.service_id  =  t2.service_id;
+SELECT town_city FROM addresses UNION SELECT state_province_county FROM addresses;
+SELECT town_city FROM addresses UNION SELECT state_province_county FROM addresses;
+SELECT count(*) FROM addresses WHERE state_province_county  =  'Colorado';
+SELECT count(*) FROM addresses WHERE state_province_county  =  'Colorado';
+SELECT payment_method_code FROM parties GROUP BY payment_method_code HAVING count(*)  >  3;
+SELECT payment_method_code FROM parties GROUP BY payment_method_code HAVING count(*)  >  3;
+SELECT organization_name FROM organizations WHERE organization_name LIKE '%Party%';
+SELECT organization_name FROM organizations WHERE organization_name LIKE '%Party%';
+SELECT count(DISTINCT payment_method_code) FROM parties;
+SELECT count(DISTINCT payment_method_code) FROM parties;
+SELECT t1.party_email FROM parties AS t1 JOIN party_services AS t2 ON t1.party_id  =  t2.customer_id GROUP BY t1.party_email ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.party_email FROM parties AS t1 JOIN party_services AS t2 ON t1.party_id  =  t2.customer_id GROUP BY t1.party_email ORDER BY count(*) DESC LIMIT 1;
+SELECT state_province_county FROM addresses WHERE line_1_number_building LIKE '%6862 Kaitlyn Knolls%';
+SELECT state_province_county FROM addresses WHERE line_1_number_building LIKE '%6862 Kaitlyn Knolls%';
+SELECT t1.organization_name FROM organizations AS t1 JOIN organization_contact_individuals AS t2 ON t1.organization_id  =  t2.organization_id GROUP BY t1.organization_name ORDER BY count(*) DESC LIMIT 1;
+SELECT t1.organization_name FROM organizations AS t1 JOIN organization_contact_individuals AS t2 ON t1.organization_id  =  t2.organization_id GROUP BY t1.organization_name ORDER BY count(*) DESC LIMIT 1;
+SELECT DISTINCT t1.individual_last_name FROM individuals AS t1 JOIN organization_contact_individuals AS t2 ON t1.individual_id  =  t2.individual_id;
+SELECT DISTINCT t1.individual_last_name FROM individuals AS t1 JOIN organization_contact_individuals AS t2 ON t1.individual_id  =  t2.individual_id;
+SELECT count(*) FROM driver;
+SELECT name ,  home_city ,  age FROM driver;
+SELECT party ,  count(*) FROM driver GROUP BY party;
+SELECT name FROM driver ORDER BY age DESC;
+SELECT DISTINCT home_city FROM driver;
+SELECT home_city FROM driver GROUP BY home_city ORDER BY count(*) DESC LIMIT 1;
+SELECT party FROM driver WHERE home_city  =  'Hartford' AND age  >  40;
+SELECT home_city FROM driver WHERE age  >  40 GROUP BY home_city HAVING count(*)  >=  2;
+SELECT home_city FROM driver EXCEPT SELECT home_city FROM driver WHERE age  >  40;
+SELECT name FROM driver WHERE driver_id NOT IN (SELECT driver_id FROM school_bus);
+SELECT TYPE FROM school GROUP BY TYPE HAVING count(*)  =  2;
+SELECT T2.school ,  T3.name FROM school_bus AS T1 JOIN school AS T2 ON T1.school_id  =  T2.school_id JOIN driver AS T3 ON T1.driver_id  =  T3.driver_id;
+SELECT max(years_working) ,  min(years_working) ,  avg(years_working) FROM school_bus;
+SELECT school ,  TYPE FROM school WHERE school_id NOT IN (SELECT school_id FROM school_bus);
+SELECT T2.type ,  count(*) FROM school_bus AS T1 JOIN school AS T2 ON T1.school_id  =  T2.school_id GROUP BY T2.type;
+SELECT count(*) FROM driver WHERE home_city  =  'Hartford' OR age  <  40;
+SELECT name FROM driver WHERE home_city  =  'Hartford' AND age  <  40;
+SELECT t1.name FROM driver AS t1 JOIN school_bus AS t2 ON t1.driver_id  =  t2.driver_id ORDER BY years_working DESC LIMIT 1;
+SELECT count(*) FROM flight WHERE velocity  >  200;
+SELECT vehicle_flight_number ,  date ,  pilot FROM flight ORDER BY altitude ASC;
+SELECT id ,  country ,  city ,  name FROM airport ORDER BY name;
+SELECT max(group_equity_shareholding) FROM operate_company;
+SELECT avg(velocity) FROM flight WHERE pilot  =  'Thompson';
+SELECT T1.name ,  T1.type FROM operate_company AS T1 JOIN flight AS t2 ON T1.id  =  T2.company_id;
+SELECT name FROM airport WHERE country != 'Iceland';
+SELECT DISTINCT T1.type FROM operate_company AS T1 JOIN flight AS t2 ON T1.id  =  T2.company_id WHERE T2.velocity  <  200;
+SELECT T1.id ,  T1.name FROM operate_company AS T1 JOIN flight AS t2 ON T1.id  =  T2.company_id GROUP BY T1.id HAVING count(*)  >  1;
+SELECT T1.id ,  T1.name ,  T1.IATA FROM airport AS T1 JOIN flight AS T2 ON T1.id  =  T2.airport_id GROUP BY T2.id ORDER BY count(*) DESC LIMIT 1;
+SELECT DISTINCT T2.pilot FROM airport AS T1 JOIN flight AS T2 ON T1.id  =  T2.airport_id WHERE T1.country  =  'United States' OR T1.name  =  'Billund Airport';
+SELECT TYPE ,  count(*) FROM operate_company GROUP BY TYPE ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM airport WHERE id NOT IN ( SELECT airport_id FROM flight WHERE pilot  =  'Thompson' );
+SELECT T2.pilot FROM operate_company AS T1 JOIN flight AS t2 ON T1.id  =  T2.company_id WHERE T1.principal_activities  =  'Cargo' INTERSECT SELECT T2.pilot FROM operate_company AS T1 JOIN flight AS t2 ON T1.id  =  T2.company_id WHERE T1.principal_activities  =  'Catering services';
+SELECT name FROM airport WHERE name LIKE '%international%';
+SELECT T3.id ,  count(*) FROM operate_company AS T1 JOIN flight AS t2 ON T1.id  =  T2.company_id JOIN airport AS T3 ON T2.airport_id  =  T3.id GROUP BY T3.id;
+SELECT count(*) ,  country FROM airport GROUP BY country;
+SELECT country FROM airport GROUP BY country HAVING count(*)  >  2;
+SELECT pilot FROM flight GROUP BY pilot ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM Accounts;
+SELECT count(*) FROM Accounts;
+SELECT account_id ,  account_details FROM Accounts;
+SELECT account_id ,  account_details FROM Accounts;
+SELECT count(*) FROM Statements;
+SELECT count(*) FROM Statements;
+SELECT STATEMENT_ID ,  statement_details FROM Statements;
+SELECT STATEMENT_ID ,  statement_details FROM Statements;
+SELECT T1.statement_id ,  T2.statement_details ,  T1.account_details FROM Accounts AS T1 JOIN Statements AS T2 ON T1.statement_id  =  T2.statement_id;
+SELECT T1.statement_id ,  T2.statement_details ,  T1.account_details FROM Accounts AS T1 JOIN Statements AS T2 ON T1.statement_id  =  T2.statement_id;
+SELECT STATEMENT_ID ,  count(*) FROM Accounts GROUP BY STATEMENT_ID;
+SELECT STATEMENT_ID ,  count(*) FROM Accounts GROUP BY STATEMENT_ID;
+SELECT T1.statement_id ,  T2.statement_details FROM Accounts AS T1 JOIN Statements AS T2 ON T1.statement_id  =  T2.statement_id GROUP BY T1.statement_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.statement_id ,  T2.statement_details FROM Accounts AS T1 JOIN Statements AS T2 ON T1.statement_id  =  T2.statement_id GROUP BY T1.statement_id ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM Documents;
+SELECT count(*) FROM Documents;
+SELECT document_type_code ,  document_name ,  document_description FROM Documents WHERE document_name  =  'Noel CV' OR document_name  =  'King Book';
+SELECT document_type_code ,  document_name ,  document_description FROM Documents WHERE document_name  =  'Noel CV' OR document_name  =  'King Book';
+SELECT document_id ,  document_name FROM Documents;
+SELECT document_id ,  document_name FROM Documents;
+SELECT document_name ,  document_id FROM Documents WHERE document_type_code  =  'BK';
+SELECT document_name ,  document_id FROM Documents WHERE document_type_code  =  'BK';
+SELECT count(*) ,  project_id FROM Documents WHERE document_type_code  =  'BK' GROUP BY project_id;
+SELECT count(*) ,  project_id FROM Documents WHERE document_type_code  =  'BK' GROUP BY project_id;
+SELECT document_name ,  document_date FROM Documents AS T1 JOIN projects AS T2 ON T1.project_id  =  T2.project_id WHERE T2.project_details  =  'Graph Database project';
+SELECT document_name ,  document_date FROM Documents AS T1 JOIN projects AS T2 ON T1.project_id  =  T2.project_id WHERE T2.project_details  =  'Graph Database project';
+SELECT project_id ,  count(*) FROM Documents GROUP BY project_id;
+SELECT project_id ,  count(*) FROM Documents GROUP BY project_id;
+SELECT project_id FROM Documents GROUP BY project_id ORDER BY count(*) ASC LIMIT 1;
+SELECT project_id FROM Documents GROUP BY project_id ORDER BY count(*) ASC LIMIT 1;
+SELECT project_id FROM Documents GROUP BY project_id HAVING count(*)  >=  2;
+SELECT project_id FROM Documents GROUP BY project_id HAVING count(*)  >=  2;
+SELECT document_type_code ,  count(*) FROM Documents GROUP BY document_type_code;
+SELECT document_type_code ,  count(*) FROM Documents GROUP BY document_type_code;
+SELECT document_type_code FROM Documents GROUP BY document_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT document_type_code FROM Documents GROUP BY document_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT document_type_code FROM Documents GROUP BY document_type_code HAVING count(*)  <  3;
+SELECT document_type_code FROM Documents GROUP BY document_type_code HAVING count(*)  <  3;
+SELECT T1.statement_details ,  T2.document_name FROM Statements AS T1 JOIN Documents AS T2 ON T1.statement_id  =  T2.document_id WHERE T1.statement_details  =  'Private Project';
+SELECT T1.statement_details ,  T2.document_name FROM Statements AS T1 JOIN Documents AS T2 ON T1.statement_id  =  T2.document_id WHERE T1.statement_details  =  'Private Project';
+SELECT document_type_code ,  document_type_name ,  document_type_description FROM Ref_document_types;
+SELECT document_type_code ,  document_type_name ,  document_type_description FROM Ref_document_types;
+SELECT document_type_description FROM Ref_document_types WHERE document_type_name  =  'Film';
+SELECT document_type_description FROM Ref_document_types WHERE document_type_name  =  'Film';
+SELECT T1.document_type_name ,  T1.document_type_description ,  T2.Document_date FROM Ref_document_types AS T1 JOIN Documents AS T2 ON T1.document_type_code  =  T2.document_type_code;
+SELECT T1.document_type_name ,  T1.document_type_description ,  T2.Document_date FROM Ref_document_types AS T1 JOIN Documents AS T2 ON T1.document_type_code  =  T2.document_type_code;
+SELECT count(*) FROM Projects;
+SELECT count(*) FROM Projects;
+SELECT project_id ,  project_details FROM Projects;
+SELECT project_id ,  project_details FROM Projects;
+SELECT T1.project_id ,  T1.project_details FROM Projects AS T1 JOIN Documents AS T2 ON T1.project_id  =  T2.project_id GROUP BY T1.project_id HAVING count(*)  >  2;
+SELECT T1.project_id ,  T1.project_details FROM Projects AS T1 JOIN Documents AS T2 ON T1.project_id  =  T2.project_id GROUP BY T1.project_id HAVING count(*)  >  2;
+SELECT T1.project_details FROM Projects AS T1 JOIN Documents AS T2 ON T1.project_id  =  T2.project_id WHERE T2.document_name  =  'King Book';
+SELECT T1.project_details FROM Projects AS T1 JOIN Documents AS T2 ON T1.project_id  =  T2.project_id WHERE T2.document_name  =  'King Book';
+SELECT count(*) FROM Ref_budget_codes;
+SELECT count(*) FROM Ref_budget_codes;
+SELECT budget_type_code ,  budget_type_description FROM Ref_budget_codes;
+SELECT budget_type_code ,  budget_type_description FROM Ref_budget_codes;
+SELECT budget_type_description FROM Ref_budget_codes WHERE budget_type_code  =  'ORG';
+SELECT budget_type_description FROM Ref_budget_codes WHERE budget_type_code  =  'ORG';
+SELECT count(*) FROM Documents_with_expenses;
+SELECT count(*) FROM Documents_with_expenses;
+SELECT document_id FROM Documents_with_expenses WHERE budget_type_code  =  'SF';
+SELECT document_id FROM Documents_with_expenses WHERE budget_type_code  =  'SF';
+SELECT T2.budget_type_code ,  T2.budget_type_description ,  T1.document_id FROM Documents_with_expenses AS T1 JOIN Ref_budget_codes AS T2 ON T1.budget_type_code  =  T2.budget_type_code;
+SELECT T2.budget_type_code ,  T2.budget_type_description ,  T1.document_id FROM Documents_with_expenses AS T1 JOIN Ref_budget_codes AS T2 ON T1.budget_type_code  =  T2.budget_type_code;
+SELECT T1.document_id FROM Documents_with_expenses AS T1 JOIN Ref_Budget_Codes AS T2 ON T1.Budget_Type_code  =  T2.Budget_Type_code WHERE T2.budget_type_Description  =  'Government';
+SELECT T1.document_id FROM Documents_with_expenses AS T1 JOIN Ref_Budget_Codes AS T2 ON T1.Budget_Type_code  =  T2.Budget_Type_code WHERE T2.budget_type_Description  =  'Government';
+SELECT budget_type_code ,  count(*) FROM Documents_with_expenses GROUP BY budget_type_code;
+SELECT budget_type_code ,  count(*) FROM Documents_with_expenses GROUP BY budget_type_code;
+SELECT budget_type_code FROM Documents_with_expenses GROUP BY budget_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT budget_type_code FROM Documents_with_expenses GROUP BY budget_type_code ORDER BY count(*) DESC LIMIT 1;
+SELECT document_id FROM Documents EXCEPT SELECT document_id FROM Documents_with_expenses;
+SELECT document_id FROM Documents EXCEPT SELECT document_id FROM Documents_with_expenses;
+SELECT document_id FROM Documents WHERE document_type_code  =  'CV' EXCEPT SELECT document_id FROM Documents_with_expenses;
+SELECT document_id FROM Documents WHERE document_type_code  =  'CV' EXCEPT SELECT document_id FROM Documents_with_expenses;
+SELECT T1.document_id FROM Documents AS T1 JOIN Documents_with_expenses AS T2 ON T1.document_id  =  T2.document_id WHERE T1.document_name LIKE '%s%';
+SELECT T1.document_id FROM Documents AS T1 JOIN Documents_with_expenses AS T2 ON T1.document_id  =  T2.document_id WHERE T1.document_name LIKE '%s%';
+SELECT count(*) FROM Documents WHERE document_id NOT IN ( SELECT document_id FROM Documents_with_expenses );
+SELECT count(*) FROM Documents WHERE document_id NOT IN ( SELECT document_id FROM Documents_with_expenses );
+SELECT T1.document_date FROM Documents AS T1 JOIN Documents_with_Expenses AS T2 ON T1.document_id  =  T2.document_id WHERE T2.budget_type_code  =  'GV' INTERSECT SELECT T1.document_date FROM Documents AS T1 JOIN Documents_with_Expenses AS T2 ON T1.document_id  =  T2.document_id WHERE T2.budget_type_code  =  'SF';
+SELECT T1.document_date FROM Documents AS T1 JOIN Documents_with_Expenses AS T2 ON T1.document_id  =  T2.document_id WHERE T2.budget_type_code  =  'GV' INTERSECT SELECT T1.document_date FROM Documents AS T1 JOIN Documents_with_Expenses AS T2 ON T1.document_id  =  T2.document_id WHERE T2.budget_type_code  =  'SF';
+SELECT max(Account_details) FROM Accounts UNION SELECT Account_details FROM Accounts WHERE Account_details LIKE '%5%';
+SELECT max(Account_details) FROM Accounts UNION SELECT Account_details FROM Accounts WHERE Account_details LIKE '%5%';
+SELECT count(*) FROM scientists;
+SELECT count(*) FROM scientists;
+SELECT sum(hours) FROM projects;
+SELECT sum(hours) FROM projects;
+SELECT count(DISTINCT scientist) FROM assignedto;
+SELECT count(DISTINCT scientist) FROM assignedto;
+SELECT count(DISTINCT name) FROM projects;
+SELECT count(DISTINCT name) FROM projects;
+SELECT avg(hours) FROM projects;
+SELECT avg(hours) FROM projects;
+SELECT name FROM projects ORDER BY hours DESC LIMIT 1;
+SELECT name FROM projects ORDER BY hours DESC LIMIT 1;
+SELECT name FROM projects WHERE hours  >  (SELECT avg(hours) FROM projects);
+SELECT name FROM projects WHERE hours  >  (SELECT avg(hours) FROM projects);
+SELECT T1.name ,  T1.hours FROM projects AS T1 JOIN assignedto AS T2 ON T1.code  =  T2.project GROUP BY T2.project ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name ,  T1.hours FROM projects AS T1 JOIN assignedto AS T2 ON T1.code  =  T2.project GROUP BY T2.project ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.name FROM assignedto AS T1 JOIN projects AS T2 ON T1.project  =  T2.code JOIN scientists AS T3 ON T1.scientist  =  T3.SSN WHERE T3.name LIKE '%Smith%';
+SELECT T2.name FROM assignedto AS T1 JOIN projects AS T2 ON T1.project  =  T2.code JOIN scientists AS T3 ON T1.scientist  =  T3.SSN WHERE T3.name LIKE '%Smith%';
+SELECT sum(T2.hours) FROM assignedto AS T1 JOIN projects AS T2 ON T1.project  =  T2.code JOIN scientists AS T3 ON T1.scientist  =  T3.SSN WHERE T3.name  =  'Michael Rogers' OR T3.name  =  'Carol Smith';
+SELECT sum(T2.hours) FROM assignedto AS T1 JOIN projects AS T2 ON T1.project  =  T2.code JOIN scientists AS T3 ON T1.scientist  =  T3.SSN WHERE T3.name  =  'Michael Rogers' OR T3.name  =  'Carol Smith';
+SELECT name FROM projects WHERE hours BETWEEN 100 AND 300;
+SELECT name FROM projects WHERE hours BETWEEN 100 AND 300;
+SELECT T3.name FROM assignedto AS T1 JOIN projects AS T2 ON T1.project  =  T2.code JOIN scientists AS T3 ON T1.scientist  =  T3.SSN WHERE T2.name  =  'Matter of Time' INTERSECT SELECT T3.name FROM assignedto AS T1 JOIN projects AS T2 ON T1.project  =  T2.code JOIN scientists AS T3 ON T1.scientist  =  T3.SSN WHERE T2.name  =  'A Puzzling Parallax';
+SELECT T3.name FROM assignedto AS T1 JOIN projects AS T2 ON T1.project  =  T2.code JOIN scientists AS T3 ON T1.scientist  =  T3.SSN WHERE T2.name  =  'Matter of Time' INTERSECT SELECT T3.name FROM assignedto AS T1 JOIN projects AS T2 ON T1.project  =  T2.code JOIN scientists AS T3 ON T1.scientist  =  T3.SSN WHERE T2.name  =  'A Puzzling Parallax';
+SELECT name FROM scientists ORDER BY name;
+SELECT name FROM scientists ORDER BY name;
+SELECT count(*) ,  T1.name FROM projects AS T1 JOIN assignedto AS T2 ON T1.code  =  T2.project GROUP BY T1.name;
+SELECT count(*) ,  T1.name FROM projects AS T1 JOIN assignedto AS T2 ON T1.code  =  T2.project GROUP BY T1.name;
+SELECT count(*) ,  T1.name FROM projects AS T1 JOIN assignedto AS T2 ON T1.code  =  T2.project WHERE T1.hours  >  300 GROUP BY T1.name;
+SELECT count(*) ,  T1.name FROM projects AS T1 JOIN assignedto AS T2 ON T1.code  =  T2.project WHERE T1.hours  >  300 GROUP BY T1.name;
+SELECT count(*) ,  T1.name FROM scientists AS T1 JOIN assignedto AS T2 ON T1.ssn  =  T2.scientist GROUP BY T1.name;
+SELECT count(*) ,  T1.name FROM scientists AS T1 JOIN assignedto AS T2 ON T1.ssn  =  T2.scientist GROUP BY T1.name;
+SELECT T3.ssn ,  T3.name FROM assignedto AS T1 JOIN projects AS T2 ON T1.project  =  T2.code JOIN scientists AS T3 ON T1.scientist  =  T3.SSN WHERE T2.hours  =  (SELECT max(hours) FROM projects);
+SELECT T3.ssn ,  T3.name FROM assignedto AS T1 JOIN projects AS T2 ON T1.project  =  T2.code JOIN scientists AS T3 ON T1.scientist  =  T3.SSN WHERE T2.hours  =  (SELECT max(hours) FROM projects);
+SELECT T2.name FROM assignedto AS T1 JOIN scientists AS T2 ON T1.scientist  =  T2.ssn;
+SELECT T2.name FROM assignedto AS T1 JOIN scientists AS T2 ON T1.scientist  =  T2.ssn;
+SELECT Name FROM Projects WHERE Code NOT IN (SELECT Project FROM AssignedTo);
+SELECT Name FROM Projects WHERE Code NOT IN (SELECT Project FROM AssignedTo);
+SELECT Name FROM scientists WHERE ssn NOT IN (SELECT scientist FROM AssignedTo);
+SELECT Name FROM scientists WHERE ssn NOT IN (SELECT scientist FROM AssignedTo);
+SELECT count(*) FROM scientists WHERE ssn NOT IN (SELECT scientist FROM AssignedTo);
+SELECT count(*) FROM scientists WHERE ssn NOT IN (SELECT scientist FROM AssignedTo);
+SELECT name FROM scientists EXCEPT SELECT T3.name FROM assignedto AS T1 JOIN projects AS T2 ON T1.project  =  T2.code JOIN scientists AS T3 ON T1.scientist  =  T3.SSN WHERE T2.hours  =  (SELECT max(hours) FROM projects);
+SELECT name FROM scientists EXCEPT SELECT T3.name FROM assignedto AS T1 JOIN projects AS T2 ON T1.project  =  T2.code JOIN scientists AS T3 ON T1.scientist  =  T3.SSN WHERE T2.hours  =  (SELECT max(hours) FROM projects);
+SELECT T1.Name ,  T3.Name ,  T3.Hours FROM Scientists AS T1 JOIN AssignedTo AS T2 ON T1.SSN = T2.Scientist JOIN Projects AS T3 ON T2.Project = T3.Code ORDER BY T3.Name ,  T1.Name;
+SELECT T1.Name ,  T3.Name ,  T3.Hours FROM Scientists AS T1 JOIN AssignedTo AS T2 ON T1.SSN = T2.Scientist JOIN Projects AS T3 ON T2.Project = T3.Code ORDER BY T3.Name ,  T1.Name;
+SELECT T2.name ,  T3.name FROM assignedto AS T1 JOIN projects AS T2 ON T1.project  =  T2.code JOIN scientists AS T3 ON T1.scientist  =  T3.SSN WHERE T2.hours  =  (SELECT min(hours) FROM projects);
+SELECT T2.name ,  T3.name FROM assignedto AS T1 JOIN projects AS T2 ON T1.project  =  T2.code JOIN scientists AS T3 ON T1.scientist  =  T3.SSN WHERE T2.hours  =  (SELECT min(hours) FROM projects);
+SELECT Name FROM WINE ORDER BY Score LIMIT 1;
+SELECT Name FROM WINE ORDER BY Score LIMIT 1;
+SELECT Winery FROM WINE ORDER BY SCORE LIMIT 1;
+SELECT Winery FROM WINE ORDER BY SCORE LIMIT 1;
+SELECT Name FROM WINE WHERE YEAR  =  '2008';
+SELECT Name FROM WINE WHERE YEAR  =  '2008';
+SELECT Grape ,  Appelation FROM WINE;
+SELECT Grape ,  Appelation FROM WINE;
+SELECT Name ,  Score FROM WINE;
+SELECT Name ,  Score FROM WINE;
+SELECT Area ,  County FROM APPELLATIONS;
+SELECT Area ,  County FROM APPELLATIONS;
+SELECT Price FROM WINE WHERE YEAR  <  2010;
+SELECT Price FROM WINE WHERE YEAR  <  2010;
+SELECT Name FROM WINE WHERE score  >  90;
+SELECT Name FROM WINE WHERE score  >  90;
+SELECT DISTINCT T2.Name FROM GRAPES AS T1 JOIN WINE AS T2 ON T1.Grape  =  T2.Grape WHERE T1.Color  =  'Red';
+SELECT DISTINCT T2.Name FROM GRAPES AS T1 JOIN WINE AS T2 ON T1.Grape  =  T2.Grape WHERE T1.Color  =  'Red';
+SELECT DISTINCT T2.Name FROM APPELLATIONs AS T1 JOIN WINE AS T2 ON T1.Appelation  =  T2.Appelation WHERE T1.Area  =  'North Coast';
+SELECT DISTINCT T2.Name FROM APPELLATIONs AS T1 JOIN WINE AS T2 ON T1.Appelation  =  T2.Appelation WHERE T1.Area  =  'North Coast';
+SELECT count(*) FROM WINE WHERE Winery  =  'Robert Biale';
+SELECT count(*) FROM WINE WHERE Winery  =  'Robert Biale';
+SELECT count(*) FROM APPELLATIONS WHERE County  =  'Napa';
+SELECT count(*) FROM APPELLATIONS WHERE County  =  'Napa';
+SELECT AVG(T2.Price) FROM APPELLATIONS AS T1 JOIN WINE AS T2 ON T1.Appelation  =  T2.Appelation WHERE T1.County  =  'Sonoma';
+SELECT AVG(T2.Price) FROM APPELLATIONS AS T1 JOIN WINE AS T2 ON T1.Appelation  =  T2.Appelation WHERE T1.County  =  'Sonoma';
+SELECT T2.Name ,   T2.Score FROM GRAPES AS T1 JOIN WINE AS T2 ON T1.Grape  =  T2.Grape WHERE T1.Color  =  'White';
+SELECT T2.Name ,   T2.Score FROM GRAPES AS T1 JOIN WINE AS T2 ON T1.Grape  =  T2.Grape WHERE T1.Color  =  'White';
+SELECT max(T2.Price) FROM APPELLATIONS AS T1 JOIN WINE AS T2 ON T1.Appelation  =  T2.Appelation WHERE T1.Area  =  'Central Coast' AND T2.year  <  2005;
+SELECT max(T2.Price) FROM APPELLATIONS AS T1 JOIN WINE AS T2 ON T1.Appelation  =  T2.Appelation WHERE T1.Area  =  'Central Coast' AND T2.year  <  2005;
+SELECT DISTINCT T1.Grape FROM GRAPES AS T1 JOIN WINE AS T2 ON T1.Grape  =  T2.Grape WHERE T1.Color  =  'White' AND T2.score  >  90;
+SELECT DISTINCT T1.Grape FROM GRAPES AS T1 JOIN WINE AS T2 ON T1.Grape  =  T2.Grape WHERE T1.Color  =  'White' AND T2.score  >  90;
+SELECT T2.Name FROM Grapes AS T1 JOIN WINE AS T2 ON T1.Grape  =  T2.Grape WHERE T1.Color  =  'Red' AND T2.price  >  50;
+SELECT T2.Name FROM Grapes AS T1 JOIN WINE AS T2 ON T1.Grape  =  T2.Grape WHERE T1.Color  =  'Red' AND T2.price  >  50;
+SELECT T2.Name FROM APPELLATIONS AS T1 JOIN WINE AS T2 ON T1.Appelation  =  T2.Appelation WHERE T1.County  =  'Monterey' AND T2.price  <  50;
+SELECT T2.Name FROM APPELLATIONS AS T1 JOIN WINE AS T2 ON T1.Appelation  =  T2.Appelation WHERE T1.County  =  'Monterey' AND T2.price  <  50;
+SELECT count(*) ,  Grape FROM WINE GROUP BY Grape;
+SELECT count(*) ,  Grape FROM WINE GROUP BY Grape;
+SELECT avg(Price) ,  YEAR FROM WINE GROUP BY YEAR;
+SELECT avg(Price) ,  YEAR FROM WINE GROUP BY YEAR;
+SELECT DISTINCT Name FROM WINE WHERE Price  >  (SELECT min(Price) FROM wine WHERE Winery  =  'John Anthony');
+SELECT DISTINCT Name FROM WINE WHERE Price  >  (SELECT min(Price) FROM wine WHERE Winery  =  'John Anthony');
+SELECT DISTINCT Name FROM WINE ORDER BY Name;
+SELECT DISTINCT Name FROM WINE ORDER BY Name;
+SELECT DISTINCT Name FROM WINE ORDER BY price;
+SELECT DISTINCT Name FROM WINE ORDER BY price;
+SELECT T1.Area FROM APPELLATIONS AS T1 JOIN WINE AS T2 ON T1.Appelation  =  T2.Appelation GROUP BY T2.Appelation HAVING T2.year  <  2010 ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.Area FROM APPELLATIONS AS T1 JOIN WINE AS T2 ON T1.Appelation  =  T2.Appelation GROUP BY T2.Appelation HAVING T2.year  <  2010 ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.Color FROM GRAPES AS T1 JOIN WINE AS T2 ON T1.Grape  =  T2.Grape GROUP BY T2.Grape ORDER BY AVG(Price) DESC LIMIT 1;
+SELECT T1.Color FROM GRAPES AS T1 JOIN WINE AS T2 ON T1.Grape  =  T2.Grape GROUP BY T2.Grape ORDER BY AVG(Price) DESC LIMIT 1;
+SELECT DISTINCT Name FROM WINE WHERE YEAR  <  2000 OR YEAR  >  2010;
+SELECT DISTINCT Name FROM WINE WHERE YEAR  <  2000 OR YEAR  >  2010;
+SELECT DISTINCT Winery FROM WINE WHERE Price BETWEEN 50 AND 100;
+SELECT DISTINCT Winery FROM WINE WHERE Price BETWEEN 50 AND 100;
+SELECT AVG(Price) ,  AVG(Cases) FROM WINE WHERE YEAR  =  2009 AND Grape  =  'Zinfandel';
+SELECT AVG(Price) ,  AVG(Cases) FROM WINE WHERE YEAR  =  2009 AND Grape  =  'Zinfandel';
+SELECT max(Price) ,  max(Score) FROM WINE WHERE Appelation  =  'St. Helena';
+SELECT max(Price) ,  max(Score) FROM WINE WHERE Appelation  =  'St. Helena';
+SELECT max(Price) ,  max(Score) ,  YEAR FROM WINE GROUP BY YEAR;
+SELECT max(Price) ,  max(Score) ,  YEAR FROM WINE GROUP BY YEAR;
+SELECT avg(Price) ,  avg(Score) ,  Appelation FROM WINE GROUP BY Appelation;
+SELECT avg(Price) ,  avg(Score) ,  Appelation FROM WINE GROUP BY Appelation;
+SELECT Winery FROM WINE GROUP BY Winery HAVING count(*)  >=  4;
+SELECT Winery FROM WINE GROUP BY Winery HAVING count(*)  >=  4;
+SELECT T1.County FROM APPELLATIONS AS T1 JOIN WINE AS T2 ON T1.Appelation  =  T2.Appelation GROUP BY T2.Appelation HAVING count(*)  <=  3;
+SELECT T1.County FROM APPELLATIONS AS T1 JOIN WINE AS T2 ON T1.Appelation  =  T2.Appelation GROUP BY T2.Appelation HAVING count(*)  <=  3;
+SELECT Name FROM WINE WHERE YEAR  <  (SELECT min(YEAR) FROM WINE WHERE Winery  =  'Brander');
+SELECT Name FROM WINE WHERE YEAR  <  (SELECT min(YEAR) FROM WINE WHERE Winery  =  'Brander');
+SELECT Name FROM WINE WHERE Price  >  (SELECT max(Price) FROM WINE WHERE YEAR  =  2006);
+SELECT Name FROM WINE WHERE Price  >  (SELECT max(Price) FROM WINE WHERE YEAR  =  2006);
+SELECT T2.Winery FROM GRAPES AS T1 JOIN WINE AS T2 ON T1.GRAPE  =  T2.GRAPE WHERE T1.Color  =  'White' GROUP BY T2.Winery ORDER BY count(*) DESC LIMIT 3;
+SELECT T2.Winery FROM GRAPES AS T1 JOIN WINE AS T2 ON T1.GRAPE  =  T2.GRAPE WHERE T1.Color  =  'White' GROUP BY T2.Winery ORDER BY count(*) DESC LIMIT 3;
+SELECT Grape ,  Winery ,  YEAR FROM WINE WHERE Price  >   100 ORDER BY YEAR;
+SELECT Grape ,  Winery ,  YEAR FROM WINE WHERE Price  >   100 ORDER BY YEAR;
+SELECT Grape ,  Appelation ,  Name FROM WINE WHERE Score  >  93 ORDER BY Name;
+SELECT Grape ,  Appelation ,  Name FROM WINE WHERE Score  >  93 ORDER BY Name;
+SELECT Appelation FROM WINE WHERE YEAR  >  2008 EXCEPT SELECT Appelation FROM APPELLATIONS WHERE Area  =  'Central Coast';
+SELECT Appelation FROM WINE WHERE YEAR  >  2008 EXCEPT SELECT Appelation FROM APPELLATIONS WHERE Area  =  'Central Coast';
+SELECT avg(price) FROM wine WHERE Appelation NOT IN (SELECT T1.Appelation FROM APPELLATIONS AS T1 JOIN WINE AS T2 ON T1.Appelation  =  T2.Appelation WHERE T1.County  =  'Sonoma');
+SELECT avg(price) FROM wine WHERE Appelation NOT IN (SELECT T1.Appelation FROM APPELLATIONS AS T1 JOIN WINE AS T2 ON T1.Appelation  =  T2.Appelation WHERE T1.County  =  'Sonoma');
+SELECT T1.County FROM APPELLATIONS AS T1 JOIN WINE AS T2 ON T1.Appelation  =  T2.Appelation WHERE T2.Score  >  90 GROUP BY T1.County ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.County FROM APPELLATIONS AS T1 JOIN WINE AS T2 ON T1.Appelation  =  T2.Appelation WHERE T2.Score  >  90 GROUP BY T1.County ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM station;
+SELECT name ,  LOCATION ,  number_of_platforms FROM station;
+SELECT DISTINCT LOCATION FROM station;
+SELECT name ,  total_passengers FROM station WHERE LOCATION != 'London';
+SELECT name ,  main_services FROM station ORDER BY total_passengers DESC LIMIT 3;
+SELECT avg(total_passengers) ,  max(total_passengers) FROM station WHERE LOCATION  =  'London' OR LOCATION  =  'Glasgow';
+SELECT LOCATION ,  sum(number_of_platforms) ,  sum(total_passengers) FROM station GROUP BY LOCATION;
+SELECT DISTINCT LOCATION FROM station WHERE number_of_platforms  >=  15 AND total_passengers  >  25;
+SELECT LOCATION FROM station EXCEPT SELECT LOCATION FROM station WHERE number_of_platforms  >=  15;
+SELECT LOCATION FROM station GROUP BY LOCATION ORDER BY count(*) DESC LIMIT 1;
+SELECT name ,  TIME ,  service FROM train;
+SELECT count(*) FROM train;
+SELECT name ,  service FROM train ORDER BY TIME;
+SELECT T2.name ,  count(*) FROM train_station AS T1 JOIN station AS T2 ON T1.station_id  =  T2.station_id GROUP BY T1.station_id;
+SELECT T2.name ,  T3.name FROM train_station AS T1 JOIN station AS T2 ON T1.station_id  =  T2.station_id JOIN train AS T3 ON T3.train_id  =  T1.train_id;
+SELECT T3.name ,  T3.time FROM train_station AS T1 JOIN station AS T2 ON T1.station_id  =  T2.station_id JOIN train AS T3 ON T3.train_id  =  T1.train_id WHERE T2.location  =  'London' ORDER BY T3.time DESC;
+SELECT T2.name FROM train_station AS T1 JOIN station AS T2 ON T1.station_id  =  T2.station_id GROUP BY T1.station_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.name FROM train_station AS T1 JOIN station AS T2 ON T1.station_id  =  T2.station_id GROUP BY T1.station_id HAVING count(*)  >=  2;
+SELECT LOCATION FROM station GROUP BY LOCATION HAVING count(*)  =  1;
+SELECT name FROM station WHERE station_id NOT IN (SELECT station_id FROM train_station);
+SELECT T2.name FROM train_station AS T1 JOIN station AS T2 ON T1.station_id  =  T2.station_id JOIN train AS T3 ON T3.train_id  =  T1.train_id WHERE T3.Name  =  'Ananthapuri Express' INTERSECT SELECT T2.name FROM train_station AS T1 JOIN station AS T2 ON T1.station_id  =  T2.station_id JOIN train AS T3 ON T3.train_id  =  T1.train_id WHERE T3.Name  =  'Guruvayur Express';
+SELECT T2.name FROM train_station AS T1 JOIN train AS T2 ON T1.train_id  =  T2.train_id WHERE T1.station_id NOT IN (SELECT T4.station_id FROM train_station AS T3 JOIN station AS T4 ON T3.station_id  =  T4.station_id WHERE t4.location  =  'London');
+SELECT name ,  LOCATION FROM station ORDER BY Annual_entry_exit ,  Annual_interchanges;
+SELECT vehicle_id FROM Vehicles;
+SELECT vehicle_id FROM Vehicles;
+SELECT count(*) FROM Vehicles;
+SELECT count(*) FROM Vehicles;
+SELECT vehicle_details FROM Vehicles WHERE vehicle_id = 1;
+SELECT vehicle_details FROM Vehicles WHERE vehicle_id = 1;
+SELECT first_name ,  middle_name ,  last_name FROM Staff;
+SELECT first_name ,  middle_name ,  last_name FROM Staff;
+SELECT date_of_birth FROM Staff WHERE first_name = 'Janessa' AND last_name = 'Sawayn';
+SELECT date_of_birth FROM Staff WHERE first_name = 'Janessa' AND last_name = 'Sawayn';
+SELECT date_joined_staff FROM Staff WHERE first_name = 'Janessa' AND last_name = 'Sawayn';
+SELECT date_joined_staff FROM Staff WHERE first_name = 'Janessa' AND last_name = 'Sawayn';
+SELECT date_left_staff FROM Staff WHERE first_name = 'Janessa' AND last_name = 'Sawayn';
+SELECT date_left_staff FROM Staff WHERE first_name = 'Janessa' AND last_name = 'Sawayn';
+SELECT count(*) FROM Staff WHERE first_name = 'Ludie';
+SELECT count(*) FROM Staff WHERE first_name = 'Ludie';
+SELECT nickname FROM Staff WHERE first_name = 'Janessa' AND last_name = 'Sawayn';
+SELECT nickname FROM Staff WHERE first_name = 'Janessa' AND last_name = 'Sawayn';
+SELECT count(*) FROM Staff;
+SELECT count(*) FROM Staff;
+SELECT T1.city FROM Addresses AS T1 JOIN Staff AS T2 ON T1.address_id = T2.staff_address_id WHERE T2.first_name = 'Janessa' AND T2.last_name = 'Sawayn';
+SELECT T1.city FROM Addresses AS T1 JOIN Staff AS T2 ON T1.address_id = T2.staff_address_id WHERE T2.first_name = 'Janessa' AND T2.last_name = 'Sawayn';
+SELECT T1.country ,  T1.state_province_county FROM Addresses AS T1 JOIN Staff AS T2 ON T1.address_id = T2.staff_address_id WHERE T2.first_name = 'Janessa' AND T2.last_name = 'Sawayn';
+SELECT T1.country ,  T1.state_province_county FROM Addresses AS T1 JOIN Staff AS T2 ON T1.address_id = T2.staff_address_id WHERE T2.first_name = 'Janessa' AND T2.last_name = 'Sawayn';
+SELECT sum(T1.lesson_time) FROM Lessons AS T1 JOIN Customers AS T2 ON T1.customer_id  = T2.customer_id WHERE T2.first_name = 'Rylan' AND T2.last_name = 'Goodwin';
+SELECT sum(T1.lesson_time) FROM Lessons AS T1 JOIN Customers AS T2 ON T1.customer_id  = T2.customer_id WHERE T2.first_name = 'Rylan' AND T2.last_name = 'Goodwin';
+SELECT T1.zip_postcode FROM Addresses AS T1 JOIN Staff AS T2 ON T1.address_id = T2.staff_address_id WHERE T2.first_name = 'Janessa' AND T2.last_name = 'Sawayn';
+SELECT T1.zip_postcode FROM Addresses AS T1 JOIN Staff AS T2 ON T1.address_id = T2.staff_address_id WHERE T2.first_name = 'Janessa' AND T2.last_name = 'Sawayn';
+SELECT count(*) FROM Addresses WHERE state_province_county = 'Georgia';
+SELECT count(*) FROM Addresses WHERE state_province_county = 'Georgia';
+SELECT T2.first_name ,  T2.last_name FROM Addresses AS T1 JOIN Staff AS T2 ON T1.address_id = T2.staff_address_id WHERE T1.city = 'Damianfort';
+SELECT T2.first_name ,  T2.last_name FROM Addresses AS T1 JOIN Staff AS T2 ON T1.address_id = T2.staff_address_id WHERE T1.city = 'Damianfort';
+SELECT T1.city ,  count(*) FROM Addresses AS T1 JOIN Staff AS T2 ON T1.address_id = T2.staff_address_id GROUP BY T1.city ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.city ,  count(*) FROM Addresses AS T1 JOIN Staff AS T2 ON T1.address_id = T2.staff_address_id GROUP BY T1.city ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.state_province_county FROM Addresses AS T1 JOIN Staff AS T2 ON T1.address_id = T2.staff_address_id GROUP BY T1.state_province_county HAVING count(*) BETWEEN 2 AND 4;
+SELECT T1.state_province_county FROM Addresses AS T1 JOIN Staff AS T2 ON T1.address_id = T2.staff_address_id GROUP BY T1.state_province_county HAVING count(*) BETWEEN 2 AND 4;
+SELECT first_name ,  last_name FROM Customers;
+SELECT first_name ,  last_name FROM Customers;
+SELECT email_address ,  date_of_birth FROM Customers WHERE first_name = 'Carole';
+SELECT email_address ,  date_of_birth FROM Customers WHERE first_name = 'Carole';
+SELECT phone_number ,  email_address FROM Customers WHERE amount_outstanding  >  2000;
+SELECT phone_number ,  email_address FROM Customers WHERE amount_outstanding  >  2000;
+SELECT customer_status_code ,  cell_mobile_phone_number ,  email_address FROM Customers WHERE first_name = 'Marina' OR last_name  =  'Kohler';
+SELECT customer_status_code ,  cell_mobile_phone_number ,  email_address FROM Customers WHERE first_name = 'Marina' OR last_name  =  'Kohler';
+SELECT date_of_birth FROM Customers WHERE customer_status_code  =  'Good Customer';
+SELECT date_of_birth FROM Customers WHERE customer_status_code  =  'Good Customer';
+SELECT date_became_customer FROM Customers WHERE first_name = 'Carole' AND last_name = 'Bernhard';
+SELECT date_became_customer FROM Customers WHERE first_name = 'Carole' AND last_name = 'Bernhard';
+SELECT count(*) FROM Customers;
+SELECT count(*) FROM Customers;
+SELECT customer_status_code , count(*) FROM Customers GROUP BY customer_status_code;
+SELECT customer_status_code , count(*) FROM Customers GROUP BY customer_status_code;
+SELECT customer_status_code FROM Customers GROUP BY customer_status_code ORDER BY count(*) ASC LIMIT 1;
+SELECT customer_status_code FROM Customers GROUP BY customer_status_code ORDER BY count(*) ASC LIMIT 1;
+SELECT count(*) FROM Lessons AS T1 JOIN Customers AS T2 ON T1.customer_id  = T2.customer_id WHERE T2.first_name = 'Rylan' AND T2.last_name = 'Goodwin' AND T1.lesson_status_code = 'Completed';
+SELECT count(*) FROM Lessons AS T1 JOIN Customers AS T2 ON T1.customer_id  = T2.customer_id WHERE T2.first_name = 'Rylan' AND T2.last_name = 'Goodwin' AND T1.lesson_status_code = 'Completed';
+SELECT max(amount_outstanding) ,  min(amount_outstanding) ,  avg(amount_outstanding) FROM Customers;
+SELECT max(amount_outstanding) ,  min(amount_outstanding) ,  avg(amount_outstanding) FROM Customers;
+SELECT first_name ,  last_name FROM Customers WHERE amount_outstanding BETWEEN 1000 AND 3000;
+SELECT first_name ,  last_name FROM Customers WHERE amount_outstanding BETWEEN 1000 AND 3000;
+SELECT T1.first_name ,  T1.last_name FROM Customers AS T1 JOIN Addresses AS T2 ON T1.customer_address_id = T2.address_id WHERE T2.city = 'Lockmanfurt';
+SELECT T1.first_name ,  T1.last_name FROM Customers AS T1 JOIN Addresses AS T2 ON T1.customer_address_id = T2.address_id WHERE T2.city = 'Lockmanfurt';
+SELECT T2.country FROM Customers AS T1 JOIN Addresses AS T2 ON T1.customer_address_id = T2.address_id WHERE T1.first_name = 'Carole' AND T1.last_name = 'Bernhard';
+SELECT T2.country FROM Customers AS T1 JOIN Addresses AS T2 ON T1.customer_address_id = T2.address_id WHERE T1.first_name = 'Carole' AND T1.last_name = 'Bernhard';
+SELECT T2.zip_postcode FROM Customers AS T1 JOIN Addresses AS T2 ON T1.customer_address_id = T2.address_id WHERE T1.first_name = 'Carole' AND T1.last_name = 'Bernhard';
+SELECT T2.zip_postcode FROM Customers AS T1 JOIN Addresses AS T2 ON T1.customer_address_id = T2.address_id WHERE T1.first_name = 'Carole' AND T1.last_name = 'Bernhard';
+SELECT T2.city FROM Customers AS T1 JOIN Addresses AS T2 ON T1.customer_address_id = T2.address_id GROUP BY T2.city ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.city FROM Customers AS T1 JOIN Addresses AS T2 ON T1.customer_address_id = T2.address_id GROUP BY T2.city ORDER BY count(*) DESC LIMIT 1;
+SELECT sum(T1.amount_payment) FROM Customer_Payments AS T1 JOIN Customers AS T2 ON T1.customer_id = T2.customer_id WHERE T2.first_name = 'Carole' AND T2.last_name = 'Bernhard';
+SELECT sum(T1.amount_payment) FROM Customer_Payments AS T1 JOIN Customers AS T2 ON T1.customer_id = T2.customer_id WHERE T2.first_name = 'Carole' AND T2.last_name = 'Bernhard';
+SELECT count(*) FROM Customers WHERE customer_id NOT IN ( SELECT customer_id FROM Customer_Payments );
+SELECT count(*) FROM Customers WHERE customer_id NOT IN ( SELECT customer_id FROM Customer_Payments );
+SELECT T2.first_name ,  T2.last_name FROM Customer_Payments AS T1 JOIN Customers AS T2 ON T1.customer_id = T2.customer_id GROUP BY T1.customer_id HAVING count(*)  >  2;
+SELECT T2.first_name ,  T2.last_name FROM Customer_Payments AS T1 JOIN Customers AS T2 ON T1.customer_id = T2.customer_id GROUP BY T1.customer_id HAVING count(*)  >  2;
+SELECT payment_method_code ,  count(*) FROM Customer_Payments GROUP BY payment_method_code;
+SELECT payment_method_code ,  count(*) FROM Customer_Payments GROUP BY payment_method_code;
+SELECT count(*) FROM Lessons WHERE lesson_status_code = 'Cancelled';
+SELECT count(*) FROM Lessons WHERE lesson_status_code = 'Cancelled';
+SELECT T1.lesson_id FROM Lessons AS T1 JOIN Staff AS T2 ON T1.staff_id = T2.staff_id WHERE T2.first_name = 'Janessa' AND T2.last_name = 'Sawayn' AND nickname LIKE '%s%';
+SELECT T1.lesson_id FROM Lessons AS T1 JOIN Staff AS T2 ON T1.staff_id = T2.staff_id WHERE T2.first_name = 'Janessa' AND T2.last_name = 'Sawayn' AND nickname LIKE '%s%';
+SELECT count(*) FROM Lessons AS T1 JOIN Staff AS T2 ON T1.staff_id = T2.staff_id WHERE T2.first_name LIKE '%a%';
+SELECT count(*) FROM Lessons AS T1 JOIN Staff AS T2 ON T1.staff_id = T2.staff_id WHERE T2.first_name LIKE '%a%';
+SELECT sum(lesson_time) FROM Lessons AS T1 JOIN Staff AS T2 ON T1.staff_id = T2.staff_id WHERE T2.first_name = 'Janessa' AND T2.last_name = 'Sawayn';
+SELECT sum(lesson_time) FROM Lessons AS T1 JOIN Staff AS T2 ON T1.staff_id = T2.staff_id WHERE T2.first_name = 'Janessa' AND T2.last_name = 'Sawayn';
+SELECT avg(price) FROM Lessons AS T1 JOIN Staff AS T2 ON T1.staff_id = T2.staff_id WHERE T2.first_name = 'Janessa' AND T2.last_name = 'Sawayn';
+SELECT avg(price) FROM Lessons AS T1 JOIN Staff AS T2 ON T1.staff_id = T2.staff_id WHERE T2.first_name = 'Janessa' AND T2.last_name = 'Sawayn';
+SELECT count(*) FROM Lessons AS T1 JOIN Customers AS T2 ON T1.customer_id  = T2.customer_id WHERE T2.first_name = 'Ray';
+SELECT count(*) FROM Lessons AS T1 JOIN Customers AS T2 ON T1.customer_id  = T2.customer_id WHERE T2.first_name = 'Ray';
+SELECT last_name FROM Customers INTERSECT SELECT last_name FROM Staff;
+SELECT last_name FROM Customers INTERSECT SELECT last_name FROM Staff;
+SELECT first_name FROM Staff EXCEPT SELECT T2.first_name FROM Lessons AS T1 JOIN Staff AS T2 ON T1.staff_id  =  T2.staff_id;
+SELECT first_name FROM Staff EXCEPT SELECT T2.first_name FROM Lessons AS T1 JOIN Staff AS T2 ON T1.staff_id  =  T2.staff_id;
+SELECT T1.vehicle_id ,  T1.vehicle_details FROM Vehicles AS T1 JOIN Lessons AS T2 ON T1.vehicle_id  =  T2.vehicle_id GROUP BY T1.vehicle_id ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM Faculty;
+SELECT count(*) FROM Faculty;
+SELECT DISTINCT rank FROM Faculty;
+SELECT DISTINCT rank FROM Faculty;
+SELECT DISTINCT building FROM Faculty;
+SELECT DISTINCT building FROM Faculty;
+SELECT rank ,  Fname ,  Lname FROM Faculty;
+SELECT rank ,  Fname ,  Lname FROM Faculty;
+SELECT Fname ,  Lname ,  phone FROM Faculty WHERE Sex  =  'F';
+SELECT Fname ,  Lname ,  phone FROM Faculty WHERE Sex  =  'F';
+SELECT FacID FROM Faculty WHERE Sex  =  'M';
+SELECT FacID FROM Faculty WHERE Sex  =  'M';
+SELECT count(*) FROM Faculty WHERE Sex  =  'F' AND Rank  =  'Professor';
+SELECT count(*) FROM Faculty WHERE Sex  =  'F' AND Rank  =  'Professor';
+SELECT phone ,  room ,  building FROM Faculty WHERE Fname  =  'Jerry' AND Lname  =  'Prince';
+SELECT phone ,  room ,  building FROM Faculty WHERE Fname  =  'Jerry' AND Lname  =  'Prince';
+SELECT count(*) FROM Faculty WHERE Rank  =  'Professor' AND building  =  'NEB';
+SELECT count(*) FROM Faculty WHERE Rank  =  'Professor' AND building  =  'NEB';
+SELECT fname ,  lname FROM Faculty WHERE Rank  =  'Instructor';
+SELECT fname ,  lname FROM Faculty WHERE Rank  =  'Instructor';
+SELECT building ,  count(*) FROM Faculty GROUP BY building;
+SELECT building ,  count(*) FROM Faculty GROUP BY building;
+SELECT building FROM Faculty GROUP BY building ORDER BY count(*) DESC LIMIT 1;
+SELECT building FROM Faculty GROUP BY building ORDER BY count(*) DESC LIMIT 1;
+SELECT building FROM Faculty WHERE rank  =  'Professor' GROUP BY building HAVING count(*)  >=  10;
+SELECT building FROM Faculty WHERE rank  =  'Professor' GROUP BY building HAVING count(*)  >=  10;
+SELECT rank ,  count(*) FROM Faculty GROUP BY rank;
+SELECT rank ,  count(*) FROM Faculty GROUP BY rank;
+SELECT rank ,  sex ,  count(*) FROM Faculty GROUP BY rank ,  sex;
+SELECT rank ,  sex ,  count(*) FROM Faculty GROUP BY rank ,  sex;
+SELECT rank FROM Faculty GROUP BY rank ORDER BY count(*) ASC LIMIT 1;
+SELECT rank FROM Faculty GROUP BY rank ORDER BY count(*) ASC LIMIT 1;
+SELECT sex ,  count(*) FROM Faculty WHERE rank  =  'AsstProf' GROUP BY sex;
+SELECT sex ,  count(*) FROM Faculty WHERE rank  =  'AsstProf' GROUP BY sex;
+SELECT T1.fname ,  T1.lname FROM Faculty AS T1 JOIN Student AS T2 ON T1.FacID  =  T2.advisor WHERE T2.fname  =  'Linda' AND T2.lname  =  'Smith';
+SELECT T1.fname ,  T1.lname FROM Faculty AS T1 JOIN Student AS T2 ON T1.FacID  =  T2.advisor WHERE T2.fname  =  'Linda' AND T2.lname  =  'Smith';
+SELECT T2.StuID FROM Faculty AS T1 JOIN Student AS T2 ON T1.FacID  =  T2.advisor WHERE T1.rank  =  'Professor';
+SELECT T2.StuID FROM Faculty AS T1 JOIN Student AS T2 ON T1.FacID  =  T2.advisor WHERE T1.rank  =  'Professor';
+SELECT T2.fname ,  T2.lname FROM Faculty AS T1 JOIN Student AS T2 ON T1.FacID  =  T2.advisor WHERE T1.fname  =  'Michael' AND T1.lname  =  'Goodrich';
+SELECT T2.fname ,  T2.lname FROM Faculty AS T1 JOIN Student AS T2 ON T1.FacID  =  T2.advisor WHERE T1.fname  =  'Michael' AND T1.lname  =  'Goodrich';
+SELECT T1.FacID ,  count(*) FROM Faculty AS T1 JOIN Student AS T2 ON T1.FacID  =  T2.advisor GROUP BY T1.FacID;
+SELECT T1.FacID ,  count(*) FROM Faculty AS T1 JOIN Student AS T2 ON T1.FacID  =  T2.advisor GROUP BY T1.FacID;
+SELECT T1.rank ,  count(*) FROM Faculty AS T1 JOIN Student AS T2 ON T1.FacID  =  T2.advisor GROUP BY T1.rank;
+SELECT T1.rank ,  count(*) FROM Faculty AS T1 JOIN Student AS T2 ON T1.FacID  =  T2.advisor GROUP BY T1.rank;
+SELECT T1.fname ,  T1.lname FROM Faculty AS T1 JOIN Student AS T2 ON T1.FacID  =  T2.advisor GROUP BY T1.FacID ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.fname ,  T1.lname FROM Faculty AS T1 JOIN Student AS T2 ON T1.FacID  =  T2.advisor GROUP BY T1.FacID ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.FacID FROM Faculty AS T1 JOIN Student AS T2 ON T1.FacID  =  T2.advisor GROUP BY T1.FacID HAVING count(*)  >=  2;
+SELECT T1.FacID FROM Faculty AS T1 JOIN Student AS T2 ON T1.FacID  =  T2.advisor GROUP BY T1.FacID HAVING count(*)  >=  2;
+SELECT FacID FROM Faculty EXCEPT SELECT advisor FROM Student;
+SELECT FacID FROM Faculty EXCEPT SELECT advisor FROM Student;
+SELECT activity_name FROM Activity;
+SELECT activity_name FROM Activity;
+SELECT count(*) FROM Activity;
+SELECT count(*) FROM Activity;
+SELECT count(DISTINCT FacID) FROM Faculty_participates_in;
+SELECT count(DISTINCT FacID) FROM Faculty_participates_in;
+SELECT FacID FROM Faculty EXCEPT SELECT FacID FROM Faculty_participates_in;
+SELECT FacID FROM Faculty EXCEPT SELECT FacID FROM Faculty_participates_in;
+SELECT FacID FROM Faculty_participates_in INTERSECT SELECT advisor FROM Student;
+SELECT FacID FROM Faculty_participates_in INTERSECT SELECT advisor FROM Student;
+SELECT count(*) FROM Faculty AS T1 JOIN Faculty_participates_in AS T2 ON T1.facID  =  T2.facID WHERE T1.fname  =  'Mark' AND T1.lname  =  'Giuliano';
+SELECT count(*) FROM Faculty AS T1 JOIN Faculty_participates_in AS T2 ON T1.facID  =  T2.facID WHERE T1.fname  =  'Mark' AND T1.lname  =  'Giuliano';
+SELECT T3.activity_name FROM Faculty AS T1 JOIN Faculty_participates_in AS T2 ON T1.facID  =  T2.facID JOIN Activity AS T3 ON T3.actid  =  T2.actid WHERE T1.fname  =  'Mark' AND T1.lname  =  'Giuliano';
+SELECT T3.activity_name FROM Faculty AS T1 JOIN Faculty_participates_in AS T2 ON T1.facID  =  T2.facID JOIN Activity AS T3 ON T3.actid  =  T2.actid WHERE T1.fname  =  'Mark' AND T1.lname  =  'Giuliano';
+SELECT  T1.fname ,  T1.lname ,  count(*) ,  T1.FacID FROM Faculty AS T1 JOIN Faculty_participates_in AS T2 ON T1.facID  =  T2.facID GROUP BY T1.FacID;
+SELECT  T1.fname ,  T1.lname ,  count(*) ,  T1.FacID FROM Faculty AS T1 JOIN Faculty_participates_in AS T2 ON T1.facID  =  T2.facID GROUP BY T1.FacID;
+SELECT T1.activity_name ,  count(*) FROM Activity AS T1 JOIN Faculty_participates_in AS T2 ON T1.actID  =  T2.actID GROUP BY T1.actID;
+SELECT T1.activity_name ,  count(*) FROM Activity AS T1 JOIN Faculty_participates_in AS T2 ON T1.actID  =  T2.actID GROUP BY T1.actID;
+SELECT  T1.fname ,  T1.lname FROM Faculty AS T1 JOIN Faculty_participates_in AS T2 ON T1.facID  =  T2.facID GROUP BY T1.FacID ORDER BY count(*) DESC LIMIT 1;
+SELECT  T1.fname ,  T1.lname FROM Faculty AS T1 JOIN Faculty_participates_in AS T2 ON T1.facID  =  T2.facID GROUP BY T1.FacID ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.activity_name FROM Activity AS T1 JOIN Faculty_participates_in AS T2 ON T1.actID  =  T2.actID GROUP BY T1.actID ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.activity_name FROM Activity AS T1 JOIN Faculty_participates_in AS T2 ON T1.actID  =  T2.actID GROUP BY T1.actID ORDER BY count(*) DESC LIMIT 1;
+SELECT StuID FROM Student EXCEPT SELECT StuID FROM Participates_in;
+SELECT StuID FROM Student EXCEPT SELECT StuID FROM Participates_in;
+SELECT StuID FROM Participates_in INTERSECT SELECT StuID FROM Student WHERE age  <  20;
+SELECT StuID FROM Participates_in INTERSECT SELECT StuID FROM Student WHERE age  <  20;
+SELECT T1.fname ,  T1.lname FROM Student AS T1 JOIN Participates_in AS T2 ON T1.StuID  =  T2.StuID GROUP BY T1.StuID ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.fname ,  T1.lname FROM Student AS T1 JOIN Participates_in AS T2 ON T1.StuID  =  T2.StuID GROUP BY T1.StuID ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.activity_name FROM Activity AS T1 JOIN Participates_in AS T2 ON T1.actID  =  T2.actID GROUP BY T1.actID ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.activity_name FROM Activity AS T1 JOIN Participates_in AS T2 ON T1.actID  =  T2.actID GROUP BY T1.actID ORDER BY count(*) DESC LIMIT 1;
+SELECT DISTINCT T1.lname FROM Faculty AS T1 JOIN Faculty_participates_in AS T2 ON T1.facID  =  T2.facID JOIN activity AS T3 ON T2.actid  =  T2.actid WHERE T3.activity_name  =  'Canoeing' OR T3.activity_name  =  'Kayaking';
+SELECT DISTINCT T1.lname FROM Faculty AS T1 JOIN Faculty_participates_in AS T2 ON T1.facID  =  T2.facID JOIN activity AS T3 ON T2.actid  =  T2.actid WHERE T3.activity_name  =  'Canoeing' OR T3.activity_name  =  'Kayaking';
+SELECT lname FROM faculty WHERE rank  =  'Professor' EXCEPT SELECT DISTINCT T1.lname FROM Faculty AS T1 JOIN Faculty_participates_in AS T2 ON T1.facID  =  T2.facID JOIN activity AS T3 ON T2.actid  =  T2.actid WHERE T3.activity_name  =  'Canoeing' OR T3.activity_name  =  'Kayaking';
+SELECT lname FROM faculty WHERE rank  =  'Professor' EXCEPT SELECT DISTINCT T1.lname FROM Faculty AS T1 JOIN Faculty_participates_in AS T2 ON T1.facID  =  T2.facID JOIN activity AS T3 ON T2.actid  =  T2.actid WHERE T3.activity_name  =  'Canoeing' OR T3.activity_name  =  'Kayaking';
+SELECT T1.lname FROM Faculty AS T1 JOIN Faculty_participates_in AS T2 ON T1.facID  =  T2.facID JOIN activity AS T3 ON T2.actid  =  T2.actid WHERE T3.activity_name  =  'Canoeing' INTERSECT SELECT T1.lname FROM Faculty AS T1 JOIN Faculty_participates_in AS T2 ON T1.facID  =  T2.facID JOIN activity AS T3 ON T2.actid  =  T2.actid WHERE T3.activity_name  =  'Kayaking';
+SELECT T1.lname FROM Faculty AS T1 JOIN Faculty_participates_in AS T2 ON T1.facID  =  T2.facID JOIN activity AS T3 ON T2.actid  =  T2.actid WHERE T3.activity_name  =  'Canoeing' INTERSECT SELECT T1.lname FROM Faculty AS T1 JOIN Faculty_participates_in AS T2 ON T1.facID  =  T2.facID JOIN activity AS T3 ON T2.actid  =  T2.actid WHERE T3.activity_name  =  'Kayaking';
+SELECT T1.stuid FROM participates_in AS T1 JOIN activity AS T2 ON T2.actid  =  T2.actid WHERE T2.activity_name  =  'Canoeing' INTERSECT SELECT T1.stuid FROM participates_in AS T1 JOIN activity AS T2 ON T2.actid  =  T2.actid WHERE T2.activity_name  =  'Kayaking';
+SELECT T1.stuid FROM participates_in AS T1 JOIN activity AS T2 ON T2.actid  =  T2.actid WHERE T2.activity_name  =  'Canoeing' INTERSECT SELECT T1.stuid FROM participates_in AS T1 JOIN activity AS T2 ON T2.actid  =  T2.actid WHERE T2.activity_name  =  'Kayaking';
+SELECT name FROM airports WHERE city  =  'Goroka';
+SELECT name FROM airports WHERE city  =  'Goroka';
+SELECT name ,  city ,  country ,  elevation FROM airports WHERE city  =  'New York';
+SELECT name ,  city ,  country ,  elevation FROM airports WHERE city  =  'New York';
+SELECT count(*) FROM airlines;
+SELECT count(*) FROM airlines;
+SELECT count(*) FROM airlines WHERE country  =  'Russia';
+SELECT count(*) FROM airlines WHERE country  =  'Russia';
+SELECT max(elevation) FROM airports WHERE country  =  'Iceland';
+SELECT max(elevation) FROM airports WHERE country  =  'Iceland';
+SELECT name FROM airports WHERE country  =  'Cuba' OR country  =  'Argentina';
+SELECT name FROM airports WHERE country  =  'Cuba' OR country  =  'Argentina';
+SELECT country FROM airlines WHERE name LIKE 'Orbit%';
+SELECT country FROM airlines WHERE name LIKE 'Orbit%';
+SELECT name FROM airports WHERE elevation BETWEEN -50 AND 50;
+SELECT name FROM airports WHERE elevation BETWEEN -50 AND 50;
+SELECT country FROM airports ORDER BY elevation DESC LIMIT 1;
+SELECT country FROM airports ORDER BY elevation DESC LIMIT 1;
+SELECT count(*) FROM airports WHERE name LIKE '%International%';
+SELECT count(*) FROM airports WHERE name LIKE '%International%';
+SELECT count(DISTINCT city) FROM airports WHERE country  =  'Greenland';
+SELECT count(DISTINCT city) FROM airports WHERE country  =  'Greenland';
+SELECT count(*) FROM airlines AS T1 JOIN routes AS T2 ON T1.alid  =  T2.alid WHERE T1.name  =  'American Airlines';
+SELECT count(*) FROM airlines AS T1 JOIN routes AS T2 ON T1.alid  =  T2.alid WHERE T1.name  =  'American Airlines';
+SELECT count(*) FROM airports AS T1 JOIN routes AS T2 ON T1.apid  =  T2.dst_apid WHERE country  =  'Canada';
+SELECT count(*) FROM airports AS T1 JOIN routes AS T2 ON T1.apid  =  T2.dst_apid WHERE country  =  'Canada';
+SELECT name ,  city ,  country FROM airports ORDER BY elevation LIMIT 1;
+SELECT name ,  city ,  country FROM airports ORDER BY elevation LIMIT 1;
+SELECT name ,  city ,  country FROM airports ORDER BY elevation DESC LIMIT 1;
+SELECT name ,  city ,  country FROM airports ORDER BY elevation DESC LIMIT 1;
+SELECT T1.name ,  T1.city ,  T2.dst_apid FROM airports AS T1 JOIN routes AS T2 ON T1.apid  =  T2.dst_apid GROUP BY T2.dst_apid ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name ,  T1.city ,  T2.dst_apid FROM airports AS T1 JOIN routes AS T2 ON T1.apid  =  T2.dst_apid GROUP BY T2.dst_apid ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name ,  T2.alid FROM airlines AS T1 JOIN routes AS T2 ON T1.alid  =  T2.alid GROUP BY T2.alid ORDER BY count(*) DESC LIMIT 10;
+SELECT T1.name ,  T2.alid FROM airlines AS T1 JOIN routes AS T2 ON T1.alid  =  T2.alid GROUP BY T2.alid ORDER BY count(*) DESC LIMIT 10;
+SELECT T1.name ,  T1.city ,  T2.src_apid FROM airports AS T1 JOIN routes AS T2 ON T1.apid  =  T2.src_apid GROUP BY T2.src_apid ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name ,  T1.city ,  T2.src_apid FROM airports AS T1 JOIN routes AS T2 ON T1.apid  =  T2.src_apid GROUP BY T2.src_apid ORDER BY count(*) DESC LIMIT 1;
+SELECT count(DISTINCT dst_apid) FROM airlines AS T1 JOIN routes AS T2 ON T1.alid  =  T2.alid WHERE T1.name  =  'American Airlines';
+SELECT count(DISTINCT dst_apid) FROM airlines AS T1 JOIN routes AS T2 ON T1.alid  =  T2.alid WHERE T1.name  =  'American Airlines';
+SELECT country FROM airlines GROUP BY country ORDER BY count(*) DESC LIMIT 1;
+SELECT country FROM airlines GROUP BY country ORDER BY count(*) DESC LIMIT 1;
+SELECT country FROM airlines WHERE active  =  'Y' GROUP BY country ORDER BY count(*) DESC LIMIT 1;
+SELECT country FROM airlines WHERE active  =  'Y' GROUP BY country ORDER BY count(*) DESC LIMIT 1;
+SELECT country ,  count(*) FROM airlines GROUP BY country ORDER BY count(*) DESC;
+SELECT country ,  count(*) FROM airlines GROUP BY country ORDER BY count(*) DESC;
+SELECT count(*) ,  country FROM airports GROUP BY country ORDER BY count(*) DESC;
+SELECT count(*) ,  country FROM airports GROUP BY country ORDER BY count(*) DESC;
+SELECT count(*) ,  city FROM airports WHERE country  =  'United States' GROUP BY city ORDER BY count(*) DESC;
+SELECT count(*) ,  city FROM airports WHERE country  =  'United States' GROUP BY city ORDER BY count(*) DESC;
+SELECT city FROM airports WHERE country  =  'United States' GROUP BY city HAVING count(*)  >  3;
+SELECT city FROM airports WHERE country  =  'United States' GROUP BY city HAVING count(*)  >  3;
+SELECT count(*) FROM (SELECT city FROM airports GROUP BY city HAVING count(*)  >  3);
+SELECT count(*) FROM (SELECT city FROM airports GROUP BY city HAVING count(*)  >  3);
+SELECT city ,  count(*) FROM airports GROUP BY city HAVING count(*)  >  1;
+SELECT city ,  count(*) FROM airports GROUP BY city HAVING count(*)  >  1;
+SELECT city FROM airports GROUP BY city HAVING count(*)  >  2 ORDER BY count(*);
+SELECT city FROM airports GROUP BY city HAVING count(*)  >  2 ORDER BY count(*);
+SELECT count(*) ,  T1.name FROM airports AS T1 JOIN routes AS T2 ON T1.apid  =  T2.src_apid GROUP BY T1.name;
+SELECT count(*) ,  T1.name FROM airports AS T1 JOIN routes AS T2 ON T1.apid  =  T2.src_apid GROUP BY T1.name;
+SELECT count(*) ,  T1.name FROM airports AS T1 JOIN routes AS T2 ON T1.apid  =  T2.src_apid GROUP BY T1.name ORDER BY count(*) DESC;
+SELECT count(*) ,  T1.name FROM airports AS T1 JOIN routes AS T2 ON T1.apid  =  T2.src_apid GROUP BY T1.name ORDER BY count(*) DESC;
+SELECT avg(elevation) ,  country FROM airports GROUP BY country;
+SELECT avg(elevation) ,  country FROM airports GROUP BY country;
+SELECT city FROM airports GROUP BY city HAVING count(*)  =  2;
+SELECT city FROM airports GROUP BY city HAVING count(*)  =  2;
+SELECT T1.country ,  T1.name ,  count(*) FROM airlines AS T1 JOIN routes AS T2 ON T1.alid  =  T2.alid GROUP BY T1.country ,  T1.name;
+SELECT T1.country ,  T1.name ,  count(*) FROM airlines AS T1 JOIN routes AS T2 ON T1.alid  =  T2.alid GROUP BY T1.country ,  T1.name;
+SELECT count(*) FROM routes AS T1 JOIN airports AS T2 ON T1.dst_apid  =  T2.apid WHERE T2.country  =  'Italy';
+SELECT count(*) FROM routes AS T1 JOIN airports AS T2 ON T1.dst_apid  =  T2.apid WHERE T2.country  =  'Italy';
+SELECT count(*) FROM routes AS T1 JOIN airports AS T2 ON T1.dst_apid  =  T2.apid JOIN airlines AS T3 ON T1.alid  =  T3.alid WHERE T2.country  =  'Italy' AND T3.name  =  'American Airlines';
+SELECT count(*) FROM routes AS T1 JOIN airports AS T2 ON T1.dst_apid  =  T2.apid JOIN airlines AS T3 ON T1.alid  =  T3.alid WHERE T2.country  =  'Italy' AND T3.name  =  'American Airlines';
+SELECT count(*) FROM airports AS T1 JOIN routes AS T2 ON T1.apid  =  T2.dst_apid WHERE T1.name  =  'John F Kennedy International Airport';
+SELECT count(*) FROM airports AS T1 JOIN routes AS T2 ON T1.apid  =  T2.dst_apid WHERE T1.name  =  'John F Kennedy International Airport';
+SELECT count(*) FROM routes WHERE dst_apid IN (SELECT apid FROM airports WHERE country  =  'Canada') AND src_apid IN (SELECT apid FROM airports WHERE country  =  'United States');
+SELECT count(*) FROM routes WHERE dst_apid IN (SELECT apid FROM airports WHERE country  =  'Canada') AND src_apid IN (SELECT apid FROM airports WHERE country  =  'United States');
+SELECT rid FROM routes WHERE dst_apid IN (SELECT apid FROM airports WHERE country  =  'United States') AND src_apid IN (SELECT apid FROM airports WHERE country  =  'United States');
+SELECT rid FROM routes WHERE dst_apid IN (SELECT apid FROM airports WHERE country  =  'United States') AND src_apid IN (SELECT apid FROM airports WHERE country  =  'United States');
+SELECT T1.name FROM airlines AS T1 JOIN routes AS T2 ON T1.alid  =  T2.alid GROUP BY T1.name ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name FROM airlines AS T1 JOIN routes AS T2 ON T1.alid  =  T2.alid GROUP BY T1.name ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name FROM airports AS T1 JOIN routes AS T2 ON T1.apid  =  T2.src_apid WHERE T1.country  =  'China' GROUP BY T1.name ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name FROM airports AS T1 JOIN routes AS T2 ON T1.apid  =  T2.src_apid WHERE T1.country  =  'China' GROUP BY T1.name ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name FROM airports AS T1 JOIN routes AS T2 ON T1.apid  =  T2.dst_apid WHERE T1.country  =  'China' GROUP BY T1.name ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.name FROM airports AS T1 JOIN routes AS T2 ON T1.apid  =  T2.dst_apid WHERE T1.country  =  'China' GROUP BY T1.name ORDER BY count(*) DESC LIMIT 1;
+SELECT order_id FROM orders ORDER BY date_order_placed DESC LIMIT 1;
+SELECT order_id FROM orders ORDER BY date_order_placed DESC LIMIT 1;
+SELECT order_id ,  customer_id FROM orders ORDER BY date_order_placed LIMIT 1;
+SELECT order_id ,  customer_id FROM orders ORDER BY date_order_placed LIMIT 1;
+SELECT order_id FROM shipments WHERE shipment_tracking_number = '3452';
+SELECT order_id FROM shipments WHERE shipment_tracking_number = '3452';
+SELECT order_item_id FROM order_items WHERE product_id = 11;
+SELECT order_item_id FROM order_items WHERE product_id = 11;
+SELECT DISTINCT T1.customer_name FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id WHERE T2.order_status = 'Packing';
+SELECT DISTINCT T1.customer_name FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id WHERE T2.order_status = 'Packing';
+SELECT DISTINCT T1.customer_details FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id WHERE T2.order_status = 'On Road';
+SELECT DISTINCT T1.customer_details FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id WHERE T2.order_status = 'On Road';
+SELECT T1.customer_name FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.customer_name FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.customer_id FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.customer_id FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id  =  T2.customer_id GROUP BY T1.customer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T2.order_id ,  T2.order_status FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id WHERE T1.customer_name = 'Jeramie';
+SELECT T2.order_id ,  T2.order_status FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id WHERE T1.customer_name = 'Jeramie';
+SELECT T2.date_order_placed FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id WHERE T1.customer_name = 'Jeramie';
+SELECT T2.date_order_placed FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id WHERE T1.customer_name = 'Jeramie';
+SELECT T1.customer_name FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id WHERE T2.date_order_placed >= '2009-01-01' AND T2.date_order_placed <= '2010-01-01';
+SELECT T1.customer_name FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id WHERE T2.date_order_placed >= '2009-01-01' AND T2.date_order_placed <= '2010-01-01';
+SELECT DISTINCT T2.product_id FROM orders AS T1 JOIN order_items AS T2 ON T1.order_id = T2.order_id WHERE T1.date_order_placed >= '1975-01-01' AND T1.date_order_placed <= '1976-01-01';
+SELECT DISTINCT T2.product_id FROM orders AS T1 JOIN order_items AS T2 ON T1.order_id = T2.order_id WHERE T1.date_order_placed >= '1975-01-01' AND T1.date_order_placed <= '1976-01-01';
+SELECT T1.customer_name FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id WHERE T2.order_status = 'On Road' INTERSECT SELECT T1.customer_name FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id WHERE T2.order_status = 'Shipped';
+SELECT T1.customer_name FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id WHERE T2.order_status = 'On Road' INTERSECT SELECT T1.customer_name FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id WHERE T2.order_status = 'Shipped';
+SELECT T1.customer_id FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id WHERE T2.order_status = 'On Road' INTERSECT SELECT T1.customer_id FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id WHERE T2.order_status = 'Shipped';
+SELECT T1.customer_id FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id WHERE T2.order_status = 'On Road' INTERSECT SELECT T1.customer_id FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id WHERE T2.order_status = 'Shipped';
+SELECT T1.date_order_placed FROM orders AS T1 JOIN shipments AS T2 ON T1.order_id = T2.order_id WHERE T2.shipment_tracking_number = 3452;
+SELECT T1.date_order_placed FROM orders AS T1 JOIN shipments AS T2 ON T1.order_id = T2.order_id WHERE T2.shipment_tracking_number = 3452;
+SELECT T1.date_order_placed FROM orders AS T1 JOIN shipments AS T2 ON T1.order_id = T2.order_id WHERE T2.invoice_number = 10;
+SELECT T1.date_order_placed FROM orders AS T1 JOIN shipments AS T2 ON T1.order_id = T2.order_id WHERE T2.invoice_number = 10;
+SELECT count(*) ,  T3.product_id FROM orders AS T1 JOIN order_items AS T2 JOIN products AS T3 ON T1.order_id = T2.order_id AND T2.product_id = T3.product_id GROUP BY T3.product_id;
+SELECT count(*) ,  T3.product_id FROM orders AS T1 JOIN order_items AS T2 JOIN products AS T3 ON T1.order_id = T2.order_id AND T2.product_id = T3.product_id GROUP BY T3.product_id;
+SELECT T3.product_name ,  count(*) FROM orders AS T1 JOIN order_items AS T2 JOIN products AS T3 ON T1.order_id = T2.order_id AND T2.product_id = T3.product_id GROUP BY T3.product_id;
+SELECT T3.product_name ,  count(*) FROM orders AS T1 JOIN order_items AS T2 JOIN products AS T3 ON T1.order_id = T2.order_id AND T2.product_id = T3.product_id GROUP BY T3.product_id;
+SELECT order_id FROM shipments WHERE shipment_date > '2000-01-01';
+SELECT order_id FROM shipments WHERE shipment_date > '2000-01-01';
+SELECT order_id FROM shipments WHERE shipment_date  =  (SELECT max(shipment_date) FROM shipments);
+SELECT order_id FROM shipments WHERE shipment_date  =  (SELECT max(shipment_date) FROM shipments);
+SELECT DISTINCT product_name FROM products ORDER BY product_name;
+SELECT DISTINCT product_name FROM products ORDER BY product_name;
+SELECT DISTINCT order_id FROM orders ORDER BY date_order_placed;
+SELECT DISTINCT order_id FROM orders ORDER BY date_order_placed;
+SELECT T1.order_id FROM orders AS T1 JOIN order_items AS T2 ON T1.order_id = T2.order_id GROUP BY T1.order_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.order_id FROM orders AS T1 JOIN order_items AS T2 ON T1.order_id = T2.order_id GROUP BY T1.order_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.customer_name FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id GROUP BY T1.customer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.customer_name FROM customers AS T1 JOIN orders AS T2 ON T1.customer_id = T2.customer_id GROUP BY T1.customer_id ORDER BY count(*) DESC LIMIT 1;
+SELECT invoice_number FROM invoices WHERE invoice_date < '1989-09-03' OR invoice_date > '2007-12-25';
+SELECT invoice_number FROM invoices WHERE invoice_date < '1989-09-03' OR invoice_date > '2007-12-25';
+SELECT DISTINCT invoice_details FROM invoices WHERE invoice_date < '1989-09-03' OR invoice_date > '2007-12-25';
+SELECT DISTINCT invoice_details FROM invoices WHERE invoice_date < '1989-09-03' OR invoice_date > '2007-12-25';
+SELECT T2.customer_name ,  count(*) FROM orders AS T1 JOIN customers AS T2 ON T1.customer_id = T2.customer_id GROUP BY T2.customer_id HAVING count(*)  >=  2;
+SELECT T2.customer_name ,  count(*) FROM orders AS T1 JOIN customers AS T2 ON T1.customer_id = T2.customer_id GROUP BY T2.customer_id HAVING count(*)  >=  2;
+SELECT T2.customer_name FROM orders AS T1 JOIN customers AS T2 ON T1.customer_id = T2.customer_id GROUP BY T2.customer_id HAVING count(*)  <=  2;
+SELECT T2.customer_name FROM orders AS T1 JOIN customers AS T2 ON T1.customer_id = T2.customer_id GROUP BY T2.customer_id HAVING count(*)  <=  2;
+SELECT T1.customer_name FROM customers AS T1 JOIN orders AS T2 JOIN order_items AS T3 JOIN products AS T4 ON T1.customer_id = T2.customer_id AND T2.order_id = T3.order_id AND T3.product_id = T4.product_id WHERE T4.product_name = 'food' GROUP BY T1.customer_id HAVING count(*)  >=  1;
+SELECT T1.customer_name FROM customers AS T1 JOIN orders AS T2 JOIN order_items AS T3 JOIN products AS T4 ON T1.customer_id = T2.customer_id AND T2.order_id = T3.order_id AND T3.product_id = T4.product_id WHERE T4.product_name = 'food' GROUP BY T1.customer_id HAVING count(*)  >=  1;
+SELECT T1.customer_name FROM customers AS T1 JOIN orders AS T2 JOIN order_items AS T3 JOIN products AS T4 ON T1.customer_id = T2.customer_id AND T2.order_id = T3.order_id AND T3.product_id = T4.product_id WHERE T3.order_item_status = 'Cancel' AND T4.product_name = 'food' GROUP BY T1.customer_id HAVING count(*)  >=  1;
+SELECT T1.customer_name FROM customers AS T1 JOIN orders AS T2 JOIN order_items AS T3 JOIN products AS T4 ON T1.customer_id = T2.customer_id AND T2.order_id = T3.order_id AND T3.product_id = T4.product_id WHERE T3.order_item_status = 'Cancel' AND T4.product_name = 'food' GROUP BY T1.customer_id HAVING count(*)  >=  1;
+SELECT count(*) FROM architect WHERE gender  =  'female';
+SELECT name ,  nationality ,  id FROM architect WHERE gender  =  'male' ORDER BY name;
+SELECT max(T1.length_meters) ,  T2.name FROM bridge AS T1 JOIN architect AS T2 ON T1.architect_id  =  T2.id;
+SELECT avg(length_feet) FROM bridge;
+SELECT name ,  built_year FROM mill WHERE TYPE  =  'Grondzeiler';
+SELECT DISTINCT T1.name ,  T1.nationality FROM architect AS T1 JOIN mill AS t2 ON T1.id  =  T2.architect_id;
+SELECT name FROM mill WHERE LOCATION != 'Donceel';
+SELECT DISTINCT T1.type FROM mill AS T1 JOIN architect AS t2 ON T1.architect_id  =  T2.id WHERE T2.nationality  =  'American' OR T2.nationality  =  'Canadian';
+SELECT T1.id ,  T1.name FROM architect AS T1 JOIN bridge AS T2 ON T1.id  =  T2.architect_id GROUP BY T1.id HAVING count(*)  >=  3;
+SELECT T1.id ,  T1.name ,  T1.nationality FROM architect AS T1 JOIN mill AS T2 ON T1.id  =  T2.architect_id GROUP BY T1.id ORDER BY count(*) DESC LIMIT 1;
+SELECT T1.id ,  T1.name ,  T1.gender FROM architect AS T1 JOIN bridge AS T2 ON T1.id  =  T2.architect_id GROUP BY T1.id HAVING count(*)  =  2 UNION SELECT T1.id ,  T1.name ,  T1.gender FROM architect AS T1 JOIN mill AS T2 ON T1.id  =  T2.architect_id GROUP BY T1.id HAVING count(*)  =  1;
+SELECT LOCATION FROM bridge WHERE name  =  'Kolob Arch' OR name  =  'Rainbow Bridge';
+SELECT name FROM mill WHERE name LIKE '%Moulin%';
+SELECT DISTINCT T1.name FROM mill AS T1 JOIN architect AS t2 ON T1.architect_id  =  T2.id JOIN bridge AS T3 ON T3.architect_id  =  T2.id WHERE T3.length_meters  >  80;
+SELECT TYPE ,  count(*) FROM mill GROUP BY TYPE ORDER BY count(*) DESC LIMIT 1;
+SELECT count(*) FROM architect WHERE id NOT IN ( SELECT architect_id FROM mill WHERE built_year  <  1850 );
+SELECT t1.name FROM bridge AS t1 JOIN architect AS t2 ON t1.architect_id  =  t2.id WHERE t2.nationality  =  'American' ORDER BY t1.length_feet;
+SELECT count(*) FROM book_club;
+SELECT count(*) FROM book_club;
+SELECT book_title ,  author_or_editor FROM book_club WHERE YEAR  >  1989;
+SELECT book_title ,  author_or_editor FROM book_club WHERE YEAR  >  1989;
+SELECT DISTINCT publisher FROM book_club;
+SELECT DISTINCT publisher FROM book_club;
+SELECT YEAR ,  book_title ,  publisher FROM book_club ORDER BY YEAR DESC;
+SELECT YEAR ,  book_title ,  publisher FROM book_club ORDER BY YEAR DESC;
+SELECT publisher ,  count(*) FROM book_club GROUP BY publisher;
+SELECT publisher ,  count(*) FROM book_club GROUP BY publisher;
+SELECT publisher FROM book_club GROUP BY publisher ORDER BY count(*) DESC LIMIT 1;
+SELECT publisher FROM book_club GROUP BY publisher ORDER BY count(*) DESC LIMIT 1;
+SELECT category ,  count(*) FROM book_club GROUP BY category;
+SELECT category ,  count(*) FROM book_club GROUP BY category;
+SELECT category FROM book_club WHERE YEAR  >  1989 GROUP BY category HAVING count(*)  >=  2;
+SELECT category FROM book_club WHERE YEAR  >  1989 GROUP BY category HAVING count(*)  >=  2;
+SELECT publisher FROM book_club WHERE YEAR  =  1989 INTERSECT SELECT publisher FROM book_club WHERE YEAR  =  1990;
+SELECT publisher FROM book_club WHERE YEAR  =  1989 INTERSECT SELECT publisher FROM book_club WHERE YEAR  =  1990;
+SELECT publisher FROM book_club EXCEPT SELECT publisher FROM book_club WHERE YEAR  =  1989;
+SELECT publisher FROM book_club EXCEPT SELECT publisher FROM book_club WHERE YEAR  =  1989;
+SELECT title ,  YEAR ,  director FROM movie ORDER BY budget_million;
+SELECT title ,  YEAR ,  director FROM movie ORDER BY budget_million;
+SELECT COUNT (DISTINCT director) FROM movie;
+SELECT COUNT (DISTINCT director) FROM movie;
+SELECT title ,  director FROM movie WHERE YEAR  <=  2000 ORDER BY gross_worldwide DESC LIMIT 1;
+SELECT title ,  director FROM movie WHERE YEAR  <=  2000 ORDER BY gross_worldwide DESC LIMIT 1;
+SELECT director FROM movie WHERE YEAR  =  2000 INTERSECT SELECT director FROM movie WHERE YEAR  =  1999;
+SELECT director FROM movie WHERE YEAR  =  2000 INTERSECT SELECT director FROM movie WHERE YEAR  =  1999;
+SELECT director FROM movie WHERE YEAR  =  1999 OR YEAR  =  2000;
+SELECT director FROM movie WHERE YEAR  =  1999 OR YEAR  =  2000;
+SELECT avg(budget_million) ,  max(budget_million) ,  min(budget_million) FROM movie WHERE YEAR  <  2000;
+SELECT avg(budget_million) ,  max(budget_million) ,  min(budget_million) FROM movie WHERE YEAR  <  2000;
+SELECT T1.company_name FROM culture_company AS T1 JOIN book_club AS T2 ON T1.book_club_id  =  T2.book_club_id WHERE T2.publisher  =  'Alyson';
+SELECT T1.company_name FROM culture_company AS T1 JOIN book_club AS T2 ON T1.book_club_id  =  T2.book_club_id WHERE T2.publisher  =  'Alyson';
+SELECT T1.title ,  T3.book_title FROM movie AS T1 JOIN culture_company AS T2 ON T1.movie_id  =  T2.movie_id JOIN book_club AS T3 ON T3.book_club_id  =  T2.book_club_id WHERE T2.incorporated_in  =  'China';
+SELECT T1.title ,  T3.book_title FROM movie AS T1 JOIN culture_company AS T2 ON T1.movie_id  =  T2.movie_id JOIN book_club AS T3 ON T3.book_club_id  =  T2.book_club_id WHERE T2.incorporated_in  =  'China';
+SELECT T2.company_name FROM movie AS T1 JOIN culture_company AS T2 ON T1.movie_id  =  T2.movie_id WHERE T1.year  =  1999;
+SELECT T2.company_name FROM movie AS T1 JOIN culture_company AS T2 ON T1.movie_id  =  T2.movie_id WHERE T1.year  =  1999;
\ No newline at end of file
diff --git a/parsing/sql2pandas.py b/parsing/sql2pandas.py
index 25b7caf9..1e97397f 100644
--- a/parsing/sql2pandas.py
+++ b/parsing/sql2pandas.py
@@ -10,18 +10,40 @@
     'user-agent': 'Mozilla/5.0 (X11 Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chromium/80.0.3987.160 Chrome/80.0.3987.163 Safari/537.36'
 }
 
-HTML_CSRF_ERROR = 'The CSRF tokens do not match.'
-HTML_CMD_START_TOKEN = '<pre><code class="code-default">'
-HTML_CMD_END_TOKEN = '</code></pre>'
+# Tokens to search for in HTML
+HTML_CSRF_ERROR_TOKEN = 'The CSRF tokens do not match.'
+
+HTML_PANDAS_CMD_START_TOKEN = '<pre><code class="code-default">'
+HTML_PANDAS_CMD_END_TOKEN = '</code></pre>'
+
+HTML_QUERY_FORMAT_ERROR_TOKEN_1 = 'Please check the submitted SQL syntax'
+HTML_QUERY_FORMAT_ERROR_TOKEN_2 = '<div class="alert alert-warning">'
+
+# Function error messages
+CSRF_ERROR = '[sql2pandas.py] Error: unable to bypass CSRF token'
+SQL_FORMAT_ERROR = '[sql2pandas.py] Error: SQL syntax incorrect or not supported'
+POST_REQUEST_ERROR_SNIPPET = '[sql2pandas.py] Error: POST request responded with status code:'
+
+GENERAL_ERROR = '[sql2pandas.py] Error: unknown'
 
 
 # Extract converted SQL2pandas command from processed HTML string using start/end tokens
 def extract_pandas_cmd(processed_html):
-    if processed_html.find(HTML_CSRF_ERROR) >= 0:
-        return 'Error: unable to bypass CSRF token'
+    if processed_html.find(HTML_CSRF_ERROR_TOKEN) >= 0:
+        return CSRF_ERROR
+
+    if processed_html.find(HTML_QUERY_FORMAT_ERROR_TOKEN_1) >= 0 and processed_html.find(HTML_QUERY_FORMAT_ERROR_TOKEN_2) >= 0:
+        return SQL_FORMAT_ERROR
+
+    temp_pieces = processed_html.split(HTML_PANDAS_CMD_START_TOKEN)
+    if len(temp_pieces) < 2:
+        return GENERAL_ERROR
 
-    temp = processed_html.split(HTML_CMD_START_TOKEN)[1]
-    temp = temp[0:temp.index(HTML_CMD_END_TOKEN)]
+    temp = temp_pieces[1]
+    if temp.find(HTML_PANDAS_CMD_END_TOKEN) < 0:
+        return GENERAL_ERROR
+
+    temp = temp[0:temp.index(HTML_PANDAS_CMD_END_TOKEN)]
     return temp
 
 
@@ -44,6 +66,10 @@ def make_post_request(query):
 
         post_response = session.post(
             SQL2PANDAS_URL, data=POST_BODY, headers=POST_HEADERS)
+
+        if post_response.status_code != 200:
+            return f"{POST_REQUEST_ERROR_SNIPPET} {post_response.status_code}: {post_response.reason}"
+
         raw_html = post_response.text
         # Decode HTML entities and special escaped chars
         processed_html = html.unescape(raw_html)
@@ -53,13 +79,23 @@ def make_post_request(query):
         return extract_pandas_cmd(processed_html)
 
 
+# API
+def sql2pandas(sql_query, should_disable_warnings=True):
+    if should_disable_warnings == True:
+        # Disable unsecure HTTPS request (SSL) warnings
+        urllib3.disable_warnings()
+
+    return make_post_request(sql_query)
+
+
+# If run as main program script
 def main():
     if len(argv) <= 1:
         print('Usage: python sandbox.py <SQL_QUERY>')
         return
 
     query = argv[1]
-    print(make_post_request(query))
+    print(sql2pandas(query))
 
 
 if __name__ == '__main__':
diff --git a/parsing/squall_playground.py b/parsing/squall_playground.py
new file mode 100644
index 00000000..83ecec21
--- /dev/null
+++ b/parsing/squall_playground.py
@@ -0,0 +1,134 @@
+from sql2pandas import sql2pandas
+import ast
+import json
+import pandas as pd
+from preprocess import get_pandas_code_snippets_from_tree, check_processed_sql_tree, preprocess_sql_query_into_tree, sql_query_to_pandas_code_snippets
+
+# Set this to the absolute path to the squall.json file
+SQUALL_PATH = "/mnt/c/Users/Stephen Yin/Desktop/14th Grade/Research Lab/squall.json"
+SQL_KEYWORDS = ["select", "distinct", "count", "avg", "sum", "max", "min", "abs", "case", "when", 
+                "then", "else", "end", "as", "from", "where", "is", "null", "not", "in", "and", 
+                "or", "between", "group", "order", "by", "having", "join", "intersect", "except", "union"]
+SQL_FUNCTIONS = ["COUNT", "AVG", "SUM", "MAX", "MIN", "CASE", "WHEN", "THEN", "ELSE", "END", "ABS"]
+
+with open(SQUALL_PATH, "r") as read_file:
+    json_data = json.load(read_file)
+
+# Tracker variables
+n = len(json_data)
+n_cols = len(json_data[0])
+
+print("There are " + str(n) + " number of entries in Squall.")
+print("There are " + str(n_cols) + " number of columns per entry in Squall.")
+
+# See the columns
+df = pd.DataFrame(json_data)
+df.info()
+
+# We see that there are two extra columsn compared to the documentation: 
+#     nl_typebio, nl_typebio_col 
+# Let's examine what they are:
+print(type(df["nl_typebio"][0]))
+print(type(df["nl_typebio_col"][0]))
+print(type(df["nl_typebio"][0][0]))
+print(type(df["nl_typebio_col"][0][5]))
+print(df["nl_typebio"].head(5))
+print(df["nl_typebio_col"].head(5))
+
+# They are both lists of STRINGS/NONE that _seem_ to be describing some tagging data on the sentence
+"""
+Begin: Preprocessing squall.json per entry, following format of ast_sandbox.py written by Troy
+"""
+def sql_join(query):
+    joined_query = ""
+    for word in query:
+        if word in SQL_FUNCTIONS:
+            joined_query += word
+        else:
+            joined_query += word + " "
+    
+
+    return joined_query[:-1] # Remove the last space
+def preprocess_sql_query(query):
+
+    # Get only SQL words
+    words = []
+    for word in query:
+        if word[0] == "Keyword" and word[1] in SQL_KEYWORDS:
+            words.append(word[1].upper())
+        else:
+            words.append(word[1])
+    
+    query = sql_join(words)
+    query = query.replace('"', '\'')
+    # print(query)
+    
+    return query if query[len(query) - 1] == ';' else query + ';'
+
+queries = []
+# bad_tokens = ['JOIN', 'INTERSECT', 'EXCEPT', 'UNION']
+bad_tokens = ["DESC"]
+
+for entry in json_data:
+    query = entry['sql']
+    queries.append(preprocess_sql_query(query))
+
+# Save the queries to examine
+with open("queries.txt", "w") as write_file:
+    write_file.write("\n".join(queries))
+
+count_total_queries = len(queries)
+
+bad_token_counts = {}
+for bad_token in bad_tokens:
+    bad_token_counts[bad_token] = 0
+
+other_bad_queries = []
+count_other_bad_queries = 0
+
+count_total_bad_queries = 0
+
+output_json = []
+
+for idx, query in enumerate(queries):
+    print(idx)
+    x = preprocess_sql_query_into_tree(query)
+    print(x)
+    # for idx in range(100):
+    #     query = queries[idx]
+    if idx % 100 == 0:
+        print(idx)
+    # for i in range(100):
+    #     query = queries[i]
+    should_skip_output_json = False
+    for sql_token in bad_tokens:
+        if query.find(sql_token) >= 0:
+            bad_token_counts[sql_token] += 1
+            # bad_queries.append(query)
+            should_skip_output_json = True
+
+    converted_query = sql2pandas(query)
+    if converted_query.find('Error:') >= 0:
+        count_other_bad_queries += 1
+        other_bad_queries.append(query)
+        should_skip_output_json = True
+
+    if should_skip_output_json == True:
+        count_total_bad_queries += 1
+        continue
+
+    full_json_entry = json_data[idx]
+    full_json_entry['pandas_converted'] = converted_query
+    output_json.append(full_json_entry)
+
+for bad_query in other_bad_queries:
+    print(bad_query)
+
+for key, value in bad_token_counts.items():
+    print(key + ': ' + str(value))
+print('Other bad queries: ' + str(count_other_bad_queries))
+print('Total bad queries: ' + str(count_total_bad_queries))
+print('Total queries: ' + str(count_total_queries))
+
+with open('squall_converted.json', 'w', encoding='utf-8') as f_write:
+    json.dump(output_json, f_write, ensure_ascii=False, indent=4)
diff --git a/requirements.txt b/requirements.txt
index c851e9b5..b0e67916 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -11,4 +11,6 @@ overrides
 astunparse
 scipy
 numpy
-wandb
\ No newline at end of file
+wandb
+
+pandas==1.4.2
\ No newline at end of file
diff --git a/stephen_playground/error_analysis.py b/stephen_playground/error_analysis.py
new file mode 100644
index 00000000..05a6e504
--- /dev/null
+++ b/stephen_playground/error_analysis.py
@@ -0,0 +1,157 @@
+import json
+from collections import Counter
+import pandas as pd
+
+SQL_KEYWORDS = [
+"ADD", 
+"ADD CONSTRAINT", 
+"ALL",
+"ALTER",
+"ALTER COLUMN",
+"ALTER TABLE",
+"AND",
+"ANY",
+"AS",
+"ASC",
+"BACKUP DATABASE",
+"BETWEEN",
+"CASE",
+"CHECK",
+"COLUMN",
+"CONSTRAINT",
+"DATABASE",
+"DEFAULT",
+"DELETE",
+"DESC",
+"DISTINCT",
+"EXEC",
+"EXISTS",
+"FOREIGN KEY",
+"FROM",
+"FULL OUTER JOIN",
+"GROUP BY",
+"HAVING",
+"IN",
+"INDEX",
+"INNER JOIN",
+"INSERT INTO",
+"INSERT INTO SELECT",
+"IS NULL",
+"IS NOT NULL",
+"JOIN",
+"LEFT JOIN",
+"LIKE",
+"LIMIT",
+"NOT",
+"NOT NULL",
+"OR",
+"ORDER BY",
+"OUTER JOIN",
+"PRIMARY KEY",
+"PROCEDURE",
+"RIGHT JOIN",
+"ROWNUM",
+"SELECT",
+"SELECT DISTINCT",
+"SELECT INTO",
+"SELECT TOP",
+"SET",
+"TABLE",
+"TOP",
+"TRUNCATE TABLE",
+"UNION",
+"UNION ALL",
+"UNIQUE",
+"UPDATE",
+"VALUES",
+"VIEW", 
+"WHERE", # BELOW MY ADDONS TO W3SCHOOLS
+"COUNT(*)",
+"AVG",
+"SUM",
+"MAX",
+"MIN",
+"INTERSECT",
+"EXCEPT",
+"(SELECT"
+]
+
+if __name__ == "__main__":
+    sample_1 = []
+    sample_5 = []
+    sample_20 = []
+
+    with open("spider_codex_conversion_k_1_n_6997.jsonl") as f:
+        for line in f:
+            sample_1.append(json.loads(line))
+    with open("spider_codex_conversion_k_5_n_2691.jsonl") as f:
+        for line in f:
+            sample_5.append(json.loads(line))
+    with open("spider_codex_conversion_k_20_n_1969.jsonl") as f:
+        for line in f:
+            sample_20.append(json.loads(line))
+
+    data = [sample_1, sample_5, sample_20]
+
+    # Keep set of all tokens 
+    # all_toks = set()
+    # for item in sample_1:
+    #     for tok in item["example"]["query_toks"]:
+    #         all_toks.add(tok)
+    
+    fail = []
+
+    for data_set in data:
+        print("Num. Examples:", len(data_set))
+        successes = []
+        fails = []
+        for item in data_set:
+            truthy_results = [x[1] for x in item["program_result_list"]]
+            if True in truthy_results:
+                successes.append(item)
+            elif False in truthy_results:
+                fails.append(item)
+            else:
+                raise Exception("Result neither true or false")
+        print("Sucesses:", len(successes))
+        print("Fails:", len(fails))
+
+        # Add fails to fail list
+        fail.append(fails)
+    
+    assert(len(fail) == 3)
+
+    fail_errs = []
+
+    for lst in fail:
+        i = 0
+        err = Counter(SQL_KEYWORDS)
+        err.subtract(err) # Reset counts to 0
+
+        for item in lst:
+            for tok in SQL_KEYWORDS:
+                if tok.lower() in item["example"]["query"].lower():
+                    err[tok] += 1
+            if i % 100 == 0:
+                print(i)
+            i += 1
+        
+        fail_errs.append(err)
+
+    print("Sample 1:")
+    for k, v in fail_errs[0].most_common():
+        print(k, v)
+    print("\n")
+
+    print("Sample 5:")
+    for k, v in fail_errs[1].most_common():
+        print(k, v)
+    print("\n")
+
+    print("Sample 20:")
+    for k, v in fail_errs[2].most_common():
+        print(k, v)
+    print("\n")
+
+        
+
diff --git a/stephen_playground/fifteen_correct_incorrect_analysis.py b/stephen_playground/fifteen_correct_incorrect_analysis.py
new file mode 100644
index 00000000..f8b8ab59
--- /dev/null
+++ b/stephen_playground/fifteen_correct_incorrect_analysis.py
@@ -0,0 +1,29 @@
+import json
+import random
+
+with open("../NLP4Code-Playground/spider_codex_conversion_k_20_n_1969.jsonl") as f:
+    data = [json.loads(item) for item in list(f)]
+
+print("Num. Examples:", len(data))
+successes = []
+fails = []
+for item in data:
+    truthy_results = [x[1] for x in item["program_result_list"]]
+    if True in truthy_results:
+        successes.append(item)
+    elif False in truthy_results:
+        fails.append(item)
+    else:
+        raise Exception("Result neither true or false")
+print("Sucesses:", len(successes))
+print("Fails:", len(fails))
+
+# analyze_success = random.sample(successes, 100)
+analyze_fail = random.sample(fails, 100)
+
+# with open("hundred_successes.jsonl", "w") as out:
+#     out.write("\n".join([json.dumps(json_og) for json_og in analyze_success]))
+with open("hundred_fails.jsonl", "w") as out:
+    out.write("\n".join([json.dumps(json_og) for json_og in analyze_fail]))
+
+
diff --git a/stephen_playground/fifteen_fails.jsonl b/stephen_playground/fifteen_fails.jsonl
new file mode 100644
index 00000000..8ed5ee90
--- /dev/null
+++ b/stephen_playground/fifteen_fails.jsonl
@@ -0,0 +1,15 @@
+{"example": {"db_id": "csu_1", "query": "SELECT T1.campus FROM campuses AS T1 JOIN faculty AS T2 ON T1.id  =  T2.campus WHERE T2.year  =  2002 AND faculty  >  (SELECT max(faculty) FROM campuses AS T1 JOIN faculty AS T2 ON T1.id  =  T2.campus WHERE T2.year  =  2002 AND T1.county  =  \"Orange\")", "query_toks": ["SELECT", "T1.campus", "FROM", "campuses", "AS", "T1", "JOIN", "faculty", "AS", "T2", "ON", "T1.id", "=", "T2.campus", "WHERE", "T2.year", "=", "2002", "AND", "faculty", ">", "(", "SELECT", "max", "(", "faculty", ")", "FROM", "campuses", "AS", "T1", "JOIN", "faculty", "AS", "T2", "ON", "T1.id", "=", "T2.campus", "WHERE", "T2.year", "=", "2002", "AND", "T1.county", "=", "``", "Orange", "''", ")"], "query_toks_no_value": ["select", "t1", ".", "campus", "from", "campuses", "as", "t1", "join", "faculty", "as", "t2", "on", "t1", ".", "id", "=", "t2", ".", "campus", "where", "t2", ".", "year", "=", "value", "and", "faculty", ">", "(", "select", "max", "(", "faculty", ")", "from", "campuses", "as", "t1", "join", "faculty", "as", "t2", "on", "t1", ".", "id", "=", "t2", ".", "campus", "where", "t2", ".", "year", "=", "value", "and", "t1", ".", "county", "=", "value", ")"], "question": "Find the names of the campus which has more faculties in 2002 than every campus in Orange county.", "question_toks": ["Find", "the", "names", "of", "the", "campus", "which", "has", "more", "faculties", "in", "2002", "than", "every", "campus", "in", "Orange", "county", "."], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 5]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 21, false], null]]}, "select": [false, [[0, [0, [0, 2, false], null]]]], "where": [[false, 2, [0, [0, 22, false], null], 2002.0, null], "and", [false, 3, [0, [0, 23, false], null], {"from": {"table_units": [["table_unit", 0], ["table_unit", 5]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 21, false], null]]}, "select": [false, [[1, [0, [0, 23, false], null]]]], "where": [[false, 2, [0, [0, 22, false], null], 2002.0, null], "and", [false, 2, [0, [0, 4, false], null], "\"Orange\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Campuses": ["Id", "Campus", "Location", "County", "Year"], "csu_fees": ["Campus", "Year", "CampusFee"], "degrees": ["Year", "Campus", "Degrees"], "discipline_enrollments": ["Campus", "Discipline", "Year", "Undergraduate", "Graduate"], "enrollments": ["Campus", "Year", "TotalEnrollment_AY", "FTE_AY"], "faculty": ["Campus", "Year", "Faculty"]}, "answer": [["Long Beach State University"], ["San Diego State University"]]}, "program_result_list": [["t1 = pd.merge(Campuses, faculty, on='id')\nt2 = t1[t1['year'] == 2002]\nt3 = t2[t2['county'] == 'Orange']\nt4 = t2.groupby('campus').agg({'faculty': 'max'}).rename(columns={'faculty': 'max_faculty'})\nt5 = pd.merge(t4, t2, on=['campus', 'max_faculty'])\nanswer = t5[t5['faculty'] > t5['max_faculty']]['campus']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = t1[t1['year'] == 2002]\nanswer = t2[t2['faculty'] > t2[t2['county'] == 'orange']['faculty'].max()]['campus']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = t1[(t1['year_x'] == 2002) & (t1['county'] == 'Orange')]['faculty'].max()\nt3 = t1.loc[t1['faculty'] > t2]\nanswer = t3['campus_x']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = t1[t1['year'] == 2002]\nt3 = t2[t2['county'] == 'Orange']\nt4 = t2[t2['faculty'] > t3['faculty'].max()]\nanswer = t4['campus']", false], ["t1 = pd.merge(Campuses, faculty, on='id')\nt2 = t1[t1['year'] == 2002]\nt3 = t2[t2['county'] == 'Orange'].groupby('campus').agg({'faculty': np.max})\nt4 = t2[t2['faculty'] > t3['faculty'].max()]\nanswer = t4['campus']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = t1[t1['year'] == 2002]\nt3 = t2[t2['county'] == 'Orange']\nt4 = t2[t2['faculty'] > t3['faculty'].max()]\nanswer = t4['campus']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = t1[t1['year'] == 2002]\nt3 = t2[t2['county'] == 'Orange']\nt4 = t3['faculty'].max()\nt5 = t1[t1['faculty'] > t4]\nanswer = t5['campus'].unique()", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = t1[t1['year'] == 2002]\nt3 = t2[t2['county'] == 'Orange']\nanswer = t1[t1['faculty'] > t3['faculty'].max()]['campus'].unique()", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = t1[t1['year'] == 2002]\nt3 = t2[t2['county'] == 'Orange']\nt4 = t2[t2['faculty'] > t3['faculty'].max()]\nanswer = t4['campus']", false], ["t1 = pd.merge(Campuses, faculty, on='id')\nt2 = t1[t1['year'] == 2002]\nt3 = t2[t2['county'] == 'Orange']\nt4 = t2[t2['faculty'] > t3['faculty'].max()]\nanswer = t4['campus'].unique()", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus', how='left')\nt2 = t1[t1['year'] == 2002]\nt3 = t1[(t1['year'] == 2002) & (t1['county'] == 'Orange')]\nt4 = t2[t2['faculty'] > t3['faculty'].max()]\nanswer = t4['campus']", false], ["t1 = pd.merge(Campuses, faculty, on='id')\nt2 = t1[t1['year'] == 2002]\nt3 = t2[t2['county'] == 'Orange']\nt4 = t2[t2['faculty'] > t3['faculty'].max()]\nanswer = t4['campus']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = t1[(t1['year_x'] == 2002) & (t1['county'] == 'Orange')]\nt3 = t1[(t1['year_x'] == 2002) & (t1['faculty'] > t2['faculty'].max())]\nanswer = t3['campus_x']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = t1[t1['year'] == 2002]\nt3 = t2[t2['county'] == 'orange']\nt4 = t2[t2['faculty'] > t3['faculty'].max()]\nanswer = t4['campus']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = t1[t1['year'] == 2002]\nt3 = t2[t2['county'] == 'orange']\nt4 = t3['faculty'].max()\nt5 = t2[t2['faculty'] > t4]\nanswer = t5['campus']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = t1[(t1['year'] == 2002) & (t1['county'] == 'Orange')]\nt3 = t2.groupby('campus').max()\nt4 = t1.groupby('campus').max()\nanswer = t4[t4['faculty'] > t3['faculty'].max()].reset_index()['campus']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = t1[(t1['year'] == 2002) & (t1['county'] == 'Orange')]\nt3 = t2.max()['faculty']\nt4 = t1[(t1['year'] == 2002) & (t1['faculty'] > t3)]\nanswer = t4['campus'].unique()", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = t1[t1['year'] == 2002]\nt3 = t2[t2['county'] == 'orange']\nt4 = t2[t2['faculty'] > t3['faculty'].max()]\nanswer = t4['campus']", false], ["t1 = pd.merge(Campuses, faculty, on='id')\nt2 = t1[t1['year'] == 2002]\nt3 = t2[t2['county'] == 'Orange']\nt4 = t2[t2['faculty'] > t3['faculty'].max()]\nanswer = t4['campus']", false], ["t1 = pd.merge(Campuses, faculty, on='campus')\nt2 = t1[t1['year'] == 2002]\nt3 = t2[t2['county'] == 'Orange']\nmax_faculty_in_orange = t3['faculty'].max()\nt4 = t2[t2['faculty'] > max_faculty_in_orange]\nanswer = t4['campus']", false]]}
+{"example": {"db_id": "products_gen_characteristics", "query": "SELECT characteristic_name ,  other_characteristic_details ,  characteristic_data_type FROM CHARACTERISTICS EXCEPT SELECT t1.characteristic_name ,  t1.other_characteristic_details ,  t1.characteristic_data_type FROM CHARACTERISTICS AS t1 JOIN product_characteristics AS t2 ON t1.characteristic_id  =  t2.characteristic_id", "query_toks": ["SELECT", "characteristic_name", ",", "other_characteristic_details", ",", "characteristic_data_type", "FROM", "CHARACTERISTICS", "EXCEPT", "SELECT", "t1.characteristic_name", ",", "t1.other_characteristic_details", ",", "t1.characteristic_data_type", "FROM", "CHARACTERISTICS", "AS", "t1", "JOIN", "product_characteristics", "AS", "t2", "ON", "t1.characteristic_id", "=", "t2.characteristic_id"], "query_toks_no_value": ["select", "characteristic_name", ",", "other_characteristic_details", ",", "characteristic_data_type", "from", "characteristics", "except", "select", "t1", ".", "characteristic_name", ",", "t1", ".", "other_characteristic_details", ",", "t1", ".", "characteristic_data_type", "from", "characteristics", "as", "t1", "join", "product_characteristics", "as", "t2", "on", "t1", ".", "characteristic_id", "=", "t2", ".", "characteristic_id"], "question": "What are the names, details and data types of the characteristics which are never used by any product?", "question_toks": ["What", "are", "the", "names", ",", "details", "and", "data", "types", "of", "the", "characteristics", "which", "are", "never", "used", "by", "any", "product", "?"], "sql": {"from": {"table_units": [["table_unit", 3]], "conds": []}, "select": [false, [[0, [0, [0, 11, false], null]], [0, [0, [0, 12, false], null]], [0, [0, [0, 10, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": {"from": {"table_units": [["table_unit", 3], ["table_unit", 5]], "conds": [[false, 2, [0, [0, 8, false], null], [0, 22, false], null]]}, "select": [false, [[0, [0, [0, 11, false], null]], [0, [0, [0, 12, false], null]], [0, [0, [0, 10, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}}, "db_table_headers": {"Ref_Characteristic_Types": ["characteristic_type_code", "characteristic_type_description"], "Ref_Colors": ["color_code", "color_description"], "Ref_Product_Categories": ["product_category_code", "product_category_description", "unit_of_measure"], "Characteristics": ["characteristic_id", "characteristic_type_code", "characteristic_data_type", "characteristic_name", "other_characteristic_details"], "Products": ["product_id", "color_code", "product_category_code", "product_name", "typical_buying_price", "typical_selling_price", "product_description", "other_product_details"], "Product_Characteristics": ["product_id", "characteristic_id", "product_characteristic_value"]}, "answer": [["cool", null, "dolores"], ["cool", null, "et"], ["cool", null, "quam"], ["hot", null, "impedit"], ["slow", null, "numquam"], ["slow", null, "placeat"]]}, "program_result_list": [["t1 = pd.merge(Characteristics, Product_Characteristics, left_on='characteristic_id', right_on='characteristic_id')\nanswer = Characteristics[~Characteristics['characteristic_id'].isin(t1['characteristic_id'])][['characteristic_name', 'other_characteristic_details', 'characteristic_data_type']]", false], ["t1 = Characteristics[~Characteristics['characteristic_id'].isin(Product_Characteristics['characteristic_id'])]\nanswer = t1[['characteristic_name', 'other_characteristic_details', 'characteristic_data_type']]", false], ["t1 = pd.merge(Characteristics, Product_Characteristics, on='characteristic_id', how='outer', indicator=True)\nt2 = t1[t1['_merge'] == 'left_only']\nanswer = t2[['characteristic_name', 'other_characteristic_details', 'characteristic_data_type']]", false], ["t1 = pd.merge(Characteristics, Product_Characteristics, left_on='characteristic_id', right_on='characteristic_id')\nt2 = Characteristics.merge(t1, how='left', indicator=True)\nanswer = t2[t2['_merge'] == 'left_only']", false], ["t1 = pd.merge(Characteristics, Product_Characteristics, left_on='characteristic_id', right_on='characteristic_id', how='outer')\nanswer = Characteristics[~Characteristics.characteristic_id.isin(t1.characteristic_id)]", false], ["t1 = pd.merge(Characteristics, Product_Characteristics, left_on='characteristic_id', right_on='characteristic_id')\nanswer = Characteristics[~Characteristics['characteristic_id'].isin(t1['characteristic_id'])][['characteristic_name', 'other_characteristic_details', 'characteristic_data_type']]", false], ["t1 = pd.merge(Characteristics, Product_Characteristics, left_on='characteristic_id', right_on='characteristic_id', how='outer')\nt2 = Characteristics.copy()\nt2.drop(t2.index, inplace=True)\nt3 = t1.append(t2)\nanswer = t3.drop_duplicates(keep=False)", false], ["t1 = pd.merge(Characteristics, Product_Characteristics, on='characteristic_id', how='left', indicator=True)\nt2 = t1[t1['_merge'] == 'left_only'].drop('_merge', axis=1)\nanswer = t2[['characteristic_name', 'other_characteristic_details', 'characteristic_data_type']]", false], ["t1 = pd.merge(Characteristics, Product_Characteristics, left_on='characteristic_id', right_on='characteristic_id')\nt2 = Characteristics.merge(t1, indicator=True, how='outer').query('_merge == \"left_only\"').drop('_merge', 1)\nanswer = t2[['characteristic_name', 'other_characteristic_details', 'characteristic_data_type']]", false], ["t1 = pd.merge(Characteristics, Product_Characteristics, left_on='characteristic_id', right_on='characteristic_id', how='outer', indicator=True)\nt2 = t1[t1['_merge'] == 'left_only']\nanswer = t2[['characteristic_name', 'other_characteristic_details', 'characteristic_data_type']]", false], ["t1 = pd.merge(Characteristics, Product_Characteristics, left_on='characteristic_id', right_on='characteristic_id')\nanswer = Characteristics[~Characteristics['characteristic_id'].isin(t1['characteristic_id'])][['characteristic_name', 'other_characteristic_details', 'characteristic_data_type']]", false], ["t1 = pd.merge(Characteristics, Product_Characteristics, left_on='characteristic_id', right_on='characteristic_id')\nanswer = Characteristics[~Characteristics['characteristic_id'].isin(t1['characteristic_id'])]", false], ["t1 = pd.merge(Characteristics, Product_Characteristics, on='characteristic_id', how='outer', indicator=True)\nt2 = t1[t1['_merge'] == 'left_only']\nanswer = t2[['characteristic_name', 'other_characteristic_details', 'characteristic_data_type']]", false], ["t1 = Characteristics.merge(Product_Characteristics, left_on='characteristic_id', right_on='characteristic_id', how='outer', indicator=True)\nt2 = t1[t1['_merge'] == 'left_only']\nanswer = t2[['characteristic_name', 'other_characteristic_details', 'characteristic_data_type']]", false], ["t1 = pd.merge(Characteristics, Product_Characteristics, left_on='characteristic_id', right_on='characteristic_id', how='outer')\nt2 = t1[t1['product_characteristic_value'].isnull()]\nanswer = t2[['characteristic_name', 'other_characteristic_details', 'characteristic_data_type']]", false], ["t1 = pd.merge(Characteristics, Product_Characteristics, left_on='characteristic_id', right_on='characteristic_id', how='outer')\nt2 = Characteristics.drop(t1.index)\nanswer = t2[['characteristic_name', 'other_characteristic_details', 'characteristic_data_type']]", false], ["t1 = pd.merge(Characteristics, Product_Characteristics, left_on='characteristic_id', right_on='characteristic_id')\nt2 = Characteristics.drop(Characteristics.index[t1.index])\nanswer = t2[['characteristic_name', 'other_characteristic_details', 'characteristic_data_type']]", false], ["t1 = pd.merge(Products, Product_Characteristics, left_on='product_id', right_on='product_id')\nt2 = pd.merge(t1, Characteristics, left_on='characteristic_id', right_on='characteristic_id')\nt3 = Characteristics.loc[~Characteristics.index.isin(t2.index)]\nanswer = t3[['characteristic_name', 'other_characteristic_details', 'characteristic_data_type']]", false], ["t1 = pd.merge(Characteristics, Product_Characteristics, on='characteristic_id')\nt2 = Characteristics[~Characteristics['characteristic_id'].isin(t1['characteristic_id'])]\nanswer = t2[['characteristic_name', 'other_characteristic_details', 'characteristic_data_type']]", false], ["t1 = pd.merge(Characteristics, Product_Characteristics, how='outer', left_on='characteristic_id', right_on='characteristic_id')\nanswer = t1[t1['characteristic_name'].isnull()][['characteristic_name', 'other_characteristic_details', 'characteristic_data_type']]", false]]}
+{"example": {"db_id": "college_2", "query": "SELECT avg(T1.salary) ,  count(*) FROM instructor AS T1 JOIN department AS T2 ON T1.dept_name  =  T2.dept_name ORDER BY T2.budget DESC LIMIT 1", "query_toks": ["SELECT", "avg", "(", "T1.salary", ")", ",", "count", "(", "*", ")", "FROM", "instructor", "AS", "T1", "JOIN", "department", "AS", "T2", "ON", "T1.dept_name", "=", "T2.dept_name", "ORDER", "BY", "T2.budget", "DESC", "LIMIT", "1"], "query_toks_no_value": ["select", "avg", "(", "t1", ".", "salary", ")", ",", "count", "(", "*", ")", "from", "instructor", "as", "t1", "join", "department", "as", "t2", "on", "t1", ".", "dept_name", "=", "t2", ".", "dept_name", "order", "by", "t2", ".", "budget", "desc", "limit", "value"], "question": "How many instructors are in the department with the highest budget, and what is their average salary?", "question_toks": ["How", "many", "instructors", "are", "in", "the", "department", "with", "the", "highest", "budget", ",", "and", "what", "is", "their", "average", "salary", "?"], "sql": {"from": {"table_units": [["table_unit", 3], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 13, false], null], [0, 4, false], null]]}, "select": [false, [[5, [0, [0, 14, false], null]], [3, [0, [0, 0, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": ["desc", [[0, [0, 6, false], null]]], "limit": 1, "intersect": null, "union": null, "except": null}, "db_table_headers": {"classroom": ["building", "room_number", "capacity"], "department": ["dept_name", "building", "budget"], "course": ["course_id", "title", "dept_name", "credits"], "instructor": ["ID", "name", "dept_name", "salary"], "section": ["course_id", "sec_id", "semester", "year", "building", "room_number", "time_slot_id"], "teaches": ["ID", "course_id", "sec_id", "semester", "year"], "student": ["ID", "name", "dept_name", "tot_cred"], "takes": ["ID", "course_id", "sec_id", "semester", "year", "grade"], "advisor": ["s_ID", "i_ID"], "time_slot": ["time_slot_id", "day", "start_hr", "start_min", "end_hr", "end_min"], "prereq": ["course_id", "prereq_id"]}, "answer": [[77600.18819999999, 50]]}, "program_result_list": [["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.groupby('dept_name').agg(['mean', 'count'])\nt2.columns = t2.columns.droplevel(0)\nt3 = t2.sort_values('budget', ascending=False).head(1)\nt3.reset_index(inplace=True)\nanswer = [t3.iloc[0]['mean'], t3.iloc[0]['count']]", false], ["t1 = pd.merge(department, instructor, on='dept_name')\nt2 = t1.sort_values('budget', ascending=False).head(1)\nt3 = t2.groupby(['dept_name'])['salary'].agg(['average', 'count']).rename(columns={'average': 'avg(t1.salary)', 'count': 'count(*)'})\nanswer = t3.reset_index()", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values(by='budget', ascending=False).head(1)\nanswer = [t2['salary'].mean(), t2.shape[0]]", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values('budget', ascending=False).head(1)\nanswer = [t2['salary'].mean(), t2['salary'].size]", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values(by='budget', ascending=False).head(1)\nanswer = [t2['salary'].mean(), t2['salary'].size]", false], ["t1 = pd.merge(department, instructor, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values(by='budget', ascending=False).head(1)\nt3 = t2.groupby('dept_name').mean().reset_index()\nt4 = t3[['dept_name', 'salary']]\nanswer = t4.rename(columns={'salary':'avg(t1.salary)'})", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.groupby('dept_name').agg({'salary': [np.mean, len]})\nt3 = t2.sort_values(by=('salary', 'budget'), ascending=False)\nt4 = t3.head(1)\nanswer = [t4[('salary', 'mean')].values[0], t4[('salary', 'len')].values[0]]", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values('budget', ascending=False).head(1)\nt3 = t2.groupby(['dept_name']).agg({'salary':['mean', 'count']})\nanswer = t3['salary']", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values('budget', ascending=False).head(1)\nanswer = [t2['salary'].mean(), t2['salary'].size]", false], ["t1 = pd.merge(instructor, department, on='dept_name')\nt2 = t1.sort_values('budget', ascending=False).head(1)\nt3 = t2.groupby(['dept_name', 'budget'])['salary'].agg(['mean', 'count']).reset_index()\nanswer = t3[['mean', 'count']]", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.groupby('dept_name').agg({'salary': 'mean', 'budget': 'max'})\nt3 = t2.sort_values('budget', ascending=False).head(1)\nanswer = t3[['salary', 'budget']]", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.groupby(['dept_name', 'budget']).agg({'salary':['mean', 'count']}).reset_index()\nt3 = t2.sort_values(by=('budget', 'budget'), ascending=False).reset_index(drop=True)\nanswer = t3.iloc[0, [1, 2]]", false], ["t1 = pd.merge(department, instructor, left_on='dept_name', right_on='dept_name')\nt2 = t1.groupby('dept_name').agg({'budget':'max', 'salary':'mean', 'name':'count'})\nt3 = t2.sort_values(by=['budget'], ascending=False).head(1).reset_index()\nanswer = t3[['salary', 'name']]", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values(by='budget', ascending=False).head(1)\nanswer = pd.Series([t2['salary'].mean(), t2['salary'].count()])", false], ["t1 = pd.merge(instructor, department, on='dept_name')\nt2 = t1.sort_values('budget', ascending=False).head(1)\nanswer = [t2['salary'].mean(), len(t2)]", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values('budget', ascending=False).head(1)\nanswer = t2.agg({'salary': ['mean', 'count']})", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values(by='budget', ascending=False).head(1)\nanswer = (t2['salary'].mean(), t2['salary'].count())", false], ["t1 = pd.merge(instructor, department, on='dept_name')\nt2 = t1.groupby(['dept_name', 'budget']).agg({'salary': ['mean', 'count']}).reset_index()\nt3 = t2.sort_values(['budget', 'dept_name'], ascending=False)\nanswer = t3.iloc[0, 2:]", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.groupby('dept_name')['salary'].agg([np.mean, np.size])\nt3 = t2.sort_values('budget', ascending=False).head(1).reset_index()\nanswer = t3[['mean', 'size']]", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.groupby('budget').agg({'salary': ['mean', 'count']}).sort_values(('budget', 'mean'), ascending=False).head(1)\nanswer = [t2.iloc[0]['salary']['mean'], t2.iloc[0]['salary']['count']]", false]]}
+{"example": {"db_id": "cre_Theme_park", "query": "SELECT other_hotel_details ,  star_rating_code FROM HOTELS ORDER BY price_range ASC LIMIT 3", "query_toks": ["SELECT", "other_hotel_details", ",", "star_rating_code", "FROM", "HOTELS", "ORDER", "BY", "price_range", "ASC", "LIMIT", "3"], "query_toks_no_value": ["select", "other_hotel_details", ",", "star_rating_code", "from", "hotels", "order", "by", "price_range", "asc", "limit", "value"], "question": "What are the details and star ratings of the three hotels with the lowest price ranges?", "question_toks": ["What", "are", "the", "details", "and", "star", "ratings", "of", "the", "three", "hotels", "with", "the", "lowest", "price", "ranges", "?"], "sql": {"from": {"table_units": [["table_unit", 5]], "conds": []}, "select": [false, [[0, [0, [0, 17, false], null]], [0, [0, [0, 14, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": ["asc", [[0, [0, 16, false], null]]], "limit": 3, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Ref_Hotel_Star_Ratings": ["star_rating_code", "star_rating_description"], "Locations": ["Location_ID", "Location_Name", "Address", "Other_Details"], "Ref_Attraction_Types": ["Attraction_Type_Code", "Attraction_Type_Description"], "Visitors": ["Tourist_ID", "Tourist_Details"], "Features": ["Feature_ID", "Feature_Details"], "Hotels": ["hotel_id", "star_rating_code", "pets_allowed_yn", "price_range", "other_hotel_details"], "Tourist_Attractions": ["Tourist_Attraction_ID", "Attraction_Type_Code", "Location_ID", "How_to_Get_There", "Name", "Description", "Opening_Hours", "Other_Details"], "Street_Markets": ["Market_ID", "Market_Details"], "Shops": ["Shop_ID", "Shop_Details"], "Museums": ["Museum_ID", "Museum_Details"], "Royal_Family": ["Royal_Family_ID", "Royal_Family_Details"], "Theme_Parks": ["Theme_Park_ID", "Theme_Park_Details"], "Visits": ["Visit_ID", "Tourist_Attraction_ID", "Tourist_ID", "Visit_Date", "Visit_Details"], "Photos": ["Photo_ID", "Tourist_Attraction_ID", "Name", "Description", "Filename", "Other_Details"], "Staff": ["Staff_ID", "Tourist_Attraction_ID", "Name", "Other_Details"], "Tourist_Attraction_Features": ["Tourist_Attraction_ID", "Feature_ID"]}, "answer": [[null, "2"], [null, "5"], [null, "1"]]}, "program_result_list": [["t1 = Hotels.sort_values('price_range')\nanswer = t1.head(3)[['other_hotel_details', 'star_rating_code']]", false], ["t1 = Hotels.sort_values(by='price_range').head(3)\nt2 = pd.merge(t1, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nanswer = t2[['other_hotel_details', 'star_rating_code']]", false], ["t1 = Hotels.sort_values('price_range').head(3)\nanswer = t1[['other_hotel_details', 'star_rating_code']]", false], ["t1 = Hotels.sort_values('price_range').head(3)\nanswer = t1[['other_hotel_details', 'star_rating_code']]", false], ["t1 = Hotels.sort_values('price_range').head(3)\nanswer = t1[['other_hotel_details', 'star_rating_code']]", false], ["t1 = Hotels.sort_values(by='price_range', ascending=True).head(3).reset_index(drop=True)\nanswer = t1[['other_hotel_details', 'star_rating_code']]", false], ["t1 = Hotels.sort_values('price_range', ascending=True).head(3)\nanswer = t1[['other_hotel_details', 'star_rating_code']]", false], ["t1 = Hotels.sort_values('price_range').head(3).reset_index(drop=True)\nanswer = t1[['other_hotel_details', 'star_rating_code']]", false], ["t1 = Hotels.sort_values(by='price_range').head(3)\nanswer = t1[['other_hotel_details', 'star_rating_code']]", false], ["t1 = Hotels.sort_values('price_range')\nanswer = t1[['other_hotel_details', 'star_rating_code']].head(n=3)", false], ["answer = Hotels[['other_hotel_details', 'star_rating_code']].sort_values(by='price_range').head(3)", false], ["answer = Hotels.sort_values(by='price_range')[:3][['other_hotel_details', 'star_rating_code']]", false], ["t1 = Hotels.sort_values(by='price_range').head(3)\nt2 = pd.merge(t1, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nanswer = t2[['other_hotel_details', 'star_rating_description']]", false], ["t1 = Hotels.sort_values('price_range').head(3)\nanswer = t1[['other_hotel_details', 'star_rating_code']]", false], ["t1 = Hotels.sort_values(['price_range'])\nt2 = t1[['other_hotel_details', 'star_rating_code']]\nanswer = t2.head(3)", false], ["t1 = Hotels.sort_values(by='price_range').head(3)\nanswer = t1[['other_hotel_details', 'star_rating_code']]", false], ["t1 = Hotels.sort_values(by='price_range', ascending=True).head(3)\nanswer = t1[['other_hotel_details', 'star_rating_code']]", false], ["t1 = Hotels.sort_values('price_range').head(3)\nanswer = t1[['other_hotel_details', 'star_rating_code']]", false], ["t1 = Hotels.sort_values(by='price_range').head(3)\nt2 = pd.merge(t1, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nanswer = t2[['other_hotel_details', 'star_rating_code']]", false], ["t1 = Hotels.sort_values('price_range').head(3)\nanswer = t1[['other_hotel_details', 'star_rating_code']]", false]]}
+{"example": {"db_id": "bike_1", "query": "SELECT date FROM weather WHERE max_temperature_f  >  85", "query_toks": ["SELECT", "date", "FROM", "weather", "WHERE", "max_temperature_f", ">", "85"], "query_toks_no_value": ["select", "date", "from", "weather", "where", "max_temperature_f", ">", "value"], "question": "Give me the dates when the max temperature was higher than 85.", "question_toks": ["Give", "me", "the", "dates", "when", "the", "max", "temperature", "was", "higher", "than", "85", "."], "sql": {"from": {"table_units": [["table_unit", 3]], "conds": []}, "select": [false, [[0, [0, [0, 23, false], null]]]], "where": [[false, 3, [0, [0, 24, false], null], 85.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"station": ["id", "name", "lat", "long", "dock_count", "city", "installation_date"], "status": ["station_id", "bikes_available", "docks_available", "time"], "trip": ["id", "duration", "start_date", "start_station_name", "start_station_id", "end_date", "end_station_name", "end_station_id", "bike_id", "subscription_type", "zip_code"], "weather": ["date", "max_temperature_f", "mean_temperature_f", "min_temperature_f", "max_dew_point_f", "mean_dew_point_f", "min_dew_point_f", "max_humidity", "mean_humidity", "min_humidity", "max_sea_level_pressure_inches", "mean_sea_level_pressure_inches", "min_sea_level_pressure_inches", "max_visibility_miles", "mean_visibility_miles", "min_visibility_miles", "max_wind_Speed_mph", "mean_wind_speed_mph", "max_gust_speed_mph", "precipitation_inches", "cloud_cover", "events", "wind_dir_degrees", "zip_code"]}, "answer": [["9/7/2013"], ["8/30/2013"], ["9/6/2013"], ["9/7/2013"], ["9/9/2013"], ["9/19/2013"], ["9/6/2013"], ["9/7/2013"], ["8/30/2013"], ["9/6/2013"], ["9/7/2013"], ["9/19/2013"], ["8/30/2013"], ["9/6/2013"], ["9/7/2013"], ["9/8/2013"], ["9/9/2013"], ["9/19/2013"], ["4/30/2014"], ["5/13/2014"], ["5/14/2014"], ["6/8/2014"], ["7/25/2014"], ["4/29/2014"], ["4/30/2014"], ["5/1/2014"], ["5/12/2014"], ["5/13/2014"], ["5/14/2014"], ["5/15/2014"], ["6/8/2014"], ["6/9/2014"], ["6/18/2014"], ["6/29/2014"], ["6/30/2014"], ["7/24/2014"], ["7/25/2014"], ["4/29/2014"], ["4/30/2014"], ["5/1/2014"], ["5/12/2014"], ["5/13/2014"], ["5/14/2014"], ["5/15/2014"], ["6/8/2014"], ["6/12/2014"], ["6/18/2014"], ["6/30/2014"], ["7/24/2014"], ["7/25/2014"], ["4/29/2014"], ["4/30/2014"], ["5/1/2014"], ["5/12/2014"], ["5/13/2014"], ["5/14/2014"], ["5/15/2014"], ["6/8/2014"], ["6/14/2014"], ["6/18/2014"], ["6/30/2014"], ["7/25/2014"], ["4/8/2014"], ["4/29/2014"], ["4/30/2014"], ["5/1/2014"], ["5/12/2014"], ["5/13/2014"], ["5/14/2014"], ["5/15/2014"], ["6/8/2014"], ["6/9/2014"], ["6/14/2014"], ["6/18/2014"], ["6/29/2014"], ["6/30/2014"], ["7/14/2014"], ["7/25/2014"], ["7/26/2014"], ["7/27/2014"], ["8/30/2014"], ["10/2/2014"], ["10/3/2014"], ["10/4/2014"], ["10/5/2014"], ["10/12/2014"], ["6/8/2015"], ["7/19/2015"], ["7/28/2015"], ["8/15/2015"], ["8/16/2015"], ["8/28/2015"], ["9/1/2014"], ["10/2/2014"], ["10/3/2014"], ["10/4/2014"], ["10/5/2014"], ["10/12/2014"], ["4/30/2015"], ["6/8/2015"], ["6/30/2015"], ["7/19/2015"], ["7/20/2015"], ["7/27/2015"], ["7/28/2015"], ["8/15/2015"], ["8/16/2015"], ["8/17/2015"], ["8/27/2015"], ["8/28/2015"], ["9/1/2014"], ["10/1/2014"], ["10/2/2014"], ["10/3/2014"], ["10/4/2014"], ["10/5/2014"], ["10/12/2014"], ["10/18/2014"], ["1/27/2015"], ["2/27/2015"], ["4/27/2015"], ["4/30/2015"], ["6/8/2015"], ["6/30/2015"], ["7/27/2015"], ["7/28/2015"], ["8/8/2015"], ["8/13/2015"], ["8/15/2015"], ["8/27/2015"], ["9/10/2014"], ["10/1/2014"], ["10/2/2014"], ["10/3/2014"], ["10/4/2014"], ["10/5/2014"], ["10/12/2014"], ["4/30/2015"], ["5/1/2015"], ["5/2/2015"], ["5/3/2015"], ["6/8/2015"], ["6/25/2015"], ["6/30/2015"], ["7/19/2015"], ["7/20/2015"], ["7/28/2015"], ["8/15/2015"], ["8/16/2015"], ["8/27/2015"], ["8/28/2015"], ["9/1/2014"], ["9/10/2014"], ["9/11/2014"], ["10/2/2014"], ["10/3/2014"], ["10/4/2014"], ["10/5/2014"], ["10/6/2014"], ["10/12/2014"], ["10/13/2014"], ["3/14/2015"], ["4/30/2015"], ["6/8/2015"], ["6/12/2015"], ["6/25/2015"], ["6/30/2015"], ["7/1/2015"], ["7/19/2015"], ["7/20/2015"], ["7/27/2015"], ["7/28/2015"], ["7/29/2015"], ["8/15/2015"], ["8/16/2015"], ["8/17/2015"], ["8/26/2015"], ["8/27/2015"], ["8/28/2015"]]}, "program_result_list": [["answer = weather[weather['max_temperature_f'] > 85]['date']", false], ["answer = weather[weather['max_temperature_f'] > 85]['date']", false], ["t1 = weather[weather['max_temperature_f'] > 85]\nanswer = t1['date']", false], ["answer = weather[weather['max_temperature_f'] > 85]['date']", false], ["answer = weather[weather['max_temperature_f'] > 85]['date']", false], ["answer = weather[weather['max_temperature_f'] > 85]['date']", false], ["answer = weather[weather['max_temperature_f'] > 85]['date']", false], ["answer = weather[weather['max_temperature_f'] > 85]['date']", false], ["t1 = weather.loc[weather['max_temperature_f'] > 85]\nanswer = t1['date']", false], ["answer = weather[weather['max_temperature_f'] > 85]['date']", false], ["answer = weather[weather['max_temperature_f'] > 85]['date']", false], ["t1 = weather[weather['max_temperature_f'] > 85]\nanswer = t1['date']", false], ["answer = weather[weather['max_temperature_f'] > 85]['date']", false], ["answer = weather[weather['max_temperature_f'] > 85]['date']", false], ["answer = weather[weather['max_temperature_f'] > 85]['date']", false], ["answer = weather[weather['max_temperature_f'] > 85]['date']", false], ["t1 = weather[weather['max_temperature_f'] > 85]\nanswer = t1['date']", false], ["t1 = weather[weather['max_temperature_f'] > 85]\nanswer = t1['date']", false], ["answer = weather[weather['max_temperature_f'] > 85]['date']", false], ["answer = weather[weather['max_temperature_f'] > 85]['date']", false]]}
+{"example": {"db_id": "behavior_monitoring", "query": "SELECT T1.teacher_id ,  T2.first_name FROM Assessment_Notes AS T1 JOIN Teachers AS T2 ON T1.teacher_id  =  T2.teacher_id GROUP BY T1.teacher_id ORDER BY count(*) DESC LIMIT 3", "query_toks": ["SELECT", "T1.teacher_id", ",", "T2.first_name", "FROM", "Assessment_Notes", "AS", "T1", "JOIN", "Teachers", "AS", "T2", "ON", "T1.teacher_id", "=", "T2.teacher_id", "GROUP", "BY", "T1.teacher_id", "ORDER", "BY", "count", "(", "*", ")", "DESC", "LIMIT", "3"], "query_toks_no_value": ["select", "t1", ".", "teacher_id", ",", "t2", ".", "first_name", "from", "assessment_notes", "as", "t1", "join", "teachers", "as", "t2", "on", "t1", ".", "teacher_id", "=", "t2", ".", "teacher_id", "group", "by", "t1", ".", "teacher_id", "order", "by", "count", "(", "*", ")", "desc", "limit", "value"], "question": "Find the ids and first names of the 3 teachers that have the most number of assessment notes?", "question_toks": ["Find", "the", "ids", "and", "first", "names", "of", "the", "3", "teachers", "that", "have", "the", "most", "number", "of", "assessment", "notes", "?"], "sql": {"from": {"table_units": [["table_unit", 6], ["table_unit", 5]], "conds": [[false, 2, [0, [0, 37, false], null], [0, 26, false], null]]}, "select": [false, [[0, [0, [0, 37, false], null]], [0, [0, [0, 28, false], null]]]], "where": [], "groupBy": [[0, 37, false]], "having": [], "orderBy": ["desc", [[0, [3, 0, false], null]]], "limit": 3, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Ref_Address_Types": ["address_type_code", "address_type_description"], "Ref_Detention_Type": ["detention_type_code", "detention_type_description"], "Ref_Incident_Type": ["incident_type_code", "incident_type_description"], "Addresses": ["address_id", "line_1", "line_2", "line_3", "city", "zip_postcode", "state_province_county", "country", "other_address_details"], "Students": ["student_id", "address_id", "first_name", "middle_name", "last_name", "cell_mobile_number", "email_address", "date_first_rental", "date_left_university", "other_student_details"], "Teachers": ["teacher_id", "address_id", "first_name", "middle_name", "last_name", "gender", "cell_mobile_number", "email_address", "other_details"], "Assessment_Notes": ["notes_id", "student_id", "teacher_id", "date_of_notes", "text_of_notes", "other_details"], "Behavior_Incident": ["incident_id", "incident_type_code", "student_id", "date_incident_start", "date_incident_end", "incident_summary", "recommendations", "other_details"], "Detention": ["detention_id", "detention_type_code", "teacher_id", "datetime_detention_start", "datetime_detention_end", "detention_summary", "other_details"], "Student_Addresses": ["student_id", "address_id", "date_address_from", "date_address_to", "monthly_rental", "other_details"], "Students_in_Detention": ["student_id", "detention_id", "incident_id"]}, "answer": [[3, "Trystan"], [15, "Hobart"], [14, "Evelyn"]]}, "program_result_list": [["t1 = pd.merge(Assessment_Notes, Teachers, left_on='teacher_id', right_on='teacher_id')\nt2 = t1.groupby(['teacher_id', 'first_name']).size().rename('count')\nt3 = t2.sort_values(ascending=False).head(3).to_frame().reset_index()\nanswer = t3[['teacher_id', 'first_name']]", false], ["t1 = pd.merge(Assessment_Notes, Teachers, left_on='teacher_id', right_on='teacher_id')\nt2 = t1.groupby('teacher_id').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(3).to_frame().reset_index()\nt4 = pd.merge(t3, Teachers, left_on='teacher_id', right_on='teacher_id')\nanswer = t4[['teacher_id', 'first_name']]", false], ["t1 = pd.merge(Assessment_Notes, Teachers, on='teacher_id')\nt2 = t1.groupby(['teacher_id', 'first_name']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(3).to_frame().reset_index()", false], ["t1 = pd.merge(Assessment_Notes, Teachers, on='teacher_id')\nt2 = t1.groupby('teacher_id').size().rename('count').sort_values(ascending=False).head(3).to_frame().reset_index()\nt3 = pd.merge(t2, Teachers, on='teacher_id')\nanswer = t3[['teacher_id', 'first_name']]", false], ["t1 = pd.merge(Assessment_Notes, Teachers, left_on='teacher_id', right_on='teacher_id')\nt2 = t1.groupby('teacher_id', as_index=False).size().rename('count').sort_values(ascending=False).head(3).to_frame().reset_index()\nt3 = pd.merge(t2, Teachers, left_on='teacher_id', right_on='teacher_id')\nanswer = t3[['teacher_id', 'first_name']]", false], ["t1 = pd.merge(Assessment_Notes, Teachers, left_on='teacher_id', right_on='teacher_id')\nt2 = t1.groupby(['teacher_id', 'first_name']).size().rename('count').to_frame().reset_index()\nt3 = t2.sort_values(by='count', ascending=False).head(3)\nanswer = t3[['teacher_id', 'first_name']]", false], ["t1 = pd.merge(Assessment_Notes, Teachers, left_on='teacher_id', right_on='teacher_id')\nt2 = t1.groupby('teacher_id').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(3).to_frame().reset_index()\nt4 = pd.merge(t3, Teachers, on='teacher_id')\nanswer = t4[['teacher_id', 'first_name']]", false], ["t1 = pd.merge(Assessment_Notes, Teachers, left_on='teacher_id', right_on='teacher_id')\nt2 = t1.groupby('teacher_id').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(3).to_frame().reset_index()\nt4 = pd.merge(t3, Teachers, left_on='teacher_id', right_on='teacher_id')\nanswer = t4[['teacher_id', 'first_name']]", false], ["t1 = pd.merge(Assessment_Notes, Teachers, left_on='teacher_id', right_on='teacher_id')\nt2 = t1.groupby('teacher_id').size().rename('count').sort_values(ascending=False).to_frame().reset_index()\nanswer = t2.head(3)", false], ["t1 = pd.merge(Assessment_Notes, Teachers, left_on='teacher_id', right_on='teacher_id')\nt2 = t1.groupby(['teacher_id', 'first_name']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(3).to_frame().reset_index()", false], ["t1 = pd.merge(Assessment_Notes, Teachers, left_on='teacher_id', right_on='teacher_id')\nt2 = t1.groupby(['teacher_id', 'first_name']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(3).to_frame().reset_index()", false], ["t1 = pd.merge(Assessment_Notes, Teachers, on='teacher_id')\nt2 = t1.groupby('teacher_id').size().rename('count').to_frame().reset_index()\nt3 = t2.sort_values('count', ascending=False).head(3).to_frame().reset_index()\nt4 = pd.merge(t3, Teachers, on='teacher_id')\nanswer = t4[['teacher_id', 'first_name']]", false], ["t1 = pd.merge(Assessment_Notes, Teachers, on='teacher_id')\nt2 = t1.groupby(['teacher_id', 'first_name']).size().to_frame().reset_index()\nt3 = t2.sort_values(by=0, ascending=False).head(3)\nanswer = t3[['teacher_id', 'first_name']]", false], ["t1 = pd.merge(Assessment_Notes, Teachers, left_on='teacher_id', right_on='teacher_id')\nt2 = t1.groupby(['teacher_id', 'first_name']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(3).to_frame().reset_index()", false], ["t1 = pd.merge(Assessment_Notes, Teachers, left_on='teacher_id', right_on='teacher_id')\nt2 = t1.groupby('teacher_id').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(3).to_frame().reset_index()\nt4 = pd.merge(t3, Teachers, left_on='teacher_id', right_on='teacher_id')\nanswer = t4[['teacher_id', 'first_name']]", false], ["t1 = pd.merge(Assessment_Notes, Teachers, on='teacher_id')\nt2 = t1.groupby('teacher_id').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(3).to_frame().reset_index()\nt4 = pd.merge(t3, Teachers, on='teacher_id')\nanswer = t4[['teacher_id', 'first_name']]", false], ["t1 = pd.merge(Assessment_Notes, Teachers, left_on='teacher_id', right_on='teacher_id')\nt2 = t1.groupby(['teacher_id', 'first_name']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(3).to_frame().reset_index()", false], ["t1 = pd.merge(Assessment_Notes, Teachers, on='teacher_id')\nanswer = t1.groupby('teacher_id').size().rename('count').sort_values(ascending=False).head(3).to_frame().reset_index()", false], ["t1 = pd.merge(Assessment_Notes, Teachers, left_on='teacher_id', right_on='teacher_id')\nt2 = t1.groupby('teacher_id').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(3).to_frame().reset_index()\nt4 = pd.merge(t3, Teachers, left_on='teacher_id', right_on='teacher_id')\nanswer = t4[['teacher_id', 'first_name']]", false], ["t1 = pd.merge(Assessment_Notes, Teachers, left_on='teacher_id', right_on='teacher_id')\nt2 = t1.groupby('teacher_id').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(3).to_frame().reset_index()\nt4 = pd.merge(t3, Teachers, left_on='teacher_id', right_on='teacher_id')\nanswer = t4[['teacher_id', 'first_name']]", false]]}
+{"example": {"db_id": "cre_Drama_Workshop_Groups", "query": "SELECT T1.Store_Phone ,  T1.Store_Email_Address FROM Drama_Workshop_Groups AS T1 JOIN Services AS T2 ON T1.Workshop_Group_ID  =  T2.Workshop_Group_ID", "query_toks": ["SELECT", "T1.Store_Phone", ",", "T1.Store_Email_Address", "FROM", "Drama_Workshop_Groups", "AS", "T1", "JOIN", "Services", "AS", "T2", "ON", "T1.Workshop_Group_ID", "=", "T2.Workshop_Group_ID"], "query_toks_no_value": ["select", "t1", ".", "store_phone", ",", "t1", ".", "store_email_address", "from", "drama_workshop_groups", "as", "t1", "join", "services", "as", "t2", "on", "t1", ".", "workshop_group_id", "=", "t2", ".", "workshop_group_id"], "question": "Give me all the phone numbers and email addresses of the workshop groups where services are performed.", "question_toks": ["Give", "me", "all", "the", "phone", "numbers", "and", "email", "addresses", "of", "the", "workshop", "groups", "where", "services", "are", "performed", "."], "sql": {"from": {"table_units": [["table_unit", 6], ["table_unit", 15]], "conds": [[false, 2, [0, [0, 27, false], null], [0, 86, false], null]]}, "select": [false, [[0, [0, [0, 32, false], null]], [0, [0, [0, 33, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Ref_Payment_Methods": ["payment_method_code", "payment_method_description"], "Ref_Service_Types": ["Service_Type_Code", "Parent_Service_Type_Code", "Service_Type_Description"], "Addresses": ["Address_ID", "Line_1", "Line_2", "City_Town", "State_County", "Other_Details"], "Products": ["Product_ID", "Product_Name", "Product_Price", "Product_Description", "Other_Product_Service_Details"], "Marketing_Regions": ["Marketing_Region_Code", "Marketing_Region_Name", "Marketing_Region_Descriptrion", "Other_Details"], "Clients": ["Client_ID", "Address_ID", "Customer_Email_Address", "Customer_Name", "Customer_Phone", "Other_Details"], "Drama_Workshop_Groups": ["Workshop_Group_ID", "Address_ID", "Currency_Code", "Marketing_Region_Code", "Store_Name", "Store_Phone", "Store_Email_Address", "Other_Details"], "Performers": ["Performer_ID", "Address_ID", "Customer_Name", "Customer_Phone", "Customer_Email_Address", "Other_Details"], "Customers": ["Customer_ID", "Address_ID", "Customer_Name", "Customer_Phone", "Customer_Email_Address", "Other_Details"], "Stores": ["Store_ID", "Address_ID", "Marketing_Region_Code", "Store_Name", "Store_Phone", "Store_Email_Address", "Other_Details"], "Bookings": ["Booking_ID", "Customer_ID", "Workshop_Group_ID", "Status_Code", "Store_ID", "Order_Date", "Planned_Delivery_Date", "Actual_Delivery_Date", "Other_Order_Details"], "Performers_in_Bookings": ["Order_ID", "Performer_ID"], "Customer_Orders": ["Order_ID", "Customer_ID", "Store_ID", "Order_Date", "Planned_Delivery_Date", "Actual_Delivery_Date", "Other_Order_Details"], "Order_Items": ["Order_Item_ID", "Order_ID", "Product_ID", "Order_Quantity", "Other_Item_Details"], "Invoices": ["Invoice_ID", "Order_ID", "payment_method_code", "Product_ID", "Order_Quantity", "Other_Item_Details", "Order_Item_ID"], "Services": ["Service_ID", "Service_Type_Code", "Workshop_Group_ID", "Product_Description", "Product_Name", "Product_Price", "Other_Product_Service_Details"], "Bookings_Services": ["Order_ID", "Product_ID"], "Invoice_Items": ["Invoice_Item_ID", "Invoice_ID", "Order_ID", "Order_Item_ID", "Product_ID", "Order_Quantity", "Other_Item_Details"]}, "answer": [["(422)705-5633", "roosevelt61@example.com"], ["1-351-773-1587x95545", "bednar.michael@example.org"], ["499-032-2149", "katherine.kling@example.org"], ["+60(6)8081312118", "arturo.orn@example.org"], ["1-811-875-3222", "waino.king@example.com"], ["(904)958-9909x0087", "harry.nicolas@example.org"], ["1-351-773-1587x95545", "bednar.michael@example.org"], ["+60(6)8081312118", "arturo.orn@example.org"], ["492-463-5967", "quigley.queen@example.org"], ["(411)133-9128", "watsica.hettie@example.com"], ["1-811-875-3222", "waino.king@example.com"], ["(256)743-0310", "kole.torp@example.org"], ["(636)443-4067", "uhuels@example.net"], ["492-463-5967", "quigley.queen@example.org"], ["1-764-337-3453", "hansen.krista@example.net"]]}, "program_result_list": [["t1 = pd.merge(Drama_Workshop_Groups, Services, left_on='workshop_group_id', right_on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, left_on='workshop_group_id', right_on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, left_on='workshop_group_id', right_on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, left_on='workshop_group_id', right_on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, left_on='workshop_group_id', right_on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, left_on='workshop_group_id', right_on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, left_on='workshop_group_id', right_on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, left_on='workshop_group_id', right_on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, left_on='workshop_group_id', right_on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false], ["t1 = pd.merge(Drama_Workshop_Groups, Services, left_on='workshop_group_id', right_on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]", false]]}
+{"example": {"db_id": "gas_company", "query": "SELECT main_industry ,  sum(market_value) FROM company GROUP BY main_industry", "query_toks": ["SELECT", "main_industry", ",", "sum", "(", "market_value", ")", "FROM", "company", "GROUP", "BY", "main_industry"], "query_toks_no_value": ["select", "main_industry", ",", "sum", "(", "market_value", ")", "from", "company", "group", "by", "main_industry"], "question": "What are the main indstries and total market value for each industry?", "question_toks": ["What", "are", "the", "main", "indstries", "and", "total", "market", "value", "for", "each", "industry", "?"], "sql": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[0, [0, [0, 5, false], null]], [4, [0, [0, 9, false], null]]]], "where": [], "groupBy": [[0, 5, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"company": ["Company_ID", "Rank", "Company", "Headquarters", "Main_Industry", "Sales_billion", "Profits_billion", "Assets_billion", "Market_Value"], "gas_station": ["Station_ID", "Open_Year", "Location", "Manager_Name", "Vice_Manager_Name", "Representative_Name"], "station_company": ["Station_ID", "Company_ID", "Rank_of_the_Year"]}, "answer": [["Banking", 580.4000000000001], ["Conglomerate", 415.9], ["Oil and gas", 1257.1000000000001]]}, "program_result_list": [["t1 = company.groupby('main_industry').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t2['main_industry'][0]", false], ["t1 = company.groupby('main_industry').sum()\nanswer = t1['market_value'].to_frame().reset_index()", false], ["t1 = company.groupby('main_industry').sum()\nanswer = t1[['market_value']]", false], ["t1 = company.groupby('main_industry').size().rename('sum')\nt2 = t1.to_frame().reset_index()\nanswer = t2[['main_industry', 'sum']]", false], ["t1 = company.groupby('main_industry').sum()\nanswer = t1['market_value'].to_frame().reset_index()", false], ["t1 = company.groupby('main_industry').sum()\nanswer = t1[['market_value']]", false], ["t1 = company.groupby('main_industry')['market_value'].sum().rename('sum_market_value').to_frame().reset_index()\nanswer = t1[['main_industry', 'sum_market_value']]", false], ["t1 = company.groupby('main_industry').sum()['market_value']\nanswer = t1.to_frame().rename(columns={'market_value': 'total_market_value'})", false], ["t1 = company.groupby('main_industry')['market_value'].sum().to_frame()\nanswer = t1.reset_index()", false], ["t1 = company.groupby('main_industry')['market_value'].sum()\nanswer = t1.to_frame().reset_index()", false], ["t1 = company.groupby('main_industry').sum()\nt2 = t1[['market_value']]\nanswer = t2.reset_index()", false], ["t1 = company.groupby('main_industry')[['market_value']].sum()\nanswer = t1.reset_index()", false], ["answer = company.groupby('main_industry')['market_value'].sum().to_frame().reset_index()", false], ["t1 = company.groupby('main_industry')['market_value'].sum().rename('sum')\nanswer = t1.to_frame().reset_index()", false], ["t1 = company.groupby('main_industry').sum().reset_index()\nanswer = t1[['main_industry', 'market_value']]", false], ["t1 = company.groupby('main_industry').sum()\nt2 = t1[['market_value']]\nanswer = t2.reset_index()", false], ["t1 = company.groupby('main_industry').size().rename('count')\nt2= t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t2['creation']", false], ["t1 = company.groupby('main_industry').sum()\nanswer = t1[['market_value']]", false], ["answer = company.groupby('main_industry')['market_value'].sum().to_frame().reset_index()", false], ["t1 = company.groupby('main_industry').sum()\nanswer = t1[['market_value']]", false]]}
+{"example": {"db_id": "hospital_1", "query": "SELECT T1.name FROM physician AS T1 JOIN trained_in AS T2 ON T1.employeeid  =  T2.physician JOIN procedures AS T3 ON T3.code  =  T2.treatment WHERE T3.cost  >  5000", "query_toks": ["SELECT", "T1.name", "FROM", "physician", "AS", "T1", "JOIN", "trained_in", "AS", "T2", "ON", "T1.employeeid", "=", "T2.physician", "JOIN", "procedures", "AS", "T3", "ON", "T3.code", "=", "T2.treatment", "WHERE", "T3.cost", ">", "5000"], "query_toks_no_value": ["select", "t1", ".", "name", "from", "physician", "as", "t1", "join", "trained_in", "as", "t2", "on", "t1", ".", "employeeid", "=", "t2", ".", "physician", "join", "procedures", "as", "t3", "on", "t3", ".", "code", "=", "t2", ".", "treatment", "where", "t3", ".", "cost", ">", "value"], "question": "Find the physicians who are trained in a procedure that costs more than 5000.", "question_toks": ["Find", "the", "physicians", "who", "are", "trained", "in", "a", "procedure", "that", "costs", "more", "than", "5000", "."], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 4], ["table_unit", 3]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 14, false], null], "and", [false, 2, [0, [0, 11, false], null], [0, 15, false], null]]}, "select": [false, [[0, [0, [0, 2, false], null]]]], "where": [[false, 3, [0, [0, 13, false], null], 5000.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Physician": ["EmployeeID", "Name", "Position", "SSN"], "Department": ["DepartmentID", "Name", "Head"], "Affiliated_With": ["Physician", "Department", "PrimaryAffiliation"], "Procedures": ["Code", "Name", "Cost"], "Trained_In": ["Physician", "Treatment", "CertificationDate", "CertificationExpires"], "Patient": ["SSN", "Name", "Address", "Phone", "InsuranceID", "PCP"], "Nurse": ["EmployeeID", "Name", "Position", "Registered", "SSN"], "Appointment": ["AppointmentID", "Patient", "PrepNurse", "Physician", "Start", "End", "ExaminationRoom"], "Medication": ["Code", "Name", "Brand", "Description"], "Prescribes": ["Physician", "Patient", "Medication", "Date", "Appointment", "Dose"], "Block": ["BlockFloor", "BlockCode"], "Room": ["RoomNumber", "RoomType", "BlockFloor", "BlockCode", "Unavailable"], "On_Call": ["Nurse", "BlockFloor", "BlockCode", "OnCallStart", "OnCallEnd"], "Stay": ["StayID", "Patient", "Room", "StayStart", "StayEnd"], "Undergoes": ["Patient", "Procedures", "Stay", "DateUndergoes", "Physician", "AssistingNurse"]}, "answer": [["Christopher Turk"], ["Todd Quinlan"], ["John Wen"], ["John Wen"]]}, "program_result_list": [["t1 = pd.merge(Physician, Trained_In, on='employeeid')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nanswer = t2[t2['cost'] > 5000]['name']", false], ["t1 = pd.merge(Physician, Trained_In, left_on='employeeid', right_on='physician')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nt3 = t2[t2['cost'] > 5000]\nanswer = t3['name']", false], ["t1 = pd.merge(Physician, Trained_In, left_on='employeeid', right_on='physician')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nanswer = t2[t2['cost'] > 5000]['name']", false], ["t1 = pd.merge(Physician, Trained_In, left_on='employeeid', right_on='physician')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nt3 = t2[t2['cost'] > 5000]\nanswer = t3['name'].unique()", false], ["t1 = pd.merge(Physician, Trained_In, left_on='employeeid', right_on='physician')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nt3 = t2[t2['cost'] > 5000]\nanswer = t3['name']", false], ["t1 = pd.merge(Physician, Trained_In, left_on='employeeid', right_on='physician')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nt3 = t2[t2['cost'] > 5000]\nanswer = t3['name']", false], ["t1 = pd.merge(Physician, Trained_In, left_on='employeeid', right_on='physician')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nt3 = t2[t2['cost'] > 5000]\nanswer = t3['name'].unique()", false], ["t1 = pd.merge(Physician, Trained_In, left_on='employeeid', right_on='physician')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nt3 = t2[t2['cost'] > 5000]\nanswer = t3['name']", false], ["t1 = pd.merge(Physician, Trained_In, left_on='employeeid', right_on='physician')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nt3 = t2[t2['cost'] > 5000]\nanswer = t3['name']", false], ["t1 = pd.merge(Physician, Trained_In, left_on='employeeid', right_on='physician')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nanswer = t2[t2['cost'] > 5000]['name']", false], ["t1 = pd.merge(Physician, Trained_In, on='employeeid')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nanswer = t2[t2['cost'] > 5000]['name']", false], ["t1 = pd.merge(Physician, Trained_In, left_on='employeeid', right_on='physician')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nt3 = t2[t2['cost'] > 5000]\nanswer = t3['name'].unique()", false], ["t1 = pd.merge(Physician, Trained_In, on='employeeid')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nt3 = t2[t2['cost'] > 5000]\nanswer = t3['name']", false], ["t1 = pd.merge(Physician, Trained_In, left_on='employeeid', right_on='physician')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nanswer = t2[t2['cost'] > 5000]['name']", false], ["t1 = pd.merge(Physician, Trained_In, left_on='employeeid', right_on='physician')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nt3 = t2[t2['cost'] > 5000]\nanswer = t3['name']", false], ["t1 = pd.merge(Physician, Trained_In, left_on='employeeid', right_on='physician')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nt3 = t2[t2['cost'] > 5000]\nanswer = t3['name'].unique()", false], ["t1 = pd.merge(Physician, Trained_In, left_on='employeeid', right_on='physician')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nt3 = t2[t2['cost'] > 5000]\nanswer = t3['name']", false], ["t1 = pd.merge(Physician, Trained_In, left_on='employeeid', right_on='physician')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nt3 = t2[t2['cost'] > 5000]\nanswer = t3['name']", false], ["t1 = pd.merge(Physician, Trained_In, left_on='employeeid', right_on='physician')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nt3 = t2[t2['cost'] > 5000]\nanswer = t3['name']", false], ["t1 = pd.merge(Physician, Trained_In, left_on='employeeid', right_on='physician')\nt2 = pd.merge(t1, Procedures, left_on='treatment', right_on='code')\nt3 = t2[t2['cost'] > 5000]\nanswer = t3['name']", false]]}
+{"example": {"db_id": "student_assessment", "query": "SELECT T2.student_id FROM courses AS T1 JOIN student_course_registrations AS T2 ON T1.course_id = T2.course_id WHERE T1.course_name = \"statistics\" ORDER BY T2.registration_date", "query_toks": ["SELECT", "T2.student_id", "FROM", "courses", "AS", "T1", "JOIN", "student_course_registrations", "AS", "T2", "ON", "T1.course_id", "=", "T2.course_id", "WHERE", "T1.course_name", "=", "``", "statistics", "''", "ORDER", "BY", "T2.registration_date"], "query_toks_no_value": ["select", "t2", ".", "student_id", "from", "courses", "as", "t1", "join", "student_course_registrations", "as", "t2", "on", "t1", ".", "course_id", "=", "t2", ".", "course_id", "where", "t1", ".", "course_name", "=", "value", "order", "by", "t2", ".", "registration_date"], "question": "What are the ids of the students who registered course statistics by order of registration date?", "question_toks": ["What", "are", "the", "ids", "of", "the", "students", "who", "registered", "course", "statistics", "by", "order", "of", "registration", "date", "?"], "sql": {"from": {"table_units": [["table_unit", 3], ["table_unit", 5]], "conds": [[false, 2, [0, [0, 18, false], null], [0, 28, false], null]]}, "select": [false, [[0, [0, [0, 27, false], null]]]], "where": [[false, 2, [0, [0, 19, false], null], "\"statistics\"", null]], "groupBy": [], "having": [], "orderBy": ["asc", [[0, [0, 29, false], null]]], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Addresses": ["address_id", "line_1", "line_2", "city", "zip_postcode", "state_province_county", "country"], "People": ["person_id", "first_name", "middle_name", "last_name", "cell_mobile_number", "email_address", "login_name", "password"], "Students": ["student_id", "student_details"], "Courses": ["course_id", "course_name", "course_description", "other_details"], "People_Addresses": ["person_address_id", "person_id", "address_id", "date_from", "date_to"], "Student_Course_Registrations": ["student_id", "course_id", "registration_date"], "Student_Course_Attendance": ["student_id", "course_id", "date_of_attendance"], "Candidates": ["candidate_id", "candidate_details"], "Candidate_Assessments": ["candidate_id", "qualification", "assessment_date", "asessment_outcome_code"]}, "answer": [[121], [111], [171], [141]]}, "program_result_list": [["t1 = pd.merge(Students, Student_Course_Registrations, on='student_id')\nt2 = pd.merge(t1, Courses, on='course_id')\nt3 = t2[t2['course_name'] == 'statistics']\nt4 = t3.sort_values(by='registration_date')\nanswer = t4['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values('registration_date')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2[['student_id']].sort_values('registration_date')", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics'].sort_values('registration_date')\nanswer = t2['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values('registration_date')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values(by='registration_date')[['student_id']]\nanswer", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values(by=['registration_date'])['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nt3 = t2.sort_values(by='registration_date')\nanswer = t3['student_id']\n \n# Dataset student_assessment:", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values('registration_date')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values(by='registration_date')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, on='course_id', how='inner')\nt2 = t1[t1['course_name'] == 'statistics']\nt3 = t2.sort_values('registration_date').reset_index(drop=True)\nanswer = t3['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values('registration_date')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values('registration_date')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values('registration_date')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics'].sort_values('registration_date')\nanswer = t2['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values(by='registration_date')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values('registration_date')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values('registration_date')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values(by='registration_date')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Registrations, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values(by='registration_date')[['student_id']]", false]]}
+{"example": {"db_id": "storm_record", "query": "SELECT name FROM storm EXCEPT SELECT T1.name FROM storm AS T1 JOIN affected_region AS T2 ON T1.storm_id  =  T2.storm_id GROUP BY T1.storm_id HAVING count(*)  >=  2", "query_toks": ["SELECT", "name", "FROM", "storm", "EXCEPT", "SELECT", "T1.name", "FROM", "storm", "AS", "T1", "JOIN", "affected_region", "AS", "T2", "ON", "T1.storm_id", "=", "T2.storm_id", "GROUP", "BY", "T1.storm_id", "HAVING", "count", "(", "*", ")", ">", "=", "2"], "query_toks_no_value": ["select", "name", "from", "storm", "except", "select", "t1", ".", "name", "from", "storm", "as", "t1", "join", "affected_region", "as", "t2", "on", "t1", ".", "storm_id", "=", "t2", ".", "storm_id", "group", "by", "t1", ".", "storm_id", "having", "count", "(", "*", ")", ">", "=", "value"], "question": "What are the names of storms that did not affect two or more regions?", "question_toks": ["What", "are", "the", "names", "of", "storms", "that", "did", "not", "affect", "two", "or", "more", "regions", "?"], "sql": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[0, [0, [0, 2, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": {"from": {"table_units": [["table_unit", 0], ["table_unit", 2]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 11, false], null]]}, "select": [false, [[0, [0, [0, 2, false], null]]]], "where": [], "groupBy": [[0, 1, false]], "having": [[false, 5, [0, [3, 0, false], null], 2.0, null]], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}}, "db_table_headers": {"storm": ["Storm_ID", "Name", "Dates_active", "Max_speed", "Damage_millions_USD", "Number_Deaths"], "region": ["Region_id", "Region_code", "Region_name"], "affected_region": ["Region_id", "Storm_ID", "Number_city_affected"]}, "answer": [["9 cyclones"], ["Eight"], ["Four"], ["Nine"], ["Seven"], ["Six"], ["Three"], ["Two"]]}, "program_result_list": [["t1 = pd.merge(storm, affected_region, left_on='storm_id', right_on='storm_id')\nt2 = t1.groupby(['storm_id', 'name']).size().rename('count')\nt3 = t2[t2 >= 2].to_frame().reset_index()\nt4 = storm[~storm.name.isin(t3['name'])]\nanswer = t4['name']", false], ["t1 = pd.merge(storm, affected_region, left_on='storm_id', right_on='storm_id')\nt2 = t1.groupby('storm_id').size().rename('count')\nt3 = t2[t2 >= 2].to_frame().reset_index()\nt4 = pd.merge(storm, t3, left_on='storm_id', right_on='storm_id', how='outer', indicator=True)\nanswer = t4[t4['_merge'] == 'left_only']['name']", false], ["t1 = pd.merge(storm, affected_region, left_on='storm_id', right_on='storm_id')\nt2 = t1.groupby(['storm_id', 'name']).size().rename('count')\nt3 = t2[t2 >= 2].to_frame().reset_index()\nt4 = storm[~storm['name'].isin(t3['name'])]\nanswer = t4['name']", false], ["t1 = pd.merge(affected_region, storm, left_on='storm_id', right_on='storm_id')\nt2 = t1.groupby('storm_id').size().rename('count')\nt3 = t2[t2 >= 2].index\nt4 = storm[~storm['storm_id'].isin(t3)]\nanswer = t4['name']", false], ["t1 = pd.merge(affected_region, storm, left_on='storm_id', right_on='storm_id')\nt2 = t1.groupby('storm_id').size().rename('count')\nt3 = t2[t2 >= 2].to_frame().reset_index()\nt4 = pd.merge(t3, storm, left_on='storm_id', right_on='storm_id')\nt5 = storm.merge(t4, on='name', how='outer', indicator=True)\nanswer = t5[t5['_merge'] == 'left_only']['name']", false], ["t1 = pd.merge(storm, affected_region, left_on='storm_id', right_on='storm_id')\nt2 = t1.groupby('name').size().rename('count')\nt3 = storm[~storm['name'].isin(t2[t2 >= 2].index)]\nanswer = t3['name']", false], ["t1 = pd.merge(storm, affected_region, on='storm_id')\nt2 = t1.groupby('storm_id').size().rename('count')\nt3 = t2[t2 >= 2].to_frame().reset_index()\nt4 = t3[['storm_id']]\nt5 = pd.merge(storm, t4, on='storm_id', how='left')\nt6 = t5[t5['storm_id'].isna()]\nanswer = t6['name']", false], ["t1 = pd.merge(storm, affected_region, left_on='storm_id', right_on='storm_id')\nt2 = t1.groupby('storm_id').size().rename('count')\nt3 = t2[t2 < 2].to_frame().reset_index()\nt4 = pd.merge(storm, t3, left_on='storm_id', right_on='storm_id')\nanswer = t4['name']", false], ["t1 = storm.set_index('storm_id')\nt2 = affected_region.groupby('storm_id').size().to_frame().reset_index()\nt3 = pd.merge(t2, t1, left_on='storm_id', right_index=True)\nt4 = t3[t3['0_x'] < 2]\nanswer = t4['name']", false], ["t1 = pd.merge(storm, affected_region, left_on='storm_id', right_on='storm_id')\nt2 = t1.groupby('name').size().rename('count')\nanswer = storm[~storm['name'].isin(t2[t2 >= 2].index)]", false], ["t1 = pd.merge(storm, affected_region, left_on='storm_id', right_on='storm_id')\nt2 = t1.groupby('storm_id').size()\nanswer = storm[storm['storm_id'].isin(t2[t2 > 1].index)]['name']", false], ["t1 = pd.merge(storm, affected_region, left_on='storm_id', right_on='storm_id')\nt2 = t1.groupby('storm_id').size().rename('count')\nt3 = t2[t2 < 2].to_frame().reset_index()\nt4 = pd.merge(storm, t3, left_on='storm_id', right_on='storm_id')\nanswer = t4['name']", false], ["t1 = pd.merge(storm, affected_region, on='storm_id')\nt2 = t1.groupby('storm_id').size().rename('count')\nt3 = t2[t2 >= 2].to_frame().reset_index()\nt4 = pd.merge(storm, t3, on='storm_id', how='left')\nanswer = t4[t4['count'].isnull()]['name']", false], ["t1 = pd.merge(storm, affected_region, left_on='storm_id', right_on='storm_id')\nt2 = t1.groupby('name').size().rename('count')\nanswer = storm[~storm['name'].isin(t2[t2 >= 2].index)]", false], ["t1 = pd.merge(storm, affected_region, left_on='storm_id', right_on='storm_id')\nt2 = t1.groupby(['storm_id', 'name']).size()\nt3 = t2[t2 >= 2].to_frame().reset_index()\nt4 = storm[~storm.name.isin(t3.name)]\nanswer = t4.name", false], ["t1 = pd.merge(storm, affected_region, on='storm_id')\nt2 = t1.groupby('storm_id').size().rename('count')\nt3 = t2[t2 >= 2].to_frame().reset_index()\nt4 = pd.merge(storm, t3, on='storm_id', how='outer', indicator=True)\nt5 = t4[t4['_merge'] == 'left_only']\nanswer = t5['name']", false], ["t1 = pd.merge(storm, affected_region, left_on='storm_id', right_on='storm_id')\nt2 = t1.groupby(['storm_id', 'name']).size().rename('count')\nt3 = t2[t2 >= 2].to_frame().reset_index()\nt4 = storm[~storm['name'].isin(t3['name'])]\nanswer = t4['name']", false], ["t1 = pd.merge(storm, affected_region, left_on='storm_id', right_on='storm_id')\nt2 = t1.groupby('storm_id').size().rename('count')\nt3 = t2[t2 >= 2].to_frame().reset_index()\nt4 = pd.merge(t3, storm, on='storm_id')\nanswer = t4['name']", false], ["t1 = storm.merge(affected_region, left_on='storm_id', right_on='storm_id')\nt2 = t1.groupby('storm_id').size().rename('count')\nt3 = t2[t2 < 2].to_frame().reset_index()\nt4 = pd.merge(storm, t3, on='storm_id')\nanswer = t4['name']", false], ["t1 = pd.merge(storm, affected_region, left_on='storm_id', right_on='storm_id')\nt2 = t1.groupby(['storm_id', 'name']).size().rename('count')\nt3 = t2[t2 >= 2].to_frame().reset_index()\nanswer = storm[~storm['name'].isin(t3['name'])]", false]]}
+{"example": {"db_id": "customers_and_invoices", "query": "SELECT T1.invoice_date ,  T1.order_id ,  T2.order_details FROM Invoices AS T1 JOIN Orders AS T2 ON T1.order_id  =  T2.order_id", "query_toks": ["SELECT", "T1.invoice_date", ",", "T1.order_id", ",", "T2.order_details", "FROM", "Invoices", "AS", "T1", "JOIN", "Orders", "AS", "T2", "ON", "T1.order_id", "=", "T2.order_id"], "query_toks_no_value": ["select", "t1", ".", "invoice_date", ",", "t1", ".", "order_id", ",", "t2", ".", "order_details", "from", "invoices", "as", "t1", "join", "orders", "as", "t2", "on", "t1", ".", "order_id", "=", "t2", ".", "order_id"], "question": "What are the invoice dates, order ids, and order details for all invoices?", "question_toks": ["What", "are", "the", "invoice", "dates", ",", "order", "ids", ",", "and", "order", "details", "for", "all", "invoices", "?"], "sql": {"from": {"table_units": [["table_unit", 2], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 18, false], null], [0, 13, false], null]]}, "select": [false, [[0, [0, [0, 19, false], null]], [0, [0, [0, 18, false], null]], [0, [0, [0, 16, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Customers": ["customer_id", "customer_first_name", "customer_middle_initial", "customer_last_name", "gender", "email_address", "login_name", "login_password", "phone_number", "town_city", "state_county_province", "country"], "Orders": ["order_id", "customer_id", "date_order_placed", "order_details"], "Invoices": ["invoice_number", "order_id", "invoice_date"], "Accounts": ["account_id", "customer_id", "date_account_opened", "account_name", "other_account_details"], "Product_Categories": ["production_type_code", "product_type_description", "vat_rating"], "Products": ["product_id", "parent_product_id", "production_type_code", "unit_price", "product_name", "product_color", "product_size"], "Financial_Transactions": ["transaction_id", "account_id", "invoice_number", "transaction_type", "transaction_date", "transaction_amount", "transaction_comment", "other_transaction_details"], "Order_Items": ["order_item_id", "order_id", "product_id", "product_quantity", "other_order_item_details"], "Invoice_Line_Items": ["order_item_id", "invoice_number", "product_id", "product_title", "product_quantity", "product_price", "derived_product_cost", "derived_vat_payable", "derived_total_cost"]}, "answer": [["2018-03-01 16:40:48", 9, null], ["2018-03-20 00:21:41", 9, null], ["2018-03-05 08:47:33", 3, null], ["2018-02-28 19:01:06", 9, null], ["2018-03-07 02:04:32", 13, null], ["2018-03-16 21:57:43", 8, null], ["2018-03-13 07:27:38", 10, null], ["2018-03-19 17:06:30", 10, null], ["2018-03-16 11:01:06", 12, null], ["2018-03-01 01:44:08", 11, null], ["2018-03-23 04:59:28", 5, null], ["2018-03-15 21:24:13", 3, null], ["2018-03-03 20:44:06", 3, null], ["2018-03-19 22:38:10", 4, null], ["2018-03-15 09:38:49", 14, null]]}, "program_result_list": [["t1 = pd.merge(Invoices, Orders, on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, left_on='order_id', right_on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, left_on='order_id', right_on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, left_on='order_id', right_on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, left_on='order_id', right_on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, left_on='order_id', right_on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, left_on='order_id', right_on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, left_on='order_id', right_on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, left_on='order_id', right_on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, left_on='order_id', right_on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, left_on='order_id', right_on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, left_on='order_id', right_on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, left_on='order_id', right_on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, left_on='order_id', right_on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false], ["t1 = pd.merge(Invoices, Orders, left_on='order_id', right_on='order_id')\nanswer = t1[['invoice_date', 'order_id', 'order_details']]", false]]}
+{"example": {"db_id": "tracking_share_transactions", "query": "SELECT T1.Investor_details FROM  INVESTORS AS T1 JOIN TRANSACTIONS AS T2 ON T1.investor_id  =  T2.investor_id WHERE T2.share_count  >  100", "query_toks": ["SELECT", "T1.Investor_details", "FROM", "INVESTORS", "AS", "T1", "JOIN", "TRANSACTIONS", "AS", "T2", "ON", "T1.investor_id", "=", "T2.investor_id", "WHERE", "T2.share_count", ">", "100"], "query_toks_no_value": ["select", "t1", ".", "investor_details", "from", "investors", "as", "t1", "join", "transactions", "as", "t2", "on", "t1", ".", "investor_id", "=", "t2", ".", "investor_id", "where", "t2", ".", "share_count", ">", "value"], "question": "Show details of all investors if they make any transaction with share count greater than 100.", "question_toks": ["Show", "details", "of", "all", "investors", "if", "they", "make", "any", "transaction", "with", "share", "count", "greater", "than", "100", "."], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 3]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 9, false], null]]}, "select": [false, [[0, [0, [0, 2, false], null]]]], "where": [[false, 3, [0, [0, 13, false], null], 100.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Investors": ["investor_id", "Investor_details"], "Lots": ["lot_id", "investor_id", "lot_details"], "Ref_Transaction_Types": ["transaction_type_code", "transaction_type_description"], "Transactions": ["transaction_id", "investor_id", "transaction_type_code", "date_of_transaction", "amount_of_transaction", "share_count", "other_details"], "Sales": ["sales_transaction_id", "sales_details"], "Purchases": ["purchase_transaction_id", "purchase_details"], "Transactions_Lots": ["transaction_id", "lot_id"]}, "answer": [["k"], ["w"], ["z"], ["w"], ["t"], ["l"], ["l"], ["k"], ["z"], ["d"], ["w"], ["d"], ["l"]]}, "program_result_list": [["t1 = pd.merge(Investors, Transactions, left_on='investor_id', right_on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2['investor_details']", false], ["t1 = pd.merge(Investors, Transactions, on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2['investor_details']", false], ["t1 = pd.merge(Investors, Transactions, left_on='investor_id', right_on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2['investor_details']", false], ["t1 = pd.merge(Investors, Transactions, left_on='investor_id', right_on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2['investor_details']", false], ["t1 = pd.merge(Investors, Transactions, left_on='investor_id', right_on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2['investor_details']", false], ["t1 = pd.merge(Investors, Transactions, left_on='investor_id', right_on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2['investor_details']", false], ["t1 = pd.merge(Investors, Transactions, left_on='investor_id', right_on='investor_id')\nanswer = t1[t1['share_count'] > 100][['investor_details']]", false], ["t1 = pd.merge(Investors, Transactions, left_on='investor_id', right_on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2['investor_details']", false], ["t1 = pd.merge(Investors, Transactions, left_on='investor_id', right_on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2['investor_details']", false], ["t1 = pd.merge(Investors, Transactions, left_on='investor_id', right_on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2[['investor_details']]", false], ["t1 = pd.merge(Investors, Transactions, left_on='investor_id', right_on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2['investor_details']", false], ["t1 = pd.merge(Investors, Transactions, left_on='investor_id', right_on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2['investor_details']", false], ["t1 = pd.merge(Investors, Transactions, left_on='investor_id', right_on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2['investor_details']", false], ["t1 = pd.merge(Investors, Transactions, left_on='investor_id', right_on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2['investor_details']", false], ["t1 = pd.merge(Investors, Transactions, left_on='investor_id', right_on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2['investor_details']", false], ["t1 = pd.merge(Investors, Transactions, left_on='investor_id', right_on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2['investor_details']", false], ["t1 = pd.merge(Investors, Transactions, left_on='investor_id', right_on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2['investor_details']", false], ["t1 = pd.merge(Investors, Transactions, left_on='investor_id', right_on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2['investor_details']", false], ["t1 = pd.merge(Investors, Transactions, left_on='investor_id', right_on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2['investor_details']", false], ["t1 = pd.merge(Investors, Transactions, on='investor_id')\nt2 = t1[t1['share_count'] > 100]\nanswer = t2['investor_details']", false]]}
+{"example": {"db_id": "college_3", "query": "SELECT Fname FROM STUDENT ORDER BY Age DESC", "query_toks": ["SELECT", "Fname", "FROM", "STUDENT", "ORDER", "BY", "Age", "DESC"], "query_toks_no_value": ["select", "fname", "from", "student", "order", "by", "age", "desc"], "question": "What are the first names of students, ordered by age from greatest to least?", "question_toks": ["What", "are", "the", "first", "names", "of", "students", ",", "ordered", "by", "age", "from", "greatest", "to", "least", "?"], "sql": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[0, [0, [0, 3, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": ["desc", [[0, [0, 4, false], null]]], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Student": ["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], "Faculty": ["FacID", "Lname", "Fname", "Rank", "Sex", "Phone", "Room", "Building"], "Department": ["DNO", "Division", "DName", "Room", "Building", "DPhone"], "Member_of": ["FacID", "DNO", "Appt_Type"], "Course": ["CID", "CName", "Credits", "Instructor", "Days", "Hours", "DNO"], "Minor_in": ["StuID", "DNO"], "Enrolled_in": ["StuID", "CID", "Grade"], "Gradeconversion": ["lettergrade", "gradepoint"]}, "answer": [["Bruce"], ["Paul"], ["Sarah"], ["David"], ["Ian"], ["Shiela"], ["Lisa"], ["Dinesh"], ["Jandy"], ["Steven"], ["Michael"], ["David"], ["Eric"], ["Sarah"], ["Eric"], ["Tracy"], ["Eric"], ["George"], ["Eric"], ["Linda"], ["Andy"], ["Lisa"], ["Charles"], ["Arthur"], ["Stacy"], ["Mark"], ["Paul"], ["William"], ["Eric"], ["Derek"], ["Mark"], ["Michael"], ["Jun"], ["Susan"]]}, "program_result_list": [["answer = Student.sort_values('age', ascending=False)['fname']", false], ["answer = Student.sort_values('age', ascending=False)['fname']", false], ["answer = Student.sort_values('age', ascending=False)['fname']", false], ["t1 = Student.sort_values(by='age', ascending=False)\nanswer = t1['fname']", false], ["answer = Student.sort_values(by='age', ascending=False)['fname']", false], ["answer = Student.sort_values('age', ascending=False)['fname']", false], ["answer = Student.sort_values('age', ascending=False)['fname']", false], ["answer = Student.sort_values(by='age', ascending=False)['fname']", false], ["t1 = Student.sort_values(by='age', ascending=False)\nanswer = t1['fname']", false], ["answer = Student.sort_values('age', ascending=False)['fname']", false], ["answer = Student.sort_values('age', ascending=False)['fname']", false], ["t1 = Student.sort_values('age', ascending=False)\nanswer = t1['fname']", false], ["t1 = Student.sort_values('age', ascending=False)\nanswer = t1['fname']", false], ["answer = Student.sort_values('age', ascending=False)['fname']", false], ["answer = Student.sort_values(by='age', ascending=False)['fname']", false], ["answer = Student.sort_values(by='age', ascending=False)['fname']", false], ["answer = Student.sort_values('age', ascending=False)['fname']", false], ["answer = Student.sort_values('age', ascending=False)['fname']", false], ["answer = Student.sort_values('age', ascending=False)['fname']", false], ["answer = Student.sort_values(by='age', ascending=False).reset_index(drop=True)['fname']", false]]}
+{"example": {"db_id": "store_product", "query": "SELECT t3.District_name FROM store AS t1 JOIN store_district AS t2 ON t1.store_id  =  t2.store_id JOIN district AS t3 ON t2.district_id  =  t3.district_id WHERE t1.Type  =  \"City Mall\" INTERSECT SELECT t3.District_name FROM store AS t1 JOIN store_district AS t2 ON t1.store_id  =  t2.store_id JOIN district AS t3 ON t2.district_id  =  t3.district_id WHERE t1.Type  =  \"Village Store\"", "query_toks": ["SELECT", "t3.District_name", "FROM", "store", "AS", "t1", "JOIN", "store_district", "AS", "t2", "ON", "t1.store_id", "=", "t2.store_id", "JOIN", "district", "AS", "t3", "ON", "t2.district_id", "=", "t3.district_id", "WHERE", "t1.Type", "=", "``", "City", "Mall", "''", "INTERSECT", "SELECT", "t3.District_name", "FROM", "store", "AS", "t1", "JOIN", "store_district", "AS", "t2", "ON", "t1.store_id", "=", "t2.store_id", "JOIN", "district", "AS", "t3", "ON", "t2.district_id", "=", "t3.district_id", "WHERE", "t1.Type", "=", "``", "Village", "Store", "''"], "query_toks_no_value": ["select", "t3", ".", "district_name", "from", "store", "as", "t1", "join", "store_district", "as", "t2", "on", "t1", ".", "store_id", "=", "t2", ".", "store_id", "join", "district", "as", "t3", "on", "t2", ".", "district_id", "=", "t3", ".", "district_id", "where", "t1", ".", "type", "=", "value", "intersect", "select", "t3", ".", "district_name", "from", "store", "as", "t1", "join", "store_district", "as", "t2", "on", "t1", ".", "store_id", "=", "t2", ".", "store_id", "join", "district", "as", "t3", "on", "t2", ".", "district_id", "=", "t3", ".", "district_id", "where", "t1", ".", "type", "=", "value"], "question": "Find the names of districts where have both city mall and village store type stores.", "question_toks": ["Find", "the", "names", "of", "districts", "where", "have", "both", "city", "mall", "and", "village", "store", "type", "stores", "."], "sql": {"from": {"table_units": [["table_unit", 1], ["table_unit", 4], ["table_unit", 2]], "conds": [[false, 2, [0, [0, 8, false], null], [0, 21, false], null], "and", [false, 2, [0, [0, 22, false], null], [0, 14, false], null]]}, "select": [false, [[0, [0, [0, 15, false], null]]]], "where": [[false, 2, [0, [0, 10, false], null], "\"City Mall\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": {"from": {"table_units": [["table_unit", 1], ["table_unit", 4], ["table_unit", 2]], "conds": [[false, 2, [0, [0, 8, false], null], [0, 21, false], null], "and", [false, 2, [0, [0, 22, false], null], [0, 14, false], null]]}, "select": [false, [[0, [0, [0, 15, false], null]]]], "where": [[false, 2, [0, [0, 10, false], null], "\"Village Store\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "union": null, "except": null}, "db_table_headers": {"product": ["product_id", "product", "dimensions", "dpi", "pages_per_minute_color", "max_page_size", "interface"], "store": ["Store_ID", "Store_Name", "Type", "Area_size", "Number_of_product_category", "Ranking"], "district": ["District_ID", "District_name", "Headquartered_City", "City_Population", "City_Area"], "store_product": ["Store_ID", "Product_ID"], "store_district": ["Store_ID", "District_ID"]}, "answer": [["Khanewal District"]]}, "program_result_list": [["t1 = pd.merge(store, store_district, left_on='store_id', right_on='store_id')\nt2 = pd.merge(t1, district, left_on='district_id', right_on='district_id')\nt3 = t2[t2['type'] == 'city mall']['district_name']\nt4 = t2[t2['type'] == 'village store']['district_name']\nanswer = t3[t3.isin(t4)].unique()", false], ["t1 = pd.merge(store, store_district, left_on='store_id', right_on='store_id')\nt2 = pd.merge(t1, district, left_on='district_id', right_on='district_id')\nt3 = t2[t2['type'] == 'city mall']['district_name']\nt4 = t2[t2['type'] == 'village store']['district_name']\nanswer = t3.intersection(t4).to_frame().reset_index(drop=True)", false], ["t1 = pd.merge(store, store_district, left_on='store_id', right_on='store_id')\nt2 = pd.merge(t1, district, left_on='district_id', right_on='district_id')\nt3 = t2[t2['type'] == 'city mall']\nt4 = t2[t2['type'] == 'village store']\nt5 = pd.concat([t3, t4], axis=0)\nt6 = t5.groupby('district_name').size().rename('count')\nanswer = t6[t6 >= 2].to_frame().reset_index()", false], ["t1= pd.merge(store,store_district, on='store_id')\nt2= pd.merge(t1,district, on='district_id')\nt3= t2[t2['type']=='city mall']\nt4= t2[t2['type']=='village store']\nanswer= pd.merge(t3,t4, on='district_name')['district_name']", false], ["t1 = pd.merge(store, store_district, on='store_id')\nt2 = pd.merge(t1, district, on='district_id')", false], ["t1 = pd.merge(store, store_district, left_on='store_id', right_on='store_id')\nt2 = pd.merge(t1, district, left_on='district_id', right_on='district_id')\nt3 = t2[t2['type'] == 'city mall']\nt4 = t2[t2['type'] == 'village store']\nanswer = t3['district_name'][t3['district_name'].isin(t4['district_name'])].unique()", false], ["t1 = pd.merge(store, store_district, left_on='store_id', right_on='store_id')\nt2 = pd.merge(t1, district, left_on='district_id', right_on='district_id')\nt3 = t2[t2['type'] == 'city mall']\nt4 = t2[t2['type'] == 'village store']\nanswer = t3['district_name'].unique()[np.in1d(t3['district_name'].unique(), t4['district_name'].unique())]", false], ["t1 = store[store['type'] == 'city mall']\nt2 = pd.merge(t1, store_district, on='store_id')\nt3 = pd.merge(t2, district, on='district_id')\nt4 = t3[['district_name']]\nt5 = store[store['type'] == 'village store']\nt6 = pd.merge(t5, store_district, on='store_id')\nt7 = pd.merge(t6, district, on='district_id')\nt8 = t7[['district_name']]\nanswer = pd.merge(t4, t8, on='district_name')", false], ["t1 = pd.merge(store, store_district, on='store_id')\nt2 = pd.merge(t1, district, left_on='district_id', right_on='district_id')\nt3 = t2[t2['type'] == 'city mall']\nt4 = t2[t2['type'] == 'village store']\nt5 = pd.merge(t3, t4, on='district_name', how='inner')\nanswer = t5['district_name'].unique()", false], ["t1 = pd.merge(store, store_district, left_on='store_id', right_on='store_id')\nt2 = pd.merge(t1, district, left_on='district_id', right_on='district_id')\nt3 = t2[t2['type'] == 'city mall']\nt4 = t2[t2['type'] == 'village store']\nanswer = pd.merge(t3, t4, on='district_name')['district_name'].unique()", false], ["t1 = pd.merge(store, store_district, on='store_id')\nt2 = pd.merge(t1, district, on='district_id')\nt3 = t2[t2['type'] == 'city mall']\nt4 = t2[t2['type'] == 'village store']\nt5 = pd.merge(t3, t4, on='district_name', how='inner')\nanswer = t5['district_name'].unique()", false], ["t1 = pd.merge(store, store_district, on='store_id')\nt2 = pd.merge(t1, district, on='district_id')\nt3 = t2[t2['type'] == 'city mall']\nt4 = t2[t2['type'] == 'village store']\nt5 = pd.merge(t3, t4, on='district_name', suffixes=('_city_mall', '_village_store'))\nanswer = t5['district_name'].unique()", false], ["t1 = pd.merge(store, store_district, left_on='store_id', right_on='store_id')\nt2 = pd.merge(t1, district, left_on='district_id', right_on='district_id')\nt3 = t2[t2['type'] == 'city mall']\nt4 = t2[t2['type'] == 'village store']\nt5 = t3['district_name'].unique()\nt6 = t4['district_name'].unique()\nanswer = np.intersect1d(t5, t6)", false], ["t1 = pd.merge(store, store_district, left_on='store_id', right_on='store_id')\nt2 = pd.merge(t1, district, left_on='district_id', right_on='district_id')\nanswer1 = t2[t2['type'] == 'city mall']['district_name']\nanswer2 = t2[t2['type'] == 'village store']['district_name']\nanswer = answer1.merge(answer2, indicator=True, how='outer').query('_merge == \"both\"').drop('_merge', axis=1)", false], ["t1 = pd.merge(store, store_district, left_on='store_id', right_on='store_id')\nt2 = pd.merge(t1, district, left_on='district_id', right_on='district_id')\nt3 = t2[t2['type'] == 'city mall']\nt4 = t2[t2['type'] == 'village store']\nanswer = t3[['district_name']].merge(t4[['district_name']], on='district_name', how='inner')", false], ["\nt1 = pd.merge(store, store_district, left_on='store_id', right_on='store_id')\nt2 = pd.merge(t1, district, left_on='district_id', right_on='district_id')\nt3 = t2[t2['type'] == 'city mall']\nt4 = t2[t2['type'] == 'village store']\nanswer = t3.merge(t4, on='district_name', how='inner')['district_name'].unique()", false], ["t1 = pd.merge(store, store_district, left_on='store_id', right_on='store_id')\nt2 = pd.merge(t1, district, left_on='district_id', right_on='district_id')\nt3 = t2[t2['type'] == 'city mall']\nt4 = t2[t2['type'] == 'village store']\nt5 = t3.merge(t4, on='district_name', how='inner')\nanswer = t5['district_name'].unique()", false], ["t1 = pd.merge(store, store_district, left_on='store_id', right_on='store_id')\nt2 = pd.merge(t1, district, left_on='district_id', right_on='district_id')\nt3 = t2[t2['type'] == 'city mall']\nt4 = t2[t2['type'] == 'village store']\nt5 = pd.merge(t3, t4, on='district_name').drop_duplicates(subset='district_name')\nanswer = t5['district_name']", false], ["t1 = pd.merge(store, store_district, left_on='store_id', right_on='store_id')\nt2 = pd.merge(t1, district, left_on='district_id', right_on='district_id')\nt3 = t2[t2['type'] == 'city mall']\nt4 = t2[t2['type'] == 'village store']\nanswer = pd.merge(t3, t4, on='district_name', how='inner')['district_name'].unique()", false], ["t1 = pd.merge(store, store_district, left_on='store_id', right_on='store_id')\nt2 = pd.merge(t1, district, left_on='district_id', right_on='district_id')\nt3 = t2[t2['type'] == 'city mall']\nt4 = t2[t2['type'] == 'village store']\nanswer = t3['district_name'].intersection(t4['district_name'])", false]]}
\ No newline at end of file
diff --git a/stephen_playground/fifteen_fails_solution.jsonl b/stephen_playground/fifteen_fails_solution.jsonl
new file mode 100644
index 00000000..f6b7e790
--- /dev/null
+++ b/stephen_playground/fifteen_fails_solution.jsonl
@@ -0,0 +1,10 @@
+"t1 = pd.merge(campuses, faculty, left_on='Id', right_on='Campus')\nt2 = t1[(t1['Year_y'] == 2002) & (t1['County'] == 'Orange')]\nt3 = t1[(t1['Year_y'] == 2002) & (t1['Faculty'] > t2['Faculty'].max())]\nanswer = t3['Campus_x']"
+"t1 = pd.merge(characteristics, product_characteristics, left_on='characteristic_id', right_on='characteristic_id', sort=True)\nanswer = characteristics[~characteristics['characteristic_id'].isin(t1['characteristic_id'])][['characteristic_name', 'other_characteristic_details', 'characteristic_data_type']]\nanswer = answer.sort_values(by=['characteristic_name', 'other_characteristic_details', 'characteristic_data_type'])"
+"t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values(by='budget', ascending=False)\nanswer = [t2['salary'].mean(), t2.shape[0]]"
+"t1 = hotels.replace('', None).sort_values('price_range')\nanswer = t1.head(3)[['other_hotel_details', 'star_rating_code']]"
+"weather = weather.replace('', None)\nanswer = weather[weather['max_temperature_f'] > 85]['date']" NOTE: Sql counts empty values as greater than 85 count as annotation noise
+"t1 = pd.merge(assessment_notes, teachers, left_on='teacher_id', right_on='teacher_id')\nt2 = t1.groupby(['teacher_id', 'first_name'], as_index=False).size().sort_values(by='size', ascending=False).head(3)\nanswer = t2[['teacher_id', 'first_name']]"
+"t1 = pd.merge(drama_workshop_groups, services, left_on='workshop_group_id', right_on='workshop_group_id')\nanswer = t1[['store_phone', 'store_email_address']]" NOTE: sorting error due to SQL non-determinist result without order by
+"t1 = company.groupby('main_industry').sum()\nanswer = t1['market_value'].to_frame().reset_index()" NOTE: floating point error in sql solution
+"t1 = pd.merge(physician, trained_in, left_on='employeeid', right_on='physician')\nt2 = pd.merge(t1, procedures, left_on='treatment', right_on='code')\nt3 = t2[t2['cost'] > 5000]\nanswer = t3['name_x']"
+"courses.course_id = courses.course_id.astype('Int64')\nt1 = pd.merge(courses, student_course_registrations, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values('registration_date')['student_id']"
diff --git a/stephen_playground/fifteen_successes.jsonl b/stephen_playground/fifteen_successes.jsonl
new file mode 100644
index 00000000..6d841fb8
--- /dev/null
+++ b/stephen_playground/fifteen_successes.jsonl
@@ -0,0 +1,15 @@
+{"example": {"db_id": "flight_4", "query": "SELECT T1.name ,  T2.alid FROM airlines AS T1 JOIN routes AS T2 ON T1.alid  =  T2.alid GROUP BY T2.alid ORDER BY count(*) DESC LIMIT 10", "query_toks": ["SELECT", "T1.name", ",", "T2.alid", "FROM", "airlines", "AS", "T1", "JOIN", "routes", "AS", "T2", "ON", "T1.alid", "=", "T2.alid", "GROUP", "BY", "T2.alid", "ORDER", "BY", "count", "(", "*", ")", "DESC", "LIMIT", "10"], "query_toks_no_value": ["select", "t1", ".", "name", ",", "t2", ".", "alid", "from", "airlines", "as", "t1", "join", "routes", "as", "t2", "on", "t1", ".", "alid", "=", "t2", ".", "alid", "group", "by", "t2", ".", "alid", "order", "by", "count", "(", "*", ")", "desc", "limit", "value"], "question": "Find the names of the top 10 airlines that operate the most number of routes.", "question_toks": ["Find", "the", "names", "of", "the", "top", "10", "airlines", "that", "operate", "the", "most", "number", "of", "routes", "."], "sql": {"from": {"table_units": [["table_unit", 2], ["table_unit", 0]], "conds": [[false, 2, [0, [0, 18, false], null], [0, 6, false], null]]}, "select": [false, [[0, [0, [0, 19, false], null]], [0, [0, [0, 6, false], null]]]], "where": [], "groupBy": [[0, 6, false]], "having": [], "orderBy": ["desc", [[0, [3, 0, false], null]]], "limit": 10, "intersect": null, "union": null, "except": null}, "db_table_headers": {"routes": ["rid", "dst_apid", "dst_ap", "src_apid", "src_ap", "alid", "airline", "codeshare"], "airports": ["apid", "name", "city", "country", "x", "y", "elevation", "iata", "icao"], "airlines": ["alid", "name", "iata", "icao", "callsign", "country", "active"]}, "answer": [["Ryanair", 4296], ["American Airlines", 24], ["United Airlines", 5209], ["Delta Air Lines", 2009], ["US Airways", 5265], ["China Southern Airlines", 1767], ["China Eastern Airlines", 1758], ["Air China", 751], ["Southwest Airlines", 4547], ["easyJet", 2297]]}, "program_result_list": [["t1 = pd.merge(airlines, routes, on='alid')\nt2 = t1.groupby('alid').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(10).to_frame().reset_index()\nt4 = pd.merge(t3, airlines, on='alid')\nanswer = t4[['name_x', 'alid']]", false], ["t1 = pd.merge(airlines, routes, left_on='alid', right_on='alid')\nt2 = t1.groupby('alid').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(10).to_frame().reset_index()\nt4 = pd.merge(t3, airlines, left_on='alid', right_on='alid')\nanswer = t4[['name_y', 'alid']]", false], ["t1 = pd.merge(airlines, routes, on='alid')\nt2 = t1.groupby('alid').size().rename('count').sort_values(ascending=False)\nt3 = t2.head(10)\nt4 = t3.reset_index()\nt5 = pd.merge(t4, airlines, left_on='alid', right_on='alid')\nanswer = t5['name']", false], ["t1 = pd.merge(airlines, routes, on='alid')\nt2 = t1.groupby('alid').size().rename('count').sort_values(ascending=False).head(10).to_frame().reset_index()\nt3 = pd.merge(t2, airlines, left_on='alid', right_on='alid')\nanswer = t3['name_y']", false], ["t1 = pd.merge(airlines, routes, on='alid')\nt2 = t1.groupby(['alid', 'name'])['alid'].count().rename('count').sort_values(ascending=False).head(10).to_frame().reset_index()\nanswer = t2['name']", false], ["t1 = pd.merge(airlines, routes, left_on='alid', right_on='alid')\nt2 = t1.groupby('alid').size().rename('count').to_frame().reset_index()\nt3 = pd.merge(airlines, t2, left_on='alid', right_on='alid')\nanswer = t3.sort_values('count', ascending=False).head(10)[['name', 'alid']]", true], ["t1 = pd.merge(airlines, routes, left_on='alid', right_on='alid')\nt2 = t1.groupby('alid').size().rename('count').to_frame().reset_index()\nt3 = pd.merge(t2, airlines, left_on='alid', right_on='alid')\nanswer = t3[['name_y', 'alid']].sort_values('count', ascending=False).head(10)", false], ["t1 = pd.merge(airlines, routes, left_on='alid', right_on='alid')\nt2 = t1.groupby('alid').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(10).to_frame().reset_index()\nt4 = pd.merge(t3, airlines, left_on='alid', right_on='alid')\nanswer = t4[['name', 'alid']]", true], ["t1 = pd.merge(airlines, routes, left_on='alid', right_on='alid')\nt2 = t1.groupby('alid')['alid'].count()\nt3 = t2.sort_values(ascending=False).head(10).to_frame().reset_index()\nt4 = pd.merge(t3, airlines, left_on='alid', right_on='alid')\nanswer = t4[['name_x', 'alid']]", false], ["t1 = pd.merge(airlines, routes, left_on='alid', right_on='alid')\nt2 = t1.groupby(['alid', 'name']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(10).to_frame().reset_index()", false], ["t1 = pd.merge(routes, airlines, left_on='alid', right_on='alid')\nt2 = t1.groupby('alid').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(10).to_frame().reset_index()\nanswer = pd.merge(t3, airlines, left_on='alid', right_on='alid')[['name', 'alid']]", true], ["t1 = pd.merge(airlines, routes, left_on='alid', right_on='alid')\nt2 = t1.groupby('alid').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(10).to_frame().reset_index()\nt4 = pd.merge(t3, airlines, left_on='alid', right_on='alid')\nanswer = t4[['name_x', 'alid']]", false], ["t1 = pd.merge(airlines, routes, on='alid')\nt2 = t1.groupby(['alid', 'name']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(10).to_frame().reset_index()\nanswer = answer[['name', 'alid']]", true], ["t1 = pd.merge(airlines, routes, on='alid')\nt2 = t1.groupby(['alid', 'name']).size().rename('count')\nt3 = t2.sort_values(ascending=False).head(10).to_frame().reset_index()\nanswer = t3[['name', 'alid']]", true], ["t1 = pd.merge(airlines, routes, left_on='alid', right_on='alid')\nt2 = t1.groupby('alid').size().rename('count')\nt3 = pd.merge(t2.to_frame().reset_index(), airlines, left_on='alid', right_on='alid')\nanswer = t3[['name', 'alid']].sort_values('count', ascending=False).head(10)", false], ["t1 = pd.merge(airlines, routes, on='alid')\nt2 = t1.groupby('alid').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(10).to_frame().reset_index()\nt4 = pd.merge(t3, airlines, on='alid')\nanswer = t4[['name_x', 'alid']]", false], ["t1 = pd.merge(airlines, routes, on='alid')\nt2 = t1.groupby('alid').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(10).to_frame().reset_index()\nt4 = pd.merge(t3, airlines, on='alid')\nanswer = t4['name']", false], ["t1 = pd.merge(airlines, routes, left_on='alid', right_on='alid')\nt2 = t1.groupby(['alid', 'name']).size().rename('count').to_frame().reset_index()\nanswer = t2.sort_values(by='count', ascending=False).head(10)[['name', 'alid']]", true], ["t1 = pd.merge(airlines, routes, left_on='alid', right_on='alid')\nt2 = t1.groupby(['name', 'alid']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(10).to_frame().reset_index()", false], ["t1 = pd.merge(airlines, routes, left_on='alid', right_on='alid')\nt2 = t1.groupby('alid').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(10).to_frame().reset_index()\nt4 = pd.merge(t3, airlines, left_on='alid', right_on='alid')\nanswer = t4[['name_x', 'alid']]", false]]}
+{"example": {"db_id": "twitter_1", "query": "SELECT count(*) FROM follows GROUP BY f1", "query_toks": ["SELECT", "count", "(", "*", ")", "FROM", "follows", "GROUP", "BY", "f1"], "query_toks_no_value": ["select", "count", "(", "*", ")", "from", "follows", "group", "by", "value"], "question": "Find the number of followers for each user.", "question_toks": ["Find", "the", "number", "of", "followers", "for", "each", "user", "."], "sql": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[3, [0, [0, 0, false], null]]]], "where": [], "groupBy": [[0, 1, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"follows": ["f1", "f2"], "tweets": ["id", "uid", "text", "createdate"], "user_profiles": ["uid", "name", "email", "partitionid", "followers"]}, "answer": [[3], [1], [2], [2], [1]]}, "program_result_list": [["answer = follows.groupby('f1').size().rename('count').to_frame().reset_index()", false], ["t1 = follows.groupby('f1').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = follows.groupby('f1').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = follows.groupby('f1').size().rename('count').to_frame().reset_index()\nanswer = t1", false], ["t1 = follows.groupby('f1').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["answer = follows.groupby('f1').size().rename('count')", true], ["t1 = follows.groupby('f1').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = follows.groupby('f1').size().rename('count').to_frame().reset_index()\nanswer = t1", false], ["t1 = follows.groupby('f1').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = follows.groupby('f1').size().rename('count')\nanswer = t1.to_frame()", true], ["t1 = follows.groupby('f1').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = follows.groupby('f1').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = follows.groupby('f1').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = follows.groupby('f1').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = follows.groupby('f1').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = follows.groupby('f1').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = follows.groupby('f1').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = follows.groupby('f1').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = follows.groupby('f1').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = follows.groupby('f1').size().rename('count')\nanswer = t1.to_frame().reset_index()", false]]}
+{"example": {"db_id": "restaurant_1", "query": "SELECT Fname , Lname FROM Student WHERE Age  >  18 AND Major != 600 AND Sex = 'F';", "query_toks": ["SELECT", "Fname", ",", "Lname", "FROM", "Student", "WHERE", "Age", ">", "18", "AND", "Major", "!", "=", "600", "AND", "Sex", "=", "'F", "'", ";"], "query_toks_no_value": ["select", "fname", ",", "lname", "from", "student", "where", "age", ">", "value", "and", "major", "!", "=", "value", "and", "sex", "=", "value"], "question": "List all female students age is older than 18 who is not majoring in 600. List students' first name and last name.", "question_toks": ["List", "all", "female", "students", "age", "is", "older", "than", "18", "who", "is", "not", "majoring", "in", "600", ".", "List", "students", "'", "first", "name", "and", "last", "name", "."], "sql": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[0, [0, [0, 3, false], null]], [0, [0, [0, 2, false], null]]]], "where": [[false, 3, [0, [0, 4, false], null], 18.0, null], "and", [false, 7, [0, [0, 6, false], null], 600.0, null], "and", [false, 2, [0, [0, 5, false], null], "\"F\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Student": ["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], "Restaurant": ["ResID", "ResName", "Address", "Rating"], "Type_Of_Restaurant": ["ResID", "ResTypeID"], "Restaurant_Type": ["ResTypeID", "ResTypeName", "ResTypeDescription"], "Visits_Restaurant": ["StuID", "ResID", "Time", "Spent"]}, "answer": [["Lisa", "Cheng"], ["Sarah", "Smith"], ["Sarah", "Schmidt"]]}, "program_result_list": [["answer = Student[(Student['age'] > 18) & (Student['major'] != 600) & (Student['sex'] == 'f')][['fname', 'lname']]", false], ["answer = Student[(Student['age'] > 18) & (Student['major'] != 600) & (Student['sex'] == 'f')][['fname', 'lname']]", false], ["t1 = Student[Student['age'] > 18]\nt2 = t1[t1['major'] != 600]\nt3 = t2[t2['sex'] == 'f']\nanswer = t3[['fname', 'lname']]", false], ["t1 = Student[(Student['age'] > 18) & (Student['major'] != 600) & (Student['sex'] == 'f')]\nanswer = t1[['fname', 'lname']]", false], ["answer = Student[(Student['age'] > 18) & (Student['major'] != 600) & (Student['sex'] == 'f')][['fname', 'lname']]", false], ["answer = Student[(Student['age'] > 18) & (Student['major'] != 600) & (Student['sex'] == 'f')][['fname', 'lname']]", false], ["answer = Student[(Student['age'] > 18) & (Student['major'] != 600) & (Student['sex'] == 'f')][['fname', 'lname']]", false], ["answer = Student[(Student['age'] > 18) & (Student['major'] != 600) & (Student['sex'] == 'f')][['fname', 'lname']]", false], ["t1 = Student[Student['age'] > 18]\nt2 = t1[t1['major'] != 600]\nt3 = t2[t2['sex'] == 'f']\nanswer = t3[['fname', 'lname']]", false], ["answer = Student[(Student['age'] > 18) & (Student['major'] != 600) & (Student['sex'] == 'f')][['fname', 'lname']]", false], ["t1 = Student[Student['age'] > 18]\nt2 = t1[t1['major'] != 600]\nt3 = t2[t2['sex'] == 'f']\nanswer = t3[['fname', 'lname']]", false], ["answer = Student[(Student['age'] > 18) & (Student['major'] != 600) & (Student['sex'] == 'f')][['fname', 'lname']]", false], ["t1 = Student[(Student['age'] > 18) & (Student['major'] != 600) & (Student['sex'] == 'f')]\nanswer = t1[['fname', 'lname']]", false], ["answer = Student[(Student['age'] > 18) & (Student['major'] != 600) & (Student['sex'] == 'F')][['fname', 'lname']]", true], ["answer = Student[(Student['age'] > 18) & (Student['major'] != 600) & (Student['sex'] == 'f')][['fname', 'lname']]", false], ["t1 = Student[Student['age'] > 18]\nt2 = t1[t1['major'] != 600]\nt3 = t2[t2['sex'] == 'f']\nanswer = t3[['fname', 'lname']]", false], ["answer = Student[(Student['major'] != 600) & (Student['sex'] == 'f') & (Student['age'] > 18)][['fname', 'lname']]", false], ["t1 = Student[Student['age'] > 18]\nt2 = t1[t1['major'] != 600]\nt3 = t2[t2['sex'] == 'f']\nanswer = t3[['fname', 'lname']]", false], ["answer = Student[(Student['age'] > 18) & (Student['major'] != 600) & (Student['sex'] == 'f')][['fname', 'lname']]", false], ["t1 = Student[Student['age'] > 18]\nt2 = t1[t1['major'] != 600]\nt3 = t2[t2['sex'] == 'F']\nanswer = t3[['fname', 'lname']]", true]]}
+{"example": {"db_id": "hospital_1", "query": "SELECT name FROM procedures ORDER BY cost LIMIT 3", "query_toks": ["SELECT", "name", "FROM", "procedures", "ORDER", "BY", "cost", "LIMIT", "3"], "query_toks_no_value": ["select", "name", "from", "procedures", "order", "by", "cost", "limit", "value"], "question": "What are the three most costly procedures?", "question_toks": ["What", "are", "the", "three", "most", "costly", "procedures", "?"], "sql": {"from": {"table_units": [["table_unit", 3]], "conds": []}, "select": [false, [[0, [0, [0, 12, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": ["asc", [[0, [0, 13, false], null]]], "limit": 3, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Physician": ["EmployeeID", "Name", "Position", "SSN"], "Department": ["DepartmentID", "Name", "Head"], "Affiliated_With": ["Physician", "Department", "PrimaryAffiliation"], "Procedures": ["Code", "Name", "Cost"], "Trained_In": ["Physician", "Treatment", "CertificationDate", "CertificationExpires"], "Patient": ["SSN", "Name", "Address", "Phone", "InsuranceID", "PCP"], "Nurse": ["EmployeeID", "Name", "Position", "Registered", "SSN"], "Appointment": ["AppointmentID", "Patient", "PrepNurse", "Physician", "Start", "End", "ExaminationRoom"], "Medication": ["Code", "Name", "Brand", "Description"], "Prescribes": ["Physician", "Patient", "Medication", "Date", "Appointment", "Dose"], "Block": ["BlockFloor", "BlockCode"], "Room": ["RoomNumber", "RoomType", "BlockFloor", "BlockCode", "Unavailable"], "On_Call": ["Nurse", "BlockFloor", "BlockCode", "OnCallStart", "OnCallEnd"], "Stay": ["StayID", "Patient", "Room", "StayStart", "StayEnd"], "Undergoes": ["Patient", "Procedures", "Stay", "DateUndergoes", "Physician", "AssistingNurse"]}, "answer": [["Follicular Demiectomy"], ["Reverse Rhinopodoplasty"], ["Obtuse Pyloric Recombobulation"]]}, "program_result_list": [["answer = Procedures.sort_values('cost', ascending=False).head(3)['name']", false], ["t1 = Procedures.sort_values(by='cost', ascending=False).head(3).reset_index()\nanswer = t1['name']", false], ["t1 = Procedures.sort_values(by='cost', ascending=False)\nanswer = t1['name'].head(3)", false], ["t1 = Procedures.sort_values(by='cost', ascending=False).head(3).reset_index()\nanswer = t1['name']", false], ["t1 = Procedures.sort_values('cost', ascending=False).head(3)\nanswer = t1['name']", false], ["t1 = Procedures.sort_values(by='cost', ascending=False)\nanswer = t1['name'].head(3)", false], ["answer = Procedures.sort_values('cost', ascending=False).head(3)['name']", false], ["t1 = Procedures.sort_values('cost', ascending=False).head(3)\nanswer = t1['name']", false], ["answer = Procedures.sort_values('cost', ascending=False).head(3)['name']", false], ["answer = Procedures.sort_values(by='cost', ascending=False).head(3)['name']", false], ["t1 = Procedures.sort_values(by='cost', ascending=False)\nanswer = t1['name'].head(3)", false], ["answer = Procedures.sort_values('cost', ascending=False).head(3)['name']", false], ["t1 = Procedures.sort_values(by='cost', ascending=False).head(3)\nanswer = t1['name']", false], ["t1 = Procedures.nlargest(3, 'cost')\nanswer = t1['name']", false], ["t1 = Procedures.sort_values('cost', ascending=False).head(3).reset_index()\nanswer = t1['name']", false], ["t1 = Procedures.sort_values('cost').head(3)\nanswer = t1['name']", true], ["answer = Procedures.sort_values('cost', ascending=False).head(3)['name']", false], ["t1 = Procedures.sort_values('cost', ascending=False).head(3)\nanswer = t1['name']", false], ["t1 = Procedures.copy()\nt1.sort_values('cost', ascending=False, inplace=True)\nanswer = t1['name'].head(3)", false], ["answer = Procedures.sort_values(by='cost').head(3)", false]]}
+{"example": {"db_id": "college_3", "query": "SELECT DISTINCT T3.Fname FROM ENROLLED_IN AS T1 JOIN GRADECONVERSION AS T2 JOIN STUDENT AS T3 ON T1.Grade  =  T2.lettergrade AND T1.StuID  =  T3.StuID WHERE T2.gradepoint  >=  3.8", "query_toks": ["SELECT", "DISTINCT", "T3.Fname", "FROM", "ENROLLED_IN", "AS", "T1", "JOIN", "GRADECONVERSION", "AS", "T2", "JOIN", "STUDENT", "AS", "T3", "ON", "T1.Grade", "=", "T2.lettergrade", "AND", "T1.StuID", "=", "T3.StuID", "WHERE", "T2.gradepoint", ">", "=", "3.8"], "query_toks_no_value": ["select", "distinct", "t3", ".", "fname", "from", "enrolled_in", "as", "t1", "join", "gradeconversion", "as", "t2", "join", "student", "as", "t3", "on", "t1", ".", "grade", "=", "t2", ".", "lettergrade", "and", "t1", ".", "stuid", "=", "t3", ".", "stuid", "where", "t2", ".", "gradepoint", ">", "=", "value"], "question": "Find the distinct student first names of all students that have grade point at least 3.8 in one course.", "question_toks": ["Find", "the", "distinct", "student", "first", "names", "of", "all", "students", "that", "have", "grade", "point", "at", "least", "3.8", "in", "one", "course", "."], "sql": {"from": {"table_units": [["table_unit", 6], ["table_unit", 7], ["table_unit", 0]], "conds": [[false, 2, [0, [0, 37, false], null], [0, 38, false], null], "and", [false, 2, [0, [0, 35, false], null], [0, 1, false], null]]}, "select": [true, [[0, [0, [0, 3, false], null]]]], "where": [[false, 5, [0, [0, 39, false], null], 3.8, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Student": ["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], "Faculty": ["FacID", "Lname", "Fname", "Rank", "Sex", "Phone", "Room", "Building"], "Department": ["DNO", "Division", "DName", "Room", "Building", "DPhone"], "Member_of": ["FacID", "DNO", "Appt_Type"], "Course": ["CID", "CName", "Credits", "Instructor", "Days", "Hours", "DNO"], "Minor_in": ["StuID", "DNO"], "Enrolled_in": ["StuID", "CID", "Grade"], "Gradeconversion": ["lettergrade", "gradepoint"]}, "answer": [["Linda"], ["Tracy"], ["Dinesh"], ["Paul"], ["Andy"], ["Lisa"], ["Jandy"], ["Eric"], ["Derek"], ["David"], ["Steven"], ["Charles"], ["Susan"], ["Mark"], ["Bruce"], ["Michael"], ["Arthur"], ["Ian"], ["George"], ["Stacy"], ["Jun"], ["Sarah"], ["William"]]}, "program_result_list": [["t1 = pd.merge(Enrolled_in, Gradeconversion, left_on='grade', right_on='lettergrade')\nt2 = pd.merge(t1, Student, left_on='stuid', right_on='stuid')\nanswer = t2[t2['gradepoint'] >= 3.8]['fname'].unique()", false], ["t1 = pd.merge(Enrolled_in, Gradeconversion, left_on='grade', right_on='lettergrade')\nt2 = pd.merge(Student, t1, left_on='stuid', right_on='stuid')\nt3 = t2[t2['gradepoint'] >= 3.8]\nanswer = t3['fname'].unique()", true], ["t1 = pd.merge(Enrolled_in, Gradeconversion, left_on='grade', right_on='lettergrade')\nt2 = t1[t1['gradepoint'] >= 3.8]\nt3 = pd.merge(t2, Student, left_on='stuid', right_on='stuid')\nanswer = t3['fname'].unique()", false], ["t1 = pd.merge(Enrolled_in, Gradeconversion, left_on='grade', right_on='lettergrade')\nt2 = pd.merge(t1, Student, left_on='stuid', right_on='stuid')\nt3 = t2[t2['gradepoint'] >= 3.8]\nanswer = t3['fname'].unique()", false], ["t1 = pd.merge(Enrolled_in, Gradeconversion, left_on='grade', right_on='lettergrade')\nt2 = pd.merge(t1, Student, left_on='stuid', right_on='stuid')\nt3 = t2[t2['gradepoint'] >= 3.8]\nanswer = t3['fname'].unique()", false], ["t1 = pd.merge(Enrolled_in, Gradeconversion, left_on='grade', right_on='lettergrade')\nt2 = pd.merge(t1, Student, left_on='stuid', right_on='stuid')\nt3 = t2[t2['gradepoint'] >= 3.8]\nanswer = t3['fname'].unique()", false], ["t1 = pd.merge(Enrolled_in, Gradeconversion, on='grade')\nt2 = pd.merge(t1, Student, on='stuid')\nt3 = t2[t2['gradepoint'] >= 3.8]\nanswer = t3['fname'].unique()", false], ["t1 = pd.merge(Enrolled_in, Gradeconversion, left_on='grade', right_on='lettergrade')\nt2 = pd.merge(t1, Student, left_on='stuid', right_on='stuid')\nt3 = t2[t2['gradepoint'] >= 3.8]\nanswer = t3['fname'].unique()", false], ["t1 = pd.merge(Enrolled_in, Gradeconversion, on='grade')\nt2 = pd.merge(t1, Student, on='stuid')\nanswer = t2[t2['gradepoint'] >= 3.8]['fname'].unique()", false], ["t1 = pd.merge(Enrolled_in, Gradeconversion, left_on='grade', right_on='lettergrade')\nt2 = pd.merge(t1, Student, left_on='stuid', right_on='stuid')\nt3 = t2[t2['gradepoint'] >= 3.8]\nanswer = t3['fname'].unique()", false], ["t1 = pd.merge(Enrolled_in, Gradeconversion, left_on='grade', right_on='lettergrade')\nt2 = pd.merge(t1, Student, left_on='stuid', right_on='stuid')\nt3 = t2[t2['gradepoint'] >= 3.8]\nanswer = t3['fname'].unique()", false], ["t1 = pd.merge(Enrolled_in, Gradeconversion, left_on='grade', right_on='lettergrade')\nt2 = pd.merge(t1, Student, left_on='stuid', right_on='stuid')\nt3 = t2[t2['gradepoint'] >= 3.8]\nanswer = t3['fname'].unique()", false], ["t1 = pd.merge(Enrolled_in, Gradeconversion, left_on='grade', right_on='lettergrade')\nt2 = pd.merge(t1, Student, left_on='stuid', right_on='stuid')\nt3 = t2[t2['gradepoint'] >= 3.8]\nanswer = t3['fname'].unique()", false], ["t1 = pd.merge(Enrolled_in, Gradeconversion, left_on='grade', right_on='lettergrade')\nt2 = pd.merge(t1, Student, left_on='stuid', right_on='stuid')\nt3 = t2[t2['gradepoint'] >= 3.8]\nanswer = t3['fname'].unique()", false], ["t1 = pd.merge(Enrolled_in, Gradeconversion, left_on='grade', right_on='lettergrade')\nt2 = pd.merge(t1, Student, left_on='stuid', right_on='stuid')\nanswer = t2[t2['gradepoint'] >= 3.8]['fname'].unique()", false], ["t1 = pd.merge(Enrolled_in, Gradeconversion, left_on='grade', right_on='lettergrade')\nt2 = pd.merge(t1, Student, left_on='stuid', right_on='stuid')\nanswer = t2[t2['gradepoint'] >= 3.8]['fname'].unique()", false], ["t1 = pd.merge(Enrolled_in, Gradeconversion, left_on='grade', right_on='lettergrade')\nt2 = pd.merge(t1, Student, left_on='stuid', right_on='stuid')\nt3 = t2[t2['gradepoint'] >= 3.8]\nanswer = t3['fname'].unique()", false], ["t1 = pd.merge(Enrolled_in, Gradeconversion, left_on='grade', right_on='lettergrade')\nt2 = pd.merge(t1, Student, left_on='stuid', right_on='stuid')\nt3 = t2[t2['gradepoint'] >= 3.8]\nanswer = t3['fname'].unique()", false], ["t1 = pd.merge(Enrolled_in, Gradeconversion, left_on='grade', right_on='lettergrade', how='outer')\nt2 = pd.merge(t1, Student, left_on='stuid', right_on='stuid', how='outer')\nt3 = t2[t2['gradepoint'] >= 3.8]\nanswer = t3['fname'].unique()", false], ["t1 = pd.merge(Enrolled_in, Gradeconversion, left_on='grade', right_on='lettergrade')\nt2 = pd.merge(t1, Student, left_on='stuid', right_on='stuid')\nt3 = t2[t2['gradepoint'] >= 3.8]\nanswer = t3['fname'].unique()", false]]}
+{"example": {"db_id": "aircraft", "query": "SELECT Aircraft FROM aircraft WHERE Aircraft_ID NOT IN (SELECT Winning_Aircraft FROM MATCH)", "query_toks": ["SELECT", "Aircraft", "FROM", "aircraft", "WHERE", "Aircraft_ID", "NOT", "IN", "(", "SELECT", "Winning_Aircraft", "FROM", "MATCH", ")"], "query_toks_no_value": ["select", "aircraft", "from", "aircraft", "where", "aircraft_id", "not", "in", "(", "select", "winning_aircraft", "from", "match", ")"], "question": "List the names of aircrafts and that did not win any match.", "question_toks": ["List", "the", "names", "of", "aircrafts", "and", "that", "did", "not", "win", "any", "match", "."], "sql": {"from": {"table_units": [["table_unit", 1]], "conds": []}, "select": [false, [[0, [0, [0, 5, false], null]]]], "where": [[true, 8, [0, [0, 4, false], null], {"from": {"table_units": [["table_unit", 2]], "conds": []}, "select": [false, [[0, [0, [0, 16, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"pilot": ["Pilot_Id", "Name", "Age"], "aircraft": ["Aircraft_ID", "Aircraft", "Description", "Max_Gross_Weight", "Total_disk_area", "Max_disk_Loading"], "match": ["Round", "Location", "Country", "Date", "Fastest_Qualifying", "Winning_Pilot", "Winning_Aircraft"], "airport": ["Airport_ID", "Airport_Name", "Total_Passengers", "%_Change_2007", "International_Passengers", "Domestic_Passengers", "Transit_Passengers", "Aircraft_Movements", "Freight_Metric_Tonnes"], "airport_aircraft": ["ID", "Airport_ID", "Aircraft_ID"]}, "answer": []}, "program_result_list": [["t1 = pd.merge(aircraft, match, on='aircraft_id', how='left')\nt2 = t1[t1['winning_aircraft'].isnull()]['aircraft']\nanswer = t2", false], ["t1 = match['winning_aircraft'].unique()\nt2 = aircraft[~aircraft['aircraft_id'].isin(t1)]\nanswer = t2['aircraft']", false], ["t1 = aircraft[~aircraft['aircraft_id'].isin(match['winning_aircraft'])]\nanswer = t1['aircraft']", false], ["t1 = match[['winning_aircraft']]\nt2 = aircraft[['aircraft_id', 'aircraft']]\nt3 = pd.merge(t1, t2, left_on='winning_aircraft', right_on='aircraft_id')\nt4 = aircraft[['aircraft']]\nanswer = t4[~t4.aircraft.isin(t3.aircraft)]", false], ["t1 = match['winning_aircraft'].unique()\nanswer = aircraft[~aircraft['aircraft_id'].isin(t1)]['aircraft']", false], ["t1 = match['winning_aircraft'].unique()\nt2 = aircraft[~aircraft['aircraft_id'].isin(t1)]\nanswer = t2['aircraft']", false], ["t1 = aircraft[aircraft['aircraft_id'].isin(match['winning_aircraft']) == False]\nanswer = t1['aircraft']", false], ["t1 = match[['winning_aircraft']].drop_duplicates()\nt2 = pd.merge(aircraft, t1, left_on='aircraft_id', right_on='winning_aircraft', how='left')\nanswer = t2[t2['winning_aircraft'].isnull()]['aircraft']", false], ["t1 = match[['winning_aircraft']]\nt2 = aircraft[~aircraft['aircraft_id'].isin(t1['winning_aircraft'])]\nanswer = t2['aircraft']", false], ["t1 = match[['winning_aircraft']]\nt2 = aircraft[~aircraft['aircraft_id'].isin(t1)]\nanswer = t2['aircraft']", false], ["t1 = match.groupby('winning_aircraft').size().rename('count')\nt2 = t1[t1 == 0].to_frame().reset_index()\nt3 = pd.merge(aircraft, t2, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t3['aircraft']", true], ["t1 = match[['winning_aircraft']]\nt2 = pd.merge(t1, aircraft, left_on='winning_aircraft', right_on='aircraft_id', how='outer')\nt3 = t2[['aircraft', 'aircraft_id']].drop_duplicates()\nt4 = aircraft.merge(t3, how='left', on='aircraft_id')\nanswer = t4[t4['aircraft_id_y'].isna()][['aircraft']]", false], ["t1 = match['winning_aircraft'].unique()\nt2 = aircraft[~aircraft['aircraft_id'].isin(t1)]\nanswer = t2['aircraft']", false], ["t1 = match['winning_aircraft'].unique()\nt2 = aircraft[~aircraft['aircraft_id'].isin(t1)]\nanswer = t2['aircraft']", false], ["t1 = match['winning_aircraft'].unique()\nt2 = aircraft[~aircraft['aircraft_id'].isin(t1)]\nanswer = t2['aircraft']", false], ["t1 = match['winning_aircraft'].unique()\nt2 = aircraft[aircraft['aircraft_id'].isin(t1) == False]\nanswer = t2['aircraft']", false], ["t1 = match.groupby('winning_aircraft').size().rename('count')\nt2 = t1[t1 == 0].to_frame().reset_index()\nt3 = pd.merge(aircraft, t2, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t3['aircraft']", true], ["t1 = match[['winning_aircraft']].drop_duplicates()\nt2 = pd.merge(aircraft, t1, left_on='aircraft_id', right_on='winning_aircraft', how='outer', indicator=True)\nanswer = t2[t2['_merge'] == 'left_only']['aircraft']", false], ["t1 = match['winning_aircraft'].to_frame()\nt2 = pd.merge(aircraft, t1, left_on='aircraft_id', right_on='winning_aircraft', how='outer')\nt3 = t2[t2['winning_aircraft'].isnull()]\nanswer = t3['aircraft']", false], ["t1 = match[['winning_aircraft']].drop_duplicates()\nt2 = pd.merge(aircraft, t1, left_on='aircraft_id', right_on='winning_aircraft', how='outer', indicator=True)\nanswer = t2[t2['_merge'] == 'left_only'][['aircraft']]", false]]}
+{"example": {"db_id": "movie_1", "query": "SELECT rID FROM Rating EXCEPT SELECT rID FROM Rating WHERE stars  =  4", "query_toks": ["SELECT", "rID", "FROM", "Rating", "EXCEPT", "SELECT", "rID", "FROM", "Rating", "WHERE", "stars", "=", "4"], "query_toks_no_value": ["select", "rid", "from", "rating", "except", "select", "rid", "from", "rating", "where", "stars", "=", "value"], "question": "find the ids of reviewers who did not give 4 star.", "question_toks": ["find", "the", "ids", "of", "reviewers", "who", "did", "not", "give", "4", "star", "."], "sql": {"from": {"table_units": [["table_unit", 2]], "conds": []}, "select": [false, [[0, [0, [0, 7, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": {"from": {"table_units": [["table_unit", 2]], "conds": []}, "select": [false, [[0, [0, [0, 7, false], null]]]], "where": [[false, 2, [0, [0, 9, false], null], 4.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}}, "db_table_headers": {"Movie": ["mID", "title", "year", "director"], "Reviewer": ["rID", "name"], "Rating": ["rID", "mID", "stars", "ratingDate"]}, "answer": [[204], [206], [207], [208]]}, "program_result_list": [["t1 = Rating['rid'].to_frame()\nt2 = Rating[Rating['stars'] == 4]['rid'].to_frame()\nanswer = t1[~t1.isin(t2)].dropna()", false], ["t1 = Rating.groupby('rid').size()\nt2 = Rating[Rating['stars'] == 4].groupby('rid').size()\nanswer = t1[~t1.index.isin(t2.index)].to_frame().reset_index()", false], ["t1 = Rating[Rating['stars'] == 4]['rid']\nanswer = Rating[~Rating['rid'].isin(t1)]['rid']", false], ["t1 = Rating[Rating['stars'] == 4]\nt2 = Rating[~Rating['rid'].isin(t1['rid'])]\nanswer = t2['rid']", false], ["t1 = Rating[Rating['stars'] == 4]\nt2 = Rating.drop_duplicates(subset='rid')\nanswer = t2[~t2['rid'].isin(t1['rid'])]['rid']", true], ["t1 = Rating[Rating['stars'] == 4]\nt2 = Rating[~Rating['rid'].isin(t1['rid'])]\nanswer = t2['rid']", false], ["t1 = Rating[Rating['stars'] != 4]\nt2 = Rating[Rating['stars'] == 4]\nanswer = t1[~t1.rid.isin(t2.rid)]", false], ["t1 = Rating.groupby('rid').size().rename('count')\nanswer = t1[t1 > 0].to_frame().reset_index()", false], ["t1 = Rating[Rating['stars'] == 4]\nanswer = Rating[~Rating['rid'].isin(t1['rid'])]", false], ["t1 = Rating[Rating['stars'] != 4]\nt2 = Rating[Rating['stars'] == 4]\nanswer = pd.concat([t1, t2]).drop_duplicates(keep=False)['rid']", false], ["t1 = Rating[Rating['stars'] != 4]\nanswer = t1['rid']", false], ["t1 = Rating[Rating['stars'] == 4]\nt2 = Rating[~Rating['rid'].isin(t1['rid'])]\nanswer = t2['rid']", false], ["t1 = Rating[Rating['stars'] == 4]\nt2 = Rating[~Rating.isin(t1)].dropna()\nanswer = t2['rid'].unique()", false], ["t1 = Rating[Rating['stars'] == 4].drop(['mid', 'stars', 'ratingdate'], axis=1)\nt2 = Rating.drop(['mid', 'stars', 'ratingdate'], axis=1)\nanswer = t2[~t2.isin(t1)].dropna()", false], ["t1 = Rating[Rating['stars'] == 4]\nt2 = Rating[~Rating['rid'].isin(t1['rid'])]\nanswer = t2['rid']", false], ["t1 = Rating[Rating['stars'] != 4]['rid']\nt2 = Rating[Rating['stars'] == 4]['rid']\nanswer = pd.concat([t1, t2])\nanswer = answer.drop_duplicates(keep=False)", false], ["t1 = Rating[Rating['stars'] == 4]\nt2 = Rating[~Rating['rid'].isin(t1['rid'])]\nanswer = t2['rid'].unique()", true], ["t1 = Rating[Rating['stars'] == 4]\nt2 = Rating[~Rating['rid'].isin(t1['rid'])]\nanswer = t2['rid']", false], ["t1 = pd.merge(Reviewer, Rating, left_on='rid', right_on='rid')\nt2 = t1[t1['stars'] == 4]\nt3 = pd.merge(Reviewer, Rating, left_on='rid', right_on='rid')\nanswer = t3[~t3['rid'].isin(t2['rid'])]", false], ["t1 = Rating[Rating['stars'] != 4]['rid']\nt2 = Rating[Rating['stars'] == 4]['rid']\nanswer = t1[~t1.isin(t2)]", false]]}
+{"example": {"db_id": "gas_company", "query": "SELECT headquarters FROM company WHERE main_industry  =  'Banking' INTERSECT SELECT headquarters FROM company WHERE main_industry  =  'Oil and gas'", "query_toks": ["SELECT", "headquarters", "FROM", "company", "WHERE", "main_industry", "=", "'Banking", "'", "INTERSECT", "SELECT", "headquarters", "FROM", "company", "WHERE", "main_industry", "=", "'Oil", "and", "gas", "'"], "query_toks_no_value": ["select", "headquarters", "from", "company", "where", "main_industry", "=", "value", "intersect", "select", "headquarters", "from", "company", "where", "main_industry", "=", "value"], "question": "Show all headquarters with both a company in banking industry and a company in Oil and gas.", "question_toks": ["Show", "all", "headquarters", "with", "both", "a", "company", "in", "banking", "industry", "and", "a", "company", "in", "Oil", "and", "gas", "."], "sql": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[0, [0, [0, 4, false], null]]]], "where": [[false, 2, [0, [0, 5, false], null], "\"Banking\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[0, [0, [0, 4, false], null]]]], "where": [[false, 2, [0, [0, 5, false], null], "\"Oil and gas\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "union": null, "except": null}, "db_table_headers": {"company": ["Company_ID", "Rank", "Company", "Headquarters", "Main_Industry", "Sales_billion", "Profits_billion", "Assets_billion", "Market_Value"], "gas_station": ["Station_ID", "Open_Year", "Location", "Manager_Name", "Vice_Manager_Name", "Representative_Name"], "station_company": ["Station_ID", "Company_ID", "Rank_of_the_Year"]}, "answer": [["China"], ["UK"], ["USA"]]}, "program_result_list": [["t1 = company[company['main_industry'] == 'Banking']\nt2 = company[company['main_industry'] == 'Oil and gas']\nt3 = pd.merge(t1, t2, on='headquarters', how='inner')\nanswer = t3['headquarters'].unique()", true], ["t1 = company[company['main_industry'] == 'banking']\nt2 = company[company['main_industry'] == 'oil and gas']\nt3 = t1[t1['headquarters'].isin(t2['headquarters'])]\nanswer = t3['headquarters'].unique()", false], ["t1 = company[company['main_industry'] == 'banking']\nt2 = company[company['main_industry'] == 'oil and gas']\nt3 = pd.merge(t1, t2, on='headquarters', how='inner')\nanswer = t3['headquarters']", false], ["t1 = company[company['main_industry'] == 'banking']\nt2 = company[company['main_industry'] == 'oil and gas']\nanswer = t1.merge(t2, on='headquarters', how='inner')", false], ["t1 = company[company['main_industry'] == 'banking']['headquarters'].unique()\nt2 = company[company['main_industry'] == 'oil and gas']['headquarters'].unique()\nanswer = np.intersect1d(t1, t2)", false], ["t1 = company[company['main_industry'] == 'Banking']\nt2 = company[company['main_industry'] == 'Oil and gas']\nanswer = t1['headquarters'].intersection(t2['headquarters'])", false], ["t1 = company[company['main_industry'] == 'Banking']\nt2 = company[company['main_industry'] == 'Oil and gas']\nt3 = pd.merge(t1, t2, on='headquarters')\nanswer = t3['headquarters'].unique()", true], ["t1 = company[company['main_industry'] == 'banking']\nt2 = company[company['main_industry'] == 'oil and gas']\nt3 = pd.merge(t1, t2, left_on='headquarters', right_on='headquarters', how='inner')\nanswer = t3['headquarters']", false], ["t1 = company[company['main_industry'] == 'banking']\nt2 = company[company['main_industry'] == 'oil and gas']\nanswer = t1['headquarters'].isin(t2['headquarters'])", false], ["t1 = company[company['main_industry'] == 'banking']\nt2 = company[company['main_industry'] == 'oil and gas']\nt3 = pd.merge(t1, t2, on='headquarters', how='inner')\nanswer = t3['headquarters'].unique()", false], ["t1 = company[company['main_industry'] == 'banking']\nt2 = company[company['main_industry'] == 'oil and gas']\nanswer = pd.merge(t1, t2, on='headquarters', how='inner')['headquarters']", false], ["t1 = company[company['main_industry'] == 'banking']\nt2 = company[company['main_industry'] == 'oil and gas']\nt3 = t1['headquarters'].unique()\nt4 = t2['headquarters'].unique()\nanswer = np.intersect1d(t3, t4)", false], ["t1 = company[company.main_industry == 'banking']\nt2 = company[company.main_industry == 'oil and gas']\nt3 = t1.merge(t2, on='headquarters', how='inner')\nanswer = t3['headquarters']", false], ["t1 = company[company['main_industry'] == 'banking']['headquarters']\nt2 = company[company['main_industry'] == 'oil and gas']['headquarters']\nanswer = t1.intersection(t2)", false], ["t1 = company[company['main_industry'] == 'Banking']\nt2 = company[company['main_industry'] == 'Oil and gas']\nt3 = t1.merge(t2, on='headquarters', how='inner')\nanswer = t3['headquarters'].unique()", true], ["t1 = company[company['main_industry'] == 'Banking']\nt2 = company[company['main_industry'] == 'Oil and gas']\nanswer = t1['headquarters'].isin(t2['headquarters'])", false], ["t1 = company[company['main_industry'] == 'Banking']\nt2 = company[company['main_industry'] == 'Oil and gas']\nanswer = pd.merge(t1, t2, on='headquarters')['headquarters']", true], ["t1 = company[company['main_industry'] == 'banking']\nt2 = company[company['main_industry'] == 'oil and gas']\nt3 = t1[t1['headquarters'].isin(t2['headquarters'])].drop_duplicates(subset='headquarters')\nanswer = t3['headquarters']", false], ["t1 = company[company['main_industry'] == 'banking']\nt2 = company[company['main_industry'] == 'oil and gas']\nanswer = t1[t1['headquarters'].isin(t2['headquarters'])]['headquarters'].unique()", false], ["t1 = company[company['main_industry'] == 'banking']\nt2 = company[company['main_industry'] == 'oil and gas']\nanswer = t1.merge(t2, on='headquarters', how='inner')['headquarters'].unique()", false]]}
+{"example": {"db_id": "college_2", "query": "SELECT avg(T1.salary) ,  count(*) FROM instructor AS T1 JOIN department AS T2 ON T1.dept_name  =  T2.dept_name ORDER BY T2.budget DESC LIMIT 1", "query_toks": ["SELECT", "avg", "(", "T1.salary", ")", ",", "count", "(", "*", ")", "FROM", "instructor", "AS", "T1", "JOIN", "department", "AS", "T2", "ON", "T1.dept_name", "=", "T2.dept_name", "ORDER", "BY", "T2.budget", "DESC", "LIMIT", "1"], "query_toks_no_value": ["select", "avg", "(", "t1", ".", "salary", ")", ",", "count", "(", "*", ")", "from", "instructor", "as", "t1", "join", "department", "as", "t2", "on", "t1", ".", "dept_name", "=", "t2", ".", "dept_name", "order", "by", "t2", ".", "budget", "desc", "limit", "value"], "question": "Find the number and averaged salary of all instructors who are in the department with the highest budget.", "question_toks": ["Find", "the", "number", "and", "averaged", "salary", "of", "all", "instructors", "who", "are", "in", "the", "department", "with", "the", "highest", "budget", "."], "sql": {"from": {"table_units": [["table_unit", 3], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 13, false], null], [0, 4, false], null]]}, "select": [false, [[5, [0, [0, 14, false], null]], [3, [0, [0, 0, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": ["desc", [[0, [0, 6, false], null]]], "limit": 1, "intersect": null, "union": null, "except": null}, "db_table_headers": {"classroom": ["building", "room_number", "capacity"], "department": ["dept_name", "building", "budget"], "course": ["course_id", "title", "dept_name", "credits"], "instructor": ["ID", "name", "dept_name", "salary"], "section": ["course_id", "sec_id", "semester", "year", "building", "room_number", "time_slot_id"], "teaches": ["ID", "course_id", "sec_id", "semester", "year"], "student": ["ID", "name", "dept_name", "tot_cred"], "takes": ["ID", "course_id", "sec_id", "semester", "year", "grade"], "advisor": ["s_ID", "i_ID"], "time_slot": ["time_slot_id", "day", "start_hr", "start_min", "end_hr", "end_min"], "prereq": ["course_id", "prereq_id"]}, "answer": [[77600.18819999999, 50]]}, "program_result_list": [["t1 = pd.merge(instructor, department, on='dept_name')\nt2 = t1.sort_values('budget', ascending=False).head(1)\nanswer = [t2['salary'].mean(), len(t2)]", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values(by='budget', ascending=False).head(1)\nanswer = [t2['salary'].mean(), t2['salary'].count()]", false], ["t1 = pd.merge(instructor, department, on='dept_name')\nt2 = t1.sort_values(by='budget', ascending=False).head(1)\nanswer = t2[['salary', 'id']].mean()", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values('budget', ascending=False).head(1)\nanswer = [t2['salary'].mean(), t2['salary'].count()]", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values('budget', ascending=False).head(1)\nanswer = t1.agg({'salary': ['mean', 'count']})", true], ["t1 = pd.merge(instructor, department, on='dept_name')\nt2 = t1.groupby('dept_name').agg({'salary': {'avg_salary': 'mean', 'num_instructors': 'count'}}).reset_index()\nt2.columns = t2.columns.droplevel()\nt3 = t2.sort_values(by='budget', ascending=False).head(1)\nanswer = t3[['avg_salary', 'num_instructors']]", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.groupby(['dept_name', 'budget']).agg({'salary': ['mean', 'count']}).reset_index()\nt3 = t2.sort_values(by=('budget', ''), ascending=False).head(1).reset_index()\nanswer = t3[['salary', 'budget']]", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values(by='budget', ascending=False).head(1)\nanswer = [t2['salary'].mean(), t2['salary'].count()]", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values(by='budget', ascending=False).head(1)\nanswer = t2[['salary']].mean()\nanswer = answer.append(pd.Series({'salary': t2.shape[0]}))", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1[['salary', 'budget']].sort_values('budget', ascending=False).head(1)\nanswer = [t2['salary'].mean(), t2['salary'].size]", false], ["t1 = pd.merge(department, instructor, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values(by=['budget'], ascending=[False]).head(1).reset_index()\nt3 = t2.groupby('id').size().rename('count')\nt4 = t2['salary'].mean()\nt5 = pd.DataFrame({'avg(t1.salary)': [t4], 'count(*)': [t3.iloc[0]]})\nanswer = t5", false], ["t1 = pd.merge(instructor, department, on='dept_name')\nt2 = t1.sort_values(by='budget', ascending=False).head(1)\nanswer = [t2['salary'].mean(), t2.shape[0]]", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values(by='budget', ascending=False)\nanswer = [t2['salary'].mean(), t2['salary'].count()]", true], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values(by='budget', ascending=False).head(1)\nanswer = t2[['salary', 'dept_name']].agg(['mean', 'count'])", false], ["t1 = pd.merge(instructor, department, on='dept_name')\nt2 = t1.groupby('dept_name').agg({'salary': 'mean', 'budget': 'max'}).rename(columns={'salary': 'avg_salary'})\nanswer = t2.sort_values('budget', ascending=False).head(1)", false], ["t1 = pd.merge(instructor, department, on='dept_name')\nt2 = t1.sort_values('budget', ascending=False).head(1)\nanswer = pd.DataFrame([[t2['salary'].mean(), t2['salary'].size]], columns=['avg(t1.salary)', 'count(*)'])", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.sort_values('budget', ascending=False).head(1)\nanswer = t2[['salary']].mean().append(t2[['salary']].count())", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.groupby('dept_name')['salary'].agg(['mean', 'count'])\nt3 = t2.sort_values(by='budget', ascending=False).head(1).reset_index()\nanswer = t3", false], ["t1 = pd.merge(department, instructor, left_on='dept_name', right_on='dept_name')\nt2 = t1.groupby(['dept_name', 'budget']).agg({'salary':['mean', 'count']}).reset_index()\nt3 = t2.sort_values(by=[('budget', '')], ascending=False).head(1).reset_index(drop=True)\nanswer = t3[('salary', 'mean')]", false], ["t1 = pd.merge(instructor, department, left_on='dept_name', right_on='dept_name')\nt2 = t1.groupby(['dept_name', 'budget']).agg({'salary': ['mean', 'count']}).reset_index()\nt2.columns = ['dept_name', 'budget', 'avg_salary', 'count']\nt3 = t2.sort_values('budget', ascending=False).head(1)\nanswer = t3[['avg_salary', 'count']]", false]]}
+{"example": {"db_id": "station_weather", "query": "SELECT min(low_temperature) ,  max(wind_speed_mph) FROM weekly_weather", "query_toks": ["SELECT", "min", "(", "low_temperature", ")", ",", "max", "(", "wind_speed_mph", ")", "FROM", "weekly_weather"], "query_toks_no_value": ["select", "min", "(", "low_temperature", ")", ",", "max", "(", "wind_speed_mph", ")", "from", "weekly_weather"], "question": "show the lowest low temperature and highest wind speed in miles per hour.", "question_toks": ["show", "the", "lowest", "low", "temperature", "and", "highest", "wind", "speed", "in", "miles", "per", "hour", "."], "sql": {"from": {"table_units": [["table_unit", 3]], "conds": []}, "select": [false, [[2, [0, [0, 17, false], null]], [1, [0, [0, 19, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"train": ["id", "train_number", "name", "origin", "destination", "time", "interval"], "station": ["id", "network_name", "services", "local_authority"], "route": ["train_id", "station_id"], "weekly_weather": ["station_id", "day_of_week", "high_temperature", "low_temperature", "precipitation", "wind_speed_mph"]}, "answer": [[46, 24]]}, "program_result_list": [["t1 = weekly_weather.min()\nt2 = t1[['low_temperature', 'wind_speed_mph']]\nanswer = t2.to_frame().transpose()", false], ["answer = weekly_weather[['low_temperature', 'wind_speed_mph']].agg(['min', 'max'])", false], ["t1 = weekly_weather.agg({'low_temperature': 'min', 'wind_speed_mph': 'max'})\nanswer = t1", true], ["answer = weekly_weather[['low_temperature', 'wind_speed_mph']].agg({'low_temperature': 'min', 'wind_speed_mph': 'max'})", true], ["answer = weekly_weather[['low_temperature', 'wind_speed_mph']].agg(['min', 'max'])", false], ["answer = weekly_weather[['low_temperature', 'wind_speed_mph']].agg(['min', 'max'])", false], ["t1 = weekly_weather.groupby(['station_id', 'day_of_week']).agg({'low_temperature': 'min', 'wind_speed_mph': 'max'})\nanswer = t1[['low_temperature', 'wind_speed_mph']]", false], ["t1 = weekly_weather.groupby().agg({'low_temperature': 'min', 'wind_speed_mph': 'max'})\nanswer = t1", false], ["t1 = weekly_weather.groupby(['station_id', 'day_of_week']).size().rename('count')\nt2= t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t2[['low_temperature', 'wind_speed_mph']]", false], ["t1 = weekly_weather['low_temperature'].min()\nt2 = weekly_weather['wind_speed_mph'].max()\nanswer = pd.DataFrame([[t1, t2]], columns=['min(low_temperature)', 'max(wind_speed_mph)'])", true], ["t1 = weekly_weather.agg({'low_temperature': 'min', 'wind_speed_mph': 'max'})\nanswer = t1.to_frame().transpose()", true], ["t1 = weekly_weather[['low_temperature', 'wind_speed_mph']].agg(['min', 'max'])\nanswer = t1.iloc[0]", false], ["t1 = weekly_weather[['low_temperature', 'wind_speed_mph']]\nanswer = t1.agg(['min', 'max'])", false], ["answer = weekly_weather[['low_temperature', 'wind_speed_mph']].agg(['min', 'max'])", false], ["answer = weekly_weather[['low_temperature', 'wind_speed_mph']].agg({'low_temperature': 'min', 'wind_speed_mph': 'max'})", true], ["answer = weekly_weather[['low_temperature', 'wind_speed_mph']].agg(['min', 'max'])", false], ["answer = weekly_weather.agg(['min', 'max'])[['low_temperature', 'wind_speed_mph']]", false], ["t1 = weekly_weather.agg({'low_temperature':'min', 'wind_speed_mph':'max'})\nanswer = t1.to_frame().reset_index()", false], ["answer = weekly_weather[['low_temperature', 'wind_speed_mph']].agg(['min', 'max'])", false], ["t1 = weekly_weather[['low_temperature', 'wind_speed_mph']]\nanswer = t1.agg({'low_temperature': np.min, 'wind_speed_mph': np.max})", false]]}
+{"example": {"db_id": "student_1", "query": "SELECT classroom ,  count(*) FROM list WHERE grade  =  \"4\" GROUP BY classroom", "query_toks": ["SELECT", "classroom", ",", "count", "(", "*", ")", "FROM", "list", "WHERE", "grade", "=", "``", "4", "''", "GROUP", "BY", "classroom"], "query_toks_no_value": ["select", "classroom", ",", "count", "(", "*", ")", "from", "list", "where", "grade", "=", "value", "group", "by", "classroom"], "question": "Report the total number of students for each fourth-grade classroom.", "question_toks": ["Report", "the", "total", "number", "of", "students", "for", "each", "fourth-grade", "classroom", "."], "sql": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[0, [0, [0, 4, false], null]], [3, [0, [0, 0, false], null]]]], "where": [[false, 2, [0, [0, 3, false], null], "\"4\"", null]], "groupBy": [[0, 4, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"list": ["LastName", "FirstName", "Grade", "Classroom"], "teachers": ["LastName", "FirstName", "Classroom"]}, "answer": [[108, 2], [110, 6], [111, 4]]}, "program_result_list": [["t1 = list[list['grade'] == '4']\nanswer = t1.groupby('classroom').size().rename('count')", false], ["t1 = list[list['grade'] == '4']\nt2 = t1.groupby('classroom').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = list[list['grade'] == '4']\nt2 = t1.groupby('classroom').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = list[list['grade'] == 4]\nt2 = t1.groupby('classroom').size().rename('count')\nanswer = t2.to_frame().reset_index()", true], ["t1 = list[list['grade'] == '4']\nt2 = t1.groupby('classroom').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["answer = list[list['grade'] == '4'].groupby('classroom').size().rename('count').to_frame().reset_index()", false], ["t1 = list[list['grade'] == '4']\nt2 = t1.groupby('classroom').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = list[list['grade'] == '4'].groupby('classroom').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["answer = list[list['grade'] == '4'].groupby('classroom').size().rename('count')", false], ["t1 = list[list['grade'] == '4']\nt2 = t1.groupby('classroom').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = list[list['grade'] == '4']\nt2 = t1.groupby('classroom').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = list[list['grade'] == '4']\nt2 = t1.groupby('classroom').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = list[list['grade'] == '4']\nanswer = t1.groupby(['classroom']).size().rename('count')", false], ["t1 = list[list['grade'] == '4']\nt2 = t1.groupby('classroom').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = list[list['grade'] == '4']\nt2 = t1.groupby('classroom').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["answer = list[list['grade'] == '4'].groupby('classroom').size().rename('count').to_frame().reset_index()", false], ["t1 = list[list['grade'] == '4']\nt2 = t1.groupby('classroom').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = list[list['grade']=='4']\nt2 = t1.groupby('classroom').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = list[list['grade'] == '4']\nt2 = t1.groupby('classroom').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = list[list['grade'] == '4']\nt2 = t1.groupby('classroom').size().rename('count')\nanswer = t2.to_frame().reset_index()", false]]}
+{"example": {"db_id": "tracking_software_problems", "query": "SELECT count(*) ,  T2.product_id FROM problems AS T1 JOIN product AS T2 ON T1.product_id = T2.product_id GROUP BY T2.product_id", "query_toks": ["SELECT", "count", "(", "*", ")", ",", "T2.product_id", "FROM", "problems", "AS", "T1", "JOIN", "product", "AS", "T2", "ON", "T1.product_id", "=", "T2.product_id", "GROUP", "BY", "T2.product_id"], "query_toks_no_value": ["select", "count", "(", "*", ")", ",", "t2", ".", "product_id", "from", "problems", "as", "t1", "join", "product", "as", "t2", "on", "t1", ".", "product_id", "=", "t2", ".", "product_id", "group", "by", "t2", ".", "product_id"], "question": "For each product with some problems, list the count of problems and the product id.", "question_toks": ["For", "each", "product", "with", "some", "problems", ",", "list", "the", "count", "of", "problems", "and", "the", "product", "id", "."], "sql": {"from": {"table_units": [["table_unit", 5], ["table_unit", 3]], "conds": [[false, 2, [0, [0, 22, false], null], [0, 14, false], null]]}, "select": [false, [[3, [0, [0, 0, false], null]], [0, [0, [0, 14, false], null]]]], "where": [], "groupBy": [[0, 14, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Problem_Category_Codes": ["problem_category_code", "problem_category_description"], "Problem_Log": ["problem_log_id", "assigned_to_staff_id", "problem_id", "problem_category_code", "problem_status_code", "log_entry_date", "log_entry_description", "log_entry_fix", "other_log_details"], "Problem_Status_Codes": ["problem_status_code", "problem_status_description"], "Product": ["product_id", "product_name", "product_details"], "Staff": ["staff_id", "staff_first_name", "staff_last_name", "other_staff_details"], "Problems": ["problem_id", "product_id", "closure_authorised_by_staff_id", "reported_by_staff_id", "date_problem_reported", "date_problem_closed", "problem_description", "other_problem_details"]}, "answer": [[4, 1], [2, 2], [3, 4], [1, 5], [1, 6], [1, 7], [1, 8], [1, 13], [1, 15]]}, "program_result_list": [["t1 = pd.merge(Problems, Product, left_on='product_id', right_on='product_id')\nt2 = t1.groupby('product_id').size().rename('count').to_frame()\nanswer = t2.reset_index()", false], ["t1 = pd.merge(Problems, Product, left_on='product_id', right_on='product_id')\nt2 = t1.groupby('product_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Problems, Product, left_on='product_id', right_on='product_id')\nt2 = t1.groupby('product_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Problems, Product, left_on='product_id', right_on='product_id')\nt2 = t1.groupby('product_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Problems, Product, left_on='product_id', right_on='product_id')\nt2 = t1.groupby('product_id').size().rename('count').to_frame().reset_index()\nanswer = t2[['count', 'product_id']]", true], ["t1 = pd.merge(Problems, Product, on='product_id')\nt2 = t1.groupby('product_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Problems, Product, on='product_id')\nt2 = t1.groupby('product_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Problems, Product, left_on='product_id', right_on='product_id')\nt2 = t1.groupby('product_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Problems, Product, left_on='product_id', right_on='product_id')\nt2 = t1.groupby('product_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Problems, Product, left_on='product_id', right_on='product_id')\nt2 = t1.groupby('product_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Problems, Product, left_on='product_id', right_on='product_id')\nt2 = t1.groupby(['product_id']).size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Problems, Product, on='product_id')\nt2 = t1.groupby('product_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Problems, Product, left_on='product_id', right_on='product_id')\nt2 = t1.groupby('product_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Problems, Product, on='product_id')\nt2 = t1['product_id'].value_counts().rename('count').to_frame().reset_index()\nanswer = t2", false], ["t1 = pd.merge(Problems, Product, left_on='product_id', right_on='product_id')\nanswer = t1.groupby('product_id').size().rename('count').to_frame().reset_index()", false], ["t1 = pd.merge(Problems, Product, left_on='product_id', right_on='product_id')\nt2 = t1['product_id'].value_counts().to_frame().reset_index()\nanswer = t2.rename(columns={'index': 'product_id', 'product_id': 'count'})", false], ["t1 = pd.merge(Problems, Product, left_on='product_id', right_on='product_id')\nt2 = t1.groupby('product_id').size().rename('count').to_frame().reset_index()\nanswer = t2", false], ["t1 = pd.merge(Problems, Product, on='product_id')\nt2 = t1.groupby('product_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Problems, Product, left_on='product_id', right_on='product_id')\nt2 = t1.groupby('product_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Problems, Product, left_on='product_id', right_on='product_id')\nt2 = t1.groupby('product_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false]]}
+{"example": {"db_id": "dorm_1", "query": "SELECT count(*) ,  sum(student_capacity) ,  gender FROM dorm GROUP BY gender", "query_toks": ["SELECT", "count", "(", "*", ")", ",", "sum", "(", "student_capacity", ")", ",", "gender", "FROM", "dorm", "GROUP", "BY", "gender"], "query_toks_no_value": ["select", "count", "(", "*", ")", ",", "sum", "(", "student_capacity", ")", ",", "gender", "from", "dorm", "group", "by", "gender"], "question": "Find the number of dorms and total capacity for each gender.", "question_toks": ["Find", "the", "number", "of", "dorms", "and", "total", "capacity", "for", "each", "gender", "."], "sql": {"from": {"table_units": [["table_unit", 1]], "conds": []}, "select": [false, [[3, [0, [0, 0, false], null]], [4, [0, [0, 11, false], null]], [0, [0, [0, 12, false], null]]]], "where": [], "groupBy": [[0, 12, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Student": ["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], "Dorm": ["dormid", "dorm_name", "student_capacity", "gender"], "Dorm_amenity": ["amenid", "amenity_name"], "Has_amenity": ["dormid", "amenid"], "Lives_in": ["stuid", "dormid", "room_number"]}, "answer": [[1, 128, "F"], [1, 116, "M"], [5, 1136, "X"]]}, "program_result_list": [["t1 = Dorm.groupby('gender').agg({'dormid' : 'count', 'student_capacity' : 'sum'})\nanswer = t1.reset_index()", false], ["answer = Dorm.groupby('gender').agg({'dormid':'count', 'student_capacity':'sum'})", false], ["t1 = Dorm.groupby('gender').agg({'dormid':'count','student_capacity':'sum'})\nt2 = t1.rename(columns={'dormid': 'count'})\nanswer = t2", false], ["t1 = Dorm.groupby('gender').size().rename('count')\nt2 = Dorm.groupby('gender')['student_capacity'].sum().rename('student_capacity')\nanswer = pd.merge(t1, t2, on='gender').to_frame().reset_index()", false], ["t1 = Dorm.groupby('gender')['student_capacity'].sum().rename('total_capacity')\nt2 = Dorm.groupby('gender').size().rename('count')\nanswer = pd.merge(t1, t2, on='gender')", false], ["t1 = Dorm.groupby(['gender']).size().rename('count')\nt2 = Dorm.groupby(['gender'])['student_capacity'].sum().rename('sum_student_capacity')\nanswer = pd.concat([t1, t2], axis=1)", false], ["t1 = Dorm.groupby('gender').agg({'dormid': 'count', 'student_capacity': 'sum'}).rename(columns={'dormid': 'count', 'student_capacity': 'sum'}).reset_index()\nanswer = t1[['count', 'sum', 'gender']]", true], ["t1 = Dorm.groupby('gender').agg({'dormid': 'count', 'student_capacity': 'sum'})\nanswer = t1.rename(columns={'dormid':'count', 'student_capacity': 'sum'})", false], ["t1 = Dorm.groupby('gender').agg({'dormid': 'count', 'student_capacity': 'sum'})\nanswer = t1", false], ["t1 = Dorm.groupby('gender').size().rename('count')\nt2 = Dorm.groupby('gender').sum()['student_capacity']\nanswer = pd.concat([t1, t2], axis=1).reset_index()", false], ["answer = Dorm.groupby('gender').agg({'dormid':'count', 'student_capacity':'sum'})", false], ["t1 = pd.merge(Dorm, Dorm_amenity, on='amenid')\nanswer = t1.groupby(['dormid', 'dorm_name', 'gender']).size().rename('count').to_frame().reset_index()", false], ["t1 = Dorm.groupby('gender').agg({'dormid': 'count', 'student_capacity': 'sum'})\nt1.columns = ['count', 'sum']\nanswer = t1.reset_index()", false], ["answer = Dorm.groupby('gender').agg({'dormid': 'count', 'student_capacity': 'sum'})", false], ["t1 = pd.merge(Dorm, Dorm_amenity, left_on='amenid', right_on='amenid')\nt2 = pd.DataFrame(t1.groupby(['dormid', 'dorm_name', 'student_capacity', 'gender'])['amenity_name'].count())\nt3 = t2.groupby(['dormid', 'dorm_name', 'student_capacity', 'gender'])['amenity_name'].count()\nt4 = t3.to_frame().reset_index()\nt5 = t4.groupby('gender')['student_capacity'].agg(['sum', 'count']).reset_index()\nt6 = t5.rename(columns={'sum': 'total_capacity', 'count': 'num_dorms'})\nanswer = t6[['gender', 'num_dorms', 'total_capacity']]", false], ["t1 = Dorm.groupby('gender').agg({'dormid': 'count', 'student_capacity' : 'sum'})\nanswer = t1", false], ["answer = Dorm.groupby('gender').agg({'dormid':'count', 'student_capacity': 'sum'})", false], ["t1 = Dorm.groupby('gender').agg({'dormid': 'count', 'student_capacity': 'sum'})\nanswer = t1.reset_index()", false], ["answer = Dorm.groupby('gender')[['student_capacity']].agg(['count', 'sum'])", false], ["t1 = Dorm.groupby(['gender']).agg({'dormid':'count', 'student_capacity':'sum'}).rename(columns={'dormid':'count'})\nanswer = t1.reset_index()", false]]}
+{"example": {"db_id": "college_2", "query": "SELECT DISTINCT name FROM  instructor ORDER BY name", "query_toks": ["SELECT", "DISTINCT", "name", "FROM", "instructor", "ORDER", "BY", "name"], "query_toks_no_value": ["select", "distinct", "name", "from", "instructor", "order", "by", "name"], "question": "List the distinct names of the instructors, ordered by name.", "question_toks": ["List", "the", "distinct", "names", "of", "the", "instructors", ",", "ordered", "by", "name", "."], "sql": {"from": {"table_units": [["table_unit", 3]], "conds": []}, "select": [true, [[0, [0, [0, 12, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": ["asc", [[0, [0, 12, false], null]]], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"classroom": ["building", "room_number", "capacity"], "department": ["dept_name", "building", "budget"], "course": ["course_id", "title", "dept_name", "credits"], "instructor": ["ID", "name", "dept_name", "salary"], "section": ["course_id", "sec_id", "semester", "year", "building", "room_number", "time_slot_id"], "teaches": ["ID", "course_id", "sec_id", "semester", "year"], "student": ["ID", "name", "dept_name", "tot_cred"], "takes": ["ID", "course_id", "sec_id", "semester", "year", "grade"], "advisor": ["s_ID", "i_ID"], "time_slot": ["time_slot_id", "day", "start_hr", "start_min", "end_hr", "end_min"], "prereq": ["course_id", "prereq_id"]}, "answer": [["Arias"], ["Arinb"], ["Atanassov"], ["Bancilhon"], ["Bawa"], ["Bertolino"], ["Bietzk"], ["Bondi"], ["Bourrier"], ["Choll"], ["DAgostino"], ["Dale"], ["Desyl"], ["Dusserre"], ["Gustafsson"], ["Gutierrez"], ["Hau"], ["Jaekel"], ["Kean"], ["Kenje"], ["Konstantinides"], ["Lembr"], ["Lent"], ["Levine"], ["Liley"], ["Luo"], ["Mahmoud"], ["McKinnon"], ["Mingoz"], ["Mird"], ["Moreira"], ["Morris"], ["Murata"], ["Pimenta"], ["Pingr"], ["Queiroz"], ["Romero"], ["Sakurai"], ["Sarkar"], ["Shuming"], ["Soisalon-Soininen"], ["Sullivan"], ["Tung"], ["Ullman "], ["Valtchev"], ["Vicentino"], ["Voronina"], ["Wieland"], ["Yazdi"], ["Yin"]]}, "program_result_list": [["t1 = instructor.drop_duplicates(subset=['name'])\nanswer = t1['name'].sort_values()", true], ["answer = instructor['name'].unique()", false], ["answer = instructor.sort_values('name')['name'].unique()", true], ["answer = instructor.name.unique()", false], ["answer = instructor['name'].unique()", false], ["answer = instructor['name'].unique()", false], ["answer = instructor['name'].unique()", false], ["answer = instructor['name'].unique()", false], ["answer = instructor['name'].unique()", false], ["answer = instructor['name'].unique()", false], ["answer = instructor['name'].unique().tolist()", false], ["answer = instructor.drop_duplicates(subset='name')['name']", false], ["t1 = instructor['name'].unique()\nanswer = t1", false], ["answer = instructor['name'].unique()", false], ["answer = instructor['name'].unique()", false], ["answer = instructor['name'].unique()", false], ["answer = instructor['name'].unique()", false], ["answer = instructor['name'].sort_values().unique()", true], ["answer = instructor['name'].unique()", false], ["answer = instructor['name'].unique()", false]]}
+{"example": {"db_id": "ship_1", "query": "SELECT rank FROM captain WHERE CLASS  =  'Cutter' INTERSECT SELECT rank FROM captain WHERE CLASS  =  'Armed schooner'", "query_toks": ["SELECT", "rank", "FROM", "captain", "WHERE", "CLASS", "=", "'Cutter", "'", "INTERSECT", "SELECT", "rank", "FROM", "captain", "WHERE", "CLASS", "=", "'Armed", "schooner", "'"], "query_toks_no_value": ["select", "rank", "from", "captain", "where", "class", "=", "value", "intersect", "select", "rank", "from", "captain", "where", "class", "=", "value"], "question": "What are the ranks of captains that are both in the Cutter and Armed schooner classes?", "question_toks": ["What", "are", "the", "ranks", "of", "captains", "that", "are", "both", "in", "the", "Cutter", "and", "Armed", "schooner", "classes", "?"], "sql": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[0, [0, [0, 6, false], null]]]], "where": [[false, 2, [0, [0, 5, false], null], "\"Cutter\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[0, [0, [0, 6, false], null]]]], "where": [[false, 2, [0, [0, 5, false], null], "\"Armed schooner\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "union": null, "except": null}, "db_table_headers": {"captain": ["Captain_ID", "Name", "Ship_ID", "age", "Class", "Rank"], "Ship": ["Ship_ID", "Name", "Type", "Built_Year", "Class", "Flag"]}, "answer": [["Midshipman"]]}, "program_result_list": [["t1 = captain[captain['class'] == 'cutter']\nt2 = captain[captain['class'] == 'armed schooner']\nt3 = t1[['rank']]\nt4 = t2[['rank']]\nanswer = t3.merge(t4, how='inner')", false], ["t1 = captain[captain['class'] == 'cutter']\nt2 = captain[captain['class'] == 'armed schooner']\nanswer = t1.merge(t2, on='rank', how='inner')['rank']", false], ["t1 = captain[captain['class'] == 'cutter']\nt2 = captain[captain['class'] == 'armed schooner']\nt3 = pd.merge(t1, t2, on='rank', indicator=True, how='inner')\nanswer = t3['rank']", false], ["t1 = captain[captain['class'] == 'cutter']\nt2 = captain[captain['class'] == 'armed schooner']\nanswer = list(set(t1['rank']) & set(t2['rank']))", false], ["t1 = captain[captain['class'] == 'Cutter']\nt2 = captain[captain['class'] == 'Armed schooner']\nanswer = t1[t1['rank'].isin(t2['rank'])]['rank'].unique()", true], ["t1 = captain[captain['class'] == 'cutter']\nt2 = captain[captain['class'] == 'armed schooner']\nanswer = t1[t1['rank'].isin(t2['rank'])]['rank']", false], ["t1 = captain[captain['class'] == 'cutter']\nt2 = captain[captain['class'] == 'armed schooner']\nanswer = pd.merge(t1, t2, on='rank', how='inner')['rank'].unique()", false], ["t1 = captain[captain['class'] == 'cutter']\nt2 = captain[captain['class'] == 'armed schooner']\nanswer = set(t1['rank']) & set(t2['rank'])", false], ["t1 = captain[captain['class'] == 'cutter']\nt2 = captain[captain['class'] == 'armed schooner']\nanswer = pd.merge(t1, t2, how='inner', on='rank')['rank'].drop_duplicates()", false], ["t1 = captain[captain['class'] == 'cutter']\nt2 = captain[captain['class'] == 'armed schooner']\nanswer = t1.merge(t2, on='rank', how='inner')['rank']", false], ["t1 = captain.loc[captain['class'] == 'cutter', 'rank']\nt2 = captain.loc[captain['class'] == 'armed schooner', 'rank']\nanswer = t1.append(t2).unique()", false], ["t1 = captain[captain['class'] == 'cutter']\nt2 = captain[captain['class'] == 'armed schooner']\nanswer = pd.merge(t1, t2, on='rank', how='inner')['rank'].unique()", false], ["t1 = captain[captain['class'] == 'cutter']\nt2 = captain[captain['class'] == 'armed schooner']\nanswer = t1[['rank']].merge(t2[['rank']], on='rank', how='inner')['rank']", false], ["t1 = captain[captain['class'] == 'cutter']\nt2 = captain[captain['class'] == 'armed schooner']\nt3 = t1.merge(t2, on='rank', how='inner')\nanswer = t3['rank']", false], ["t1 = captain[captain['class'] == 'cutter']\nt2 = captain[captain['class'] == 'armed schooner']\nanswer = t1[t1['rank'].isin(t2['rank'])]['rank'].unique()", false], ["t1 = captain[captain['class'] == 'cutter']\nt2 = captain[captain['class'] == 'armed schooner']\nanswer = pd.merge(t1, t2, on='rank', how='inner')['rank']", false], ["t1 = captain[captain['class'] == 'cutter']\nt2 = captain[captain['class'] == 'armed schooner']\nanswer = np.intersect1d(t1['rank'], t2['rank'])", false], ["t1 = captain[captain['class'] == 'cutter'][['rank']]\nt2 = captain[captain['class'] == 'armed schooner'][['rank']]\nanswer = t1[t1['rank'].isin(t2['rank'])]", false], ["t1 = captain[captain['class'] == 'cutter']\nt2 = captain[captain['class'] == 'armed schooner']\nanswer = t1[t1.rank.isin(t2.rank)].rank.unique()", false], ["t1 = captain[captain['class'] == 'cutter']\nt2 = captain[captain['class'] == 'armed schooner']\nanswer = t1.merge(t2, on='rank')['rank']", false]]}
\ No newline at end of file
diff --git a/stephen_playground/filter.py b/stephen_playground/filter.py
new file mode 100644
index 00000000..d61589a8
--- /dev/null
+++ b/stephen_playground/filter.py
@@ -0,0 +1,37 @@
+import json
+
+if __name__ == "__main__":
+    with open("spider_codex_conversion_k_1_n_6997.jsonl") as f:
+        sample_1 = list(f)
+    sample_1 = [json.loads(json_str) for json_str in sample_1]
+    
+    print("Num. Examples:", len(sample_1))
+    successes = []
+    fails = []
+    for item in sample_1:
+        truthy_results = [x[1] for x in item["program_result_list"]]
+        if True in truthy_results:
+            successes.append(item)
+        elif False in truthy_results:
+            fails.append(item)
+        else:
+            raise Exception("Result neither true or false")
+    print("Sucesses:", len(successes))
+    print("Fails:", len(fails))
+
+    string_sample_1 = [json.dumps(json_og) for json_og in fails]
+    fails_example =  [json.dumps(json_og["example"]) for json_og in fails]
+
+    with open("spider_codex_k5_true.jsonl", "w") as out:
+        out.write("\n".join(string_sample_1))
+        
+    with open("spider_codex_conversion_k_5_n_2691.jsonl") as f:
+        sample_2 = list(f)
+    sample_2 = [json.dumps(json.loads(json_str)["example"]) for json_str in sample_2]
+
+    diff = set(fails_example) ^ set(sample_2)
+
+    with open("spider_codex_diff.jsonl", "w") as out:
+         out.write("\n".join(diff))
+
+    
\ No newline at end of file
diff --git a/stephen_playground/hundred_fails.jsonl b/stephen_playground/hundred_fails.jsonl
new file mode 100644
index 00000000..3f669a16
--- /dev/null
+++ b/stephen_playground/hundred_fails.jsonl
@@ -0,0 +1,100 @@
+{"example": {"db_id": "flight_4", "query": "SELECT max(elevation) FROM airports WHERE country  =  'Iceland'", "query_toks": ["SELECT", "max", "(", "elevation", ")", "FROM", "airports", "WHERE", "country", "=", "'Iceland", "'"], "query_toks_no_value": ["select", "max", "(", "elevation", ")", "from", "airports", "where", "country", "=", "value"], "question": "What is the highest elevation of an airport in the country of Iceland?", "question_toks": ["What", "is", "the", "highest", "elevation", "of", "an", "airport", "in", "the", "country", "of", "Iceland", "?"], "sql": {"from": {"table_units": [["table_unit", 1]], "conds": []}, "select": [false, [[1, [0, [0, 15, false], null]]]], "where": [[false, 2, [0, [0, 12, false], null], "\"Iceland\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"routes": ["rid", "dst_apid", "dst_ap", "src_apid", "src_ap", "alid", "airline", "codeshare"], "airports": ["apid", "name", "city", "country", "x", "y", "elevation", "iata", "icao"], "airlines": ["alid", "name", "iata", "icao", "callsign", "country", "active"]}, "answer": [[1030]], "pandas_converted": "pd.Series(airports[airports['country'] == 'Iceland']['elevation'].max())"}, "program_result_list": [["t1 = airports[airports['country'] == 'Iceland']\nanswer = t1['elevation'].max()", false], ["t1 = airports[airports['country'] == 'Iceland']\nanswer = t1['elevation'].max()", false], ["answer = airports[airports['country'] == 'Iceland']['elevation'].max()", false], ["t1 = airports[airports['country'] == 'Iceland']\nanswer = t1['elevation'].max()", false], ["t1 = airports[airports['country'] == 'Iceland']\nanswer = t1['elevation'].max()", false], ["answer = airports[airports['country'] == 'Iceland']['elevation'].max()", false], ["t1 = airports[airports['country'] == 'Iceland']\nanswer = t1['elevation'].max()", false], ["t1 = airports[airports['country'] == 'Iceland']\nanswer = t1['elevation'].max()", false], ["t1 = airports[airports['country'] == 'Iceland']\nanswer = t1['elevation'].max()", false], ["t1 = airports[airports['country'] == 'Iceland']\nanswer = t1['elevation'].max()", false], ["t1 = airports[airports.country == 'Iceland']\nanswer = t1.elevation.max()", false], ["t1 = airports[airports['country'] == 'Iceland']\nanswer = t1['elevation'].max()", false], ["t1 = airports[airports['country'] == 'Iceland']\nanswer = t1['elevation'].max()", false], ["answer = airports[airports['country'] == 'Iceland']['elevation'].max()", false], ["answer = airports[airports['country'] == 'Iceland']['elevation'].max()", false], ["t1 = airports[airports['country'] == 'Iceland']\nanswer = t1['elevation'].max()", false], ["t1 = airports[airports['country'] == 'Iceland']\nanswer = t1['elevation'].max()", false], ["t1 = airports[airports['country'] == 'Iceland']\nanswer = t1['elevation'].max()", false], ["answer = airports[airports['country'] == 'Iceland']['elevation'].max()", false], ["t1 = airports[airports['country'] == 'Iceland']\nanswer = t1['elevation'].max()", false]]}
+{"example": {"db_id": "aircraft", "query": "SELECT T2.Location ,  T1.Aircraft FROM aircraft AS T1 JOIN MATCH AS T2 ON T1.Aircraft_ID  =  T2.Winning_Aircraft", "query_toks": ["SELECT", "T2.Location", ",", "T1.Aircraft", "FROM", "aircraft", "AS", "T1", "JOIN", "MATCH", "AS", "T2", "ON", "T1.Aircraft_ID", "=", "T2.Winning_Aircraft"], "query_toks_no_value": ["select", "t2", ".", "location", ",", "t1", ".", "aircraft", "from", "aircraft", "as", "t1", "join", "match", "as", "t2", "on", "t1", ".", "aircraft_id", "=", "t2", ".", "winning_aircraft"], "question": "What is the location and name of the winning aircraft?", "question_toks": ["What", "is", "the", "location", "and", "name", "of", "the", "winning", "aircraft", "?"], "sql": {"from": {"table_units": [["table_unit", 1], ["table_unit", 2]], "conds": [[false, 2, [0, [0, 4, false], null], [0, 16, false], null]]}, "select": [false, [[0, [0, [0, 11, false], null]], [0, [0, [0, 5, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"pilot": ["Pilot_Id", "Name", "Age"], "aircraft": ["Aircraft_ID", "Aircraft", "Description", "Max_Gross_Weight", "Total_disk_area", "Max_disk_Loading"], "match": ["Round", "Location", "Country", "Date", "Fastest_Qualifying", "Winning_Pilot", "Winning_Aircraft"], "airport": ["Airport_ID", "Airport_Name", "Total_Passengers", "%_Change_2007", "International_Passengers", "Domestic_Passengers", "Transit_Passengers", "Aircraft_Movements", "Freight_Metric_Tonnes"], "airport_aircraft": ["ID", "Airport_ID", "Aircraft_ID"]}, "answer": [["Mina' Zayid , Abu Dhabi", "Robinson R-22"], ["Swan River , Perth", "Robinson R-22"], ["Flamengo Beach , Rio de Janeiro", "Bell 206B3 JetRanger"], ["Windsor , Ontario", "Mil Mi-26"], ["New York City", "CH-47D Chinook"], ["EuroSpeedway Lausitz", "Mil Mi-26"], ["River Danube , Budapest", "CH-53E Super Stallion"]]}, "program_result_list": [["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false]]}
+{"example": {"db_id": "bike_1", "query": "SELECT avg(T1.lat) ,  avg(T1.long) FROM station AS T1 JOIN trip AS T2 ON T1.id  =  T2.start_station_id", "query_toks": ["SELECT", "avg", "(", "T1.lat", ")", ",", "avg", "(", "T1.long", ")", "FROM", "station", "AS", "T1", "JOIN", "trip", "AS", "T2", "ON", "T1.id", "=", "T2.start_station_id"], "query_toks_no_value": ["select", "avg", "(", "t1", ".", "lat", ")", ",", "avg", "(", "t1", ".", "long", ")", "from", "station", "as", "t1", "join", "trip", "as", "t2", "on", "t1", ".", "id", "=", "t2", ".", "start_station_id"], "question": "What is the average latitude and longitude of all starting stations for the trips?", "question_toks": ["What", "is", "the", "average", "latitude", "and", "longitude", "of", "all", "starting", "stations", "for", "the", "trips", "?"], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 2]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 16, false], null]]}, "select": [false, [[5, [0, [0, 3, false], null]], [5, [0, [0, 4, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"station": ["id", "name", "lat", "long", "dock_count", "city", "installation_date"], "status": ["station_id", "bikes_available", "docks_available", "time"], "trip": ["id", "duration", "start_date", "start_station_name", "start_station_id", "end_date", "end_station_name", "end_station_id", "bike_id", "subscription_type", "zip_code"], "weather": ["date", "max_temperature_f", "mean_temperature_f", "min_temperature_f", "max_dew_point_f", "mean_dew_point_f", "min_dew_point_f", "max_humidity", "mean_humidity", "min_humidity", "max_sea_level_pressure_inches", "mean_sea_level_pressure_inches", "min_sea_level_pressure_inches", "max_visibility_miles", "mean_visibility_miles", "min_visibility_miles", "max_wind_Speed_mph", "mean_wind_speed_mph", "max_gust_speed_mph", "precipitation_inches", "cloud_cover", "events", "wind_dir_degrees", "zip_code"]}, "answer": [[37.74618133538529, -122.36134722346611]]}, "program_result_list": [["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nanswer = t1[['lat', 'long']].agg(np.mean)", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nanswer = t1[['lat', 'long']].mean()", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nanswer = t1[['lat', 'long']].mean()", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nt2 = t1[['lat', 'long']].mean()\nanswer = t2", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nanswer = t1[['lat', 'long']].mean()", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nanswer = t1[['lat', 'long']].mean()", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nanswer = t1[['lat', 'long']].mean()", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nanswer = t1[['lat', 'long']].mean()", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nanswer = t1[['lat', 'long']].mean()", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nanswer = t1[['lat', 'long']].mean()", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nanswer = t1[['lat', 'long']].mean()", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id' )\nanswer = t1[['lat', 'long']].mean()", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nanswer = t1[['lat', 'long']].mean()", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nanswer = t1[['lat', 'long']].mean()", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nanswer = t1[['lat', 'long']].mean()", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nanswer = t1[['lat', 'long']].mean()", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nt2 = t1.groupby(['start_station_id', 'start_station_name']).agg({'lat': np.mean, 'long': np.mean})\nanswer = t2.reset_index()", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nanswer = t1[['lat', 'long']].mean()", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nanswer = [t1['lat'].mean(), t1['long'].mean()]", false], ["t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nt2 = t1[['lat', 'long']].mean(axis=0)\nanswer = t2.to_frame().transpose()", false]]}
+{"example": {"db_id": "student_1", "query": "SELECT DISTINCT firstname ,  lastname FROM teachers", "query_toks": ["SELECT", "DISTINCT", "firstname", ",", "lastname", "FROM", "teachers"], "query_toks_no_value": ["select", "distinct", "firstname", ",", "lastname", "from", "teachers"], "question": "Report the first name and last name of all the teachers.", "question_toks": ["Report", "the", "first", "name", "and", "last", "name", "of", "all", "the", "teachers", "."], "sql": {"from": {"table_units": [["table_unit", 1]], "conds": []}, "select": [true, [[0, [0, [0, 6, false], null]], [0, [0, [0, 5, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"list": ["LastName", "FirstName", "Grade", "Classroom"], "teachers": ["LastName", "FirstName", "Classroom"]}, "answer": [[" JEROME", "COVIN"], [" GORDON", "KAWA"], [" BILLIE", "KRIENER"], [" MIN", "MACROSTIE"], [" KIRK", "MARROTTE"], [" OTHA", "MOYER"], [" JERLENE", "NIBLER"], [" LORIA", "ONDERSMA"], [" ALFREDA", "SUGAI"], [" GEORGETTA", "SUMPTION"], [" LEIA", "TARRING"], [" CHARMAINE", "URSERY"]]}, "program_result_list": [["answer = teachers[['firstname', 'lastname']].drop_duplicates()", false], ["answer = teachers.drop_duplicates()", false], ["answer = teachers[['firstname', 'lastname']].drop_duplicates()", false], ["answer = teachers.drop_duplicates(['firstname', 'lastname'])", false], ["answer = teachers.drop_duplicates(subset=['firstname', 'lastname'])", false], ["answer = teachers.drop_duplicates(subset=['firstname', 'lastname'])", false], ["answer = teachers.drop_duplicates(subset=['firstname', 'lastname'])", false], ["answer = teachers[['firstname', 'lastname']].drop_duplicates()", false], ["answer = teachers[['firstname','lastname']].drop_duplicates()", false], ["answer = teachers[['firstname', 'lastname']].drop_duplicates()", false], ["answer = teachers.drop_duplicates(['firstname', 'lastname'])", false], ["answer = teachers.drop_duplicates()", false], ["answer = teachers[['firstname', 'lastname']].drop_duplicates()", false], ["answer = teachers.drop_duplicates(['firstname', 'lastname'])", false], ["t1 = teachers.drop_duplicates(['firstname', 'lastname'])\nanswer = t1[['firstname', 'lastname']]", false], ["answer = teachers.drop_duplicates(subset=['firstname', 'lastname'])", false], ["t1 = teachers.drop_duplicates()\nanswer = t1[['firstname', 'lastname']]", false], ["answer = teachers[['firstname', 'lastname']].drop_duplicates()", false], ["answer = teachers.drop_duplicates(['firstname', 'lastname'])", false], ["answer = teachers.drop_duplicates(['firstname', 'lastname'])", false]]}
+{"example": {"db_id": "music_1", "query": "SELECT count(*) ,  formats FROM files GROUP BY formats", "query_toks": ["SELECT", "count", "(", "*", ")", ",", "formats", "FROM", "files", "GROUP", "BY", "formats"], "query_toks_no_value": ["select", "count", "(", "*", ")", ",", "formats", "from", "files", "group", "by", "formats"], "question": "For each file format, return the number of artists who released songs in that format.", "question_toks": ["For", "each", "file", "format", ",", "return", "the", "number", "of", "artists", "who", "released", "songs", "in", "that", "format", "."], "sql": {"from": {"table_units": [["table_unit", 2]], "conds": []}, "select": [false, [[3, [0, [0, 0, false], null]], [0, [0, [0, 12, false], null]]]], "where": [], "groupBy": [[0, 12, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"genre": ["g_name", "rating", "most_popular_in"], "artist": ["artist_name", "country", "gender", "preferred_genre"], "files": ["f_id", "artist_name", "file_size", "duration", "formats"], "song": ["song_name", "artist_name", "country", "f_id", "genre_is", "rating", "languages", "releasedate", "resolution"]}, "answer": [[2, "mp3"], [4, "mp4"]]}, "program_result_list": [["answer = files.groupby('formats').size().rename('count').to_frame().reset_index()", false], ["t1 = files.groupby('formats').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = files.groupby('formats').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["answer = files.groupby('formats').size().rename('count').to_frame().reset_index()", false], ["t1 = files.groupby('formats').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["answer = files.groupby('formats').size().rename('count').to_frame()", false], ["t1 = files.groupby('formats').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = files.groupby('formats').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["answer = files.groupby('formats').size().rename('count')", false], ["t1 = files.groupby('formats').size().rename('count')\nanswer = t1.to_frame()", false], ["answer = files.groupby('formats').size().rename('count').to_frame().reset_index()", false], ["t1 = files.groupby(['formats']).size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = files.groupby('formats').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = files.groupby('formats').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["answer = files.groupby('formats').size().rename('count').to_frame().reset_index()", false], ["t1 = files.groupby('formats').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = files.groupby('formats').size().rename('count').to_frame().reset_index()\nanswer = t1", false], ["t1 = files.groupby('formats').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = files.groupby('formats').size().rename('count').to_frame()\nanswer = t1.reset_index()", false], ["t1 = files.groupby('formats').size().rename('count')\nanswer = t1.to_frame().reset_index()", false]]}
+{"example": {"db_id": "bike_1", "query": "SELECT T1.name ,  T1.id FROM station AS T1 JOIN status AS T2 ON T1.id  =  T2.station_id GROUP BY T2.station_id HAVING avg(T2.bikes_available)  >  14 UNION SELECT name ,  id FROM station WHERE installation_date LIKE \"12/%\"", "query_toks": ["SELECT", "T1.name", ",", "T1.id", "FROM", "station", "AS", "T1", "JOIN", "status", "AS", "T2", "ON", "T1.id", "=", "T2.station_id", "GROUP", "BY", "T2.station_id", "HAVING", "avg", "(", "T2.bikes_available", ")", ">", "14", "UNION", "SELECT", "name", ",", "id", "FROM", "station", "WHERE", "installation_date", "LIKE", "``", "12/", "%", "''"], "query_toks_no_value": ["select", "t1", ".", "name", ",", "t1", ".", "id", "from", "station", "as", "t1", "join", "status", "as", "t2", "on", "t1", ".", "id", "=", "t2", ".", "station_id", "group", "by", "t2", ".", "station_id", "having", "avg", "(", "t2", ".", "bikes_available", ")", ">", "value", "union", "select", "name", ",", "id", "from", "station", "where", "installation_date", "like", "value"], "question": "What are the names and ids of stations that had more than 14 bikes available on average or were installed in December?", "question_toks": ["What", "are", "the", "names", "and", "ids", "of", "stations", "that", "had", "more", "than", "14", "bikes", "available", "on", "average", "or", "were", "installed", "in", "December", "?"], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 8, false], null]]}, "select": [false, [[0, [0, [0, 2, false], null]], [0, [0, [0, 1, false], null]]]], "where": [], "groupBy": [[0, 8, false]], "having": [[false, 3, [0, [5, 9, false], null], 14.0, null]], "orderBy": [], "limit": null, "intersect": null, "union": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[0, [0, [0, 2, false], null]], [0, [0, [0, 1, false], null]]]], "where": [[false, 9, [0, [0, 7, false], null], "\"12/%\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "except": null}, "db_table_headers": {"station": ["id", "name", "lat", "long", "dock_count", "city", "installation_date"], "status": ["station_id", "bikes_available", "docks_available", "time"], "trip": ["id", "duration", "start_date", "start_station_name", "start_station_id", "end_date", "end_station_name", "end_station_id", "bike_id", "subscription_type", "zip_code"], "weather": ["date", "max_temperature_f", "mean_temperature_f", "min_temperature_f", "max_dew_point_f", "mean_dew_point_f", "min_dew_point_f", "max_humidity", "mean_humidity", "min_humidity", "max_sea_level_pressure_inches", "mean_sea_level_pressure_inches", "min_sea_level_pressure_inches", "max_visibility_miles", "mean_visibility_miles", "min_visibility_miles", "max_wind_Speed_mph", "mean_wind_speed_mph", "max_gust_speed_mph", "precipitation_inches", "cloud_cover", "events", "wind_dir_degrees", "zip_code"]}, "answer": [["Castro Street and El Camino Real", 32], ["Civic Center BART (7th at Market)", 72], ["Market at Sansome", 77], ["San Antonio Shopping Center", 31], ["Santa Clara County Civic Center", 80]]}, "program_result_list": [["t1 = pd.merge(station, status, left_on='id', right_on='station_id')\nt2 = t1.groupby('id').agg({'bikes_available': 'mean'}).rename(columns={'bikes_available': 'bikes_avg'})\nt3 = t2[t2['bikes_avg'] > 14].to_frame().reset_index()\nt4 = pd.merge(station, t3, left_on='id', right_on='id')\nt5 = station[station['installation_date'].str.contains('12/')][['name', 'id']]\nanswer = pd.concat([t4[['name', 'id']], t5], ignore_index=True).drop_duplicates(['name', 'id']).reset_index(drop=True)", false], ["t1 = pd.merge(station, status, left_on='id', right_on='station_id')\nt2 = t1.groupby(['id', 'name']).mean()\nt3 = t2[t2['bikes_available'] > 14]\nt4 = station[station['installation_date'].str.contains('12/')]\nt5 = pd.concat([t3, t4], ignore_index=True)\nanswer = t5[['name', 'id']].drop_duplicates()", false], ["t1 = pd.merge(station, status, left_on='id', right_on='station_id')\nt2 = t1.groupby(['name', 'id']).agg({'bikes_available': 'mean'}).rename(columns={'bikes_available': 'avg_bikes_available'})\nt3 = t2[t2['avg_bikes_available'] > 14]\nt4 = station[station['installation_date'].str.contains('12/')]\nt5 = t3.append(t4).drop_duplicates()\nanswer = t5[['name', 'id']].reset_index(drop=True)", false], ["t1 = pd.merge(station, status, left_on='id', right_on='station_id')\nt2 = t1.groupby('station_id', as_index=False).mean()\nt3 = t2[t2['bikes_available'] > 14]\nt4 = t1[t1['installation_date'].str.contains('12/')].drop_duplicates()\nt5 = t3.append(t4, sort=False).drop_duplicates()\nanswer = t5[['name', 'id']]", false], ["t1 = pd.merge(station, status, on='id')\nt2 = t1.groupby(['id', 'name']).mean()['bikes_available'].rename('avg_bikes_available')\nt3 = t2[t2 > 14].to_frame().reset_index()\nt4 = station[station['installation_date'].str.contains('12/')][['id', 'name']]\nanswer = pd.concat([t3, t4])", false], ["t1 = pd.merge(station, status, left_on='id', right_on='station_id').groupby('station_id').mean().rename(columns={'bikes_available': 'mean'})\nt2 = t1[t1['mean'] > 14].to_frame().reset_index()\nt3 = station[station['installation_date'].str.contains('12/')][['name', 'id']]\nanswer = pd.concat([t2['name'], t2['id'], t3], ignore_index=True)", false], ["t1 = pd.merge(station, status, on='id')\nt2 = t1.groupby('id').agg({'bikes_available': 'mean'})\nt3 = t2[t2['bikes_available'] > 14].reset_index()\nt4 = pd.merge(t3, station, on='id')\nt5 = station[station['installation_date'].str.contains(\"12/\")]\nanswer = pd.concat([t4[['name', 'id']], t5[['name', 'id']]])", false], ["t1 = pd.merge(station, status, left_on='id', right_on='station_id')\nt2 = t1[t1['bikes_available'] > 14]\nt3 = t2['station_id'].unique()\nt4 = station[station['installation_date'].str.contains('12/')]\nt5 = pd.concat([t4, t3])\nanswer = t5[['name', 'id']]", false], ["t1 = pd.merge(station, status, left_on='id', right_on='station_id')\nt2 = t1.groupby('station_id').mean()\nt3 = t2[t2['bikes_available'] > 14].reset_index()\nt4 = t3['station_id'].to_frame()\nt5 = pd.merge(station, t4, left_on='id', right_on='station_id')\nt6 = station[station['installation_date'].str.contains('12/')]\nanswer = pd.concat([t5[['id', 'name']], t6[['id', 'name']]]).drop_duplicates()", false], ["t1 = pd.merge(station, status, left_on='id', right_on='station_id')\nt2 = t1.groupby('station_id').agg({'name': 'first', 'id': 'first', 'bikes_available': 'mean'})\nt3 = t2[t2['bikes_available'] > 14][['name', 'id']]\nt4 = station[station['installation_date'].str.contains('12/')][['name', 'id']]\nanswer = pd.concat([t3, t4], ignore_index=True).drop_duplicates(subset=['name', 'id'])", false], ["t1 = pd.merge(station, status, left_on='id', right_on='station_id')\nt2 = t1.groupby('station_id').bikes_available.mean().rename('avg_bikes_available').to_frame().reset_index()\nt3 = t2[t2['avg_bikes_available'] > 14]\nt4 = station[station['installation_date'].str.contains('/12/')]\nanswer = pd.concat([t4[['name', 'id']], t3[['station_id', 'avg_bikes_available']]]).drop_duplicates()", false], ["t1 = pd.merge(station, status, left_on='id', right_on='station_id')\nt2 = t1.groupby('station_id').mean().reset_index()\nt3 = t2[t2['bikes_available'] > 14]\nt4 = station[station['installation_date'].str.contains('12/')]\nt5 = pd.concat([t3, t4])\nanswer = t5[['name', 'id']]", false], ["t1 = pd.merge(status, station, left_on='station_id', right_on='id')\nt2 = t1.groupby('station_id').mean()\nt3 = t2[t2['bikes_available'] > 14]\nt4 = station[station['installation_date'].str.contains('12/')]\nt5 = pd.merge(t3, station, left_on='station_id', right_on='id')\nt6 = pd.merge(t4, t5, how='outer')\nanswer = t6[['name_x', 'id_x']]", false], ["t1 = pd.merge(station, status, left_on='id', right_on='station_id')\nt2 = t1.groupby('station_id').mean()\nt3 = t2[t2['bikes_available'] > 14].reset_index()\nt4 = t3['station_id'].unique()\nt5 = station[station['id'].isin(t4)]\nt6 = station[station['installation_date'].str.contains('12/')]\nanswer = t5.append(t6)", false], ["t1 = pd.merge(station, status, left_on='id', right_on='station_id')\nt2 = t1.groupby(['name', 'id'])['bikes_available'].mean().rename('avg_bikes_available').to_frame().reset_index()\nt3 = t2[t2['avg_bikes_available'] > 14]\nt4 = station[station['installation_date'].str.contains('/12/')]\nanswer = pd.concat([t3, t4])", false], ["t1 = status.groupby('station_id').mean()[['bikes_available']].rename(columns={'bikes_available':'avg_bikes_available'})\nt2 = station[station['installation_date'].str.contains('12/')][['name', 'id']]\nt3 = t1[t1['avg_bikes_available'] > 14][['avg_bikes_available']].join(station)\nanswer = pd.concat([t2, t3[['name', 'id']]])", false], ["t1 = station[station['installation_date'].str.contains('12/')].drop_duplicates(['id', 'name'])\nt2 = station.merge(status, left_on='id', right_on='station_id').groupby('station_id').agg({'name':'first', 'id':'first', 'bikes_available':'mean'})\nt3 = t2[t2['bikes_available'] > 14]\nt4 = t1.append(t3).drop_duplicates(['id', 'name'])\nanswer = t4[['name', 'id']]", false], ["t1 = pd.merge(station, status, left_on='id', right_on='station_id')\nt2 = t1.groupby('station_id').bikes_available.mean().rename('avg').to_frame().reset_index()\nt3 = t2[t2['avg'] > 14]\nt4 = station[station['installation_date'].str.contains('12/')]\nt5 = pd.concat([t4, t3]).drop_duplicates()\nanswer = t5[['name', 'id']]", false], ["t1 = pd.merge(station, status, left_on='id', right_on='station_id')\nt2 = t1.groupby('station_id').mean()\nt3 = t2[t2['bikes_available'] > 14].reset_index()\nt4 = station[station['installation_date'].str.contains('12/')]\nt5 = t3[['name', 'id']].append(t4[['name', 'id']])\nanswer = t5.drop_duplicates()", false], ["t1 = pd.merge(station, status, left_on='id', right_on='station_id')\nt2 = t1.groupby('station_id').agg({'name': lambda x: x.iloc[0], 'id': lambda x: x.iloc[0], 'bikes_available': np.mean}).rename(columns={'bikes_available': 'avg_bikes_available'})\nt3 = t2[t2['avg_bikes_available'] > 14]\nt4 = station[station['installation_date'].str.contains('12/')]\nanswer = pd.concat([t3[['name', 'id']], t4[['name', 'id']]])", false]]}
+{"example": {"db_id": "college_1", "query": "SELECT T1.stu_fname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num  =  T2.stu_num JOIN CLASS AS T3 ON T2.class_code  =  T3.class_code JOIN course AS T4 ON T3.crs_code  =  T4.crs_code JOIN department AS T5 ON T5.dept_code  =  T4.dept_code WHERE T5.dept_name  =  'Accounting' INTERSECT SELECT T1.stu_fname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num  =  T2.stu_num JOIN CLASS AS T3 ON T2.class_code  =  T3.class_code JOIN course AS T4 ON T3.crs_code  =  T4.crs_code JOIN department AS T5 ON T5.dept_code  =  T4.dept_code WHERE T5.dept_name  =  'Computer Info. Systems'", "query_toks": ["SELECT", "T1.stu_fname", "FROM", "student", "AS", "T1", "JOIN", "enroll", "AS", "T2", "ON", "T1.stu_num", "=", "T2.stu_num", "JOIN", "CLASS", "AS", "T3", "ON", "T2.class_code", "=", "T3.class_code", "JOIN", "course", "AS", "T4", "ON", "T3.crs_code", "=", "T4.crs_code", "JOIN", "department", "AS", "T5", "ON", "T5.dept_code", "=", "T4.dept_code", "WHERE", "T5.dept_name", "=", "'Accounting", "'", "INTERSECT", "SELECT", "T1.stu_fname", "FROM", "student", "AS", "T1", "JOIN", "enroll", "AS", "T2", "ON", "T1.stu_num", "=", "T2.stu_num", "JOIN", "CLASS", "AS", "T3", "ON", "T2.class_code", "=", "T3.class_code", "JOIN", "course", "AS", "T4", "ON", "T3.crs_code", "=", "T4.crs_code", "JOIN", "department", "AS", "T5", "ON", "T5.dept_code", "=", "T4.dept_code", "WHERE", "T5.dept_name", "=", "'Computer", "Info", ".", "Systems", "'"], "query_toks_no_value": ["select", "t1", ".", "stu_fname", "from", "student", "as", "t1", "join", "enroll", "as", "t2", "on", "t1", ".", "stu_num", "=", "t2", ".", "stu_num", "join", "class", "as", "t3", "on", "t2", ".", "class_code", "=", "t3", ".", "class_code", "join", "course", "as", "t4", "on", "t3", ".", "crs_code", "=", "t4", ".", "crs_code", "join", "department", "as", "t5", "on", "t5", ".", "dept_code", "=", "t4", ".", "dept_code", "where", "t5", ".", "dept_name", "=", "value", "intersect", "select", "t1", ".", "stu_fname", "from", "student", "as", "t1", "join", "enroll", "as", "t2", "on", "t1", ".", "stu_num", "=", "t2", ".", "stu_num", "join", "class", "as", "t3", "on", "t2", ".", "class_code", "=", "t3", ".", "class_code", "join", "course", "as", "t4", "on", "t3", ".", "crs_code", "=", "t4", ".", "crs_code", "join", "department", "as", "t5", "on", "t5", ".", "dept_code", "=", "t4", ".", "dept_code", "where", "t5", ".", "dept_name", "=", "value"], "question": "Find the first name of student who is taking classes from accounting and Computer Info. Systems departments", "question_toks": ["Find", "the", "first", "name", "of", "student", "who", "is", "taking", "classes", "from", "accounting", "and", "Computer", "Info", ".", "Systems", "departments"], "sql": {"from": {"table_units": [["table_unit", 6], ["table_unit", 4], ["table_unit", 0], ["table_unit", 1], ["table_unit", 2]], "conds": [[false, 2, [0, [0, 32, false], null], [0, 25, false], null], "and", [false, 2, [0, [0, 24, false], null], [0, 1, false], null], "and", [false, 2, [0, [0, 2, false], null], [0, 7, false], null], "and", [false, 2, [0, [0, 11, false], null], [0, 8, false], null]]}, "select": [false, [[0, [0, [0, 34, false], null]]]], "where": [[false, 2, [0, [0, 12, false], null], "\"Accounting\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": {"from": {"table_units": [["table_unit", 6], ["table_unit", 4], ["table_unit", 0], ["table_unit", 1], ["table_unit", 2]], "conds": [[false, 2, [0, [0, 32, false], null], [0, 25, false], null], "and", [false, 2, [0, [0, 24, false], null], [0, 1, false], null], "and", [false, 2, [0, [0, 2, false], null], [0, 7, false], null], "and", [false, 2, [0, [0, 11, false], null], [0, 8, false], null]]}, "select": [false, [[0, [0, [0, 34, false], null]]]], "where": [[false, 2, [0, [0, 12, false], null], "\"Computer Info. Systems\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "union": null, "except": null}, "db_table_headers": {"CLASS": ["CLASS_CODE", "CRS_CODE", "CLASS_SECTION", "CLASS_TIME", "CLASS_ROOM", "PROF_NUM"], "COURSE": ["CRS_CODE", "DEPT_CODE", "CRS_DESCRIPTION", "CRS_CREDIT"], "DEPARTMENT": ["DEPT_CODE", "DEPT_NAME", "SCHOOL_CODE", "EMP_NUM", "DEPT_ADDRESS", "DEPT_EXTENSION"], "EMPLOYEE": ["EMP_NUM", "EMP_LNAME", "EMP_FNAME", "EMP_INITIAL", "EMP_JOBCODE", "EMP_HIREDATE", "EMP_DOB"], "ENROLL": ["CLASS_CODE", "STU_NUM", "ENROLL_GRADE"], "PROFESSOR": ["EMP_NUM", "DEPT_CODE", "PROF_OFFICE", "PROF_EXTENSION", "PROF_HIGH_DEGREE"], "STUDENT": ["STU_NUM", "STU_LNAME", "STU_FNAME", "STU_INIT", "STU_DOB", "STU_HRS", "STU_CLASS", "STU_GPA", "STU_TRANSFER", "DEPT_CODE", "STU_PHONE", "PROF_NUM"]}, "answer": [["Anne"], ["William"]]}, "program_result_list": [["t1 = pd.merge(STUDENT, ENROLL, how='inner', left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, CLASS, how='inner', left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, COURSE, how='inner', left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, how='inner', left_on='dept_code', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'accounting']\nt6 = pd.merge(t1, CLASS, how='inner', left_on='class_code', right_on='class_code')\nt7 = pd.merge(t6, COURSE, how='inner', left_on='crs_code', right_on='crs_code')\nt8 = pd.merge(t", false], ["t1 = pd.merge(STUDENT, ENROLL, left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, CLASS, left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, COURSE, left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'accounting']\nt6 = t4[t4['dept_name'] == 'computer info. systems']\nanswer = pd.merge(t5, t6, left_on='stu_fname', right_on='stu_fname')['stu_fname'].unique()", false], ["t1 = pd.merge(STUDENT, ENROLL, left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, CLASS, left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, COURSE, left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'Accounting']\nt6 = t4[t4['dept_name'] == 'Computer Info. Systems']\nanswer = t5[t5['stu_fname'].isin(t6['stu_fname'])]['stu_fname']", false], ["t1 = pd.merge(STUDENT, ENROLL, left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, CLASS, left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, COURSE, left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'accounting']\nt6 = t4[t4['dept_name'] == 'computer info. systems']\nt7 = t5[['stu_fname']].merge(t6[['stu_fname']], on='stu_fname')\nanswer = t7['stu_fname'].unique()", false], ["t1 = pd.merge(STUDENT, ENROLL, on=['stu_num'])\nt2 = pd.merge(t1, CLASS, on=['class_code'])\nt3 = pd.merge(t2, COURSE, on=['crs_code'])\nt4 = pd.merge(t3, DEPARTMENT, on=['dept_code'])\nt5 = t4[t4['dept_name'] == 'Accounting']\nt6 = t4[t4['dept_name'] == 'Computer Info. Systems']\nt7 = pd.merge(t5, t6, on='stu_fname', how='inner')\nanswer = t7['stu_fname']", false], ["t1 = pd.merge(STUDENT, ENROLL, left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, CLASS, left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, COURSE, left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'Accounting']\nt6 = t4[t4['dept_name'] == 'Computer Info. Systems']\nanswer = t5[t5['stu_fname'].isin(t6['stu_fname'])]\n \n# Dataset college_1:", false], ["t1 = pd.merge(STUDENT, ENROLL, left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, CLASS, left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, COURSE, left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'Accounting']\nt6 = t4[t4['dept_name'] == 'Computer Info. Systems']\nt7 = pd.merge(t5, t6, on='stu_fname', how='inner')\nanswer = t7['stu_fname']", false], ["t1 = pd.merge(STUDENT, ENROLL, left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, CLASS, left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, COURSE, left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'Accounting']\nt6 = t4[t4['dept_name'] == 'Computer Info. Systems']\nanswer = t5[['stu_fname']].merge(t6[['stu_fname']], on='stu_fname')", false], ["t1 = pd.merge(STUDENT, ENROLL, left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, CLASS, left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, COURSE, left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'accounting']\nt6 = t4[t4['dept_name'] == 'computer info. systems']\nt7 = t5['stu_fname'].append(t6['stu_fname'])\nanswer = t7.unique()", false], ["t1 = pd.merge(STUDENT, ENROLL, left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, CLASS, left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, COURSE, left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'accounting']\nt6 = pd.merge(STUDENT, ENROLL, left_on='stu_num', right_on='stu_num')\nt7 = pd.merge(t6, CLASS, left_on='class_code', right_on='class_code')\nt8 = pd.merge(t7, COURSE, left_on='crs_code', right_on='crs_code", false], ["t1 = pd.merge(STUDENT, ENROLL, on='stu_num')\nt2 = pd.merge(t1, CLASS, on='class_code')\nt3 = pd.merge(t2, COURSE, on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, on='dept_code')\nt5 = t4[t4['dept_name'] == 'Accounting']\nt6 = t4[t4['dept_name'] == 'Computer Info. Systems']\nt7 = pd.merge(t5, t6, on='stu_fname', how='inner')\nanswer = t7['stu_fname']", false], ["t1 = pd.merge(STUDENT, ENROLL, left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, CLASS, left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, COURSE, left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'Accounting']\nt6 = t4[t4['dept_name'] == 'Computer Info. Systems']\nanswer = t5.merge(t6, on='stu_fname', how='intersection')['stu_fname']", false], ["t1 = pd.merge(STUDENT, ENROLL, left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, CLASS, left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, COURSE, left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'accounting']\nt6 = t4[t4['dept_name'] == 'computer info. systems']\nanswer = list(set(t5['stu_fname']).intersection(set(t6['stu_fname'])))", false], ["t1 = pd.merge(STUDENT, ENROLL, left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, CLASS, left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, COURSE, left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'Accounting']\nt6 = t4[t4['dept_name'] == 'Computer Info. Systems']\nt7 = pd.merge(t5, t6, on='stu_fname', how='inner')\nanswer = t7['stu_fname']", false], ["t1 = pd.merge(STUDENT, ENROLL, left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, CLASS, left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, COURSE, left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'Accounting']\nt6 = t4[t4['dept_name'] == 'Computer Info. Systems']\nt7 = pd.merge(t5, t6, on='stu_fname', how='inner')\nanswer = t7['stu_fname']", false], ["t1 = pd.merge(STUDENT, ENROLL, left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, CLASS, left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, COURSE, left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'Accounting']\nt6 = t4[t4['dept_name'] == 'Computer Info. Systems']\nt7 = t5.merge(t6, on='stu_fname', how='inner')\nanswer = t7['stu_fname']", false], ["t1 = pd.merge(STUDENT, ENROLL, left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, CLASS, left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, COURSE, left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'accounting']\nt6 = pd.merge(t1, CLASS, left_on='class_code', right_on='class_code')\nt7 = pd.merge(t6, COURSE, left_on='crs_code', right_on='crs_code')\nt8 = pd.merge(t7, DEPARTMENT, left_on='dept_code', right_on='dept_code')", false], ["t1 = pd.merge(STUDENT, ENROLL, left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, CLASS, left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, COURSE, left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'Accounting']\nt6 = t5[['stu_fname']]\nt7 = t4[t4['dept_name'] == 'Computer Info. Systems']\nt8 = t7[['stu_fname']]\nt9 = pd.merge(t6, t8, on='stu_fname')\nanswer = t9['stu_fname']", false], ["t1 = pd.merge(STUDENT, ENROLL, left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, CLASS, left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, COURSE, left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'Accounting']\nt6 = t4[t4['dept_name'] == 'Computer Info. Systems']\nt7 = pd.merge(t5, t6, on='stu_fname', how='inner')\nanswer = t7['stu_fname']", false], ["t1 = pd.merge(STUDENT, ENROLL, left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, CLASS, left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, COURSE, left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'Accounting']\nt6 = t4[t4['dept_name'] == 'Computer Info. Systems']\nt7 = t5[['stu_fname']].append(t6[['stu_fname']])\nanswer = t7['stu_fname'].unique()", false]]}
+{"example": {"db_id": "music_1", "query": "SELECT DISTINCT artist_name FROM artist WHERE country  =  \"Bangladesh\" EXCEPT SELECT DISTINCT artist_name FROM song WHERE rating  >  7", "query_toks": ["SELECT", "DISTINCT", "artist_name", "FROM", "artist", "WHERE", "country", "=", "``", "Bangladesh", "''", "EXCEPT", "SELECT", "DISTINCT", "artist_name", "FROM", "song", "WHERE", "rating", ">", "7"], "query_toks_no_value": ["select", "distinct", "artist_name", "from", "artist", "where", "country", "=", "value", "except", "select", "distinct", "artist_name", "from", "song", "where", "rating", ">", "value"], "question": "Find the names of the artists who are from Bangladesh and have never received rating higher than 7.", "question_toks": ["Find", "the", "names", "of", "the", "artists", "who", "are", "from", "Bangladesh", "and", "have", "never", "received", "rating", "higher", "than", "7", "."], "sql": {"from": {"table_units": [["table_unit", 1]], "conds": []}, "select": [true, [[0, [0, [0, 4, false], null]]]], "where": [[false, 2, [0, [0, 5, false], null], "\"Bangladesh\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": {"from": {"table_units": [["table_unit", 3]], "conds": []}, "select": [true, [[0, [0, [0, 14, false], null]]]], "where": [[false, 3, [0, [0, 18, false], null], 7.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}}, "db_table_headers": {"genre": ["g_name", "rating", "most_popular_in"], "artist": ["artist_name", "country", "gender", "preferred_genre"], "files": ["f_id", "artist_name", "file_size", "duration", "formats"], "song": ["song_name", "artist_name", "country", "f_id", "genre_is", "rating", "languages", "releasedate", "resolution"]}, "answer": [["Farida"], ["Prity"]]}, "program_result_list": [["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = song[song['rating'] > 7]\nanswer = t1[~t1['artist_name'].isin(t2['artist_name'])]", false], ["t1 = artist[artist['country'] == 'bangladesh']['artist_name']\nt2 = song[song['rating'] > 7]['artist_name']\nanswer = t1.append(t2).drop_duplicates(keep=False)", false], ["t1 = artist[artist['country'] == 'Bangladesh']['artist_name']\nt2 = song[song['rating'] > 7]['artist_name']\nanswer = t1[~t1.isin(t2)].to_frame().reset_index(drop=True)", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = song[song['rating'] > 7]\nt3 = pd.merge(t1, t2, on='artist_name', how='outer', indicator=True)\nanswer = t3[t3['_merge'] == 'left_only']['artist_name']", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = t1['artist_name'].to_frame()\nt3 = song[song['rating'] > 7]\nt4 = t3['artist_name'].to_frame()\nanswer = t2[~t2.isin(t4)].dropna(how='any')", false], ["t1 = artist[artist['country'] == 'Bangladesh'].drop_duplicates()\nt2 = song[song['rating'] > 7].drop_duplicates()\nt3 = pd.merge(t1, t2, on='artist_name', how='left', indicator=True)\nanswer = t3[t3['_merge'] == 'left_only']['artist_name'].drop_duplicates()", false], ["t1 = artist[artist['country'] == 'Bangladesh']['artist_name'].unique()\nt2 = song[song['rating'] > 7]['artist_name'].unique()\nt3 = np.setdiff1d(t1, t2)\nanswer = pd.DataFrame({'artist_name': t3})", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = song[song['rating'] > 7]\nanswer = t1[~t1['artist_name'].isin(t2['artist_name'])]['artist_name']", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = song[song['rating'] > 7]\nanswer = t1[~t1['artist_name'].isin(t2['artist_name'])]['artist_name'].unique()", false], ["t1 = artist[artist['country'] == 'Bangladesh']['artist_name'].to_frame()\nt2 = song[song['rating'] > 7]['artist_name'].to_frame()\nt3 = t1.merge(t2, left_on='artist_name', right_on='artist_name', how='outer', indicator=True)\nanswer = t3[t3['_merge'] == 'left_only']['artist_name']", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = song[song['rating'] > 7]\nanswer = t1[~t1['artist_name'].isin(t2['artist_name'])]", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = song[song['rating'] > 7]\nt3 = t1[~t1.artist_name.isin(t2.artist_name)]\nanswer = t3['artist_name']", false], ["t1 = artist[artist['country'] == 'Bangladesh'].drop_duplicates(subset='artist_name')\nt2 = song[song['rating'] > 7].drop_duplicates(subset='artist_name')\nanswer = t1.loc[~t1['artist_name'].isin(t2['artist_name'])]", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = t1['artist_name'].unique()\nt3 = song[song['rating'] > 7]\nanswer = t2[~t2.isin(t3['artist_name'])]", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = song[song['rating'] > 7]\nanswer = t1[~t1['artist_name'].isin(t2['artist_name'])]", false], ["t1 = artist[artist['country'] == 'Bangladesh'].drop_duplicates()\nt2 = song[song['rating'] > 7].drop_duplicates()\nt3 = pd.merge(t1, t2, on='artist_name', how='left')\nanswer = t3[t3['rating'].isnull()]['artist_name']", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = t1['artist_name'].unique()\nt3 = song[song['rating'] > 7]\nt4 = t3['artist_name'].unique()\nanswer = np.setdiff1d(t2, t4)", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = song[song['rating'] > 7]\nanswer = t1.append(t2).drop_duplicates(keep=False)", false], ["\nt1 = artist[artist['country'] == 'Bangladesh']['artist_name'].unique()\nt2 = song[song['rating'] > 7]['artist_name'].unique()\nanswer = np.setdiff1d(t1, t2)", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = song[song['rating'] > 7]\nanswer = t1[~t1.artist_name.isin(t2.artist_name)]", false]]}
+{"example": {"db_id": "gas_company", "query": "SELECT T3.location FROM station_company AS T1 JOIN company AS T2 ON T1.company_id  =  T2.company_id JOIN gas_station AS T3 ON T1.station_id  =  T3.station_id WHERE T2.market_value  >  100", "query_toks": ["SELECT", "T3.location", "FROM", "station_company", "AS", "T1", "JOIN", "company", "AS", "T2", "ON", "T1.company_id", "=", "T2.company_id", "JOIN", "gas_station", "AS", "T3", "ON", "T1.station_id", "=", "T3.station_id", "WHERE", "T2.market_value", ">", "100"], "query_toks_no_value": ["select", "t3", ".", "location", "from", "station_company", "as", "t1", "join", "company", "as", "t2", "on", "t1", ".", "company_id", "=", "t2", ".", "company_id", "join", "gas_station", "as", "t3", "on", "t1", ".", "station_id", "=", "t3", ".", "station_id", "where", "t2", ".", "market_value", ">", "value"], "question": "Show all locations where a gas station for company with market value greater than 100 is located.", "question_toks": ["Show", "all", "locations", "where", "a", "gas", "station", "for", "company", "with", "market", "value", "greater", "than", "100", "is", "located", "."], "sql": {"from": {"table_units": [["table_unit", 2], ["table_unit", 0], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 17, false], null], [0, 1, false], null], "and", [false, 2, [0, [0, 16, false], null], [0, 10, false], null]]}, "select": [false, [[0, [0, [0, 12, false], null]]]], "where": [[false, 3, [0, [0, 9, false], null], 100.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"company": ["Company_ID", "Rank", "Company", "Headquarters", "Main_Industry", "Sales_billion", "Profits_billion", "Assets_billion", "Market_Value"], "gas_station": ["Station_ID", "Open_Year", "Location", "Manager_Name", "Vice_Manager_Name", "Representative_Name"], "station_company": ["Station_ID", "Company_ID", "Rank_of_the_Year"]}, "answer": [["Herne Hill"], ["Herne St"], ["Herne Ave"], ["Hennry Hill"], ["Maindy Ave"], ["Newport Rd"]]}, "program_result_list": [["t1 = pd.merge(station_company, company, left_on='company_id', right_on='company_id')\nt2 = pd.merge(t1, gas_station, left_on='station_id', right_on='station_id')\nt3 = t2[t2['market_value'] > 100]\nanswer = t3['location']", false], ["t1 = pd.merge(station_company, company, on='company_id')\nt2 = pd.merge(t1, gas_station, on='station_id')\nt3 = t2[t2['market_value'] > 100]\nanswer = t3['location']", false], ["t1 = pd.merge(company, station_company, left_on='company_id', right_on='company_id')\nt2 = pd.merge(t1, gas_station, left_on='station_id', right_on='station_id')\nt3 = t2[t2['market_value'] > 100]\nanswer = t3['location']", false], ["t1 = pd.merge(station_company, company, left_on='company_id', right_on='company_id')\nt2 = pd.merge(t1, gas_station, left_on='station_id', right_on='station_id')\nanswer = t2[t2['market_value'] > 100]['location']", false], ["t1 = pd.merge(station_company, company, left_on='company_id', right_on='company_id')\nt2 = pd.merge(t1, gas_station, left_on='station_id', right_on='station_id')\nt3 = t2[t2['market_value'] > 100]\nanswer = t3['location']", false], ["t1 = pd.merge(station_company, company, left_on='company_id', right_on='company_id')\nt2 = pd.merge(t1, gas_station, left_on='station_id', right_on='station_id')\nanswer = t2[t2['market_value'] > 100]['location']", false], ["t1 = pd.merge(station_company, company, left_on='company_id', right_on='company_id')\nt2 = pd.merge(t1, gas_station, left_on='station_id', right_on='station_id')\nt3 = t2[t2['market_value'] > 100]\nanswer = t3['location']", false], ["t1 = pd.merge(station_company, company, left_on='company_id', right_on='company_id')\nt2 = pd.merge(t1, gas_station, left_on='station_id', right_on='station_id')\nt3 = t2[t2['market_value'] > 100]\nanswer = t3['location']", false], ["t1 = pd.merge(station_company, company, left_on='company_id', right_on='company_id')\nt2 = pd.merge(t1, gas_station, left_on='station_id', right_on='station_id')\nt3 = t2[t2['market_value'] > 100]\nanswer = t3['location']", false], ["t1 = pd.merge(station_company, company, left_on='company_id', right_on='company_id')\nt2 = pd.merge(t1, gas_station, left_on='station_id', right_on='station_id')\nt3 = t2[t2['market_value'] > 100]\nanswer = t3['location']", false], ["t1 = pd.merge(station_company, company, left_on='company_id', right_on='company_id')\nt2 = pd.merge(t1, gas_station, left_on='station_id', right_on='station_id')\nanswer = t2[t2['market_value'] > 100]['location']", false], ["t1 = pd.merge(station_company, company, on='company_id')\nt2 = pd.merge(t1, gas_station, on='station_id')\nt3 = t2[t2['market_value'] > 100]\nanswer = t3['location']", false], ["t1 = pd.merge(station_company, company, left_on='company_id', right_on='company_id')\nt2 = pd.merge(t1, gas_station, left_on='station_id', right_on='station_id')\nt3 = t2[t2['market_value'] > 100]\nanswer = t3['location']", false], ["t1 = pd.merge(station_company, company, on='company_id')\nt2 = pd.merge(t1, gas_station, on='station_id')\nanswer = t2[t2['market_value'] > 100]['location']", false], ["t1 = pd.merge(station_company, company, on='company_id')\nt2 = pd.merge(t1, gas_station, on='station_id')\nt3 = t2[t2['market_value'] > 100]\nanswer = t3['location']", false], ["t1 = pd.merge(station_company, company, on='company_id')\nt2 = pd.merge(t1, gas_station, on='station_id')\nanswer = t2[t2['market_value'] > 100]['location']", false], ["t1 = pd.merge(station_company, company, left_on='company_id', right_on='company_id')\nt2 = pd.merge(t1, gas_station, left_on='station_id', right_on='station_id')\nanswer = t2[t2['market_value'] > 100]['location']", false], ["t1 = pd.merge(station_company, company, left_on='company_id', right_on='company_id')\nt2 = pd.merge(t1, gas_station, left_on='station_id', right_on='station_id')\nt3 = t2[t2['market_value'] > 100]\nanswer = t3['location']", false], ["t1 = pd.merge(station_company, company, left_on='company_id', right_on='company_id')\nt2 = pd.merge(t1, gas_station, left_on='station_id', right_on='station_id')\nt3 = t2[t2['market_value'] > 100]\nanswer = t3['location']", false], ["t1 = pd.merge(station_company, company, left_on='company_id', right_on='company_id')\nt2 = pd.merge(t1, gas_station, left_on='station_id', right_on='station_id')\nanswer = t2[t2['market_value'] > 100]['location']", false]]}
+{"example": {"db_id": "inn_1", "query": "SELECT sum(beds) FROM Rooms WHERE bedtype  =  'King';", "query_toks": ["SELECT", "sum", "(", "beds", ")", "FROM", "Rooms", "WHERE", "bedtype", "=", "'King", "'", ";"], "query_toks_no_value": ["select", "sum", "(", "beds", ")", "from", "rooms", "where", "bedtype", "=", "value"], "question": "How many king beds are there?", "question_toks": ["How", "many", "king", "beds", "are", "there", "?"], "sql": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[4, [0, [0, 3, false], null]]]], "where": [[false, 2, [0, [0, 4, false], null], "\"King\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Rooms": ["RoomId", "roomName", "beds", "bedType", "maxOccupancy", "basePrice", "decor"], "Reservations": ["Code", "Room", "CheckIn", "CheckOut", "Rate", "LastName", "FirstName", "Adults", "Kids"]}, "answer": [[6]]}, "program_result_list": [["answer = Rooms[Rooms['bedtype'] == 'king']['beds'].sum()", false], ["t1 = Rooms[Rooms['bedtype'] == 'king']\nanswer = t1['beds'].sum()", false], ["answer = Rooms[Rooms['bedtype'] == 'king']['beds'].sum()", false], ["t1 = Rooms[Rooms['bedtype'] == 'king']\nanswer = t1['beds'].sum()", false], ["t1 = Rooms[Rooms['bedtype'] == 'king']\nanswer = t1['beds'].sum()", false], ["t1 = Rooms[Rooms['bedtype'] == 'king']\nt2 = t1['beds'].sum()\nanswer = t2", false], ["answer = Rooms[Rooms['bedtype'] == 'king']['beds'].sum()", false], ["answer = Rooms[Rooms['bedtype'] == 'king']['beds'].sum()", false], ["t1 = Rooms[Rooms['bedtype'] == 'king']\nanswer = t1['beds'].sum()", false], ["answer = Rooms[Rooms['bedtype'] == 'king']['beds'].sum()", false], ["t1 = Rooms[Rooms['bedtype'] == 'king']\nanswer = t1['beds'].sum()", false], ["answer = Rooms[Rooms['bedtype'] == 'king']['beds'].sum()", false], ["answer = Rooms[Rooms['bedtype'] == 'king']['beds'].sum()", false], ["t1 = Rooms[Rooms['bedtype'] == 'king']\nanswer = t1['beds'].sum()", false], ["t1 = Rooms[Rooms['bedtype'] == 'king']\nanswer = t1['beds'].sum()", false], ["answer = Rooms[Rooms['bedtype'] == 'king']['beds'].sum()", false], ["t1 = Rooms[Rooms['bedtype'] == 'king']\nanswer = t1['beds'].sum()", false], ["answer = Rooms[Rooms['bedtype'] == 'king']['beds'].sum()", false], ["t1 = Rooms[Rooms['bedtype'] == 'king']\nanswer = t1['beds'].sum()", false], ["t1 = Rooms[Rooms['bedtype'] == 'king']\nanswer = t1['beds'].sum()", false]]}
+{"example": {"db_id": "hospital_1", "query": "SELECT count(*) ,  T1.name FROM medication AS T1 JOIN prescribes AS T2 ON T1.code = T2.medication GROUP BY T1.brand", "query_toks": ["SELECT", "count", "(", "*", ")", ",", "T1.name", "FROM", "medication", "AS", "T1", "JOIN", "prescribes", "AS", "T2", "ON", "T1.code", "=", "T2.medication", "GROUP", "BY", "T1.brand"], "query_toks_no_value": ["select", "count", "(", "*", ")", ",", "t1", ".", "name", "from", "medication", "as", "t1", "join", "prescribes", "as", "t2", "on", "t1", ".", "code", "=", "t2", ".", "medication", "group", "by", "t1", ".", "brand"], "question": "How many medications are prescribed for each brand?", "question_toks": ["How", "many", "medications", "are", "prescribed", "for", "each", "brand", "?"], "sql": {"from": {"table_units": [["table_unit", 8], ["table_unit", 9]], "conds": [[false, 2, [0, [0, 36, false], null], [0, 42, false], null]]}, "select": [false, [[3, [0, [0, 0, false], null]], [0, [0, [0, 37, false], null]]]], "where": [], "groupBy": [[0, 38, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Physician": ["EmployeeID", "Name", "Position", "SSN"], "Department": ["DepartmentID", "Name", "Head"], "Affiliated_With": ["Physician", "Department", "PrimaryAffiliation"], "Procedures": ["Code", "Name", "Cost"], "Trained_In": ["Physician", "Treatment", "CertificationDate", "CertificationExpires"], "Patient": ["SSN", "Name", "Address", "Phone", "InsuranceID", "PCP"], "Nurse": ["EmployeeID", "Name", "Position", "Registered", "SSN"], "Appointment": ["AppointmentID", "Patient", "PrepNurse", "Physician", "Start", "End", "ExaminationRoom"], "Medication": ["Code", "Name", "Brand", "Description"], "Prescribes": ["Physician", "Patient", "Medication", "Date", "Appointment", "Dose"], "Block": ["BlockFloor", "BlockCode"], "Room": ["RoomNumber", "RoomType", "BlockFloor", "BlockCode", "Unavailable"], "On_Call": ["Nurse", "BlockFloor", "BlockCode", "OnCallStart", "OnCallEnd"], "Stay": ["StayID", "Patient", "Room", "StayStart", "StayEnd"], "Undergoes": ["Patient", "Procedures", "Stay", "DateUndergoes", "Physician", "AssistingNurse"]}, "answer": [[2, "Thesisin"], [1, "Procrastin-X"]]}, "program_result_list": [["t1 = pd.merge(Medication, Prescribes, left_on='code', right_on='medication')\nt2 = t1.groupby('brand').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Medication, Prescribes, left_on='code', right_on='medication')\nanswer = t1.groupby('brand')['medication'].count().rename('count').to_frame().reset_index()", false], ["t1 = pd.merge(Medication, Prescribes, left_on='code', right_on='medication')\nt2 = t1.groupby('name').size().rename('count').to_frame().reset_index()\nanswer = t2", false], ["t1 = pd.merge(Medication, Prescribes, left_on='code', right_on='medication')\nt2 = t1.groupby('name').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Medication, Prescribes, left_on='code', right_on='medication')\nt2 = t1.groupby(['name']).size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Medication, Prescribes, left_on='code', right_on='medication')\nanswer = t1.groupby('name').size().rename('count')", false], ["t1 = pd.merge(Medication, Prescribes, on='code')\nt2 = t1.groupby('name').size().rename('count').to_frame().reset_index()\nanswer = t2", false], ["t1 = pd.merge(Medication, Prescribes, left_on='code', right_on='medication')\nt2 = t1.groupby('name').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Medication, Prescribes, left_on='code', right_on='medication')\nanswer = t1.groupby('brand').size().rename('count').to_frame().reset_index()", false], ["t1 = pd.merge(Medication, Prescribes, left_on='code', right_on='medication')\nanswer = t1.groupby('brand').size().rename('count')", false], ["t1 = pd.merge(Medication, Prescribes, left_on='code', right_on='medication')\nt2 = t1.groupby('brand').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Medication, Prescribes, left_on='code', right_on='medication')\nt2 = t1.groupby('name').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Medication, Prescribes, left_on='code', right_on='medication')\nt2 = t1.groupby('brand').size().rename('count').to_frame().reset_index()\nanswer = t2", false], ["t1 = pd.merge(Medication, Prescribes, left_on='code', right_on='medication')\nt2 = t1.groupby('brand').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Medication, Prescribes, left_on='code', right_on='medication')\nanswer = t1.groupby('brand').size().rename('count').to_frame().reset_index()", false], ["t1 = pd.merge(Medication, Prescribes, left_on='code', right_on='medication')\nt2 = t1.groupby('brand').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Medication, Prescribes, on='code')\nt2 = t1.groupby('brand').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Medication, Prescribes, left_on='code', right_on='medication')\nanswer = t1.groupby('brand').size().rename('count').to_frame().reset_index()", false], ["t1 = pd.merge(Medication, Prescribes, left_on='code', right_on='medication')\nt2 = t1.groupby('brand').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Medication, Prescribes, left_on='code', right_on='medication')\nanswer = t1.groupby(['name'])['medication'].count().to_frame()", false]]}
+{"example": {"db_id": "film_rank", "query": "SELECT T1.Title ,  T2.Type FROM film AS T1 JOIN film_market_estimation AS T2 ON T1.Film_ID  =  T2.Film_ID", "query_toks": ["SELECT", "T1.Title", ",", "T2.Type", "FROM", "film", "AS", "T1", "JOIN", "film_market_estimation", "AS", "T2", "ON", "T1.Film_ID", "=", "T2.Film_ID"], "query_toks_no_value": ["select", "t1", ".", "title", ",", "t2", ".", "type", "from", "film", "as", "t1", "join", "film_market_estimation", "as", "t2", "on", "t1", ".", "film_id", "=", "t2", ".", "film_id"], "question": "Please show the titles of films and the types of market estimations.", "question_toks": ["Please", "show", "the", "titles", "of", "films", "and", "the", "types", "of", "market", "estimations", "."], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 2]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 12, false], null]]}, "select": [false, [[0, [0, [0, 2, false], null]], [0, [0, [0, 13, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"film": ["Film_ID", "Title", "Studio", "Director", "Gross_in_dollar"], "market": ["Market_ID", "Country", "Number_cities"], "film_market_estimation": ["Estimation_ID", "Low_Estimate", "High_Estimate", "Film_ID", "Type", "Market_ID", "Year"]}, "answer": [["ET the Extra-Terrestrial", "Mass suicide murder"], ["Tootsie", "Mass suicide"], ["An Officer and a Gentleman", "Mass human sacrifice"], ["Rocky III", "Mass suicide"], ["Rocky III", "Mass suicide murder"], ["Rocky III", "Mass suicide"], ["Rocky III", "Mass suicide"], ["ET the Extra-Terrestrial", "Mass suicide"], ["ET the Extra-Terrestrial", "Mass suicide"]]}, "program_result_list": [["t1 = pd.merge(film, film_market_estimation, left_on='film_id', right_on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, left_on='film_id', right_on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, left_on='film_id', right_on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, left_on='film_id', right_on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, left_on='film_id', right_on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, left_on='film_id', right_on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, left_on='film_id', right_on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, left_on='film_id', right_on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, left_on='film_id', right_on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, left_on='film_id', right_on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, left_on='film_id', right_on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, left_on='film_id', right_on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, left_on='film_id', right_on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, left_on='film_id', right_on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, left_on='film_id', right_on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, left_on='film_id', right_on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, left_on='film_id', right_on='film_id')\nanswer = t1[['title', 'type']]", false], ["t1 = pd.merge(film, film_market_estimation, left_on='film_id', right_on='film_id')\nanswer = t1[['title', 'type']]", false]]}
+{"example": {"db_id": "music_1", "query": "SELECT T1.artist_name ,  T1.gender FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T2.releasedate LIKE \"%Mar%\"", "query_toks": ["SELECT", "T1.artist_name", ",", "T1.gender", "FROM", "artist", "AS", "T1", "JOIN", "song", "AS", "T2", "ON", "T1.artist_name", "=", "T2.artist_name", "WHERE", "T2.releasedate", "LIKE", "``", "%", "Mar", "%", "''"], "query_toks_no_value": ["select", "t1", ".", "artist_name", ",", "t1", ".", "gender", "from", "artist", "as", "t1", "join", "song", "as", "t2", "on", "t1", ".", "artist_name", "=", "t2", ".", "artist_name", "where", "t2", ".", "releasedate", "like", "value"], "question": "What are the names and genders of all artists who released songs in the month of March?", "question_toks": ["What", "are", "the", "names", "and", "genders", "of", "all", "artists", "who", "released", "songs", "in", "the", "month", "of", "March", "?"], "sql": {"from": {"table_units": [["table_unit", 1], ["table_unit", 3]], "conds": [[false, 2, [0, [0, 4, false], null], [0, 14, false], null]]}, "select": [false, [[0, [0, [0, 4, false], null]], [0, [0, [0, 6, false], null]]]], "where": [[false, 9, [0, [0, 20, false], null], "\"%Mar%\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"genre": ["g_name", "rating", "most_popular_in"], "artist": ["artist_name", "country", "gender", "preferred_genre"], "files": ["f_id", "artist_name", "file_size", "duration", "formats"], "song": ["song_name", "artist_name", "country", "f_id", "genre_is", "rating", "languages", "releasedate", "resolution"]}, "answer": [["Michel", "Male"], ["Topu", "Female"]]}, "program_result_list": [["t1 = pd.merge(artist, song, on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('mar')]\nanswer = t2[['artist_name', 'gender']]", false], ["t1 = pd.merge(artist, song, on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('Mar')]\nanswer = t2[['artist_name', 'gender']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('mar')]\nanswer = t2[['artist_name', 'gender']]", false], ["t1 = pd.merge(artist, song, on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('Mar')]\nanswer = t2[['artist_name', 'gender']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('Mar')]\nanswer = t2[['artist_name', 'gender']]", false], ["t1 = pd.merge(artist, song, on='artist_name')\nanswer = t1[t1['releasedate'].str.contains('Mar')][['artist_name', 'gender']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('mar', case=False)]\nanswer = t2[['artist_name', 'gender']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('mar', case=False, na=False)]\nanswer = t2[['artist_name', 'gender']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('Mar')]\nanswer = t2[['artist_name', 'gender']]", false], ["t1 = pd.merge(artist, song, on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('Mar')]\nanswer = t2[['artist_name', 'gender']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('mar', case=False, na=False)]\nanswer = t2[['artist_name', 'gender']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('mar')]\nanswer = t2[['artist_name', 'gender']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('mar', case=False)]\nanswer = t2[['artist_name', 'gender']]", false], ["t1 = pd.merge(artist, song, on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('mar')]\nanswer = t2[['artist_name', 'gender']]", false], ["t1 = pd.merge(artist, song, on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('mar', case=False)]\nanswer = t2[['artist_name', 'gender']]", false], ["t1 = pd.merge(artist, song, on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('mar')]\nanswer = t2[['artist_name', 'gender']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('Mar')]\nanswer = t2[['artist_name', 'gender']]\n ", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('Mar')]\nanswer = t2[['artist_name', 'gender']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('Mar')]\nanswer = t2[['artist_name', 'gender']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['releasedate'].str.contains('mar')]\nanswer = t2[['artist_name', 'gender']]", false]]}
+{"example": {"db_id": "wrestler", "query": "SELECT Name FROM wrestler WHERE Days_held  <  100", "query_toks": ["SELECT", "Name", "FROM", "wrestler", "WHERE", "Days_held", "<", "100"], "query_toks_no_value": ["select", "name", "from", "wrestler", "where", "days_held", "<", "value"], "question": "Return the names of wrestlers with fewer than 100 days held.", "question_toks": ["Return", "the", "names", "of", "wrestlers", "with", "fewer", "than", "100", "days", "held", "."], "sql": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[0, [0, [0, 2, false], null]]]], "where": [[false, 4, [0, [0, 4, false], null], 100.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"wrestler": ["Wrestler_ID", "Name", "Reign", "Days_held", "Location", "Event"], "Elimination": ["Elimination_ID", "Wrestler_ID", "Team", "Eliminated_By", "Elimination_Move", "Time"]}, "answer": [["El Samurai"]]}, "program_result_list": [["t1 = wrestler[wrestler['days_held'] < 100]\nanswer = t1['name']", false], ["t1 = wrestler.groupby('name').size().rename('count')\nt2 = wrestler[wrestler['days_held'] < 100]\nanswer = t2['name']", false], ["answer = wrestler[wrestler['days_held'] < 100]['name']", false], ["answer = wrestler[wrestler['days_held'] < 100]['name']", false], ["answer = wrestler[wrestler['days_held'] < 100]['name']", false], ["answer = wrestler[wrestler['days_held'] < 100]['name']", false], ["t1 = wrestler[wrestler['days_held'] < 100]\nanswer = t1['name']", false], ["answer = wrestler[wrestler['days_held'] < 100]['name']", false], ["answer = wrestler[wrestler['days_held'] < 100]['name']", false], ["answer = wrestler[wrestler['days_held'] < 100]['name']", false], ["answer = wrestler[wrestler['days_held'] < 100]['name']", false], ["answer = wrestler[wrestler['days_held'] < 100]['name']", false], ["t1 = wrestler[wrestler['days_held'] < 100]\nanswer = t1['name']", false], ["answer = wrestler[wrestler['days_held'] < 100]['name']", false], ["t1 = wrestler[wrestler['days_held'] < 100]\nanswer = t1['name']", false], ["answer = wrestler[wrestler['days_held'] < 100]['name']", false], ["answer = wrestler[wrestler['days_held'] < 100]['name']", false], ["answer = wrestler[wrestler['days_held'] < 100]['name']", false], ["answer = wrestler[wrestler['days_held'] < 100]['name']", false], ["answer = wrestler[wrestler['days_held'] < 100]['name']", false]]}
+{"example": {"db_id": "store_product", "query": "SELECT sum(city_population) FROM district ORDER BY city_area DESC LIMIT 3", "query_toks": ["SELECT", "sum", "(", "city_population", ")", "FROM", "district", "ORDER", "BY", "city_area", "DESC", "LIMIT", "3"], "query_toks_no_value": ["select", "sum", "(", "city_population", ")", "from", "district", "order", "by", "city_area", "desc", "limit", "value"], "question": "Find the total population of the top 3 districts with the largest area.", "question_toks": ["Find", "the", "total", "population", "of", "the", "top", "3", "districts", "with", "the", "largest", "area", "."], "sql": {"from": {"table_units": [["table_unit", 2]], "conds": []}, "select": [false, [[4, [0, [0, 17, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": ["desc", [[0, [0, 18, false], null]]], "limit": 3, "intersect": null, "union": null, "except": null}, "db_table_headers": {"product": ["product_id", "product", "dimensions", "dpi", "pages_per_minute_color", "max_page_size", "interface"], "store": ["Store_ID", "Store_Name", "Type", "Area_size", "Number_of_product_category", "Ranking"], "district": ["District_ID", "District_name", "Headquartered_City", "City_Population", "City_Area"], "store_product": ["Store_ID", "Product_ID"], "store_district": ["Store_ID", "District_ID"]}, "answer": [[7478157.0]]}, "program_result_list": [["t1 = pd.merge(district, store_district, on='district_id')\nt2 = pd.merge(t1, store, on='store_id')\nt3 = t2.groupby(['district_id', 'district_name', 'headquartered_city', 'city_population', 'city_area']).size().rename('count')\nt4 = t3.sort_values(ascending=False).head(3).to_frame().reset_index()\nanswer = t4['city_population'].sum()", false], ["t1 = district.sort_values(by='city_area', ascending=False).head(3)\nanswer = t1['city_population'].sum()", false], ["t1 = district.sort_values('city_area', ascending=False).head(3)\nanswer = t1['city_population'].sum()", false], ["t1 = district.sort_values('city_area', ascending=False).head(3)\nanswer = t1['city_population'].sum()", false], ["t1 = district.sort_values('city_area', ascending=False).head(3)\nanswer = t1['city_population'].sum()", false], ["t1 = district.sort_values(by='city_area', ascending=False).head(3)\nanswer = t1['city_population'].sum()", false], ["t1 = district.sort_values(by='city_area', ascending=False).head(3)\nanswer = t1['city_population'].sum()", false], ["t1 = district.sort_values('city_area', ascending=False).head(3).reset_index()\nanswer = t1['city_population'].sum()", false], ["t1 = pd.merge(store_district, district, left_on='district_id', right_on='district_id')\nt2 = t1.groupby('district_id').sum().sort_values('city_area', ascending=False)\nanswer = t2['city_population'][:3].sum()", false], ["t1 = district.sort_values('city_area', ascending=False).head(3)\nanswer = t1['city_population'].sum()", false], ["t1 = district.sort_values('city_area', ascending=False).head(3)\nanswer = t1['city_population'].sum()", false], ["t1 = pd.merge(store, district, left_on='district_id', right_on='district_id')\nt2 = t1.groupby(['district_id', 'district_name', 'headquartered_city', 'city_population', 'city_area'])['store_id'].size().to_frame().reset_index()\nt2 = t2.sort_values('city_area', ascending=False).head(3)\nanswer = t2['city_population'].sum()", false], ["t1 = district.sort_values('city_area', ascending=False).head(3).sum()\nanswer = t1['city_population']", false], ["t1 = pd.merge(store_district, district, on='district_id')\nt2 = t1.groupby('district_id').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(3).to_frame().reset_index()\nt4 = pd.merge(t3, district, on='district_id')\nanswer = t4['city_population'].sum()", false], ["t1 = district.sort_values(by='city_area', ascending=False).head(3)\nanswer = t1['city_population'].sum()", false], ["t1 = district.sort_values('city_area', ascending=False).head(3)\nanswer = t1['city_population'].sum()", false], ["t1 = district.sort_values(by='city_area', ascending=False).head(3)\nanswer = t1['city_population'].sum()", false], ["t1 = district.sort_values('city_area', ascending=False).head(3)\nanswer = t1['city_population'].sum()", false], ["t1 = district.sort_values(by='city_area', ascending=False).head(3)\nanswer = t1['city_population'].sum()", false], ["t1 = district.sort_values('city_area', ascending=False).head(3)\nanswer = t1['city_population'].sum()", false]]}
+{"example": {"db_id": "apartment_rentals", "query": "SELECT apt_type_code FROM Apartments GROUP BY apt_type_code ORDER BY avg(room_count) DESC LIMIT 3", "query_toks": ["SELECT", "apt_type_code", "FROM", "Apartments", "GROUP", "BY", "apt_type_code", "ORDER", "BY", "avg", "(", "room_count", ")", "DESC", "LIMIT", "3"], "query_toks_no_value": ["select", "apt_type_code", "from", "apartments", "group", "by", "apt_type_code", "order", "by", "avg", "(", "room_count", ")", "desc", "limit", "value"], "question": "What are the top three apartment types in terms of the average room count? Give me the", "question_toks": ["What", "are", "the", "top", "three", "apartment", "types", "in", "terms", "of", "the", "average", "room", "count", "?", "Give", "me", "the"], "sql": {"from": {"table_units": [["table_unit", 1]], "conds": []}, "select": [false, [[0, [0, [0, 10, false], null]]]], "where": [], "groupBy": [[0, 10, false]], "having": [], "orderBy": ["desc", [[0, [5, 14, false], null]]], "limit": 3, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Apartment_Buildings": ["building_id", "building_short_name", "building_full_name", "building_description", "building_address", "building_manager", "building_phone"], "Apartments": ["apt_id", "building_id", "apt_type_code", "apt_number", "bathroom_count", "bedroom_count", "room_count"], "Apartment_Facilities": ["apt_id", "facility_code"], "Guests": ["guest_id", "gender_code", "guest_first_name", "guest_last_name", "date_of_birth"], "Apartment_Bookings": ["apt_booking_id", "apt_id", "guest_id", "booking_status_code", "booking_start_date", "booking_end_date"], "View_Unit_Status": ["apt_id", "apt_booking_id", "status_date", "available_yn"]}, "answer": [["Studio"], ["Flat"], ["Duplex"]]}, "program_result_list": [["t1 = Apartments.groupby('apt_type_code').mean()['room_count'].rename('avg_room_count')\nt2 = t1.sort_values(ascending=False).head(3).to_frame().reset_index()\nanswer = t2['apt_type_code']", false], ["t1 = Apartments.groupby('apt_type_code')['room_count'].mean().rename('avg_room_count')\nanswer = t1.sort_values(ascending=False).head(3).to_frame().reset_index()['apt_type_code']", false], ["t1 = Apartments.groupby('apt_type_code').mean()\nt2 = t1.sort_values('room_count', ascending=False)\nanswer = t2.head(3).index", false], ["t1 = Apartments.groupby('apt_type_code').agg({'room_count': 'mean'}).rename(columns={'room_count': 'avg_room_count'})\nt2 = t1.sort_values(by='avg_room_count', ascending=False).head(3).reset_index()\nanswer = t2['apt_type_code']", false], ["t1 = Apartments.groupby('apt_type_code').agg({'room_count': 'mean'})\nt2 = t1.sort_values('room_count', ascending=False).head(3).index.tolist()\nanswer = pd.DataFrame(t2, columns=['apt_type_code'])", false], ["t1 = Apartments.groupby('apt_type_code').room_count.mean().rename('avg_room_count')\nt2 = t1.sort_values(ascending=False).head(3).to_frame().reset_index()\nanswer = t2['apt_type_code'].to_frame()", false], ["t1 = Apartments.groupby('apt_type_code').agg({'room_count': 'mean'}).rename('avg_rooms').sort_values(ascending=False).head(3).reset_index()\nanswer = t1['apt_type_code']", false], ["t1 = Apartments.groupby('apt_type_code')['room_count'].mean().rename('avg_room_count')\nt2 = t1.sort_values(ascending=False).head(3).to_frame().reset_index()\nanswer = t2['apt_type_code']", false], ["t1 = Apartments.groupby('apt_type_code').mean()\nt2 = t1.sort_values('room_count', ascending=False).head(3).to_frame().reset_index()\nanswer = t2['apt_type_code']", false], ["t1 = Apartments.groupby('apt_type_code')['room_count'].mean().rename('avg_room_count').to_frame().reset_index()\nt2 = t1.sort_values('avg_room_count', ascending=False).head(3)\nanswer = t2['apt_type_code']", false], ["answer = Apartments.groupby('apt_type_code').agg({'room_count': 'mean'}).sort_values('room_count', ascending=False).head(3).index.tolist()", false], ["t1 = Apartments.groupby('apt_type_code')['room_count'].mean().rename('avg')\nanswer = t1.sort_values(ascending=False).head(3).to_frame().reset_index()", false], ["t1 = Apartments.groupby('apt_type_code').agg({'room_count':'mean'})\nt2 = t1.sort_values(by='room_count', ascending=False).head(3).reset_index()\nanswer = t2['apt_type_code']", false], ["t1 = Apartments.groupby('apt_type_code')['room_count'].mean().rename('room_count').to_frame()\nt2 = t1.sort_values(by='room_count', ascending=False).head(3).reset_index()\nanswer = t2['apt_type_code']", false], ["t1 = Apartments.groupby('apt_type_code').mean().rename({'room_count':'mean_room_count'}, axis=1).reset_index()\nt2 = t1.sort_values('mean_room_count', ascending=False).head(3).reset_index()\nanswer = t2['apt_type_code']", false], ["t1 = Apartments.groupby('apt_type_code').mean()\nt2 = t1.sort_values('room_count', ascending=False)\nanswer = t2.head(3).index.to_frame(index=False).rename(columns={'apt_type_code': 'apt_type_code'})", false], ["answer = Apartments.groupby('apt_type_code')['room_count'].mean().sort_values(ascending=False).head(3)", false], ["t1 = Apartments.groupby('apt_type_code')['room_count'].mean().rename('avg_room_count')\nt2 = t1.sort_values(ascending=False).head(3).to_frame().reset_index()\nanswer = t2['apt_type_code']", false], ["t1 = Apartments.groupby('apt_type_code').agg({'room_count': 'mean'}).rename(columns={'room_count': 'avg_room_count'})\nt2 = t1.sort_values('avg_room_count', ascending=False).head(3)\nanswer = t2.index", false], ["t1 = Apartments.groupby('apt_type_code').agg({'room_count': 'mean'}).rename(columns={'room_count': 'mean_room_count'})\nt2 = t1.sort_values('mean_room_count', ascending=False).head(3).to_frame().reset_index()\nanswer = t2['apt_type_code']", false]]}
+{"example": {"db_id": "cinema", "query": "SELECT LOCATION FROM cinema WHERE openning_year  >=  2010 GROUP BY LOCATION ORDER BY count(*) DESC LIMIT 1", "query_toks": ["SELECT", "LOCATION", "FROM", "cinema", "WHERE", "openning_year", ">", "=", "2010", "GROUP", "BY", "LOCATION", "ORDER", "BY", "count", "(", "*", ")", "DESC", "LIMIT", "1"], "query_toks_no_value": ["select", "location", "from", "cinema", "where", "openning_year", ">", "=", "value", "group", "by", "location", "order", "by", "count", "(", "*", ")", "desc", "limit", "value"], "question": "What is the location with the most cinemas opened in year 2010 or later?", "question_toks": ["What", "is", "the", "location", "with", "the", "most", "cinemas", "opened", "in", "year", "2010", "or", "later", "?"], "sql": {"from": {"table_units": [["table_unit", 1]], "conds": []}, "select": [false, [[0, [0, [0, 12, false], null]]]], "where": [[false, 5, [0, [0, 10, false], null], 2010.0, null]], "groupBy": [[0, 12, false]], "having": [], "orderBy": ["desc", [[0, [3, 0, false], null]]], "limit": 1, "intersect": null, "union": null, "except": null}, "db_table_headers": {"film": ["Film_ID", "Rank_in_series", "Number_in_season", "Title", "Directed_by", "Original_air_date", "Production_code"], "cinema": ["Cinema_ID", "Name", "Openning_year", "Capacity", "Location"], "schedule": ["Cinema_ID", "Film_ID", "Date", "Show_times_per_day", "Price"]}, "answer": [["County Tipperary"]]}, "program_result_list": [["t1 = cinema[cinema['openning_year'] >= 2010]\nt2 = t1.groupby('location').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3['location']", false], ["answer = cinema[cinema['openning_year'] >= 2010].groupby('location')['cinema_id'].count().sort_values(ascending=False).head(1).to_frame().reset_index()", false], ["t1 = cinema[cinema['openning_year'] >= 2010]\nt2 = t1.groupby('location').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3['location']", false], ["t1 = cinema[cinema['openning_year'] >= 2010]\nt2 = t1.groupby('location').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3['location']", false], ["t1 = cinema[cinema['openning_year'] >= 2010].groupby('location').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t2['location']", false], ["t1 = cinema[cinema['openning_year'] >= 2010]\nt2 = t1.groupby('location').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3['location']", false], ["t1 = cinema[cinema['openning_year'] >= 2010]\nt2 = t1.groupby('location').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3['location']", false], ["t1 = cinema[cinema['openning_year'] >= 2010].groupby('location').size().rename('count')\nanswer = t1.sort_values(ascending=False).head(1).to_frame().reset_index()['location']", false], ["t1 = cinema[cinema['openning_year'] >= 2010]\nt2 = t1.groupby('location').size().rename('count')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index().iloc[0,0]", false], ["t1 = cinema[cinema['openning_year'] >= 2010]\nt2 = t1.groupby('location').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3['location']", false], ["t1 = cinema[cinema['openning_year'] >= 2010]\nt2 = t1.groupby('location').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3['location']", false], ["t1 = cinema[cinema['openning_year'] >= 2010]\nt2 = t1.groupby('location').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3['location']", false], ["t1 = cinema[cinema['openning_year'] >= 2010].groupby('location').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t2['location']", false], ["t1 = cinema[cinema['openning_year'] >= 2010]\nt2 = t1.groupby('location').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3['location']", false], ["t1 = cinema[cinema['openning_year'] >= 2010]\nt2 = t1.groupby('location').size().rename('count')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index()['location']", false], ["t1 = cinema[cinema['openning_year'] >= 2010]\nt2 = t1.groupby('location').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3['location']", false], ["t1 = cinema[cinema['openning_year'] >= 2010]\nt2 = t1.groupby('location').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3['location']", false], ["t1 = cinema[cinema['openning_year'] >= 2010]\nt2 = t1.groupby('location').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3['location']", false], ["t1 = cinema[cinema['openning_year'] >= 2010]\nt2 = t1.groupby('location').size().rename('count')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index()['location']", false], ["t1 = cinema[cinema['openning_year'] >= 2010].groupby('location').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t2['location']", false]]}
+{"example": {"db_id": "cre_Theme_park", "query": "SELECT hotel_id ,  star_rating_code FROM HOTELS ORDER BY price_range ASC", "query_toks": ["SELECT", "hotel_id", ",", "star_rating_code", "FROM", "HOTELS", "ORDER", "BY", "price_range", "ASC"], "query_toks_no_value": ["select", "hotel_id", ",", "star_rating_code", "from", "hotels", "order", "by", "price_range", "asc"], "question": "Find the id and star rating of each hotel and sort them in increasing order of price.", "question_toks": ["Find", "the", "id", "and", "star", "rating", "of", "each", "hotel", "and", "sort", "them", "in", "increasing", "order", "of", "price", "."], "sql": {"from": {"table_units": [["table_unit", 5]], "conds": []}, "select": [false, [[0, [0, [0, 13, false], null]], [0, [0, [0, 14, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": ["asc", [[0, [0, 16, false], null]]], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Ref_Hotel_Star_Ratings": ["star_rating_code", "star_rating_description"], "Locations": ["Location_ID", "Location_Name", "Address", "Other_Details"], "Ref_Attraction_Types": ["Attraction_Type_Code", "Attraction_Type_Description"], "Visitors": ["Tourist_ID", "Tourist_Details"], "Features": ["Feature_ID", "Feature_Details"], "Hotels": ["hotel_id", "star_rating_code", "pets_allowed_yn", "price_range", "other_hotel_details"], "Tourist_Attractions": ["Tourist_Attraction_ID", "Attraction_Type_Code", "Location_ID", "How_to_Get_There", "Name", "Description", "Opening_Hours", "Other_Details"], "Street_Markets": ["Market_ID", "Market_Details"], "Shops": ["Shop_ID", "Shop_Details"], "Museums": ["Museum_ID", "Museum_Details"], "Royal_Family": ["Royal_Family_ID", "Royal_Family_Details"], "Theme_Parks": ["Theme_Park_ID", "Theme_Park_Details"], "Visits": ["Visit_ID", "Tourist_Attraction_ID", "Tourist_ID", "Visit_Date", "Visit_Details"], "Photos": ["Photo_ID", "Tourist_Attraction_ID", "Name", "Description", "Filename", "Other_Details"], "Staff": ["Staff_ID", "Tourist_Attraction_ID", "Name", "Other_Details"], "Tourist_Attraction_Features": ["Tourist_Attraction_ID", "Feature_ID"]}, "answer": [[431, "2"], [426, "5"], [473, "1"], [555, "5"], [420, "5"], [172, "5"], [264, "1"], [442, "2"], [514, "5"], [123, "5"], [421, "3"], [379, "4"], [373, "5"], [314, "5"], [144, "4"], [222, "5"], [239, "3"], [331, "1"], [350, "1"], [376, "2"]]}, "program_result_list": [["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nanswer = t1.sort_values(by=['price_range'])[['hotel_id', 'star_rating_code']]", false], ["answer = Hotels[['hotel_id', 'star_rating_code']].sort_values(by='price_range')", false], ["answer = Hotels.sort_values(by='price_range')[['hotel_id', 'star_rating_code']]", false], ["answer = Hotels[['hotel_id', 'star_rating_code']].sort_values('price_range')", false], ["answer = Hotels[['hotel_id', 'star_rating_code']].sort_values(by='price_range')", false], ["answer = Hotels[['hotel_id', 'star_rating_code']].sort_values(by='price_range')", false], ["answer = Hotels[['hotel_id', 'star_rating_code']].sort_values(by='price_range')", false], ["answer = Hotels[['hotel_id', 'star_rating_code']].sort_values(by='price_range')", false], ["t1 = Hotels[['hotel_id', 'star_rating_code']].sort_values(by='price_range', ascending=True)\nanswer = t1", false], ["answer = Hotels[['hotel_id', 'star_rating_code']].sort_values(by=['price_range'])", false], ["answer = Hotels.sort_values('price_range')[['hotel_id', 'star_rating_code']]", false], ["answer = Hotels.sort_values('price_range')[['hotel_id', 'star_rating_code']]", false], ["answer = Hotels[['hotel_id', 'star_rating_code']].sort_values('price_range')", false], ["answer = Hotels[['hotel_id', 'star_rating_code']].sort_values(by='price_range')", false], ["answer = Hotels[['hotel_id', 'star_rating_code']].sort_values('price_range')", false], ["answer = Hotels[['hotel_id', 'star_rating_code']].sort_values('price_range')", false], ["answer = Hotels[['hotel_id', 'star_rating_code']].sort_values(by='price_range')", false], ["answer = Hotels[['hotel_id', 'star_rating_code']].sort_values('price_range')", false], ["answer = Hotels[['hotel_id', 'star_rating_code']].sort_values('price_range', ascending=True)", false], ["answer = Hotels[['hotel_id', 'star_rating_code']].sort_values('price_range')", false]]}
+{"example": {"db_id": "flight_1", "query": "SELECT name ,  salary FROM Employee ORDER BY salary", "query_toks": ["SELECT", "name", ",", "salary", "FROM", "Employee", "ORDER", "BY", "salary"], "query_toks_no_value": ["select", "name", ",", "salary", "from", "employee", "order", "by", "salary"], "question": "Show name and salary for all employees sorted by salary.", "question_toks": ["Show", "name", "and", "salary", "for", "all", "employees", "sorted", "by", "salary", "."], "sql": {"from": {"table_units": [["table_unit", 2]], "conds": []}, "select": [false, [[0, [0, [0, 13, false], null]], [0, [0, [0, 14, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": ["asc", [[0, [0, 14, false], null]]], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"flight": ["flno", "origin", "destination", "distance", "departure_date", "arrival_date", "price", "aid"], "aircraft": ["aid", "name", "distance"], "employee": ["eid", "name", "salary"], "certificate": ["eid", "aid"]}, "answer": [["Milo Brooks", 20], ["Donald King", 18050], ["Richard Jackson", 23980], ["Patricia Jones", 24450], ["Linda Davis", 27984], ["Elizabeth Taylor", 32021], ["Haywood Kelly", 32899], ["Chad Stewart", 33546], ["David Anderson", 43001], ["Barbara Wilson", 43723], ["Robert Brown", 44740], ["Michael Miller", 48090], ["William Moore", 48250], ["Jennifer Thomas", 54921], ["William Ward", 84476], ["Michael Miller", 99890], ["Larry West", 101745], ["William Jones", 105743], ["Eric Cooper", 114323], ["James Smith", 120433], ["Dorthy Lewis", 152013], ["John Williams", 153972], ["Mary Johnson", 178345], ["Karen Scott", 205187], ["Mark Young", 205187], ["Lawrence Sperry", 212156], ["Angela Martinez", 212156], ["Joseph Thompson", 212156], ["Betty Adams", 227489], ["Lisa Walker", 256481], ["George Wright", 289950]]}, "program_result_list": [["answer = employee.sort_values('salary')", false], ["answer = employee.sort_values('salary')[['name', 'salary']]", false], ["answer = employee.sort_values('salary')", false], ["answer = employee.sort_values('salary')", false], ["answer = employee.sort_values(by='salary')[['name', 'salary']]", false], ["t1 = employee.sort_values('salary')\nanswer = t1[['name', 'salary']]", false], ["t1 = employee.sort_values('salary')\nanswer = t1[['name', 'salary']]", false], ["t1 = employee.sort_values(by='salary')\nanswer = t1[['name', 'salary']]", false], ["answer = employee.sort_values('salary')[['name', 'salary']]", false], ["t1 = employee.sort_values('salary')\nanswer = t1[['name', 'salary']]", false], ["t1 = employee.sort_values('salary')\nanswer = t1[['name', 'salary']]", false], ["answer = employee.sort_values('salary', ascending=True)[['name', 'salary']]", false], ["answer = employee.sort_values('salary')[['name', 'salary']]", false], ["t1 = employee.sort_values('salary')\nanswer = t1[['name', 'salary']]", false], ["answer = employee.sort_values(by='salary')[['name', 'salary']]", false], ["answer = employee.sort_values('salary', ascending=False)[['name', 'salary']]", false], ["answer = employee[['name', 'salary']].sort_values('salary')", false], ["t1 = employee.sort_values('salary').reset_index()\nanswer = t1[['name', 'salary']]", false], ["t1 = employee.sort_values('salary')\nanswer = t1[['name', 'salary']]", false], ["t1 = employee.sort_values('salary')\nanswer = t1[['name', 'salary']]", false]]}
+{"example": {"db_id": "shop_membership", "query": "SELECT count(*) FROM branch WHERE membership_amount  >  (SELECT avg(membership_amount) FROM branch)", "query_toks": ["SELECT", "count", "(", "*", ")", "FROM", "branch", "WHERE", "membership_amount", ">", "(", "SELECT", "avg", "(", "membership_amount", ")", "FROM", "branch", ")"], "query_toks_no_value": ["select", "count", "(", "*", ")", "from", "branch", "where", "membership_amount", ">", "(", "select", "avg", "(", "membership_amount", ")", "from", "branch", ")"], "question": "How many branches where have more than average number of memberships are there?", "question_toks": ["How", "many", "branches", "where", "have", "more", "than", "average", "number", "of", "memberships", "are", "there", "?"], "sql": {"from": {"table_units": [["table_unit", 1]], "conds": []}, "select": [false, [[3, [0, [0, 0, false], null]]]], "where": [[false, 3, [0, [0, 11, false], null], {"from": {"table_units": [["table_unit", 1]], "conds": []}, "select": [false, [[5, [0, [0, 11, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"member": ["Member_ID", "Card_Number", "Name", "Hometown", "Level"], "branch": ["Branch_ID", "Name", "Open_year", "Address_road", "City", "membership_amount"], "membership_register_branch": ["Member_ID", "Branch_ID", "Register_Year"], "purchase": ["Member_ID", "Branch_ID", "Year", "Total_pounds"]}, "answer": [[5]]}, "program_result_list": [["t1 = branch[branch['membership_amount'] > branch['membership_amount'].mean()]\nanswer = t1.shape[0]", false], ["t1 = branch['membership_amount'].mean()\nanswer = branch[branch['membership_amount'] > t1].shape[0]", false], ["t1 = branch['membership_amount'].mean()\nt2 = branch[branch['membership_amount'] > t1]\nanswer = t2['membership_amount'].count()", false], ["t1 = branch['membership_amount'].mean()\nanswer = len(branch[branch['membership_amount'] > t1])", false], ["t1 = branch['membership_amount'].mean()\nanswer = branch[branch['membership_amount'] > t1].shape[0]", false], ["t1 = branch['membership_amount'].mean()\nt2 = branch[branch['membership_amount'] > t1]\nanswer = t2['branch_id'].count()", false], ["t1 = branch[branch['membership_amount'] > branch['membership_amount'].mean()]\nanswer = t1['branch_id'].count()", false], ["t1 = branch[branch['membership_amount'] > branch['membership_amount'].mean()]\nanswer = t1.shape[0]", false], ["t1 = branch['membership_amount'].mean()\nt2 = branch[branch['membership_amount'] > t1]\nanswer = len(t2)", false], ["t1 = branch[branch['membership_amount'] > branch['membership_amount'].mean()].shape[0]\nanswer = t1", false], ["answer = branch[branch['membership_amount'] > branch['membership_amount'].mean()].shape[0]", false], ["answer = branch[branch['membership_amount'] > branch['membership_amount'].mean()].shape[0]", false], ["answer = branch[branch['membership_amount'] > branch['membership_amount'].mean()].shape[0]", false], ["t1 = branch.groupby('membership_amount').size().rename('count')\nt2 = t1[t1 > t1.mean()].to_frame().reset_index()\nanswer = t2['count'].sum()", false], ["t1 = branch['membership_amount'].mean()\nt2 = branch[branch['membership_amount'] > t1].size\nanswer = t2", false], ["t1 = branch['membership_amount'].mean()\nanswer = len(branch[branch['membership_amount'] > t1])", false], ["t1 = branch['membership_amount'].mean()\nanswer = branch[branch['membership_amount'] > t1].count()", false], ["t1 = branch[branch['membership_amount'] > branch['membership_amount'].mean()]\nanswer = len(t1)", false], ["t1 = branch['membership_amount']\nt2 = branch[t1 > t1.mean()]\nanswer = len(t2)", false], ["t1 = branch.groupby('membership_amount')['membership_amount'].mean()\nt2 = t1[t1 > t1.mean()]\nanswer = len(t2)", false]]}
+{"example": {"db_id": "customers_and_addresses", "query": "SELECT sum(t2.order_quantity) FROM customer_orders AS t1 JOIN order_items AS t2 ON t1.order_id  =  t2.order_id WHERE t1.order_status  =  \"Cancelled\"", "query_toks": ["SELECT", "sum", "(", "t2.order_quantity", ")", "FROM", "customer_orders", "AS", "t1", "JOIN", "order_items", "AS", "t2", "ON", "t1.order_id", "=", "t2.order_id", "WHERE", "t1.order_status", "=", "``", "Cancelled", "''"], "query_toks_no_value": ["select", "sum", "(", "t2", ".", "order_quantity", ")", "from", "customer_orders", "as", "t1", "join", "order_items", "as", "t2", "on", "t1", ".", "order_id", "=", "t2", ".", "order_id", "where", "t1", ".", "order_status", "=", "value"], "question": "What is the total number of products that are in orders with status \"Cancelled\"?", "question_toks": ["What", "is", "the", "total", "number", "of", "products", "that", "are", "in", "orders", "with", "status", "``", "Cancelled", "''", "?"], "sql": {"from": {"table_units": [["table_unit", 5], ["table_unit", 6]], "conds": [[false, 2, [0, [0, 25, false], null], [0, 30, false], null]]}, "select": [false, [[4, [0, [0, 32, false], null]]]], "where": [[false, 2, [0, [0, 27, false], null], "\"Cancelled\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Addresses": ["address_id", "address_content", "city", "zip_postcode", "state_province_county", "country", "other_address_details"], "Products": ["product_id", "product_details"], "Customers": ["customer_id", "payment_method", "customer_name", "date_became_customer", "other_customer_details"], "Customer_Addresses": ["customer_id", "address_id", "date_address_from", "address_type", "date_address_to"], "Customer_Contact_Channels": ["customer_id", "channel_code", "active_from_date", "active_to_date", "contact_number"], "Customer_Orders": ["order_id", "customer_id", "order_status", "order_date", "order_details"], "Order_Items": ["order_id", "product_id", "order_quantity"]}, "answer": [[77]]}, "program_result_list": [["t1 = pd.merge(Customer_Orders, Order_Items, left_on='order_id', right_on='order_id')\nt2 = t1[t1['order_status'] == 'Cancelled']\nanswer = t2['order_quantity'].sum()", false], ["t1 = pd.merge(Customer_Orders, Order_Items, left_on='order_id', right_on='order_id')\nt2 = t1[t1['order_status'] == 'Cancelled']\nanswer = t2['order_quantity'].sum()", false], ["t1 = pd.merge(Customer_Orders, Order_Items, left_on='order_id', right_on='order_id')\nt2 = t1[t1['order_status'] == 'Cancelled']\nanswer = t2['order_quantity'].sum()", false], ["t1 = pd.merge(Customer_Orders, Order_Items, left_on='order_id', right_on='order_id')\nt2 = t1[t1['order_status'] == 'Cancelled']\nanswer = t2['order_quantity'].sum()", false], ["t1 = pd.merge(Customer_Orders, Order_Items, left_on='order_id', right_on='order_id')\nt2 = t1[t1['order_status'] == 'Cancelled']\nanswer = t2['order_quantity'].sum()", false], ["t1 = pd.merge(Customer_Orders, Order_Items, left_on='order_id', right_on='order_id')\nt2 = t1[t1['order_status'] == 'Cancelled']\nanswer = t2['order_quantity'].sum()", false], ["t1 = pd.merge(Customer_Orders, Order_Items, left_on='order_id', right_on='order_id')\nt2 = t1[t1['order_status'] == 'Cancelled']\nanswer = t2['order_quantity'].sum()", false], ["t1 = pd.merge(Customer_Orders, Order_Items, on='order_id')\nt2 = t1[t1['order_status'] == 'Cancelled']\nanswer = t2['order_quantity'].sum()", false], ["t1 = pd.merge(Customer_Orders, Order_Items, left_on='order_id', right_on='order_id')\nt2 = t1[t1['order_status'] == 'Cancelled']\nanswer = t2['order_quantity'].sum()", false], ["t1 = pd.merge(Customer_Orders, Order_Items, left_on='order_id', right_on='order_id')\nt2 = t1[t1['order_status'] == 'Cancelled']\nanswer = t2['order_quantity'].sum()", false], ["t1 = pd.merge(Customer_Orders, Order_Items, on='order_id')\nt2 = t1[t1['order_status'] == 'Cancelled']\nanswer = t2['order_quantity'].sum()", false], ["t1 = pd.merge(Customer_Orders, Order_Items, left_on='order_id', right_on='order_id')\nt2 = t1[t1['order_status'] == 'Cancelled']\nanswer = t2['order_quantity'].sum()", false], ["t1 = pd.merge(Customer_Orders, Order_Items, left_on='order_id', right_on='order_id')\nanswer = t1[t1['order_status'] == 'Cancelled']['order_quantity'].sum()", false], ["t1 = pd.merge(Customer_Orders, Order_Items, left_on='order_id', right_on='order_id')\nt2 = t1[t1['order_status'] == 'cancelled']\nanswer = sum(t2['order_quantity'])", false], ["t1 = pd.merge(Customer_Orders, Order_Items, left_on='order_id', right_on='order_id')\nt2 = t1[t1['order_status'] == 'Cancelled']\nanswer = t2['order_quantity'].sum()", false], ["t1 = pd.merge(Customer_Orders, Order_Items, left_on='order_id', right_on='order_id')\nt2 = t1[t1['order_status'] == 'Cancelled']\nanswer = t2['order_quantity'].sum()", false], ["t1 = pd.merge(Customer_Orders, Order_Items, left_on='order_id', right_on='order_id')\nt2 = t1[t1['order_status'] == 'Cancelled']\nanswer = t2['order_quantity'].sum()", false], ["t1 = pd.merge(Customer_Orders, Order_Items, on='order_id')\nt2 = t1[t1['order_status'] == 'Cancelled']\nanswer = t2['order_quantity'].sum()", false], ["t1 = pd.merge(Customer_Orders, Order_Items, left_on='order_id', right_on='order_id')\nt2 = t1[t1['order_status'] == 'Cancelled']\nanswer = t2['order_quantity'].sum()", false], ["t1 = pd.merge(Customer_Orders, Order_Items, on='order_id')\nt2 = t1[t1['order_status'] == 'Cancelled']\nanswer = t2['order_quantity'].sum()", false]]}
+{"example": {"db_id": "insurance_policies", "query": "SELECT sum(Amount_Settled) FROM Settlements", "query_toks": ["SELECT", "sum", "(", "Amount_Settled", ")", "FROM", "Settlements"], "query_toks_no_value": ["select", "sum", "(", "amount_settled", ")", "from", "settlements"], "question": "What is the total amount of settlement made for all the settlements?", "question_toks": ["What", "is", "the", "total", "amount", "of", "settlement", "made", "for", "all", "the", "settlements", "?"], "sql": {"from": {"table_units": [["table_unit", 3]], "conds": []}, "select": [false, [[4, [0, [0, 19, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Customers": ["Customer_ID", "Customer_Details"], "Customer_Policies": ["Policy_ID", "Customer_ID", "Policy_Type_Code", "Start_Date", "End_Date"], "Claims": ["Claim_ID", "Policy_ID", "Date_Claim_Made", "Date_Claim_Settled", "Amount_Claimed", "Amount_Settled"], "Settlements": ["Settlement_ID", "Claim_ID", "Date_Claim_Made", "Date_Claim_Settled", "Amount_Claimed", "Amount_Settled", "Customer_Policy_ID"], "Payments": ["Payment_ID", "Settlement_ID", "Payment_Method_Code", "Date_Payment_Made", "Amount_Payment"]}, "answer": [[21993]]}, "program_result_list": [["answer = Settlements['amount_settled'].sum()", false], ["answer = Settlements['amount_settled'].sum()", false], ["answer = Settlements['amount_settled'].sum()", false], ["answer = Settlements['amount_settled'].sum()", false], ["answer = Settlements['amount_settled'].sum()", false], ["t1 = Settlements.groupby('settlement_id').size().rename('count')\nanswer = Settlements['amount_settled'].sum()", false], ["answer = Settlements['amount_settled'].sum()", false], ["answer = Settlements['amount_settled'].sum()", false], ["answer = Settlements['amount_settled'].sum()", false], ["answer = Settlements['amount_settled'].sum()", false], ["answer = Settlements['amount_settled'].sum()", false], ["answer = Settlements['amount_settled'].sum()", false], ["answer = Settlements['amount_settled'].sum()", false], ["answer = Settlements['amount_settled'].sum()", false], ["t1 = Settlements.groupby('settlement_id').sum()\nanswer = t1['amount_settled']", false], ["answer = Settlements['amount_settled'].sum()", false], ["answer = Settlements['amount_settled'].sum()", false], ["answer = Settlements['amount_settled'].sum()", false], ["answer = Settlements['amount_settled'].sum()", false], ["answer = Settlements['amount_settled'].sum()", false]]}
+{"example": {"db_id": "race_track", "query": "SELECT T1.name ,  T1.date ,  T2.name FROM race AS T1 JOIN track AS T2 ON T1.track_id  =  T2.track_id", "query_toks": ["SELECT", "T1.name", ",", "T1.date", ",", "T2.name", "FROM", "race", "AS", "T1", "JOIN", "track", "AS", "T2", "ON", "T1.track_id", "=", "T2.track_id"], "query_toks_no_value": ["select", "t1", ".", "name", ",", "t1", ".", "date", ",", "t2", ".", "name", "from", "race", "as", "t1", "join", "track", "as", "t2", "on", "t1", ".", "track_id", "=", "t2", ".", "track_id"], "question": "What are the names and dates of races, and the names of the tracks where they are held?", "question_toks": ["What", "are", "the", "names", "and", "dates", "of", "races", ",", "and", "the", "names", "of", "the", "tracks", "where", "they", "are", "held", "?"], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 5, false], null], [0, 6, false], null]]}, "select": [false, [[0, [0, [0, 2, false], null]], [0, [0, [0, 4, false], null]], [0, [0, [0, 7, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"race": ["Race_ID", "Name", "Class", "Date", "Track_ID"], "track": ["Track_ID", "Name", "Location", "Seating", "Year_Opened"]}, "answer": [["Rolex 24 At Daytona", "January 26 January 27", "Auto Club Speedway"], ["Gainsco Grand Prix of Miami", "March 29", "Chicagoland Speedway"], ["Mexico City 250", "April 19", "Chicagoland Speedway"], ["Bosch Engineering 250 at VIR", "April 27", "Daytona International Speedway"], ["RumBum.com 250", "May 17", "Homestead-Miami Speedway"], ["Lime Rock GT Classic 250", "May 26", "Kansas Speedway"], ["Sahlen's Six Hours of the Glen", "June 7", "Martinsville Speedway"]]}, "program_result_list": [["t1 = pd.merge(race, track, left_on='track_id', right_on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, left_on='track_id', right_on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, left_on='track_id', right_on='track_id')\nanswer = t1[['race_id', 'name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, left_on='track_id', right_on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, left_on='track_id', right_on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, left_on='track_id', right_on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, left_on='track_id', right_on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, left_on='track_id', right_on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, left_on='track_id', right_on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, left_on='track_id', right_on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, left_on='track_id', right_on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, left_on='track_id', right_on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, left_on='track_id', right_on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, left_on='track_id', right_on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, left_on='track_id', right_on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false], ["t1 = pd.merge(race, track, left_on='track_id', right_on='track_id')\nanswer = t1[['name_x', 'date', 'name_y']]", false]]}
+{"example": {"db_id": "medicine_enzyme_interaction", "query": "SELECT count(*) FROM medicine WHERE FDA_approved  =  'No'", "query_toks": ["SELECT", "count", "(", "*", ")", "FROM", "medicine", "WHERE", "FDA_approved", "=", "'No", "'"], "query_toks_no_value": ["select", "count", "(", "*", ")", "from", "medicine", "where", "fda_approved", "=", "value"], "question": "How many medicines were not approved by the FDA?", "question_toks": ["How", "many", "medicines", "were", "not", "approved", "by", "the", "FDA", "?"], "sql": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[3, [0, [0, 0, false], null]]]], "where": [[false, 2, [0, [0, 4, false], null], "\"No\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"medicine": ["id", "name", "Trade_Name", "FDA_approved"], "enzyme": ["id", "name", "Location", "Product", "Chromosome", "OMIM", "Porphyria"], "medicine_enzyme_interaction": ["enzyme_id", "medicine_id", "interaction_type"]}, "answer": [[11]]}, "program_result_list": [["answer = medicine[medicine['fda_approved'] == 'no'].shape[0]", false], ["t1 = medicine[medicine['fda_approved'] == 'no']\nanswer = t1.shape[0]", false], ["answer = medicine[medicine['fda_approved'] == 'no'].shape[0]", false], ["answer = medicine[medicine['fda_approved'] == 'no'].size", false], ["answer = medicine[medicine['fda_approved'] == 'no']['id'].count()", false], ["answer = len(medicine[medicine['fda_approved'] == 'no'])", false], ["t1 = medicine.loc[medicine['fda_approved'] == 'no']\nanswer = t1['id'].count()", false], ["answer = medicine[medicine['fda_approved'] == 'no']['id'].count()", false], ["t1 = medicine[medicine['fda_approved'] == 'no']\nanswer = len(t1)", false], ["answer = medicine[medicine['fda_approved'] == 'no'].shape[0]", false], ["t1 = medicine[medicine['fda_approved'] == 'no']\nanswer = t1.shape[0]", false], ["answer = len(medicine[medicine['fda_approved'] == 'no'])", false], ["t1 = medicine[medicine['fda_approved'] == 'no']\nanswer = t1['id'].count()", false], ["answer = medicine[medicine['fda_approved'] == 'no'].shape[0]", false], ["answer = medicine[medicine['fda_approved'] == 'no'].shape[0]", false], ["answer = medicine[medicine['fda_approved'] == 'no'].shape[0]", false], ["answer = medicine[medicine['fda_approved'] == 'no']['id'].count()", false], ["answer = medicine[medicine['fda_approved'] == 'no'].shape[0]", false], ["t1 = medicine[medicine['fda_approved'] == 'no'].count()\nanswer = t1['fda_approved']", false], ["answer = len(medicine[medicine['fda_approved'] == 'no'])", false]]}
+{"example": {"db_id": "manufactory_1", "query": "SELECT AVG(Price) ,  Manufacturer FROM Products GROUP BY Manufacturer", "query_toks": ["SELECT", "AVG", "(", "Price", ")", ",", "Manufacturer", "FROM", "Products", "GROUP", "BY", "Manufacturer"], "query_toks_no_value": ["select", "avg", "(", "price", ")", ",", "manufacturer", "from", "products", "group", "by", "manufacturer"], "question": "What are the average prices of products, grouped by manufacturer code?", "question_toks": ["What", "are", "the", "average", "prices", "of", "products", ",", "grouped", "by", "manufacturer", "code", "?"], "sql": {"from": {"table_units": [["table_unit", 1]], "conds": []}, "select": [false, [[5, [0, [0, 8, false], null]], [0, [0, [0, 9, false], null]]]], "where": [], "groupBy": [[0, 9, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Manufacturers": ["Code", "Name", "Headquarter", "Founder", "Revenue"], "Products": ["Code", "Name", "Price", "Manufacturer"]}, "answer": [[240.0, 1], [150.0, 2], [162.0, 3], [150.0, 4], [240.0, 5], [62.5, 6]]}, "program_result_list": [["answer = Products.groupby('manufacturer').mean()['price']", false], ["answer = Products.groupby('manufacturer')['price'].mean().to_frame().rename(columns={'price': 'avg(price)'})", false], ["t1 = Products.groupby('manufacturer').mean()\nanswer = t1['price']", false], ["t1 = Products.groupby('manufacturer')['price'].mean().rename('average')\nanswer = t1.to_frame().reset_index()", false], ["t1 = Products.groupby('manufacturer').agg({'price': 'mean'}).rename(columns={'price': 'avg(price)'}).reset_index()\nanswer = t1", false], ["answer = Products.groupby('manufacturer').agg({'price': 'mean'})", false], ["t1 = Products.groupby('manufacturer')['price'].mean().rename('avg_price').to_frame()\nanswer = t1", false], ["answer = Products.groupby('manufacturer').mean().rename(columns={'price': 'avg(price)'})", false], ["t1 = Products.groupby('manufacturer')['price'].mean().rename('avg_price')\nanswer = t1.to_frame().reset_index()", false], ["t1 = Products.groupby('manufacturer')['price'].mean().rename('avg_price')\nanswer = pd.DataFrame(t1).reset_index()", false], ["answer = Products.groupby('manufacturer')['price'].mean().rename('avg_price').to_frame().reset_index()", false], ["t1 = Products.groupby('manufacturer').agg({'price':'mean'}).rename(columns={'price':'avg_price'})\nanswer = t1.reset_index()", false], ["t1 = Products.groupby('manufacturer')['price'].mean().rename('avg(price)')\nanswer = t1.to_frame().reset_index()", false], ["t1 = Products.groupby('manufacturer').mean()\nanswer = t1['price']", false], ["t1 = Products.groupby('manufacturer').mean()\nanswer = t1['price']", false], ["t1 = Products.groupby('manufacturer').mean()\nanswer = t1['price'].rename('average_price')", false], ["answer = Products.groupby('manufacturer').agg({'price': 'mean'})", false], ["t1 = Products.groupby('manufacturer')['price'].mean()\nanswer = t1.to_frame()", false], ["t1 = Products.groupby('manufacturer').agg({'price': 'mean'})\nanswer = t1.rename(columns={'price': 'avg(price)'}).reset_index()", false], ["t1 = Products.groupby('manufacturer')['price'].mean().rename('avg_price').to_frame().reset_index()\nanswer = t1", false]]}
+{"example": {"db_id": "dorm_1", "query": "SELECT count(*) ,  sex FROM student WHERE age  >  (SELECT avg(age) FROM student) GROUP BY sex", "query_toks": ["SELECT", "count", "(", "*", ")", ",", "sex", "FROM", "student", "WHERE", "age", ">", "(", "SELECT", "avg", "(", "age", ")", "FROM", "student", ")", "GROUP", "BY", "sex"], "query_toks_no_value": ["select", "count", "(", "*", ")", ",", "sex", "from", "student", "where", "age", ">", "(", "select", "avg", "(", "age", ")", "from", "student", ")", "group", "by", "sex"], "question": "How many students are older than average for each gender?", "question_toks": ["How", "many", "students", "are", "older", "than", "average", "for", "each", "gender", "?"], "sql": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[3, [0, [0, 0, false], null]], [0, [0, [0, 5, false], null]]]], "where": [[false, 3, [0, [0, 4, false], null], {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[5, [0, [0, 4, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, null]], "groupBy": [[0, 5, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Student": ["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], "Dorm": ["dormid", "dorm_name", "student_capacity", "gender"], "Dorm_amenity": ["amenid", "amenity_name"], "Has_amenity": ["dormid", "amenid"], "Lives_in": ["stuid", "dormid", "room_number"]}, "answer": [[5, "F"], [10, "M"]]}, "program_result_list": [["t1 = Student.groupby('age').size().rename('count')\nt2 = t1[t1 > Student['age'].mean()].to_frame().reset_index()\nt3 = pd.merge(t2, Student, left_on='age', right_on='age')\nanswer = t3.groupby('sex').size().rename('count')", false], ["t1 = Student[Student['age'] > Student['age'].mean()]\nt2 = t1.groupby('sex').size().rename('count').to_frame().reset_index()\nanswer = t2", false], ["t1 = Student[Student['age'] > Student['age'].mean()]\nanswer = t1.groupby('sex').size().rename('count').to_frame().reset_index()", false], ["t1 = Student.groupby('age').size().rename('count')\nt2 = t1.mean()\nt3 = Student[Student['age'] > t2]\nanswer = t3.groupby('sex').size().rename('count')", false], ["t1 = Student[Student['age'] > Student['age'].mean()]\nt2 = t1.groupby('sex').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = Student[Student['age'] > Student['age'].mean()].groupby('sex').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = Student[Student['age'] > Student['age'].mean()]\nt2 = t1.groupby('sex').size().rename('count')\nanswer = t2.to_frame()", false], ["t1 = Student[Student['age'] > Student['age'].mean()].groupby('sex').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = Student[Student['age'] > Student['age'].mean()].groupby('sex').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = Student.groupby('sex')['age'].mean().rename('avg_age')\nt2 = Student[Student['age'] > t1[Student['sex']].values].groupby('sex').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = Student.groupby('sex').mean()\nt2 = Student[Student['age'] > t1['age'][0]]\nanswer = t2.groupby('sex').size().rename('count').to_frame().reset_index()", false], ["t1 = Student.groupby('sex').mean().rename(columns={'age': 'avg_age'})\nt2 = pd.merge(Student, t1, on='sex')\nt3 = t2[t2['age_x'] > t2['age_y']]\nanswer = t3.groupby('sex').count()[['age_x']].rename(columns={'age_x': 'count'})", false], ["t1 = Student.groupby('sex').mean()['age']\nt2 = Student[Student['age'] > t1]\nanswer = t2.groupby('sex').size().rename('count')", false], ["t1 = Student.groupby('sex').agg({'age': 'mean'})\nt2 = Student[Student['age'] > t1['age'][0]]\nanswer = t2.groupby('sex').size().rename('count')", false], ["t1 = Student[Student['age'] > Student['age'].mean()]\nt2 = t1.groupby('sex').size().rename('count').to_frame().reset_index()\nanswer = t2", false], ["t1 = Student[Student['age'] > Student['age'].mean()]\nt2 = t1.groupby('sex').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = Student[Student['age'] > Student['age'].mean()]\nt2 = t1.groupby('sex').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = Student[Student['age'] > Student['age'].mean()].groupby('sex').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = Student.groupby('sex').size().rename('count')\nt2 = Student.groupby('sex')['age'].mean().rename('age_avg')\nt3 = pd.merge(t1, t2, left_index=True, right_index=True)\nt4 = Student[Student['age'] > t3['age_avg']]\nanswer = t4.groupby('sex').size().rename('count')", false], ["t1 = Student[Student['age'] > Student['age'].mean()].groupby('sex').size().rename('count')\nanswer = t1.to_frame().reset_index()", false]]}
+{"example": {"db_id": "department_store", "query": "SELECT product_name FROM products WHERE product_type_code  =  'Hardware' ORDER BY product_price DESC LIMIT 1", "query_toks": ["SELECT", "product_name", "FROM", "products", "WHERE", "product_type_code", "=", "'Hardware", "'", "ORDER", "BY", "product_price", "DESC", "LIMIT", "1"], "query_toks_no_value": ["select", "product_name", "from", "products", "where", "product_type_code", "=", "value", "order", "by", "product_price", "desc", "limit", "value"], "question": "Find the name of the most expensive hardware product.", "question_toks": ["Find", "the", "name", "of", "the", "most", "expensive", "hardware", "product", "."], "sql": {"from": {"table_units": [["table_unit", 5]], "conds": []}, "select": [false, [[0, [0, [0, 20, false], null]]]], "where": [[false, 2, [0, [0, 19, false], null], "\"Hardware\"", null]], "groupBy": [], "having": [], "orderBy": ["desc", [[0, [0, 21, false], null]]], "limit": 1, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Addresses": ["address_id", "address_details"], "Staff": ["staff_id", "staff_gender", "staff_name"], "Suppliers": ["supplier_id", "supplier_name", "supplier_phone"], "Department_Store_Chain": ["dept_store_chain_id", "dept_store_chain_name"], "Customers": ["customer_id", "payment_method_code", "customer_code", "customer_name", "customer_address", "customer_phone", "customer_email"], "Products": ["product_id", "product_type_code", "product_name", "product_price"], "Supplier_Addresses": ["supplier_id", "address_id", "date_from", "date_to"], "Customer_Addresses": ["customer_id", "address_id", "date_from", "date_to"], "Customer_Orders": ["order_id", "customer_id", "order_status_code", "order_date"], "Department_Stores": ["dept_store_id", "dept_store_chain_id", "store_name", "store_address", "store_phone", "store_email"], "Departments": ["department_id", "dept_store_id", "department_name"], "Order_Items": ["order_item_id", "order_id", "product_id"], "Product_Suppliers": ["product_id", "supplier_id", "date_supplied_from", "date_supplied_to", "total_amount_purchased", "total_value_purchased"], "Staff_Department_Assignments": ["staff_id", "department_id", "date_assigned_from", "job_title_code", "date_assigned_to"]}, "answer": [["mic"]]}, "program_result_list": [["t1 = Products[Products['product_type_code'] == 'hardware']\nt2 = t1.sort_values('product_price', ascending=False).head(1).reset_index()\nanswer = t2['product_name']", false], ["t1 = Products[Products['product_type_code'] == 'hardware']\nanswer = t1.sort_values('product_price', ascending=False).head(1)['product_name']", false], ["t1 = Products[Products['product_type_code'] == 'hardware']\nanswer = t1['product_name'].head(1)", false], ["t1 = Products[Products['product_type_code'] == 'hardware']\nt2 = t1.sort_values(['product_price'], ascending=False).head(1)\nt3 = t2.reset_index()\nanswer = t3['product_name']", false], ["t1 = Products[Products['product_type_code'] == 'hardware'].sort_values(by='product_price', ascending=False).head(1)\nanswer = t1['product_name']", false], ["t1 = Products[Products['product_type_code'] == 'hardware']\nt2 = t1.sort_values(by='product_price', ascending=False).head(1)\nanswer = t2['product_name']", false], ["t1 = Products[Products['product_type_code'] == 'hardware']\nt2 = t1.sort_values('product_price', ascending=False).head(1)\nanswer = t2['product_name']", false], ["t1 = Products[Products['product_type_code'] == 'hardware']\nt2 = t1.sort_values('product_price', ascending=False).head(1)\nanswer = t2['product_name']", false], ["t1 = Products[Products['product_type_code'] == 'hardware']\nanswer = t1.sort_values(by='product_price', ascending=False).head(1)['product_name']", false], ["t1 = Products[Products['product_type_code'] == 'hardware']\nt2 = t1.sort_values('product_price', ascending=False).head(1)\nanswer = t2['product_name']", false], ["t1 = Products[Products['product_type_code'] == 'hardware']\nt2 = t1.sort_values('product_price', ascending=False).head(1)\nanswer = t2['product_name']", false], ["t1 = Products[Products['product_type_code'] == 'hardware']\nt2 = t1.sort_values('product_price', ascending=False).head(1)\nanswer = t2['product_name']", false], ["t1 = Products[Products['product_type_code'] == 'hardware']\nt2 = t1.sort_values('product_price', ascending=False).head(1)\nanswer = t2['product_name']", false], ["t1 = Products[Products['product_type_code'] == 'hardware']\nt2 = t1.sort_values('product_price', ascending=False)\nanswer = t2['product_name'].head(1)", false], ["t1 = Products[Products['product_type_code'] == 'hardware']\nt2 = t1.sort_values(by='product_price', ascending=False).head(1)\nanswer = t2['product_name']", false], ["t1 = Products[Products['product_type_code'] == 'hardware']\nt2 = t1.sort_values(by='product_price', ascending=False)\nanswer = t2.head(1)['product_name']", false], ["t1 = Products[Products['product_type_code'] == 'hardware']\nt2 = t1.sort_values('product_price', ascending=False)\nanswer = t2.head(1)['product_name']", false], ["t1 = Products[Products['product_type_code'] == 'hardware']\nt2 = t1.sort_values(by='product_price', ascending=False)\nanswer = t2.head(1)['product_name']", false], ["t1 = Products[Products['product_type_code'] == 'hardware']\nanswer = t1.sort_values(by='product_price', ascending=False).head(1)['product_name']", false], ["t1 = Products[Products['product_type_code'] == 'hardware']\nt2 = t1.sort_values(by='product_price', ascending=False).head(1)\nanswer = t2['product_name']", false]]}
+{"example": {"db_id": "bike_1", "query": "SELECT COUNT(*) FROM weather WHERE mean_humidity  >  50 AND mean_visibility_miles  >  8", "query_toks": ["SELECT", "COUNT", "(", "*", ")", "FROM", "weather", "WHERE", "mean_humidity", ">", "50", "AND", "mean_visibility_miles", ">", "8"], "query_toks_no_value": ["select", "count", "(", "*", ")", "from", "weather", "where", "mean_humidity", ">", "value", "and", "mean_visibility_miles", ">", "value"], "question": "What is the number of days that had an average humity above 50 and an average visibility above 8?", "question_toks": ["What", "is", "the", "number", "of", "days", "that", "had", "an", "average", "humity", "above", "50", "and", "an", "average", "visibility", "above", "8", "?"], "sql": {"from": {"table_units": [["table_unit", 3]], "conds": []}, "select": [false, [[3, [0, [0, 0, false], null]]]], "where": [[false, 3, [0, [0, 31, false], null], 50.0, null], "and", [false, 3, [0, [0, 37, false], null], 8.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"station": ["id", "name", "lat", "long", "dock_count", "city", "installation_date"], "status": ["station_id", "bikes_available", "docks_available", "time"], "trip": ["id", "duration", "start_date", "start_station_name", "start_station_id", "end_date", "end_station_name", "end_station_id", "bike_id", "subscription_type", "zip_code"], "weather": ["date", "max_temperature_f", "mean_temperature_f", "min_temperature_f", "max_dew_point_f", "mean_dew_point_f", "min_dew_point_f", "max_humidity", "mean_humidity", "min_humidity", "max_sea_level_pressure_inches", "mean_sea_level_pressure_inches", "min_sea_level_pressure_inches", "max_visibility_miles", "mean_visibility_miles", "min_visibility_miles", "max_wind_Speed_mph", "mean_wind_speed_mph", "max_gust_speed_mph", "precipitation_inches", "cloud_cover", "events", "wind_dir_degrees", "zip_code"]}, "answer": [[3013]]}, "program_result_list": [["t1 = weather[(weather['mean_humidity'] > 50) & (weather['mean_visibility_miles'] > 8)]\nanswer = t1.shape[0]", false], ["answer = weather[(weather['mean_humidity'] > 50) & (weather['mean_visibility_miles'] > 8)]", false], ["answer = len(weather[(weather['mean_humidity'] > 50) & (weather['mean_visibility_miles'] > 8)])", false], ["answer = weather[(weather['mean_humidity'] > 50) & (weather['mean_visibility_miles'] > 8)].groupby('date').size().rename('count').to_frame().reset_index()", false], ["t1 = weather[(weather['mean_humidity'] > 50) & (weather['mean_visibility_miles'] > 8)]\nanswer = len(t1.date.unique())", false], ["t1 = weather[(weather['mean_humidity'] > 50) & (weather['mean_visibility_miles'] > 8)]\nanswer = t1.groupby(['date']).size().rename('count').size", false], ["t1 = weather[(weather['mean_humidity'] > 50) & (weather['mean_visibility_miles'] > 8)]\nanswer = t1['date'].nunique()", false], ["answer = weather[(weather['mean_humidity'] > 50) & (weather['mean_visibility_miles'] > 8)].shape[0]", false], ["t1 = weather[(weather['mean_humidity'] > 50) & (weather['mean_visibility_miles'] > 8)]\nanswer = t1.groupby(['date']).size().rename('count').to_frame().reset_index()", false], ["", false], ["answer = weather[(weather['mean_humidity'] > 50) & (weather['mean_visibility_miles'] > 8)].shape[0]", false], ["t1 = weather[(weather['mean_humidity'] > 50) & (weather['mean_visibility_miles'] > 8)]\nt2 = t1.groupby('date').size().rename('count')\nanswer = len(t2)", false], ["t1 = weather[weather['mean_humidity'] > 50]\nt2 = t1[t1['mean_visibility_miles'] > 8]\nanswer = t2['mean_humidity'].count()", false], ["t1 = weather[(weather['mean_humidity'] > 50) & (weather['mean_visibility_miles'] > 8)]\nanswer = t1['date'].count()", false], ["t1 = weather[(weather['mean_humidity'] > 50) & (weather['mean_visibility_miles'] > 8)]\nanswer = t1['date'].nunique()", false], ["t1 = weather[weather['mean_humidity'] > 50]\nt2 = t1[t1['mean_visibility_miles'] > 8]\nanswer = t2.shape[0]", false], ["t1 = weather[(weather['mean_humidity'] > 50) & (weather['mean_visibility_miles'] > 8)]\nanswer = t1['date'].nunique()", false], ["t1 = weather[['date', 'mean_humidity', 'mean_visibility_miles']].copy()\nt2 = t1[(t1['mean_humidity'] > 50) & (t1['mean_visibility_miles'] > 8)]\nanswer = t2['date'].nunique()", false], ["t1 = weather[(weather['mean_humidity'] > 50) & (weather['mean_visibility_miles'] > 8)]\nanswer = len(t1['date'].unique())", false], ["t1 = weather[weather['mean_humidity'] > 50]\nt2 = t1[t1['mean_visibility_miles'] > 8]\nanswer = t2.shape[0]", false]]}
+{"example": {"db_id": "cre_Theme_park", "query": "SELECT T2.star_rating_description FROM HOTELS AS T1 JOIN Ref_Hotel_Star_Ratings AS T2 ON T1.star_rating_code  =  T2.star_rating_code WHERE T1.price_range  >  10000", "query_toks": ["SELECT", "T2.star_rating_description", "FROM", "HOTELS", "AS", "T1", "JOIN", "Ref_Hotel_Star_Ratings", "AS", "T2", "ON", "T1.star_rating_code", "=", "T2.star_rating_code", "WHERE", "T1.price_range", ">", "10000"], "query_toks_no_value": ["select", "t2", ".", "star_rating_description", "from", "hotels", "as", "t1", "join", "ref_hotel_star_ratings", "as", "t2", "on", "t1", ".", "star_rating_code", "=", "t2", ".", "star_rating_code", "where", "t1", ".", "price_range", ">", "value"], "question": "Give me the star rating descriptions of the hotels that cost more than 10000.", "question_toks": ["Give", "me", "the", "star", "rating", "descriptions", "of", "the", "hotels", "that", "cost", "more", "than", "10000", "."], "sql": {"from": {"table_units": [["table_unit", 5], ["table_unit", 0]], "conds": [[false, 2, [0, [0, 14, false], null], [0, 1, false], null]]}, "select": [false, [[0, [0, [0, 2, false], null]]]], "where": [[false, 3, [0, [0, 16, false], null], 10000.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Ref_Hotel_Star_Ratings": ["star_rating_code", "star_rating_description"], "Locations": ["Location_ID", "Location_Name", "Address", "Other_Details"], "Ref_Attraction_Types": ["Attraction_Type_Code", "Attraction_Type_Description"], "Visitors": ["Tourist_ID", "Tourist_Details"], "Features": ["Feature_ID", "Feature_Details"], "Hotels": ["hotel_id", "star_rating_code", "pets_allowed_yn", "price_range", "other_hotel_details"], "Tourist_Attractions": ["Tourist_Attraction_ID", "Attraction_Type_Code", "Location_ID", "How_to_Get_There", "Name", "Description", "Opening_Hours", "Other_Details"], "Street_Markets": ["Market_ID", "Market_Details"], "Shops": ["Shop_ID", "Shop_Details"], "Museums": ["Museum_ID", "Museum_Details"], "Royal_Family": ["Royal_Family_ID", "Royal_Family_Details"], "Theme_Parks": ["Theme_Park_ID", "Theme_Park_Details"], "Visits": ["Visit_ID", "Tourist_Attraction_ID", "Tourist_ID", "Visit_Date", "Visit_Details"], "Photos": ["Photo_ID", "Tourist_Attraction_ID", "Name", "Description", "Filename", "Other_Details"], "Staff": ["Staff_ID", "Tourist_Attraction_ID", "Name", "Other_Details"], "Tourist_Attraction_Features": ["Tourist_Attraction_ID", "Feature_ID"]}, "answer": [["star"], ["star"], ["star"], ["star"], ["star"], ["star"], ["star"], ["star"], ["star"], ["star"], ["star"], ["star"], ["star"], ["star"], ["star"]]}, "program_result_list": [["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nt2 = t1[t1['price_range'] > 10000]\nanswer = t2['star_rating_description']", false], ["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nt2 = t1[t1['price_range'] > 10000]\nanswer = t2['star_rating_description']", false], ["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nt2 = t1[t1['price_range'] > 10000]\nanswer = t2['star_rating_description']", false], ["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, on='star_rating_code')\nt2 = t1[t1['price_range'] > 10000]\nanswer = t2['star_rating_description']", false], ["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nt2 = t1[t1['price_range'] > 10000]\nanswer = t2['star_rating_description']", false], ["t1 = pd.merge(Hotels[Hotels['price_range'] > 10000], Ref_Hotel_Star_Ratings, on='star_rating_code')\nanswer = t1['star_rating_description']", false], ["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nt2 = t1[t1['price_range'] > 10000]\nanswer = t2['star_rating_description']", false], ["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nt2 = t1[t1['price_range'] > 10000]\nanswer = t2['star_rating_description']", false], ["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nt2 = t1[t1['price_range'] > 10000]\nanswer = t2['star_rating_description']", false], ["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nt2 = t1[t1['price_range'] > 10000]\nanswer = t2['star_rating_description']", false], ["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nanswer = t1[t1['price_range'] > 10000]['star_rating_description']", false], ["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nt2 = t1[t1['price_range'] > 10000]\nanswer = t2['star_rating_description']", false], ["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nt2 = t1[t1['price_range'] > 10000]\nanswer = t2['star_rating_description'].unique()", false], ["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nt2 = t1[t1['price_range'] > 10000]\nanswer = t2['star_rating_description']", false], ["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nt2 = t1[t1['price_range'] > 10000]\nanswer = t2['star_rating_description']", false], ["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nt2 = t1[t1['price_range'] > 10000]\nanswer = t2['star_rating_description']", false], ["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nt2 = t1[t1['price_range'] > 10000]\nanswer = t2['star_rating_description']", false], ["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nt2 = t1[t1['price_range'] > 10000]\nanswer = t2['star_rating_description']", false], ["t1 = pd.merge(Ref_Hotel_Star_Ratings, Hotels, on='star_rating_code')\nt2 = t1[t1['price_range'] > 10000]\nanswer = t2['star_rating_description']", false], ["t1 = pd.merge(Hotels, Ref_Hotel_Star_Ratings, left_on='star_rating_code', right_on='star_rating_code')\nanswer = t1[t1['price_range'] > 10000]['star_rating_description']", false]]}
+{"example": {"db_id": "culture_company", "query": "SELECT T1.company_name FROM culture_company AS T1 JOIN book_club AS T2 ON T1.book_club_id  =  T2.book_club_id WHERE T2.publisher  =  'Alyson'", "query_toks": ["SELECT", "T1.company_name", "FROM", "culture_company", "AS", "T1", "JOIN", "book_club", "AS", "T2", "ON", "T1.book_club_id", "=", "T2.book_club_id", "WHERE", "T2.publisher", "=", "'Alyson", "'"], "query_toks_no_value": ["select", "t1", ".", "company_name", "from", "culture_company", "as", "t1", "join", "book_club", "as", "t2", "on", "t1", ".", "book_club_id", "=", "t2", ".", "book_club_id", "where", "t2", ".", "publisher", "=", "value"], "question": "List all company names with a book published by Alyson.", "question_toks": ["List", "all", "company", "names", "with", "a", "book", "published", "by", "Alyson", "."], "sql": {"from": {"table_units": [["table_unit", 2], ["table_unit", 0]], "conds": [[false, 2, [0, [0, 18, false], null], [0, 1, false], null]]}, "select": [false, [[0, [0, [0, 14, false], null]]]], "where": [[false, 2, [0, [0, 5, false], null], "\"Alyson\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"book_club": ["book_club_id", "Year", "Author_or_Editor", "Book_Title", "Publisher", "Category", "Result"], "movie": ["movie_id", "Title", "Year", "Director", "Budget_million", "Gross_worldwide"], "culture_company": ["Company_name", "Type", "Incorporated_in", "Group_Equity_Shareholding", "book_club_id", "movie_id"]}, "answer": [["Culture China"]]}, "program_result_list": [["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nt2 = t1[t1['publisher'] == 'Alyson']\nanswer = t2['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nanswer = t1[t1['publisher'] == 'Alyson']['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nt2 = t1[t1['publisher'] == 'Alyson']\nanswer = t2['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nt2 = t1[t1['publisher'] == 'Alyson']\nanswer = t2['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nt2 = t1[t1['publisher'] == 'Alyson']\nanswer = t2['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nanswer = t1[t1['publisher'] == 'Alyson']['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nt2 = t1[t1['publisher'] == 'Alyson']\nanswer = t2['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nanswer = t1[t1['publisher'] == 'Alyson']['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nt2 = t1[t1['publisher'] == 'Alyson']\nanswer = t2['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nt2 = t1[t1['publisher'] == 'Alyson']\nanswer = t2['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nt2 = t1[t1['publisher'] == 'Alyson']\nanswer = t2['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nt2 = t1[t1['publisher'] == 'Alyson']\nanswer = t2['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nt2 = t1[t1['publisher'] == 'Alyson']\nanswer = t2['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nt2 = t1[t1['publisher'] == 'Alyson']\nanswer = t2['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nt2 = t1[t1['publisher'] == 'Alyson']\nanswer = t2['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nt2 = t1[t1['publisher'] == 'Alyson']\nanswer = t2['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nt2 = t1[t1['publisher'] == 'Alyson']\nanswer = t2['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nt2 = t1[t1['publisher'] == 'Alyson']\nanswer = t2['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nt2 = t1[t1['publisher'] == 'Alyson']\nanswer = t2['company_name']", false], ["t1 = pd.merge(culture_company, book_club, left_on='book_club_id', right_on='book_club_id')\nt2 = t1[t1['publisher'] == 'Alyson']\nanswer = t2['company_name']", false]]}
+{"example": {"db_id": "music_1", "query": "SELECT T1.artist_name ,  T1.country FROM artist AS T1 JOIN song AS T2 ON T1.artist_name  =  T2.artist_name WHERE T2.song_name LIKE \"%love%\"", "query_toks": ["SELECT", "T1.artist_name", ",", "T1.country", "FROM", "artist", "AS", "T1", "JOIN", "song", "AS", "T2", "ON", "T1.artist_name", "=", "T2.artist_name", "WHERE", "T2.song_name", "LIKE", "``", "%", "love", "%", "''"], "query_toks_no_value": ["select", "t1", ".", "artist_name", ",", "t1", ".", "country", "from", "artist", "as", "t1", "join", "song", "as", "t2", "on", "t1", ".", "artist_name", "=", "t2", ".", "artist_name", "where", "t2", ".", "song_name", "like", "value"], "question": "What is the name and country of origin of the artist who released a song that has \"love\" in its title?", "question_toks": ["What", "is", "the", "name", "and", "country", "of", "origin", "of", "the", "artist", "who", "released", "a", "song", "that", "has", "``", "love", "''", "in", "its", "title", "?"], "sql": {"from": {"table_units": [["table_unit", 1], ["table_unit", 3]], "conds": [[false, 2, [0, [0, 4, false], null], [0, 14, false], null]]}, "select": [false, [[0, [0, [0, 4, false], null]], [0, [0, [0, 5, false], null]]]], "where": [[false, 9, [0, [0, 13, false], null], "\"%love%\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"genre": ["g_name", "rating", "most_popular_in"], "artist": ["artist_name", "country", "gender", "preferred_genre"], "files": ["f_id", "artist_name", "file_size", "duration", "formats"], "song": ["song_name", "artist_name", "country", "f_id", "genre_is", "rating", "languages", "releasedate", "resolution"]}, "answer": [["Enrique", "USA"]]}, "program_result_list": [["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['song_name'].str.contains('love', case=False)]\nanswer = t2[['artist_name', 'country']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['song_name'].str.contains('love')]\nanswer = t2[['artist_name', 'country']]", false], ["t1 = pd.merge(artist, song, on='artist_name')\nanswer = t1[t1['song_name'].str.contains('love')][['artist_name', 'country']]", false], ["t1 = pd.merge(artist, song, on='artist_name')\nt2 = t1[t1['song_name'].str.contains('love', case=False)]\nanswer = t2[['artist_name', 'country']]", false], ["t1 = pd.merge(artist, song, on='artist_name')\nt2 = t1[t1['song_name'].str.contains('love', case=False)]\nanswer = t2[['artist_name', 'country']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['song_name'].str.contains('love', case=False)]\nanswer = t2[['artist_name', 'country']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['song_name'].str.contains('love')]\nanswer = t2[['artist_name', 'country']]", false], ["t1 = pd.merge(artist, song, on='artist_name')\nt2 = t1[t1['song_name'].str.contains('love', case=False)]\nanswer = t2[['artist_name', 'country']]", false], ["t1 = pd.merge(artist, song, on='artist_name')\nanswer = t1[t1['song_name'].str.contains('love')][['artist_name', 'country']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nanswer = t1[t1['song_name'].str.contains('love')][['artist_name', 'country']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['song_name'].str.contains('love')]\nanswer = t2[['artist_name', 'country']]", false], ["t1 = pd.merge(artist, song, on='artist_name')\nt2 = t1[t1['song_name'].str.contains('love', case=False)]\nanswer = t2[['artist_name', 'country']]", false], ["t1 = pd.merge(song, artist, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['song_name'].str.contains('love', case=False)]\nanswer = t2[['artist_name', 'country']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['song_name'].str.contains('love')]\nanswer = t2[['artist_name', 'country']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['song_name'].str.contains('love', case=False)]\nanswer = t2[['artist_name', 'country']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['song_name'].str.contains('love', case=False)]\nanswer = t2[['artist_name', 'country']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['song_name'].str.contains('love')]\nanswer = t2[['artist_name', 'country']]", false], ["t1 = pd.merge(artist, song, left_on='artist_name', right_on='artist_name')\nt2 = t1[t1['song_name'].str.contains('love', case=False)]\nanswer = t2[['artist_name', 'country']]", false], ["t1 = pd.merge(artist, song, on='artist_name')\nt2 = t1[t1['song_name'].str.contains('love')]\nanswer = t2[['artist_name', 'country']]", false], ["t1 = pd.merge(artist, song, on='artist_name')\nt2 = t1[t1['song_name'].str.contains('love', case=False)]\nanswer = t2[['artist_name', 'country']]", false]]}
+{"example": {"db_id": "small_bank_1", "query": "SELECT T2.balance ,  T3.balance ,  T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid  =  T2.custid JOIN savings AS T3 ON T1.custid  =  T3.custid", "query_toks": ["SELECT", "T2.balance", ",", "T3.balance", ",", "T1.name", "FROM", "accounts", "AS", "T1", "JOIN", "checking", "AS", "T2", "ON", "T1.custid", "=", "T2.custid", "JOIN", "savings", "AS", "T3", "ON", "T1.custid", "=", "T3.custid"], "query_toks_no_value": ["select", "t2", ".", "balance", ",", "t3", ".", "balance", ",", "t1", ".", "name", "from", "accounts", "as", "t1", "join", "checking", "as", "t2", "on", "t1", ".", "custid", "=", "t2", ".", "custid", "join", "savings", "as", "t3", "on", "t1", ".", "custid", "=", "t3", ".", "custid"], "question": "What are the names, checking balances, and savings balances for all customers?", "question_toks": ["What", "are", "the", "names", ",", "checking", "balances", ",", "and", "savings", "balances", "for", "all", "customers", "?"], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 2], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 5, false], null], "and", [false, 2, [0, [0, 1, false], null], [0, 3, false], null]]}, "select": [false, [[0, [0, [0, 6, false], null]], [0, [0, [0, 4, false], null]], [0, [0, [0, 2, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"ACCOUNTS": ["custid", "name"], "SAVINGS": ["custid", "balance"], "CHECKING": ["custid", "balance"]}, "answer": [[10000.0, 200000.0, "Brown"], [2000.0, 999999999.0, "Wang"], [3000.0, 230000.0, "O'mahony"], [7000.0, 60.0, "Weeks"], [10000.0, 80000.0, "Granger"], [77.0, 240.0, "Porter"]]}, "program_result_list": [["t1 = pd.merge(ACCOUNTS, CHECKING, on='custid')\nt2 = pd.merge(t1, SAVINGS, on='custid')\nanswer = t2[['name', 'balance_x', 'balance_y']]", false], ["t1 = pd.merge(ACCOUNTS, CHECKING, left_on='custid', right_on='custid')\nt2 = pd.merge(t1, SAVINGS, left_on='custid', right_on='custid')\nanswer = t2[['name', 'balance_x', 'balance_y']]", false], ["t1 = pd.merge(ACCOUNTS, CHECKING, on='custid')\nt2 = pd.merge(t1, SAVINGS, on='custid')\nanswer = t2[['name', 'balance_x', 'balance_y']]", false], ["t1 = pd.merge(ACCOUNTS, CHECKING, left_on='custid', right_on='custid')\nt2 = pd.merge(t1, SAVINGS, left_on='custid', right_on='custid')\nanswer = t2[['name', 'balance_x', 'balance_y']]", false], ["t1 = pd.merge(ACCOUNTS, CHECKING, left_on='custid', right_on='custid')\nt2 = pd.merge(t1, SAVINGS, left_on='custid', right_on='custid')\nanswer = t2[['name', 'balance_x', 'balance_y']]", false], ["t1 = pd.merge(ACCOUNTS, CHECKING, left_on='custid', right_on='custid')\nt2 = pd.merge(t1, SAVINGS, left_on='custid', right_on='custid')\nanswer = t2[['name', 'balance_x', 'balance_y']]", false], ["t1 = pd.merge(ACCOUNTS, CHECKING, on='custid')\nt2 = pd.merge(t1, SAVINGS, on='custid')\nanswer = t2[['name', 'balance_x', 'balance_y']]", false], ["t1 = pd.merge(ACCOUNTS, CHECKING, left_on='custid', right_on='custid')\nt2 = pd.merge(t1, SAVINGS, left_on='custid', right_on='custid')\nanswer = t2[['name', 'checking_balance', 'savings_balance']]", false], ["t1 = pd.merge(ACCOUNTS, CHECKING, on='custid')\nt2 = pd.merge(t1, SAVINGS, on='custid')\nanswer = t2[['name', 'balance_x', 'balance_y']]", false], ["t1 = pd.merge(ACCOUNTS, CHECKING, left_on='custid', right_on='custid')\nt2 = pd.merge(t1, SAVINGS, left_on='custid', right_on='custid')\nanswer = t2[['name', 'balance_x', 'balance_y']]", false], ["t1 = pd.merge(ACCOUNTS, CHECKING, on='custid')\nt2 = pd.merge(t1, SAVINGS, on='custid')\nanswer = t2[['name', 'balance_x', 'balance_y']]", false], ["t1 = pd.merge(ACCOUNTS, CHECKING, on='custid')\nt2 = pd.merge(t1, SAVINGS, on='custid')\nanswer = t2[['name', 'balance_x', 'balance_y']]", false], ["t1 = pd.merge(ACCOUNTS, SAVINGS, on='custid')\nt2 = pd.merge(t1, CHECKING, on='custid')\nanswer = t2[['balance_x', 'balance_y', 'name']]", false], ["t1 = pd.merge(ACCOUNTS, SAVINGS, on='custid')\nt2 = pd.merge(t1, CHECKING, on='custid')\nanswer = t2[['name', 'balance_x', 'balance_y']]", false], ["t1 = pd.merge(ACCOUNTS, CHECKING, on='custid')\nt2 = pd.merge(t1, SAVINGS, on='custid')\nanswer = t2[['name', 'balance_x', 'balance_y']]", false], ["t1 = pd.merge(ACCOUNTS, CHECKING, on='custid')\nt2 = pd.merge(t1, SAVINGS, on='custid')\nanswer = t2[['name', 'balance_x', 'balance_y']]", false], ["t1 = pd.merge(ACCOUNTS, CHECKING, on='custid')\nt2 = pd.merge(t1, SAVINGS, on='custid')\nanswer = t2[['name', 'balance_x', 'balance_y']]", false], ["t1 = pd.merge(ACCOUNTS, SAVINGS, on='custid')\nt2 = pd.merge(t1, CHECKING, on='custid')\nanswer = t2[['balance_x', 'balance_y', 'name']]", false], ["t1 = pd.merge(ACCOUNTS, CHECKING, left_on='custid', right_on='custid')\nt2 = pd.merge(t1, SAVINGS, left_on='custid', right_on='custid')\nanswer = t2[['name', 'balance_x', 'balance_y']]", false], ["t1 = pd.merge(ACCOUNTS, CHECKING, left_on='custid', right_on='custid')\nt2 = pd.merge(t1, SAVINGS, left_on='custid', right_on='custid')\nanswer = t2[['name', 'balance_x', 'balance_y']]", false]]}
+{"example": {"db_id": "flight_4", "query": "SELECT count(*) ,  city FROM airports WHERE country  =  'United States' GROUP BY city ORDER BY count(*) DESC", "query_toks": ["SELECT", "count", "(", "*", ")", ",", "city", "FROM", "airports", "WHERE", "country", "=", "'United", "States", "'", "GROUP", "BY", "city", "ORDER", "BY", "count", "(", "*", ")", "DESC"], "query_toks_no_value": ["select", "count", "(", "*", ")", ",", "city", "from", "airports", "where", "country", "=", "value", "group", "by", "city", "order", "by", "count", "(", "*", ")", "desc"], "question": "How many airports are there per city in the United States? Order the cities by decreasing number of airports.", "question_toks": ["How", "many", "airports", "are", "there", "per", "city", "in", "the", "United", "States", "?", "Order", "the", "cities", "by", "decreasing", "number", "of", "airports", "."], "sql": {"from": {"table_units": [["table_unit", 1]], "conds": []}, "select": [false, [[3, [0, [0, 0, false], null]], [0, [0, [0, 11, false], null]]]], "where": [[false, 2, [0, [0, 12, false], null], "\"United States\"", null]], "groupBy": [[0, 11, false]], "having": [], "orderBy": ["desc", [[0, [3, 0, false], null]]], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"routes": ["rid", "dst_apid", "dst_ap", "src_apid", "src_ap", "alid", "airline", "codeshare"], "airports": ["apid", "name", "city", "country", "x", "y", "elevation", "iata", "icao"], "airlines": ["alid", "name", "iata", "icao", "callsign", "country", "active"]}, "answer": [[7, "Columbus"], [6, "Houston"], [5, "Jacksonville"], [5, "Greenville"], [5, "Atlanta"], [4, "Tucson"], [4, "San Antonio"], [4, "Sacramento"], [4, "New York"], [4, "Las Vegas"], [4, "Georgetown"], [4, "Chicago"], [4, "Burlington"], [4, "Anchorage"], [3, "Wilmington"], [3, "Wichita"], [3, "Washington"], [3, "Tampa"], [3, "Springfield"], [3, "Spokane"], [3, "Shreveport"], [3, "San Diego"], [3, "Rochester"], [3, "Riverside"], [3, "Richmond"], [3, "Phoenix"], [3, "Philadelphia"], [3, "Panama City"], [3, "Palm Springs"], [3, "Oxford"], [3, "Oklahoma City"], [3, "New Orleans"], [3, "Mobile"], [3, "Miami"], [3, "Macon"], [3, "Key West"], [3, "Jackson"], [3, "Huntsville"], [3, "Douglas"], [3, "Detroit"], [3, "Denver"], [3, "Dayton"], [3, "Dallas"], [3, "Columbia"], [3, "Cleveland"], [3, "Charleston"], [3, "Brunswick"], [3, "Augusta"], [3, "Athens"], [3, "Alexandria"], [3, "Akron"], [2, "West Palm Beach"], [2, "Watertown"], [2, "Waco"], [2, "Valdosta"], [2, "Uvalde"], [2, "Tulsa"], [2, "Topeka"], [2, "Tonopah"], [2, "Tok"], [2, "Tacoma"], [2, "St. Petersburg"], [2, "Sidney"], [2, "Seattle"], [2, "Scranton"], [2, "Santa Barbara"], [2, "San Jose"], [2, "Salt Lake City"], [2, "Rome"], [2, "Rapid City"], [2, "Portland"], [2, "Port Angeles"], [2, "Pittsburgh"], [2, "Petersburg"], [2, "Peru"], [2, "Perry"], [2, "Pensacola"], [2, "Orlando"], [2, "Ontario"], [2, "Omaha"], [2, "Olathe"], [2, "Ogden"], [2, "Null"], [2, "Norfolk"], [2, "Mountain Home"], [2, "Morristown"], [2, "Montgomery"], [2, "Monroe"], [2, "Molokai"], [2, "Minot"], [2, "Milwaukee"], [2, "Mesa"], [2, "Merced"], [2, "Memphis"], [2, "Marysville"], [2, "Marshfield"], [2, "Marshall"], [2, "Madison"], [2, "Louisville"], [2, "Los Angeles"], [2, "Longview"], [2, "London"], [2, "Lompoc"], [2, "Lincoln"], [2, "Lewiston"], [2, "Leesburg"], [2, "Lawrence"], [2, "Lansing"], [2, "Lancaster"], [2, "Lafayette"], [2, "Knoxville"], [2, "Klawock"], [2, "Kansas City"], [2, "Jasper"], [2, "Jamestown"], [2, "Indianapolis"], [2, "Hillsboro"], [2, "Hayward"], [2, "Harrisburg"], [2, "Hampton"], [2, "Hamilton"], [2, "Glasgow"], [2, "Gainesville"], [2, "Fort Worth"], [2, "Fort Myers"], [2, "Fort Lauderdale"], [2, "Flagler"], [2, "Fayetteville"], [2, "Fairbanks"], [2, "Ely"], [2, "Elkhart"], [2, "El Paso"], [2, "Dillingham"], [2, "Del Rio"], [2, "Danville"], [2, "Crestview"], [2, "Corpus Christi"], [2, "Concord"], [2, "Cold Bay"], [2, "Clovis"], [2, "Clinton"], [2, "Cincinnati"], [2, "Carlsbad"], [2, "Bloomington"], [2, "Bedford"], [2, "Beaumont"], [2, "Baltimore"], [2, "Austin"], [2, "Arlington"], [2, "Albany"], [2, "Alamogordo"], [2, "Abilene"], [2, "Aberdeen"], [1, "Zuni Pueblo"], [1, "Zephyrhills"], [1, "Zelienople"], [1, "Zanesville"], [1, "Yuma"], [1, "Yuba City"], [1, "Youngstown"], [1, "Yankton"], [1, "Yakutat"], [1, "Yakima"], [1, "Yakataga"], [1, "Wuchula"], [1, "Wrightstown"], [1, "Wright"], [1, "Wrangell"], [1, "Worland"], [1, "Worcester"], [1, "Woodward"], [1, "Wolf Point"], [1, "Wisconsin Rapids"], [1, "Winter Haven"], [1, "Winston-salem"], [1, "Winslow"], [1, "Winnsboro"], [1, "Winnemucca"], [1, "Wink"], [1, "Winfield"], [1, "Windsor Locks"], [1, "Windom"], [1, "Winder"], [1, "Willow Grove"], [1, "Willoughby"], [1, "Williston"], [1, "Willimantic"], [1, "Williamsport"], [1, "Williamson"], [1, "Williamsburg"], [1, "Wilkes-Barre"], [1, "Wildwood"], [1, "Wickenburg"], [1, "Wichita Falls"], [1, "Whittier"], [1, "White Sands"], [1, "White Plains"], [1, "White Mountain"], [1, "Whidbey Island"], [1, "Wheeling"], [1, "Weyers Cave"], [1, "Westfield"], [1, "West Yellowstone"], [1, "West Hampton Beach"], [1, "West Goshen Township"], [1, "West Chicago"], [1, "Wendover"], [1, "Wenatchee"], [1, "Wellington"], [1, "Wausau"], [1, "Waupaca"], [1, "Watsonville"], [1, "Waterloo"], [1, "Wassau"], [1, "Wasilla"], [1, "Washington County"], [1, "Warsaw"], [1, "Wallops Island"], [1, "Wallace"], [1, "Walla Walla"], [1, "Wales"], [1, "Wainwright"], [1, "Waikoloa Village"], [1, "Wahoo"], [1, "Wahiawa"], [1, "Visalia"], [1, "Vineyard Haven MA"], [1, "Villa Rica"], [1, "Victorville"], [1, "Victoria"], [1, "Vero Beach"], [1, "Vernal"], [1, "Venice"], [1, "Venetie"], [1, "Van Wert"], [1, "Van Nuys"], [1, "Valparaiso IN"], [1, "Valparaiso"], [1, "Valentine"], [1, "Valdez"], [1, "Vail"], [1, "Upland"], [1, "Unalaska"], [1, "Unalakleet"], [1, "Ulysses"], [1, "Ukiah"], [1, "Tyler"], [1, "Twin Falls"], [1, "Twenty Nine Palms"], [1, "Tuscaloosa AL"], [1, "Tupelo"], [1, "Tunica"], [1, "Tucumcari"], [1, "Truth Or Consequences"], [1, "Truckee"], [1, "Troy"], [1, "Troutdale"], [1, "Trenton"], [1, "Traverse City"], [1, "Torrance"], [1, "Tomah"], [1, "Toledo"], [1, "Toksook Bay"], [1, "Togiak Village"], [1, "Toccoa"], [1, "Titusville"], [1, "Tin City"], [1, "Tillamook"], [1, "Tifton"], [1, "Thomson"], [1, "Thomasville"], [1, "Thief River Falls"], [1, "The Dalles"], [1, "Texarkana"], [1, "Teterboro"], [1, "Terre Haute"], [1, "Temple"], [1, "Telluride"], [1, "Teller"], [1, "Taunton"], [1, "Tatalina"], [1, "Tallahassee"], [1, "Talladega"], [1, "Talkeetna"], [1, "Syracuse"], [1, "Sylvania"], [1, "Sylacauga"], [1, "Sutton"], [1, "Sumter"], [1, "Summit"], [1, "Sugar Land"], [1, "Sublette"], [1, "Stuart"], [1, "Stratford"], [1, "Stockton"], [1, "Stinson"], [1, "Stillwater"], [1, "Stevens Point"], [1, "Sterling"], [1, "Stephenville"], [1, "Steamboat Springs"], [1, "Statesville"], [1, "State College Pennsylvania"], [1, "St. Paul Island"], [1, "St. Paul"], [1, "St. Michael"], [1, "St. Louis"], [1, "St. Augustine Airport"], [1, "St Mary's"], [1, "Spencer"], [1, "Spearfish-South Dakota"], [1, "Sparrevohn"], [1, "South Naknek"], [1, "South Lake Tahoe"], [1, "South Haven"], [1, "South Bend"], [1, "Sonora"], [1, "Somerville"], [1, "Somerset"], [1, "Soldotna"], [1, "Smyrna"], [1, "Smithfield"], [1, "Sleetmute"], [1, "Skagway"], [1, "Sitka"], [1, "Sioux Falls"], [1, "Sioux City"], [1, "Silver Springs"], [1, "Sikeston"], [1, "Shungnak"], [1, "Show Low"], [1, "Shishmaref"], [1, "Sheridan"], [1, "Shelton"], [1, "Shell Knob"], [1, "Shelbyville"], [1, "Sheboygan"], [1, "Shaktoolik"], [1, "Shageluk"], [1, "Seward"], [1, "Sewanee"], [1, "Selma"], [1, "Selawik"], [1, "Sedona"], [1, "Sebring"], [1, "Scottsdale"], [1, "Scottsbluff"], [1, "Scott City"], [1, "Scotia NY"], [1, "Scammon Bay"], [1, "Savoonga"], [1, "Savannah"], [1, "Sault Ste Marie"], [1, "Sarasota"], [1, "Saranac Lake"], [1, "Santa Rosa"], [1, "Santa Monica"], [1, "Santa Maria"], [1, "Santa Fe"], [1, "Santa Ana"], [1, "Sanford"], [1, "Sandusky"], [1, "Sand Point"], [1, "San Nicolas Island"], [1, "San Marcos"], [1, "San Luis Obispo"], [1, "San Luis"], [1, "San Francisco"], [1, "San Clemente Island"], [1, "San Carlos"], [1, "San Bernardino"], [1, "San Angelo"], [1, "Saluda"], [1, "Salmon"], [1, "Sallisaw"], [1, "Salisbury"], [1, "Salinas"], [1, "Salina"], [1, "Salem"], [1, "Saint George"], [1, "Saint Cloud"], [1, "Saginaw"], [1, "Safford"], [1, "Sabetha"], [1, "SARATOGA"], [1, "Rutland"], [1, "Russian Mission"], [1, "Russell"], [1, "Ruidoso"], [1, "Ruby"], [1, "Roxboro"], [1, "Roswell"], [1, "Rosecrans"], [1, "Rocky Mount"], [1, "Rockport"], [1, "Rockland"], [1, "Rockingham"], [1, "Rockford"], [1, "Rock Springs"], [1, "Rock Hill"], [1, "Robinson"], [1, "Roanoke VA"], [1, "Riverton WY"], [1, "Rifle"], [1, "Ridgely"], [1, "Richmond Heights"], [1, "Richfield"], [1, "Rhinelander"], [1, "Rhinebeck"], [1, "Renton"], [1, "Reno"], [1, "Redwood Falls"], [1, "Redstone"], [1, "Redmond-Bend"], [1, "Redlands"], [1, "Redding"], [1, "Red River"], [1, "Red Bluff"], [1, "Reading"], [1, "Rawlins"], [1, "Rancho Murieta"], [1, "Ramona"], [1, "Raleigh-durham"], [1, "Racine"], [1, "Quitman"], [1, "Quinhagak"], [1, "Quincy"], [1, "Queensbury"], [1, "Quantico"], [1, "Quakertown"], [1, "Punta Gorda"], [1, "Pullman"], [1, "Pueblo"], [1, "Provo"], [1, "Provincetown"], [1, "Providence"], [1, "Prosser"], [1, "Prospect Creek"], [1, "Prineville"], [1, "Princeton"], [1, "Price"], [1, "Presque Isle"], [1, "Prescott"], [1, "Pratt"], [1, "Poughkeepsie"], [1, "Poteau"], [1, "Portsmouth"], [1, "Port O\\\\'Connor"], [1, "Port Moller"], [1, "Port Huron"], [1, "Port Heiden"], [1, "Port Clinton"], [1, "Poplar Bluff"], [1, "Pontiac"], [1, "Ponca City"], [1, "Pompano Beach"], [1, "Point Mugu"], [1, "Point Lay"], [1, "Point Barrow"], [1, "Pocatello"], [1, "Plymouth"], [1, "Plattsburgh"], [1, "Placida"], [1, "Pineville"], [1, "Pinehurst-Southern Pines"], [1, "Pinedale"], [1, "Pine Mountain"], [1, "Pine Bluff"], [1, "Pilot Point"], [1, "Pikeville"], [1, "Pierre"], [1, "Phoenix "], [1, "Phillips"], [1, "Perryville"], [1, "Peoria"], [1, "Pendleton"], [1, "Pembina"], [1, "Pellston"], [1, "Pecos"], [1, "Patuxent River"], [1, "Paso Robles"], [1, "Pasco"], [1, "Paris"], [1, "Palo Alto"], [1, "Palmer"], [1, "Palmdale"], [1, "Palacios"], [1, "Pahokee"], [1, "Page"], [1, "PARKERSBURG"], [1, "PADUCAH"], [1, "Ozona"], [1, "Oxnard"], [1, "Owensboro"], [1, "Ottumwa"], [1, "Oshkosh"], [1, "Oscoda"], [1, "Ormond Beach"], [1, "Orangeburg"], [1, "Opolu"], [1, "Olympia"], [1, "Olive Branch"], [1, "Okmulgee"], [1, "Okeechobee"], [1, "Ogdensburg"], [1, "Oconomowoc"], [1, "Oceana"], [1, "Ocean Reef Club Airport"], [1, "Ocala"], [1, "Oakley"], [1, "Oakland"], [1, "Oak Island"], [1, "Nulato"], [1, "Nuiqsut"], [1, "Novato"], [1, "Norwood"], [1, "Northway"], [1, "North Wilkesboro"], [1, "North Platte"], [1, "North Myrtle Beach"], [1, "North Kingstown"], [1, "North Bend"], [1, "Norfolk  Nebraska"], [1, "Noorvik"], [1, "Nondalton"], [1, "Nome"], [1, "Nogales"], [1, "Noatak"], [1, "Nikolai"], [1, "Nightmute"], [1, "Niagara Falls"], [1, "Newton"], [1, "Newport News"], [1, "Newport"], [1, "Newnan"], [1, "Newburgh"], [1, "Newberry"], [1, "Newark"], [1, "New Stuyahok"], [1, "New Richmond"], [1, "New Philadelpha"], [1, "New Haven"], [1, "New Castle"], [1, "New Bern"], [1, "New Bedford"], [1, "Neodesha"], [1, "Nenana"], [1, "Nelson Lagoon"], [1, "Needles"], [1, "Nashville"], [1, "Nashua"], [1, "Nappanee "], [1, "Naples"], [1, "Napaskiak"], [1, "Napakiak"], [1, "Napa"], [1, "Nantucket"], [1, "Myrtle Beach"], [1, "Muskogee"], [1, "Muskegon"], [1, "Muscle Shoals"], [1, "Murrieta-Temecula"], [1, "Muncie"], [1, "Muir"], [1, "Mountain Village"], [1, "Mountain View"], [1, "Mount Sterling"], [1, "Mount Pocono"], [1, "Mount Pleasant"], [1, "Mount Holly"], [1, "Mount Clemens"], [1, "Morrisville"], [1, "Morgantown"], [1, "Morganton"], [1, "Monument Valley"], [1, "Montrose CO"], [1, "Montpelier"], [1, "Monticello"], [1, "Monterey"], [1, "Montauk"], [1, "Moline"], [1, "Mojave"], [1, "Modesto"], [1, "Mobridge"], [1, "Moab"], [1, "Missoula"], [1, "Misquite"], [1, "Miramar"], [1, "Minocqua - Woodruff"], [1, "Minneapolis"], [1, "Mineral Wells"], [1, "Milton"], [1, "Millville"], [1, "Millinocket"], [1, "Millington"], [1, "Milledgeville"], [1, "Miles City"], [1, "Midland"], [1, "Michigan City"], [1, "Metakatla"], [1, "Meridian"], [1, "Meriden"], [1, "Mercury"], [1, "Melbourne"], [1, "Mekoryuk"], [1, "Medford"], [1, "Meade"], [1, "Mcgrath"], [1, "Mcallen"], [1, "Mcalester"], [1, "McKinley Park"], [1, "McCook"], [1, "McCall"], [1, "Mc Pherson"], [1, "Mc Comb"], [1, "Mayport"], [1, "Mattawa"], [1, "Massena"], [1, "Mason City"], [1, "Mary Esther"], [1, "Martinsburg"], [1, "Marquette"], [1, "Mariposa"], [1, "Marion"], [1, "Marietta"], [1, "Marfa"], [1, "Marco Island Airport"], [1, "Marathon"], [1, "Marana"], [1, "Manteo"], [1, "Mansfield"], [1, "Manokotak"], [1, "Manley Hot Springs"], [1, "Mankato"], [1, "Manistee"], [1, "Manhattan"], [1, "Manchester NH"], [1, "Manassas"], [1, "Mammoth Lakes"], [1, "Malad City"], [1, "Madera"], [1, "Mackminnville"], [1, "MONTGOMERY"], [1, "Lynchburg"], [1, "Lumberton"], [1, "Lufkin"], [1, "Ludington"], [1, "Lubbock"], [1, "Lovelock"], [1, "Louisiana"], [1, "Los Alamos"], [1, "Lorain-Elyria"], [1, "Lopez"], [1, "Long Beach"], [1, "Lone Rock"], [1, "Logan"], [1, "Locust Grove"], [1, "Lockport"], [1, "Lock Haven"], [1, "Livingston-Montana"], [1, "Livermore"], [1, "Little Rock"], [1, "Linden"], [1, "Lima"], [1, "Lihue"], [1, "Liberty"], [1, "Liberal"], [1, "Lexington KY"], [1, "Lexington"], [1, "Lewistown"], [1, "Lewisburg"], [1, "Lemoore"], [1, "Lebanon"], [1, "Lawton"], [1, "Lawrenceville"], [1, "Latrobe"], [1, "Las Cruces"], [1, "Larsen Bay"], [1, "Larned"], [1, "Laredo"], [1, "Laramie"], [1, "Lanai"], [1, "Lampasas"], [1, "Lamar"], [1, "Lakeway"], [1, "Lakeview"], [1, "Lakeland"], [1, "Lakehurst"], [1, "Lake Placid"], [1, "Lake Minchumina"], [1, "Lake Havasu City"], [1, "Lake City"], [1, "Lake Charles"], [1, "Lahania-kapalua"], [1, "Lago Vista"], [1, "LaGrange"], [1, "La Verne"], [1, "La Junta"], [1, "La Grande"], [1, "La Crosse"], [1, "Kwigillingok"], [1, "Kwethluk"], [1, "Kuparuk"], [1, "Koyukuk"], [1, "Koyuk"], [1, "Kotzebue"], [1, "Kotlik"], [1, "Kongiganak"], [1, "Kona"], [1, "Koliganek"], [1, "Kokomo"], [1, "Kodiak"], [1, "Kobuk"], [1, "Knobnoster"], [1, "Klamath Falls"], [1, "Kivalina"], [1, "Kissimmee"], [1, "Kirksville"], [1, "Kipnuk"], [1, "Kinston"], [1, "King Salmon"], [1, "King Cove"], [1, "Killeen"], [1, "Kill Devil Hills"], [1, "Kiana"], [1, "Ketchikan"], [1, "Kerrville"], [1, "Keokuk"], [1, "Kenosha"], [1, "Kendall-tamiami"], [1, "Kenai"], [1, "Kelso"], [1, "Keene"], [1, "Kasigluk"], [1, "Karluk"], [1, "Kapolei"], [1, "Kankakee"], [1, "Kaneohe Bay"], [1, "Kamuela"], [1, "Kaltag"], [1, "Kalskag"], [1, "Kalispell"], [1, "Kalamazoo"], [1, "Kake"], [1, "Kaiser Lake Ozark"], [1, "Kahului"], [1, "Juneau"], [1, "Junction"], [1, "Joplin"], [1, "Jonesboro"], [1, "Joliet"], [1, "Johnstown"], [1, "Jesup"], [1, "Jefferson City"], [1, "Janesville"], [1, "Jacksonville NC"], [1, "Jacksn Hole"], [1, "Ithaca"], [1, "Islip"], [1, "Iron Mountain"], [1, "Iraan"], [1, "Iowa City"], [1, "Inyokern"], [1, "International Falls"], [1, "Indian Springs"], [1, "Indian Mountains"], [1, "Independence"], [1, "Imperial"], [1, "Immokalee "], [1, "Iliamna"], [1, "Igiugig"], [1, "Idaho Falls"], [1, "Hydaburg"], [1, "Hutchinson"], [1, "Huslia"], [1, "Huron"], [1, "Huntington"], [1, "Hunter Aaf"], [1, "Hughes"], [1, "Houlton"], [1, "Houghton Lake"], [1, "Hot Springs"], [1, "Hoquiam"], [1, "Hopkinsville"], [1, "Hooper Bay"], [1, "Hoonah"], [1, "Honolulu"], [1, "Hondo"], [1, "Homestead"], [1, "Homer"], [1, "Holy Cross"], [1, "Hollywood"], [1, "Holland"], [1, "Holdredge"], [1, "Hobbs"], [1, "Hobart"], [1, "Hilton Head Island"], [1, "Hilo"], [1, "Hickory"], [1, "Hibbing"], [1, "Henderson"], [1, "Helena"], [1, "Healy"], [1, "Hazleton"], [1, "Hays"], [1, "Hayden"], [1, "Hawthorne"], [1, "Havre"], [1, "Hattiesburg/Laurel"], [1, "Hattiesburg"], [1, "Hartsville"], [1, "Hartford"], [1, "Harrison"], [1, "Harlingen"], [1, "Hancock"], [1, "Hana"], [1, "Half Moon Bay"], [1, "Haines"], [1, "Hailey"], [1, "Hagerstown"], [1, "Gwinn"], [1, "Guymon"], [1, "Gustavus"], [1, "Gunnison"], [1, "Gulkana"], [1, "Gulfport"], [1, "Gulf Shores"], [1, "Grove"], [1, "Groton CT"], [1, "Groom Lake"], [1, "Greenwood"], [1, "Greenvile"], [1, "Greensboro"], [1, "Green Cove Springs"], [1, "Green Bay"], [1, "Greeley"], [1, "Great Falls"], [1, "Great Bend"], [1, "Grants"], [1, "Grant County Airport"], [1, "Grand Rapids MN"], [1, "Grand Rapids"], [1, "Grand Marais"], [1, "Grand Junction"], [1, "Grand Island"], [1, "Grand Forks"], [1, "Grand Canyon"], [1, "Goodyear"], [1, "Goodland"], [1, "Golovin"], [1, "Goldsboro"], [1, "Glendive"], [1, "Glendale"], [1, "Gladwin"], [1, "Gillette"], [1, "Gila Bend"], [1, "Gary"], [1, "Garden City"], [1, "Gambell"], [1, "Galveston"], [1, "Gallup"], [1, "Galion"], [1, "Galesburg"], [1, "Galena"], [1, "Gaithersburg"], [1, "Gadsden"], [1, "Funter Bay"], [1, "Fullerton"], [1, "Fryeburg"], [1, "Friday Harbor"], [1, "Fresno"], [1, "Frenchville"], [1, "Fremont"], [1, "Fredericksburg"], [1, "Franklin"], [1, "Frankfort"], [1, "Fostoria"], [1, "Fort Yukon"], [1, "Fort Wayne IN"], [1, "Fort Wayne"], [1, "Fort Wainwright"], [1, "Fort Stockton"], [1, "Fort Smith"], [1, "Fort Sill"], [1, "Fort Rucker/Ozark"], [1, "Fort Riley"], [1, "Fort Richardson"], [1, "Fort Polk"], [1, "Fort Pierce"], [1, "Fort Ord"], [1, "Fort Meade"], [1, "Fort Lewis"], [1, "Fort Leonardwood"], [1, "Fort Leavenworth"], [1, "Fort Knox"], [1, "Fort Irwin"], [1, "Fort Huachuca"], [1, "Fort Hood"], [1, "Fort Eustis"], [1, "Fort Drum"], [1, "Fort Dodge"], [1, "Fort Collins"], [1, "Fort Carson"], [1, "Fort Bridger"], [1, "Fort Bragg"], [1, "Fort Benning"], [1, "Fort Belvoir"], [1, "Fond du Lac"], [1, "Florence"], [1, "Flint"], [1, "Flagstaff"], [1, "Fitzgerald"], [1, "Fitchburg"], [1, "Findley"], [1, "Farmington"], [1, "Farmingdale"], [1, "Fargo"], [1, "False Pass"], [1, "Fairfield"], [1, "Everett"], [1, "Evansville"], [1, "Evanston"], [1, "Eureka"], [1, "Eugene"], [1, "Eufala"], [1, "Escanaba"], [1, "Erie"], [1, "Enumclaw"], [1, "Enterprise"], [1, "Enid"], [1, "Engelhard"], [1, "Endicott"], [1, "Emporia"], [1, "Emmonak"], [1, "Elmira"], [1, "Ellicott"], [1, "Elko"], [1, "Elkins"], [1, "Elim"], [1, "Elfin Cove"], [1, "El dorado springs"], [1, "El Monte"], [1, "El Dorado"], [1, "El Centro"], [1, "El Cajon"], [1, "Egegik"], [1, "Eek"], [1, "Edwards Afb"], [1, "Eden Prairie"], [1, "Eau Claire"], [1, "Eastsound"], [1, "Eastport"], [1, "Eastover"], [1, "Easton"], [1, "East Troy"], [1, "East Tawas"], [1, "Eagle River"], [1, "Eagle"], [1, "Durango"], [1, "Dunkirk"], [1, "Duncan"], [1, "Duluth"], [1, "Dubuque IA"], [1, "Dublin"], [1, "Du Bois"], [1, "Drummond Island"], [1, "Doylestown"], [1, "Dover"], [1, "Dothan"], [1, "Dodge City"], [1, "Dickinson"], [1, "Devils Lake"], [1, "Destin"], [1, "Des Moines"], [1, "Deridder"], [1, "Denton"], [1, "Deming"], [1, "Delta Junction"], [1, "Delta"], [1, "Dell"], [1, "Deering"], [1, "Decatur"], [1, "Deadhorse"], [1, "DeLand"], [1, "Daytona Beach"], [1, "Davis-Woodland-Winters"], [1, "Darlington"], [1, "Danbury"], [1, "Dalton"], [1, "Dallas-Fort Worth"], [1, "Dalhart"], [1, "Daggett"], [1, "DALLAS"], [1, "Cutbank"], [1, "Cushing"], [1, "Cumberland"], [1, "Crystal River"], [1, "Crystal"], [1, "Crossville"], [1, "Cross City"], [1, "Crescent City"], [1, "Council Bluffs"], [1, "Cotulla"], [1, "Cottonwood"], [1, "Corvallis"], [1, "Cortez"], [1, "Cornelia"], [1, "Cordova"], [1, "Cordele"], [1, "Cooldige"], [1, "Conway"], [1, "Conroe"], [1, "Connellsville"], [1, "Concord NH"], [1, "Columbus Mississippi"], [1, "Colorado Springs"], [1, "Colombus"], [1, "College Station"], [1, "Coldwater"], [1, "Coffeyville"], [1, "Coeur d'Alene"], [1, "Cody"], [1, "Cocoa Beach"], [1, "Coco Beach"], [1, "Coatesville"], [1, "Clemson"], [1, "Clearwater"], [1, "Clear Mews"], [1, "Clarksville"], [1, "Clarksburg"], [1, "Circle"], [1, "Christmas Valley"], [1, "Chino"], [1, "China Lake"], [1, "Childress"], [1, "Chicopee Falls"], [1, "Chico"], [1, "Chicago-Wheeling"], [1, "Cheyenne"], [1, "Chevak"], [1, "Cherry Point"], [1, "Cheraw"], [1, "Chenega"], [1, "Chehalis"], [1, "Chefornak"], [1, "Chattanooga"], [1, "Chatsworth"], [1, "Charlottesville VA"], [1, "Charlotte"], [1, "Charles City"], [1, "Charelvoix"], [1, "Chapel Hill"], [1, "Chanute"], [1, "Chandler"], [1, "Champaign"], [1, "Chalkyitsik"], [1, "Chadron"], [1, "Centre"], [1, "Central"], [1, "Cedar Rapids"], [1, "Cedar City"], [1, "Catalina Island"], [1, "Casper"], [1, "Casa Grande"], [1, "Cartersville"], [1, "Carrollton"], [1, "Caribou"], [1, "Carefree"], [1, "Carbondale/Murphysboro"], [1, "Cape Romanzof"], [1, "Cape Newenham"], [1, "Cape Lisburne"], [1, "Cape Girardeau"], [1, "Camp Springs"], [1, "Camp Douglas"], [1, "Camden"], [1, "Cambria"], [1, "Camarillo - CA"], [1, "Calhoun"], [1, "Calexico"], [1, "Caldwell"], [1, "Butte"], [1, "Bush Field"], [1, "Burns"], [1, "Burley"], [1, "Burbank"], [1, "Buffalo"], [1, "Buckley"], [1, "Buckland"], [1, "Buckeye"], [1, "Bryce Canyon"], [1, "Bryan"], [1, "Brownsville"], [1, "Broomfield-CO"], [1, "Brookneal"], [1, "Brigham City"], [1, "Brevig Mission"], [1, "Brenham"], [1, "Bremerton"], [1, "Breckenridge"], [1, "Brainerd"], [1, "Brady"], [1, "Bradshaw Field"], [1, "Bradford"], [1, "Bozeman"], [1, "Bowling Green"], [1, "Boulder"], [1, "Boston"], [1, "Boise"], [1, "Boca Raton"], [1, "Blytheville"], [1, "Blythe"], [1, "Bluefield"], [1, "Block Island"], [1, "Bismarck"], [1, "Bishop"], [1, "Birmingham"], [1, "Binghamton"], [1, "Biloxi"], [1, "Billings"], [1, "Big Timber"], [1, "Big Piney"], [1, "Big Mountain"], [1, "Beverly"], [1, "Bettles"], [1, "Bethel"], [1, "Bessemer"], [1, "Bentonville"], [1, "Benton"], [1, "Bend"], [1, "Bemidji"], [1, "Bellingham"], [1, "Belleville"], [1, "Beckley"], [1, "Beaver Falls"], [1, "Beaver"], [1, "Beaufort"], [1, "Bay City"], [1, "Baudette"], [1, "Battle Creek"], [1, "Baton Rouge"], [1, "Batavia"], [1, "Bartow"], [1, "Barter Island"], [1, "Barrow"], [1, "Barnstable"], [1, "Barking Sands"], [1, "Baraboo"], [1, "Bar Harbor"], [1, "Banning"], [1, "Bangor"], [1, "Ballston Spa"], [1, "Bakersfield"], [1, "Baker City"], [1, "Bainbridge"], [1, "BRISTOL"], [1, "Avon Park"], [1, "Aurora"], [1, "Auburn"], [1, "Atqasuk"], [1, "Atlantic City"], [1, "Atka"], [1, "Astoria"], [1, "Aspen"], [1, "Asheville"], [1, "Asheboro"], [1, "Ardmore"], [1, "Arctic Village"], [1, "Arcata CA"], [1, "Appleton"], [1, "Apalachicola"], [1, "Anvik"], [1, "Anoka"], [1, "Anniston"], [1, "Annette Island"], [1, "Annapolis"], [1, "Ann Arbor"], [1, "Ankeny"], [1, "Aniak"], [1, "Angoon"], [1, "Angola"], [1, "Angleton"], [1, "Andersen"], [1, "Anaktuvuk Pass"], [1, "Americus"], [1, "Ambler"], [1, "Amarillo"], [1, "Altus"], [1, "Altoona"], [1, "Alton/St Louis"], [1, "Alpine"], [1, "Alpena"], [1, "Alliance"], [1, "Allentown"], [1, "Allakaket"], [1, "Alice"], [1, "Alexander City"], [1, "Aleknagik"], [1, "Albuquerque"], [1, "Alamosa"], [1, "Alakanuk"], [1, "Alabaster"], [1, "Akutan"], [1, "Akiak"], [1, "Akhiok"], [1, "Aiken"], [1, "Addison"], [1, "Adak Island"], [1, "Ada"]]}, "program_result_list": [["t1 = airports[airports['country'] == 'United States']\nt2 = t1.groupby('city').size().rename('count')\nanswer = t2.sort_values(ascending=False).to_frame().reset_index()", false], ["t1 = airports[airports['country'] == 'United States']\nt2 = t1.groupby('city').size().rename('count')\nanswer = t2.sort_values(ascending=False).to_frame().reset_index()", false], ["t1 = airports[airports['country'] == 'United States']\nt2 = t1.groupby('city').size().rename('count').sort_values(ascending=False).to_frame().reset_index()\nanswer = t2", false], ["t1 = airports[airports['country'] == 'United States']\nt2 = t1.groupby('city').size().rename('count').to_frame().reset_index()\nanswer = t2.sort_values(by='count', ascending=False)", false], ["t1 = airports[airports['country'] == 'United States']\nanswer = t1.groupby('city').size().rename('count').sort_values(ascending=False).to_frame().reset_index()", false], ["t1 = airports[airports['country'] == 'United States']\nt2 = t1.groupby('city').size().rename('count')\nanswer = t2.sort_values(ascending=False).to_frame().reset_index()", false], ["t1 = airports[airports['country'] == 'United States']\nt2 = t1.groupby('city').size().rename('count')\nanswer = t2.sort_values(ascending=False).to_frame().reset_index()", false], ["t1 = airports[airports['country'] == 'United States'].groupby('city').size().rename('count')\nanswer = t1.sort_values(ascending=False).to_frame().reset_index()", false], ["t1 = airports[airports['country'] == 'United States']\nt2 = t1.groupby('city').size().rename('count').to_frame().reset_index()\nanswer = t2.sort_values('count', ascending=False)", false], ["t1 = airports[airports['country'] == 'United States']\nt2 = t1.groupby('city').size().rename('count')\nanswer = t2.sort_values(ascending=False).to_frame().reset_index()", false], ["t1 = airports[airports['country'] == 'United States']\nt2 = t1.groupby('city').size().rename('count').to_frame().reset_index()\nanswer = t2.sort_values('count', ascending=False)", false], ["t1 = airports[airports['country'] == 'United States']\nt2 = t1.groupby('city').size().rename('count').sort_values(ascending=False).to_frame().reset_index()\nanswer = t2", false], ["t1 = airports[airports['country'] == 'United States']\nt2 = t1.groupby('city').size().rename('count').to_frame().reset_index()\nanswer = t2.sort_values('count', ascending=False)", false], ["t1 = airports[airports['country'] == 'United States']\nt2 = t1.groupby('city').size().rename('count').sort_values(ascending=False).to_frame()\nanswer = t2.reset_index()", false], ["t1 = airports[airports['country'] == 'United States']\nanswer = t1.groupby('city').size().rename('count').sort_values(ascending=False).to_frame().reset_index()", false], ["t1 = airports[airports['country'] == 'United States']\nt2 = t1.groupby('city').size().rename('count')\nanswer = t2.sort_values(ascending=False).to_frame().reset_index()", false], ["t1 = airports[airports['country'] == 'United States']\nt2 = t1.groupby('city').size().rename('count').to_frame().reset_index()\nanswer = t2.sort_values('count', ascending=False)", false], ["t1 = airports[airports['country'] == 'United States']\nanswer = t1.groupby('city').size().rename('count').sort_values(ascending=False).to_frame().reset_index()", false], ["t1 = airports[airports['country'] == 'United States']\nt2 = t1.groupby('city').size().rename('count').to_frame().reset_index()\nanswer = t2.sort_values(by='count', ascending=False)", false], ["t1 = airports[airports['country'] == 'United States']\nt2 = t1.groupby('city').size().rename('count').to_frame().reset_index()\nanswer = t2.sort_values(by='count', ascending=False)", false]]}
+{"example": {"db_id": "college_2", "query": "SELECT name FROM student WHERE id IN (SELECT id FROM takes WHERE semester  =  'Fall' AND YEAR  =  2003)", "query_toks": ["SELECT", "name", "FROM", "student", "WHERE", "id", "IN", "(", "SELECT", "id", "FROM", "takes", "WHERE", "semester", "=", "'Fall", "'", "AND", "YEAR", "=", "2003", ")"], "query_toks_no_value": ["select", "name", "from", "student", "where", "id", "in", "(", "select", "id", "from", "takes", "where", "semester", "=", "value", "and", "year", "=", "value", ")"], "question": "What are the names of students who took a course in the Fall of 2003?", "question_toks": ["What", "are", "the", "names", "of", "students", "who", "took", "a", "course", "in", "the", "Fall", "of", "2003", "?"], "sql": {"from": {"table_units": [["table_unit", 6]], "conds": []}, "select": [false, [[0, [0, [0, 28, false], null]]]], "where": [[false, 8, [0, [0, 27, false], null], {"from": {"table_units": [["table_unit", 7]], "conds": []}, "select": [false, [[0, [0, [0, 31, false], null]]]], "where": [[false, 2, [0, [0, 34, false], null], "\"Fall\"", null], "and", [false, 2, [0, [0, 35, false], null], 2003.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"classroom": ["building", "room_number", "capacity"], "department": ["dept_name", "building", "budget"], "course": ["course_id", "title", "dept_name", "credits"], "instructor": ["ID", "name", "dept_name", "salary"], "section": ["course_id", "sec_id", "semester", "year", "building", "room_number", "time_slot_id"], "teaches": ["ID", "course_id", "sec_id", "semester", "year"], "student": ["ID", "name", "dept_name", "tot_cred"], "takes": ["ID", "course_id", "sec_id", "semester", "year", "grade"], "advisor": ["s_ID", "i_ID"], "time_slot": ["time_slot_id", "day", "start_hr", "start_min", "end_hr", "end_min"], "prereq": ["course_id", "prereq_id"]}, "answer": [["Manber"], ["Zelty"], ["Colin"], ["Rzecz"], ["Ugarte"], ["Kieras"], ["Reed"], ["Okaf"], ["Allard"], ["Veselovsky"], ["Dhav"], ["Xue"], ["More"], ["Marlet"], ["Roses"], ["Jerns"], ["Morales"], ["Prasad"], ["Kerridge"], ["Schultz"], ["Tzeng"], ["Englund"], ["Al-Tahat"], ["Heckman"], ["Rokhs"], ["Foels"], ["Jr"], ["Peyse"], ["Rakoj"], ["Ng"], ["Kwan"], ["Kowe"], ["Skeen"], ["Knutson"], ["Thimm"], ["Griffiths"], ["Wingb"], ["Marcus"], ["Watson"], ["Power"], ["Sin"], ["Szczerban"], ["Ren"], ["Serrano"], ["Okano"], ["Alqui"], ["Bonatto"], ["Wolter"], ["Latour"], ["Adam"], ["Ignj"], ["Bedny"], ["Lazos"], ["Berger"], ["Belhadji"], ["Starker"], ["Miao"], ["Moszkowski"], ["Yoneda"], ["Takeshi"], ["Whitley"], ["Axte"], ["Ramadan"], ["Haigh"], ["Vagn"], ["Martinsen"], ["Lehtinen"], ["Malinen"], ["Philippe"], ["Kawahara"], ["Massour"], ["Enokib"], ["Sun"], ["Lapio"], ["Chien"], ["She"], ["Silbert"], ["Sasso"], ["Arinb"], ["Januszewski"], ["Anis"], ["Yeung"], ["Dink"], ["Marques"], ["Ross"], ["Wicki"], ["Sacchi"], ["Bandekar"], ["Liao"], ["Rahman"], ["Kanata"], ["Meyl"], ["Suppan"], ["Angs"], ["Stratulat"], ["Kuwadak"], ["Kantors"], ["Biehl"], ["Hakkinen"], ["Akaiw"], ["Swartj"], ["King"], ["Hazemi"], ["Chuon"], ["Kang"], ["Mathur"], ["Peter"], ["Ould"], ["Michel"], ["Pearlman"], ["Audeh"], ["Tavan"], ["Hamarn"], ["Brailsford"], ["Nirenbu"], ["Dhav"], ["Kangs"], ["Marinov"], ["Goodwin"], ["Coppens"], ["Ma"], ["Mori"], ["Araya"], ["Williamson"], ["Pampal"], ["Benson"], ["Masini"], ["Rubio"], ["Denecker"], ["Erdem"], ["Cavalcanti"], ["Hayashi"], ["Cai"], ["Chang"], ["Bouzeghoub"], ["Asahara"], ["Canas"], ["Colu"], ["Dawson"], ["Sahm"], ["Vanrell"], ["Savelieva"], ["Tiwari"], ["Fontana"], ["Adda"], ["Lewis"], ["Finney"], ["Lomi"], ["Laak"], ["Shuming"], ["Cheed"], ["Wunderli"], ["Geon"], ["Held"], ["Juchn"], ["McCarter"], ["Rais"], ["Nardi"], ["Agarwal"], ["Patel"], ["Garze"], ["Bradshaw"], ["Negron"], ["Cox"], ["Cheah"], ["Bates"], ["Sauer"], ["Cal"], ["Ockerb"], ["Gerstend"], ["Sundho"], ["Read"], ["Kinney"], ["Gordon"], ["Larion"], ["Daat"], ["Dang"], ["Oevers"], ["Wecker"], ["Wodn"], ["Dias"], ["Silverman"], ["Lykin"], ["Ceze"], ["Sahani"], ["Kempn"], ["Bouras"], ["Brunet"], ["Fierro-"], ["Lang"], ["Noda"], ["Shakhnovich"], ["Cecchi"], ["Kinney"], ["DAgostino"], ["Kalisz"], ["Russa"], ["Fauth"], ["Andert"], ["Juol"], ["\u00c3\u0081lvarez"], ["Jo"], ["Yap"], ["Greene"], ["Halbert"], ["Engeldr"], ["Tran-"], ["Segars"], ["Cotterill"], ["Palomo"], ["Visr"], ["Fenwick"], ["Boulah"], ["Baker"], ["Vassileva"], ["Hunter"], ["Moreira"], ["Sivew"], ["Calles"], ["Seyfert"], ["Ashmi"], ["Bertranp"], ["Simmel"], ["Tanaka"], ["Richter"], ["Goldbu"], ["Ende"], ["Neru"], ["Heers"], ["Veselovsky"], ["Baber"], ["Markin"], ["Veerar"], ["Trur"], ["Garze"], ["Fitzpatrick"], ["Date"], ["Ssu"], ["Leitner"], ["Tabarr"], ["Duong"], ["Brunt"], ["Y\u00c3\u00bcksel"], ["McQuillan"], ["Savelieva"], ["Evano"], ["Rougemont"], ["Grange"], ["Rajan"], ["Mai"], ["Sorensen"], ["Hirasawa"], ["Carr"], ["Catani"], ["Mathur"], ["Canellas"], ["Reiss"], ["Kennedy"], ["Chatterton"], ["Williamson"], ["Duxbury"], ["Berthold"], ["Reuver"], ["Gomez"], ["Simmel"], ["Aufr"], ["Sutter"], ["Daues"], ["Michael"], ["Oller"], ["Lykin"], ["Stokic"], ["Stetson"], ["Gaspar"], ["Beavis"], ["Solar"], ["Kogure"], ["Mateo"], ["Youseffi"], ["Ibrahim"], ["Humphrey"], ["Grude"], ["Ende"], ["Porr"], ["Lepp"], ["Folkers"], ["Marsh"], ["Arakawa"], ["Chaudhuri"], ["Mali"], ["Inoue"], ["Fonseca"], ["Shaffer"], ["Rueda"], ["Botha"], ["Arnoux"], ["Canas"], ["Zulueta"], ["Lhomme"], ["Porter"], ["Kawasaki"], ["Kacpr"], ["Resa"], ["Qian"], ["Rotter"], ["Bessou"], ["Okubo"], ["Neld"], ["Ueda"], ["Frost"], ["Tapia"], ["Nagashima"], ["Saariluoma"], ["Chormo"], ["Ouaz"], ["Nakajima"], ["Park"], ["Atre"], ["McCracken"], ["Suzuki"], ["Robins"], ["Leventhal"], ["Damas"], ["Curutchet"], ["Liley"], ["McCormack"], ["Stead"], ["Peck"], ["Otterm"], ["Saguez"], ["Kawakami"], ["Seike"], ["Mowbray"], ["Wood"], ["Asahara"], ["Orono"], ["Schreitm"], ["Mantzo"], ["Chan"], ["Bosnjak"], ["Nestor"], ["Baba"], ["Kacpr"], ["Hagedorn"], ["Lauciu"], ["Fries"], ["Nakajima"], ["Farr"], ["Basile"], ["Arndt"], ["Barkov"], ["Glaho"], ["Quimby"], ["Loc"], ["Shilv"], ["Ebou"], ["Cao"], ["Gradino"], ["Peskin"], ["Byrd"], ["Yamamoto"], ["John"], ["Letouzey"], ["Suppan"], ["\u00c3\u2013zel"], ["ODono"], ["Quaranta"], ["Szendrei"], ["Neuhold"], ["Souza"], ["Nair"], ["Story"], ["Karniel"], ["Baccou"], ["Narayanan"], ["Fathi"], ["Okaf"], ["Dalton"], ["Kamae"], ["Grieng"], ["Falconer"], ["Poize"], ["Warren"], ["Sohn"], ["Norman"], ["Frasinc"], ["Mes"], ["Valtchev"], ["Campbell"], ["Westbrook"], ["Kamata"], ["Signah"], ["Jiao"], ["Gay"], ["Beeu"], ["Vrato"], ["Zuyev"], ["Kosken"], ["Matsuda"], ["Hayat"], ["Harada"], ["Rzecz"], ["Fok"], ["Spector"], ["Swien"], ["Theodores"], ["Kreutz"], ["Murphy"], ["Maglioni"], ["Narasimhamu"], ["Loull"], ["Elme"], ["Reinhardt"], ["Soper"], ["Holloway"], ["Tsukamoto"], ["Macias"], ["Androutsopoulos"], ["Dwyer"], ["Wood"], ["Urano"], ["Dellwo"], ["Bartels"], ["Drig"], ["Jode"], ["Llam"], ["Xie"], ["Bouras"], ["Pacie"], ["Rowe"], ["Dima"], ["Coppens"], ["Rioult"], ["Petzo"], ["Fok"], ["Hubr"], ["Birtz"], ["Tola"], ["Beavis"], ["Meneses"], ["April"], ["Pietkiewicz"], ["Kaufman"], ["Tassel"], ["Chriso"], ["Curl"], ["Ortmann"], ["Jordan"], ["Gall"], ["Koch"], ["Freib"], ["Mezzar"], ["Fournier"], ["Nikut"], ["Wright"], ["Tsantis"], ["Amr"], ["Holland"], ["Lin"], ["Ram"], ["McGinn"], ["Kaiser"], ["Tanaka"], ["Shim"], ["Venturini"], ["Scher"], ["Yoon"], ["Cui"], ["Tam"], ["Meneses"], ["Tallis"], ["Papakir"], ["Makarychev"], ["Halbert"], ["Gilmour"], ["Steinmetz"], ["Sowerby"], ["Cox"], ["Labroc"], ["Yoshioka"], ["Nguyen"], ["Lanfr"], ["Abraham"], ["Cochran"], ["Stasko"], ["Zelek"], ["Ahmadian"], ["Hoyos"], ["Kiltz"], ["Yeoh"], ["Beekw"], ["Zaniolo"], ["Teng"], ["Hirasawa"], ["Pledg"], ["Kruglyak"], ["Bloom"], ["Houtsm"], ["Richardson"], ["Zander"], ["Kagd"], ["Ryoo"], ["Masamura"], ["Richi"], ["Barry"], ["Rafiq"], ["Correia"], ["Bakirc"], ["Albinal"], ["Wakamiya"], ["Warren"], ["Odell"], ["Xue"], ["Tso"], ["Lopes"], ["Gustafsson"], ["Krone"], ["Kolodko"], ["Elias"], ["Martyno"], ["Penneb"], ["Brown"], ["Emam"], ["Tuomisto"], ["Pup"], ["Wehen"], ["Ende"], ["Bonvin"], ["Vogel"], ["Chikar"], ["Wilson"], ["Rammer"], ["Rhyne"], ["Gotoh"], ["Karande"], ["Androutsopoulos"], ["Petersen"], ["Nikut"], ["Seike"], ["Haigh"], ["Milanic"], ["Urano"], ["Kurt"], ["Spengler"], ["Landau"], ["She"], ["Rajnov"], ["Recc"], ["Blanchard"], ["Lindner"], ["Concilio"], ["Allen"], ["Kandadai"], ["Pah"], ["Syng"], ["Lahtinen"], ["Ang"], ["Rao"], ["Willis"], ["Klivansky"], ["Sharpe"], ["Noga"], ["MacIntyre"], ["Guthk"], ["Peterson"], ["Nicol"], ["Nadg"], ["Engen"], ["Garg"], ["Noda"], ["Feyr"], ["Redw"], ["Nagle"], ["Fritsch"], ["Suwanno"], ["Wolff"], ["Boons"], ["Higuchi"], ["Recc"], ["Sznajder"], ["\u00c3\u2026str\u00c3\u00b6m"], ["Hobbs"], ["Saito"], ["Dahmann"], ["Hashim"], ["Adeni"], ["Keps"], ["Brandt"], ["Sayre"], ["Juan"], ["Lemoine"], ["Hasan"], ["Reina-"], ["Cashman"], ["Neubert"], ["Schwet"], ["Cordt"], ["Eck"], ["Byun"], ["Okabe"], ["Ludwig"], ["Diana"], ["Chun"], ["Gregga"], ["Tam"], ["Zarat\u00c3\u00a9"], ["Hancock"], ["Yu"], ["Reiss"], ["Thie"], ["Feng"], ["Levitan"], ["Pohlem"], ["Ivanov"], ["Thadani"], ["Kihn"], ["Apostolov"], ["Choung"], ["Bhat"], ["Mohamed"], ["Kameda"], ["Someren"], ["Fettes"], ["Zamani"], ["Strieg"], ["Schelten"], ["Yusop"], ["Souza"], ["Rolland"], ["Perozzo"], ["Lansi"], ["Sadry"], ["DeMil"], ["Zarpell"], ["Carey"], ["Osaka"], ["Swain"], ["Rotom"], ["Gray"], ["Janssen"], ["Katsik"], ["Margetts"], ["Haigh"], ["Theuniss"], ["Rehd"], ["Chatfield"], ["Roeder"], ["Soricu"], ["Holloway"], ["Stauf"], ["Pulido"], ["Albuquerque"], ["Fries"], ["Alexandri"], ["Aarde"], ["Weller"], ["Pelletier"], ["Lao"], ["Gei\u00c3\u0178l"], ["Lum"], ["Tiamp"], ["Afim"], ["Xiong"], ["Deng"], ["Lutes"], ["Ivanov"], ["Cronin"], ["Choung"], ["Hampp"], ["Fengl"], ["Ray"], ["Collet"], ["Reichl"], ["Belmes"], ["Badran"], ["Godfrey"], ["Morris"], ["Harrison"], ["Kothari"], ["Schoenfl"], ["Mertens"], ["Barwin"], ["Cox"], ["Dano"], ["Samel"], ["Nishida"], ["Dage"], ["Zander"], ["Nagal"], ["Emms"], ["Pietkiewicz"], ["Jovicic"], ["Bai"], ["Tuki"], ["Kangs"], ["Fukui"], ["Canon"], ["Barranco"], ["Bhavs"], ["Maity"], ["Tanno"], ["Vries"], ["Kissel"], ["Roytman"], ["Hartmann"], ["Sohn"], ["Baccou"], ["Towsey"], ["Brookh"], ["Cheed"], ["Durrant"], ["Im"], ["Komatsu"], ["Soni"], ["Hoffman"], ["Bocchi"], ["Camme"], ["Durrant"], ["Stylian"], ["Conradie"], ["Anderson"], ["Makowski"], ["Wagner"], ["Hochri"], ["Hoov"], ["Dostal"], ["Stilla"], ["So"], ["Luan"], ["Djurd"], ["Sellink"], ["Enokib"], ["Xie"], ["Pulido"], ["Fischer"], ["Mandviwall"], ["Bayn"], ["Kuo"], ["Kelly"], ["Aarde"], ["Mennif"], ["Mansint"], ["Doran"], ["Tiroz"], ["Midu"], ["Zander"], ["Lucas"], ["Pradhan"], ["Namer"], ["Desp"], ["Kane"], ["Kubo"], ["Kamez"], ["Deupree"], ["Yuanq"], ["Rossettin"], ["Hendrickson"], ["Katzenb"], ["Halbert"], ["Gall"], ["Papakir"], ["Spengler"], ["Winter"], ["Butler"], ["Karlsson"], ["Riser"], ["Schwarze"], ["Rossos"], ["Horecz"], ["Saad"], ["Yagit"], ["Aufr"], ["Roses"], ["Loudn"], ["Goldman"], ["Hughes"], ["Novak"], ["Planti"], ["Shishkin"], ["Morrison"], ["Szczerban"], ["Crick"], ["Brochhause"], ["Qvi"], ["Hsueh"], ["Schelten"], ["Gibson"], ["Xi"], ["Amberg"], ["Shevade"], ["Goldman"], ["Spengler"], ["Sui"], ["Westervelt"], ["Catona"], ["Chenu"], ["Aufr"], ["Yun"], ["Labroc"], ["Westphal"], ["Scherze"], ["Heilprin"], ["Ueno"], ["Dubu"], ["Garcia-Ferr"], ["Yoneda"], ["Cooper"], ["LaCo"], ["Psil"], ["Jones"], ["Sandberg"], ["Beeu"], ["Newitt"], ["Yamamoto"], ["Pigd"], ["Forestiero"], ["Blecken"], ["Cameron"], ["Teo"], ["Kim"], ["Hugo"], ["Tillmann"], ["Roessler"], ["Becker"], ["Jessup"], ["Kjellmer"], ["Kouan"], ["Larsson"], ["Hill"], ["Portillo"], ["Stratulat"], ["Sciore"], ["Mejia"], ["Masum"], ["Zaharak"], ["Gierl"], ["Aly"], ["Prabhakaran"], ["Bouamama"], ["Peip"], ["Barbosa"], ["Beichn"], ["Tan"], ["Putru"], ["Alexandri"], ["Boken"], ["Hoshi"], ["Mu\u00c3\u00b1oz"], ["Ra\u00c3\u00afev"], ["Aydin"], ["Konno"], ["Zhiyong"], ["Zle"], ["Hirvas"], ["Shevade"], ["Ballew"], ["Gryts"], ["Al-Hu"], ["Holn"], ["Kaska"], ["Holland"], ["Rajnov"], ["Holn"], ["Flecker"], ["Fredrickso"], ["Amann"], ["Scheine"], ["Stephenn"], ["Hayat"], ["Otsuki"], ["Eller"], ["Pace"], ["Wetzel"], ["Mulet"], ["Caleff"], ["Tuki"], ["Kashima"], ["Schmitz"], ["Queiroz"], ["Guix"], ["Akroy"], ["Fox"], ["Schweitzer"], ["Fall"], ["Stokic"], ["Baccou"], ["Blasbe"], ["Schulz"], ["Hwang"], ["Richardson"], ["Sachse"], ["Gubar"], ["Moei"], ["Sandberg"], ["Chettao"], ["Ledermann"], ["Ranno"], ["Cerime"], ["Eggers"], ["Vulp"], ["Giuffrida"], ["Wall"], ["Lao"], ["Hamagi"], ["Havill"], ["Chowdhury"], ["Rical"], ["Basturk"], ["Ravindranath"], ["Hughes"], ["Huo"], ["Miao"], ["Oller"], ["Griffin"], ["Simmel"], ["Tabor"], ["Morton"], ["Keuk"], ["Hahn-"], ["Yoshimoto"], ["Liedm"], ["Tavan"], ["Sakanushi"], ["Williamson"], ["Jovicic"], ["Potry"], ["Pettersen"], ["Maw"], ["Qian"], ["Dellwo"], ["Maher"], ["\u00c3\u2021ivi"], ["Miliko"], ["Rani"], ["Oblak"], ["Monger"], ["Alart"], ["Lemoine"], ["Cherchi"], ["Bollen"], ["Tyler"], ["Klepper"], ["Lohman"], ["Roschew"], ["Hayrapetyan"], ["Benitez"], ["Morales"], ["Krohn"], ["Lui"], ["Yap"], ["Erdem"], ["Visr"], ["Michael"], ["Xiong"], ["Someren"], ["Douss"], ["Kurt"], ["Beeu"], ["Dai"], ["Chakraborty"], ["Marlet"], ["Sherman"], ["Abdul-Rahman"], ["Shavel"], ["Koltso"], ["Lingamp"], ["Sanchez"], ["Velikovs"], ["Rumat"], ["Frost"], ["Jacobs"], ["Marongiu"], ["Martel-"], ["Schopp"], ["Marquis"], ["Breed"], ["Ikeda"], ["Witty"], ["Chiari"], ["Corr"], ["Clemme"], ["Boudjelo"], ["Heng"], ["Tsuji"], ["Mittelm"], ["Karv"], ["Bansal"], ["Nakamura"], ["Holz"], ["Eynd"], ["Almeida"], ["Homyk"], ["Mozayani"], ["Strzem"], ["Perna"], ["Atkins"], ["Masri"], ["Nirenbu"], ["Cordt"], ["Paddock"], ["Farr"], ["Ching"], ["Chiu"], ["Urwin"], ["Arora"], ["Perei"], ["Kereth"], ["Verma"], ["Hendrickson"], ["Towsey"], ["Peeri"], ["Koizumi"], ["Conti"], ["Grant"], ["Breuer"], ["Wyes"], ["Pomy"], ["Nirenbu"], ["Rote"], ["Das"], ["So"], ["Peip"], ["Duncan"], ["Ladu"], ["Harmon"], ["Caporali"], ["Ariav"], ["Tauber"], ["Chow"], ["Quinta"], ["Shoji"], ["Mehra"], ["Wunderli"], ["Whitten"], ["Godfrey"], ["Achilles"], ["Krasser"], ["Kaminsky"], ["Gregory"], ["Stenv"], ["Stylian"], ["Jordan"], ["Sakanushi"], ["Kuwadak"], ["Jode"], ["Jordan"], ["Schrefl"], ["Kurata"], ["Levie"], ["Christiansen"], ["Bra\u00c3\u00b1a"], ["Rammer"], ["Hennig"], ["Randers"], ["Luk"], ["Zuo"], ["Fernandez-Gonzalez"], ["Chaney"], ["Vicentino"], ["Yihn"], ["Kocsis"], ["Erol"], ["Bocchi"], ["Chapman"], ["Setiawan"], ["Bogdanova"], ["Champes"], ["Betho"], ["Kashima"], ["Aufr"], ["Stoltzfus"], ["Harass"], ["Levitan"], ["Kahs"], ["Juchn"], ["Spikov"], ["Moscarini"], ["Kaar"], ["Loher"], ["Cordt"], ["Martin"], ["Dair"], ["Peter"], ["Houtsm"], ["Franchet"], ["Unger"], ["Viani"], ["Berthold"], ["Hansch"], ["Gani"], ["Pinkus"], ["Koenig"], ["Kim"], ["Scheffer"], ["Carrera"], ["Liang"], ["Nakao"], ["McCracken"], ["Liepelt"], ["Turunen"], ["Reiss"], ["Zacharias"], ["Bouamama"], ["Stanko"], ["Lesaffre"], ["Yarmush"], ["Reiss"], ["Bongio"], ["Henriksen"], ["Mitsuhashi"], ["Slaw"], ["Warner"], ["Levine"], ["Smoro"], ["Frost"], ["Fonseca"], ["Stasko"], ["Wodn"], ["Palaniswami"], ["Guthk"], ["Juan"], ["Mitsuhashi"], ["Brenner"], ["Janssen"], ["Anse"], ["Kl\u00c3\u00b6pper"], ["Loyka"], ["Marques"], ["Walker"], ["Zubai"], ["Heilprin"], ["Maesf"], ["Unay"], ["Donofrio"], ["Barberis"], ["Esparza"], ["Mohan"], ["Koppit"], ["Jo"], ["Giannoulis"], ["Bruderm"], ["Godfrey"], ["Tomason"], ["Mohamed"], ["Lenhart"], ["Finance"], ["Grange"], ["Dair"], ["Ockerb"], ["Sud"], ["Clarkson"], ["Frangeu"], ["Schill"], ["Ohno"], ["Hoffman"], ["Streitf"], ["Steinmetz"], ["Sutter"], ["Tchuri"], ["Pourkas"], ["DAtri"], ["Nicol"], ["Dubink"], ["Enokib"], ["Mathias"], ["Mattor"], ["Makinen"], ["Cirsto"], ["Flossmann"], ["Rogers"], ["Mesne"], ["Gibbs"], ["McQuillan"], ["Carey"], ["Gault"], ["Rees-"], ["Graham"], ["Sendlm"], ["Mellor"], ["Smoro"], ["Zahrani"], ["Oberholzer"], ["Simon"], ["Winkler"], ["Bengtss"], ["Unger"], ["Milner"], ["Gierl"], ["Pampal"], ["Yip"], ["Wrzesz"], ["Sarnak"], ["Pettersen"], ["Ailamaki"], ["Neff"], ["Buril"], ["Chakraborty"], ["Xiang"], ["Kozlov"], ["Crimm"], ["Lykin"], ["Krishnakumar"], ["Bauer"], ["Mercank"], ["Chien"], ["Dickson"], ["Guiho"], ["Yang"], ["Hammond"], ["Hands"], ["Morgan"], ["Isogai"], ["Pampal"], ["Tellez"], ["Gruns"], ["Jakobsen"], ["Schrefl"], ["Cabr"], ["Batano"], ["Nakajima"], ["Beeu"], ["Marcol"], ["Sugavanam"], ["Nisso"], ["Hay"], ["Yasuda"], ["Koic"], ["Vulp"], ["Tong"], ["Sudirm"], ["Anty"], ["Katehakis"], ["Kobayashi"], ["Rieger"], ["Jamro"], ["Mohamed"], ["Bussel"], ["Bulash"], ["Rajnov"], ["Noda"], ["Labaye"], ["Rammer"], ["Awano"], ["Sacchi"], ["ODono"], ["Schinag"], ["Ciepl"], ["Furukawa"], ["Ratcliff"], ["Saill"], ["Patne"], ["Nilsson"], ["Alfaro"], ["Neru"], ["Frolova"], ["Julier"], ["Kun"], ["Hawthorne"], ["Hao"], ["Makarychev"], ["McGarr"], ["Pircher"], ["Tobin"], ["Fickl"], ["Bouzeghoub"], ["Wilks"], ["Strader"], ["Schrefl"], ["Bellman"], ["Beckert"], ["Savolainen"], ["Bennett"], ["Courtney"], ["Califieri"], ["Lv"], ["Epley"], ["Bravo"], ["Samo"]]}, "program_result_list": [["t1 = takes[(takes['semester'] == 'Fall') & (takes['year'] == 2003)]\nt2 = pd.merge(t1, student, left_on='id', right_on='id')\nanswer = t2['name']", false], ["t1 = takes[takes['semester'] == 'Fall']\nt2 = t1[t1['year'] == 2003]\nt3 = pd.merge(t2, student, left_on='id', right_on='id')\nanswer = t3['name']", false], ["t1 = takes[(takes['semester'] == 'Fall') & (takes['year'] == 2003)]\nt2 = pd.merge(t1, student, on='id')\nanswer = t2['name']", false], ["t1 = takes[(takes['semester'] == 'Fall') & (takes['year'] == 2003)]\nt2 = pd.merge(student, t1, on='id')\nanswer = t2['name']", false], ["t1 = takes[(takes['semester'] == 'Fall') & (takes['year'] == 2003)]\nt2 = pd.merge(t1, student, left_on='id', right_on='id')\nanswer = t2['name']", false], ["t1 = pd.merge(student, takes, on='id')\nt2 = t1[(t1['semester'] == 'Fall') & (t1['year'] == 2003)]\nanswer = t2['name']", false], ["t1 = takes[(takes['semester'] == 'Fall') & (takes['year'] == 2003)]['id']\nt2 = student[student['id'].isin(t1)]\nanswer = t2['name']", false], ["t1 = takes[(takes['semester'] == 'Fall') & (takes['year'] == 2003)]\nt2 = pd.merge(t1, student, left_on='id', right_on='id')\nanswer = t2['name']", false], ["t1 = takes[takes['semester'] == 'Fall']\nt2 = t1[t1['year'] == 2003]\nt3 = pd.merge(t2, student, left_on='id', right_on='id')\nanswer = t3['name']", false], ["t1 = takes[takes['semester'] == 'Fall']\nt2 = t1[t1['year'] == 2003]\nt3 = pd.merge(t2, student, left_on='id', right_on='id')\nanswer = t3['name']", false], ["t1 = takes[(takes['semester'] == 'Fall') & (takes['year'] == 2003)]\nt2 = pd.merge(student, t1, left_on='id', right_on='id')\nanswer = t2['name']", false], ["t1 = takes[takes['semester'] == 'Fall']\nt2 = t1[t1['year'] == 2003]\nt3 = pd.merge(student, t2, on='id')\nanswer = t3['name']", false], ["t1 = takes[(takes['semester'] == 'Fall') & (takes['year'] == 2003)]\nt2 = pd.merge(t1, student, on='id')\nanswer = t2['name']", false], ["t1 = takes[(takes['semester'] == 'Fall') & (takes['year'] == 2003)]\nt2 = pd.merge(t1, student, on='id')\nanswer = t2['name']", false], ["t1 = takes[(takes['semester'] == 'Fall') & (takes['year'] == 2003)]\nt2 = pd.merge(student, t1, left_on='id', right_on='id')\nanswer = t2['name']", false], ["t1 = takes[(takes['semester'] == 'Fall') & (takes['year'] == 2003)]\nt2 = pd.merge(student, t1, left_on='id', right_on='id')\nanswer = t2['name']", false], ["t1 = takes[(takes['semester'] == 'Fall') & (takes['year'] == 2003)]\nt2 = pd.merge(student, t1, left_on='id', right_on='id')\nanswer = t2['name']", false], ["t1 = takes[(takes['semester'] == 'Fall') & (takes['year'] == 2003)]\nt2 = pd.merge(t1, student, left_on='id', right_on='id')\nanswer = t2['name']", false], ["t1 = takes[(takes['semester'] == 'Fall') & (takes['year'] == 2003)]\nt2 = pd.merge(student, t1, left_on='id', right_on='id')\nanswer = t2['name']", false], ["t1 = takes[(takes['semester'] == 'Fall') & (takes['year'] == 2003)]\nt2 = pd.merge(student, t1, on='id', how='right')\nanswer = t2['name']", false]]}
+{"example": {"db_id": "tracking_grants_for_research", "query": "SELECT date_from ,  date_to FROM Project_Staff WHERE project_id IN( SELECT project_id FROM Project_Staff GROUP BY project_id ORDER BY count(*) DESC LIMIT 1 ) UNION SELECT date_from ,  date_to FROM Project_Staff WHERE role_code  =  'leader'", "query_toks": ["SELECT", "date_from", ",", "date_to", "FROM", "Project_Staff", "WHERE", "project_id", "IN", "(", "SELECT", "project_id", "FROM", "Project_Staff", "GROUP", "BY", "project_id", "ORDER", "BY", "count", "(", "*", ")", "DESC", "LIMIT", "1", ")", "UNION", "SELECT", "date_from", ",", "date_to", "FROM", "Project_Staff", "WHERE", "role_code", "=", "'leader", "'"], "query_toks_no_value": ["select", "date_from", ",", "date_to", "from", "project_staff", "where", "project_id", "in", "(", "select", "project_id", "from", "project_staff", "group", "by", "project_id", "order", "by", "count", "(", "*", ")", "desc", "limit", "value", ")", "union", "select", "date_from", ",", "date_to", "from", "project_staff", "where", "role_code", "=", "value"], "question": "From what date and to what date do the staff work on a project that has the most staff and has staff in a leader role?", "question_toks": ["From", "what", "date", "and", "to", "what", "date", "do", "the", "staff", "work", "on", "a", "project", "that", "has", "the", "most", "staff", "and", "has", "staff", "in", "a", "leader", "role", "?"], "sql": {"from": {"table_units": [["table_unit", 6]], "conds": []}, "select": [false, [[0, [0, [0, 26, false], null]], [0, [0, [0, 27, false], null]]]], "where": [[false, 8, [0, [0, 24, false], null], {"from": {"table_units": [["table_unit", 6]], "conds": []}, "select": [false, [[0, [0, [0, 24, false], null]]]], "where": [], "groupBy": [[0, 24, false]], "having": [], "orderBy": ["desc", [[0, [3, 0, false], null]]], "limit": 1, "intersect": null, "union": null, "except": null}, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": {"from": {"table_units": [["table_unit", 6]], "conds": []}, "select": [false, [[0, [0, [0, 26, false], null]], [0, [0, [0, 27, false], null]]]], "where": [[false, 2, [0, [0, 25, false], null], "\"leader\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "except": null}, "db_table_headers": {"Document_Types": ["document_type_code", "document_description"], "Documents": ["document_id", "document_type_code", "grant_id", "sent_date", "response_received_date", "other_details"], "Grants": ["grant_id", "organisation_id", "grant_amount", "grant_start_date", "grant_end_date", "other_details"], "Organisation_Types": ["organisation_type", "organisation_type_description"], "Organisations": ["organisation_id", "organisation_type", "organisation_details"], "Project_Outcomes": ["project_id", "outcome_code", "outcome_details"], "Project_Staff": ["staff_id", "project_id", "role_code", "date_from", "date_to", "other_details"], "Projects": ["project_id", "organisation_id", "project_details"], "Research_Outcomes": ["outcome_code", "outcome_description"], "Research_Staff": ["staff_id", "employer_organisation_id", "staff_details"], "Staff_Roles": ["role_code", "role_description"], "Tasks": ["task_id", "project_id", "task_details", "eg Agree Objectives"]}, "answer": [["1970-01-02 15:35:05", "1985-09-22 09:06:08"], ["1970-04-06 15:50:21", "1983-03-19 16:06:31"], ["1973-12-12 11:46:28", "1971-07-19 22:49:05"], ["1981-10-04 22:44:50", "1985-05-30 22:26:30"], ["1981-10-09 21:32:53", "2004-12-16 13:03:36"], ["1999-10-21 22:07:15", "2008-09-25 20:06:28"], ["2003-04-19 15:06:20", "2010-12-08 11:55:36"], ["2004-11-01 23:52:38", "1988-03-04 19:30:05"]]}, "program_result_list": [["t1 = Project_Staff.groupby('project_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = Project_Staff[Project_Staff['project_id'].isin(t2['project_id'])]\nt4 = Project_Staff[Project_Staff['role_code'] == 'leader']\nt5 = pd.concat([t3, t4])\nanswer = t5[['date_from', 'date_to']]", false], ["t1 = Project_Staff.groupby('project_id').size().rename('count').idxmax()\nt2 = Project_Staff[Project_Staff['project_id'] == t1]\nt3 = Project_Staff[Project_Staff['role_code'] == 'leader']\nt4 = t2.append(t3)\nanswer = t4[['date_from', 'date_to']]", false], ["t1 = Project_Staff.groupby('project_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = pd.merge(Project_Staff, t2, on='project_id')\nt4 = Project_Staff[Project_Staff['role_code'] == 'leader']\nt5 = pd.merge(t3, t4, on=['date_from', 'date_to'], how='outer')\nanswer = t5[['date_from', 'date_to']]", false], ["t1 = Project_Staff.groupby('project_id').size().rename('count').sort_values(ascending=False).head(1).to_frame().reset_index()\nt2 = Project_Staff[Project_Staff['project_id'] == t1['project_id'].values[0]]\nt3 = Project_Staff[Project_Staff['role_code'] == 'leader']\nanswer = t2.append(t3)[['date_from', 'date_to']]", false], ["t1 = Project_Staff.groupby('project_id').size().rename('count').to_frame().reset_index()\nt2 = t1.sort_values(by='count', ascending=False).head(1).to_frame().reset_index()\nt3 = Project_Staff[Project_Staff['project_id'].isin(t2['project_id'])]\nt4 = Project_Staff[Project_Staff['role_code'] == 'leader']\nt5 = pd.concat([t3, t4])\nanswer = t5[['date_from', 'date_to']]", false], ["t1 = Project_Staff.groupby('project_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = Project_Staff[Project_Staff['project_id'].isin(t2['project_id'])]\nt4 = Project_Staff[Project_Staff['role_code'] == 'leader']\nt5 = pd.concat([t3, t4])\nanswer = t5[['date_from', 'date_to']]", false], ["t1 = Project_Staff.groupby('project_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = Project_Staff[Project_Staff['project_id'].isin(t2['project_id'])]\nt4 = Project_Staff[Project_Staff['project_code'] == 'leader']\nt5 = t3.append(t4)\nanswer = t5[['date_from', 'date_to']].drop_duplicates()", false], ["t1 = Project_Staff.groupby('project_id').size().rename('count').to_frame().reset_index()\nt2 = t1.sort_values('count', ascending=False).head(1).to_frame().reset_index()\nt3 = Project_Staff[Project_Staff['project_id'] == t2['project_id'].values[0]]\nt4 = Project_Staff[Project_Staff['role_code'] == 'leader']\nt5 = pd.concat([t3[['date_from', 'date_to']], t4[['date_from', 'date_to']]], ignore_index=True)\nanswer = t5", false], ["t1 = Project_Staff.groupby('project_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = Project_Staff[Project_Staff['project_id'].isin(t2['project_id'])]\nt4 = Project_Staff[Project_Staff['role_code'] == 'leader']\nt5 = pd.concat([t3, t4])\nanswer = t5[['date_from', 'date_to']]", false], ["t1 = Project_Staff.groupby('project_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = Project_Staff[Project_Staff['project_id'] == t2['project_id'].iloc[0]]\nt4 = t3[['date_from', 'date_to']]\nt5 = Project_Staff[Project_Staff['role_code'] == 'leader']\nanswer = pd.concat([t4, t5[['date_from', 'date_to']]]).reset_index(drop=True)", false], ["t1 = Project_Staff.groupby('project_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = Project_Staff.merge(t2, on='project_id', how='inner')\nt4 = Project_Staff[Project_Staff['role_code'] == 'leader']\nanswer = pd.concat([t3[['date_from', 'date_to']], t4[['date_from', 'date_to']]])", false], ["t1 = Project_Staff.groupby('project_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = t2.merge(Project_Staff, on='project_id', how='inner')\nt4 = t3[['date_from', 'date_to']]\nt5 = Project_Staff[Project_Staff['role_code'] == 'leader']\nanswer = pd.concat([t4, t5[['date_from', 'date_to']]])", false], ["t1 = Project_Staff.groupby('project_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = Project_Staff[Project_Staff['project_id'] == t2['project_id'].iloc[0]]\nt4 = Project_Staff[Project_Staff['role_code'] == 'leader']\nt5 = pd.concat([t3, t4])\nanswer = t5[['date_from', 'date_to']].drop_duplicates()", false], ["t1 = Project_Staff.groupby('project_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = Project_Staff.merge(t2, left_on='project_id', right_on='project_id')\nt4 = Project_Staff[Project_Staff['role_code'] == 'leader']\nt5 = t3.append(t4, ignore_index=True)\nanswer = t5[['date_from', 'date_to']]", false], ["t1 = Project_Staff.groupby('project_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = pd.merge(Project_Staff, t2, on='project_id')\nt4 = t3[['date_from', 'date_to']]\nt5 = Project_Staff[Project_Staff['role_code'] == 'leader']\nt6 = t5[['date_from', 'date_to']]\nanswer = pd.concat([t4, t6])", false], ["t1 = Project_Staff.groupby('project_id').size().rename('count').to_frame().reset_index()\nt2 = t1.sort_values('count', ascending=False).head(1)\nt3 = pd.merge(t2, Project_Staff, left_on='project_id', right_on='project_id')\nt4 = t3[['date_from', 'date_to']]\nt5 = Project_Staff[Project_Staff['role_code'] == 'leader']\nt6 = t4.append(t5[['date_from', 'date_to']])\nanswer = t6", false], ["t1 = Project_Staff.groupby('project_id').size().rename('count').to_frame().reset_index()\nt2 = t1.sort_values('count', ascending=False).head(1).to_frame().reset_index()\nt3 = Project_Staff[Project_Staff['project_id'].isin(t2['project_id'])]\nt4 = Project_Staff[Project_Staff['role_code'] == 'leader']\nanswer = pd.concat([t3, t4])[['date_from', 'date_to']]", false], ["t1 = Project_Staff.groupby('project_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = Project_Staff[Project_Staff['project_id'].isin(t2['project_id'])]\nt4 = Project_Staff[Project_Staff['role_code'] == 'leader']\nanswer = pd.concat([t3[['date_from', 'date_to']], t4[['date_from', 'date_to']]])", false], ["t1 = Project_Staff.groupby(Project_Staff['project_id']).size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = Project_Staff[Project_Staff['project_id'].isin(t2['project_id'])]\nt4 = Project_Staff[Project_Staff['role_code'] == 'leader']\nanswer = pd.concat([t3[['date_from', 'date_to']], t4[['date_from', 'date_to']]])", false], ["t1 = Project_Staff.groupby('project_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = Project_Staff[Project_Staff['project_id'].isin(t2['project_id'])]\nt4 = Project_Staff[Project_Staff['role_code'] == 'leader']\nanswer = pd.concat([t3, t4], ignore_index=True)", false]]}
+{"example": {"db_id": "store_1", "query": "SELECT T1.first_name , T1.last_name FROM employees AS T1 JOIN customers AS T2 ON T1.id  =  T2.support_rep_id GROUP BY T1.id ORDER BY count(*) DESC LIMIT 1", "query_toks": ["SELECT", "T1.first_name", ",", "T1.last_name", "FROM", "employees", "AS", "T1", "JOIN", "customers", "AS", "T2", "ON", "T1.id", "=", "T2.support_rep_id", "GROUP", "BY", "T1.id", "ORDER", "BY", "count", "(", "*", ")", "DESC", "LIMIT", "1"], "query_toks_no_value": ["select", "t1", ".", "first_name", ",", "t1", ".", "last_name", "from", "employees", "as", "t1", "join", "customers", "as", "t2", "on", "t1", ".", "id", "=", "t2", ".", "support_rep_id", "group", "by", "t1", ".", "id", "order", "by", "count", "(", "*", ")", "desc", "limit", "value"], "question": "What is the full name of the employee who has the most customers?", "question_toks": ["What", "is", "the", "full", "name", "of", "the", "employee", "who", "has", "the", "most", "customers", "?"], "sql": {"from": {"table_units": [["table_unit", 3], ["table_unit", 4]], "conds": [[false, 2, [0, [0, 8, false], null], [0, 35, false], null]]}, "select": [false, [[0, [0, [0, 10, false], null]], [0, [0, [0, 9, false], null]]]], "where": [], "groupBy": [[0, 8, false]], "having": [], "orderBy": ["desc", [[0, [3, 0, false], null]]], "limit": 1, "intersect": null, "union": null, "except": null}, "db_table_headers": {"artists": ["id", "name"], "sqlite_sequence": ["name", "seq"], "albums": ["id", "title", "artist_id"], "employees": ["id", "last_name", "first_name", "title", "reports_to", "birth_date", "hire_date", "address", "city", "state", "country", "postal_code", "phone", "fax", "email"], "customers": ["id", "first_name", "last_name", "company", "address", "city", "state", "country", "postal_code", "phone", "fax", "email", "support_rep_id"], "genres": ["id", "name"], "invoices": ["id", "customer_id", "invoice_date", "billing_address", "billing_city", "billing_state", "billing_country", "billing_postal_code", "total"], "media_types": ["id", "name"], "tracks": ["id", "name", "album_id", "media_type_id", "genre_id", "composer", "milliseconds", "bytes", "unit_price"], "invoice_lines": ["id", "invoice_id", "track_id", "unit_price", "quantity"], "playlists": ["id", "name"], "playlist_tracks": ["playlist_id", "track_id"]}, "answer": [["Jane", "Peacock"]]}, "program_result_list": [["t1 = pd.merge(employees, customers, left_on='id', right_on='support_rep_id')\nt2 = t1.groupby(['id', 'first_name', 'last_name']).size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3[['first_name', 'last_name']]", false], ["t1 = pd.merge(employees, customers, on='id')\nt2 = t1.groupby('id').size().rename('count').to_frame().reset_index()\nt3 = pd.merge(t2, employees, on='id')\nanswer = t3.loc[t3['count'].idxmax(), ['first_name', 'last_name']]", false], ["t1 = pd.merge(customers, employees, left_on='support_rep_id', right_on='id')\nt2 = t1.groupby('id').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nt4 = pd.merge(t3, employees, left_on='id', right_on='id')\nanswer = t4[['first_name', 'last_name']]", false], ["t1 = pd.merge(customers, employees, left_on='support_rep_id', right_on='id')\nt2 = t1.groupby(['id', 'first_name', 'last_name']).size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3[['first_name', 'last_name']]", false], ["t1 = pd.merge(employees, customers, left_on='id', right_on='support_rep_id')\nt2 = t1.groupby('id').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nt4 = pd.merge(t3, employees, left_on='id', right_on='id')\nanswer = t4[['first_name', 'last_name']]", false], ["t1 = pd.merge(employees, customers, left_on='id', right_on='support_rep_id')\nt2 = t1.groupby(['first_name', 'last_name']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index()", false], ["t1 = pd.merge(artists, albums, left_on='id', right_on='artist_id')\nt2 = pd.merge(t1, employees, left_on='id', right_on='id')\nt3 = pd.merge(t2, customers, left_on='id', right_on='support_rep_id')\nt4 = t3.groupby(['first_name', 'last_name']).size().rename('count')\nanswer = t4.sort_values(ascending=False).head(1).to_frame().reset_index()", false], ["t1 = pd.merge(employees, customers, left_on='id', right_on='support_rep_id')\nt2 = t1.groupby(['first_name', 'last_name'])['first_name'].size().rename('count').sort_values(ascending=False)\nanswer = t2.head(1).to_frame().reset_index()", false], ["t1 = pd.merge(employees, customers, left_on='id', right_on='support_rep_id')\nt2 = t1.groupby('id').size().rename('count')\nt3 = pd.merge(employees, t2, left_on='id', right_index=True)\nanswer = t3[t3['count'] == t3['count'].max()][['first_name', 'last_name']]", false], ["t1 = pd.merge(employees, customers, left_on='id', right_on='support_rep_id')\nt2 = t1.groupby(['id', 'first_name', 'last_name']).size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3[['first_name', 'last_name']]", false], ["t1 = pd.merge(customers, employees, left_on='support_rep_id', right_on='id')\nt2 = t1.groupby('id').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nt4 = pd.merge(t3, employees, left_on='id', right_on='id')\nanswer = t4[['first_name', 'last_name']]", false], ["t1 = pd.merge(employees, customers, left_on='id', right_on='support_rep_id')\nt2 = t1.groupby('id').size().rename('count')\nt3 = pd.merge(t2.to_frame().reset_index(), employees, left_on='id', right_on='id')\nanswer = t3[['first_name', 'last_name']].head(1)", false], ["t1 = pd.merge(employees, customers, left_on='id', right_on='support_rep_id')\nt1 = t1.groupby('id').size().rename('count').to_frame().reset_index()\nt2 = pd.merge(t1, employees, left_on='id', right_on='id')\nanswer = t2.sort_values(by='count', ascending=False).head(1)[['first_name', 'last_name']]", false], ["t1 = pd.merge(employees, customers, left_on='id', right_on='support_rep_id')\nt2 = t1.groupby('id').size().rename('count')\nt3 = pd.merge(t2.to_frame().reset_index(), employees, left_on='id', right_on='id')\nanswer = t3[['first_name', 'last_name']].iloc[0]", false], ["t1 = pd.merge(employees, customers, left_on='id', right_on='support_rep_id')\nt2 = t1.groupby('id').size().rename('count')\nt3 = t2.to_frame().reset_index()\nt4 = pd.merge(t3, employees, on='id')\nanswer = t4.loc[t4['count'].idxmax(), ['first_name', 'last_name']]", false], ["t1 = pd.merge(customers, employees, left_on='support_rep_id', right_on='id')\nt2 = t1.groupby(['first_name', 'last_name']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index()", false], ["t1 = pd.merge(employees, customers, left_on='id', right_on='support_rep_id')\nt2 = t1.groupby(['id', 'first_name', 'last_name']).size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3[['first_name', 'last_name']]", false], ["t1 = pd.merge(employees, customers, left_on='id', right_on='support_rep_id')\nt2 = t1.groupby('id').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nt4 = pd.merge(t3, employees, left_on='id', right_on='id')\nanswer = t4[['first_name', 'last_name']]", false], ["t1 = pd.merge(customers, employees, left_on='support_rep_id', right_on='id')\nt2 = t1.groupby('id').size().rename('count').to_frame().reset_index()\nt3 = pd.merge(t2, employees, left_on='id', right_on='id')\nanswer = t3[['first_name', 'last_name']].head(1)", false], ["t1 = pd.merge(employees, customers, left_on='id', right_on='support_rep_id')\nt2 = t1.groupby('id').size().rename('count')\nt3 = pd.merge(t2.to_frame().reset_index(), employees, on='id')\nanswer = t3.sort_values(by='count', ascending=False).head(1)[['first_name', 'last_name']]", false]]}
+{"example": {"db_id": "city_record", "query": "SELECT T1.city FROM city AS T1 JOIN hosting_city AS T2 ON T1.city_id = T2.host_city WHERE T2.year  >  2010", "query_toks": ["SELECT", "T1.city", "FROM", "city", "AS", "T1", "JOIN", "hosting_city", "AS", "T2", "ON", "T1.city_id", "=", "T2.host_city", "WHERE", "T2.year", ">", "2010"], "query_toks_no_value": ["select", "t1", ".", "city", "from", "city", "as", "t1", "join", "hosting_city", "as", "t2", "on", "t1", ".", "city_id", "=", "t2", ".", "host_city", "where", "t2", ".", "year", ">", "value"], "question": "Which cities served as a host city after 2010?", "question_toks": ["Which", "cities", "served", "as", "a", "host", "city", "after", "2010", "?"], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 3]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 27, false], null]]}, "select": [false, [[0, [0, [0, 2, false], null]]]], "where": [[false, 3, [0, [0, 25, false], null], 2010.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"city": ["City_ID", "City", "Hanzi", "Hanyu_Pinyin", "Regional_Population", "GDP"], "match": ["Match_ID", "Date", "Venue", "Score", "Result", "Competition"], "temperature": ["City_ID", "Jan", "Feb", "Mar", "Apr", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"], "hosting_city": ["Year", "Match_ID", "Host_City"]}, "answer": [["Wuxi ( Jiangsu )"], ["Taizhou ( Zhejiang )"], ["Jiaxing ( Zhejiang )"]]}, "program_result_list": [["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nt2 = t1[t1['year'] > 2010]\nanswer = t2['city']", false], ["t1 = pd.merge(hosting_city, city, left_on='host_city', right_on='city_id')\nt2 = t1[t1['year'] > 2010]\nanswer = t2['city']", false], ["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nanswer = t1[t1['year'] > 2010]['city']", false], ["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nanswer = t1[t1['year'] > 2010]['city']", false], ["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nanswer = t1[t1['year'] > 2010]['city']", false], ["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nanswer = t1[t1['year'] > 2010]['city']", false], ["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nt2 = t1[t1['year'] > 2010]\nanswer = t2['city'].unique()", false], ["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nt2 = t1[t1['year'] > 2010]\nanswer = t2['city']", false], ["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nt2 = t1[t1['year'] > 2010]\nanswer = t2['city']", false], ["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nt2 = t1[t2['year'] > 2010]\nanswer = t2['city']", false], ["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nanswer = t1[t1['year'] > 2010]['city']", false], ["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nt2 = t1[t1['year'] > 2010]\nanswer = t2['city']", false], ["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nanswer = t1[t1['year'] > 2010]['city']", false], ["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nt2 = t1[t1['year'] > 2010]\nanswer = t2['city']", false], ["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nt2 = t1[t1['year'] > 2010]\nanswer = t2['city']", false], ["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nt2 = t1[t1['year'] > 2010]\nanswer = t2['city']", false], ["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nt2 = t1[t1['year'] > 2010]\nanswer = t2['city']", false], ["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nanswer = t1[t1['year'] > 2010]['city']", false], ["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nt2 = t1[t1['year'] > 2010]\nanswer = t2['city']", false], ["t1 = pd.merge(city, hosting_city, left_on='city_id', right_on='host_city')\nanswer = t1[t1['year'] > 2010]['city']", false]]}
+{"example": {"db_id": "apartment_rentals", "query": "SELECT sum(T2.room_count) FROM Apartment_Buildings AS T1 JOIN Apartments AS T2 ON T1.building_id  =  T2.building_id WHERE T1.building_short_name  =  \"Columbus Square\"", "query_toks": ["SELECT", "sum", "(", "T2.room_count", ")", "FROM", "Apartment_Buildings", "AS", "T1", "JOIN", "Apartments", "AS", "T2", "ON", "T1.building_id", "=", "T2.building_id", "WHERE", "T1.building_short_name", "=", "``", "Columbus", "Square", "''"], "query_toks_no_value": ["select", "sum", "(", "t2", ".", "room_count", ")", "from", "apartment_buildings", "as", "t1", "join", "apartments", "as", "t2", "on", "t1", ".", "building_id", "=", "t2", ".", "building_id", "where", "t1", ".", "building_short_name", "=", "value"], "question": "Show the total number of rooms of the apartments in the building with short name \"Columbus Square\".", "question_toks": ["Show", "the", "total", "number", "of", "rooms", "of", "the", "apartments", "in", "the", "building", "with", "short", "name", "``", "Columbus", "Square", "''", "."], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 9, false], null]]}, "select": [false, [[4, [0, [0, 14, false], null]]]], "where": [[false, 2, [0, [0, 2, false], null], "\"Columbus Square\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Apartment_Buildings": ["building_id", "building_short_name", "building_full_name", "building_description", "building_address", "building_manager", "building_phone"], "Apartments": ["apt_id", "building_id", "apt_type_code", "apt_number", "bathroom_count", "bedroom_count", "room_count"], "Apartment_Facilities": ["apt_id", "facility_code"], "Guests": ["guest_id", "gender_code", "guest_first_name", "guest_last_name", "date_of_birth"], "Apartment_Bookings": ["apt_booking_id", "apt_id", "guest_id", "booking_status_code", "booking_start_date", "booking_end_date"], "View_Unit_Status": ["apt_id", "apt_booking_id", "status_date", "available_yn"]}, "answer": [[29]]}, "program_result_list": [["t1 = pd.merge(Apartment_Buildings, Apartments, left_on='building_id', right_on='building_id')\nt2 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t2['room_count'].sum()", false], ["t1 = pd.merge(Apartment_Buildings, Apartments, on='building_id')\nt2 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t2['room_count'].sum()", false], ["t1 = pd.merge(Apartment_Buildings, Apartments, on='building_id')\nt2 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t2['room_count'].sum()", false], ["t1 = pd.merge(Apartment_Buildings, Apartments, left_on='building_id', right_on='building_id')\nt2 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t2['room_count'].sum()", false], ["t1 = pd.merge(Apartment_Buildings, Apartments, left_on='building_id', right_on='building_id')\nt2 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t2['room_count'].sum()", false], ["t1 = pd.merge(Apartment_Buildings, Apartments, left_on='building_id', right_on='building_id')\nt2 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t2['room_count'].sum()", false], ["t1 = pd.merge(Apartment_Buildings, Apartments, left_on='building_id', right_on='building_id')\nt2 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t2.room_count.sum()", false], ["t1 = pd.merge(Apartment_Buildings, Apartments, left_on='building_id', right_on='building_id')\nt2 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t2['room_count'].sum()", false], ["t1 = pd.merge(Apartment_Buildings, Apartments, on='building_id')\nt2 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t2['room_count'].sum()", false], ["t1 = pd.merge(Apartment_Buildings, Apartments, left_on='building_id', right_on='building_id')\nt2 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t2['room_count'].sum()", false], ["t1 = pd.merge(Apartment_Buildings, Apartments, left_on='building_id', right_on='building_id')\nt2 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t2['room_count'].sum()", false], ["t1 = pd.merge(Apartment_Buildings, Apartments, left_on='building_id', right_on='building_id')\nt1 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t1['room_count'].sum()", false], ["t1 = pd.merge(Apartment_Buildings, Apartments, left_on='building_id', right_on='building_id')\nt2 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t2['room_count'].sum()", false], ["t1 = pd.merge(Apartment_Buildings, Apartments, left_on='building_id', right_on='building_id')\nanswer = t1[t1['building_short_name'] == 'Columbus Square']['room_count'].sum()", false], ["t1 = pd.merge(Apartment_Buildings, Apartments, on='building_id')\nt2 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t2['room_count'].sum()", false], ["t1 = pd.merge(Apartment_Buildings, Apartments, on='building_id')\nt2 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t2['room_count'].sum()", false], ["t1 = pd.merge(Apartment_Buildings, Apartments, left_on='building_id', right_on='building_id')\nt2 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t2['room_count'].sum()", false], ["t1 = pd.merge(Apartment_Buildings, Apartments, left_on='building_id', right_on='building_id')\nt2 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t2['room_count'].sum()", false], ["t1 = pd.merge(Apartments, Apartment_Buildings, left_on='building_id', right_on='building_id')\nt2 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t2['room_count'].sum()", false], ["t1 = pd.merge(Apartment_Buildings, Apartments, left_on='building_id', right_on='building_id')\nt2 = t1[t1['building_short_name'] == 'Columbus Square']\nanswer = t2['room_count'].sum()", false]]}
+{"example": {"db_id": "student_assessment", "query": "SELECT T2.student_id FROM courses AS T1 JOIN student_course_attendance AS T2 ON T1.course_id = T2.course_id WHERE T1.course_name = \"statistics\" ORDER BY T2.date_of_attendance", "query_toks": ["SELECT", "T2.student_id", "FROM", "courses", "AS", "T1", "JOIN", "student_course_attendance", "AS", "T2", "ON", "T1.course_id", "=", "T2.course_id", "WHERE", "T1.course_name", "=", "``", "statistics", "''", "ORDER", "BY", "T2.date_of_attendance"], "query_toks_no_value": ["select", "t2", ".", "student_id", "from", "courses", "as", "t1", "join", "student_course_attendance", "as", "t2", "on", "t1", ".", "course_id", "=", "t2", ".", "course_id", "where", "t1", ".", "course_name", "=", "value", "order", "by", "t2", ".", "date_of_attendance"], "question": "List the id of students who attended  statistics courses in the order of attendance date.", "question_toks": ["List", "the", "id", "of", "students", "who", "attended", "statistics", "courses", "in", "the", "order", "of", "attendance", "date", "."], "sql": {"from": {"table_units": [["table_unit", 3], ["table_unit", 6]], "conds": [[false, 2, [0, [0, 18, false], null], [0, 31, false], null]]}, "select": [false, [[0, [0, [0, 30, false], null]]]], "where": [[false, 2, [0, [0, 19, false], null], "\"statistics\"", null]], "groupBy": [], "having": [], "orderBy": ["asc", [[0, [0, 32, false], null]]], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Addresses": ["address_id", "line_1", "line_2", "city", "zip_postcode", "state_province_county", "country"], "People": ["person_id", "first_name", "middle_name", "last_name", "cell_mobile_number", "email_address", "login_name", "password"], "Students": ["student_id", "student_details"], "Courses": ["course_id", "course_name", "course_description", "other_details"], "People_Addresses": ["person_address_id", "person_id", "address_id", "date_from", "date_to"], "Student_Course_Registrations": ["student_id", "course_id", "registration_date"], "Student_Course_Attendance": ["student_id", "course_id", "date_of_attendance"], "Candidates": ["candidate_id", "candidate_details"], "Candidate_Assessments": ["candidate_id", "qualification", "assessment_date", "asessment_outcome_code"]}, "answer": [[111], [121], [141], [171]]}, "program_result_list": [["t1 = pd.merge(Courses, Student_Course_Attendance, on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values('date_of_attendance')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Attendance, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values(by='date_of_attendance')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Attendance, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values(by='date_of_attendance')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Attendance, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics'].sort_values('date_of_attendance')\nanswer = t2['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Attendance, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values(by='date_of_attendance')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Attendance, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values(by='date_of_attendance')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Attendance, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values(by='date_of_attendance')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Attendance, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2[['student_id']].sort_values(by='date_of_attendance')", false], ["t1 = pd.merge(Courses, Student_Course_Attendance, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values(by='date_of_attendance')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Attendance, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values(by='date_of_attendance')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Attendance, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2[['student_id']].sort_values(by='date_of_attendance')", false], ["t1 = pd.merge(Courses, Student_Course_Attendance, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics'].sort_values(by='date_of_attendance')\nanswer = t2['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Attendance, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values(by='date_of_attendance', ascending=False)['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Attendance, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nt3 = t2.sort_values('date_of_attendance')\nanswer = t3['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Attendance, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values('date_of_attendance')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Attendance, on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2.sort_values(by='date_of_attendance')['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Attendance, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics'].sort_values(by='date_of_attendance')\nanswer = t2['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Attendance, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics'].sort_values(by='date_of_attendance')\nanswer = t2['student_id']", false], ["t1 = pd.merge(Courses[Courses['course_name'] == 'statistics'], Student_Course_Attendance, left_on='course_id', right_on='course_id')\nt2 = t1.sort_values('date_of_attendance')\nanswer = t2['student_id']", false], ["t1 = pd.merge(Courses, Student_Course_Attendance, left_on='course_id', right_on='course_id')\nt2 = t1[t1['course_name'] == 'statistics']\nanswer = t2[['student_id']].sort_values('date_of_attendance')", false]]}
+{"example": {"db_id": "dorm_1", "query": "SELECT count(*) ,  avg(age) ,  city_code FROM student GROUP BY city_code", "query_toks": ["SELECT", "count", "(", "*", ")", ",", "avg", "(", "age", ")", ",", "city_code", "FROM", "student", "GROUP", "BY", "city_code"], "query_toks_no_value": ["select", "count", "(", "*", ")", ",", "avg", "(", "age", ")", ",", "city_code", "from", "student", "group", "by", "city_code"], "question": "How many students live in each city and what are their average ages?", "question_toks": ["How", "many", "students", "live", "in", "each", "city", "and", "what", "are", "their", "average", "ages", "?"], "sql": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[3, [0, [0, 0, false], null]], [5, [0, [0, 4, false], null]], [0, [0, [0, 8, false], null]]]], "where": [], "groupBy": [[0, 8, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Student": ["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], "Dorm": ["dormid", "dorm_name", "student_capacity", "gender"], "Dorm_amenity": ["amenid", "amenity_name"], "Has_amenity": ["dormid", "amenid"], "Lives_in": ["stuid", "dormid", "room_number"]}, "answer": [[1, 20.0, "ATL"], [4, 18.5, "BAL"], [1, 18.0, "BOS"], [1, 20.0, "CHI"], [1, 18.0, "DAL"], [1, 17.0, "DET"], [3, 18.0, "HKG"], [1, 17.0, "HOU"], [1, 27.0, "LON"], [1, 18.0, "LOS"], [1, 18.0, "NAR"], [3, 20.333333333333332, "NYC"], [1, 17.0, "PEK"], [3, 19.666666666666668, "PHL"], [4, 19.0, "PIT"], [1, 20.0, "ROC"], [1, 21.0, "SFO"], [3, 21.666666666666668, "WAS"], [2, 22.5, "YYZ"]]}, "program_result_list": [["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('city_code')['age'].agg(['count', 'mean'])\nanswer = t2.rename(columns={'count': 'student_count', 'mean': 'average_age'})", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('city_code').size().rename('count')\nt3 = t1.groupby('city_code')['age'].mean().rename('avg')\nanswer = pd.merge(t2, t3, left_on='city_code', right_on='city_code').reset_index()", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('city_code').agg({'age': np.mean, 'stuid': np.size}).rename(columns={'age': 'average_age', 'stuid': 'count'})\nanswer = t2.reset_index()", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('city_code').agg({'age': [np.mean, 'count']})\nanswer = t2.reset_index()", false], ["t1 = pd.merge(Student, Lives_in, on='stuid')\nt2 = t1.groupby('city_code').agg({'age': np.mean}).rename(columns={'age':'avg_age'})\nt3 = t1.groupby('city_code').size().rename('count')\nanswer = pd.concat([t2, t3], axis=1)", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('city_code').agg({'age': ['mean', 'count']})\nanswer = t2", false], ["t1 = pd.merge(Lives_in, Student, left_on='stuid', right_on='stuid')\nt2 = t1.groupby(['city_code']).agg({'stuid': 'count', 'age': 'mean'}).rename(columns={'stuid': 'count', 'age': 'avg_age'})\nanswer = t2.reset_index()", false], ["t1 = Student.groupby('city_code').agg({'age': 'mean'}).rename(columns={'age': 'avg_age'})\nt2 = Student.groupby('city_code').size().rename('count')\nt3 = pd.merge(t1, t2, left_index=True, right_index=True)\nanswer = t3.reset_index()", false], ["t1 = Student.groupby('city_code').agg({'age': np.mean, 'city_code': 'count'}).rename(columns={'age': 'average_age', 'city_code': 'student_count'})\nanswer = t1.reset_index()", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nanswer = t1.groupby('city_code').agg({'age': 'mean', 'stuid': 'count'}).rename(columns={'age': 'average_age', 'stuid': 'count'})", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('city_code').agg({'age':'mean'}).rename(columns={'age':'mean_age'})\nt3 = t1.groupby('city_code').size().rename('count')\nanswer = pd.concat([t2, t3], axis=1)", false], ["t1 = Student.groupby('city_code').agg({'age': np.mean, 'stuid': np.size}).rename(columns={'stuid':'count', 'age':'avg_age'})\nanswer = t1.reset_index()", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('city_code').agg({'age': 'mean', 'stuid': 'count'})\nanswer = t2.rename(columns={'age': 'avg_age', 'stuid': 'count'})", false], ["t1 = pd.merge(Student, Lives_in, on='stuid')\nt2 = t1.groupby('city_code').agg({'stuid':'count', 'age':'mean'}).rename(columns={'stuid':'count', 'age':'average_age'})\nanswer = t2.reset_index()", false], ["answer = Student.groupby('city_code').agg({'age': 'mean', 'city_code': 'count'})", false], ["answer = pd.merge(Student, Lives_in, on='stuid')[['age', 'city_code']].groupby(['city_code']).agg({'age': ['count', 'mean']})", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('city_code').agg({'age':'mean', 'stuid':'count'}).rename(columns={'age':'avg_age'})\nanswer = t2.reset_index()", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('city_code').agg({'age': ['mean', 'count']})\nanswer = t2", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('city_code').agg({'age': 'mean'})\nt3 = t1.groupby('city_code').size().rename('count')\nanswer = pd.merge(t2, t3, on='city_code')", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('city_code').agg({'age': 'mean'}).rename({'age': 'average_age'}, axis=1)\nt3 = t1.groupby('city_code').size().rename('count')\nanswer = pd.concat([t3, t2], axis=1)", false]]}
+{"example": {"db_id": "manufacturer", "query": "SELECT sum(num_of_component) FROM furniture", "query_toks": ["SELECT", "sum", "(", "num_of_component", ")", "FROM", "furniture"], "query_toks_no_value": ["select", "sum", "(", "num_of_component", ")", "from", "furniture"], "question": "How many furniture components are there in total?", "question_toks": ["How", "many", "furniture", "components", "are", "there", "in", "total", "?"], "sql": {"from": {"table_units": [["table_unit", 1]], "conds": []}, "select": [false, [[4, [0, [0, 8, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"manufacturer": ["Manufacturer_ID", "Open_Year", "Name", "Num_of_Factories", "Num_of_Shops"], "furniture": ["Furniture_ID", "Name", "Num_of_Component", "Market_Rate"], "furniture_manufacte": ["Manufacturer_ID", "Furniture_ID", "Price_in_Dollar"]}, "answer": [[49]], "pandas_converted": "pd.Series(furniture['num_of_component'].sum())"}, "program_result_list": [["answer = furniture['num_of_component'].sum()", false], ["answer = furniture['num_of_component'].sum()", false], ["answer = furniture['num_of_component'].sum()", false], ["answer = furniture['num_of_component'].sum()", false], ["answer = furniture['num_of_component'].sum()", false], ["answer = furniture['num_of_component'].sum()", false], ["answer = furniture['num_of_component'].sum()", false], ["answer = furniture['num_of_component'].sum()", false], ["answer = furniture['num_of_component'].sum()", false], ["answer = furniture['num_of_component'].sum()", false], ["answer = furniture['num_of_component'].sum()", false], ["answer = furniture['num_of_component'].sum()", false], ["answer = furniture['num_of_component'].sum()", false], ["answer = furniture['num_of_component'].sum()", false], ["answer = furniture['num_of_component'].sum()", false], ["answer = furniture['num_of_component'].sum()", false], ["answer = furniture['num_of_component'].sum()", false], ["t1 = furniture['num_of_component'].sum()\nanswer = t1", false], ["answer = furniture['num_of_component'].sum()", false], ["t1 = furniture['num_of_component'].sum()\nanswer = t1", false]]}
+{"example": {"db_id": "candidate_poll", "query": "SELECT t1.name ,  t1.sex ,  min(oppose_rate) FROM people AS t1 JOIN candidate AS t2 ON t1.people_id  =  t2.people_id GROUP BY t1.sex", "query_toks": ["SELECT", "t1.name", ",", "t1.sex", ",", "min", "(", "oppose_rate", ")", "FROM", "people", "AS", "t1", "JOIN", "candidate", "AS", "t2", "ON", "t1.people_id", "=", "t2.people_id", "GROUP", "BY", "t1.sex"], "query_toks_no_value": ["select", "t1", ".", "name", ",", "t1", ".", "sex", ",", "min", "(", "oppose_rate", ")", "from", "people", "as", "t1", "join", "candidate", "as", "t2", "on", "t1", ".", "people_id", "=", "t2", ".", "people_id", "group", "by", "t1", ".", "sex"], "question": "Find the name of the candidates whose oppose percentage is the lowest for each sex.", "question_toks": ["Find", "the", "name", "of", "the", "candidates", "whose", "oppose", "percentage", "is", "the", "lowest", "for", "each", "sex", "."], "sql": {"from": {"table_units": [["table_unit", 1], ["table_unit", 0]], "conds": [[false, 2, [0, [0, 9, false], null], [0, 2, false], null]]}, "select": [false, [[0, [0, [0, 11, false], null]], [0, [0, [0, 10, false], null]], [2, [0, [0, 7, false], null]]]], "where": [], "groupBy": [[0, 10, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"candidate": ["Candidate_ID", "People_ID", "Poll_Source", "Date", "Support_rate", "Consider_rate", "Oppose_rate", "Unsure_rate"], "people": ["People_ID", "Sex", "Name", "Date_of_Birth", "Height", "Weight"]}, "answer": [["St\u00e9phane Antiga", "F", 0.32], ["Lo\u00efc De Kergret", "M", 0.32]]}, "program_result_list": [["t1 = pd.merge(people, candidate, left_on='people_id', right_on='people_id')\nt2 = t1.groupby(['name', 'sex']).agg({'oppose_rate': 'min'})\nanswer = t2.reset_index()", false], ["t1 = pd.merge(people, candidate, on='people_id')\nt2 = t1.groupby(['name', 'sex'])['oppose_rate'].min().to_frame().reset_index()\nanswer = t2.sort_values(by=['sex','oppose_rate'], ascending=False)", false], ["t1 = pd.merge(people, candidate, left_on='people_id', right_on='people_id')\nt2 = t1.groupby(['name', 'sex']).agg({'oppose_rate': 'min'})\nt3 = t2.groupby('sex').first()\nanswer = t3.reset_index()", false], ["t1 = pd.merge(people, candidate, left_on='people_id', right_on='people_id')\nt2 = t1.groupby('sex').agg(min_oppose_rate=('oppose_rate', 'min'))\nt3 = pd.merge(t1, t2, on='sex')\nanswer = t3[t3['oppose_rate'] == t3['min_oppose_rate']][['name', 'sex', 'min_oppose_rate']]", false], ["t1 = pd.merge(people, candidate, left_on='people_id', right_on='people_id')\nt2 = t1.groupby(['name', 'sex'])['oppose_rate'].min().rename('min_oppose_rate')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(people, candidate, left_on='people_id', right_on='people_id')\nt2 = t1.groupby(['name', 'sex'])['oppose_rate'].min().rename('min_oppose_rate')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(people, candidate, left_on='people_id', right_on='people_id')\nt2 = t1.groupby(['name', 'sex'])['oppose_rate'].min().rename('min_oppose_rate').to_frame().reset_index()\nanswer = t2", false], ["t1 = pd.merge(people, candidate, left_on='people_id', right_on='people_id')\nt2 = t1.groupby(['name', 'sex'])['oppose_rate'].min()\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(people, candidate, left_on='people_id', right_on='people_id')\nt2 = t1.groupby('sex')['oppose_rate'].min().rename('min_oppose_rate')\nt3 = pd.merge(t1, t2.to_frame().reset_index(), on=['sex', 'oppose_rate'])\nanswer = t3[['name', 'sex']]", false], ["t1 = pd.merge(people, candidate, on='people_id')\nt2 = t1.groupby(['name', 'sex']).agg({'oppose_rate':'min'})\nanswer = t2.reset_index()", false], ["t1 = pd.merge(people, candidate, left_on='people_id', right_on='people_id')\nt2 = t1.groupby(['name', 'sex']).agg({'oppose_rate':'min'}).reset_index()\nanswer = t2[['name', 'sex', 'oppose_rate']]", false], ["t1 = pd.merge(people, candidate, left_on='people_id', right_on='people_id')\nt2 = t1.groupby(['name', 'sex']).min()['oppose_rate'].rename('min_oppose').to_frame().reset_index()\nanswer = t2.sort_values(['sex', 'min_oppose'])[['name', 'sex']]", false], ["t1 = pd.merge(people, candidate, left_on='people_id', right_on='people_id')\nt2 = t1.groupby(['name', 'sex']).min().reset_index()\nanswer = t2[['name', 'sex', 'oppose_rate']]", false], ["t1 = pd.merge(people, candidate, left_on='people_id', right_on='people_id')\nt2 = t1.groupby(['name', 'sex']).agg({'oppose_rate': 'min'})\nanswer = t2.reset_index()", false], ["t1 = pd.merge(people, candidate, left_on='people_id', right_on='people_id')\nt2 = t1.groupby('sex').agg({'name': 'min', 'oppose_rate': 'min'})\nanswer = t2.reset_index()", false], ["t1 = pd.merge(people, candidate, left_on='people_id', right_on='people_id')\nt2 = t1.groupby(['name', 'sex'])['oppose_rate'].min().reset_index()\nanswer = t2[['name', 'sex']]", false], ["t1 = pd.merge(people, candidate, left_on='people_id', right_on='people_id')\nt2 = t1.groupby('sex')['oppose_rate'].min().rename('min_oppose_rate')\nt3 = pd.merge(t1, t2.to_frame(), left_on=['sex', 'oppose_rate'], right_on=['sex', 'min_oppose_rate'])\nanswer = t3[['name', 'sex']]", false], ["t1 = pd.merge(people, candidate, left_on='people_id', right_on='people_id')\nt2 = t1.groupby(['name', 'sex']).agg({'oppose_rate': np.min}).reset_index()\nanswer = t2[['name', 'sex', 'oppose_rate']]", false], ["t1 = pd.merge(people, candidate, left_on='people_id', right_on='people_id')\nt2 = t1.groupby(['name', 'sex']).apply(lambda x: x['oppose_rate'].min()).to_frame().reset_index()\nanswer = t2.sort_values('name')", false], ["t1 = pd.merge(people, candidate, left_on='people_id', right_on='people_id')\nt2 = t1.groupby(['sex', 'name'])['oppose_rate'].min()\nanswer = t2.to_frame().reset_index()", false]]}
+{"example": {"db_id": "hospital_1", "query": "SELECT count(*) ,  T1.blockcode FROM BLOCK AS T1 JOIN room AS T2 ON T1.blockfloor  =  T2.blockfloor AND T1.blockcode  =  T2.blockcode GROUP BY T1.blockcode", "query_toks": ["SELECT", "count", "(", "*", ")", ",", "T1.blockcode", "FROM", "BLOCK", "AS", "T1", "JOIN", "room", "AS", "T2", "ON", "T1.blockfloor", "=", "T2.blockfloor", "AND", "T1.blockcode", "=", "T2.blockcode", "GROUP", "BY", "T1.blockcode"], "query_toks_no_value": ["select", "count", "(", "*", ")", ",", "t1", ".", "blockcode", "from", "block", "as", "t1", "join", "room", "as", "t2", "on", "t1", ".", "blockfloor", "=", "t2", ".", "blockfloor", "and", "t1", ".", "blockcode", "=", "t2", ".", "blockcode", "group", "by", "t1", ".", "blockcode"], "question": "How many rooms are located for each block code?", "question_toks": ["How", "many", "rooms", "are", "located", "for", "each", "block", "code", "?"], "sql": {"from": {"table_units": [["table_unit", 10], ["table_unit", 11]], "conds": [[false, 2, [0, [0, 46, false], null], [0, 50, false], null], "and", [false, 2, [0, [0, 47, false], null], [0, 51, false], null]]}, "select": [false, [[3, [0, [0, 0, false], null]], [0, [0, [0, 47, false], null]]]], "where": [], "groupBy": [[0, 47, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Physician": ["EmployeeID", "Name", "Position", "SSN"], "Department": ["DepartmentID", "Name", "Head"], "Affiliated_With": ["Physician", "Department", "PrimaryAffiliation"], "Procedures": ["Code", "Name", "Cost"], "Trained_In": ["Physician", "Treatment", "CertificationDate", "CertificationExpires"], "Patient": ["SSN", "Name", "Address", "Phone", "InsuranceID", "PCP"], "Nurse": ["EmployeeID", "Name", "Position", "Registered", "SSN"], "Appointment": ["AppointmentID", "Patient", "PrepNurse", "Physician", "Start", "End", "ExaminationRoom"], "Medication": ["Code", "Name", "Brand", "Description"], "Prescribes": ["Physician", "Patient", "Medication", "Date", "Appointment", "Dose"], "Block": ["BlockFloor", "BlockCode"], "Room": ["RoomNumber", "RoomType", "BlockFloor", "BlockCode", "Unavailable"], "On_Call": ["Nurse", "BlockFloor", "BlockCode", "OnCallStart", "OnCallEnd"], "Stay": ["StayID", "Patient", "Room", "StayStart", "StayEnd"], "Undergoes": ["Patient", "Procedures", "Stay", "DateUndergoes", "Physician", "AssistingNurse"]}, "answer": [[12, 1], [12, 2], [12, 3]]}, "program_result_list": [["t1 = pd.merge(Block, Room, left_on=['blockfloor', 'blockcode'], right_on=['blockfloor', 'blockcode'])\nt2 = t1.groupby('blockcode').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Block, Room, left_on=['blockfloor', 'blockcode'], right_on=['blockfloor', 'blockcode'])\nanswer = t1.groupby('blockcode').size().rename('count').to_frame().reset_index()", false], ["t1 = pd.merge(Block, Room, left_on=['blockfloor', 'blockcode'], right_on=['blockfloor', 'blockcode'])\nt2 = t1.groupby(['blockcode']).size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Block, Room, left_on=['blockfloor', 'blockcode'], right_on=['blockfloor', 'blockcode'])\nanswer = t1.groupby('blockcode').size().rename('count').to_frame().reset_index()", false], ["t1 = pd.merge(Block, Room, left_on=['blockfloor', 'blockcode'], right_on=['blockfloor', 'blockcode'])\nt2 = t1.groupby('blockcode').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Block, Room, left_on=['blockfloor', 'blockcode'], right_on=['blockfloor', 'blockcode'])\nt2 = t1.groupby('blockcode').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Block, Room, left_on=['blockfloor', 'blockcode'], right_on=['blockfloor', 'blockcode'])\nt2 = t1.groupby('blockcode').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Block, Room, left_on=['blockfloor', 'blockcode'], right_on=['blockfloor', 'blockcode'])\nanswer = t1.groupby('blockcode').size().rename('count')", false], ["t1 = pd.merge(Block, Room, left_on=['blockfloor', 'blockcode'], right_on=['blockfloor', 'blockcode'])\nanswer = t1.groupby('blockcode').size().rename('count').to_frame().reset_index()", false], ["t1 = pd.merge(block, room, left_on=['blockfloor', 'blockcode'], right_on=['blockfloor', 'blockcode'])\nanswer = t1.groupby('blockcode').size().rename('count').to_frame().reset_index()", false], ["t1 = pd.merge(Block, Room, left_on=['blockfloor', 'blockcode'], right_on=['blockfloor', 'blockcode'])\nanswer = t1.groupby('blockcode').size().rename('count').to_frame().reset_index()", false], ["t1 = pd.merge(Block, Room, left_on=['blockfloor', 'blockcode'], right_on=['blockfloor', 'blockcode'])\nt2 = t1.groupby('blockcode').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Block, Room, left_on=['blockfloor', 'blockcode'], right_on=['blockfloor', 'blockcode'])\nt2 = t1.groupby('blockcode').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Block, Room, left_on=['blockfloor', 'blockcode'], right_on=['blockfloor', 'blockcode'])\nt2 = t1.groupby('blockcode').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Block, Room, left_on=['blockfloor', 'blockcode'], right_on=['blockfloor', 'blockcode'])\nanswer = t1.groupby('blockcode').size().rename('count').to_frame().reset_index()", false], ["t1 = pd.merge(Block, Room, on=['blockfloor', 'blockcode'])\nt2 = t1.groupby('blockcode').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Block, Room, left_on=['blockfloor','blockcode'], right_on=['blockfloor','blockcode'])\nt2 = t1.groupby('blockcode').size().rename('count').to_frame().reset_index()\nanswer = t2", false], ["t1 = pd.merge(Block, Room, left_on=['blockfloor', 'blockcode'], right_on=['blockfloor', 'blockcode'])\nt2 = t1.groupby('blockcode').size().rename('count').to_frame().reset_index()\nanswer = t2", false], ["t1 = pd.merge(Block, Room, left_on=['blockfloor', 'blockcode'], right_on=['blockfloor', 'blockcode'])\nt2 = t1.groupby('blockcode').size().rename('count').to_frame().reset_index()\nanswer = t2", false], ["t1 = pd.merge(Block, Room, left_on=['blockfloor', 'blockcode'], right_on=['blockfloor', 'blockcode'])\nt2 = t1.groupby('blockcode').size().rename('count').to_frame().reset_index()\nanswer = t2", false]]}
+{"example": {"db_id": "store_1", "query": "SELECT billing_country ,  AVG(total) FROM invoices GROUP BY billing_country ORDER BY AVG(total) DESC LIMIT 10;", "query_toks": ["SELECT", "billing_country", ",", "AVG", "(", "total", ")", "FROM", "invoices", "GROUP", "BY", "billing_country", "ORDER", "BY", "AVG", "(", "total", ")", "DESC", "LIMIT", "10", ";"], "query_toks_no_value": ["select", "billing_country", ",", "avg", "(", "total", ")", "from", "invoices", "group", "by", "billing_country", "order", "by", "avg", "(", "total", ")", "desc", "limit", "value"], "question": "What are the names of the countries and average invoice size of the top countries by size?", "question_toks": ["What", "are", "the", "names", "of", "the", "countries", "and", "average", "invoice", "size", "of", "the", "top", "countries", "by", "size", "?"], "sql": {"from": {"table_units": [["table_unit", 6]], "conds": []}, "select": [false, [[0, [0, [0, 44, false], null]], [5, [0, [0, 46, false], null]]]], "where": [], "groupBy": [[0, 44, false]], "having": [], "orderBy": ["desc", [[0, [5, 46, false], null]]], "limit": 10, "intersect": null, "union": null, "except": null}, "db_table_headers": {"artists": ["id", "name"], "sqlite_sequence": ["name", "seq"], "albums": ["id", "title", "artist_id"], "employees": ["id", "last_name", "first_name", "title", "reports_to", "birth_date", "hire_date", "address", "city", "state", "country", "postal_code", "phone", "fax", "email"], "customers": ["id", "first_name", "last_name", "company", "address", "city", "state", "country", "postal_code", "phone", "fax", "email", "support_rep_id"], "genres": ["id", "name"], "invoices": ["id", "customer_id", "invoice_date", "billing_address", "billing_city", "billing_state", "billing_country", "billing_postal_code", "total"], "media_types": ["id", "name"], "tracks": ["id", "name", "album_id", "media_type_id", "genre_id", "composer", "milliseconds", "bytes", "unit_price"], "invoice_lines": ["id", "invoice_id", "track_id", "unit_price", "quantity"], "playlists": ["id", "name"], "playlist_tracks": ["playlist_id", "track_id"]}, "answer": [["Chile", 6.659999999999999], ["Ireland", 6.517142857142857], ["Hungary", 6.517142857142857], ["Czech Republic", 6.445714285714287], ["Austria", 6.088571428571428], ["Finland", 5.945714285714287], ["Netherlands", 5.8028571428571425], ["India", 5.789230769230769], ["USA", 5.747912087912091], ["Norway", 5.659999999999999]]}, "program_result_list": [["t1 = invoices.groupby('billing_country').size().rename('count')\nt2 = invoices.groupby('billing_country').mean()['total'].rename('avg')\nt3 = pd.merge(t1, t2, left_index=True, right_index=True)\nt4 = t3.sort_values(by='total', ascending=False).head(10).reset_index()\nanswer = t4[['billing_country', 'avg']]", false], ["t1 = invoices.groupby('billing_country').mean()['total'].rename('avg_total')\nanswer = t1.sort_values(ascending=False).head(10).to_frame().reset_index()", false], ["t1 = invoices.groupby('billing_country')['total'].mean().rename('avg').head(10)\nanswer = t1.to_frame().reset_index()", false], ["t1 = invoices.groupby('billing_country').agg({'total': 'mean'}).rename(columns={'total': 'avg'}).sort_values(by='avg', ascending=False)\nanswer = t1.head(10).reset_index()", false], ["t1 = invoices.groupby('billing_country').agg({'total':'mean'}).sort_values('total', ascending=False).head(10)\nanswer = t1.reset_index()", false], ["t1 = invoices.groupby('billing_country')['total'].mean().rename('avg').sort_values(ascending=False).head(10).to_frame().reset_index()\nanswer = t1[['billing_country', 'avg']]", false], ["t1 = invoices.groupby('billing_country').mean()['total'].rename('avg').to_frame()\nt2 = t1.sort_values(by='avg', ascending=False).head(10).reset_index()\nanswer = t2[['billing_country', 'avg']]", false], ["t1 = invoices.groupby('billing_country').mean().rename(columns={'total':'avg_total'}).reset_index()\nt2 = t1.sort_values(by='avg_total', ascending=False).head(10).reset_index()\nanswer = t2[['billing_country', 'avg_total']]", false], ["t1 = invoices.groupby('billing_country').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(10).to_frame().reset_index()\nt3 = pd.merge(invoices, t2, on='billing_country')\nt4 = t3.groupby('billing_country').agg({'total': 'mean'})\nanswer = t4.reset_index()", false], ["t1 = invoices.groupby('billing_country').agg({'total': 'mean'}).rename(columns={'total': 'avg_total'}).sort_values('avg_total', ascending=False).head(10)\nanswer = t1.reset_index()", false], ["t1 = invoices.groupby('billing_country').mean().sort_values('total', ascending=False).head(10).reset_index()\nanswer = t1[['billing_country', 'total']]", false], ["t1 = invoices.groupby('billing_country').mean().rename(columns={'total': 'avg'})\nt2 = t1.sort_values(by=['avg'], ascending=False).head(10)\nanswer = t2.reset_index()[['billing_country', 'avg']]", false], ["t1 = invoices.groupby('billing_country').mean()\nt2 = t1.sort_values('total', ascending=False).head(10)\nanswer = t2[['total']]", false], ["t1 = invoices.groupby('billing_country').mean()['total'].rename('avg')\nt2 = t1.sort_values(ascending=False).head(10).to_frame().reset_index()\nanswer = t2[['billing_country', 'avg']]", false], ["t1 = invoices.groupby('billing_country').mean().rename(columns={'total': 'avg_total'})\nt2 = t1.sort_values('avg_total', ascending=False).head(10).reset_index()\nanswer = t2[['billing_country', 'avg_total']]", false], ["# Top countries by size:\n# 1. United States\n# 2. Canada\n# 3. Brazil\n# 4. France\n# 5. Germany\n# 6. United Kingdom\n# 7. Poland\n# 8. Portugal\n# 9. Italy\n# 10. Japan\nt1 = invoices.groupby('billing_country').mean().reset_index()\nt2 = t1.sort_values('total', ascending=False).head(10)\nanswer = t2[['billing_country', 'total']]", false], ["top10 = invoices.groupby('billing_country').agg({'total': np.mean}).sort_values('total', ascending=False).head(10)\nanswer = top10.reset_index()", false], ["t1 = invoices.groupby('billing_country').total.mean().rename('avg')\nt2 = t1.sort_values(ascending=False).head(10).to_frame().reset_index()\nanswer = t2.loc[:, ['billing_country', 'avg']]", false], ["t1 = invoices.groupby('billing_country')['total'].mean().rename('average_invoice_size')\nanswer = t1.sort_values(ascending=False).head(10).to_frame().reset_index()", false], ["t1 = invoices.groupby('billing_country').mean().rename(columns={'total': 'avg'}).sort_values(by='avg', ascending=False).head(10)\nanswer = t1.reset_index()", false]]}
+{"example": {"db_id": "tracking_software_problems", "query": "SELECT product_id FROM problems AS T1 JOIN staff AS T2 ON T1.reported_by_staff_id = T2.staff_id WHERE T2.staff_first_name = \"Dameon\" AND T2.staff_last_name = \"Frami\" UNION SELECT product_id FROM problems AS T1 JOIN staff AS T2 ON T1.reported_by_staff_id = T2.staff_id WHERE T2.staff_first_name = \"Jolie\" AND T2.staff_last_name = \"Weber\"", "query_toks": ["SELECT", "product_id", "FROM", "problems", "AS", "T1", "JOIN", "staff", "AS", "T2", "ON", "T1.reported_by_staff_id", "=", "T2.staff_id", "WHERE", "T2.staff_first_name", "=", "``", "Dameon", "''", "AND", "T2.staff_last_name", "=", "``", "Frami", "''", "UNION", "SELECT", "product_id", "FROM", "problems", "AS", "T1", "JOIN", "staff", "AS", "T2", "ON", "T1.reported_by_staff_id", "=", "T2.staff_id", "WHERE", "T2.staff_first_name", "=", "``", "Jolie", "''", "AND", "T2.staff_last_name", "=", "``", "Weber", "''"], "query_toks_no_value": ["select", "product_id", "from", "problems", "as", "t1", "join", "staff", "as", "t2", "on", "t1", ".", "reported_by_staff_id", "=", "t2", ".", "staff_id", "where", "t2", ".", "staff_first_name", "=", "value", "and", "t2", ".", "staff_last_name", "=", "value", "union", "select", "product_id", "from", "problems", "as", "t1", "join", "staff", "as", "t2", "on", "t1", ".", "reported_by_staff_id", "=", "t2", ".", "staff_id", "where", "t2", ".", "staff_first_name", "=", "value", "and", "t2", ".", "staff_last_name", "=", "value"], "question": "Which problems were reported by the staff named Dameon Frami or Jolie Weber? Give me the ids of the problems.", "question_toks": ["Which", "problems", "were", "reported", "by", "the", "staff", "named", "Dameon", "Frami", "or", "Jolie", "Weber", "?", "Give", "me", "the", "ids", "of", "the", "problems", "."], "sql": {"from": {"table_units": [["table_unit", 5], ["table_unit", 4]], "conds": [[false, 2, [0, [0, 24, false], null], [0, 17, false], null]]}, "select": [false, [[0, [0, [0, 22, false], null]]]], "where": [[false, 2, [0, [0, 18, false], null], "\"Dameon\"", null], "and", [false, 2, [0, [0, 19, false], null], "\"Frami\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": {"from": {"table_units": [["table_unit", 5], ["table_unit", 4]], "conds": [[false, 2, [0, [0, 24, false], null], [0, 17, false], null]]}, "select": [false, [[0, [0, [0, 22, false], null]]]], "where": [[false, 2, [0, [0, 18, false], null], "\"Jolie\"", null], "and", [false, 2, [0, [0, 19, false], null], "\"Weber\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "except": null}, "db_table_headers": {"Problem_Category_Codes": ["problem_category_code", "problem_category_description"], "Problem_Log": ["problem_log_id", "assigned_to_staff_id", "problem_id", "problem_category_code", "problem_status_code", "log_entry_date", "log_entry_description", "log_entry_fix", "other_log_details"], "Problem_Status_Codes": ["problem_status_code", "problem_status_description"], "Product": ["product_id", "product_name", "product_details"], "Staff": ["staff_id", "staff_first_name", "staff_last_name", "other_staff_details"], "Problems": ["problem_id", "product_id", "closure_authorised_by_staff_id", "reported_by_staff_id", "date_problem_reported", "date_problem_closed", "problem_description", "other_problem_details"]}, "answer": [[1], [4], [6]]}, "program_result_list": [["t1 = pd.merge(Problems, Staff, left_on='reported_by_staff_id', right_on='staff_id')\nt2 = t1[(t1['staff_first_name'] == 'Dameon') & (t1['staff_last_name'] == 'Frami')]\nt3 = t1[(t1['staff_first_name'] == 'Jolie') & (t1['staff_last_name'] == 'Weber')]\nt4 = pd.concat([t2, t3])\nanswer = t4['product_id']", false], ["t1 = pd.merge(Problems, Staff, left_on='reported_by_staff_id', right_on='staff_id')\nt2 = t1[(t1['staff_first_name'] == 'Dameon') & (t1['staff_last_name'] == 'Frami')]\nt3 = t1[(t1['staff_first_name'] == 'Jolie') & (t1['staff_last_name'] == 'Weber')]\nt4 = t2['product_id'].append(t3['product_id'])\nanswer = t4.unique()", false], ["t1 = pd.merge(Problems, Staff, left_on='reported_by_staff_id', right_on='staff_id')\nt2 = t1[((t1['staff_first_name'] == 'Dameon') & (t1['staff_last_name'] == 'Frami')) | ((t1['staff_first_name'] == 'Jolie') & (t1['staff_last_name'] == 'Weber'))]\nanswer = t2['product_id']", false], ["t1 = pd.merge(Problems, Staff, on='reported_by_staff_id')\nt2 = t1[(t1['staff_first_name']=='Dameon') & (t1['staff_last_name']=='Frami')]\nt3 = t1[(t1['staff_first_name']=='Jolie') & (t1['staff_last_name']=='Weber')]\nt4 = pd.concat([t2, t3])\nanswer = t4['product_id']", false], ["t1 = pd.merge(Problems, Staff, left_on='reported_by_staff_id', right_on='staff_id')\nt2 = t1[(t1['staff_first_name'] == 'Dameon') & (t1['staff_last_name'] == 'Frami')]\nt3 = t1[(t1['staff_first_name'] == 'Jolie') & (t1['staff_last_name'] == 'Weber')]\nt4 = pd.concat([t2['product_id'], t3['product_id']], axis=0)\nanswer = t4.unique()", false], ["t1 = pd.merge(Problems, Staff, left_on='reported_by_staff_id', right_on='staff_id')\nt2 = t1[(t1['staff_first_name'] == 'Dameon') & (t1['staff_last_name'] == 'Frami')]\nt3 = t1[(t1['staff_first_name'] == 'Jolie') & (t1['staff_last_name'] == 'Weber')]\nt4 = t2.append(t3)\nanswer = t4['product_id']", false], ["t1 = pd.merge(Problems, Staff, left_on='reported_by_staff_id', right_on='staff_id')\nt2 = t1[(t1['staff_first_name'] == 'Dameon') & (t1['staff_last_name'] == 'Frami')]\nt3 = t1[(t1['staff_first_name'] == 'Jolie') & (t1['staff_last_name'] == 'Weber')]\nt4 = pd.concat([t2,t3])\nanswer = t4['product_id']", false], ["t1 = pd.merge(Problems, Staff, left_on='reported_by_staff_id', right_on='staff_id')\nt2 = t1[(t1['staff_first_name'] == 'Dameon') & (t1['staff_last_name'] == 'Frami')]\nt3 = t1[(t1['staff_first_name'] == 'Jolie') & (t1['staff_last_name'] == 'Weber')]\nt4 = pd.concat([t2, t3])\nanswer = t4['product_id'].unique()", false], ["t1 = pd.merge(Problems, Staff, left_on='reported_by_staff_id', right_on='staff_id')\nt2 = t1[(t1['staff_first_name'] == 'Dameon') & (t1['staff_last_name'] == 'Frami')]\nt3 = t1[(t1['staff_first_name'] == 'Jolie') & (t1['staff_last_name'] == 'Weber')]\nanswer = pd.concat([t2['product_id'], t3['product_id']]).drop_duplicates()", false], ["t1 = pd.merge(Problems, Staff, left_on='reported_by_staff_id', right_on='staff_id')\nt2 = t1[t1['staff_first_name'] == 'Dameon']\nt3 = t2[t2['staff_last_name'] == 'Frami']\nt4 = t1[t1['staff_first_name'] == 'Jolie']\nt5 = t4[t4['staff_last_name'] == 'Weber']\nt6 = t3.append(t5)\nanswer = t6['product_id']", false], ["t1 = pd.merge(Problems, Staff, left_on='reported_by_staff_id', right_on='staff_id')\nt2 = t1[t1['staff_first_name'] == 'Dameon']\nt3 = t2[t2['staff_last_name'] == 'Frami']\nt4 = t1[t1['staff_first_name'] == 'Jolie']\nt5 = t4[t4['staff_last_name'] == 'Weber']\nt6 = t3.append(t5)\nanswer = t6['product_id']", false], ["t1 = pd.merge(Problems, Staff, left_on='reported_by_staff_id', right_on='staff_id')\nt2 = t1[(t1['staff_first_name'] == 'Dameon') & (t1['staff_last_name'] == 'Frami')]\nt3 = t1[(t1['staff_first_name'] == 'Jolie') & (t1['staff_last_name'] == 'Weber')]\nt4 = t2[['product_id']].append(t3[['product_id']])\nanswer = t4['product_id'].unique()", false], ["t1 = pd.merge(Problems, Staff, on='reported_by_staff_id')\nt2 = t1[((t1['staff_first_name'] == 'Dameon') & (t1['staff_last_name'] == 'Frami')) | ((t1['staff_first_name'] == 'Jolie') & (t1['staff_last_name'] == 'Weber'))]\nanswer = t2['product_id']", false], ["t1 = pd.merge(Problems, Staff, left_on='reported_by_staff_id', right_on='staff_id')\nt2 = t1[(t1['staff_first_name'] == 'Dameon') & (t1['staff_last_name'] == 'Frami')]\nt3 = t1[(t1['staff_first_name'] == 'Jolie') & (t1['staff_last_name'] == 'Weber')]\nt4 = pd.concat([t2, t3])\nanswer = t4['product_id']", false], ["t1 = pd.merge(Problems, Staff, left_on='reported_by_staff_id', right_on='staff_id')\nt2 = t1[t1['staff_first_name'] == 'Dameon']\nt3 = t2[t2['staff_last_name'] == 'Frami']\nt4 = t1[t1['staff_first_name'] == 'Jolie']\nt5 = t4[t4['staff_last_name'] == 'Weber']\nt6 = pd.concat([t3, t5])\nanswer = t6['product_id'].unique()", false], ["t1 = pd.merge(Problems, Staff, left_on='reported_by_staff_id', right_on='staff_id')\nt2 = t1[(t1['staff_first_name'] == 'Dameon') & (t1['staff_last_name'] == 'Frami')]['product_id']\nt3 = t1[(t1['staff_first_name'] == 'Jolie') & (t1['staff_last_name'] == 'Weber')]['product_id']\nanswer = pd.concat([t2, t3])", false], ["t1 = pd.merge(Problems, Staff, left_on='reported_by_staff_id', right_on='staff_id')\nt2 = t1[(t1['staff_first_name'] == 'Dameon') & (t1['staff_last_name'] == 'Frami')]\nt3 = t1[(t1['staff_first_name'] == 'Jolie') & (t1['staff_last_name'] == 'Weber')]\nanswer = pd.concat([t2, t3])['product_id'].unique()", false], ["t1 = pd.merge(Problems, Staff, left_on='reported_by_staff_id', right_on='staff_id')\nt2 = t1[(t1['staff_first_name'] == 'Dameon') & (t1['staff_last_name'] == 'Frami')]\nt3 = t1[(t1['staff_first_name'] == 'Jolie') & (t1['staff_last_name'] == 'Weber')]\nt4 = t2.append(t3)\nanswer = t4['product_id'].unique()", false], ["t1 = pd.merge(Problems, Staff, left_on='reported_by_staff_id', right_on='staff_id')\nt2 = t1[(t1['staff_first_name'] == 'Dameon') & (t1['staff_last_name'] == 'Frami')]\nt3 = t1[(t1['staff_first_name'] == 'Jolie') & (t1['staff_last_name'] == 'Weber')]\nt4 = t2['product_id'].append(t3['product_id'])\nanswer = t4.unique()", false], ["t1 = pd.merge(Problems, Staff, left_on='reported_by_staff_id', right_on='staff_id')\nt2 = t1[((t1['staff_first_name'] == 'Dameon') & (t1['staff_last_name'] == 'Frami')) | ((t1['staff_first_name'] == 'Jolie') & (t1['staff_last_name'] == 'Weber'))]\nanswer = t2['product_id']", false]]}
+{"example": {"db_id": "flight_1", "query": "SELECT eid FROM Employee EXCEPT SELECT eid FROM Certificate", "query_toks": ["SELECT", "eid", "FROM", "Employee", "EXCEPT", "SELECT", "eid", "FROM", "Certificate"], "query_toks_no_value": ["select", "eid", "from", "employee", "except", "select", "eid", "from", "certificate"], "question": "What are the ids of all employees that don't have certificates?", "question_toks": ["What", "are", "the", "ids", "of", "all", "employees", "that", "do", "n't", "have", "certificates", "?"], "sql": {"from": {"table_units": [["table_unit", 2]], "conds": []}, "select": [false, [[0, [0, [0, 12, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": {"from": {"table_units": [["table_unit", 3]], "conds": []}, "select": [false, [[0, [0, [0, 15, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}}, "db_table_headers": {"flight": ["flno", "origin", "destination", "distance", "departure_date", "arrival_date", "price", "aid"], "aircraft": ["aid", "name", "distance"], "employee": ["eid", "name", "salary"], "certificate": ["eid", "aid"]}, "answer": [[15645489], [248965255], [254099823], [287321212], [310454877], [348121549], [486512566], [489221823], [489456522], [552455348], [619023588]]}, "program_result_list": [["t1 = employee[['eid']]\nt2 = certificate[['eid']]\nanswer = t1[~t1.isin(t2).all(1)]", false], ["answer = employee[~employee['eid'].isin(certificate['eid'])]['eid']", false], ["t1 = employee[['eid']]\nt2 = certificate[['eid']]\nanswer = t1[~t1.isin(t2)].dropna()", false], ["t1 = employee.copy()\nt2 = certificate.copy()\nt3 = pd.merge(t1, t2, on='eid', how='left', indicator=True)\nt4 = t3[t3['_merge'] == 'left_only']\nanswer = t4['eid']", false], ["t1 = employee[['eid']]\nt2 = certificate[['eid']]\nanswer = t1.merge(t2, on='eid', how='left', indicator=True).query('_merge == \"left_only\"').drop('_merge', axis=1)", false], ["t1 = employee[['eid']].drop_duplicates()\nt2 = certificate[['eid']].drop_duplicates()\nanswer = t1[~t1.eid.isin(t2.eid)]", false], ["t1 = employee.merge(certificate, how='left', on='eid', indicator=True)\nanswer = t1.query('_merge == \"left_only\"')['eid']", false], ["t1 = employee[['eid']]\nt2 = certificate[['eid']]\nanswer = t1[~t1.isin(t2)].dropna()", false], ["t1 = employee['eid']\nt2 = certificate['eid']\nt3 = t1[~t1.isin(t2)]\nanswer = t3.to_frame().reset_index(drop=True)", false], ["t1 = employee[['eid']].rename(columns={'eid':'eid1'})\nt2 = certificate[['eid']].rename(columns={'eid':'eid2'})\nanswer = t1[~t1.eid1.isin(t2.eid2)]", false], ["t1 = employee[['eid']]\nt2 = certificate[['eid']]\nanswer = t1[~t1.isin(t2)].dropna()", false], ["answer = pd.concat([employee['eid'], certificate['eid']]).drop_duplicates(keep=False)", false], ["t1 = employee['eid']\nt2 = certificate['eid']\nanswer = t1[~t1.isin(t2)]", false], ["t1 = employee.set_index('eid')\nt2 = certificate.set_index('eid')\nanswer = t1.index.difference(t2.index)", false], ["t1 = employee[['eid']]\nt2 = certificate[['eid']]\nanswer = t1[~t1.isin(t2)].dropna()", false], ["t1 = employee.merge(certificate, on='eid', how='left')\nt2 = t1[t1['aid'].isnull()]\nanswer = t2['eid']", false], ["t1 = employee.copy()\nt2 = certificate.copy()\nt3 = pd.merge(t1, t2, how='left', left_on='eid', right_on='eid')\nt4 = t3[t3['aid'].isnull()].drop('aid', axis=1)\nanswer = t4['eid']", false], ["t1 = employee['eid'].to_frame()\nt2 = certificate['eid'].to_frame()\nanswer = t1[~t1.isin(t2)].dropna()", false], ["answer = employee[~employee['eid'].isin(certificate['eid'])]['eid']", false], ["t1 = employee[['eid']].copy()\nt2 = certificate[['eid']].copy()\nanswer = pd.concat([t1, t2]).drop_duplicates(keep=False)", false]]}
+{"example": {"db_id": "college_2", "query": "SELECT name ,  course_id FROM instructor AS T1 JOIN teaches AS T2 ON T1.ID  =  T2.ID", "query_toks": ["SELECT", "name", ",", "course_id", "FROM", "instructor", "AS", "T1", "JOIN", "teaches", "AS", "T2", "ON", "T1.ID", "=", "T2.ID"], "query_toks_no_value": ["select", "name", ",", "course_id", "from", "instructor", "as", "t1", "join", "teaches", "as", "t2", "on", "t1", ".", "id", "=", "t2", ".", "id"], "question": "Find the names of all instructors who have taught some course and the course_id.", "question_toks": ["Find", "the", "names", "of", "all", "instructors", "who", "have", "taught", "some", "course", "and", "the", "course_id", "."], "sql": {"from": {"table_units": [["table_unit", 3], ["table_unit", 5]], "conds": [[false, 2, [0, [0, 11, false], null], [0, 22, false], null]]}, "select": [false, [[0, [0, [0, 12, false], null]], [0, [0, [0, 23, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"classroom": ["building", "room_number", "capacity"], "department": ["dept_name", "building", "budget"], "course": ["course_id", "title", "dept_name", "credits"], "instructor": ["ID", "name", "dept_name", "salary"], "section": ["course_id", "sec_id", "semester", "year", "building", "room_number", "time_slot_id"], "teaches": ["ID", "course_id", "sec_id", "semester", "year"], "student": ["ID", "name", "dept_name", "tot_cred"], "takes": ["ID", "course_id", "sec_id", "semester", "year", "grade"], "advisor": ["s_ID", "i_ID"], "time_slot": ["time_slot_id", "day", "start_hr", "start_min", "end_hr", "end_min"], "prereq": ["course_id", "prereq_id"]}, "answer": [["Bondi", "747"], ["Gustafsson", "169"], ["Mingoz", "445"], ["Kean", "808"], ["DAgostino", "962"], ["Bietzk", "158"], ["Gustafsson", "561"], ["Bondi", "274"], ["Bondi", "571"], ["Ullman ", "345"], ["Queiroz", "559"], ["DAgostino", "991"], ["DAgostino", "400"], ["Voronina", "376"], ["Sullivan", "694"], ["Mingoz", "137"], ["Sakurai", "270"], ["Morris", "795"], ["Voronina", "443"], ["Mahmoud", "486"], ["Ullman ", "408"], ["Mingoz", "362"], ["Mingoz", "527"], ["Tung", "401"], ["Ullman ", "408"], ["Mahmoud", "493"], ["Luo", "679"], ["Dale", "237"], ["Sakurai", "258"], ["Jaekel", "334"], ["Kean", "366"], ["Wieland", "581"], ["Vicentino", "793"], ["Voronina", "959"], ["Tung", "421"], ["Mingoz", "426"], ["Mingoz", "362"], ["Mahmoud", "864"], ["Bourrier", "960"], ["Romero", "105"], ["Lent", "626"], ["Dale", "893"], ["DAgostino", "663"], ["Liley", "192"], ["Mingoz", "349"], ["Dale", "237"], ["DAgostino", "972"], ["Mahmoud", "704"], ["Morris", "242"], ["Dale", "158"], ["Wieland", "545"], ["Morris", "791"], ["DAgostino", "642"], ["Sakurai", "468"], ["Bourrier", "949"], ["Dale", "748"], ["Romero", "489"], ["Atanassov", "603"], ["Valtchev", "415"], ["Sarkar", "867"], ["Gustafsson", "631"], ["Voronina", "443"], ["Valtchev", "702"], ["Lembr", "843"], ["Voronina", "612"], ["Dale", "629"], ["Romero", "105"], ["Ullman ", "200"], ["Dale", "496"], ["Shuming", "468"], ["Morris", "696"], ["Mahmoud", "735"], ["Lembr", "200"], ["Choll", "461"], ["Mahmoud", "735"], ["Mingoz", "362"], ["Romero", "476"], ["Voronina", "239"], ["Mingoz", "304"], ["DAgostino", "599"], ["Jaekel", "852"], ["Ullman ", "760"], ["Wieland", "591"], ["Gustafsson", "169"], ["Morris", "313"], ["Tung", "692"], ["DAgostino", "867"], ["DAgostino", "482"], ["Bawa", "457"], ["Dale", "802"], ["DAgostino", "338"], ["DAgostino", "352"], ["DAgostino", "338"], ["Sakurai", "960"], ["Pimenta", "875"], ["DAgostino", "400"], ["Dale", "927"], ["Ullman ", "974"], ["Atanassov", "604"], ["Mingoz", "319"]]}, "program_result_list": [["t1 = pd.merge(instructor, teaches, left_on='id', right_on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, left_on='id', right_on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, left_on='id', right_on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, left_on='id', right_on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, left_on='id', right_on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, left_on='id', right_on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, left_on='id', right_on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, left_on='id', right_on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, left_on='id', right_on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, left_on='id', right_on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, left_on='id', right_on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, left_on='id', right_on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, on='id')\nanswer = t1[['name', 'course_id']]", false], ["t1 = pd.merge(instructor, teaches, left_on='id', right_on='id')\nanswer = t1[['name', 'course_id']]", false]]}
+{"example": {"db_id": "bike_1", "query": "SELECT date ,  mean_temperature_f ,  mean_humidity FROM weather ORDER BY max_gust_speed_mph DESC LIMIT 3", "query_toks": ["SELECT", "date", ",", "mean_temperature_f", ",", "mean_humidity", "FROM", "weather", "ORDER", "BY", "max_gust_speed_mph", "DESC", "LIMIT", "3"], "query_toks_no_value": ["select", "date", ",", "mean_temperature_f", ",", "mean_humidity", "from", "weather", "order", "by", "max_gust_speed_mph", "desc", "limit", "value"], "question": "What are the date, mean temperature and mean humidity for the top 3 days with the largest max gust speeds?", "question_toks": ["What", "are", "the", "date", ",", "mean", "temperature", "and", "mean", "humidity", "for", "the", "top", "3", "days", "with", "the", "largest", "max", "gust", "speeds", "?"], "sql": {"from": {"table_units": [["table_unit", 3]], "conds": []}, "select": [false, [[0, [0, [0, 23, false], null]], [0, [0, [0, 25, false], null]], [0, [0, [0, 31, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": ["desc", [[0, [0, 41, false], null]]], "limit": 3, "intersect": null, "union": null, "except": null}, "db_table_headers": {"station": ["id", "name", "lat", "long", "dock_count", "city", "installation_date"], "status": ["station_id", "bikes_available", "docks_available", "time"], "trip": ["id", "duration", "start_date", "start_station_name", "start_station_id", "end_date", "end_station_name", "end_station_id", "bike_id", "subscription_type", "zip_code"], "weather": ["date", "max_temperature_f", "mean_temperature_f", "min_temperature_f", "max_dew_point_f", "mean_dew_point_f", "min_dew_point_f", "max_humidity", "mean_humidity", "min_humidity", "max_sea_level_pressure_inches", "mean_sea_level_pressure_inches", "min_sea_level_pressure_inches", "max_visibility_miles", "mean_visibility_miles", "min_visibility_miles", "max_wind_Speed_mph", "mean_wind_speed_mph", "max_gust_speed_mph", "precipitation_inches", "cloud_cover", "events", "wind_dir_degrees", "zip_code"]}, "answer": [["10/14/2013", 64, 49], ["10/31/2013", 59, 54], ["8/31/2013", 68, 72]]}, "program_result_list": [["t1 = weather[['date', 'mean_temperature_f', 'mean_humidity']].sort_values('max_gust_speed_mph', ascending=False).head(3)\nanswer = t1", false], ["t1 = weather.sort_values(by='max_gust_speed_mph', ascending=False)\nanswer = t1.head(3)[['date', 'mean_temperature_f', 'mean_humidity']]", false], ["t1 = weather.sort_values(by='max_gust_speed_mph', ascending=False).head(3)\nanswer = t1[['date', 'mean_temperature_f', 'mean_humidity']]", false], ["t1 = weather.sort_values(by='max_gust_speed_mph', ascending=False).head(3)\nanswer = t1[['date', 'mean_temperature_f', 'mean_humidity']]", false], ["t1 = weather.sort_values('max_gust_speed_mph', ascending=False).head(3).reset_index()\nanswer = t1[['date', 'mean_temperature_f', 'mean_humidity']]", false], ["answer = weather[['date', 'mean_temperature_f', 'mean_humidity']].sort_values('max_gust_speed_mph', ascending=False).head(3)", false], ["t1 = weather.sort_values('max_gust_speed_mph', ascending=False).head(3).reset_index()\nanswer = t1[['date', 'mean_temperature_f', 'mean_humidity']]", false], ["t1 = weather.sort_values(by='max_gust_speed_mph', ascending=False).head(3)\nanswer = t1[['date', 'mean_temperature_f', 'mean_humidity']]", false], ["t1 = weather.sort_values(by='max_gust_speed_mph', ascending=False).head(3)\nanswer = t1[['date', 'mean_temperature_f', 'mean_humidity']]", false], ["t1 = weather.sort_values('max_gust_speed_mph', ascending=False).head(3)\nanswer = t1[['date', 'mean_temperature_f', 'mean_humidity']]", false], ["answer = weather.sort_values(by='max_gust_speed_mph', ascending=False).head(3)[['date', 'mean_temperature_f', 'mean_humidity']]", false], ["t1 = weather.sort_values(by='max_gust_speed_mph', ascending=False).head(3)\nanswer = t1[['date', 'mean_temperature_f', 'mean_humidity']]", false], ["t1 = weather.sort_values('max_gust_speed_mph', ascending=False).head(3).reset_index()\nanswer = t1[['date', 'mean_temperature_f', 'mean_humidity']]", false], ["t1 = weather.sort_values('max_gust_speed_mph', ascending=False).head(3)\nanswer = t1[['date', 'mean_temperature_f', 'mean_humidity']]", false], ["t1 = weather.sort_values(by='max_gust_speed_mph', ascending=False).head(3)\nanswer = t1[['date', 'mean_temperature_f', 'mean_humidity']]", false], ["answer = weather[['date', 'mean_temperature_f', 'mean_humidity']].sort_values('max_gust_speed_mph', ascending=False).head(3)", false], ["t1 = weather.sort_values(by='max_gust_speed_mph', ascending=False).head(3).reset_index()\nanswer = t1[['date', 'mean_temperature_f', 'mean_humidity']]", false], ["t1 = weather.sort_values('max_gust_speed_mph', ascending=False).head(3).reset_index(drop=True)\nanswer = t1[['date', 'mean_temperature_f', 'mean_humidity']]", false], ["t1 = weather.sort_values(by='max_gust_speed_mph', ascending=False).head(3).reset_index(drop=True)\nanswer = t1[['date', 'mean_temperature_f', 'mean_humidity']]", false], ["answer = weather[['date', 'mean_temperature_f', 'mean_humidity']].sort_values(by='max_gust_speed_mph', ascending=False).head(3)", false]]}
+{"example": {"db_id": "party_people", "query": "SELECT T1.member_name FROM Member AS T1 JOIN party AS T2 ON T1.party_id  =  T2.party_id WHERE T2.Party_name != \"Progress Party\"", "query_toks": ["SELECT", "T1.member_name", "FROM", "Member", "AS", "T1", "JOIN", "party", "AS", "T2", "ON", "T1.party_id", "=", "T2.party_id", "WHERE", "T2.Party_name", "!", "=", "``", "Progress", "Party", "''"], "query_toks_no_value": ["select", "t1", ".", "member_name", "from", "member", "as", "t1", "join", "party", "as", "t2", "on", "t1", ".", "party_id", "=", "t2", ".", "party_id", "where", "t2", ".", "party_name", "!", "=", "value"], "question": "Which member names corresponding to members who are not in the Progress Party?", "question_toks": ["Which", "member", "names", "corresponding", "to", "members", "who", "are", "not", "in", "the", "Progress", "Party", "?"], "sql": {"from": {"table_units": [["table_unit", 2], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 15, false], null], [0, 7, false], null]]}, "select": [false, [[0, [0, [0, 14, false], null]]]], "where": [[false, 7, [0, [0, 12, false], null], "\"Progress Party\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"region": ["Region_ID", "Region_name", "Date", "Label", "Format", "Catalogue"], "party": ["Party_ID", "Minister", "Took_office", "Left_office", "Region_ID", "Party_name"], "member": ["Member_ID", "Member_Name", "Party_ID", "In_office"], "party_events": ["Event_ID", "Event_Name", "Party_ID", "Member_in_charge_ID"]}, "answer": [["Hon Tony Abbott"], ["Hon John Anderson"], ["Peter Andren"], ["Hon Kevin Andrews"], ["Hon Fran Bailey"], ["Hon Bruce Baird"], ["Mark Baker"], ["Hon Bob Baldwin"], ["Phil Barresi"], ["Kerry Bartlett"], ["Hon Bruce Billson"]]}, "program_result_list": [["t1 = pd.merge(member, party, on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name'].unique()", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false], ["t1 = pd.merge(member, party, on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false], ["t1 = pd.merge(member, party, on='party_id', how='left')\nt2 = t1[t1['party_name'] != 'Progress Party']\nanswer = t2['member_name']", false]]}
+{"example": {"db_id": "student_1", "query": "SELECT T2.firstname ,  T2.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom  =  T2.classroom GROUP BY T2.firstname ,  T2.lastname ORDER BY count(*) DESC LIMIT 1", "query_toks": ["SELECT", "T2.firstname", ",", "T2.lastname", "FROM", "list", "AS", "T1", "JOIN", "teachers", "AS", "T2", "ON", "T1.classroom", "=", "T2.classroom", "GROUP", "BY", "T2.firstname", ",", "T2.lastname", "ORDER", "BY", "count", "(", "*", ")", "DESC", "LIMIT", "1"], "query_toks_no_value": ["select", "t2", ".", "firstname", ",", "t2", ".", "lastname", "from", "list", "as", "t1", "join", "teachers", "as", "t2", "on", "t1", ".", "classroom", "=", "t2", ".", "classroom", "group", "by", "t2", ".", "firstname", ",", "t2", ".", "lastname", "order", "by", "count", "(", "*", ")", "desc", "limit", "value"], "question": "Find the name of the teacher who teaches the largest number of students.", "question_toks": ["Find", "the", "name", "of", "the", "teacher", "who", "teaches", "the", "largest", "number", "of", "students", "."], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 4, false], null], [0, 7, false], null]]}, "select": [false, [[0, [0, [0, 6, false], null]], [0, [0, [0, 5, false], null]]]], "where": [], "groupBy": [[0, 6, false], [0, 5, false]], "having": [], "orderBy": ["desc", [[0, [3, 0, false], null]]], "limit": 1, "intersect": null, "union": null, "except": null}, "db_table_headers": {"list": ["LastName", "FirstName", "Grade", "Classroom"], "teachers": ["LastName", "FirstName", "Classroom"]}, "answer": [[" GORDON", "KAWA"]]}, "program_result_list": [["t1 = pd.merge(list, teachers, left_on='classroom', right_on='classroom')\nt2 = t1.groupby(['firstname', 'lastname']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index()[['firstname', 'lastname']]", false], ["t1 = pd.merge(list, teachers, on='classroom')\nt2 = t1.groupby(['firstname', 'lastname']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index()", false], ["t1 = pd.merge(list, teachers, on='classroom')\nt2 = t1.groupby(['firstname', 'lastname']).size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3[['firstname', 'lastname']]", false], ["t1 = pd.merge(list, teachers, left_on='classroom', right_on='classroom')\nt2 = t1.groupby(['firstname', 'lastname']).size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3[['firstname', 'lastname']]", false], ["t1 = pd.merge(list, teachers, left_on='classroom', right_on='classroom')\nt2 = t1.groupby(['firstname', 'lastname']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index()", false], ["t1 = pd.merge(list, teachers, left_on='classroom', right_on='classroom')\nt2 = t1.groupby(['firstname', 'lastname']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index()", false], ["t1 = pd.merge(list, teachers, on='classroom')\nt2 = t1.groupby(['firstname', 'lastname']).size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3[['firstname', 'lastname']]", false], ["t1 = pd.merge(list, teachers, left_on='classroom', right_on='classroom')\nt2 = t1.groupby(['firstname', 'lastname']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(1)", false], ["t1 = pd.merge(list, teachers, left_on='classroom', right_on='classroom')\nt2 = t1.groupby(['firstname', 'lastname']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index().loc[:, ['firstname', 'lastname']]", false], ["t1 = pd.merge(list, teachers, left_on='classroom', right_on='classroom')\nt2 = t1.groupby(['firstname', 'lastname']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index().iloc[:,[1,2]]", false], ["t1 = pd.merge(list, teachers, on='classroom')\nt2 = t1.groupby(['firstname', 'lastname']).size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3[['firstname', 'lastname']]", false], ["t1 = pd.merge(list, teachers, on='classroom')\nt2 = t1.groupby(['lastname', 'firstname']).size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3[['firstname', 'lastname']]", false], ["t1 = pd.merge(list, teachers, left_on='classroom', right_on='classroom')\nt2 = t1.groupby(['firstname', 'lastname']).size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3[['firstname', 'lastname']]", false], ["t1 = pd.merge(list, teachers, left_on='classroom', right_on='classroom')\nt2 = t1.groupby(['firstname', 'lastname']).size().rename('count').to_frame().reset_index()\nt3 = t2.sort_values('count', ascending=False).head(1)\nanswer = t3[['firstname', 'lastname']]", false], ["t1 = pd.merge(list, teachers, left_on='classroom', right_on='classroom')\nt2 = t1.groupby(['firstname', 'lastname']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index()", false], ["t1 = pd.merge(list, teachers, left_on='classroom', right_on='classroom')\nt2 = t1.groupby(['firstname', 'lastname']).size().rename('count')\nt3 = t2.sort_values(ascending=False).to_frame().reset_index().head(1)\nanswer = t3[['firstname', 'lastname']]", false], ["t1 = pd.merge(list, teachers, left_on='classroom', right_on='classroom')\nt2 = t1.groupby(['firstname', 'lastname']).size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3[['firstname', 'lastname']]", false], ["t1 = pd.merge(list, teachers, on='classroom')\nt2 = t1.groupby(['firstname', 'lastname']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index()", false], ["t1 = pd.merge(list, teachers, on='classroom')\nt2 = t1.groupby(['firstname','lastname']).size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3[['firstname', 'lastname']]", false], ["t1 = pd.merge(list, teachers, on='classroom')\nt2 = t1.groupby(['firstname', 'lastname']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index()[['firstname', 'lastname']]", false]]}
+{"example": {"db_id": "program_share", "query": "SELECT t1.name FROM channel AS t1 JOIN broadcast AS t2 ON t1.channel_id  =  t2.channel_id WHERE t2.time_of_day  =  'Morning'", "query_toks": ["SELECT", "t1.name", "FROM", "channel", "AS", "t1", "JOIN", "broadcast", "AS", "t2", "ON", "t1.channel_id", "=", "t2.channel_id", "WHERE", "t2.time_of_day", "=", "'Morning", "'"], "query_toks_no_value": ["select", "t1", ".", "name", "from", "channel", "as", "t1", "join", "broadcast", "as", "t2", "on", "t1", ".", "channel_id", "=", "t2", ".", "channel_id", "where", "t2", ".", "time_of_day", "=", "value"], "question": "Which channels are broadcast in the morning? Give me the channel names.", "question_toks": ["Which", "channels", "are", "broadcast", "in", "the", "morning", "?", "Give", "me", "the", "channel", "names", "."], "sql": {"from": {"table_units": [["table_unit", 1], ["table_unit", 2]], "conds": [[false, 2, [0, [0, 6, false], null], [0, 11, false], null]]}, "select": [false, [[0, [0, [0, 7, false], null]]]], "where": [[false, 2, [0, [0, 13, false], null], "\"Morning\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"program": ["Program_ID", "Name", "Origin", "Launch", "Owner"], "channel": ["Channel_ID", "Name", "Owner", "Share_in_percent", "Rating_in_percent"], "broadcast": ["Channel_ID", "Program_ID", "Time_of_day"], "broadcast_share": ["Channel_ID", "Program_ID", "Date", "Share_in_percent"]}, "answer": [["CCTV-1"], ["CCTV-8"], ["CCTV-3"], ["CCTV-6"]]}, "program_result_list": [["t1 = pd.merge(broadcast, channel, on=['channel_id'])\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name'].unique()", false], ["t1 = pd.merge(channel, broadcast, left_on='channel_id', right_on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name']", false], ["t1 = pd.merge(channel, broadcast, on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name'].unique()", false], ["t1 = pd.merge(channel, broadcast, left_on='channel_id', right_on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name']", false], ["t1 = pd.merge(channel, broadcast, left_on='channel_id', right_on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name'].unique()", false], ["t1 = pd.merge(channel, broadcast, left_on='channel_id', right_on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name']", false], ["t1 = pd.merge(channel, broadcast, left_on='channel_id', right_on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name'].unique()", false], ["t1 = pd.merge(channel, broadcast, left_on='channel_id', right_on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name']", false], ["t1 = pd.merge(broadcast, channel, on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name'].unique()", false], ["t1 = pd.merge(channel, broadcast, left_on='channel_id', right_on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name']", false], ["t1 = pd.merge(channel, broadcast, left_on='channel_id', right_on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name']", false], ["t1 = pd.merge(channel, broadcast, left_on='channel_id', right_on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name']", false], ["t1 = pd.merge(channel, broadcast, left_on='channel_id', right_on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name']", false], ["t1 = pd.merge(channel, broadcast, on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name'].unique()", false], ["t1 = pd.merge(channel, broadcast, left_on='channel_id', right_on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name'].unique()", false], ["t1 = pd.merge(channel, broadcast, left_on='channel_id', right_on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name']", false], ["t1 = pd.merge(channel, broadcast, left_on='channel_id', right_on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name'].unique()", false], ["t1 = pd.merge(channel, broadcast, on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name'].unique()", false], ["t1 = pd.merge(channel, broadcast, left_on='channel_id', right_on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name'].unique()", false], ["t1 = pd.merge(channel, broadcast, left_on='channel_id', right_on='channel_id')\nt2 = t1[t1['time_of_day'] == 'morning']\nanswer = t2['name']", false]]}
+{"example": {"db_id": "allergy_1", "query": "SELECT DISTINCT T1.fname ,  T1.city_code FROM Student AS T1 JOIN Has_Allergy AS T2 ON T1.stuid  =  T2.stuid WHERE T2.Allergy  =  \"Milk\" OR T2.Allergy  =  \"Cat\"", "query_toks": ["SELECT", "DISTINCT", "T1.fname", ",", "T1.city_code", "FROM", "Student", "AS", "T1", "JOIN", "Has_Allergy", "AS", "T2", "ON", "T1.stuid", "=", "T2.stuid", "WHERE", "T2.Allergy", "=", "``", "Milk", "''", "OR", "T2.Allergy", "=", "``", "Cat", "''"], "query_toks_no_value": ["select", "distinct", "t1", ".", "fname", ",", "t1", ".", "city_code", "from", "student", "as", "t1", "join", "has_allergy", "as", "t2", "on", "t1", ".", "stuid", "=", "t2", ".", "stuid", "where", "t2", ".", "allergy", "=", "value", "or", "t2", ".", "allergy", "=", "value"], "question": "What are the distinct first names and cities of the students who have allergy either to milk or to cat?", "question_toks": ["What", "are", "the", "distinct", "first", "names", "and", "cities", "of", "the", "students", "who", "have", "allergy", "either", "to", "milk", "or", "to", "cat", "?"], "sql": {"from": {"table_units": [["table_unit", 2], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 5, false], null], [0, 3, false], null]]}, "select": [true, [[0, [0, [0, 7, false], null]], [0, [0, [0, 12, false], null]]]], "where": [[false, 2, [0, [0, 4, false], null], "\"Milk\"", null], "or", [false, 2, [0, [0, 4, false], null], "\"Cat\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Allergy_Type": ["Allergy", "AllergyType"], "Has_Allergy": ["StuID", "Allergy"], "Student": ["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"]}, "answer": [["Linda", "BAL"], ["Lisa", "PIT"], ["Derek", "HOU"], ["Mark", "DET"], ["David", "NYC"]]}, "program_result_list": [["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nanswer = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')][['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[ (t1['allergy'] == 'milk') | (t1['allergy'] == 'cat') ]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'cat') | (t1['allergy'] == 'milk')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Has_Allergy, Student, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nanswer = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')][['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false]]}
+{"example": {"db_id": "music_1", "query": "SELECT T2.song_name FROM files AS T1 JOIN song AS T2 ON T1.f_id  =  T2.f_id WHERE T1.duration LIKE \"4:%\" UNION SELECT song_name FROM song WHERE languages  =  \"english\"", "query_toks": ["SELECT", "T2.song_name", "FROM", "files", "AS", "T1", "JOIN", "song", "AS", "T2", "ON", "T1.f_id", "=", "T2.f_id", "WHERE", "T1.duration", "LIKE", "``", "4", ":", "%", "''", "UNION", "SELECT", "song_name", "FROM", "song", "WHERE", "languages", "=", "``", "english", "''"], "query_toks_no_value": ["select", "t2", ".", "song_name", "from", "files", "as", "t1", "join", "song", "as", "t2", "on", "t1", ".", "f_id", "=", "t2", ".", "f_id", "where", "t1", ".", "duration", "like", "value", "union", "select", "song_name", "from", "song", "where", "languages", "=", "value"], "question": "What are the names of all songs that are approximately 4 minutes long or are in English?", "question_toks": ["What", "are", "the", "names", "of", "all", "songs", "that", "are", "approximately", "4", "minutes", "long", "or", "are", "in", "English", "?"], "sql": {"from": {"table_units": [["table_unit", 2], ["table_unit", 3]], "conds": [[false, 2, [0, [0, 8, false], null], [0, 16, false], null]]}, "select": [false, [[0, [0, [0, 13, false], null]]]], "where": [[false, 9, [0, [0, 11, false], null], "\"4:%\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": {"from": {"table_units": [["table_unit", 3]], "conds": []}, "select": [false, [[0, [0, [0, 13, false], null]]]], "where": [[false, 2, [0, [0, 19, false], null], "\"english\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "except": null}, "db_table_headers": {"genre": ["g_name", "rating", "most_popular_in"], "artist": ["artist_name", "country", "gender", "preferred_genre"], "files": ["f_id", "artist_name", "file_size", "duration", "formats"], "song": ["song_name", "artist_name", "country", "f_id", "genre_is", "rating", "languages", "releasedate", "resolution"]}, "answer": [["Aj ei akash"], ["Ami opar hoye"], ["Just beat it"], ["My love"]]}, "program_result_list": [["t1 = pd.merge(files, song, left_on='f_id', right_on='f_id')\nt2 = t1[t1['duration'].str.contains(\"4:\")]['song_name']\nt3 = song[song['languages'] == 'english']['song_name']\nanswer = t2.append(t3).drop_duplicates()", false], ["t1 = pd.merge(files, song, left_on='f_id', right_on='f_id')\nt2 = t1[t1['duration'].str.contains('4:')]\nt3 = song[song['languages'] == 'English']\nt4 = pd.concat([t2, t3], ignore_index=True)\nanswer = t4.song_name.unique()", false], ["t1 = pd.merge(files, song, left_on='f_id', right_on='f_id')\nt2 = t1[t1['duration'].str.contains('4:')]\nt3 = song[song['languages'] == 'english']\nanswer = t2['song_name'].append(t3['song_name']).drop_duplicates()", false], ["t1 = pd.merge(files, song, left_on='f_id', right_on='f_id')\nt2 = t1[t1['duration'].str.contains('4:')]\nt3 = song[song['languages'] == 'english']\nanswer = pd.concat([t2['song_name'], t3['song_name']]).drop_duplicates()", false], ["t1 = pd.merge(files, song, left_on='f_id', right_on='f_id')\nt2 = t1[t1['duration'].str.contains('4:')]\nanswer = t2[['song_name']].union(song[song['languages'] == 'english']['song_name']).sort_values(by='song_name')", false], ["t1 = pd.merge(files, song, left_on='f_id', right_on='f_id')\nt2 = t1[t1['duration'].str.contains('4:')]\nt3 = t2[['song_name']]\nt4 = song[song['languages'] == 'english']\nt5 = t4[['song_name']]\nanswer = pd.concat([t3, t5])", false], ["t1 = pd.merge(files, song, left_on='f_id', right_on='f_id')\nt2 = t1[t1['duration'].str.contains('4:')]\nt3 = song[song['languages'] == 'english']\nanswer = pd.concat([t2['song_name'], t3['song_name']]).unique()", false], ["t1 = pd.merge(files, song, on='f_id')\nt2 = t1[t1['duration'].str.contains('4:')]\nt3 = song[song['languages'] == 'english']\nanswer = pd.concat([t2['song_name'], t3['song_name']]).drop_duplicates()", false], ["t1 = pd.merge(files, song, on='f_id')\nt2 = t1[t1['duration'].str.contains(\"4:\")]\nt3 = song[song['languages'] == \"english\"]\nanswer = t2[['song_name']].append(t3[['song_name']]).drop_duplicates()", false], ["t1 = pd.merge(files, song, left_on='f_id', right_on='f_id')\nt2 = t1[t1['duration'].str.contains('4:')]\nt3 = song[song['languages'] == 'english']\nanswer = pd.concat([t2['song_name'], t3['song_name']]).unique()", false], ["t1 = pd.merge(files, song, left_on='f_id', right_on='f_id')\nt2 = t1[t1['duration'].str.contains('4:')]\nanswer = pd.concat([t2['song_name'], song[song['languages'] == 'english']['song_name']])", false], ["t1 = pd.merge(files, song, left_on='f_id', right_on='f_id')\nt2 = t1[t1['duration'].str.startswith('4:')]\nt3 = song[song['languages'] == 'english']\nanswer = t2.append(t3).drop_duplicates()['song_name']", false], ["t1 = pd.merge(files, song, left_on='f_id', right_on='f_id')\nt2 = t1[t1['duration'].str.contains(\"4:\")]\nt3 = song[song['languages'] == 'english']\nanswer = t2['song_name'].append(t3['song_name'])", false], ["t1 = pd.merge(files, song, left_on='f_id', right_on='f_id')\nt2 = t1[t1['duration'].str.contains(\"4:\")]\nt3 = song[song['languages'] == \"English\"]\nt4 = pd.merge(t2, t3, how='outer')\nanswer = t4['song_name']", false], ["t1 = pd.merge(files, song, left_on='f_id', right_on='f_id')\nt2 = t1[t1['duration'].str.contains(r'4:')].song_name\nt3 = song[song['languages'] == 'english'].song_name\nanswer = t2.append(t3).unique()", false], ["t1 = pd.merge(files, song, left_on='f_id', right_on='f_id')\nt2 = t1[t1['duration'].str.contains('4:')]\nt3 = song[song['languages'] == 'english']\nanswer = pd.concat([t2['song_name'], t3['song_name']]).drop_duplicates().reset_index(drop=True)", false], ["t1 = pd.merge(files, song, left_on='f_id', right_on='f_id')\nt2 = t1[t1['duration'].str.contains('4:')]['song_name']\nt3 = song[song['languages'] == 'english']['song_name']\nanswer = pd.concat([t2, t3])", false], ["t1 = pd.merge(files, song, left_on='f_id', right_on='f_id')\nt2 = t1[t1['duration'].str.contains('4:')]\nanswer = t2['song_name'].append(song[song['languages'] == 'english']['song_name']).unique()", false], ["t1 = pd.merge(files, song, on='f_id')\nt2 = t1[t1['duration'].str.contains('4:')]\nt3 = song[song['languages'] == 'english']\nanswer = pd.concat([t2['song_name'], t3['song_name']]).drop_duplicates()", false], ["t1 = pd.merge(files, song, left_on='f_id', right_on='f_id')\nt2 = t1[t1['duration'].str.contains('4:')]\nt3 = song[song['languages'] == 'English']\nanswer = pd.concat([t2['song_name'], t3['song_name']]).drop_duplicates()", false]]}
+{"example": {"db_id": "csu_1", "query": "SELECT T1.campus ,  sum(T2.degrees) FROM campuses AS T1 JOIN degrees AS T2 ON T1.id  =  T2.campus WHERE T2.year  >=  1998 AND T2.year  <=  2002 GROUP BY T1.campus", "query_toks": ["SELECT", "T1.campus", ",", "sum", "(", "T2.degrees", ")", "FROM", "campuses", "AS", "T1", "JOIN", "degrees", "AS", "T2", "ON", "T1.id", "=", "T2.campus", "WHERE", "T2.year", ">", "=", "1998", "AND", "T2.year", "<", "=", "2002", "GROUP", "BY", "T1.campus"], "query_toks_no_value": ["select", "t1", ".", "campus", ",", "sum", "(", "t2", ".", "degrees", ")", "from", "campuses", "as", "t1", "join", "degrees", "as", "t2", "on", "t1", ".", "id", "=", "t2", ".", "campus", "where", "t2", ".", "year", ">", "=", "value", "and", "t2", ".", "year", "<", "=", "value", "group", "by", "t1", ".", "campus"], "question": "report the total number of degrees granted between 1998 and 2002.", "question_toks": ["report", "the", "total", "number", "of", "degrees", "granted", "between", "1998", "and", "2002", "."], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 2]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 10, false], null]]}, "select": [false, [[0, [0, [0, 2, false], null]], [4, [0, [0, 11, false], null]]]], "where": [[false, 5, [0, [0, 9, false], null], 1998.0, null], "and", [false, 6, [0, [0, 9, false], null], 2002.0, null]], "groupBy": [[0, 2, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Campuses": ["Id", "Campus", "Location", "County", "Year"], "csu_fees": ["Campus", "Year", "CampusFee"], "degrees": ["Year", "Campus", "Degrees"], "discipline_enrollments": ["Campus", "Discipline", "Year", "Undergraduate", "Graduate"], "enrollments": ["Campus", "Year", "TotalEnrollment_AY", "FTE_AY"], "faculty": ["Campus", "Year", "Faculty"]}, "answer": [["California  Maritime Academy", 405], ["California Polytechnic State University-San Luis Obispo", 16402], ["California State Polytechnic University-Pomona", 13493], ["California State University-Bakersfield", 4967], ["California State University-Channel Islands", 2], ["California State University-Chico", 13762], ["California State University-Dominguez Hills", 8681], ["California State University-East Bay", 11622], ["California State University-Fullerton", 23457], ["California State University-Los Angeles", 13032], ["California State University-Monterey Bay", 1626], ["California State University-Northridge", 22141], ["California State University-Sacramento", 20155], ["California State University-San Bernardino", 10959], ["California State University-San Marcos", 5275], ["California State University-Stanislaus", 6181], ["Fresno State University", 14060], ["Humboldt State University", 6963], ["Long Beach State University", 22502], ["San Diego State University", 26418], ["San Francisco State University", 21323], ["San Jose State University", 19945], ["Sonoma State University", 7204]]}, "program_result_list": [["t1 = pd.merge(Campuses, degrees, left_on='id', right_on='campus', how='left')\nt2 = t1[(t1['year_x'] >= 1998) & (t1['year_x'] <= 2002)]\nt3 = t2.groupby('campus_x').sum()\nanswer = t3['degrees'].to_frame().reset_index()", false], ["t1 = pd.merge(Campuses, degrees, left_on='id', right_on='campus')\nt2 = t1[(t1['year_x'] >= 1998) & (t1['year_x'] <= 2002)]\nanswer = t2.groupby('campus_x').sum()", false], ["t1 = pd.merge(Campuses, degrees, on='id')\nt2 = t1[(t1['year_x'] >= 1998) & (t1['year_x'] <= 2002)]\nanswer = t2.groupby('campus_x')['degrees'].sum().to_frame().reset_index()", false], ["t1 = pd.merge(Campuses, degrees, left_on='id', right_on='campus')\nt2 = t1[(t1['year_x'] >= 1998) & (t1['year_x'] <= 2002)]\nt3 = t2.groupby('campus_x').sum()\nanswer = t3['degrees']", false], ["t1 = pd.merge(Campuses, degrees, left_on='id', right_on='campus')\nt2 = t1[(t1['year_x'] >= 1998) & (t1['year_x'] <= 2002)]\nanswer = t2.groupby('campus_x').sum()['degrees']", false], ["t1 = pd.merge(Campuses, degrees, left_on='id', right_on='campus')\nanswer = t1[(t1['year_x'] >= 1998) & (t1['year_x'] <= 2002)].groupby('campus_x').sum().drop(columns=['id', 'year_x'])", false], ["t1 = pd.merge(Campuses, degrees, left_on='id', right_on='campus')\nt2 = t1[(t1['year_x'] >= 1998) & (t1['year_x'] <= 2002)]\nt3 = t2.groupby('campus_x').sum()\nanswer = t3['degrees']", false], ["t1 = pd.merge(Campuses, degrees, left_on='id', right_on='campus')\nt2 = t1[(t1['year_x'] >= 1998) & (t1['year_x'] <= 2002)]\nt3 = t2.groupby('campus_x').sum()\nanswer = t3['degrees']", false], ["t1 = pd.merge(Campuses, degrees, left_on='id', right_on='campus')\nt1 = t1[(t1['year_x'] >= 1998) & (t1['year_x'] <= 2002)]\nt2 = t1.groupby('campus_x').sum().reset_index()\nanswer = t2[['campus_x', 'degrees']]", false], ["t1 = pd.merge(Campuses, degrees, left_on='id', right_on='campus')\nt2 = t1[t1['year'] >= 1998]\nt3 = t2[t2['year'] <= 2002]\nt4 = t3.groupby('campus').sum()\nanswer = t4[['degrees']]", false], ["t1 = pd.merge(Campuses, degrees, left_on='id', right_on='campus')\nt2 = t1[(t1['year_x'] >= 1998) & (t1['year_x'] <= 2002)]\nt3 = t2.groupby('campus_x').sum()\nanswer = t3.reset_index()[['campus_x', 'degrees']]", false], ["t1 = pd.merge(Campuses, degrees, left_on='id', right_on='campus')\nt2 = t1[(t1['year_x'] >= 1998) & (t1['year_x'] <= 2002)]\nt3 = t2.groupby('campus_x')['degrees'].sum().to_frame().reset_index()\nanswer = t3[['campus_x', 'degrees']]", false], ["t1 = pd.merge(Campuses, degrees, on='campus')\nt2 = t1[(t1['year_y'] >= 1998) & (t1['year_y'] <= 2002)]\nt3 = t2.groupby('campus_id').sum()\nt4 = t3.rename(columns={'degrees':'sum_degrees'}).reset_index()\nanswer = t4[['campus_id', 'sum_degrees']]", false], ["t1 = pd.merge(Campuses, degrees, left_on='id', right_on='campus')\nt2 = t1[(t1['year_x'] >= 1998) & (t1['year_x'] <= 2002)].groupby('campus_x').sum()\nanswer = t2.rename(columns={'degrees': 'total_degrees'})", false], ["t1 = pd.merge(Campuses, degrees, left_on='id', right_on='campus')\nt2 = t1[(t1['year'] >= 1998) & (t1['year'] <= 2002)]\nt3 = t2.groupby('campus').sum()\nanswer = t3['degrees']", false], ["t1 = pd.merge(Campuses, degrees, left_on='id', right_on='campus')\nt2 = t1[(t1['year_x'] >= 1998) & (t1['year_x'] <= 2002)]\nt3 = t2.groupby('campus_x').sum()['degrees']\nanswer = t3", false], ["t1 = pd.merge(Campuses, degrees, left_on='id', right_on='campus')\nt2 = t1[(t1['year_x'] >= 1998) & (t1['year_x'] <= 2002)]\nt3 = t2.groupby('campus_x').sum()['degrees']\nanswer = t3.to_frame().reset_index()", false], ["t1 = pd.merge(Campuses, degrees, left_on='id', right_on='campus')\nt2 = t1[(t1['year_x'] >= 1998) & (t1['year_x'] <= 2002)]\nt3 = t2.groupby('campus_x').sum().reset_index()\nanswer = t3[['campus_x', 'degrees']]", false], ["t1 = pd.merge(Campuses, degrees, left_on='id', right_on='campus')\nanswer = t1[(t1['year'] >= 1998) & (t1['year'] <= 2002)].groupby('campus').sum()['degrees']", false], ["t1 = pd.merge(Campuses, degrees, left_on='id', right_on='campus')\nt2 = t1[(t1['year_x'] >= 1998) & (t1['year_x'] <= 2002)]\nt3 = t2.groupby('campus_x').sum()\nanswer = t3['degrees']", false]]}
+{"example": {"db_id": "game_1", "query": "SELECT T2.Fname ,  T2.Lname FROM Sportsinfo AS T1 JOIN Student AS T2 ON T1.StuID  =  T2.StuID GROUP BY T1.StuID ORDER BY count(*) DESC LIMIT 1", "query_toks": ["SELECT", "T2.Fname", ",", "T2.Lname", "FROM", "Sportsinfo", "AS", "T1", "JOIN", "Student", "AS", "T2", "ON", "T1.StuID", "=", "T2.StuID", "GROUP", "BY", "T1.StuID", "ORDER", "BY", "count", "(", "*", ")", "DESC", "LIMIT", "1"], "query_toks_no_value": ["select", "t2", ".", "fname", ",", "t2", ".", "lname", "from", "sportsinfo", "as", "t1", "join", "student", "as", "t2", "on", "t1", ".", "stuid", "=", "t2", ".", "stuid", "group", "by", "t1", ".", "stuid", "order", "by", "count", "(", "*", ")", "desc", "limit", "value"], "question": "What is the first name and last name of the student who have most number of sports?", "question_toks": ["What", "is", "the", "first", "name", "and", "last", "name", "of", "the", "student", "who", "have", "most", "number", "of", "sports", "?"], "sql": {"from": {"table_units": [["table_unit", 3], ["table_unit", 0]], "conds": [[false, 2, [0, [0, 15, false], null], [0, 1, false], null]]}, "select": [false, [[0, [0, [0, 3, false], null]], [0, [0, [0, 2, false], null]]]], "where": [], "groupBy": [[0, 15, false]], "having": [], "orderBy": ["desc", [[0, [3, 0, false], null]]], "limit": 1, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Student": ["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], "Video_Games": ["GameID", "GName", "GType"], "Plays_Games": ["StuID", "GameID", "Hours_Played"], "SportsInfo": ["StuID", "SportName", "HoursPerWeek", "GamesPlayed", "OnScholarship"]}, "answer": [["David", "Shieber"]]}, "program_result_list": [["t1 = pd.merge(SportsInfo, Student, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('stuid').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nt4 = pd.merge(t3, Student, left_on='stuid', right_on='stuid')\nanswer = t4[['fname', 'lname']]", false], ["t1 = pd.merge(SportsInfo, Student, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('stuid').size().rename('count').to_frame().reset_index()\nanswer = t2.sort_values('count', ascending=False).head(1)[['fname', 'lname']]", false], ["t1 = pd.merge(SportsInfo, Student, left_on='stuid', right_on='stuid')\nanswer = t1.groupby(['fname', 'lname']).size().rename('count').sort_values(ascending=False).head(1).to_frame().reset_index()", false], ["t1 = pd.merge(SportsInfo, Student, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('stuid').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nt4 = pd.merge(t3, Student, left_on='stuid', right_on='stuid')\nanswer = t4[['fname', 'lname']]", false], ["t1 = pd.merge(SportsInfo, Student, left_on='stuid', right_on='stuid')\nt2 = t1.groupby(['stuid'])['sportname'].agg('count').rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3['stuid']", false], ["t1 = pd.merge(SportsInfo, Student, left_on='stuid', right_on='stuid')\nt2 = t1.groupby(['fname', 'lname']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index()", false], ["t1 = pd.merge(SportsInfo, Student, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('stuid').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nt4 = pd.merge(t3, t1, left_on='stuid', right_on='stuid')\nanswer = t4[['fname', 'lname']]", false], ["t1 = pd.merge(SportsInfo, Student, left_on='stuid', right_on='stuid')\nt2 = t1.groupby(['stuid', 'fname', 'lname']).size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3[['fname', 'lname']]", false], ["t1 = pd.merge(SportsInfo, Student, left_on='stuid', right_on='stuid')\nt2 = t1.groupby(['fname', 'lname']).size().rename('count')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index()", false], ["t1 = pd.merge(SportsInfo, Student, on='stuid')\nt2 = t1.groupby('stuid').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nt4 = pd.merge(t3, Student, left_on='stuid', right_on='stuid')\nanswer = t4[['fname', 'lname']]", false], ["t1 = pd.merge(SportsInfo, Student, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('stuid').size().rename('count').to_frame().reset_index()\nt3 = t2.sort_values(by='count', ascending=False).head(1)\nanswer = pd.merge(t3, Student, left_on='stuid', right_on='stuid')[['fname', 'lname']]", false], ["t1 = pd.merge(SportsInfo, Student, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('stuid').size().rename('count').to_frame().reset_index()\nt3 = pd.merge(t2, Student, left_on='stuid', right_on='stuid')\nanswer = t3[['fname', 'lname']].head(1)", false], ["t1 = pd.merge(SportsInfo, Student, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('stuid').size().rename('count')\nt3 = pd.merge(t2.to_frame(), Student, on='stuid').sort_values(by='count', ascending=False).head(1)\nanswer = t3[['fname', 'lname']]", false], ["t1 = pd.merge(SportsInfo, Student, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('stuid').size().rename('count').sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t2.merge(Student, on='stuid')[['fname', 'lname']]", false], ["t1 = pd.merge(SportsInfo, Student, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('stuid').size().rename('count')\nt3= t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nt4 = pd.merge(t3, Student, left_on='stuid', right_on='stuid')\nanswer = t4[['fname','lname']]", false], ["t1 = pd.merge(SportsInfo, Student, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('stuid').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nt4 = pd.merge(t3, Student, left_on='stuid', right_on='stuid')\nanswer = t4[['fname', 'lname']]", false], ["t1 = pd.merge(SportsInfo, Student, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('stuid').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nt4 = pd.merge(t3, Student, left_on='stuid', right_on='stuid')\nanswer = t4[['fname', 'lname']]", false], ["t1 = pd.merge(SportsInfo, Student, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('stuid').size().rename('count').to_frame().reset_index()\nt3 = t2.sort_values(by=['count'], ascending=False).head(1)\nt4 = pd.merge(t3, Student, on='stuid')\nanswer = t4[['fname', 'lname']]", false], ["t1 = pd.merge(Student, SportsInfo, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('stuid').size().to_frame().reset_index()\nt3 = pd.merge(Student, t2, left_on='stuid', right_on='stuid')\nanswer = t3.sort_values(by=0, ascending=False).head(1)[['lname', 'fname']]", false], ["t1 = pd.merge(SportsInfo, Student, left_on='stuid', right_on='stuid')\nt2 = t1.groupby('stuid').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nt4 = pd.merge(t3, Student, left_on='stuid', right_on='stuid')\nanswer = t4[['fname', 'lname']]", false]]}
+{"example": {"db_id": "dorm_1", "query": "SELECT avg(T1.age) ,  T3.dorm_name FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid  =  T2.stuid JOIN dorm AS T3 ON T3.dormid  =  T2.dormid GROUP BY T3.dorm_name", "query_toks": ["SELECT", "avg", "(", "T1.age", ")", ",", "T3.dorm_name", "FROM", "student", "AS", "T1", "JOIN", "lives_in", "AS", "T2", "ON", "T1.stuid", "=", "T2.stuid", "JOIN", "dorm", "AS", "T3", "ON", "T3.dormid", "=", "T2.dormid", "GROUP", "BY", "T3.dorm_name"], "query_toks_no_value": ["select", "avg", "(", "t1", ".", "age", ")", ",", "t3", ".", "dorm_name", "from", "student", "as", "t1", "join", "lives_in", "as", "t2", "on", "t1", ".", "stuid", "=", "t2", ".", "stuid", "join", "dorm", "as", "t3", "on", "t3", ".", "dormid", "=", "t2", ".", "dormid", "group", "by", "t3", ".", "dorm_name"], "question": "What is the average age for each dorm and what are the names of each dorm?", "question_toks": ["What", "is", "the", "average", "age", "for", "each", "dorm", "and", "what", "are", "the", "names", "of", "each", "dorm", "?"], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 4], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 17, false], null], "and", [false, 2, [0, [0, 9, false], null], [0, 18, false], null]]}, "select": [false, [[5, [0, [0, 4, false], null]], [0, [0, [0, 10, false], null]]]], "where": [], "groupBy": [[0, 10, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Student": ["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], "Dorm": ["dormid", "dorm_name", "student_capacity", "gender"], "Dorm_amenity": ["amenid", "amenity_name"], "Has_amenity": ["dormid", "amenid"], "Lives_in": ["stuid", "dormid", "room_number"]}, "answer": [[19.666666666666668, "Anonymous Donor Hall"], [20.0, "Bud Jones Hall"], [19.6, "Dorm-plex 2000"], [19.77777777777778, "Fawlty Towers"], [18.0, "Grad Student Asylum"], [20.166666666666668, "Smith Hall"], [18.0, "University Hovels"]]}, "program_result_list": [["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nanswer = t2.groupby('dorm_name').agg({'age': 'mean'}).reset_index()", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nt3 = t2.groupby('dorm_name').mean().rename(columns={'age':'avg_age'})\nanswer = t3[['avg_age']]", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nanswer = t2.groupby('dorm_name')['age'].mean().rename('avg').to_frame().reset_index()", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nt3 = t2.groupby('dorm_name').agg({'age': 'mean'}).rename({'age': 'average_age'}, axis=1)\nanswer = t3.reset_index()", false], ["t1 = pd.merge(Student, Lives_in, on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nt3 = t2.groupby('dorm_name')['age'].mean().rename('avg_age')\nanswer = t3.to_frame().reset_index()", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nt3 = t2.groupby('dorm_name').agg({'age':'mean'})\nanswer = t3.reset_index()", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nt3 = t2.groupby('dorm_name').agg({'age':'mean'}).rename(columns={'age':'average_age'}).reset_index()\nanswer = t3", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nt3 = t2.groupby('dorm_name').age.mean().rename('avg_age').to_frame().reset_index()\nanswer = t3.sort_values('avg_age', ascending=False)", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nt3 = t2.groupby('dorm_name').agg({'age': 'mean'})\nanswer = t3", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nt3 = t2.groupby('dorm_name').agg({'age': ['mean']}).reset_index()\nt3.columns = ['_'.join(tup) for tup in t3.columns.tolist()]\nanswer = t3", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nt3 = t2.groupby('dorm_name').agg({'age': 'mean'})\nanswer = t3.reset_index()", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nt3 = t2.groupby('dorm_name')['age'].mean().rename('avg(t1.age)')\nanswer = t3.to_frame().reset_index()", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nt3 = t2.groupby('dorm_name')['age'].mean().rename('avg_age').to_frame().reset_index()\nanswer = t3", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nt3 = t2.groupby('dorm_name').agg({'age': np.average}).rename(columns={'age': 'average_age'})\nanswer = t3.reset_index()", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nt3 = t2.groupby('dorm_name').mean()\nanswer = t3", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nt3 = t2.groupby('dorm_name').agg({'age':'mean'})\nanswer = t3", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nt3 = t2.groupby('dorm_name').mean()['age'].rename('average_age').to_frame().reset_index()\nanswer = t3", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nt3 = t2.groupby('dorm_name').mean().reset_index()\nanswer = t3[['dorm_name', 'age']]", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nt3 = t2.groupby(['dorm_name'])['age'].agg('mean').rename('avg(age)')\nanswer = t3.to_frame().reset_index()", false], ["t1 = pd.merge(Student, Lives_in, left_on='stuid', right_on='stuid')\nt2 = pd.merge(t1, Dorm, left_on='dormid', right_on='dormid')\nt3 = t2.groupby('dorm_name').agg({'age': 'mean'}).reset_index()\nanswer = t3", false]]}
+{"example": {"db_id": "soccer_1", "query": "SELECT player_api_id FROM Player WHERE height  >=  180 INTERSECT SELECT player_api_id FROM Player_Attributes WHERE overall_rating  >  85", "query_toks": ["SELECT", "player_api_id", "FROM", "Player", "WHERE", "height", ">", "=", "180", "INTERSECT", "SELECT", "player_api_id", "FROM", "Player_Attributes", "WHERE", "overall_rating", ">", "85"], "query_toks_no_value": ["select", "player_api_id", "from", "player", "where", "height", ">", "=", "value", "intersect", "select", "player_api_id", "from", "player_attributes", "where", "overall_rating", ">", "value"], "question": "List all of the player ids with a height of at least 180cm and an overall rating higher than 85.", "question_toks": ["List", "all", "of", "the", "player", "ids", "with", "a", "height", "of", "at", "least", "180cm", "and", "an", "overall", "rating", "higher", "than", "85", "."], "sql": {"from": {"table_units": [["table_unit", 2]], "conds": []}, "select": [false, [[0, [0, [0, 46, false], null]]]], "where": [[false, 5, [0, [0, 50, false], null], 180.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[0, [0, [0, 3, false], null]]]], "where": [[false, 3, [0, [0, 5, false], null], 85.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "union": null, "except": null}, "db_table_headers": {"Player_Attributes": ["id", "player_fifa_api_id", "player_api_id", "date", "overall_rating", "potential", "preferred_foot", "attacking_work_rate", "defensive_work_rate", "crossing", "finishing", "heading_accuracy", "short_passing", "volleys", "dribbling", "curve", "free_kick_accuracy", "long_passing", "ball_control", "acceleration", "sprint_speed", "agility", "reactions", "balance", "shot_power", "jumping", "stamina", "strength", "long_shots", "aggression", "interceptions", "positioning", "vision", "penalties", "marking", "standing_tackle", "sliding_tackle", "gk_diving", "gk_handling", "gk_kicking", "gk_positioning", "gk_reflexes"], "sqlite_sequence": ["name", "seq"], "Player": ["id", "player_api_id", "player_name", "player_fifa_api_id", "birthday", "height", "weight"], "League": ["id", "country_id", "name"], "Country": ["id", "name"], "Team": ["id", "team_api_id", "team_fifa_api_id", "team_long_name", "team_short_name"], "Team_Attributes": ["id", "team_fifa_api_id", "team_api_id", "date", "buildUpPlaySpeed", "buildUpPlaySpeedClass", "buildUpPlayDribbling", "buildUpPlayDribblingClass", "buildUpPlayPassing", "buildUpPlayPassingClass", "buildUpPlayPositioningClass", "chanceCreationPassing", "chanceCreationPassingClass", "chanceCreationCrossing", "chanceCreationCrossingClass", "chanceCreationShooting", "chanceCreationShootingClass", "chanceCreationPositioningClass", "defencePressure", "defencePressureClass", "defenceAggression", "defenceAggressionClass", "defenceTeamWidth", "defenceTeamWidthClass", "defenceDefenderLineClass"]}, "answer": [[19243], [24503], [25759], [26098], [26166], [26173], [26295], [26564], [27299], [27301], [27326], [27424], [27430], [30549], [30592], [30612], [30617], [30618], [30620], [30622], [30626], [30627], [30631], [30638], [30648], [30649], [30657], [30679], [30682], [30690], [30696], [30699], [30706], [30709], [30714], [30716], [30717], [30720], [30723], [30726], [30728], [30740], [30743], [30822], [30834], [30843], [30853], [30859], [30865], [30872], [30889], [30893], [30911], [30931], [30960], [30962], [30989], [31921], [31996], [32118], [32867], [33074], [33085], [33761], [34106], [34305], [34534], [34602], [34947], [35561], [35606], [35724], [36183], [36378], [36388], [36615], [37259], [37482], [38460], [39027], [39774], [39989], [40636], [41044], [41884], [46509], [49677], [49939], [56678], [80562], [93447], [95078], [116772], [129944], [154257], [164684], [169200], [170323], [182917], [248453]]}, "program_result_list": [["t1 = Player[Player['height'] >= 180]['player_api_id']\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85]['player_api_id']\nanswer = t1.merge(t2, how='inner')", false], ["t1 = Player[Player['height'] >= 180]['player_api_id']\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85]['player_api_id']\nanswer = t1.intersection(t2)", false], ["t1 = Player[Player['height'] >= 180]\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85]\nanswer = t1[t1['player_api_id'].isin(t2['player_api_id'])]", false], ["t1 = Player[Player['height'] >= 180]\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85]\nanswer = t1.merge(t2, how='inner')['player_api_id']", false], ["t1 = Player[Player['height'] >= 180]['player_api_id']\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85]['player_api_id']\nanswer = t1.merge(t2, how='inner')", false], ["t1 = Player[Player['height'] >= 180]['player_api_id']\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85]['player_api_id']\nanswer = pd.merge(t1, t2, on='player_api_id')", false], ["t1 = Player[Player['height'] >= 180]\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85]\nt3 = pd.merge(t1, t2, on='player_api_id')\nanswer = t3['player_api_id'].unique()", false], ["t1 = Player[Player['height'] >= 180]\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85]\nt3 = t1.merge(t2, on='player_api_id')\nanswer = t3['player_api_id']", false], ["t1 = Player[Player['height'] >= 180]\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85]\nanswer = t1[t1['player_api_id'].isin(t2['player_api_id'])]['player_api_id']", false], ["t1 = Player[Player['height'] >= 180]\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85]\nanswer = pd.merge(t1, t2, on='player_api_id', how='inner')['player_api_id']", false], ["t1 = Player[Player['height'] >= 180]['player_api_id']\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85]['player_api_id']\nanswer = t1.merge(t2, on='player_api_id', how='inner')", false], ["t1 = Player[Player['height'] >= 180]\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85]\nanswer = t1.merge(t2, left_on='player_api_id', right_on='player_api_id')[['player_api_id']]", false], ["t1 = Player[Player['height'] >= 180]\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85]\nt3 = pd.merge(t1, t2, left_on='player_api_id', right_on='player_api_id')\nanswer = t3['player_api_id']", false], ["t1 = Player[Player['height'] >= 180][['player_api_id']]\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85][['player_api_id']]\nanswer = t1[t1['player_api_id'].isin(t2['player_api_id'])]", false], ["t1 = Player[Player['height'] >= 180]\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85]\nanswer = pd.merge(t1, t2, on='player_api_id')['player_api_id'].to_frame().drop_duplicates()", false], ["t1 = Player[Player['height'] > 180]\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85]\nanswer = t1.merge(t2, left_on='player_api_id', right_on='player_api_id')['player_api_id']", false], ["t1 = Player[Player['height'] >= 180]\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85]\nanswer = pd.merge(t1, t2, on='player_api_id')['player_api_id']", false], ["height_over_180 = Player[Player['height'] >= 180]['player_api_id']\noverall_rating_over_85 = Player_Attributes[Player_Attributes['overall_rating'] > 85]['player_api_id']\nanswer = height_over_180.intersection(overall_rating_over_85)", false], ["t1 = Player[Player['height'] >= 180]['player_api_id']\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85]['player_api_id']\nt3 = pd.merge(t1, t2, on='player_api_id', how='inner')\nanswer = t3['player_api_id']", false], ["t1 = Player[Player['height'] >= 180][['player_api_id']]\nt2 = Player_Attributes[Player_Attributes['overall_rating'] > 85][['player_api_id']]\nanswer = pd.merge(t1, t2, on='player_api_id', how='inner')", false]]}
+{"example": {"db_id": "cre_Docs_and_Epenses", "query": "SELECT count(*) ,  project_id FROM Documents WHERE document_type_code  =  \"BK\" GROUP BY project_id", "query_toks": ["SELECT", "count", "(", "*", ")", ",", "project_id", "FROM", "Documents", "WHERE", "document_type_code", "=", "``", "BK", "''", "GROUP", "BY", "project_id"], "query_toks_no_value": ["select", "count", "(", "*", ")", ",", "project_id", "from", "documents", "where", "document_type_code", "=", "value", "group", "by", "project_id"], "question": "How many documents are with document type code BK for each product id?", "question_toks": ["How", "many", "documents", "are", "with", "document", "type", "code", "BK", "for", "each", "product", "id", "?"], "sql": {"from": {"table_units": [["table_unit", 3]], "conds": []}, "select": [false, [[3, [0, [0, 0, false], null]], [0, [0, [0, 10, false], null]]]], "where": [[false, 2, [0, [0, 9, false], null], "\"BK\"", null]], "groupBy": [[0, 10, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Ref_Document_Types": ["Document_Type_Code", "Document_Type_Name", "Document_Type_Description"], "Ref_Budget_Codes": ["Budget_Type_Code", "Budget_Type_Description"], "Projects": ["Project_ID", "Project_Details"], "Documents": ["Document_ID", "Document_Type_Code", "Project_ID", "Document_Date", "Document_Name", "Document_Description", "Other_Details"], "Statements": ["Statement_ID", "Statement_Details"], "Documents_with_Expenses": ["Document_ID", "Budget_Type_Code", "Document_Details"], "Accounts": ["Account_ID", "Statement_ID", "Account_Details"]}, "answer": [[2, 30], [1, 35], [3, 105]]}, "program_result_list": [["t1 = Documents[Documents['document_type_code'] == 'bk']\nt2 = t1.groupby('project_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = Documents[Documents['document_type_code'] == 'BK']\nt2 = t1.groupby('project_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = Documents[Documents['document_type_code'] == 'BK'].groupby('project_id').size().rename('count')\nanswer = t1.to_frame().reset_index()", false], ["t1 = Documents[Documents['document_type_code'] == 'BK']\nt2 = t1.groupby('project_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = Documents[Documents['document_type_code'] == 'BK'].groupby('project_id').size().rename('count').to_frame().reset_index()\nanswer = t1", false], ["t1 = Documents[Documents['document_type_code'] == 'BK']\nt2 = t1.groupby('project_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = Documents[Documents['document_type_code'] == 'BK']\nt2 = t1.groupby('project_id').size().rename('count').to_frame().reset_index()\nanswer = t2", false], ["t1 = Documents[Documents['document_type_code'] == 'BK']\nanswer = t1.groupby('project_id').size().rename('count').to_frame().reset_index()", false], ["t1 = Documents[Documents['document_type_code'] == 'BK']\nt2 = t1.groupby('project_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = Documents.groupby(['project_id']).size().rename('count')\nt2 = t1.to_frame().reset_index()\nanswer = t2", false], ["t1 = Documents[Documents['document_type_code'] == 'BK']\nt2 = t1.groupby('project_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = Documents[Documents['document_type_code'] == 'BK']\nt2 = t1.groupby('project_id').size().rename('count').to_frame().reset_index()\nanswer = t2", false], ["t1 = Documents[Documents['document_type_code'] == 'BK']\nt2 = t1.groupby('project_id').size().rename('count').to_frame().reset_index()\nanswer = t2", false], ["t1 = Documents[Documents['document_type_code'] == 'BK']\nt2 = t1.groupby('project_id').size().rename('count').to_frame().reset_index()\nanswer = t2", false], ["t1 = Documents[Documents['document_type_code'] == 'bk']\nanswer = t1.groupby('project_id').size().rename('count').to_frame().reset_index()", false], ["t1 = Documents[Documents['document_type_code'] == 'BK']\nt2 = t1.groupby('project_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Documents, Ref_Document_Types, left_on='document_type_code', right_on='document_type_code')\nt2 = t1[t1['document_type_code'] == 'BK']\nanswer = t2.groupby('project_id').size().rename('count').to_frame().reset_index()", false], ["t1 = Documents[Documents['document_type_code'] == 'BK']\nanswer = t1.groupby('project_id').size().rename('count').to_frame().reset_index()", false], ["t1 = Documents[Documents['document_type_code'] == 'BK']\nanswer = t1.groupby('project_id').size().rename('count').to_frame().reset_index()", false], ["t1 = Documents[Documents['document_type_code'] == 'BK']\nt2 = t1.groupby('project_id').size().rename('count')\nanswer = t2.to_frame().reset_index()", false]]}
+{"example": {"db_id": "customers_and_addresses", "query": "SELECT avg(active_to_date - active_from_date) FROM customer_contact_channels", "query_toks": ["SELECT", "avg", "(", "active_to_date", "-", "active_from_date", ")", "FROM", "customer_contact_channels"], "query_toks_no_value": ["select", "avg", "(", "active_to_date", "-", "active_from_date", ")", "from", "customer_contact_channels"], "question": "What is the average time span of contact channels in the database?", "question_toks": ["What", "is", "the", "average", "time", "span", "of", "contact", "channels", "in", "the", "database", "?"], "sql": {"from": {"table_units": [["table_unit", 4]], "conds": []}, "select": [false, [[5, [1, [0, 23, false], [0, 22, false]]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Addresses": ["address_id", "address_content", "city", "zip_postcode", "state_province_county", "country", "other_address_details"], "Products": ["product_id", "product_details"], "Customers": ["customer_id", "payment_method", "customer_name", "date_became_customer", "other_customer_details"], "Customer_Addresses": ["customer_id", "address_id", "date_address_from", "address_type", "date_address_to"], "Customer_Contact_Channels": ["customer_id", "channel_code", "active_from_date", "active_to_date", "contact_number"], "Customer_Orders": ["order_id", "customer_id", "order_status", "order_date", "order_details"], "Order_Items": ["order_id", "product_id", "order_quantity"]}, "answer": [[0.85]]}, "program_result_list": [["t1 = Customer_Contact_Channels.copy()\nt1['active_to_date'] = pd.to_datetime(t1['active_to_date'])\nt1['active_from_date'] = pd.to_datetime(t1['active_from_date'])\nanswer = (t1['active_to_date'] - t1['active_from_date']).mean()", false], ["t1 = Customer_Contact_Channels['active_to_date'] - Customer_Contact_Channels['active_from_date']\nanswer = t1.mean()", false], ["t1 = Customer_Contact_Channels['active_to_date'] - Customer_Contact_Channels['active_from_date']\nanswer = t1.mean()", false], ["t1 = Customer_Contact_Channels.copy()\nt1['active_to_date'] = pd.to_datetime(t1['active_to_date'])\nt1['active_from_date'] = pd.to_datetime(t1['active_from_date'])\nt1['time_span'] = t1['active_to_date'] - t1['active_from_date']\nanswer = t1['time_span'].mean()", false], ["answer = Customer_Contact_Channels['active_to_date'] - Customer_Contact_Channels['active_from_date']\nanswer = answer.mean()", false], ["t1 = Customer_Contact_Channels.assign(time_span=Customer_Contact_Channels['active_to_date'] - Customer_Contact_Channels['active_from_date'])\nanswer = t1['time_span'].mean()", false], ["answer = Customer_Contact_Channels['active_to_date'] - Customer_Contact_Channels['active_from_date']", false], ["answer = Customer_Contact_Channels['active_to_date'] - Customer_Contact_Channels['active_from_date']", false], ["t1 = Customer_Contact_Channels['active_to_date'] - Customer_Contact_Channels['active_from_date']\nanswer = t1.mean()", false], ["t1 = Customer_Contact_Channels.copy()\nt1['active_to_date'] = pd.to_datetime(t1['active_to_date'])\nt1['active_from_date'] = pd.to_datetime(t1['active_from_date'])\nt2 = t1['active_to_date'] - t1['active_from_date']\nanswer = t2.mean()", false], ["t1 = Customer_Contact_Channels.copy()\nt1['diff'] = t1['active_to_date'] - t1['active_from_date']\nanswer = t1['diff'].mean()", false], ["t1 = Customer_Contact_Channels.copy()\nt1['date_diff'] = t1['active_to_date'] - t1['active_from_date']\nanswer = t1['date_diff'].mean()", false], ["t1 = Customer_Contact_Channels.copy()\nt1['active_to_date'] = pd.to_datetime(Customer_Contact_Channels['active_to_date'])\nt1['active_from_date'] = pd.to_datetime(Customer_Contact_Channels['active_from_date'])\nt2 = t1['active_to_date'] - t1['active_from_date']\nanswer = t2.mean()", false], ["t1 = Customer_Contact_Channels\nanswer = t1['active_to_date'] - t1['active_from_date']", false], ["answer = Customer_Contact_Channels['active_to_date'] - Customer_Contact_Channels['active_from_date']", false], ["answer = Customer_Contact_Channels['active_to_date'] - Customer_Contact_Channels['active_from_date']\nanswer = answer.mean()", false], ["t1 = Customer_Contact_Channels.copy()\nt1['active_to_date'] = pd.to_datetime(t1['active_to_date'])\nt1['active_from_date'] = pd.to_datetime(t1['active_from_date'])\nt1['time_span'] = t1['active_to_date'] - t1['active_from_date']\nanswer = t1['time_span'].mean()", false], ["t1 = Customer_Contact_Channels['active_to_date'] - Customer_Contact_Channels['active_from_date']\nanswer = t1.mean()", false], ["t1 = Customer_Contact_Channels['active_to_date'] - Customer_Contact_Channels['active_from_date']\nanswer = t1.mean()", false], ["t1 = Customer_Contact_Channels.copy()\nt1['diff'] = t1['active_to_date'] - t1['active_from_date']\nanswer = t1['diff'].mean()", false]]}
+{"example": {"db_id": "college_3", "query": "SELECT DName FROM DEPARTMENT WHERE Division  =  \"AS\" UNION SELECT DName FROM DEPARTMENT WHERE Division  =  \"EN\" AND Building  =  \"NEB\"", "query_toks": ["SELECT", "DName", "FROM", "DEPARTMENT", "WHERE", "Division", "=", "``", "AS", "''", "UNION", "SELECT", "DName", "FROM", "DEPARTMENT", "WHERE", "Division", "=", "``", "EN", "''", "AND", "Building", "=", "``", "NEB", "''"], "query_toks_no_value": ["select", "dname", "from", "department", "where", "division", "=", "value", "union", "select", "dname", "from", "department", "where", "division", "=", "value", "and", "building", "=", "value"], "question": "Find the names of departments that are either in division AS or in division EN and in Building NEB.", "question_toks": ["Find", "the", "names", "of", "departments", "that", "are", "either", "in", "division", "AS", "or", "in", "division", "EN", "and", "in", "Building", "NEB", "."], "sql": {"from": {"table_units": [["table_unit", 2]], "conds": []}, "select": [false, [[0, [0, [0, 19, false], null]]]], "where": [[false, 2, [0, [0, 18, false], null], "\"AS\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": {"from": {"table_units": [["table_unit", 2]], "conds": []}, "select": [false, [[0, [0, [0, 19, false], null]]]], "where": [[false, 2, [0, [0, 18, false], null], "\"EN\"", null], "and", [false, 2, [0, [0, 21, false], null], "\"NEB\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "except": null}, "db_table_headers": {"Student": ["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], "Faculty": ["FacID", "Lname", "Fname", "Rank", "Sex", "Phone", "Room", "Building"], "Department": ["DNO", "Division", "DName", "Room", "Building", "DPhone"], "Member_of": ["FacID", "DNO", "Appt_Type"], "Course": ["CID", "CName", "Credits", "Instructor", "Days", "Hours", "DNO"], "Minor_in": ["StuID", "DNO"], "Enrolled_in": ["StuID", "CID", "Grade"], "Gradeconversion": ["lettergrade", "gradepoint"]}, "answer": [["Anthropology"], ["Biology"], ["Biomedical Engineering"], ["Chemical Engineering"], ["Chemistry"], ["Classics"], ["Cognitive Science"], ["Computer Science"], ["Economics"], ["English"], ["French"], ["German"], ["Hispanic/Italian Studies"], ["History"], ["History of Art"], ["History of Science"], ["Mathematics"], ["Near Eastern Studies"], ["Philosophy"], ["Physics and Astronomy"], ["Political Science"], ["Psychology"]]}, "program_result_list": [["t1 = Department[Department['division'] == 'AS']\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')]\nanswer = pd.concat([t1, t2]).drop_duplicates(['dname'])['dname']", false], ["t1 = Department[Department['division'] == 'AS']\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')]\nanswer = pd.concat([t1, t2], axis=0)[['dname']]", false], ["t1 = Department[Department['division'] == 'AS']\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')]\nanswer = pd.concat([t1, t2])['dname'].unique()", false], ["t1 = Department[Department['division'] == 'AS']\nt2 = Department[Department['division'] == 'EN']\nt3 = Department[Department['building'] == 'NEB']\nt4 = pd.concat([t1, t2, t3])\nt5 = t4.drop_duplicates(subset=['dname'])\nanswer = t5['dname']", false], ["t1 = Department[Department['division'] == 'AS']\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')]\nanswer = pd.concat([t1, t2])[['dname']]", false], ["t1 = Department[Department['division'] == 'AS']\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')]\nt3 = pd.concat([t1, t2])\nanswer = t3['dname']", false], ["t1 = Department[Department['division'] == 'AS'].copy()\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')].copy()\nt3 = pd.concat([t1, t2])\nanswer = t3['dname']", false], ["t1 = Department[Department['division'] == 'AS']\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')]\nanswer = pd.concat([t1, t2])[['dname']]", false], ["t1 = Department[Department['division'] == 'AS']\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')]\nanswer = pd.concat([t1, t2])['dname'].drop_duplicates()", false], ["t1 = Department[Department['division'] == 'AS']\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')]\nanswer = pd.concat([t1, t2])['dname']", false], ["t1 = Department[Department['division'] == 'AS']\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')]\nanswer = pd.concat([t1, t2], axis=0)['dname'].unique()", false], ["t1 = Department[Department['division'] == 'AS']\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')]\nanswer = pd.concat([t1, t2])['dname'].unique()", false], ["t1 = Department[Department['division'] == 'AS']\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')]\nt3 = pd.merge(t1, t2, how='outer', on=['dno', 'division', 'dname', 'room', 'building', 'dphone'])\nanswer = t3['dname']", false], ["t1 = Department[Department['division'] == 'AS']\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')]\nanswer = pd.concat([t1, t2], ignore_index=True)['dname']", false], ["t1 = Department[Department['division'] == 'AS']\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')]\nt3 = pd.concat([t1, t2])\nanswer = t3['dname']", false], ["t1 = Department[Department['division'] == 'AS']\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')]\nanswer = pd.concat([t1['dname'], t2['dname']]).unique()", false], ["t1 = Department[Department['division'] == 'AS'][['dname']]\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')][['dname']]\nanswer = pd.concat([t1, t2]).drop_duplicates()", false], ["t1 = Department[Department['division'] == 'AS'][['dname']]\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')][['dname']]\nanswer = pd.concat([t1, t2]).drop_duplicates(keep='first')", false], ["t1 = Department[Department['division'] == 'AS']\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')]\nanswer = pd.concat([t1, t2], ignore_index=True)['dname']", false], ["t1 = Department[Department['division'] == 'AS']\nt2 = Department[(Department['division'] == 'EN') & (Department['building'] == 'NEB')]\nt3 = pd.concat([t1, t2])\nanswer = t3['dname']", false]]}
+{"example": {"db_id": "college_1", "query": "SELECT dept_address FROM department WHERE dept_name  =  'History'", "query_toks": ["SELECT", "dept_address", "FROM", "department", "WHERE", "dept_name", "=", "'History", "'"], "query_toks_no_value": ["select", "dept_address", "from", "department", "where", "dept_name", "=", "value"], "question": "Where is the history department?", "question_toks": ["Where", "is", "the", "history", "department", "?"], "sql": {"from": {"table_units": [["table_unit", 2]], "conds": []}, "select": [false, [[0, [0, [0, 15, false], null]]]], "where": [[false, 2, [0, [0, 12, false], null], "\"History\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"CLASS": ["CLASS_CODE", "CRS_CODE", "CLASS_SECTION", "CLASS_TIME", "CLASS_ROOM", "PROF_NUM"], "COURSE": ["CRS_CODE", "DEPT_CODE", "CRS_DESCRIPTION", "CRS_CREDIT"], "DEPARTMENT": ["DEPT_CODE", "DEPT_NAME", "SCHOOL_CODE", "EMP_NUM", "DEPT_ADDRESS", "DEPT_EXTENSION"], "EMPLOYEE": ["EMP_NUM", "EMP_LNAME", "EMP_FNAME", "EMP_INITIAL", "EMP_JOBCODE", "EMP_HIREDATE", "EMP_DOB"], "ENROLL": ["CLASS_CODE", "STU_NUM", "ENROLL_GRADE"], "PROFESSOR": ["EMP_NUM", "DEPT_CODE", "PROF_OFFICE", "PROF_EXTENSION", "PROF_HIGH_DEGREE"], "STUDENT": ["STU_NUM", "STU_LNAME", "STU_FNAME", "STU_INIT", "STU_DOB", "STU_HRS", "STU_CLASS", "STU_GPA", "STU_TRANSFER", "DEPT_CODE", "STU_PHONE", "PROF_NUM"]}, "answer": [["DRE 156, Box 284"]]}, "program_result_list": [["t1 = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']\nanswer = t1['dept_address']", false], ["t1 = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']\nanswer = t1['dept_address']", false], ["answer = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']['dept_address']", false], ["t1 = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']\nanswer = t1['dept_address']", false], ["t1 = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']\nanswer = t1['dept_address']", false], ["answer = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']['dept_address']", false], ["answer = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']['dept_address']", false], ["answer = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']['dept_address']", false], ["answer = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']['dept_address']", false], ["t1 = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']\nanswer = t1['dept_address']", false], ["t1 = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']\nanswer = t1['dept_address']", false], ["t1 = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']\nanswer = t1['dept_address']", false], ["t1 = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']\nanswer = t1['dept_address']", false], ["t1 = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']\nanswer = t1['dept_address']", false], ["t1 = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']\nanswer = t1['dept_address']", false], ["t1 = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']\nanswer = t1['dept_address']", false], ["answer = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']['dept_address']", false], ["answer = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']['dept_address']", false], ["t1 = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']\nanswer = t1['dept_address']", false], ["t1 = DEPARTMENT[DEPARTMENT['dept_name'] == 'history']\nanswer = t1['dept_address']", false]]}
+{"example": {"db_id": "driving_school", "query": "SELECT sum(T1.lesson_time) FROM Lessons AS T1 JOIN Customers AS T2 ON T1.customer_id  = T2.customer_id WHERE T2.first_name = \"Rylan\" AND T2.last_name = \"Goodwin\";", "query_toks": ["SELECT", "sum", "(", "T1.lesson_time", ")", "FROM", "Lessons", "AS", "T1", "JOIN", "Customers", "AS", "T2", "ON", "T1.customer_id", "=", "T2.customer_id", "WHERE", "T2.first_name", "=", "``", "Rylan", "''", "AND", "T2.last_name", "=", "``", "Goodwin", "''", ";"], "query_toks_no_value": ["select", "sum", "(", "t1", ".", "lesson_time", ")", "from", "lessons", "as", "t1", "join", "customers", "as", "t2", "on", "t1", ".", "customer_id", "=", "t2", ".", "customer_id", "where", "t2", ".", "first_name", "=", "value", "and", "t2", ".", "last_name", "=", "value"], "question": "How long is the total lesson time took by the customer named Rylan Goodwin?", "question_toks": ["How", "long", "is", "the", "total", "lesson", "time", "took", "by", "the", "customer", "named", "Rylan", "Goodwin", "?"], "sql": {"from": {"table_units": [["table_unit", 5], ["table_unit", 3]], "conds": [[false, 2, [0, [0, 34, false], null], [0, 18, false], null]]}, "select": [false, [[4, [0, [0, 39, false], null]]]], "where": [[false, 2, [0, [0, 23, false], null], "\"Rylan\"", null], "and", [false, 2, [0, [0, 24, false], null], "\"Goodwin\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Addresses": ["address_id", "line_1_number_building", "city", "zip_postcode", "state_province_county", "country"], "Staff": ["staff_id", "staff_address_id", "nickname", "first_name", "middle_name", "last_name", "date_of_birth", "date_joined_staff", "date_left_staff"], "Vehicles": ["vehicle_id", "vehicle_details"], "Customers": ["customer_id", "customer_address_id", "customer_status_code", "date_became_customer", "date_of_birth", "first_name", "last_name", "amount_outstanding", "email_address", "phone_number", "cell_mobile_phone_number"], "Customer_Payments": ["customer_id", "datetime_payment", "payment_method_code", "amount_payment"], "Lessons": ["lesson_id", "customer_id", "lesson_status_code", "staff_id", "vehicle_id", "lesson_date", "lesson_time", "price"]}, "answer": [[11]]}, "program_result_list": [["t1 = pd.merge(Lessons, Customers, left_on='customer_id', right_on='customer_id')\nt2 = t1[(t1['first_name'] == 'Rylan') & (t1['last_name'] == 'Goodwin')]\nanswer = t2['lesson_time'].sum()", false], ["t1 = pd.merge(Lessons, Customers, left_on='customer_id', right_on='customer_id')\nt2 = t1[(t1['first_name'] == 'Rylan') & (t1['last_name'] == 'Goodwin')]\nanswer = t2['lesson_time'].sum()", false], ["t1 = pd.merge(Lessons, Customers, left_on='customer_id', right_on='customer_id')\nt2 = t1[(t1['first_name'] == 'Rylan') & (t1['last_name'] == 'Goodwin')]\nanswer = t2['lesson_time'].sum()", false], ["t1 = pd.merge(Lessons, Customers, left_on='customer_id', right_on='customer_id')\nt2 = t1[(t1['first_name'] == 'Rylan') & (t1['last_name'] == 'Goodwin')]\nanswer = t2['lesson_time'].sum()", false], ["t1 = pd.merge(Lessons, Customers, left_on='customer_id', right_on='customer_id')\nt2 = t1[(t1['first_name'] == 'Rylan') & (t1['last_name'] == 'Goodwin')]\nanswer = t2['lesson_time'].sum()", false], ["t1 = pd.merge(Lessons, Customers, left_on='customer_id', right_on='customer_id')\nt2 = t1[(t1['first_name'] == 'Rylan') & (t1['last_name'] == 'Goodwin')]\nanswer = t2['lesson_time'].sum()", false], ["t3 = Lessons.merge(Customers, left_on='customer_id', right_on='customer_id')\nt4 = t3[(t3['first_name'] == 'Rylan') & (t3['last_name'] == 'Goodwin')]\nanswer = t4['lesson_time'].sum()", false], ["t1 = pd.merge(Lessons, Customers, left_on='customer_id', right_on='customer_id')\nt2 = t1[(t1['first_name'] == 'Rylan') & (t1['last_name'] == 'Goodwin')]\nanswer = t2['lesson_time'].sum()", false], ["t1 = pd.merge(Lessons, Customers, left_on='customer_id', right_on='customer_id')\nt2 = t1[(t1['first_name'] == 'Rylan') & (t1['last_name'] == 'Goodwin')]\nanswer = t2['lesson_time'].sum()", false], ["t1 = pd.merge(Lessons, Customers, left_on='customer_id', right_on='customer_id')\nt2 = t1[(t1['first_name'] == 'Rylan') & (t1['last_name'] == 'Goodwin')]\nanswer = t2['lesson_time'].sum()", false], ["t1 = pd.merge(Customers, Lessons, left_on='customer_id', right_on='customer_id')\nt2 = t1[(t1['first_name'] == 'Rylan') & (t1['last_name'] == 'Goodwin')]\nanswer = t2['lesson_time'].sum()", false], ["t1 = pd.merge(Lessons, Customers, left_on='customer_id', right_on='customer_id')\nt2 = t1[t1['first_name'] == 'Rylan']\nt3 = t2[t2['last_name'] == 'Goodwin']\nanswer = t3['lesson_time'].sum()", false], ["t1 = pd.merge(Lessons, Customers, left_on='customer_id', right_on='customer_id')\nt2 = t1[(t1['first_name'] == 'Rylan') & (t1['last_name'] == 'Goodwin')]\nanswer = t2['lesson_time'].sum()", false], ["t1 = pd.merge(Lessons, Customers, left_on='customer_id', right_on='customer_id')\nt2 = t1[(t1['first_name'] == 'Rylan') & (t1['last_name'] == 'Goodwin')]\nanswer = t2['lesson_time'].sum()", false], ["t1 = pd.merge(Lessons, Customers, left_on='customer_id', right_on='customer_id')\nt2 = t1[(t1['first_name'] == 'Rylan') & (t1['last_name'] == 'Goodwin')]\nanswer = t2['lesson_time'].sum()", false], ["t1 = pd.merge(Lessons, Customers, left_on='customer_id', right_on='customer_id')\nt2 = t1[(t1['first_name'] == 'Rylan') & (t1['last_name'] == 'Goodwin')]\nanswer = t2['lesson_time'].sum()", false], ["t1 = pd.merge(Lessons, Customers, left_on='customer_id', right_on='customer_id')\nt1 = t1[(t1['first_name'] == 'Rylan') & (t1['last_name'] == 'Goodwin')]\nanswer = t1['lesson_time'].sum()", false], ["t1 = pd.merge(Lessons, Customers, left_on='customer_id', right_on='customer_id')\nt2 = t1[(t1['first_name'] == 'Rylan') & (t1['last_name'] == 'Goodwin')]\nanswer = t2['lesson_time'].sum()", false], ["t1 = pd.merge(Lessons, Customers, left_on='customer_id', right_on='customer_id')\nt2 = t1[(t1['first_name'] == 'Rylan') & (t1['last_name'] == 'Goodwin')]\nanswer = t2['lesson_time'].sum()", false], ["t1 = pd.merge(Lessons, Customers, left_on='customer_id', right_on='customer_id')\nt2 = t1[(t1['first_name'] == 'Rylan') & (t1['last_name'] == 'Goodwin')]\nanswer = t2['lesson_time'].sum()", false]]}
+{"example": {"db_id": "tracking_grants_for_research", "query": "SELECT count(*) FROM Project_outcomes WHERE outcome_code  =  'Patent'", "query_toks": ["SELECT", "count", "(", "*", ")", "FROM", "Project_outcomes", "WHERE", "outcome_code", "=", "'Patent", "'"], "query_toks_no_value": ["select", "count", "(", "*", ")", "from", "project_outcomes", "where", "outcome_code", "=", "value"], "question": "How many Patent outcomes are generated from all the projects?", "question_toks": ["How", "many", "Patent", "outcomes", "are", "generated", "from", "all", "the", "projects", "?"], "sql": {"from": {"table_units": [["table_unit", 5]], "conds": []}, "select": [false, [[3, [0, [0, 0, false], null]]]], "where": [[false, 2, [0, [0, 21, false], null], "\"Patent\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Document_Types": ["document_type_code", "document_description"], "Documents": ["document_id", "document_type_code", "grant_id", "sent_date", "response_received_date", "other_details"], "Grants": ["grant_id", "organisation_id", "grant_amount", "grant_start_date", "grant_end_date", "other_details"], "Organisation_Types": ["organisation_type", "organisation_type_description"], "Organisations": ["organisation_id", "organisation_type", "organisation_details"], "Project_Outcomes": ["project_id", "outcome_code", "outcome_details"], "Project_Staff": ["staff_id", "project_id", "role_code", "date_from", "date_to", "other_details"], "Projects": ["project_id", "organisation_id", "project_details"], "Research_Outcomes": ["outcome_code", "outcome_description"], "Research_Staff": ["staff_id", "employer_organisation_id", "staff_details"], "Staff_Roles": ["role_code", "role_description"], "Tasks": ["task_id", "project_id", "task_details", "eg Agree Objectives"]}, "answer": [[5]]}, "program_result_list": [["t1 = Project_Outcomes[Project_Outcomes['outcome_code'] == 'patent']\nanswer = t1.shape[0]", false], ["t1 = pd.merge(Project_Outcomes, Research_Outcomes, on='outcome_code')\nanswer = t1[t1['outcome_description'] == 'patent'].shape[0]", false], ["t1 = pd.merge(Project_Outcomes, Research_Outcomes, left_on='outcome_code', right_on='outcome_code')\nt2 = t1[t1['outcome_description'] == 'Patent']\nanswer = t2.shape[0]", false], ["t1 = Project_Outcomes[Project_Outcomes['outcome_code'] == 'patent']\nanswer = t1.count()[0]", false], ["t1 = Project_Outcomes[Project_Outcomes['outcome_code'] == 'patent']\nanswer = t1.size", false], ["t1 = Project_Outcomes[Project_Outcomes['outcome_code'] == 'patent']\nanswer = t1.shape[0]", false], ["t1 = Project_Outcomes[Project_Outcomes['outcome_code'] == 'patent']\nanswer = t1['outcome_code'].count()", false], ["t1 = Project_Outcomes[Project_Outcomes['outcome_code'] == 'patent'].groupby('project_id')\nanswer = t1.size().sum()", false], ["t1 = Project_Outcomes[Project_Outcomes['outcome_code'] == 'patent']\nanswer = t1.size", false], ["t1 = Project_Outcomes[Project_Outcomes['outcome_code'] == 'patent']\nanswer = t1.shape[0]", false], ["t1 = Project_Outcomes[Project_Outcomes['outcome_code'] == 'patent']\nanswer = t1.shape[0]", false], ["t1 = Project_Outcomes[Project_Outcomes['outcome_code'] == 'patent']\nanswer = t1['outcome_code'].count()", false], ["t1 = Project_Outcomes[Project_Outcomes['outcome_code'] == 'patent']\nanswer = t1.count()", false], ["t1 = Project_Outcomes[Project_Outcomes['outcome_code'] == 'patent']\nanswer = t1.count()[0]", false], ["t1 = pd.merge(Project_Outcomes, Research_Outcomes, on='outcome_code')\nanswer = t1[t1['outcome_description'] == 'patent'].count()", false], ["t1 = Project_Outcomes[Project_Outcomes['outcome_code'] == 'patent']\nanswer = len(t1)", false], ["t1 = Project_Outcomes[Project_Outcomes['outcome_code'] == 'patent']\nanswer = t1.shape[0]", false], ["t1 = pd.merge(Project_Outcomes, Research_Outcomes, left_on='outcome_code', right_on='outcome_code')\nt2 = t1[t1['outcome_description'] == 'Patent']\nanswer = t2['outcome_code'].count()", false], ["t1 = Project_Outcomes[Project_Outcomes['outcome_code'] == 'patent']\nanswer = t1['outcome_code'].count()", false], ["t1 = Project_Outcomes[Project_Outcomes['outcome_code'] == 'patent']\nanswer = t1['outcome_code'].count()", false]]}
+{"example": {"db_id": "e_learning", "query": "SELECT count(course_id) FROM Student_Course_Enrolment", "query_toks": ["SELECT", "count", "(", "course_id", ")", "FROM", "Student_Course_Enrolment"], "query_toks_no_value": ["select", "count", "(", "course_id", ")", "from", "student_course_enrolment"], "question": "Find the number of distinct courses that have enrolled students.", "question_toks": ["Find", "the", "number", "of", "distinct", "courses", "that", "have", "enrolled", "students", "."], "sql": {"from": {"table_units": [["table_unit", 4]], "conds": []}, "select": [false, [[3, [0, [0, 27, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Course_Authors_and_Tutors": ["author_id", "author_tutor_ATB", "login_name", "password", "personal_name", "middle_name", "family_name", "gender_mf", "address_line_1"], "Students": ["student_id", "date_of_registration", "date_of_latest_logon", "login_name", "password", "personal_name", "middle_name", "family_name"], "Subjects": ["subject_id", "subject_name"], "Courses": ["course_id", "author_id", "subject_id", "course_name", "course_description"], "Student_Course_Enrolment": ["registration_id", "student_id", "course_id", "date_of_enrolment", "date_of_completion"], "Student_Tests_Taken": ["registration_id", "date_test_taken", "test_result"]}, "answer": [[20]]}, "program_result_list": [["answer = Student_Course_Enrolment['course_id'].nunique()", false], ["t1 = Student_Course_Enrolment['course_id'].nunique()\nt2 = Student_Course_Enrolment['course_id'].count()\nanswer = t1", false], ["t1 = Student_Course_Enrolment['course_id'].nunique()\nanswer = t1", false], ["answer = len(Student_Course_Enrolment['course_id'].unique())", false], ["t1 = Student_Course_Enrolment.groupby('course_id').size().rename('count').to_frame().reset_index()\nanswer = t1['count'].sum()", false], ["answer = Student_Course_Enrolment['course_id'].nunique()", false], ["t1 = Student_Course_Enrolment['course_id'].unique().size\nanswer = t1", false], ["t1 = Student_Course_Enrolment.groupby('course_id').size().rename('count')\nanswer = t1[t1 > 0].count()", false], ["answer = Student_Course_Enrolment['course_id'].nunique()", false], ["t1 = Student_Course_Enrolment['course_id'].unique().size\nanswer = t1", false], ["t1 = Student_Course_Enrolment['course_id'].unique()\nanswer = len(t1)", false], ["t1 = Student_Course_Enrolment['course_id'].unique()\nanswer = len(t1)", false], ["answer = Student_Course_Enrolment['course_id'].unique().size", false], ["t1 = Student_Course_Enrolment.groupby('course_id').size().rename('count')\nanswer = t1.size", false], ["answer = Student_Course_Enrolment['course_id'].nunique()", false], ["t1 = Student_Course_Enrolment.groupby('course_id').size().rename('count').to_frame().reset_index()\nanswer = t1['count'].sum()", false], ["answer = Student_Course_Enrolment['course_id'].nunique()", false], ["t1 = Student_Course_Enrolment['course_id'].unique()\nanswer = len(t1)", false], ["t1 = Student_Course_Enrolment['course_id'].nunique()\nanswer = t1", false], ["t1 = Student_Course_Enrolment[['course_id']]\nanswer = [len(t1['course_id'].unique())]", false]]}
+{"example": {"db_id": "csu_1", "query": "SELECT T2.faculty FROM campuses AS T1 JOIN faculty AS T2 ON T1.id  =  t2.campus JOIN degrees AS T3 ON T1.id  =  t3.campus AND t2.year  =  t3.year WHERE t2.year  =  2002 ORDER BY t3.degrees DESC LIMIT 1", "query_toks": ["SELECT", "T2.faculty", "FROM", "campuses", "AS", "T1", "JOIN", "faculty", "AS", "T2", "ON", "T1.id", "=", "t2.campus", "JOIN", "degrees", "AS", "T3", "ON", "T1.id", "=", "t3.campus", "AND", "t2.year", "=", "t3.year", "WHERE", "t2.year", "=", "2002", "ORDER", "BY", "t3.degrees", "DESC", "LIMIT", "1"], "query_toks_no_value": ["select", "t2", ".", "faculty", "from", "campuses", "as", "t1", "join", "faculty", "as", "t2", "on", "t1", ".", "id", "=", "t2", ".", "campus", "join", "degrees", "as", "t3", "on", "t1", ".", "id", "=", "t3", ".", "campus", "and", "t2", ".", "year", "=", "t3", ".", "year", "where", "t2", ".", "year", "=", "value", "order", "by", "t3", ".", "degrees", "desc", "limit", "value"], "question": "How many faculty lines are there in the university that conferred the most number of degrees in year 2002?", "question_toks": ["How", "many", "faculty", "lines", "are", "there", "in", "the", "university", "that", "conferred", "the", "most", "number", "of", "degrees", "in", "year", "2002", "?"], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 5], ["table_unit", 2]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 21, false], null], "and", [false, 2, [0, [0, 1, false], null], [0, 10, false], null], "and", [false, 2, [0, [0, 22, false], null], [0, 9, false], null]]}, "select": [false, [[0, [0, [0, 23, false], null]]]], "where": [[false, 2, [0, [0, 22, false], null], 2002.0, null]], "groupBy": [], "having": [], "orderBy": ["desc", [[0, [0, 11, false], null]]], "limit": 1, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Campuses": ["Id", "Campus", "Location", "County", "Year"], "csu_fees": ["Campus", "Year", "CampusFee"], "degrees": ["Year", "Campus", "Degrees"], "discipline_enrollments": ["Campus", "Discipline", "Year", "Undergraduate", "Graduate"], "enrollments": ["Campus", "Year", "TotalEnrollment_AY", "FTE_AY"], "faculty": ["Campus", "Year", "Faculty"]}, "answer": [[1555.7]]}, "program_result_list": [["t1 = pd.merge(Campuses, faculty, on='id')\nt2 = pd.merge(t1, degrees, left_on=['id', 'year'], right_on=['campus', 'year'])\nt3 = t2[t2['year'] == 2002]\nt4 = t3.sort_values('degrees', ascending=False).head(1)\nanswer = t4['faculty']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = pd.merge(t1, degrees, left_on=['id', 'year'], right_on=['campus', 'year'])\nt3 = t2[t2['year'] == 2002]\nt4 = t3.sort_values(by='degrees', ascending=False).head(1)\nanswer = t4['faculty']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = pd.merge(t1, degrees, left_on=['id', 'year'], right_on=['campus', 'year'])\nt3 = t2[t2['year'] == 2002].sort_values(by='degrees', ascending=False).head(1)\nanswer = t3['faculty']", false], ["t1 = pd.merge(Campuses, faculty, on='campus')\nt2 = pd.merge(t1, degrees, on=['campus', 'year'])\nt3 = t2[t2['year'] == 2002]\nanswer = t3.sort_values('degrees', ascending=False).head(1)[['faculty']]", false], ["t1 = pd.merge(Campuses, faculty, on='id')\nt2 = pd.merge(t1, degrees, on=['id', 'year'])\nt3 = t2[t2['year'] == 2002]\nt4 = t3.sort_values('degrees', ascending=False).head(1)\nanswer = t4['faculty']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = pd.merge(t1, degrees, left_on=['id', 'year'], right_on=['campus', 'year'])\nt3 = t2[t2['year'] == 2002]\nanswer = t3.sort_values('degrees', ascending=False).head(1)['faculty']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = pd.merge(t1, degrees, left_on=['id', 'year'], right_on=['campus', 'year'])\nt3 = t2[t2['year'] == 2002].sort_values(by='degrees', ascending=False).head(1)\nanswer = t3['faculty']", false], ["t1 = pd.merge(Campuses, faculty, left_on=['id', 'year'], right_on=['campus', 'year'])\nt2 = pd.merge(t1, degrees, left_on=['id', 'year'], right_on=['campus', 'year'])\nt3 = t2[t2['year'] == 2002].sort_values(by=['degrees'], ascending=False).head(1)\nanswer = t3['faculty']", false], ["t1 = pd.merge(Campuses, faculty, on='id')\nt2 = pd.merge(t1, degrees, on=['id', 'year'])\nt3 = t2[t2['year'] == 2002]\nt4 = t3.sort_values('degrees', ascending=False).head(1)\nanswer = t4['faculty']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = pd.merge(t1, degrees, left_on=['id', 'year'], right_on=['campus', 'year'])\nt3 = t2[t2['year'] == 2002]\nanswer = t3['faculty'].head(1)", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus', suffixes=('_Campuses', '_faculty'))\nt2 = pd.merge(t1, degrees, left_on=['id', 'year'], right_on=['campus', 'year'])\nt3 = t2[t2['year'] == 2002]\nanswer = t3.sort_values(by=['degrees'], ascending=False).head(1)['faculty']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = pd.merge(t1, degrees, left_on=['id', 'year'], right_on=['campus', 'year'])\nt3 = t2.sort_values('degrees', ascending=False).head(1)\nanswer = t3['faculty']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = pd.merge(t1, degrees, left_on=['id', 'year'], right_on=['campus', 'year'])\nt3 = t2[t2['year'] == 2002].sort_values(by='degrees', ascending=False).head(1)\nanswer = t3['faculty']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = pd.merge(t1, degrees, left_on=['id', 'year'], right_on=['campus', 'year'])\nt3 = t2[t2['year'] == 2002].sort_values('degrees', ascending=False).head(1).reset_index(drop=True)\nanswer = t3['faculty']", false], ["t1 = pd.merge(Campuses, faculty, left_on=['id', 'year'], right_on=['campus', 'year'])\nt2 = pd.merge(t1, degrees, left_on=['campus', 'year'], right_on=['campus', 'year'])\nt3 = t2[t2['year'] == 2002].sort_values('degrees', ascending=False).head(1)\nanswer = t3['faculty']", false], ["t1 = pd.merge(Campuses, faculty, on='campus')\nt2 = pd.merge(t1, degrees, on=['campus', 'year'])\nt3 = t2[t2['year'] == 2002].sort_values('degrees', ascending=False).head(1).to_frame().reset_index()\nanswer = t3['faculty']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = pd.merge(t1, degrees, left_on=['id', 'year'], right_on=['campus', 'year'])\nt3 = t2[t2['year'] == 2002].sort_values(by='degrees', ascending=False).head(1).to_frame()\nanswer = t3['faculty']", false], ["t1 = pd.merge(Campuses, faculty, left_on='id', right_on='campus')\nt2 = pd.merge(t1, degrees, left_on=['id', 'year'], right_on=['campus', 'year'])\nt3 = t2[t2['year'] == 2002].sort_values('degrees', ascending=False).head(1).reset_index()\nanswer = t3['faculty']", false], ["t1 = pd.merge(Campuses, faculty, on=['id', 'year'])\nt2 = pd.merge(t1, degrees, on=['campus', 'year'])\nt3 = t2[t2['year'] == 2002]\nt4 = t3.sort_values('degrees', ascending=False).head(1)\nanswer = t4['faculty']", false], ["t1 = pd.merge(Campuses, faculty, left_on=['id', 'year'], right_on=['campus', 'year'])\nt2 = pd.merge(t1, degrees, left_on=['id', 'year'], right_on=['campus', 'year'])\nt3 = t2[t2['year'] == 2002].sort_values('degrees', ascending=False).head(1).reset_index()\nanswer = t3['faculty']", false]]}
+{"example": {"db_id": "formula_1", "query": "SELECT DISTINCT T1.forename FROM drivers AS T1 JOIN driverstandings AS T2 ON T1.driverid = T2.driverid WHERE T2.position = 1 AND T2.wins = 1", "query_toks": ["SELECT", "DISTINCT", "T1.forename", "FROM", "drivers", "AS", "T1", "JOIN", "driverstandings", "AS", "T2", "ON", "T1.driverid", "=", "T2.driverid", "WHERE", "T2.position", "=", "1", "AND", "T2.wins", "=", "1"], "query_toks_no_value": ["select", "distinct", "t1", ".", "forename", "from", "drivers", "as", "t1", "join", "driverstandings", "as", "t2", "on", "t1", ".", "driverid", "=", "t2", ".", "driverid", "where", "t2", ".", "position", "=", "value", "and", "t2", ".", "wins", "=", "value"], "question": "What are all the different first names of the drivers who are in position as standing and won?", "question_toks": ["What", "are", "all", "the", "different", "first", "names", "of", "the", "drivers", "who", "are", "in", "position", "as", "standing", "and", "won", "?"], "sql": {"from": {"table_units": [["table_unit", 2], ["table_unit", 8]], "conds": [[false, 2, [0, [0, 18, false], null], [0, 63, false], null]]}, "select": [true, [[0, [0, [0, 22, false], null]]]], "where": [[false, 2, [0, [0, 65, false], null], 1.0, null], "and", [false, 2, [0, [0, 67, false], null], 1.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"circuits": ["circuitId", "circuitRef", "name", "location", "country", "lat", "lng", "alt", "url"], "races": ["raceId", "year", "round", "circuitId", "name", "date", "time", "url"], "drivers": ["driverId", "driverRef", "number", "code", "forename", "surname", "dob", "nationality", "url"], "status": ["statusId", "status"], "seasons": ["year", "url"], "constructors": ["constructorId", "constructorRef", "name", "nationality", "url"], "constructorStandings": ["constructorStandingsId", "raceId", "constructorId", "points", "position", "positionText", "wins"], "results": ["resultId", "raceId", "driverId", "constructorId", "number", "grid", "position", "positionText", "positionOrder", "points", "laps", "time", "milliseconds", "fastestLap", "rank", "fastestLapTime", "fastestLapSpeed", "statusId"], "driverStandings": ["driverStandingsId", "raceId", "driverId", "points", "position", "positionText", "wins"], "constructorResults": ["constructorResultsId", "raceId", "constructorId", "points", "status"], "qualifying": ["qualifyId", "raceId", "driverId", "constructorId", "number", "position", "q1", "q2", "q3"], "pitStops": ["raceId", "driverId", "stop", "lap", "time", "duration", "milliseconds"], "lapTimes": ["raceId", "driverId", "lap", "position", "time", "milliseconds"]}, "answer": [["Lewis"], ["Kimi"], ["Robert"], ["Giancarlo"], ["Fernando"], ["Michael"], ["Mika"], ["Damon"], ["Nigel"], ["Jenson"], ["Alain"], ["Ayrton"], ["David"], ["Eddie"], ["Nelson"], ["Elio"], ["Michele"], ["Keke"], ["Alan"], ["Carlos"], ["Nino"], ["Juan"], ["Piero"], ["Alberto"], ["Maurice"], ["Peter"], ["Stirling"], ["Phil"], ["Graham"], ["Jackie"], ["Jack"], ["Mike"], ["Bruce"], ["Jim"], ["Pedro"], ["Denny"], ["Mario"], ["Emerson"], ["Clay"], ["Niki"], ["Patrick"], ["Jody"], ["Jacques"], ["Sebastian"], ["Nico"]]}, "program_result_list": [["t1 = pd.merge(drivers, driverStandings, on='driverid')\nt1 = t1[(t1['position'] == 1) & (t1['wins'] == 1)][['forename']]\nanswer = t1['forename'].unique()", false], ["t1 = pd.merge(drivers, driverStandings, left_on='driverid', right_on='driverid')\nt2 = t1[(t1['position'] == 1) & (t1['wins'] == 1)]\nanswer = t2['forename'].unique()", false], ["t1 = pd.merge(drivers, driverStandings, left_on='driverid', right_on='driverid')\nt2 = t1[(t1['position'] == 1) & (t1['wins'] == 1)]\nanswer = t2['forename'].unique()", false], ["t1 = pd.merge(driverStandings, drivers, left_on='driverid', right_on='driverid')\nt2 = t1[(t1['position'] == 1) & (t1['wins'] == 1)]\nanswer =t2['forename'].unique()", false], ["t1 = pd.merge(drivers, driverStandings, left_on='driverid', right_on='driverid')\nt2 = t1[(t1['position'] == 1) & (t1['wins'] == 1)]\nanswer = t2['forename'].unique()", false], ["t1 = pd.merge(drivers, driverStandings, left_on='driverid', right_on='driverid')\nt2 = t1[(t1['position'] == 1) & (t1['wins'] == 1)]\nanswer = t2['forename'].unique()", false], ["t1 = pd.merge(drivers, driverStandings, left_on='driverid', right_on='driverid')\nt2 = t1[(t1['position'] == 1) & (t1['wins'] == 1)]\nanswer = t2['forename'].unique()", false], ["t1 = pd.merge(drivers, driverStandings, left_on='driverid', right_on='driverid')\nt2 = t1[(t1['position'] == 1) & (t1['wins'] == 1)]\nanswer = t2['forename'].unique()", false], ["t1 = pd.merge(drivers, driverStandings, left_on='driverid', right_on='driverid')\nt2 = t1[(t1['position'] == 1) & (t1['wins'] == 1)]\nanswer = t2['forename'].unique()", false], ["t1 = pd.merge(drivers, driverStandings, left_on='driverid', right_on='driverid')\nt2 = t1[(t1['position'] == 1) & (t1['wins'] == 1)]\nanswer = t2['forename'].unique()", false], ["t1 = pd.merge(drivers, driverStandings, left_on='driverid', right_on='driverid')\nt2 = t1[(t1['position'] == 1) & (t1['wins'] == 1)]\nanswer = t2['forename'].unique()", false], ["t1 = pd.merge(drivers, driverStandings, left_on='driverid', right_on='driverid')\nt2 = t1[(t1['position'] == 1) & (t1['wins'] == 1)]\nanswer = t2['forename'].unique()", false], ["t1 = pd.merge(drivers, driverStandings, left_on='driverid', right_on='driverid')\nt2 = t1[(t1['position'] == 1) & (t1['wins'] == 1)]\nanswer = t2['forename'].unique()", false], ["t1 = pd.merge(drivers, driverStandings, left_on='driverid', right_on='driverid')\nt2 = t1[(t1['position'] == 1) & (t1['wins'] == 1)]\nanswer = t2['forename'].unique()", false], ["t1 = pd.merge(drivers, driverStandings, on='driverid')\nt2 = t1[(t1['position'] == 1) & (t1['wins'] == 1)]\nanswer = t2['forename'].unique()", false], ["t1 = pd.merge(drivers, driverStandings, left_on='driverid', right_on='driverid')\nt2 = t1[(t1['position'] == 1) & (t1['wins'] == 1)]\nanswer = t2['forename'].unique()", false], ["t1 = pd.merge(drivers, driverStandings, left_on='driverid', right_on='driverid')\nt2 = t1[(t1['position'] == 1) & (t1['wins'] == 1)]\nanswer = t2['forename'].unique()", false], ["t1 = pd.merge(drivers, driverStandings, left_on='driverid', right_on='driverid')\nt2 = t1[t1['position'] == 1]\nt3 = t2[t2['wins'] == 1]\nanswer = t3['forename'].unique()", false], ["t1 = pd.merge(drivers, driverStandings, left_on='driverid', right_on='driverid')\nt2 = t1[(t1['position']==1) & (t1['wins']==1)]\nanswer = t2['forename'].unique()", false], ["t1 = pd.merge(drivers, driverStandings, left_on='driverid', right_on='driverid')\nt2 = t1[(t1['position'] == 1) & (t1['wins'] == 1)]\nanswer = t2['forename'].unique()", false]]}
+{"example": {"db_id": "e_government", "query": "SELECT t1.party_email FROM parties AS t1 JOIN party_forms AS t2 ON t1.party_id  =  t2.party_id WHERE t2.form_id  =  (SELECT form_id FROM party_forms GROUP BY form_id ORDER BY count(*) DESC LIMIT 1)", "query_toks": ["SELECT", "t1.party_email", "FROM", "parties", "AS", "t1", "JOIN", "party_forms", "AS", "t2", "ON", "t1.party_id", "=", "t2.party_id", "WHERE", "t2.form_id", "=", "(", "SELECT", "form_id", "FROM", "party_forms", "GROUP", "BY", "form_id", "ORDER", "BY", "count", "(", "*", ")", "DESC", "LIMIT", "1", ")"], "query_toks_no_value": ["select", "t1", ".", "party_email", "from", "parties", "as", "t1", "join", "party_forms", "as", "t2", "on", "t1", ".", "party_id", "=", "t2", ".", "party_id", "where", "t2", ".", "form_id", "=", "(", "select", "form_id", "from", "party_forms", "group", "by", "form_id", "order", "by", "count", "(", "*", ")", "desc", "limit", "value", ")"], "question": "What are the party emails associated with parties that used the party form that is the most common?", "question_toks": ["What", "are", "the", "party", "emails", "associated", "with", "parties", "that", "used", "the", "party", "form", "that", "is", "the", "most", "common", "?"], "sql": {"from": {"table_units": [["table_unit", 5], ["table_unit", 8]], "conds": [[false, 2, [0, [0, 28, false], null], [0, 41, false], null]]}, "select": [false, [[0, [0, [0, 31, false], null]]]], "where": [[false, 2, [0, [0, 42, false], null], {"from": {"table_units": [["table_unit", 8]], "conds": []}, "select": [false, [[0, [0, [0, 42, false], null]]]], "where": [], "groupBy": [[0, 42, false]], "having": [], "orderBy": ["desc", [[0, [3, 0, false], null]]], "limit": 1, "intersect": null, "union": null, "except": null}, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Addresses": ["address_id", "line_1_number_building", "town_city", "zip_postcode", "state_province_county", "country"], "Services": ["service_id", "service_type_code", "service_name", "service_descriptio"], "Forms": ["form_id", "form_type_code", "service_id", "form_number", "form_name", "form_description"], "Individuals": ["individual_id", "individual_first_name", "individual_middle_name", "inidividual_phone", "individual_email", "individual_address", "individual_last_name"], "Organizations": ["organization_id", "date_formed", "organization_name", "uk_vat_number"], "Parties": ["party_id", "payment_method_code", "party_phone", "party_email"], "Organization_Contact_Individuals": ["individual_id", "organization_id", "date_contact_from", "date_contact_to"], "Party_Addresses": ["party_id", "address_id", "date_address_from", "address_type_code", "date_address_to"], "Party_Forms": ["party_id", "form_id", "date_completion_started", "form_status_code", "date_fully_completed"], "Party_Services": ["booking_id", "customer_id", "service_id", "service_datetime", "booking_made_date"]}, "answer": [["alanna.boehm@example.net"], ["lucinda83@example.org"], ["erick.mills@example.com"]]}, "program_result_list": [["t1 = pd.merge(Parties, Party_Forms, left_on='party_id', right_on='party_id')\nt2 = t1.groupby('form_id').size().rename('count').to_frame().reset_index()\nform_id = t2.sort_values('count', ascending=False).head(1)['form_id'].values[0]\nt3 = t1[t1['form_id'] == form_id]\nanswer = t3['party_email']", false], ["t1 = pd.merge(Parties, Party_Forms, left_on='party_id', right_on='party_id')\nt2 = t1.groupby('form_id').size().rename('count').sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = pd.merge(t1, t2, on='form_id')\nanswer = t3['party_email']", false], ["t1 = pd.merge(Parties, Party_Forms, left_on='party_id', right_on='party_id')\nt2 = t1.groupby(['form_id']).size().rename('count').to_frame().reset_index()\nt3 = t2.sort_values('count', ascending=False).head(1)\nt4 = pd.merge(t3, t1, on='form_id')\nanswer = t4['party_email']", false], ["t1 = pd.merge(Forms, Party_Forms, left_on='form_id', right_on='form_id')\nt2 = pd.merge(Parties, t1, left_on='party_id', right_on='party_id')\nt3 = t2.groupby('form_id').size().sort_values(ascending=False)\nt4 = t3.head(1).to_frame().reset_index()\nt5 = t4.iloc[0]['form_id']\nanswer = t2[t2['form_id'] == t5][['party_email']]", false], ["t1 = pd.merge(Parties, Party_Forms, left_on='party_id', right_on='party_id')\nmost_common_form = Party_Forms.groupby('form_id').size().sort_values(ascending=False).head(1).reset_index()[['form_id']]\nt2 = pd.merge(t1, most_common_form, left_on='form_id', right_on='form_id')\nanswer = t2['party_email']", false], ["t1 = pd.merge(Parties, Party_Forms, left_on='party_id', right_on='party_id')\nt2 = t1.groupby('form_id').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nt4 = t1[t1['form_id'] == t3['form_id'].values[0]]\nanswer = t4['party_email']", false], ["t1 = pd.merge(parties, party_forms, left_on='party_id', right_on='party_id')\nt2 = t1.groupby('form_id').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = t3.iloc[0]['form_id']\nt4 = t1[t1['form_id'] == t3]\nanswer = t4['party_email']", false], ["t1 = pd.merge(Parties, Party_Forms, left_on='party_id', right_on='party_id')\nt2 = t1.groupby('form_id').size().rename('count').sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = pd.merge(t1, t2, on='form_id')\nanswer = t3['party_email']", false], ["t1 = pd.merge(Parties, Party_Forms, left_on='party_id', right_on='party_id')\nt2 = Party_Forms.groupby('form_id').size().sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = pd.merge(t1, t2, on='form_id')\nanswer = t3['party_email']", false], ["t1 = pd.merge(Party_Forms, Parties, left_on='party_id', right_on='party_id')\nt2 = t1.groupby('form_id').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nt4 = t3.loc[0, 'form_id']\nt5 = t1[t1['form_id'] == t4]\nanswer = t5['party_email']", false], ["t1 = pd.merge(Parties, Party_Forms, left_on='party_id', right_on='party_id')\nanswer = t1[t1['form_id'] == t1['form_id'].value_counts().index[0]]['party_email']", false], ["t1 = pd.merge(Parties, Party_Forms, left_on='party_id', right_on='party_id')\nt2 = t1[t1['form_id'] == Party_Forms.groupby('form_id').size().sort_values(ascending=False).head(1).index[0]]\nanswer = t2['party_email']", false], ["t1 = pd.merge(Parties, Party_Forms, on='party_id')\nt2 = t1.groupby('form_id').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nt4 = pd.merge(t3, t1, on='form_id')\nanswer = t4['party_email']", false], ["t1 = Party_Forms.groupby('form_id').size().rename('count').sort_values(ascending=False).head(1).to_frame().reset_index()\nt2 = pd.merge(Party_Forms, Parties, left_on='party_id', right_on='party_id')\nt3 = pd.merge(t2, t1, on='form_id')\nanswer = t3['party_email']", false], ["t1 = pd.merge(Parties, Party_Forms, left_on='party_id', right_on='party_id')\nt2 = t1.groupby('form_id').size().rename('count').to_frame().reset_index()\nt3 = t2.sort_values(by='count', ascending=False).head(1).to_frame().reset_index()\nt4 = t1[t1['form_id'] == t3.iloc[0,0]]\nanswer = t4['party_email']", false], ["t1 = pd.merge(Parties, Party_Forms, left_on='party_id', right_on='party_id')\nt2 = t1.groupby('form_id').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nt4 = t3[['form_id']]\nt5 = pd.merge(t1, t4, on='form_id')\nanswer = t5['party_email']", false], ["t1 = pd.merge(Parties, Party_Forms, left_on='party_id', right_on='party_id')\nt2 = t1.groupby('form_id').size().rename('count').to_frame().reset_index()\nt3 = t2.sort_values(by='count', ascending=False).head(1).reset_index()\nt4 = t1[t1['form_id'] == t3.iloc[0]['form_id']]\nanswer = t4['party_email']", false], ["t1 = pd.merge(Parties, Party_Forms, left_on='party_id', right_on='party_id')\nt2 = Party_Forms.groupby('form_id').size().rename('count').to_frame().reset_index().sort_values(by='count', ascending=False).head(1)\nt3 = pd.merge(t1, t2, on='form_id')\nanswer = t3['party_email']", false], ["t1 = pd.merge(Party_Forms, Parties, left_on='party_id', right_on='party_id')\nt2 = Party_Forms.groupby('form_id').size().rename('count')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nt4 = pd.merge(t1, t3, left_on='form_id', right_on='form_id')\nanswer = t4['party_email']", false], ["t1 = pd.merge(Parties, Party_Forms, left_on='party_id', right_on='party_id')\nanswer = t1[t1['form_id'] == t1['form_id'].value_counts().head(1).index[0]]['party_email']", false]]}
+{"example": {"db_id": "hr_1", "query": "SELECT first_name ,  last_name ,  hire_date ,  salary ,  department_id FROM employees WHERE first_name NOT LIKE '%M%' ORDER BY department_id", "query_toks": ["SELECT", "first_name", ",", "last_name", ",", "hire_date", ",", "salary", ",", "department_id", "FROM", "employees", "WHERE", "first_name", "NOT", "LIKE", "'", "%", "M", "%", "'", "ORDER", "BY", "department_id"], "query_toks_no_value": ["select", "first_name", ",", "last_name", ",", "hire_date", ",", "salary", ",", "department_id", "from", "employees", "where", "first_name", "not", "like", "value", "order", "by", "department_id"], "question": "What are the full name, hire data, salary and department id for employees without the letter M in their first name, ordered by ascending department id?", "question_toks": ["What", "are", "the", "full", "name", ",", "hire", "data", ",", "salary", "and", "department", "id", "for", "employees", "without", "the", "letter", "M", "in", "their", "first", "name", ",", "ordered", "by", "ascending", "department", "id", "?"], "sql": {"from": {"table_units": [["table_unit", 4]], "conds": []}, "select": [false, [[0, [0, [0, 15, false], null]], [0, [0, [0, 16, false], null]], [0, [0, [0, 19, false], null]], [0, [0, [0, 21, false], null]], [0, [0, [0, 24, false], null]]]], "where": [[true, 9, [0, [0, 15, false], null], "\"%M%\"", null]], "groupBy": [], "having": [], "orderBy": ["asc", [[0, [0, 24, false], null]]], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"regions": ["REGION_ID", "REGION_NAME"], "countries": ["COUNTRY_ID", "COUNTRY_NAME", "REGION_ID"], "departments": ["DEPARTMENT_ID", "DEPARTMENT_NAME", "MANAGER_ID", "LOCATION_ID"], "jobs": ["JOB_ID", "JOB_TITLE", "MIN_SALARY", "MAX_SALARY"], "employees": ["EMPLOYEE_ID", "FIRST_NAME", "LAST_NAME", "EMAIL", "PHONE_NUMBER", "HIRE_DATE", "JOB_ID", "SALARY", "COMMISSION_PCT", "MANAGER_ID", "DEPARTMENT_ID"], "job_history": ["EMPLOYEE_ID", "START_DATE", "END_DATE", "JOB_ID", "DEPARTMENT_ID"], "locations": ["LOCATION_ID", "STREET_ADDRESS", "POSTAL_CODE", "CITY", "STATE_PROVINCE", "COUNTRY_ID"]}, "answer": [["Jennifer", "Whalen", "1987-09-25", 4400, 10], ["Pat", "Fay", "1987-09-27", 6000, 20], ["Den", "Raphaely", "1987-07-01", 11000, 30], ["Alexander", "Khoo", "1987-07-02", 3100, 30], ["Shelli", "Baida", "1987-07-03", 2900, 30], ["Sigal", "Tobias", "1987-07-04", 2800, 30], ["Guy", "Himuro", "1987-07-05", 2600, 30], ["Karen", "Colmenares", "1987-07-06", 2500, 30], ["Susan", "Mavris", "1987-09-28", 6500, 40], ["Shanta", "Vollman", "1987-07-10", 6500, 50], ["Kevin", "Mourgos", "1987-07-11", 5800, 50], ["Julia", "Nayer", "1987-07-12", 3200, 50], ["Irene", "Mikkilineni", "1987-07-13", 2700, 50], ["Steven", "Markle", "1987-07-15", 2200, 50], ["Laura", "Bissot", "1987-07-16", 3300, 50], ["TJ", "Olson", "1987-07-19", 2100, 50], ["Jason", "Mallin", "1987-07-20", 3300, 50], ["Ki", "Gee", "1987-07-22", 2400, 50], ["Hazel", "Philtanker", "1987-07-23", 2200, 50], ["Renske", "Ladwig", "1987-07-24", 3600, 50], ["Stephen", "Stiles", "1987-07-25", 3200, 50], ["John", "Seo", "1987-07-26", 2700, 50], ["Joshua", "Patel", "1987-07-27", 2500, 50], ["Trenna", "Rajs", "1987-07-28", 3500, 50], ["Curtis", "Davies", "1987-07-29", 3100, 50], ["Randall", "Matos", "1987-07-30", 2600, 50], ["Peter", "Vargas", "1987-07-31", 2500, 50], ["Winston", "Taylor", "1987-09-05", 3200, 50], ["Jean", "Fleaur", "1987-09-06", 3100, 50], ["Girard", "Geoni", "1987-09-08", 2800, 50], ["Nandita", "Sarchand", "1987-09-09", 4200, 50], ["Alexis", "Bull", "1987-09-10", 4100, 50], ["Julia", "Dellinger", "1987-09-11", 3400, 50], ["Anthony", "Cabrio", "1987-09-12", 3000, 50], ["Kelly", "Chung", "1987-09-13", 3800, 50], ["Jennifer", "Dilly", "1987-09-14", 3600, 50], ["Randall", "Perkins", "1987-09-16", 2500, 50], ["Sarah", "Bell", "1987-09-17", 4000, 50], ["Britney", "Everett", "1987-09-18", 3900, 50], ["Vance", "Jones", "1987-09-20", 2800, 50], ["Alana", "Walsh", "1987-09-21", 3100, 50], ["Kevin", "Feeney", "1987-09-22", 3000, 50], ["Donald", "OConnell", "1987-09-23", 2600, 50], ["Douglas", "Grant", "1987-09-24", 2600, 50], ["Alexander", "Hunold", "1987-06-20", 9000, 60], ["Bruce", "Ernst", "1987-06-21", 6000, 60], ["David", "Austin", "1987-06-22", 4800, 60], ["Valli", "Pataballa", "1987-06-23", 4800, 60], ["Diana", "Lorentz", "1987-06-24", 4200, 60], ["John", "Russell", "1987-08-01", 14000, 80], ["Karen", "Partners", "1987-08-02", 13500, 80], ["Alberto", "Errazuriz", "1987-08-03", 12000, 80], ["Gerald", "Cambrault", "1987-08-04", 11000, 80], ["Eleni", "Zlotkey", "1987-08-05", 10500, 80], ["Peter", "Tucker", "1987-08-06", 10000, 80], ["David", "Bernstein", "1987-08-07", 9500, 80], ["Peter", "Hall", "1987-08-08", 9000, 80], ["Christopher", "Olsen", "1987-08-09", 8000, 80], ["Nanette", "Cambrault", "1987-08-10", 7500, 80], ["Oliver", "Tuvault", "1987-08-11", 7000, 80], ["Janette", "King", "1987-08-12", 10000, 80], ["Patrick", "Sully", "1987-08-13", 9500, 80], ["Allan", "McEwen", "1987-08-14", 9000, 80], ["Lindsey", "Smith", "1987-08-15", 8000, 80], ["Louise", "Doran", "1987-08-16", 7500, 80], ["Sarath", "Sewall", "1987-08-17", 7000, 80], ["Clara", "Vishney", "1987-08-18", 10500, 80], ["Danielle", "Greene", "1987-08-19", 9500, 80], ["David", "Lee", "1987-08-21", 6800, 80], ["Sundar", "Ande", "1987-08-22", 6400, 80], ["Lisa", "Ozer", "1987-08-24", 11500, 80], ["Harrison", "Bloom", "1987-08-25", 10000, 80], ["Tayler", "Fox", "1987-08-26", 9600, 80], ["Elizabeth", "Bates", "1987-08-28", 7300, 80], ["Sundita", "Kumar", "1987-08-29", 6100, 80], ["Ellen", "Abel", "1987-08-30", 11000, 80], ["Alyssa", "Hutton", "1987-08-31", 8800, 80], ["Jonathon", "Taylor", "1987-09-01", 8600, 80], ["Jack", "Livingston", "1987-09-02", 8400, 80], ["Charles", "Johnson", "1987-09-04", 6200, 80], ["Steven", "King", "1987-06-17", 24000, 90], ["Neena", "Kochhar", "1987-06-18", 17000, 90], ["Lex", "De Haan", "1987-06-19", 17000, 90], ["Nancy", "Greenberg", "1987-06-25", 12000, 100], ["Daniel", "Faviet", "1987-06-26", 9000, 100], ["John", "Chen", "1987-06-27", 8200, 100], ["Luis", "Popp", "1987-06-30", 6900, 100], ["Shelley", "Higgins", "1987-09-30", 12000, 110]]}, "program_result_list": [["answer = employees[~employees['first_name'].str.contains('m', case=False, na=False)]\nanswer = answer[['first_name', 'last_name', 'hire_date', 'salary', 'department_id']].sort_values(by='department_id')", false], ["answer = employees[employees['first_name'].str.contains('M', regex=False) == False][['first_name', 'last_name', 'hire_date', 'salary', 'department_id']].sort_values(by='department_id')", false], ["t1 = employees[employees['first_name'].str.contains('M', regex=False) == False]\nanswer = t1[['first_name', 'last_name', 'hire_date', 'salary', 'department_id']].sort_values('department_id')", false], ["t1 = employees[employees['first_name'].str.contains('M') == False]\nanswer = t1[['first_name', 'last_name', 'hire_date', 'salary', 'department_id']].sort_values(by='department_id')", false], ["answer = employees[~employees['first_name'].str.contains('M')].sort_values('department_id')[['first_name', 'last_name', 'hire_date', 'salary', 'department_id']]", false], ["t1 = employees[employees['first_name'].str.contains('M', case=False) == False]\nanswer = t1[['first_name', 'last_name', 'hire_date', 'salary', 'department_id']].sort_values(by='department_id')", false], ["t1 = employees[employees['first_name'].str.contains('M') == False]\nanswer = t1[['first_name', 'last_name', 'hire_date', 'salary', 'department_id']].sort_values(by='department_id')", false], ["t1 = employees[employees['first_name'].str.contains(\"M\") == False]\nanswer = t1[['first_name', 'last_name', 'hire_date', 'salary', 'department_id']].sort_values('department_id')", false], ["answer = employees[~employees['first_name'].str.contains('M')].sort_values(by='department_id')[['first_name', 'last_name', 'hire_date', 'salary', 'department_id']]", false], ["t1 = employees[employees['first_name'].str.contains('m', case=False) == False]\nanswer = t1[['first_name', 'last_name', 'hire_date', 'salary', 'department_id']].sort_values('department_id')", false], ["t1 = employees[employees['first_name'].str.contains('m') == False]\nanswer = t1[['first_name', 'last_name', 'hire_date', 'salary', 'department_id']].sort_values('department_id')", false], ["answer = employees[employees['first_name'].str.contains('M') == False][['first_name', 'last_name', 'hire_date', 'salary', 'department_id']].sort_values(by='department_id')", false], ["t1 = employees[employees['first_name'].str.contains('M', regex=False) == False]\nanswer = t1[['first_name', 'last_name', 'hire_date', 'salary', 'department_id']].sort_values('department_id')", false], ["t1 = employees[employees['first_name'].str.contains('m') == False]\nanswer = t1.sort_values('department_id', ascending=True)[['first_name', 'last_name', 'hire_date', 'salary', 'department_id']]", false], ["answer = employees[~employees['first_name'].str.contains('M')][['first_name', 'last_name', 'hire_date', 'salary', 'department_id']].sort_values(by='department_id')", false], ["t1 = employees[employees['first_name'].str.contains('m', regex=False, case=False) == False]\nanswer = t1[['first_name', 'last_name', 'hire_date', 'salary', 'department_id']].sort_values(by=['department_id'])", false], ["t1 = employees[employees['first_name'].str.contains('[mM]') == False]\nanswer = t1.sort_values('department_id')[['first_name', 'last_name', 'hire_date', 'salary', 'department_id']]", false], ["answer = employees[employees['first_name'].str.contains('M', case=False) == False][['first_name', 'last_name', 'hire_date', 'salary', 'department_id']].sort_values(by='department_id')", false], ["t1 = employees[employees['first_name'].str.contains('m', case=False) == False]\nanswer = t1[['first_name', 'last_name', 'hire_date', 'salary', 'department_id']].sort_values(by='department_id')", false], ["t1 = employees[employees['first_name'].str.contains('M', na=False) == False]\nanswer = t1[['first_name', 'last_name', 'hire_date', 'salary', 'department_id']].sort_values('department_id')", false]]}
+{"example": {"db_id": "voter_2", "query": "SELECT DISTINCT T1.Advisor FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID  =  T2.Treasurer_Vote WHERE T2.Election_Cycle  =  \"Spring\"", "query_toks": ["SELECT", "DISTINCT", "T1.Advisor", "FROM", "STUDENT", "AS", "T1", "JOIN", "VOTING_RECORD", "AS", "T2", "ON", "T1.StuID", "=", "T2.Treasurer_Vote", "WHERE", "T2.Election_Cycle", "=", "``", "Spring", "''"], "query_toks_no_value": ["select", "distinct", "t1", ".", "advisor", "from", "student", "as", "t1", "join", "voting_record", "as", "t2", "on", "t1", ".", "stuid", "=", "t2", ".", "treasurer_vote", "where", "t2", ".", "election_cycle", "=", "value"], "question": "Find the distinct Advisor of students who have treasurer votes in the spring election cycle.", "question_toks": ["Find", "the", "distinct", "Advisor", "of", "students", "who", "have", "treasurer", "votes", "in", "the", "spring", "election", "cycle", "."], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 15, false], null]]}, "select": [true, [[0, [0, [0, 7, false], null]]]], "where": [[false, 2, [0, [0, 11, false], null], "\"Spring\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Student": ["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], "Voting_record": ["StuID", "Registration_Date", "Election_Cycle", "President_Vote", "Vice_President_Vote", "Secretary_Vote", "Treasurer_Vote", "Class_President_Vote", "Class_Senator_Vote"]}, "answer": [[5718], [7134]]}, "program_result_list": [["t1 = pd.merge(Student, Voting_record, left_on='stuid', right_on='treasurer_vote')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, on='stuid')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, left_on='stuid', right_on='treasurer_vote')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, on='stuid')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, left_on='stuid', right_on='treasurer_vote')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, left_on='stuid', right_on='treasurer_vote')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, left_on='stuid', right_on='stuid')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, left_on='stuid', right_on='stuid')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, left_on='stuid', right_on='stuid')\nt2 = t1[t1['election_cycle']== 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, left_on='stuid', right_on='treasurer_vote')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, left_on='stuid', right_on='treasurer_vote')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, left_on='stuid', right_on='stuid')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, left_on='stuid', right_on='treasurer_vote')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, left_on='stuid', right_on='treasurer_vote')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, left_on='stuid', right_on='treasurer_vote')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, left_on='stuid', right_on='treasurer_vote')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, left_on='stuid', right_on='treasurer_vote')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, left_on='stuid', right_on='stuid')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, left_on='stuid', right_on='treasurer_vote')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false], ["t1 = pd.merge(Student, Voting_record, on='stuid')\nt2 = t1[t1['election_cycle'] == 'spring']\nanswer = t2['advisor'].unique()", false]]}
+{"example": {"db_id": "college_2", "query": "SELECT i_id FROM advisor AS T1 JOIN student AS T2 ON T1.s_id  =  T2.id WHERE T2.dept_name  =  'History'", "query_toks": ["SELECT", "i_id", "FROM", "advisor", "AS", "T1", "JOIN", "student", "AS", "T2", "ON", "T1.s_id", "=", "T2.id", "WHERE", "T2.dept_name", "=", "'History", "'"], "query_toks_no_value": ["select", "i_id", "from", "advisor", "as", "t1", "join", "student", "as", "t2", "on", "t1", ".", "s_id", "=", "t2", ".", "id", "where", "t2", ".", "dept_name", "=", "value"], "question": "What is the id of the instructor who advises of all students from History department?", "question_toks": ["What", "is", "the", "id", "of", "the", "instructor", "who", "advises", "of", "all", "students", "from", "History", "department", "?"], "sql": {"from": {"table_units": [["table_unit", 8], ["table_unit", 6]], "conds": [[false, 2, [0, [0, 37, false], null], [0, 27, false], null]]}, "select": [false, [[0, [0, [0, 38, false], null]]]], "where": [[false, 2, [0, [0, 29, false], null], "\"History\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"classroom": ["building", "room_number", "capacity"], "department": ["dept_name", "building", "budget"], "course": ["course_id", "title", "dept_name", "credits"], "instructor": ["ID", "name", "dept_name", "salary"], "section": ["course_id", "sec_id", "semester", "year", "building", "room_number", "time_slot_id"], "teaches": ["ID", "course_id", "sec_id", "semester", "year"], "student": ["ID", "name", "dept_name", "tot_cred"], "takes": ["ID", "course_id", "sec_id", "semester", "year", "grade"], "advisor": ["s_ID", "i_ID"], "time_slot": ["time_slot_id", "day", "start_hr", "start_min", "end_hr", "end_min"], "prereq": ["course_id", "prereq_id"]}, "answer": [["19368"], ["90643"], ["73623"], ["79081"], ["58558"], ["74426"], ["22591"], ["63395"], ["50885"], ["3335"], ["35579"], ["64871"], ["50330"], ["80759"], ["99052"], ["4233"], ["50885"], ["48507"], ["74426"], ["90376"], ["19368"], ["4034"], ["79653"], ["50885"], ["43779"], ["6569"], ["81991"], ["63287"], ["37687"], ["59795"], ["35579"], ["65931"], ["77346"], ["63395"], ["81991"], ["95709"], ["48507"], ["42782"], ["57180"], ["16807"], ["35579"], ["73623"], ["41930"], ["41930"], ["97302"], ["15347"], ["63287"], ["31955"], ["16807"], ["4034"], ["25946"], ["50330"], ["77346"], ["74426"], ["74426"], ["41930"], ["97302"], ["14365"], ["90643"], ["36897"], ["37687"], ["79653"], ["48570"], ["57180"], ["64871"], ["28400"], ["52647"], ["90376"], ["36897"], ["48570"], ["4233"], ["65931"], ["37687"], ["63395"], ["48570"], ["3199"], ["37687"], ["77346"], ["48507"], ["77346"], ["64871"], ["58558"], ["14365"], ["74426"], ["96895"], ["52647"], ["64871"], ["79653"], ["78699"], ["3335"], ["15347"], ["3335"], ["97302"], ["90376"], ["48507"], ["16807"], ["65931"], ["31955"], ["4233"], ["58558"], ["74426"], ["79081"], ["48507"], ["42782"], ["74426"], ["16807"], ["42782"], ["36897"], ["80759"], ["95709"], ["74420"], ["35579"], ["6569"], ["36897"], ["48507"], ["4034"], ["34175"]]}, "program_result_list": [["t1 = pd.merge(advisor, student, left_on='s_id', right_on='id')\nt2 = t1[t1['dept_name_x'] == 'History']\nanswer = t2['i_id'].unique()", false], ["t1 = pd.merge(advisor, student, left_on='s_id', right_on='id')\nt2 = t1[t1['dept_name_x'] == 'history']\nanswer = t2['i_id'].unique()", false], ["t1 = pd.merge(advisor, student, left_on='s_id', right_on='id')\nt2 = t1[t1['dept_name_x'] == 'History']\nanswer = t2['i_id'].unique()", false], ["t1 = pd.merge(advisor, student, left_on='s_id', right_on='id')\nt2 = t1[t1['dept_name'] == 'History']\nanswer = t2['i_id'].unique()", false], ["t1 = pd.merge(advisor, student, on='id')\nt2 = t1[t1['dept_name'] == 'History']\nanswer = t2['i_id']", false], ["t1 = pd.merge(advisor, student, left_on='s_id', right_on='id')\nt2 = t1[t1['dept_name'] == 'History']\nanswer = t2['i_id'].unique()", false], ["t1 = pd.merge(advisor, student, left_on='s_id', right_on='id')\nt2 = t1[t1['dept_name_y'] == 'History']\nanswer = t2['i_id'].unique()", false], ["t1 = pd.merge(advisor, student, left_on='s_id', right_on='id')\nt2 = t1[t1['dept_name'] == 'History']\nanswer = t2['i_id'].unique()", false], ["t1 = pd.merge(advisor, student, left_on='s_id', right_on='id')\nt2 = t1[t1['dept_name_x'] == 'History']\nanswer = t2['i_id'].unique()", false], ["t1 = pd.merge(advisor, student, left_on='s_id', right_on='id')\nt2 = t1[t1['dept_name_y'] == 'History']\nanswer = t2['i_id'].unique()", false], ["t1 = pd.merge(advisor, student, left_on='s_id', right_on='id')\nt2 = t1[t1['dept_name_x'] == 'history']\nanswer = t2['i_id'].unique()", false], ["t1 = pd.merge(advisor, student, left_on='s_id', right_on='id')\nt2 = t1[t1['dept_name_x'] == 'History']\nanswer = t2['i_id'].unique()", false], ["t1 = pd.merge(advisor, student, left_on='s_id', right_on='id')\nt2 = t1[t1['dept_name_x'] == 'History'].drop_duplicates(subset='i_id')\nanswer = t2['i_id']", false], ["t1 = pd.merge(advisor, student, left_on='s_id', right_on='id')\nt2 = t1[t1['dept_name_x'] == 'history']\nanswer = t2['i_id'].unique()", false], ["t1 = pd.merge(advisor, student, left_on='s_id', right_on='id')\nt2 = t1[t1['dept_name_x'] == 'history']\nanswer = t2['i_id'].unique()", false], ["t1 = pd.merge(advisor, student, left_on='s_id', right_on='id')\nt2 = t1[t1['dept_name'] == 'history']\nanswer = t2['i_id'].unique()", false], ["t1 = pd.merge(advisor, student, left_on='s_id', right_on='id')\nt2 = t1[t1['dept_name_y'] == 'History']\nanswer = t2['i_id'].unique()", false], ["t1 = pd.merge(advisor, student, left_on='s_id', right_on='id')\nt2 = t1[t1['dept_name_x'] == 'History']\nanswer = t2['i_id'].unique()", false], ["t1 = pd.merge(advisor, student, left_on='s_id', right_on='id')\nt2 = t1[t1['dept_name_x'] == 'history']\nanswer = t2['i_id'].unique()", false], ["t1= pd.merge(student[student['dept_name'] == 'History'], advisor, left_on='id', right_on='s_id')\nt2 = t1.groupby('i_id').size().rename('count')\nanswer = t2[t2 == t2.max()].to_frame().reset_index()", false]]}
+{"example": {"db_id": "wrestler", "query": "SELECT T2.Name ,  T1.Team FROM elimination AS T1 JOIN wrestler AS T2 ON T1.Wrestler_ID  =  T2.Wrestler_ID ORDER BY T2.Days_held DESC", "query_toks": ["SELECT", "T2.Name", ",", "T1.Team", "FROM", "elimination", "AS", "T1", "JOIN", "wrestler", "AS", "T2", "ON", "T1.Wrestler_ID", "=", "T2.Wrestler_ID", "ORDER", "BY", "T2.Days_held", "DESC"], "query_toks_no_value": ["select", "t2", ".", "name", ",", "t1", ".", "team", "from", "elimination", "as", "t1", "join", "wrestler", "as", "t2", "on", "t1", ".", "wrestler_id", "=", "t2", ".", "wrestler_id", "order", "by", "t2", ".", "days_held", "desc"], "question": "What are the names of wrestlers and their teams in elimination, ordered descending by days held?", "question_toks": ["What", "are", "the", "names", "of", "wrestlers", "and", "their", "teams", "in", "elimination", ",", "ordered", "descending", "by", "days", "held", "?"], "sql": {"from": {"table_units": [["table_unit", 1], ["table_unit", 0]], "conds": [[false, 2, [0, [0, 8, false], null], [0, 1, false], null]]}, "select": [false, [[0, [0, [0, 2, false], null]], [0, [0, [0, 9, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": ["desc", [[0, [0, 4, false], null]]], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"wrestler": ["Wrestler_ID", "Name", "Reign", "Days_held", "Location", "Event"], "Elimination": ["Elimination_ID", "Wrestler_ID", "Team", "Eliminated_By", "Elimination_Move", "Time"]}, "answer": [["Gran Hamada", "Team Batista"], ["\u00daltimo Drag\u00f3n \u00a7", "Team Batista"], ["El Samurai \u00a7", "Team Orton"], ["Rey Misterio Sr.", "Team Orton"], ["Fishman", "Team Batista"], ["El Samurai", "Team Batista"]]}, "program_result_list": [["t1 = pd.merge(Elimination, wrestler, left_on='wrestler_id', right_on='wrestler_id')\nanswer = t1[['name', 'team']].sort_values('days_held', ascending=False)", false], ["t1 = pd.merge(Elimination, wrestler, left_on='wrestler_id', right_on='wrestler_id')\nt2 = t1.sort_values(by='days_held', ascending=False)\nanswer = t2[['name', 'team']]", false], ["t1 = pd.merge(Elimination, wrestler, left_on='wrestler_id', right_on='wrestler_id')\nanswer = t1[['name', 'team']].sort_values('days_held', ascending=False)", false], ["t1 = pd.merge(Elimination, wrestler, left_on='wrestler_id', right_on='wrestler_id')\nanswer = t1[['name', 'team']].sort_values(by=['days_held'], ascending=False)", false], ["t1 = pd.merge(wrestler, Elimination, left_on='wrestler_id', right_on='wrestler_id')\nt2 = t1.sort_values(by='days_held', ascending=False)\nanswer = t2[['name', 'team']]", false], ["t1 = pd.merge(Elimination, wrestler, left_on='wrestler_id', right_on='wrestler_id')\nanswer = t1[['name', 'team']].sort_values('days_held', ascending=False)", false], ["t1 = pd.merge(Elimination, wrestler, left_on='wrestler_id', right_on='wrestler_id')\nanswer = t1.sort_values(by='days_held', ascending=False)[['name', 'team']]", false], ["t1 = pd.merge(Elimination, wrestler, left_on='wrestler_id', right_on='wrestler_id')\nanswer = t1[['name', 'team']].sort_values(by='days_held', ascending=False)", false], ["t1 = pd.merge(Elimination, wrestler, left_on='wrestler_id', right_on='wrestler_id')\nanswer = t1[['name', 'team']].sort_values('days_held', ascending=False)", false], ["t1 = pd.merge(wrestler, Elimination, on='wrestler_id')\nanswer = t1[['name', 'team']].sort_values('days_held', ascending=False)", false], ["t1 = pd.merge(wrestler, Elimination, left_on='wrestler_id', right_on='wrestler_id')\nanswer = t1[['name', 'team']].sort_values('days_held', ascending=False)", false], ["t1 = pd.merge(Elimination, wrestler, left_on='wrestler_id', right_on='wrestler_id')\nanswer = t1[['name', 'team']].sort_values('days_held', ascending=False)", false], ["t1 = pd.merge(Elimination, wrestler, on='wrestler_id')\nt2 = t1[['name', 'team']]\nanswer = t2.sort_values('days_held', ascending=False)", false], ["t1 = pd.merge(Elimination, wrestler, on='wrestler_id')\nt2 = t1.sort_values(by='days_held', ascending=False)\nanswer = t2[['name', 'team']]", false], ["t1 = pd.merge(Elimination, wrestler, left_on='wrestler_id', right_on='wrestler_id')\nanswer = t1[['name', 'team']].sort_values('days_held', ascending=False)", false], ["t1 = pd.merge(Elimination, wrestler, left_on='wrestler_id', right_on='wrestler_id')\nanswer = t1[['name', 'team']].sort_values('days_held', ascending=False)", false], ["t1 = pd.merge(Elimination, wrestler, left_on='wrestler_id', right_on='wrestler_id')\nanswer = t1[['name', 'team']].sort_values(by='days_held', ascending=False)", false], ["t1 = pd.merge(Elimination, wrestler, left_on='wrestler_id', right_on='wrestler_id')\nanswer = t1[['name', 'team']].sort_values('days_held', ascending=False)", false], ["t1 = pd.merge(Elimination, wrestler, left_on='wrestler_id', right_on='wrestler_id')\nanswer = t1[['name', 'team']].sort_values('days_held', ascending=False)", false], ["t1 = pd.merge(wrestler, Elimination, left_on='wrestler_id', right_on='wrestler_id')\nanswer = t1[['name', 'team']].sort_values(by='days_held', ascending=False)", false]]}
+{"example": {"db_id": "allergy_1", "query": "SELECT DISTINCT T1.fname ,  T1.city_code FROM Student AS T1 JOIN Has_Allergy AS T2 ON T1.stuid  =  T2.stuid WHERE T2.Allergy  =  \"Milk\" OR T2.Allergy  =  \"Cat\"", "query_toks": ["SELECT", "DISTINCT", "T1.fname", ",", "T1.city_code", "FROM", "Student", "AS", "T1", "JOIN", "Has_Allergy", "AS", "T2", "ON", "T1.stuid", "=", "T2.stuid", "WHERE", "T2.Allergy", "=", "``", "Milk", "''", "OR", "T2.Allergy", "=", "``", "Cat", "''"], "query_toks_no_value": ["select", "distinct", "t1", ".", "fname", ",", "t1", ".", "city_code", "from", "student", "as", "t1", "join", "has_allergy", "as", "t2", "on", "t1", ".", "stuid", "=", "t2", ".", "stuid", "where", "t2", ".", "allergy", "=", "value", "or", "t2", ".", "allergy", "=", "value"], "question": "Find the different first names and cities of the students who have allergy to milk or cat.", "question_toks": ["Find", "the", "different", "first", "names", "and", "cities", "of", "the", "students", "who", "have", "allergy", "to", "milk", "or", "cat", "."], "sql": {"from": {"table_units": [["table_unit", 2], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 5, false], null], [0, 3, false], null]]}, "select": [true, [[0, [0, [0, 7, false], null]], [0, [0, [0, 12, false], null]]]], "where": [[false, 2, [0, [0, 4, false], null], "\"Milk\"", null], "or", [false, 2, [0, [0, 4, false], null], "\"Cat\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Allergy_Type": ["Allergy", "AllergyType"], "Has_Allergy": ["StuID", "Allergy"], "Student": ["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"]}, "answer": [["Linda", "BAL"], ["Lisa", "PIT"], ["Derek", "HOU"], ["Mark", "DET"], ["David", "NYC"]]}, "program_result_list": [["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[t1['allergy'].isin(['milk', 'cat'])]\nanswer = t2.drop_duplicates(subset=['fname', 'city_code'])[['fname', 'city_code']]", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, on='stuid')\nt2 = t1[t1['allergy'].isin(['milk', 'cat'])]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[t1['allergy'].isin(['milk', 'cat'])]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nanswer = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')][['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[t1['allergy'].isin(['milk', 'cat'])]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, on='stuid')\nt2 = t1[t1['allergy'].isin(['milk', 'cat'])]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[t1['allergy'].isin(['milk', 'cat'])]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(student, has_allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false], ["t1 = pd.merge(Student, Has_Allergy, left_on='stuid', right_on='stuid')\nt2 = t1[(t1['allergy'] == 'milk') | (t1['allergy'] == 'cat')]\nanswer = t2[['fname', 'city_code']].drop_duplicates()", false]]}
+{"example": {"db_id": "products_gen_characteristics", "query": "SELECT count(*) FROM products WHERE product_category_code  =  \"Spices\" AND typical_buying_price  >  1000", "query_toks": ["SELECT", "count", "(", "*", ")", "FROM", "products", "WHERE", "product_category_code", "=", "``", "Spices", "''", "AND", "typical_buying_price", ">", "1000"], "query_toks_no_value": ["select", "count", "(", "*", ")", "from", "products", "where", "product_category_code", "=", "value", "and", "typical_buying_price", ">", "value"], "question": "How many products are in the 'Spices' category and have a typical price of over 1000?", "question_toks": ["How", "many", "products", "are", "in", "the", "'Spices", "'", "category", "and", "have", "a", "typical", "price", "of", "over", "1000", "?"], "sql": {"from": {"table_units": [["table_unit", 4]], "conds": []}, "select": [false, [[3, [0, [0, 0, false], null]]]], "where": [[false, 2, [0, [0, 15, false], null], "\"Spices\"", null], "and", [false, 3, [0, [0, 17, false], null], 1000.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Ref_Characteristic_Types": ["characteristic_type_code", "characteristic_type_description"], "Ref_Colors": ["color_code", "color_description"], "Ref_Product_Categories": ["product_category_code", "product_category_description", "unit_of_measure"], "Characteristics": ["characteristic_id", "characteristic_type_code", "characteristic_data_type", "characteristic_name", "other_characteristic_details"], "Products": ["product_id", "color_code", "product_category_code", "product_name", "typical_buying_price", "typical_selling_price", "product_description", "other_product_details"], "Product_Characteristics": ["product_id", "characteristic_id", "product_characteristic_value"]}, "answer": [[2]]}, "program_result_list": [["t1 = pd.merge(Products, Ref_Product_Categories, left_on='product_category_code', right_on='product_category_code')\nt2 = t1[t1['product_category_description'] == 'Spices']\nanswer = t2[t2['typical_buying_price'] > 1000].shape[0]", false], ["t1 = pd.merge(Products, Ref_Product_Categories, left_on='product_category_code', right_on='product_category_code')\nt2 = t1[(t1['product_category_description'] == 'Spices') & (t1['typical_buying_price'] > 1000)]\nanswer = len(t2)", false], ["t1 = pd.merge(Products, Ref_Product_Categories, left_on='product_category_code', right_on='product_category_code')\nt2 = t1[t1['product_category_description'] == 'Spices']\nanswer = t2[t2['typical_buying_price'] > 1000]['product_id'].count()", false], ["t1 = pd.merge(Products, Ref_Product_Categories, left_on='product_category_code', right_on='product_category_code')\nt2 = t1[(t1['product_category_description'] == 'Spices') & (t1['typical_buying_price'] > 1000)]\nanswer = t2.shape[0]", false], ["t1 = pd.merge(Products, Ref_Product_Categories, left_on='product_category_code', right_on='product_category_code')\nt2 = t1[t1['product_category_description'] == 'Spices']\nanswer = t2[t2['typical_buying_price'] > 1000].shape[0]", false], ["t1 = pd.merge(Products, Ref_Product_Categories, left_on='product_category_code', right_on='product_category_code')\nt2 = t1[(t1['product_category_description'] == 'Spices') & (t1['typical_buying_price'] > 1000)]\nanswer = t2.shape[0]", false], ["t1 = pd.merge(Products, Ref_Product_Categories, on='product_category_code')\nt2 = t1[(t1['product_category_description'] == 'Spices') & (t1['typical_buying_price'] > 1000)]\nanswer = len(t2)", false], ["t1 = pd.merge(Products, Ref_Product_Categories, left_on='product_category_code', right_on='product_category_code')\nt2 = t1[(t1['product_category_description'] == 'Spices') & (t1['typical_buying_price'] > 1000)]\nanswer = t2.shape[0]", false], ["t1 = pd.merge(Products, Ref_Product_Categories, left_on='product_category_code', right_on='product_category_code')\nt2 = t1[t1['product_category_description'] == 'Spices']\nt3 = t2[t2['typical_buying_price'] > 1000]\nanswer = t3.shape[0]", false], ["t1 = pd.merge(Products, Ref_Product_Categories, left_on='product_category_code', right_on='product_category_code')\nanswer = t1[(t1['product_category_code'] == 'spices') & (t1['typical_buying_price'] > 1000)].count()", false], ["t1 = Products[Products['product_category_code'] == 'Spices']\nt2 = t1[t1['typical_buying_price'] > 1000]\nanswer = t2.size", false], ["t1 = pd.merge(Products, Ref_Product_Categories, left_on='product_category_code', right_on='product_category_code')\nt2 = t1[(t1['product_category_description'] == 'Spices') & (t1['typical_buying_price'] > 1000)]\nanswer = t2.shape[0]", false], ["t1 = pd.merge(Products, Ref_Product_Categories, left_on='product_category_code', right_on='product_category_code')\nt2 = t1[(t1['product_category_description'] == 'Spices') & (t1['typical_buying_price'] > 1000)]\nanswer = t2['product_id'].count()", false], ["t1 = pd.merge(Products, Ref_Product_Categories, left_on='product_category_code', right_on='product_category_code')\nt2 = t1[(t1['product_category_code'] == 'Spices') & (t1['typical_buying_price'] > 1000)]\nanswer = t2.shape[0]", false], ["t1 = pd.merge(Products, Ref_Product_Categories, left_on='product_category_code', right_on='product_category_code')\nt2 = t1[(t1['product_category_description'] == 'Spices') & (t1['typical_buying_price'] > 1000)]\nanswer = len(t2)", false], ["t1 = pd.merge(Products, Ref_Product_Categories, left_on='product_category_code', right_on='product_category_code')\nt2 = t1[t1['product_category_description'] == 'Spices']\nanswer = len(t2[t2['typical_buying_price'] > 1000])", false], ["t1 = pd.merge(Products, Ref_Product_Categories, left_on='product_category_code', right_on='product_category_code')\nt2 = t1[t1['product_category_description'] == 'Spices']\nanswer = t2[t2['typical_buying_price'] > 1000]['product_id'].count()", false], ["t1 = pd.merge(Products, Ref_Product_Categories, left_on='product_category_code', right_on='product_category_code')\nanswer = t1[(t1['product_category_description'] == 'Spices') & (t1['typical_buying_price'] > 1000)].shape[0]", false], ["t1 = pd.merge(Products, Ref_Product_Categories, left_on='product_category_code', right_on='product_category_code')\nt2 = t1[t1['product_category_description'] == 'Spices']\nt3 = t2[t2['typical_buying_price'] > 1000]\nanswer = len(t3)", false], ["t1 = pd.merge(Products, Ref_Product_Categories, left_on='product_category_code', right_on='product_category_code')\nt2 = t1[(t1['product_category_description'] == 'Spices') & (t1['typical_buying_price'] > 1000)]\nanswer = len(t2)", false]]}
+{"example": {"db_id": "insurance_and_eClaims", "query": "SELECT customer_details FROM customers UNION SELECT staff_details FROM staff", "query_toks": ["SELECT", "customer_details", "FROM", "customers", "UNION", "SELECT", "staff_details", "FROM", "staff"], "query_toks_no_value": ["select", "customer_details", "from", "customers", "union", "select", "staff_details", "from", "staff"], "question": "Find the names of all the customers and staff members.", "question_toks": ["Find", "the", "names", "of", "all", "the", "customers", "and", "staff", "members", "."], "sql": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[0, [0, [0, 2, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": {"from": {"table_units": [["table_unit", 1]], "conds": []}, "select": [false, [[0, [0, [0, 4, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "except": null}, "db_table_headers": {"Customers": ["Customer_ID", "Customer_Details"], "Staff": ["Staff_ID", "Staff_Details"], "Policies": ["Policy_ID", "Customer_ID", "Policy_Type_Code", "Start_Date", "End_Date"], "Claim_Headers": ["Claim_Header_ID", "Claim_Status_Code", "Claim_Type_Code", "Policy_ID", "Date_of_Claim", "Date_of_Settlement", "Amount_Claimed", "Amount_Piad"], "Claims_Documents": ["Claim_ID", "Document_Type_Code", "Created_by_Staff_ID", "Created_Date"], "Claims_Processing_Stages": ["Claim_Stage_ID", "Next_Claim_Stage_ID", "Claim_Status_Name", "Claim_Status_Description"], "Claims_Processing": ["Claim_Processing_ID", "Claim_ID", "Claim_Outcome_Code", "Claim_Stage_ID", "Staff_ID"]}, "answer": [["Alexander"], ["Allen"], ["America Jaskolski"], ["Augustine Kerluke"], ["Bo"], ["Buddy Marquardt"], ["Cathryn"], ["Clifton"], ["Dayana Robel"], ["Dr. Diana Rath"], ["Eladio"], ["Ellsworth Paucek"], ["Evans"], ["Gideon"], ["Julius"], ["Kaci"], ["Lauriane Ferry PhD"], ["Margaretta"], ["Maryse"], ["Miss Felicita Reichel"], ["Mr. Edwardo Blanda I"], ["Mr. Randal Lynch III"], ["Mrs. Hanna Willms"], ["Mrs. Lilly Graham III"], ["Mrs. Liza Heller V"], ["Roman"], ["Selena Gerhold"], ["Sydnie Friesen"], ["Vernie"], ["Zachery"]]}, "program_result_list": [["t1 = Customers['customer_details']\nt2 = Staff['staff_details']\nanswer = pd.concat([t1, t2]).unique()", false], ["answer = pd.concat([Customers.customer_details, Staff.staff_details]).unique()", false], ["t1 = Customers['customer_details']\nt2 = Staff['staff_details']\nanswer = pd.concat([t1, t2]).unique()", false], ["t1 = pd.concat([Customers['customer_details'], Staff['staff_details']]).unique()\nanswer = pd.DataFrame(t1, columns=['name'])", false], ["t1 = Customers['customer_details'].to_frame().reset_index()\nt2 = Staff['staff_details'].to_frame().reset_index()\nt3 = pd.concat([t1, t2])\nanswer = t3['staff_details']", false], ["t1 = Customers['customer_details']\nt2 = Staff['staff_details']\nanswer = pd.concat([t1, t2]).unique()", false], ["t1 = Customers['customer_details'].to_frame()\nt2 = Staff['staff_details'].to_frame()\nanswer = pd.concat([t1, t2]).drop_duplicates()", false], ["t1 = Customers['customer_details']\nt2 = Staff['staff_details']\nanswer = t1.append(t2).to_frame().reset_index()", false], ["t1 = Customers['customer_details'].to_frame().rename(columns={'customer_details':'details'})\nt2 = Staff['staff_details'].to_frame().rename(columns={'staff_details':'details'})\nanswer = pd.concat([t1, t2])", false], ["t1 = pd.concat([Customers, Staff])\nanswer = t1.rename(columns={'customer_details': 'name', 'staff_details': 'name'})[['name']]", false], ["answer = pd.concat([Customers['customer_details'], Staff['staff_details']]).unique()", false], ["t1 = Customers[['customer_details']].rename(columns={'customer_details':'name'})\nt2 = Staff[['staff_details']].rename(columns={'staff_details':'name'})\nanswer = pd.concat([t1, t2]).reset_index(drop=True)", false], ["t1 = Customers.rename(columns={'customer_details': 'name'})\nt2 = Staff.rename(columns={'staff_details': 'name'})\nanswer = pd.concat([t1, t2])", false], ["t1 = Customers['customer_details'].to_frame().rename(columns={'customer_details':'name'})\nt2 = Staff['staff_details'].to_frame().rename(columns={'staff_details':'name'})\nanswer = pd.concat([t1, t2]).reset_index(drop=True)", false], ["t1 = Customers['customer_details']\nt2 = Staff['staff_details']\nanswer = t1.append(t2)", false], ["t1 = Customers.rename(columns = {'customer_details': 'details'})\nt2 = Staff.rename(columns = {'staff_details': 'details'})\nanswer = pd.concat([t1, t2]).drop_duplicates()", false], ["t1 = Customers['customer_details']\nt2 = Staff['staff_details']\nanswer = pd.concat([t1, t2]).unique()", false], ["t1 = Customers['customer_details'].to_frame()\nt2 = Staff['staff_details'].to_frame()\nanswer = t1.append(t2)", false], ["t1 = Customers['customer_details'].to_frame()\nt2 = Staff['staff_details'].to_frame()\nt3 = pd.merge(t1, t2, on='staff_details', how='outer')\nanswer = t3['staff_details']", false], ["answer = pd.concat([Customers.drop('customer_id', axis=1), Staff.drop('staff_id', axis=1)]).rename(columns={'customer_details': 'name'})", false]]}
+{"example": {"db_id": "riding_club", "query": "SELECT T3.Player_name ,  T3.gender FROM player_coach AS T1 JOIN coach AS T2 ON T1.Coach_ID  =  T2.Coach_ID JOIN player AS T3 ON T1.Player_ID  =  T3.Player_ID WHERE T1.Starting_year  >  2011", "query_toks": ["SELECT", "T3.Player_name", ",", "T3.gender", "FROM", "player_coach", "AS", "T1", "JOIN", "coach", "AS", "T2", "ON", "T1.Coach_ID", "=", "T2.Coach_ID", "JOIN", "player", "AS", "T3", "ON", "T1.Player_ID", "=", "T3.Player_ID", "WHERE", "T1.Starting_year", ">", "2011"], "query_toks_no_value": ["select", "t3", ".", "player_name", ",", "t3", ".", "gender", "from", "player_coach", "as", "t1", "join", "coach", "as", "t2", "on", "t1", ".", "coach_id", "=", "t2", ".", "coach_id", "join", "player", "as", "t3", "on", "t1", ".", "player_id", "=", "t3", ".", "player_id", "where", "t1", ".", "starting_year", ">", "value"], "question": "Show the names and genders of players with a coach starting after 2011.", "question_toks": ["Show", "the", "names", "and", "genders", "of", "players", "with", "a", "coach", "starting", "after", "2011", "."], "sql": {"from": {"table_units": [["table_unit", 3], ["table_unit", 2], ["table_unit", 0]], "conds": [[false, 2, [0, [0, 19, false], null], [0, 13, false], null], "and", [false, 2, [0, [0, 18, false], null], [0, 1, false], null]]}, "select": [false, [[0, [0, [0, 3, false], null]], [0, [0, [0, 4, false], null]]]], "where": [[false, 3, [0, [0, 20, false], null], 2011.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"player": ["Player_ID", "Sponsor_name", "Player_name", "Gender", "Residence", "Occupation", "Votes", "Rank"], "club": ["Club_ID", "Club_name", "Region", "Start_year"], "coach": ["Coach_ID", "Coach_name", "Gender", "Club_ID", "Rank"], "player_coach": ["Player_ID", "Coach_ID", "Starting_year"], "match_result": ["Rank", "Club_ID", "Gold", "Big_Silver", "Small_Silver", "Bronze", "Points"]}, "answer": [["Niki Ashton", "F"], ["Ron Strynadka", "M"], ["Ross Eadie", "M"]]}, "program_result_list": [["t1 = pd.merge(player_coach, coach, left_on='coach_id', right_on='coach_id')\nt2 = pd.merge(t1, player, left_on='player_id', right_on='player_id')\nt3 = t2[t2['starting_year'] > 2011]\nanswer = t3[['player_name', 'gender']]", false], ["t1 = pd.merge(player_coach, coach, on='coach_id')\nt2 = pd.merge(t1, player, on='player_id')\nt3 = t2[t2['starting_year'] > 2011]\nanswer = t3[['player_name', 'gender']]", false], ["t1 = pd.merge(player_coach, coach, on='coach_id')\nt2 = pd.merge(t1, player, on='player_id')\nt3 = t2[t2['starting_year'] > 2011]\nanswer = t3[['player_name', 'gender']]", false], ["t1 = pd.merge(player, player_coach, left_on='player_id', right_on='player_id')\nt2 = pd.merge(t1, coach, left_on='coach_id', right_on='coach_id')\nt3 = t2[t2['starting_year'] > 2011]\nanswer = t3[['player_name', 'gender']]", false], ["t1 = pd.merge(player_coach, coach, left_on='coach_id', right_on='coach_id')\nt2 = pd.merge(t1, player, left_on='player_id', right_on='player_id')\nt3 = t2[t2['starting_year'] > 2011]\nanswer = t3[['player_name', 'gender']]", false], ["t1 = pd.merge(player_coach, coach, on='coach_id')\nt2 = pd.merge(t1, player, on='player_id')\nanswer = t2[t2['starting_year'] > 2011][['player_name', 'gender']]", false], ["t1 = pd.merge(player_coach, coach, left_on='coach_id', right_on='coach_id')\nt2 = pd.merge(t1, player, left_on='player_id', right_on='player_id')\nanswer = t2[t2['starting_year'] > 2011][['player_name', 'gender']]", false], ["t1 = pd.merge(player_coach, coach, left_on='coach_id', right_on='coach_id')\nt2 = pd.merge(t1, player, left_on='player_id', right_on='player_id')\nt3 = t2[t2['starting_year'] > 2011]\nanswer = t3[['player_name', 'gender']]", false], ["t1 = pd.merge(player_coach, coach, left_on='coach_id', right_on='coach_id')\nt2 = pd.merge(t1, player, left_on='player_id', right_on='player_id')\nt3 = t2[t2['starting_year'] > 2011]\nanswer = t3[['player_name', 'gender']]", false], ["t1 = pd.merge(player_coach, coach, left_on='coach_id', right_on='coach_id')\nt2 = pd.merge(t1, player, left_on='player_id', right_on='player_id')\nanswer = t2[t2['starting_year'] > 2011][['player_name', 'gender']]", false], ["t1 = pd.merge(player_coach, coach, on='coach_id')\nt2 = pd.merge(t1, player, on='player_id')\nanswer = t2[t2['starting_year'] > 2011][['player_name', 'gender']]", false], ["t1 = pd.merge(player_coach, coach, left_on='coach_id', right_on='coach_id')\nt2 = pd.merge(t1, player, left_on='player_id', right_on='player_id')\nt3 = t2[t2['starting_year'] > 2011]\nanswer = t3[['player_name', 'gender']]", false], ["t1 = pd.merge(player_coach, coach, left_on='coach_id', right_on='coach_id')\nt2 = pd.merge(t1, player, left_on='player_id', right_on='player_id')\nt3 = t2[t2['starting_year'] > 2011]\nanswer = t3[['player_name', 'gender']]", false], ["t1 = pd.merge(player_coach, coach, left_on='coach_id', right_on='coach_id')\nt2 = pd.merge(t1, player, left_on='player_id', right_on='player_id')\nt3 = t2[t2['starting_year'] > 2011]\nanswer = t3[['player_name', 'gender']]", false], ["t1 = pd.merge(player_coach, coach, left_on='coach_id', right_on='coach_id')\nt2 = pd.merge(t1, player, left_on='player_id', right_on='player_id')\nt3 = t2[t2['starting_year'] > 2011]\nanswer = t3[['player_name', 'gender']]", false], ["t1 = pd.merge(player_coach, coach, left_on='coach_id', right_on='coach_id')\nt2 = pd.merge(t1, player, left_on='player_id', right_on='player_id')\nt3 = t2[t2['starting_year'] > 2011]\nanswer = t3[['player_name', 'gender']]", false], ["t1 = pd.merge(player_coach, coach, left_on='coach_id', right_on='coach_id')\nt2 = pd.merge(t1, player, left_on='player_id', right_on='player_id')\nt3 = t2[t2['starting_year'] > 2011]\nanswer = t3[['player_name', 'gender']]", false], ["t1 = pd.merge(player_coach, coach, left_on='coach_id', right_on='coach_id')\nt2 = pd.merge(t1, player, left_on='player_id', right_on='player_id')\nt3 = t2[t2['starting_year'] > 2011]\nanswer = t3[['player_name', 'gender']]", false], ["t1 = pd.merge(player_coach, coach, left_on='coach_id', right_on='coach_id')\nt2 = pd.merge(t1, player, left_on='player_id', right_on='player_id')\nt3 = t2[t2['starting_year'] > 2011]\nanswer = t3[['player_name', 'gender']]", false], ["t1 = pd.merge(player_coach, coach, left_on='coach_id', right_on='coach_id')\nt2 = pd.merge(t1, player, left_on='player_id', right_on='player_id')\nt3 = t2[t2['starting_year'] > 2011]\nanswer = t3[['player_name', 'gender']]", false]]}
+{"example": {"db_id": "dorm_1", "query": "SELECT avg(age) ,  max(age) ,  sex FROM student GROUP BY sex", "query_toks": ["SELECT", "avg", "(", "age", ")", ",", "max", "(", "age", ")", ",", "sex", "FROM", "student", "GROUP", "BY", "sex"], "query_toks_no_value": ["select", "avg", "(", "age", ")", ",", "max", "(", "age", ")", ",", "sex", "from", "student", "group", "by", "sex"], "question": "What is the average and oldest age for each gender of student?", "question_toks": ["What", "is", "the", "average", "and", "oldest", "age", "for", "each", "gender", "of", "student", "?"], "sql": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[5, [0, [0, 4, false], null]], [1, [0, [0, 4, false], null]], [0, [0, [0, 5, false], null]]]], "where": [], "groupBy": [[0, 5, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Student": ["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], "Dorm": ["dormid", "dorm_name", "student_capacity", "gender"], "Dorm_amenity": ["amenid", "amenity_name"], "Has_amenity": ["dormid", "amenid"], "Lives_in": ["stuid", "dormid", "room_number"]}, "answer": [[19.7, 26, "F"], [19.5, 27, "M"]]}, "program_result_list": [["t1 = Student.groupby('sex').agg({'age': [np.mean, np.max]})\nanswer = t1", false], ["t1 = Student.groupby('sex').agg({'age': ['mean', 'max']})\nanswer = t1.reset_index()", false], ["t1 = Student.groupby('sex').agg({'age': ['mean', 'max']})\nanswer = t1.reset_index()", false], ["t1 = Student.groupby('sex')\nanswer = t1[['age']].agg(['mean', 'max'])", false], ["t1 = Student.groupby('sex').agg({'age': ['mean', 'max']}).reset_index()\nanswer = t1[['sex', 'age', 'mean', 'max']]", false], ["t1 = Student.groupby('sex').agg({'age': ['mean', 'max']}).rename({'mean':'avg', 'max':'oldest'}, axis=1)\nanswer = t1.reset_index()", false], ["answer = Student.groupby('sex').agg({'age': ['mean', 'max']})", false], ["t1 = Student.groupby('sex')\nanswer = t1['age'].agg(['mean', 'max']).reset_index()", false], ["t1 = Student.groupby('sex').agg({'age': [np.mean, np.max]})\nanswer = t1.reset_index()", false], ["t1 = Student.groupby('sex')\nanswer = t1[['age']].agg(['mean', 'max'])", false], ["t1 = Student.groupby('sex').agg({'age': ['mean', 'max']}).reset_index()\nt2 = t1.rename(columns={'age': 'avg_age', 'age': 'oldest_age'})\nanswer = t2", false], ["t1 = Student.groupby(['sex']).agg({'age': ['mean', 'max']}).reset_index()\nanswer = t1", false], ["t1 = Student.groupby('sex').agg({'age': ['mean', 'max']})\nanswer = t1.reset_index()", false], ["t1 = Student.groupby('sex').agg({'age': ['mean', 'max']})\nanswer = t1.columns = ['avg_age', 'max_age']", false], ["t1 = Student.groupby('sex').agg({'age': [np.mean, np.max]}).reset_index()\nt1.columns = ['sex', 'average_age', 'oldest']\nanswer = t1[['sex', 'average_age', 'oldest']]", false], ["t1 = Student.groupby('sex')[['age']].agg(['mean','max']).reset_index()\nanswer = t1", false], ["answer = Student.groupby('sex').agg({'age': [np.mean, np.max]})", false], ["t1 = Student.groupby('sex').agg({'age': ['mean', 'max']})\nanswer = t1.reset_index()", false], ["t1 = Student.groupby('sex').agg({'age': [np.mean, np.max]})\nanswer = t1.reset_index()", false], ["t1 = Student.groupby('sex')[['age']].agg(['mean', 'max'])\nanswer = t1.reset_index()", false]]}
+{"example": {"db_id": "customers_and_products_contacts", "query": "SELECT T1.customer_name FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.order_status_code  =  'Completed' INTERSECT SELECT T1.customer_name FROM customers AS T1 JOIN customer_orders AS T2 ON T1.customer_id  =  T2.customer_id WHERE T2.order_status_code  =  'Part'", "query_toks": ["SELECT", "T1.customer_name", "FROM", "customers", "AS", "T1", "JOIN", "customer_orders", "AS", "T2", "ON", "T1.customer_id", "=", "T2.customer_id", "WHERE", "T2.order_status_code", "=", "'Completed", "'", "INTERSECT", "SELECT", "T1.customer_name", "FROM", "customers", "AS", "T1", "JOIN", "customer_orders", "AS", "T2", "ON", "T1.customer_id", "=", "T2.customer_id", "WHERE", "T2.order_status_code", "=", "'Part", "'"], "query_toks_no_value": ["select", "t1", ".", "customer_name", "from", "customers", "as", "t1", "join", "customer_orders", "as", "t2", "on", "t1", ".", "customer_id", "=", "t2", ".", "customer_id", "where", "t2", ".", "order_status_code", "=", "value", "intersect", "select", "t1", ".", "customer_name", "from", "customers", "as", "t1", "join", "customer_orders", "as", "t2", "on", "t1", ".", "customer_id", "=", "t2", ".", "customer_id", "where", "t2", ".", "order_status_code", "=", "value"], "question": "Show the names of customers who have both an order in completed status and an order in part status.", "question_toks": ["Show", "the", "names", "of", "customers", "who", "have", "both", "an", "order", "in", "completed", "status", "and", "an", "order", "in", "part", "status", "."], "sql": {"from": {"table_units": [["table_unit", 2], ["table_unit", 5]], "conds": [[false, 2, [0, [0, 11, false], null], [0, 29, false], null]]}, "select": [false, [[0, [0, [0, 14, false], null]]]], "where": [[false, 2, [0, [0, 31, false], null], "\"Completed\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": {"from": {"table_units": [["table_unit", 2], ["table_unit", 5]], "conds": [[false, 2, [0, [0, 11, false], null], [0, 29, false], null]]}, "select": [false, [[0, [0, [0, 14, false], null]]]], "where": [[false, 2, [0, [0, 31, false], null], "\"Part\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "union": null, "except": null}, "db_table_headers": {"Addresses": ["address_id", "line_1_number_building", "city", "zip_postcode", "state_province_county", "country"], "Products": ["product_id", "product_type_code", "product_name", "product_price"], "Customers": ["customer_id", "payment_method_code", "customer_number", "customer_name", "customer_address", "customer_phone", "customer_email"], "Contacts": ["contact_id", "customer_id", "gender", "first_name", "last_name", "contact_phone"], "Customer_Address_History": ["customer_id", "address_id", "date_from", "date_to"], "Customer_Orders": ["order_id", "customer_id", "order_date", "order_status_code"], "Order_Items": ["order_item_id", "order_id", "product_id", "order_quantity"]}, "answer": [["Cleo"], ["Lela"], ["Sterling"]]}, "program_result_list": [["t1 = pd.merge(Customers, Customer_Orders, left_on='customer_id', right_on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']\nt3 = t1[t1['order_status_code'] == 'part']\nt4 = pd.merge(t2, t3, on='customer_name', how='inner')\nanswer = t4['customer_name'].unique()", false], ["t1 = pd.merge(Customers, Customer_Orders, on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']\nt3 = t1[t1['order_status_code'] == 'part']\nt4 = pd.merge(t2, t3, on='customer_name')\nanswer = t4['customer_name'].unique()", false], ["t1 = pd.merge(Customers, Customer_Orders, left_on='customer_id', right_on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']\nt3 = t1[t1['order_status_code'] == 'part']\nanswer = t2.merge(t3, on='customer_name', how='inner')['customer_name']", false], ["t1 = pd.merge(Customers, Customer_Orders, left_on='customer_id', right_on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']\nt3 = t1[t1['order_status_code'] == 'part']\nt4 = t2.merge(t3, on='customer_name', how='inner')\nanswer = t4['customer_name'].unique()", false], ["t1 = pd.merge(Customers, Customer_Orders, left_on='customer_id', right_on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']\nt3 = t1[t1['order_status_code'] == 'part']\nanswer = t2.merge(t3, how='inner')['customer_name'].unique()", false], ["t1 = pd.merge(Customers, Customer_Orders, on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']\nt3 = pd.merge(Customers, Customer_Orders, on='customer_id')\nt4 = t3[t3['order_status_code'] == 'part']\nt5 = pd.merge(t2, t4, on='customer_name')\nanswer = t5['customer_name'].unique()", false], ["t1 = pd.merge(Customers, Customer_Orders, left_on='customer_id', right_on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']\nt3 = t1[t1['order_status_code'] == 'part']\nt4 = pd.merge(t2, t3, on='customer_name', how='inner')\nanswer = t4['customer_name']", false], ["t1 = pd.merge(Customers, Customer_Orders, left_on='customer_id', right_on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']\nt3 = t1[t1['order_status_code'] == 'part']\nanswer = t2.merge(t3, on=['customer_name'], how='inner')['customer_name'].unique()", false], ["t1 = pd.merge(Customers, Customer_Orders, left_on='customer_id', right_on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']\nt3 = t1[t1['order_status_code'] == 'part']\nanswer = t2[t2['customer_name'].isin(t3['customer_name'])]['customer_name'].unique()", false], ["t1 = pd.merge(Customers, Customer_Orders, left_on='customer_id', right_on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']\nt3 = t1[t1['order_status_code'] == 'part']\nt4 = t2[['customer_name']].merge(t3[['customer_name']], how='inner')\nanswer = t4['customer_name']", false], ["t1 = Customers.merge(Customer_Orders, left_on='customer_id', right_on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']\nt3 = Customers.merge(Customer_Orders, left_on='customer_id', right_on='customer_id')\nt4 = t3[t3['order_status_code'] == 'part']\nt5 = pd.merge(t2, t4, how='inner', on='customer_name')\nanswer = t5['customer_name'].unique()", false], ["t1 = pd.merge(Customers, Customer_Orders, left_on='customer_id', right_on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']\nt3 = pd.merge(Customers, Customer_Orders, left_on='customer_id', right_on='customer_id')\nt4 = t3[t3['order_status_code'] == 'part']\nt5 = pd.merge(t2, t4, left_on='customer_name', right_on='customer_name')\nanswer = t5['customer_name']", false], ["t1 = pd.merge(Customers, Customer_Orders, left_on='customer_id', right_on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']\nt3 = t1[t1['order_status_code'] == 'part']\nt4 = t2.merge(t3, how='inner', on=['customer_name'])\nanswer = t4['customer_name'].unique()", false], ["t1 = pd.merge(Customers, Customer_Orders, left_on='customer_id', right_on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']['customer_name']\nt3 = t1[t1['order_status_code'] == 'part']['customer_name']\nanswer = t2.intersection(t3)", false], ["t1 = pd.merge(Customers, Customer_Orders, left_on='customer_id', right_on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']\nt3 = t1[t1['order_status_code'] == 'part']\nt4 = pd.merge(t2, t3, on='customer_name')\nanswer = t4['customer_name']", false], ["t1 = pd.merge(Customers, Customer_Orders, left_on='customer_id', right_on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']\nt3 = t1[t1['order_status_code'] == 'part']\nt4 = pd.merge(t2, t3, on='customer_name', how='inner')\nanswer = t4['customer_name'].unique()", false], ["t1 = pd.merge(Customers, Customer_Orders, left_on='customer_id', right_on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed'][['customer_name']]\nt3 = pd.merge(Customers, Customer_Orders, left_on='customer_id', right_on='customer_id')\nt4 = t3[t3['order_status_code'] == 'part'][['customer_name']]\nt5 = pd.merge(t2, t4, on='customer_name', how='inner')\nanswer = t5['customer_name']", false], ["t1 = pd.merge(Customers, Customer_Orders, left_on='customer_id', right_on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']\nt3 = t1[t1['order_status_code'] == 'part']\nanswer = t2.merge(t3, on='customer_name', how='inner')['customer_name'].drop_duplicates()", false], ["t1 = pd.merge(Customers, Customer_Orders, on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']\nt3 = t1[t1['order_status_code'] == 'part']\nt4 = pd.merge(t2, t3, on='customer_name')\nanswer = t4['customer_name'].unique()", false], ["t1 = pd.merge(Customers, Customer_Orders, on='customer_id')\nt2 = t1[t1['order_status_code'] == 'completed']\nt3 = t1[t1['order_status_code'] == 'part']\nt4 = pd.merge(t2, t3, on='customer_name')\nanswer = t4['customer_name']", false]]}
+{"example": {"db_id": "college_1", "query": "SELECT T2.emp_fname ,  T3.crs_description FROM CLASS AS T1 JOIN employee AS T2 ON T1.prof_num  =  T2.emp_num JOIN course AS T3 ON T1.crs_code  =  T3.crs_code", "query_toks": ["SELECT", "T2.emp_fname", ",", "T3.crs_description", "FROM", "CLASS", "AS", "T1", "JOIN", "employee", "AS", "T2", "ON", "T1.prof_num", "=", "T2.emp_num", "JOIN", "course", "AS", "T3", "ON", "T1.crs_code", "=", "T3.crs_code"], "query_toks_no_value": ["select", "t2", ".", "emp_fname", ",", "t3", ".", "crs_description", "from", "class", "as", "t1", "join", "employee", "as", "t2", "on", "t1", ".", "prof_num", "=", "t2", ".", "emp_num", "join", "course", "as", "t3", "on", "t1", ".", "crs_code", "=", "t3", ".", "crs_code"], "question": "What are the first names of all teachers who have taught a course and the corresponding descriptions?", "question_toks": ["What", "are", "the", "first", "names", "of", "all", "teachers", "who", "have", "taught", "a", "course", "and", "the", "corresponding", "descriptions", "?"], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 3], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 6, false], null], [0, 17, false], null], "and", [false, 2, [0, [0, 2, false], null], [0, 7, false], null]]}, "select": [false, [[0, [0, [0, 19, false], null]], [0, [0, [0, 9, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"CLASS": ["CLASS_CODE", "CRS_CODE", "CLASS_SECTION", "CLASS_TIME", "CLASS_ROOM", "PROF_NUM"], "COURSE": ["CRS_CODE", "DEPT_CODE", "CRS_DESCRIPTION", "CRS_CREDIT"], "DEPARTMENT": ["DEPT_CODE", "DEPT_NAME", "SCHOOL_CODE", "EMP_NUM", "DEPT_ADDRESS", "DEPT_EXTENSION"], "EMPLOYEE": ["EMP_NUM", "EMP_LNAME", "EMP_FNAME", "EMP_INITIAL", "EMP_JOBCODE", "EMP_HIREDATE", "EMP_DOB"], "ENROLL": ["CLASS_CODE", "STU_NUM", "ENROLL_GRADE"], "PROFESSOR": ["EMP_NUM", "DEPT_CODE", "PROF_OFFICE", "PROF_EXTENSION", "PROF_HIGH_DEGREE"], "STUDENT": ["STU_NUM", "STU_LNAME", "STU_FNAME", "STU_INIT", "STU_DOB", "STU_HRS", "STU_CLASS", "STU_GPA", "STU_TRANSFER", "DEPT_CODE", "STU_PHONE", "PROF_NUM"]}, "answer": [["Arnelle", "Accounting I"], ["Arnelle", "Accounting I"], ["Robert", "Accounting I"], ["Ismael", "Accounting II"], ["Ismael", "Accounting II"], ["Carlos", "Intro. to Microcomputing"], ["Gerald", "Intro. to Microcomputing"], ["Carlos", "Intro. to Microcomputing"], ["Peter", "Database Design and Implementation"], ["Gerald", "Intro. to Statistics"], ["Gerald", "Intro. to Statistics"], ["Peter", "Statistical Applications"], ["Peter", "Statistical Applications"]]}, "program_result_list": [["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, COURSE, left_on='crs_code', right_on='crs_code')\nanswer = t2[['emp_fname', 'crs_description']]", false]]}
+{"example": {"db_id": "election", "query": "SELECT T1.Committee FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID WHERE T2.Party  =  \"Democratic\" INTERSECT SELECT T1.Committee FROM election AS T1 JOIN party AS T2 ON T1.Party  =  T2.Party_ID WHERE T2.Party  =  \"Liberal\"", "query_toks": ["SELECT", "T1.Committee", "FROM", "election", "AS", "T1", "JOIN", "party", "AS", "T2", "ON", "T1.Party", "=", "T2.Party_ID", "WHERE", "T2.Party", "=", "``", "Democratic", "''", "INTERSECT", "SELECT", "T1.Committee", "FROM", "election", "AS", "T1", "JOIN", "party", "AS", "T2", "ON", "T1.Party", "=", "T2.Party_ID", "WHERE", "T2.Party", "=", "``", "Liberal", "''"], "query_toks_no_value": ["select", "t1", ".", "committee", "from", "election", "as", "t1", "join", "party", "as", "t2", "on", "t1", ".", "party", "=", "t2", ".", "party_id", "where", "t2", ".", "party", "=", "value", "intersect", "select", "t1", ".", "committee", "from", "election", "as", "t1", "join", "party", "as", "t2", "on", "t1", ".", "party", "=", "t2", ".", "party_id", "where", "t2", ".", "party", "=", "value"], "question": "Which committees have delegates from both democratic party and liberal party?", "question_toks": ["Which", "committees", "have", "delegates", "from", "both", "democratic", "party", "and", "liberal", "party", "?"], "sql": {"from": {"table_units": [["table_unit", 2], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 17, false], null], [0, 5, false], null]]}, "select": [false, [[0, [0, [0, 19, false], null]]]], "where": [[false, 2, [0, [0, 7, false], null], "\"Democratic\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": {"from": {"table_units": [["table_unit", 2], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 17, false], null], [0, 5, false], null]]}, "select": [false, [[0, [0, [0, 19, false], null]]]], "where": [[false, 2, [0, [0, 7, false], null], "\"Liberal\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "union": null, "except": null}, "db_table_headers": {"county": ["County_Id", "County_name", "Population", "Zip_code"], "party": ["Party_ID", "Year", "Party", "Governor", "Lieutenant_Governor", "Comptroller", "Attorney_General", "US_Senate"], "election": ["Election_ID", "Counties_Represented", "District", "Delegate", "Party", "First_Elected", "Committee"]}, "answer": [["Appropriations"]]}, "program_result_list": [["t1 = pd.merge(election, party, on='party')\nt2 = t1[t1['party'] == 'democratic']\nt3 = t1[t1['party'] == 'liberal']\nanswer = t2['committee'].intersection(t3['committee'])", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'democratic']\nt3 = pd.merge(election, party, left_on='party', right_on='party_id')\nt4 = t3[t3['party'] == 'liberal']\nanswer = pd.merge(t2, t4, on='committee')['committee'].unique()", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'democratic']\nt3 = pd.merge(election, party, left_on='party', right_on='party_id')\nt4 = t3[t3['party'] == 'liberal']\nunion = pd.merge(t2, t4, on='committee', how='inner')\nanswer = union['committee'].unique()", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'Democratic']['committee'].unique()\nt3 = t1[t1['party'] == 'Liberal']['committee'].unique()\nanswer = np.intersect1d(t2, t3)", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'democratic']\nt3 = t1[t1['party'] == 'liberal']\nt4 = t2.merge(t3, how='inner', on='committee')\nanswer = t4['committee'].unique()", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'democratic']\nt3 = t1[t1['party'] == 'liberal']\nt4 = set(t2['committee']) & set(t3['committee'])\nanswer = pd.Series(list(t4)).to_frame().rename(columns={0: 'committee'})", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'democratic']\nt3 = t2['committee'].unique()\nt4 = pd.merge(election, party, left_on='party', right_on='party_id')\nt5 = t4[t4['party'] == 'liberal']\nt6 = t5['committee'].unique()\nanswer = list(set(t3) & set(t6))", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'democratic']\nt3 = t2[['committee']]\nt4 = pd.merge(election, party, left_on='party', right_on='party_id')\nt5 = t4[t4['party'] == 'liberal']\nt6 = t5[['committee']]\nanswer = t3.merge(t6, how='inner')['committee'].unique()", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'Democratic']['committee']\nt3 = pd.merge(election, party, left_on='party', right_on='party_id')\nt4 = t3[t3['party'] == 'Liberal']['committee']\nanswer = t2.intersection(t4)", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'democratic']\nt3 = t1[t1['party'] == 'liberal']\nt4 = t2.merge(t3, on='committee', how='inner')\nanswer = t4['committee'].unique()", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'democratic']\nt3 = pd.merge(election, party, left_on='party', right_on='party_id')\nt4 = t3[t3['party'] == 'liberal']\nt5 = pd.merge(t2, t4, on='committee')\nanswer = t5['committee'].unique()", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'Democratic']\nt3 = t2[['committee']]\nt4 = pd.merge(election, party, left_on='party', right_on='party_id')\nt5 = t4[t4['party'] == 'Liberal']\nt6 = t5[['committee']]\nt7 = pd.merge(t3, t6, on='committee', how='inner')\nanswer = t7['committee'].unique()", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'Democratic']['committee'].to_frame()\nt3 = pd.merge(election, party, left_on='party', right_on='party_id')\nt4 = t3[t3['party'] == 'Liberal']['committee'].to_frame()\nanswer = pd.merge(t2, t4, on='committee')", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'democratic']\nt3 = pd.merge(election, party, left_on='party', right_on='party_id')\nt4 = t3[t3['party'] == 'liberal']\nt5 = t2[['committee']].merge(t4[['committee']], how='inner')\nanswer = t5['committee'].unique()", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'Democratic']\nt3 = t2[['committee']]\nt4 = pd.merge(election, party, left_on='party', right_on='party_id')\nt5 = t4[t4['party'] == 'Liberal']\nt6 = t5[['committee']]\nanswer = t3.merge(t6, how=\"inner\")", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'Democratic']\nt3 = t1[t1['party'] == 'Liberal']\nt4 = t2['committee'].unique()\nt5 = t3['committee'].unique()\nanswer = np.intersect1d(t4, t5)", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'democratic']\nt3 = pd.merge(election, party, left_on='party', right_on='party_id')\nt4 = t3[t3['party'] == 'liberal']\nt5 = pd.merge(t2, t4)\nanswer = t5['committee'].unique()", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'Democratic']\nt3 = pd.merge(election, party, left_on='party', right_on='party_id')\nt4 = t3[t3['party'] == 'Liberal']\nt5 = t2[['committee']].merge(t4[['committee']], on='committee', how='inner')\nanswer = t5['committee']", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'democratic']\nt3 = t1[t1['party'] == 'liberal']\nanswer = t2.merge(t3, on='committee')['committee'].unique()", false], ["t1 = pd.merge(election, party, left_on='party', right_on='party_id')\nt2 = t1[t1['party'] == 'democratic']\nt3 = pd.merge(election, party, left_on='party', right_on='party_id')\nt4 = t3[t3['party'] == 'liberal']\nanswer = t2[t2['committee'].isin(t4['committee'])]['committee'].unique()", false]]}
+{"example": {"db_id": "party_people", "query": "SELECT T1.member_name ,  T2.party_name FROM Member AS T1 JOIN party AS T2 ON T1.party_id  =  T2.party_id", "query_toks": ["SELECT", "T1.member_name", ",", "T2.party_name", "FROM", "Member", "AS", "T1", "JOIN", "party", "AS", "T2", "ON", "T1.party_id", "=", "T2.party_id"], "query_toks_no_value": ["select", "t1", ".", "member_name", ",", "t2", ".", "party_name", "from", "member", "as", "t1", "join", "party", "as", "t2", "on", "t1", ".", "party_id", "=", "t2", ".", "party_id"], "question": "List member names and their party names.", "question_toks": ["List", "member", "names", "and", "their", "party", "names", "."], "sql": {"from": {"table_units": [["table_unit", 2], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 15, false], null], [0, 7, false], null]]}, "select": [false, [[0, [0, [0, 14, false], null]], [0, [0, [0, 12, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"region": ["Region_ID", "Region_name", "Date", "Label", "Format", "Catalogue"], "party": ["Party_ID", "Minister", "Took_office", "Left_office", "Region_ID", "Party_name"], "member": ["Member_ID", "Member_Name", "Party_ID", "In_office"], "party_events": ["Event_ID", "Event_Name", "Party_ID", "Member_in_charge_ID"]}, "answer": [["Hon Tony Abbott", "3"], ["Hon Dick Adams", "Progress Party"], ["Anthony Albanese", "Progress Party"], ["Hon John Anderson", "Convention Peoples Party"], ["Peter Andren", "3"], ["Hon Kevin Andrews", "3"], ["Hon Fran Bailey", "3"], ["Hon Bruce Baird", "3"], ["Mark Baker", "3"], ["Hon Bob Baldwin", "3"], ["Phil Barresi", "3"], ["Kerry Bartlett", "Convention Peoples Party"], ["Hon Kim Beazley", "Progress Party"], ["Hon Arch Bevis", "Progress Party"], ["Hon Bruce Billson", "3"]]}, "program_result_list": [["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nanswer = t1[['member_name', 'party_name']]", false], ["t1 = pd.merge(member, party, left_on='party_id', right_on='party_id')\nanswer = t1[['member_name', 'party_name']]", false]]}
+{"example": {"db_id": "phone_1", "query": "SELECT DISTINCT T2.Hardware_Model_name FROM screen_mode AS T1 JOIN phone AS T2 ON T1.Graphics_mode = T2.screen_mode WHERE T1.Type  =  \"Graphics\" OR t2.Company_name  =  \"Nokia Corporation\"", "query_toks": ["SELECT", "DISTINCT", "T2.Hardware_Model_name", "FROM", "screen_mode", "AS", "T1", "JOIN", "phone", "AS", "T2", "ON", "T1.Graphics_mode", "=", "T2.screen_mode", "WHERE", "T1.Type", "=", "``", "Graphics", "''", "OR", "t2.Company_name", "=", "``", "Nokia", "Corporation", "''"], "query_toks_no_value": ["select", "distinct", "t2", ".", "hardware_model_name", "from", "screen_mode", "as", "t1", "join", "phone", "as", "t2", "on", "t1", ".", "graphics_mode", "=", "t2", ".", "screen_mode", "where", "t1", ".", "type", "=", "value", "or", "t2", ".", "company_name", "=", "value"], "question": "List the hardware model name for the phones that were produced by \"Nokia Corporation\" or whose screen mode type is \"Graphics.\"", "question_toks": ["List", "the", "hardware", "model", "name", "for", "the", "phones", "that", "were", "produced", "by", "``", "Nokia", "Corporation", "''", "or", "whose", "screen", "mode", "type", "is", "``", "Graphics", ".", "''"], "sql": {"from": {"table_units": [["table_unit", 1], ["table_unit", 2]], "conds": [[false, 2, [0, [0, 8, false], null], [0, 21, false], null]]}, "select": [true, [[0, [0, [0, 16, false], null]]]], "where": [[false, 2, [0, [0, 14, false], null], "\"Graphics\"", null], "or", [false, 2, [0, [0, 15, false], null], "\"Nokia Corporation\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"chip_model": ["Model_name", "Launch_year", "RAM_MiB", "ROM_MiB", "Slots", "WiFi", "Bluetooth"], "screen_mode": ["Graphics_mode", "Char_cells", "Pixels", "Hardware_colours", "used_kb", "map", "Type"], "phone": ["Company_name", "Hardware_Model_name", "Accreditation_type", "Accreditation_level", "Date", "chip_model", "screen_mode"]}, "answer": [["GT-I9100"], ["GT-I9300"], ["Lumia 920, Lumia 820, Lumia 620"], ["Nokia 700"], ["XPERIA T, XPERIA J"]]}, "program_result_list": [["t1 = pd.merge(screen_mode, phone, left_on='graphics_mode', right_on='screen_mode')\nanswer = t1[(t1['type'] == 'graphics') | (t1['company_name'] == 'Nokia Corporation')]['hardware_model_name'].unique()", false], ["t1 = pd.merge(screen_mode, phone, left_on='graphics_mode', right_on='screen_mode')\nt2 = t1[(t1['type'] == 'Graphics') | (t1['company_name'] == 'Nokia Corporation')]\nanswer = t2['hardware_model_name'].unique()", false], ["t1 = pd.merge(phone, screen_mode, on='screen_mode')\nt2 = t1[(t1['type'] == 'Graphics') | (t1['company_name'] == 'Nokia Corporation')]\nanswer = t2['hardware_model_name'].unique()", false], ["t1 = pd.merge(screen_mode, phone, left_on='graphics_mode', right_on='screen_mode')\nt2 = t1[(t1['type'] == 'Graphics') | (t1['company_name'] == 'Nokia Corporation')]\nanswer = t2['hardware_model_name'].unique()", false], ["t1 = pd.merge(screen_mode, phone, left_on='graphics_mode', right_on='screen_mode')\nt2 = t1[(t1['type'] == 'Graphics') | (t1['company_name'] == 'Nokia Corporation')]\nanswer = t2['hardware_model_name'].unique()", false], ["t1 = pd.merge(screen_mode, phone, on='screen_mode')\nt2 = t1[(t1['type'] == 'Graphics') | (t1['company_name'] == 'Nokia Corporation')]\nanswer = t2['hardware_model_name'].unique()", false], ["t1 = pd.merge(screen_mode, phone, left_on='graphics_mode', right_on='screen_mode')\nt2 = t1[(t1['type'] == 'Graphics') | (t1['company_name'] == 'Nokia Corporation')]\nanswer = t2['hardware_model_name'].unique()", false], ["t1 = pd.merge(screen_mode, phone, left_on='graphics_mode', right_on='screen_mode')\nt2 = t1[(t1['type'] == 'graphics') | (t1['company_name'] == 'Nokia Corporation')]\nanswer = t2['hardware_model_name'].unique()", false], ["t1 = pd.merge(screen_mode, phone, left_on='graphics_mode', right_on='screen_mode')\nt2 = t1[(t1['type'] == 'Graphics') | (t1['company_name'] == 'Nokia Corporation')]\nanswer = t2['hardware_model_name'].unique()", false], ["t1 = phone[phone['company_name'] == 'Nokia Corporation']\nt2 = pd.merge(screen_mode, phone, how='inner', left_on='graphics_mode', right_on='screen_mode')\nt3 = t2[t2['type'] == 'Graphics']\nanswer = t1['hardware_model_name'].append(t3['hardware_model_name']).unique()", false], ["t1 = pd.merge(screen_mode, phone, on='graphics_mode')\nt2 = t1[(t1['type'] == 'Graphics') | (t1['company_name'] == 'Nokia Corporation')]\nanswer = t2['hardware_model_name'].unique()", false], ["t1 = pd.merge(screen_mode, phone, left_on='graphics_mode', right_on='screen_mode')\nt2 = t1[(t1['type'] == 'Graphics') | (t1['company_name'] == 'Nokia Corporation')]\nanswer = t2['hardware_model_name'].unique()", false], ["t1 = pd.merge(screen_mode, phone, left_on='graphics_mode', right_on='screen_mode')\nt2 = t1[(t1['type'] == 'graphics') | (t1['company_name'] == 'Nokia Corporation')]\nanswer = t2['hardware_model_name'].unique()", false], ["t1 = pd.merge(screen_mode, phone, left_on='graphics_mode', right_on='screen_mode', how='left')\nt2 = t1[(t1['type'] == 'Graphics') | (t1['company_name'] == 'Nokia Corporation')].drop_duplicates()\nanswer = t2['hardware_model_name']", false], ["t1 = pd.merge(screen_mode, phone, left_on='graphics_mode', right_on='screen_mode')\nt2 = t1[(t1['type'] == 'Graphics') | (t1['company_name'] == 'Nokia Corporation')]\nanswer = t2['hardware_model_name'].unique()", false], ["t1 = pd.merge(screen_mode, phone, left_on='graphics_mode', right_on='screen_mode')\nt2 = t1[(t1['type'] == 'Graphics') | (t1['company_name'] == 'Nokia Corporation')]\nanswer = t2['hardware_model_name'].unique()", false], ["t1 = pd.merge(screen_mode, phone, left_on='graphics_mode', right_on='screen_mode')\nt2 = t1[(t1['type'] == 'Graphics') | (t1['company_name'] == 'Nokia Corporation')]\nanswer = t2['hardware_model_name'].unique()", false], ["t1 = pd.merge(screen_mode, phone, left_on='graphics_mode', right_on='screen_mode')\nt2 = t1[(t1['type'] == 'Graphics') | (t1['company_name'] == 'Nokia Corporation')]\nanswer = t2[['hardware_model_name']].drop_duplicates()", false], ["t1 = pd.merge(screen_mode, phone, left_on='graphics_mode', right_on='screen_mode')\nt2 = t1[(t1['type'] == 'Graphics') | (t1['company_name'] == 'Nokia Corporation')]\nanswer = t2['hardware_model_name'].unique()", false], ["t1 = pd.merge(screen_mode, phone, left_on='graphics_mode', right_on='screen_mode')\nt2 = t1[(t1['type'] == 'Graphics') | (t1['company_name'] == 'Nokia Corporation')]\nanswer = t2['hardware_model_name'].unique()", false]]}
+{"example": {"db_id": "baseball_1", "query": "SELECT name_first ,  name_last FROM player AS T1 JOIN all_star AS T2 ON T1.player_id  =  T2.player_id WHERE YEAR  =  1998", "query_toks": ["SELECT", "name_first", ",", "name_last", "FROM", "player", "AS", "T1", "JOIN", "all_star", "AS", "T2", "ON", "T1.player_id", "=", "T2.player_id", "WHERE", "YEAR", "=", "1998"], "query_toks_no_value": ["select", "name_first", ",", "name_last", "from", "player", "as", "t1", "join", "all_star", "as", "t2", "on", "t1", ".", "player_id", "=", "t2", ".", "player_id", "where", "year", "=", "value"], "question": "What are first and last names of players participating in all star game in 1998?", "question_toks": ["What", "are", "first", "and", "last", "names", "of", "players", "participating", "in", "all", "star", "game", "in", "1998", "?"], "sql": {"from": {"table_units": [["table_unit", 16], ["table_unit", 0]], "conds": [[false, 2, [0, [0, 182, false], null], [0, 1, false], null]]}, "select": [false, [[0, [0, [0, 195, false], null]], [0, [0, [0, 196, false], null]]]], "where": [[false, 2, [0, [0, 2, false], null], 1998.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"all_star": ["player_id", "year", "game_num", "game_id", "team_id", "league_id", "gp", "starting_pos"], "appearances": ["year", "team_id", "league_id", "player_id", "g_all", "gs", "g_batting", "g_defense", "g_p", "g_c", "g_1b", "g_2b", "g_3b", "g_ss", "g_lf", "g_cf", "g_rf", "g_of", "g_dh", "g_ph", "g_pr"], "manager_award": ["player_id", "award_id", "year", "league_id", "tie", "notes"], "player_award": ["player_id", "award_id", "year", "league_id", "tie", "notes"], "manager_award_vote": ["award_id", "year", "league_id", "player_id", "points_won", "points_max", "votes_first"], "player_award_vote": ["award_id", "year", "league_id", "player_id", "points_won", "points_max", "votes_first"], "batting": ["player_id", "year", "stint", "team_id", "league_id", "g", "ab", "r", "h", "double", "triple", "hr", "rbi", "sb", "cs", "bb", "so", "ibb", "hbp", "sh", "sf", "g_idp"], "batting_postseason": ["year", "round", "player_id", "team_id", "league_id", "g", "ab", "r", "h", "double", "triple", "hr", "rbi", "sb", "cs", "bb", "so", "ibb", "hbp", "sh", "sf", "g_idp"], "player_college": ["player_id", "college_id", "year"], "fielding": ["player_id", "year", "stint", "team_id", "league_id", "pos", "g", "gs", "inn_outs", "po", "a", "e", "dp", "pb", "wp", "sb", "cs", "zr"], "fielding_outfield": ["player_id", "year", "stint", "glf", "gcf", "grf"], "fielding_postseason": ["player_id", "year", "team_id", "league_id", "round", "pos", "g", "gs", "inn_outs", "po", "a", "e", "dp", "tp", "pb", "sb", "cs"], "hall_of_fame": ["player_id", "yearid", "votedby", "ballots", "needed", "votes", "inducted", "category", "needed_note"], "home_game": ["year", "league_id", "team_id", "park_id", "span_first", "span_last", "games", "openings", "attendance"], "manager": ["player_id", "year", "team_id", "league_id", "inseason", "g", "w", "l", "rank", "plyr_mgr"], "manager_half": ["player_id", "year", "team_id", "league_id", "inseason", "half", "g", "w", "l", "rank"], "player": ["player_id", "birth_year", "birth_month", "birth_day", "birth_country", "birth_state", "birth_city", "death_year", "death_month", "death_day", "death_country", "death_state", "death_city", "name_first", "name_last", "name_given", "weight", "height", "bats", "throws", "debut", "final_game", "retro_id", "bbref_id"], "park": ["park_id", "park_name", "park_alias", "city", "state", "country"], "pitching": ["player_id", "year", "stint", "team_id", "league_id", "w", "l", "g", "gs", "cg", "sho", "sv", "ipouts", "h", "er", "hr", "bb", "so", "baopp", "era", "ibb", "wp", "hbp", "bk", "bfp", "gf", "r", "sh", "sf", "g_idp"], "pitching_postseason": ["player_id", "year", "round", "team_id", "league_id", "w", "l", "g", "gs", "cg", "sho", "sv", "ipouts", "h", "er", "hr", "bb", "so", "baopp", "era", "ibb", "wp", "hbp", "bk", "bfp", "gf", "r", "sh", "sf", "g_idp"], "salary": ["year", "team_id", "league_id", "player_id", "salary"], "college": ["college_id", "name_full", "city", "state", "country"], "postseason": ["year", "round", "team_id_winner", "league_id_winner", "team_id_loser", "league_id_loser", "wins", "losses", "ties"], "team": ["year", "league_id", "team_id", "franchise_id", "div_id", "rank", "g", "ghome", "w", "l", "div_win", "wc_win", "lg_win", "ws_win", "r", "ab", "h", "double", "triple", "hr", "bb", "so", "sb", "cs", "hbp", "sf", "ra", "er", "era", "cg", "sho", "sv", "ipouts", "ha", "hra", "bba", "soa", "e", "dp", "fp", "name", "park", "attendance", "bpf", "ppf", "team_id_br", "team_id_lahman45", "team_id_retro"], "team_franchise": ["franchise_id", "franchise_name", "active", "na_assoc"], "team_half": ["year", "league_id", "team_id", "half", "div_id", "div_win", "rank", "g", "w", "l"]}, "answer": [["David", "Wells"], ["Ivan", "Rodriguez"], ["Jim", "Thome"], ["Roberto", "Alomar"], ["Cal", "Ripken"], ["Alex", "Rodriguez"], ["Kenny", "Lofton"], ["Ken", "Griffey"], ["Juan", "Gonzalez"], ["Sandy", "Alomar"], ["Rolando", "Arrojo"], ["Scott", "Brosius"], ["Roger", "Clemens"], ["Bartolo", "Colon"], ["Ray", "Durham"], ["Damion", "Easley"], ["Darin", "Erstad"], ["Tom", "Gordon"], ["Ben", "Grieve"], ["Derek", "Jeter"], ["Pedro", "Martinez"], ["Paul", "O'Neill"], ["Dean", "Palmer"], ["Rafael", "Palmeiro"], ["Troy", "Percival"], ["Brad", "Radke"], ["Manny", "Ramirez"], ["Aaron", "Sele"], ["Mo", "Vaughn"], ["Omar", "Vizquel"], ["John", "Wetteland"], ["Bernie", "Williams"], ["Greg", "Maddux"], ["Mike", "Piazza"], ["Mark", "McGwire"], ["Craig", "Biggio"], ["Chipper", "Jones"], ["Walt", "Weiss"], ["Barry", "Bonds"], ["Larry", "Walker"], ["Tony", "Gwynn"], ["Moises", "Alou"], ["Andy", "Ashby"], ["Dante", "Bichette"], ["Bret", "Boone"], ["Kevin", "Brown"], ["Vinny", "Castilla"], ["Andres", "Galarraga"], ["Tom", "Glavine"], ["Trevor", "Hoffman"], ["Jason", "Kendall"], ["Javy", "Lopez"], ["Robb", "Nen"], ["Rick", "Reed"], ["Edgar", "Renteria"], ["Curt", "Schilling"], ["Jeff", "Shaw"], ["Gary", "Sheffield"], ["Sammy", "Sosa"], ["Ugueth", "Urbina"], ["Greg", "Vaughn"], ["Fernando", "Vina"], ["Devon", "White"]]}, "program_result_list": [["t1 = pd.merge(player, all_star, left_on='player_id', right_on='player_id')\nt2 = t1[t1['year'] == 1998]\nanswer = t2[['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, left_on='player_id', right_on='player_id')\nt2 = t1[t1['year'] == 1998]\nanswer = t2[['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, on='player_id')\nt2 = t1[t1['year'] == 1998]\nanswer = t2[['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, left_on='player_id', right_on='player_id')\nt2 = t1[t1['year'] == 1998]\nanswer = t2[['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, left_on='player_id', right_on='player_id')\nt2 = t1[t1['year'] == 1998]\nanswer = t2[['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, left_on='player_id', right_on='player_id')\nt2 = t1[t1['year'] == 1998]\nanswer = t2[['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, on='player_id')\nt2 = t1[t1['year'] == 1998]\nanswer = t2[['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, left_on='player_id', right_on='player_id')\nanswer = t1[t1['year'] == 1998][['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, on='player_id')\nt2 = t1[t1['year'] == 1998]\nanswer = t2[['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, left_on='player_id', right_on='player_id')\nt2 = t1[t1['year'] == 1998]\nanswer = t2[['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, on='player_id')\nt2 = t1[t1['year'] == 1998]\nanswer = t2[['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, on='player_id')\nt2 = t1[t1['year'] == 1998]\nanswer = t2[['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, left_on='player_id', right_on='player_id')\nt2 = t1[t1['year'] == 1998]\nanswer = t2[['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, left_on='player_id', right_on='player_id')\nt2 = t1[t1['year'] == 1998]\nanswer = t2[['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, left_on='player_id', right_on='player_id')\nt2 = t1[t1['year'] == 1998]\nanswer = t2[['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, left_on='player_id', right_on='player_id')\nanswer = t1[t1['year'] == 1998][['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, left_on='player_id', right_on='player_id')\nt2 = t1[t1['year'] == 1998]\nanswer = t2[['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, left_on='player_id', right_on='player_id')\nt2 = t1[t1['year'] == 1998]\nanswer = t2[['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, left_on='player_id', right_on='player_id')\nt2 = t1[t1['year'] == 1998]\nanswer = t2[['name_first', 'name_last']]", false], ["t1 = pd.merge(player, all_star, on='player_id')\nt2 = t1[t1['year'] == 1998]\nanswer = t2[['name_first', 'name_last']]", false]]}
+{"example": {"db_id": "network_2", "query": "SELECT avg(age) ,  job FROM Person WHERE gender  =  'male' GROUP BY job", "query_toks": ["SELECT", "avg", "(", "age", ")", ",", "job", "FROM", "Person", "WHERE", "gender", "=", "'male", "'", "GROUP", "BY", "job"], "query_toks_no_value": ["select", "avg", "(", "age", ")", ",", "job", "from", "person", "where", "gender", "=", "value", "group", "by", "job"], "question": "What is average age of male for different job title?", "question_toks": ["What", "is", "average", "age", "of", "male", "for", "different", "job", "title", "?"], "sql": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[5, [0, [0, 2, false], null]], [0, [0, [0, 5, false], null]]]], "where": [[false, 2, [0, [0, 4, false], null], "\"male\"", null]], "groupBy": [[0, 5, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Person": ["name", "age", "city", "gender", "job"], "PersonFriend": ["name", "friend", "year"]}, "answer": [[45.0, "doctor"], [35.0, "engineer"]]}, "program_result_list": [["t1 = Person[Person['gender'] == 'male'].groupby('job')\nanswer = t1['age'].mean().to_frame().reset_index()", false], ["t1 = Person[Person['gender'] == 'male']\nanswer = t1.groupby('job')['age'].mean().to_frame().reset_index()", false], ["t1 = Person[Person['gender'] == 'male']\nt2 = t1.groupby('job')['age'].mean().rename('avg(age)').to_frame()\nanswer = t2.reset_index()", false], ["t1 = Person[Person['gender'] == 'male']\nt2 = t1.groupby('job')['age'].mean().rename('avg_age').to_frame()\nanswer = t2", false], ["t1 = Person[Person['gender'] == 'male']\nt2 = t1.groupby('job').mean().reset_index()\nanswer = t2[['job', 'age']]", false], ["t1 = Person[Person['gender'] == 'male']\nt2 = t1.groupby('job')['age'].mean().to_frame().reset_index()\nanswer = t2", false], ["t1 = Person[Person['gender'] == 'male']\nt2 = t1.groupby(['job']).agg({'age': 'mean'})\nanswer = t2", false], ["t1 = Person[Person['gender'] == 'male'].groupby('job').agg({'age': 'mean'})\nanswer = t1.reset_index()", false], ["answer = Person[Person['gender'] == 'male'].groupby('job').agg({'age': 'mean'})", false], ["t1 = Person[Person['gender'] == 'male']\nt2 = t1.groupby('job').mean()\nanswer = t2[['age']]", false], ["t1 = Person[Person['gender'] == 'male']\nt2 = t1.groupby('job')['age'].mean().rename('avg_age')\nanswer = t2.to_frame().reset_index()", false], ["t1 = Person[Person['gender'] == 'male']\nt2 = t1.groupby('job').agg({'age': 'mean'}).reset_index()\nanswer = t2", false], ["t1 = Person[Person['gender'] == 'male']\nt2 = t1.groupby('job').agg({'age': 'mean'}).rename(columns={'age': 'avg_age'})\nanswer = t2.reset_index()", false], ["answer = Person[Person['gender'] == 'male'].groupby('job')['age'].mean().to_frame().reset_index()", false], ["t1 = Person[Person['gender'] == 'male']\nt2 = t1.groupby('job').agg({'age': 'mean'}).rename(columns={'age': 'avg_age'})\nanswer = t2", false], ["t1 = Person[Person['gender'] == 'male']\nt2 = t1.groupby('job')['age'].mean().rename('avg(age)').to_frame().reset_index()\nanswer = t2", false], ["t1 = Person[Person['gender'] == 'Male']\nt2 = t1.groupby('job')['age'].mean()\nanswer = t2", false], ["answer = Person[Person['gender'] == 'male'].groupby('job')['age'].mean().to_frame().reset_index()", false], ["t1 = Person[Person['gender'] == 'male']\nt2 = t1.groupby('job').mean()\nanswer = t2.reset_index().rename(columns={'age': 'avg_age'})", false], ["t1 = Person[Person['gender']=='male']\nt2 = t1.groupby('job')['age'].mean().rename('avg_age')\nanswer = t2.to_frame().reset_index()", false]]}
+{"example": {"db_id": "college_1", "query": "SELECT DISTINCT T2.emp_fname ,  T3.prof_high_degree FROM CLASS AS T1 JOIN employee AS T2 ON T1.prof_num  =  T2.emp_num JOIN professor AS T3 ON T2.emp_num  =  T3.emp_num JOIN department AS T4 ON T4.dept_code  =  T3.dept_code WHERE T4.dept_name  =  'Computer Info. Systems'", "query_toks": ["SELECT", "DISTINCT", "T2.emp_fname", ",", "T3.prof_high_degree", "FROM", "CLASS", "AS", "T1", "JOIN", "employee", "AS", "T2", "ON", "T1.prof_num", "=", "T2.emp_num", "JOIN", "professor", "AS", "T3", "ON", "T2.emp_num", "=", "T3.emp_num", "JOIN", "department", "AS", "T4", "ON", "T4.dept_code", "=", "T3.dept_code", "WHERE", "T4.dept_name", "=", "'Computer", "Info", ".", "Systems", "'"], "query_toks_no_value": ["select", "distinct", "t2", ".", "emp_fname", ",", "t3", ".", "prof_high_degree", "from", "class", "as", "t1", "join", "employee", "as", "t2", "on", "t1", ".", "prof_num", "=", "t2", ".", "emp_num", "join", "professor", "as", "t3", "on", "t2", ".", "emp_num", "=", "t3", ".", "emp_num", "join", "department", "as", "t4", "on", "t4", ".", "dept_code", "=", "t3", ".", "dept_code", "where", "t4", ".", "dept_name", "=", "value"], "question": "Find the first names and degree of all professors who are teaching some class in Computer Info. Systems department.", "question_toks": ["Find", "the", "first", "names", "and", "degree", "of", "all", "professors", "who", "are", "teaching", "some", "class", "in", "Computer", "Info", ".", "Systems", "department", "."], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 3], ["table_unit", 5], ["table_unit", 2]], "conds": [[false, 2, [0, [0, 6, false], null], [0, 17, false], null], "and", [false, 2, [0, [0, 17, false], null], [0, 27, false], null], "and", [false, 2, [0, [0, 11, false], null], [0, 28, false], null]]}, "select": [true, [[0, [0, [0, 19, false], null]], [0, [0, [0, 31, false], null]]]], "where": [[false, 2, [0, [0, 12, false], null], "\"Computer Info. Systems\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"CLASS": ["CLASS_CODE", "CRS_CODE", "CLASS_SECTION", "CLASS_TIME", "CLASS_ROOM", "PROF_NUM"], "COURSE": ["CRS_CODE", "DEPT_CODE", "CRS_DESCRIPTION", "CRS_CREDIT"], "DEPARTMENT": ["DEPT_CODE", "DEPT_NAME", "SCHOOL_CODE", "EMP_NUM", "DEPT_ADDRESS", "DEPT_EXTENSION"], "EMPLOYEE": ["EMP_NUM", "EMP_LNAME", "EMP_FNAME", "EMP_INITIAL", "EMP_JOBCODE", "EMP_HIREDATE", "EMP_DOB"], "ENROLL": ["CLASS_CODE", "STU_NUM", "ENROLL_GRADE"], "PROFESSOR": ["EMP_NUM", "DEPT_CODE", "PROF_OFFICE", "PROF_EXTENSION", "PROF_HIGH_DEGREE"], "STUDENT": ["STU_NUM", "STU_LNAME", "STU_FNAME", "STU_INIT", "STU_DOB", "STU_HRS", "STU_CLASS", "STU_GPA", "STU_TRANSFER", "DEPT_CODE", "STU_PHONE", "PROF_NUM"]}, "answer": [["Peter", "Ph.D."], ["Carlos", "Ph.D."]]}, "program_result_list": [["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, left_on='emp_num', right_on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt4 = t3[t3['dept_name'] == 'computer info. systems']\nanswer = t4[['emp_fname', 'prof_high_degree']].drop_duplicates()", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, left_on='emp_num', right_on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt4 = t3[t3['dept_name'] == 'Computer Info. Systems']\nanswer = t4[['emp_fname', 'prof_high_degree']].drop_duplicates()", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, left_on='emp_num', right_on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt4 = t3[t3['dept_name'] == 'Computer Info. Systems']\nanswer = t4.drop_duplicates(['emp_fname', 'prof_high_degree'])[['emp_fname', 'prof_high_degree']]", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, left_on='emp_num', right_on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt4 = t3[t3['dept_name'] == 'Computer Info. Systems']\nanswer = t4[['emp_fname', 'prof_high_degree']].drop_duplicates()", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, left_on='emp_num', right_on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt4 = t3[t3['dept_name'] == 'Computer Info. Systems']\nanswer = t4[['emp_fname', 'prof_high_degree']].drop_duplicates()", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nanswer = t3[t3['dept_name'] == 'computer info. systems'][['emp_fname', 'prof_high_degree']].drop_duplicates()", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, on='dept_code')\nt4 = t3[t3['dept_name'] == 'Computer Info. Systems']\nanswer = t4[['emp_fname', 'prof_high_degree']].drop_duplicates()", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, left_on='emp_num', right_on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt4 = t3[t3['dept_name'] == 'Computer Info. Systems']\nanswer = t4[['emp_fname', 'prof_high_degree']].drop_duplicates()", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, left_on='emp_num', right_on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt4 = t3[t3['dept_name'] == 'computer info. systems']\nanswer = t4[['emp_fname', 'prof_high_degree']].drop_duplicates()", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, left_on='emp_num', right_on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt4 = t3[t3['dept_name'] == 'Computer Info. Systems']\nanswer = t4[['emp_fname', 'prof_high_degree']].drop_duplicates()", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, left_on='dept_code_x', right_on='dept_code')\nt4 = t3[t3['dept_name'] == 'computer info. systems']\nanswer = t4[['emp_fname', 'prof_high_degree']].drop_duplicates()", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, left_on='emp_num', right_on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt4 = t3[t3['dept_name'] == 'computer info. systems']\nanswer = t4[['emp_fname', 'prof_high_degree']].drop_duplicates()", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, left_on='emp_num', right_on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nanswer = t3[t3['dept_name'] == 'Computer Info. Systems'][['emp_fname', 'prof_high_degree']].drop_duplicates()", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt4 = t3[t3['dept_name'] == 'computer info. systems']\nanswer = t4[['emp_fname', 'prof_high_degree']].drop_duplicates()", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, left_on='emp_num', right_on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt4 = t3[t3['dept_name'] == 'computer info. systems']\nanswer = t4[['emp_fname', 'prof_high_degree']].drop_duplicates()", false], ["t1 = pd.merge(CLASS, EMPLOYEE, on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, on='dept_code')\nanswer = t3[t3['dept_name'] == 'computer info. systems'][['emp_fname', 'prof_high_degree']].drop_duplicates()", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, left_on='emp_num', right_on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nanswer = t3[t3['dept_name'] == 'computer info. systems']['emp_fname', 'prof_high_degree'].drop_duplicates()", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, left_on='emp_num', right_on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt4 = t3[t3['dept_name'] == 'Computer Info. Systems']\nanswer = t4[['emp_fname', 'prof_high_degree']].drop_duplicates()", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, left_on='prof_num', right_on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt4 = t3[t3['dept_name'] == 'computer info. systems']\nanswer = t4[['emp_fname', 'prof_high_degree']].drop_duplicates()", false], ["t1 = pd.merge(CLASS, EMPLOYEE, left_on='prof_num', right_on='emp_num')\nt2 = pd.merge(t1, PROFESSOR, left_on='emp_num', right_on='emp_num')\nt3 = pd.merge(t2, DEPARTMENT, left_on='dept_code', right_on='dept_code')\nt4 = t3[t3['dept_name'] == 'Computer Info. Systems']\nanswer = t4[['emp_fname', 'prof_high_degree']].drop_duplicates()", false]]}
+{"example": {"db_id": "inn_1", "query": "SELECT roomName FROM Rooms WHERE bedType  =  \"King\" OR bedType  =  \"Queen\";", "query_toks": ["SELECT", "roomName", "FROM", "Rooms", "WHERE", "bedType", "=", "``", "King", "''", "OR", "bedType", "=", "``", "Queen", "''", ";"], "query_toks_no_value": ["select", "roomname", "from", "rooms", "where", "bedtype", "=", "value", "or", "bedtype", "=", "value"], "question": "What are the names of rooms that have either king or queen bed?", "question_toks": ["What", "are", "the", "names", "of", "rooms", "that", "have", "either", "king", "or", "queen", "bed", "?"], "sql": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[0, [0, [0, 2, false], null]]]], "where": [[false, 2, [0, [0, 4, false], null], "\"King\"", null], "or", [false, 2, [0, [0, 4, false], null], "\"Queen\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Rooms": ["RoomId", "roomName", "beds", "bedType", "maxOccupancy", "basePrice", "decor"], "Reservations": ["Code", "Room", "CheckIn", "CheckOut", "Rate", "LastName", "FirstName", "Adults", "Kids"]}, "answer": [["Recluse and defiance"], ["Interim but salutary"], ["Abscond or bolster"], ["Harbinger but bequest"], ["Immutable before decorum"], ["Convoke and sanguine"], ["Riddle to exculpate"], ["Frugal not apropos"]]}, "program_result_list": [["answer = Rooms[(Rooms['bedtype'] == 'king') | (Rooms['bedtype'] == 'queen')]['roomname']", false], ["answer = Rooms[(Rooms['bedtype'] == 'king') | (Rooms['bedtype'] == 'queen')]['roomname']", false], ["t1 = Rooms[(Rooms['bedtype'] == 'king') | (Rooms['bedtype'] == 'queen')]\nanswer = t1['roomname']", false], ["t1 = Rooms[Rooms['bedtype'] == 'king']\nt2 = Rooms[Rooms['bedtype'] == 'queen']\nt3 = pd.concat([t1, t2], axis=0)\nanswer = t3['roomname'].unique()", false], ["answer = Rooms[(Rooms['bedtype'] == 'king') | (Rooms['bedtype'] == 'queen')]['roomname']", false], ["t1 = Rooms[Rooms['bedtype'] == 'king']\nt2 = Rooms[Rooms['bedtype'] == 'queen']\nanswer = pd.concat([t1, t2])['roomname'].unique()", false], ["t1 = Rooms[(Rooms['bedtype'] == 'king') | (Rooms['bedtype'] == 'queen')]\nanswer = t1['roomname']", false], ["t1 = Rooms[(Rooms['bedtype'] == 'king') | (Rooms['bedtype'] == 'queen')]\nanswer = t1['roomname']", false], ["t1 = Rooms[Rooms['bedtype'].isin(['king', 'queen'])]\nanswer = t1['roomname']", false], ["t1 = Rooms[(Rooms['bedtype'] == 'king') | (Rooms['bedtype'] == 'queen')]\nanswer = t1['roomname']", false], ["t1 = Rooms[(Rooms['bedtype'] == 'king') | (Rooms['bedtype'] == 'queen')]\nanswer = t1['roomname']", false], ["t1 = Rooms[Rooms['bedtype'] == 'king']\nt2 = Rooms[Rooms['bedtype'] == 'queen']\nanswer = pd.concat([t1, t2])['roomname'].unique()", false], ["t1 = Rooms[Rooms['bedtype'] == 'king']\nt2 = Rooms[Rooms['bedtype'] == 'queen']\nanswer = pd.concat([t1, t2])['roomname'].unique()", false], ["t1 = Rooms[Rooms['bedtype'].isin(['king', 'queen'])]\nanswer = t1['roomname']", false], ["t1 = Rooms[(Rooms['bedtype'] == 'king') | (Rooms['bedtype'] == 'queen')]\nanswer = t1['roomname']", false], ["answer = Rooms[Rooms['bedtype'].isin(['king', 'queen'])]['roomname']", false], ["t1 = Rooms.query(\"bedtype == 'queen' or bedtype == 'king'\")\nanswer = t1['roomname']", false], ["t1 = Rooms[(Rooms['bedtype'] == 'king') | (Rooms['bedtype'] == 'queen')]\nanswer = t1['roomname']", false], ["t1 = Rooms[Rooms['bedtype'].isin(['king', 'queen'])]\nanswer = t1['roomname']", false], ["t1 = Rooms[(Rooms['bedtype'] == 'king') | (Rooms['bedtype'] == 'queen')]\nanswer = t1['roomname']", false]]}
+{"example": {"db_id": "dorm_1", "query": "SELECT T3.amenity_name FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid  =  T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid  =  T3.amenid WHERE T1.dorm_name  =  'Smith Hall'", "query_toks": ["SELECT", "T3.amenity_name", "FROM", "dorm", "AS", "T1", "JOIN", "has_amenity", "AS", "T2", "ON", "T1.dormid", "=", "T2.dormid", "JOIN", "dorm_amenity", "AS", "T3", "ON", "T2.amenid", "=", "T3.amenid", "WHERE", "T1.dorm_name", "=", "'Smith", "Hall", "'"], "query_toks_no_value": ["select", "t3", ".", "amenity_name", "from", "dorm", "as", "t1", "join", "has_amenity", "as", "t2", "on", "t1", ".", "dormid", "=", "t2", ".", "dormid", "join", "dorm_amenity", "as", "t3", "on", "t2", ".", "amenid", "=", "t3", ".", "amenid", "where", "t1", ".", "dorm_name", "=", "value"], "question": "Find the name of amenities Smith Hall dorm have.", "question_toks": ["Find", "the", "name", "of", "amenities", "Smith", "Hall", "dorm", "have", "."], "sql": {"from": {"table_units": [["table_unit", 1], ["table_unit", 3], ["table_unit", 2]], "conds": [[false, 2, [0, [0, 9, false], null], [0, 15, false], null], "and", [false, 2, [0, [0, 16, false], null], [0, 13, false], null]]}, "select": [false, [[0, [0, [0, 14, false], null]]]], "where": [[false, 2, [0, [0, 10, false], null], "\"Smith Hall\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Student": ["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], "Dorm": ["dormid", "dorm_name", "student_capacity", "gender"], "Dorm_amenity": ["amenid", "amenity_name"], "Has_amenity": ["dormid", "amenid"], "Lives_in": ["stuid", "dormid", "room_number"]}, "answer": [["Study Room"], ["Carpeted Rooms"], ["4 Walls"], ["Heat"], ["Roof"]]}, "program_result_list": [["t1 = pd.merge(Dorm, Has_amenity, left_on='dormid', right_on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, left_on='amenid', right_on='amenid')\nt3 = t2[t2['dorm_name']=='Smith Hall']['amenity_name']\nanswer = t3", false], ["t1 = pd.merge(Dorm, Has_amenity, left_on='dormid', right_on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, left_on='amenid', right_on='amenid')\nt3 = t2[t2['dorm_name'] == 'Smith Hall']\nanswer = t3['amenity_name']", false], ["t1 = pd.merge(Dorm, Has_amenity, on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, on='amenid')\nt3 = t2[t2['dorm_name'] == 'Smith Hall']\nanswer = t3['amenity_name']", false], ["t1 = pd.merge(Dorm, Has_amenity, left_on='dormid', right_on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, left_on='amenid', right_on='amenid')\nt3 = t2[t2['dorm_name'] == 'smith hall']\nanswer = t3['amenity_name']", false], ["t1 = pd.merge(Dorm, Has_amenity, on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, on='amenid')\nt3 = t2[t2['dorm_name'] == 'Smith Hall']\nanswer = t3['amenity_name']", false], ["t1 = pd.merge(Dorm, Has_amenity, left_on='dormid', right_on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, left_on='amenid', right_on='amenid')\nt3 = t2[t2['dorm_name'] == 'Smith Hall']\nanswer = t3['amenity_name']", false], ["t1 = pd.merge(Dorm, Has_amenity, on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, on='amenid')\nt3 = t2[t2['dorm_name'] == 'Smith Hall']\nanswer = t3['amenity_name']", false], ["t1 = pd.merge(Dorm, Has_amenity, left_on='dormid', right_on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, left_on='amenid', right_on='amenid')\nt3 = t2[t2['dorm_name'] == 'Smith Hall']\nanswer = t3['amenity_name']", false], ["t1 = pd.merge(Dorm, Has_amenity, left_on='dormid', right_on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, left_on='amenid', right_on='amenid')\nt3 = t2[t2['dorm_name'] == 'Smith Hall']\nanswer = t3['amenity_name']", false], ["t1 = pd.merge(Dorm, Has_amenity, left_on='dormid', right_on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, left_on='amenid', right_on='amenid')\nt3 = t2[t2['dorm_name'] == 'Smith Hall']\nanswer = t3['amenity_name']", false], ["t1 = pd.merge(Dorm, Has_amenity, left_on='dormid', right_on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, left_on='amenid', right_on='amenid')\nt3 = t2[t2['dorm_name'] == 'Smith Hall']\nanswer = t3['amenity_name']", false], ["t1 = pd.merge(Dorm, Has_amenity, left_on='dormid', right_on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, left_on='amenid', right_on='amenid')\nt3 = t2[t2['dorm_name'] == 'smith hall']\nanswer = t3['amenity_name']", false], ["t1 = pd.merge(Dorm, Has_amenity, left_on='dormid', right_on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, left_on='amenid', right_on='amenid')\nt3 = t2[t2['dorm_name'] == 'Smith Hall']\nanswer = t3['amenity_name']", false], ["t1 = pd.merge(Dorm, Has_amenity, left_on='dormid', right_on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, left_on='amenid', right_on='amenid')\nt3 = t2[t2['dorm_name'] == 'smith hall']\nanswer = t3['amenity_name']", false], ["t1 = pd.merge(Dorm, Has_amenity, left_on='dormid', right_on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, left_on='amenid', right_on='amenid')\nt3 = t2[t2['dorm_name'] == 'Smith Hall']\nanswer = t3['amenity_name']", false], ["t1 = pd.merge(Dorm, Has_amenity, left_on='dormid', right_on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, left_on='amenid', right_on='amenid')\nt3 = t2[t2['dorm_name'] == 'Smith Hall']\nanswer = t3['amenity_name']", false], ["t1 = pd.merge(Dorm, Has_amenity, left_on='dormid', right_on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, left_on='amenid', right_on='amenid')\nt3 = t2[t2['dorm_name'] == 'smith hall']\nanswer = t3['amenity_name']", false], ["t1 = pd.merge(Dorm, Has_amenity, left_on='dormid', right_on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, left_on='amenid', right_on='amenid')\nt3 = t2[t2['dorm_name'] == 'Smith Hall']\nanswer = t3['amenity_name']", false], ["t1 = pd.merge(Dorm, Has_amenity, left_on='dormid', right_on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, left_on='amenid', right_on='amenid')\nt3 = t2[t2['dorm_name'] == 'Smith Hall']\nanswer = t3['amenity_name']", false], ["t1 = pd.merge(Dorm, Has_amenity, left_on='dormid', right_on='dormid')\nt2 = pd.merge(t1, Dorm_amenity, left_on='amenid', right_on='amenid')\nt3 = t2[t2['dorm_name'] == 'smith hall']\nanswer = t3['amenity_name']", false]]}
+{"example": {"db_id": "college_2", "query": "SELECT course_id FROM SECTION WHERE semester  =  'Fall' AND YEAR  =  2009 UNION SELECT course_id FROM SECTION WHERE semester  =  'Spring' AND YEAR  =  2010", "query_toks": ["SELECT", "course_id", "FROM", "SECTION", "WHERE", "semester", "=", "'Fall", "'", "AND", "YEAR", "=", "2009", "UNION", "SELECT", "course_id", "FROM", "SECTION", "WHERE", "semester", "=", "'Spring", "'", "AND", "YEAR", "=", "2010"], "query_toks_no_value": ["select", "course_id", "from", "section", "where", "semester", "=", "value", "and", "year", "=", "value", "union", "select", "course_id", "from", "section", "where", "semester", "=", "value", "and", "year", "=", "value"], "question": "Find courses that ran in Fall 2009 or in Spring 2010.", "question_toks": ["Find", "courses", "that", "ran", "in", "Fall", "2009", "or", "in", "Spring", "2010", "."], "sql": {"from": {"table_units": [["table_unit", 4]], "conds": []}, "select": [false, [[0, [0, [0, 15, false], null]]]], "where": [[false, 2, [0, [0, 17, false], null], "\"Fall\"", null], "and", [false, 2, [0, [0, 18, false], null], 2009.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": {"from": {"table_units": [["table_unit", 4]], "conds": []}, "select": [false, [[0, [0, [0, 15, false], null]]]], "where": [[false, 2, [0, [0, 17, false], null], "\"Spring\"", null], "and", [false, 2, [0, [0, 18, false], null], 2010.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "except": null}, "db_table_headers": {"classroom": ["building", "room_number", "capacity"], "department": ["dept_name", "building", "budget"], "course": ["course_id", "title", "dept_name", "credits"], "instructor": ["ID", "name", "dept_name", "salary"], "section": ["course_id", "sec_id", "semester", "year", "building", "room_number", "time_slot_id"], "teaches": ["ID", "course_id", "sec_id", "semester", "year"], "student": ["ID", "name", "dept_name", "tot_cred"], "takes": ["ID", "course_id", "sec_id", "semester", "year", "grade"], "advisor": ["s_ID", "i_ID"], "time_slot": ["time_slot_id", "day", "start_hr", "start_min", "end_hr", "end_min"], "prereq": ["course_id", "prereq_id"]}, "answer": [["105"], ["237"], ["242"], ["270"], ["304"], ["334"], ["443"], ["486"], ["493"], ["679"], ["692"], ["735"], ["960"]]}, "program_result_list": [["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]\nt3 = pd.concat([t1, t2])\nanswer = t3['course_id'].unique()", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]\nt3 = pd.concat([t1, t2])\nanswer = t3['course_id'].unique()", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]['course_id']\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]['course_id']\nanswer = pd.concat([t1,t2]).unique()", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]\nt3 = t1.append(t2)\nanswer = t3['course_id'].unique()", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]\nt3 = pd.concat([t1, t2])\nanswer = t3['course_id']", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]['course_id']\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]['course_id']\nanswer = pd.concat([t1, t2]).unique()", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]\nt3 = pd.concat([t1, t2])\nanswer = t3['course_id'].unique()", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]\nanswer = pd.concat([t1, t2])['course_id'].unique()", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]\nt3 = pd.concat([t1,t2]).drop_duplicates()\nanswer = t3['course_id'].unique()", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]\nt3 = pd.concat([t1, t2])\nanswer = t3['course_id'].unique()", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]\nanswer = pd.concat([t1['course_id'], t2['course_id']]).drop_duplicates().to_frame().reset_index(drop=True)", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]\nt3 = pd.concat([t1, t2])\nanswer = t3['course_id'].unique()", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]\nt3 = pd.concat([t1, t2])\nanswer = t3['course_id'].unique()", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)][['course_id']]\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)][['course_id']]\nt3 = pd.concat([t1,t2])\nanswer = t3['course_id'].unique()", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]\nt3 = pd.concat([t1, t2])\nanswer = t3['course_id'].unique()", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]\nt3 = t1.append(t2)\nanswer = t3['course_id'].unique()", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]\nanswer = pd.concat([t1, t2])['course_id'].unique()", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]\nanswer = pd.concat([t1, t2])['course_id'].unique()", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]\nt3 = pd.concat([t1, t2])\nanswer = t3['course_id'].unique()", false], ["t1 = section[(section['semester'] == 'Fall') & (section['year'] == 2009)]\nt2 = section[(section['semester'] == 'Spring') & (section['year'] == 2010)]\nt3 = pd.concat([t1, t2], ignore_index=True)\nt4 = t3['course_id'].unique()\nanswer = t4", false]]}
+{"example": {"db_id": "music_4", "query": "SELECT T2.Song FROM artist AS T1 JOIN volume AS T2 ON T1.Artist_ID  =  T2.Artist_ID WHERE T1.age  >=  32", "query_toks": ["SELECT", "T2.Song", "FROM", "artist", "AS", "T1", "JOIN", "volume", "AS", "T2", "ON", "T1.Artist_ID", "=", "T2.Artist_ID", "WHERE", "T1.age", ">", "=", "32"], "query_toks_no_value": ["select", "t2", ".", "song", "from", "artist", "as", "t1", "join", "volume", "as", "t2", "on", "t1", ".", "artist_id", "=", "t2", ".", "artist_id", "where", "t1", ".", "age", ">", "=", "value"], "question": "What are the songs in volumes associated with the artist aged 32 or older?", "question_toks": ["What", "are", "the", "songs", "in", "volumes", "associated", "with", "the", "artist", "aged", "32", "or", "older", "?"], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 11, false], null]]}, "select": [false, [[0, [0, [0, 10, false], null]]]], "where": [[false, 5, [0, [0, 3, false], null], 32.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"artist": ["Artist_ID", "Artist", "Age", "Famous_Title", "Famous_Release_date"], "volume": ["Volume_ID", "Volume_Issue", "Issue_Date", "Weeks_on_Top", "Song", "Artist_ID"], "music_festival": ["ID", "Music_Festival", "Date_of_ceremony", "Category", "Volume", "Result"]}, "answer": [[" The Way"], [" Everybody Have Fun Tonight "], [" Walk Like an Egyptian "], [" Touch Me (I Want Your Body) "], [" With or Without You "], [" (I Just) Died in Your Arms "], ["Looking for a New Love"], ["Head to Toe "]]}, "program_result_list": [["t1 = pd.merge(artist, volume, left_on='artist_id', right_on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, left_on='artist_id', right_on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, left_on='artist_id', right_on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, left_on='artist_id', right_on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, left_on='artist_id', right_on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, left_on='artist_id', right_on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, left_on='artist_id', right_on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, left_on='artist_id', right_on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, left_on='artist_id', right_on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, left_on='artist_id', right_on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, left_on='artist_id', right_on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, left_on='artist_id', right_on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, left_on='artist_id', right_on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, left_on = 'artist_id', right_on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false], ["t1 = pd.merge(artist, volume, left_on='artist_id', right_on='artist_id')\nt2 = t1[t1['age'] >= 32]\nanswer = t2['song']", false]]}
+{"example": {"db_id": "network_2", "query": "SELECT T2.name ,  avg(T1.age) FROM Person AS T1 JOIN PersonFriend AS T2 ON T1.name  =  T2.friend GROUP BY T2.name ORDER BY avg(T1.age) DESC LIMIT 1", "query_toks": ["SELECT", "T2.name", ",", "avg", "(", "T1.age", ")", "FROM", "Person", "AS", "T1", "JOIN", "PersonFriend", "AS", "T2", "ON", "T1.name", "=", "T2.friend", "GROUP", "BY", "T2.name", "ORDER", "BY", "avg", "(", "T1.age", ")", "DESC", "LIMIT", "1"], "query_toks_no_value": ["select", "t2", ".", "name", ",", "avg", "(", "t1", ".", "age", ")", "from", "person", "as", "t1", "join", "personfriend", "as", "t2", "on", "t1", ".", "name", "=", "t2", ".", "friend", "group", "by", "t2", ".", "name", "order", "by", "avg", "(", "t1", ".", "age", ")", "desc", "limit", "value"], "question": "Which person whose friends have the oldest average age?", "question_toks": ["Which", "person", "whose", "friends", "have", "the", "oldest", "average", "age", "?"], "sql": {"from": {"table_units": [["table_unit", 0], ["table_unit", 1]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 7, false], null]]}, "select": [false, [[0, [0, [0, 6, false], null]], [5, [0, [0, 2, false], null]]]], "where": [], "groupBy": [[0, 6, false]], "having": [], "orderBy": ["desc", [[0, [5, 2, false], null]]], "limit": 1, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Person": ["name", "age", "city", "gender", "job"], "PersonFriend": ["name", "friend", "year"]}, "answer": [["Bob", 45.0]]}, "program_result_list": [["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby('name').mean().rename(columns={'age': 'avg_age'})\nanswer = t2.sort_values('avg_age', ascending=False).head(1)", false], ["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby('name')['age'].mean().rename('avg_age')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3['name']", false], ["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby('name').agg({'age': np.mean}).rename(columns={'age': 'average_age'})\nt3 = t2.sort_values(by='average_age', ascending=False).head(1)\nanswer = t3.index", false], ["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby('name').agg(['mean'])['age']\nanswer = t2.sort_values('mean', ascending=False).head(1)", false], ["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby('name')['age'].mean().rename('average_age')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index()", false], ["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby(['name']).agg({'age': 'mean'})\nt3 = t2.sort_values(by=['age'], ascending=False)\nanswer = t3.head(1)", false], ["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby('name').agg({'age': 'mean'}).rename(columns={'age': 'avg'})\nanswer = t2.sort_values(by='avg', ascending=False).head(1).reset_index()[['name']]", false], ["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby('name').mean().rename(columns={'age': 'avg_age'})\nanswer = t2.sort_values(by='avg_age', ascending=False).head(1)", false], ["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby('name')['age'].mean().rename('avg_age')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3['name']", false], ["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby('name').size()\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3['name']", false], ["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby('name')['age'].mean().rename('avg_age')\nt3 = t2.sort_values(ascending=False).head(1).to_frame().reset_index()\nanswer = t3['name']", false], ["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby('name').mean()\nanswer = t2.sort_values(by='age', ascending=False).head(1)['age']", false], ["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby('name').age.mean().rename('avg(t1.age)')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index()", false], ["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby('name').mean()\nanswer = t2[['age']].sort_values('age', ascending=False).head(1)", false], ["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby('name')['age'].mean().rename('avg_age')\nanswer = t2.sort_values(ascending=False).head(1).to_frame().reset_index()", false], ["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby('name').agg({'age': 'mean'}).rename(columns={'age': 'avg_age'})\nanswer = t2.sort_values('avg_age', ascending=False).head(1).reset_index()", false], ["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby('name').agg({'age': np.mean}).rename(columns={'age': 'avg_age'})\nanswer = t2.sort_values(by='avg_age', ascending=False).head(1).reset_index()[['name', 'avg_age']]", false], ["t1 = pd.merge(Person, PersonFriend, on='name')\nt2 = t1.groupby('friend').agg({'age':'mean'}).rename(columns={'age':'avg_age'})\nanswer = t2.sort_values(by='avg_age', ascending=False).head(1).reset_index()['friend']", false], ["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby('name').mean()\nanswer = t2['age'].sort_values(ascending=False).head(1).to_frame().reset_index()", false], ["t1 = pd.merge(Person, PersonFriend, left_on='name', right_on='friend')\nt2 = t1.groupby('name').age.mean().rename('avg_age').to_frame().reset_index()\nanswer = t2.sort_values(by='avg_age', ascending=False).iloc[[0]]", false]]}
+{"example": {"db_id": "manufactory_1", "query": "SELECT count(DISTINCT T1.name) ,  T2.Headquarter FROM products AS T1 JOIN manufacturers AS T2 ON T1.Manufacturer  =  T2.code GROUP BY T2.Headquarter", "query_toks": ["SELECT", "count", "(", "DISTINCT", "T1.name", ")", ",", "T2.Headquarter", "FROM", "products", "AS", "T1", "JOIN", "manufacturers", "AS", "T2", "ON", "T1.Manufacturer", "=", "T2.code", "GROUP", "BY", "T2.Headquarter"], "query_toks_no_value": ["select", "count", "(", "distinct", "t1", ".", "name", ")", ",", "t2", ".", "headquarter", "from", "products", "as", "t1", "join", "manufacturers", "as", "t2", "on", "t1", ".", "manufacturer", "=", "t2", ".", "code", "group", "by", "t2", ".", "headquarter"], "question": "Find the number of different products that are produced by companies at different headquarter cities.", "question_toks": ["Find", "the", "number", "of", "different", "products", "that", "are", "produced", "by", "companies", "at", "different", "headquarter", "cities", "."], "sql": {"from": {"table_units": [["table_unit", 1], ["table_unit", 0]], "conds": [[false, 2, [0, [0, 9, false], null], [0, 1, false], null]]}, "select": [false, [[3, [0, [0, 7, true], null]], [0, [0, [0, 3, false], null]]]], "where": [], "groupBy": [[0, 3, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Manufacturers": ["Code", "Name", "Headquarter", "Founder", "Revenue"], "Products": ["Code", "Name", "Price", "Manufacturer"]}, "answer": [[3, "Austin"], [1, "Beijing"], [3, "Los Angeles"], [2, "Paris"], [1, "Taiwan"], [1, "Tokyo"]]}, "program_result_list": [["t1 = pd.merge(Products, Manufacturers, left_on='manufacturer', right_on='code')\nt2 = t1[['name', 'headquarter']].drop_duplicates().groupby('headquarter')['name'].count().rename('count').to_frame().reset_index()\nanswer = t2", false], ["t1 = pd.merge(Products, Manufacturers, left_on='manufacturer', right_on='code')\nt2 = t1.groupby('headquarter')['name'].nunique().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Products, Manufacturers, left_on='manufacturer', right_on='code')\nt2 = t1.groupby('headquarter').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Products, Manufacturers, left_on='manufacturer', right_on='code')\nanswer = t1.groupby('headquarter').agg({'name': 'nunique'}).rename(columns={'name': 'count'})", false], ["t1 = pd.merge(Products, Manufacturers, left_on='manufacturer', right_on='code')\nt2 = t1.groupby('headquarter')['name_x'].nunique().rename('count').to_frame().reset_index()\nanswer = t2['count']", false], ["t1 = pd.merge(Products, Manufacturers, left_on='manufacturer', right_on='code')\nt2 = t1.groupby('headquarter')['name'].nunique()\nanswer = t2.to_frame().rename(columns={'name': 'count'})", false], ["t1 = pd.merge(Products, Manufacturers, left_on='manufacturer', right_on='code')\nt2 = t1.groupby('headquarter').nunique()\nanswer = t2['name']", false], ["t1 = pd.merge(Manufacturers, Products, left_on='code', right_on='manufacturer')\nt2 = t1.groupby('headquarter').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Products, Manufacturers, left_on='manufacturer', right_on='code')\nt2 = t1.groupby('headquarter').agg({'name': 'nunique'})\nanswer = t2", false], ["t1 = pd.merge(Products, Manufacturers, left_on='manufacturer', right_on='code')\nt2 = t1.groupby('headquarter')['name'].nunique()\nanswer = t2.to_frame().reset_index()\nanswer.columns = ['headquarter', 'count']", false], ["t1 = pd.merge(Products, Manufacturers, left_on='manufacturer', right_on='code')\nt2 = t1.groupby('headquarter').agg({'name': pd.Series.nunique}).reset_index()\nanswer = t2", false], ["t1 = pd.merge(Products, Manufacturers, left_on='manufacturer', right_on='code')\nt2 = t1.groupby('headquarter')['name'].nunique().to_frame().reset_index()\nanswer = t2['headquarter'].size", false], ["t1 = pd.merge(Manufacturers, Products, left_on='code', right_on='manufacturer')\nt2 = t1.groupby('headquarter')['name'].nunique().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Products, Manufacturers, left_on='manufacturer', right_on='code')\nt2 = t1.groupby('headquarter').agg({'name': lambda x: len(x.unique())})\nanswer = t2.rename(columns={'name': 'count'})", false], ["t1 = pd.merge(Products, Manufacturers, left_on='manufacturer', right_on='code')\nt2 = t1.groupby('headquarter')['name'].nunique()\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Products, Manufacturers, left_on='manufacturer', right_on='code')\nt2 = t1.groupby(['headquarter'])['name'].nunique()\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Products, Manufacturers, left_on='manufacturer', right_on='code')\nt2 = t1.groupby('headquarter').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(Products, Manufacturers, on='code')\nt2 = t1.groupby('headquarter').agg({'name': pd.Series.nunique}).rename(columns={'name': 'count'})\nanswer = t2", false], ["t1 = pd.merge(Products, Manufacturers, left_on='manufacturer', right_on='code')\nt2 = t1.groupby('headquarter').size().rename('count').to_frame().reset_index()\nanswer = t2['count']", false], ["t1 = pd.merge(Products, Manufacturers, left_on='manufacturer', right_on='code')\nt2 = t1.groupby(['headquarter'])['name_x'].nunique().rename('count').to_frame().reset_index()\nanswer = t2['count']", false]]}
+{"example": {"db_id": "aircraft", "query": "SELECT T2.Location ,  T1.Aircraft FROM aircraft AS T1 JOIN MATCH AS T2 ON T1.Aircraft_ID  =  T2.Winning_Aircraft", "query_toks": ["SELECT", "T2.Location", ",", "T1.Aircraft", "FROM", "aircraft", "AS", "T1", "JOIN", "MATCH", "AS", "T2", "ON", "T1.Aircraft_ID", "=", "T2.Winning_Aircraft"], "query_toks_no_value": ["select", "t2", ".", "location", ",", "t1", ".", "aircraft", "from", "aircraft", "as", "t1", "join", "match", "as", "t2", "on", "t1", ".", "aircraft_id", "=", "t2", ".", "winning_aircraft"], "question": "Please list the location and the winning aircraft name.", "question_toks": ["Please", "list", "the", "location", "and", "the", "winning", "aircraft", "name", "."], "sql": {"from": {"table_units": [["table_unit", 1], ["table_unit", 2]], "conds": [[false, 2, [0, [0, 4, false], null], [0, 16, false], null]]}, "select": [false, [[0, [0, [0, 11, false], null]], [0, [0, [0, 5, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"pilot": ["Pilot_Id", "Name", "Age"], "aircraft": ["Aircraft_ID", "Aircraft", "Description", "Max_Gross_Weight", "Total_disk_area", "Max_disk_Loading"], "match": ["Round", "Location", "Country", "Date", "Fastest_Qualifying", "Winning_Pilot", "Winning_Aircraft"], "airport": ["Airport_ID", "Airport_Name", "Total_Passengers", "%_Change_2007", "International_Passengers", "Domestic_Passengers", "Transit_Passengers", "Aircraft_Movements", "Freight_Metric_Tonnes"], "airport_aircraft": ["ID", "Airport_ID", "Aircraft_ID"]}, "answer": [["Mina' Zayid , Abu Dhabi", "Robinson R-22"], ["Swan River , Perth", "Robinson R-22"], ["Flamengo Beach , Rio de Janeiro", "Bell 206B3 JetRanger"], ["Windsor , Ontario", "Mil Mi-26"], ["New York City", "CH-47D Chinook"], ["EuroSpeedway Lausitz", "Mil Mi-26"], ["River Danube , Budapest", "CH-53E Super Stallion"]]}, "program_result_list": [["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(match, aircraft, left_on='winning_aircraft', right_on='aircraft_id')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false], ["t1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]", false]]}
+{"example": {"db_id": "wine_1", "query": "SELECT avg(Price) ,  avg(Score) ,  Appelation FROM WINE GROUP BY Appelation", "query_toks": ["SELECT", "avg", "(", "Price", ")", ",", "avg", "(", "Score", ")", ",", "Appelation", "FROM", "WINE", "GROUP", "BY", "Appelation"], "query_toks_no_value": ["select", "avg", "(", "price", ")", ",", "avg", "(", "score", ")", ",", "appelation", "from", "wine", "group", "by", "appelation"], "question": "What are the average price and score of wines grouped by appelation?", "question_toks": ["What", "are", "the", "average", "price", "and", "score", "of", "wines", "grouped", "by", "appelation", "?"], "sql": {"from": {"table_units": [["table_unit", 2]], "conds": []}, "select": [false, [[5, [0, [0, 17, false], null]], [5, [0, [0, 18, false], null]], [0, [0, [0, 13, false], null]]]], "where": [], "groupBy": [[0, 13, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"grapes": ["ID", "Grape", "Color"], "appellations": ["No", "Appelation", "County", "State", "Area", "isAVA"], "wine": ["No", "Grape", "Winery", "Appelation", "State", "Name", "Year", "Price", "Score", "Cases", "Drink"]}, "answer": [[29.142857142857142, 88.14285714285714, "Alexander Valley"], [31.5, 90.0, "Amador County"], [18.0, 87.0, "Amador-Mendocino-Sonoma Counties"], [53.333333333333336, 92.33333333333333, "Anderson Valley"], [31.0, 92.0, "Arroyo Grande Valley"], [125.0, 88.0, "Atlas Peak"], [46.5, 91.0, "Bennett Valley"], [20.0, 84.0, "Calaveras County"], [39.48275862068966, 87.82758620689656, "California"], [36.166666666666664, 90.0, "Carneros"], [13.0, 87.0, "Central Coast"], [52.5, 92.0, "Chalk Hill"], [25.0, 92.0, "Chalone"], [27.8, 87.2, "Contra Costa County"], [27.653846153846153, 89.3076923076923, "Dry Creek Valley"], [29.5, 89.0, "Edna Valley"], [25.0, 78.0, "Fiddletown"], [40.0, 91.0, "Green Valley of Russian River Valley"], [20.0, 87.0, "Guenoc Valley"], [21.0, 88.0, "Happy Canyon of Santa Barbara"], [29.0, 93.0, "Howell Mountain"], [37.5, 89.5, "Knights Valley"], [26.0, 87.0, "Lake County"], [40.0, 89.0, "Livermore Valley"], [21.5, 87.5, "Lodi"], [15.333333333333334, 86.33333333333333, "Mendocino County"], [22.5, 89.0, "Monterey County"], [38.333333333333336, 90.66666666666667, "Mount Harlan"], [53.333333333333336, 90.66666666666667, "Mount Vedeer"], [59.135416666666664, 90.47916666666667, "Napa Valley"], [18.0, 86.0, "Napa-Sonoma counties"], [12.0, 85.0, "North Coast"], [95.0, 90.5, "Oakville"], [32.285714285714285, 88.42857142857143, "Paso Robles"], [49.0, 90.5, "Red Hills Lake County"], [38.0, 90.0, "Rockpile"], [49.60377358490566, 91.79245283018868, "Russian River Valley"], [45.714285714285715, 89.42857142857143, "Rutherford"], [31.5, 89.5, "San Luis Obispo County"], [28.0, 89.41666666666667, "Santa Barbara County"], [40.0, 93.0, "Santa Cruz Mountains"], [46.285714285714285, 91.23809523809524, "Santa Lucia Highlands"], [41.5, 91.25, "Santa Maria Valley"], [29.333333333333332, 88.73333333333333, "Santa Ynez Valley"], [25.0, 86.0, "Shenandoah Valley"], [10.0, 82.0, "Sierra Foothills"], [35.0, 89.0, "Solano County"], [49.02564102564103, 90.82051282051282, "Sonoma Coast"], [43.48148148148148, 90.4074074074074, "Sonoma County"], [42.68421052631579, 89.78947368421052, "Sonoma Valley"], [15.0, 86.0, "Sonoma-Lake Counties"], [70.66666666666667, 89.33333333333333, "Spring Mountain District"], [77.5, 91.5, "St. Helena"], [60.35294117647059, 90.88235294117646, "Sta. Rita Hills"], [84.5, 92.25, "Stags Leap District"], [25.0, 91.0, "Yolo County"], [90.0, 90.0, "Yountville"]]}, "program_result_list": [["t1 = wine.groupby('appelation')\nt2 = t1[['price', 'score']].mean().rename(columns = {'price': 'avg_price', 'score': 'avg_score'})\nanswer = t2", false], ["t1 = wine.groupby('appelation').mean()\nanswer = t1[['price', 'score']]", false], ["t1 = wine.groupby('appelation').mean()[['price', 'score']].rename(columns={'price': 'avg_price', 'score': 'avg_score'})\nanswer = t1.reset_index()", false], ["t1 = wine.groupby('appelation')[['price', 'score']].mean()\nanswer = t1.reset_index()", false], ["t1 = wine.groupby('appelation').agg({'price': 'mean', 'score': 'mean'}).rename(columns={'price': 'avg_price', 'score': 'avg_score'})\nanswer = t1.reset_index()", false], ["t1 = wine.groupby('appelation').agg({'price': 'mean', 'score': 'mean'}).rename(columns={'price': 'avg_price', 'score': 'avg_score'})\nanswer = t1.reset_index()", false], ["t1 = wine.groupby('appelation').agg({'price': 'mean', 'score': 'mean'}).rename(columns={'price': 'avg_price', 'score': 'avg_score'})\nanswer = t1.reset_index()", false], ["t1 = wine.groupby('appelation').mean()\nanswer = t1[['price', 'score']]", false], ["t1 = wine.groupby('appelation').agg({'price': 'mean', 'score': 'mean'}).reset_index()\nt1.rename(columns={'price': 'avg(price)', 'score': 'avg(score)'}, inplace=True)\nanswer = t1", false], ["answer = wine.groupby('appelation').agg({'price': 'mean', 'score': 'mean'}).reset_index()", false], ["t1 = wine.groupby('appelation', as_index=False).agg({'price': 'mean', 'score': 'mean'})\nanswer = t1[['appelation', 'price', 'score']]", false], ["t1 = wine.groupby('appelation')\nanswer = t1[['price', 'score']].mean()", false], ["t1 = wine.groupby('appelation')\nanswer = t1.agg({'price': np.mean, 'score': np.mean}).reset_index()", false], ["answer = wine.groupby('appelation').agg({'price': 'mean', 'score': 'mean'})", false], ["t1 = wine.groupby('appelation').agg({'price': np.mean, 'score': np.mean}).rename(columns={'price': 'avg_price', 'score': 'avg_score'})\nanswer = t1[['avg_price', 'avg_score']]", false], ["t1 = wine.groupby('appelation')\nanswer = t1['price', 'score'].mean()", false], ["t1 = wine.groupby('appelation')['price', 'score'].mean().rename(columns={'price': 'avg_price', 'score': 'avg_score'})\nanswer = t1.reset_index()", false], ["t1 = wine.groupby('appelation').agg({'price': np.mean, 'score': np.mean})\nanswer = t1.reset_index()", false], ["t1 = wine.groupby('appelation')\nanswer = t1[['price', 'score']].mean().reset_index()", false], ["t1 = wine.groupby('appelation').agg({'price': 'mean', 'score': 'mean'})\nanswer = t1.reset_index()", false]]}
+{"example": {"db_id": "college_1", "query": "SELECT count(*) ,  T1.school_code FROM department AS T1 JOIN professor AS T2 ON T1.dept_code  =  T2.dept_code GROUP BY T1.school_code", "query_toks": ["SELECT", "count", "(", "*", ")", ",", "T1.school_code", "FROM", "department", "AS", "T1", "JOIN", "professor", "AS", "T2", "ON", "T1.dept_code", "=", "T2.dept_code", "GROUP", "BY", "T1.school_code"], "query_toks_no_value": ["select", "count", "(", "*", ")", ",", "t1", ".", "school_code", "from", "department", "as", "t1", "join", "professor", "as", "t2", "on", "t1", ".", "dept_code", "=", "t2", ".", "dept_code", "group", "by", "t1", ".", "school_code"], "question": "What is the number of professors for different school?", "question_toks": ["What", "is", "the", "number", "of", "professors", "for", "different", "school", "?"], "sql": {"from": {"table_units": [["table_unit", 2], ["table_unit", 5]], "conds": [[false, 2, [0, [0, 11, false], null], [0, 28, false], null]]}, "select": [false, [[3, [0, [0, 0, false], null]], [0, [0, [0, 13, false], null]]]], "where": [], "groupBy": [[0, 13, false]], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"CLASS": ["CLASS_CODE", "CRS_CODE", "CLASS_SECTION", "CLASS_TIME", "CLASS_ROOM", "PROF_NUM"], "COURSE": ["CRS_CODE", "DEPT_CODE", "CRS_DESCRIPTION", "CRS_CREDIT"], "DEPARTMENT": ["DEPT_CODE", "DEPT_NAME", "SCHOOL_CODE", "EMP_NUM", "DEPT_ADDRESS", "DEPT_EXTENSION"], "EMPLOYEE": ["EMP_NUM", "EMP_LNAME", "EMP_FNAME", "EMP_INITIAL", "EMP_JOBCODE", "EMP_HIREDATE", "EMP_DOB"], "ENROLL": ["CLASS_CODE", "STU_NUM", "ENROLL_GRADE"], "PROFESSOR": ["EMP_NUM", "DEPT_CODE", "PROF_OFFICE", "PROF_EXTENSION", "PROF_HIGH_DEGREE"], "STUDENT": ["STU_NUM", "STU_LNAME", "STU_FNAME", "STU_INIT", "STU_DOB", "STU_HRS", "STU_CLASS", "STU_GPA", "STU_TRANSFER", "DEPT_CODE", "STU_PHONE", "PROF_NUM"]}, "answer": [[12, "A&SCI"], [10, "BUS"]]}, "program_result_list": [["t1 = pd.merge(DEPARTMENT, PROFESSOR, left_on='dept_code', right_on='dept_code')\nt2 = t1.groupby('school_code').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, left_on='dept_code', right_on='dept_code')\nt2 = t1.groupby('school_code').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, on='dept_code')\nanswer = t1.groupby('school_code').size().rename('count').to_frame().reset_index()", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, left_on='dept_code', right_on='dept_code')\nanswer = t1.groupby('school_code').size().rename('count')", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, on='dept_code')\nanswer = t1.groupby('school_code').size().rename('count')", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, left_on='dept_code', right_on='dept_code')\nt2 = t1.groupby('school_code').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, left_on='dept_code', right_on='dept_code')\nt2 = t1.groupby('school_code').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, left_on='dept_code', right_on='dept_code')\nanswer = t1.groupby('school_code').size().rename('count').to_frame().reset_index()", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, left_on='dept_code', right_on='dept_code')\nt2 = t1.groupby('school_code').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, on='dept_code')\nt2 = t1.groupby('school_code').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, left_on='dept_code', right_on='dept_code')\nt2 = t1.groupby('school_code').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, left_on='dept_code', right_on='dept_code')\nt2 = t1.groupby('school_code').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, left_on='dept_code', right_on='dept_code')\nt2 = t1.groupby('school_code').size().rename('count').to_frame().reset_index()\nanswer = t2", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, left_on='dept_code', right_on='dept_code')\nt2 = t1.groupby('school_code').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, left_on='dept_code', right_on='dept_code')\nanswer = t1.groupby('school_code').size().rename('count').to_frame().reset_index()", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, left_on='dept_code', right_on='dept_code')\nt2 = t1.groupby('school_code').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, left_on='dept_code', right_on='dept_code')\nt2 = t1.groupby('school_code').size().rename('count')\nanswer = t2.to_frame().reset_index()", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, left_on='dept_code', right_on='dept_code')\nt2 = t1.groupby('school_code').size().rename('count').to_frame().reset_index()\nanswer = t2", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, left_on='dept_code', right_on='dept_code')\nt2 = t1.groupby('school_code').size().rename('count').to_frame().reset_index()\nanswer = t2", false], ["t1 = pd.merge(DEPARTMENT, PROFESSOR, left_on='dept_code', right_on='dept_code')\nt2 = t1.groupby('school_code').size().rename('count')\nanswer = t2.to_frame().reset_index()", false]]}
+{"example": {"db_id": "activity_1", "query": "SELECT T1.stuid FROM participates_in AS T1 JOIN activity AS T2 ON T2.actid  =  T2.actid WHERE T2.activity_name  =  'Canoeing' INTERSECT SELECT T1.stuid FROM participates_in AS T1 JOIN activity AS T2 ON T2.actid  =  T2.actid WHERE T2.activity_name  =  'Kayaking'", "query_toks": ["SELECT", "T1.stuid", "FROM", "participates_in", "AS", "T1", "JOIN", "activity", "AS", "T2", "ON", "T2.actid", "=", "T2.actid", "WHERE", "T2.activity_name", "=", "'Canoeing", "'", "INTERSECT", "SELECT", "T1.stuid", "FROM", "participates_in", "AS", "T1", "JOIN", "activity", "AS", "T2", "ON", "T2.actid", "=", "T2.actid", "WHERE", "T2.activity_name", "=", "'Kayaking", "'"], "query_toks_no_value": ["select", "t1", ".", "stuid", "from", "participates_in", "as", "t1", "join", "activity", "as", "t2", "on", "t2", ".", "actid", "=", "t2", ".", "actid", "where", "t2", ".", "activity_name", "=", "value", "intersect", "select", "t1", ".", "stuid", "from", "participates_in", "as", "t1", "join", "activity", "as", "t2", "on", "t2", ".", "actid", "=", "t2", ".", "actid", "where", "t2", ".", "activity_name", "=", "value"], "question": "Which students participate in both Canoeing and Kayaking as their activities? Tell me their student ids.", "question_toks": ["Which", "students", "participate", "in", "both", "Canoeing", "and", "Kayaking", "as", "their", "activities", "?", "Tell", "me", "their", "student", "ids", "."], "sql": {"from": {"table_units": [["table_unit", 1], ["table_unit", 0]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 1, false], null]]}, "select": [false, [[0, [0, [0, 3, false], null]]]], "where": [[false, 2, [0, [0, 2, false], null], "\"Canoeing\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": {"from": {"table_units": [["table_unit", 1], ["table_unit", 0]], "conds": [[false, 2, [0, [0, 1, false], null], [0, 1, false], null]]}, "select": [false, [[0, [0, [0, 3, false], null]]]], "where": [[false, 2, [0, [0, 2, false], null], "\"Kayaking\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "union": null, "except": null}, "db_table_headers": {"Activity": ["actid", "activity_name"], "Participates_in": ["stuid", "actid"], "Faculty_Participates_in": ["FacID", "actid"], "Student": ["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], "Faculty": ["FacID", "Lname", "Fname", "Rank", "Sex", "Phone", "Room", "Building"]}, "answer": [[1001], [1002], [1003], [1004], [1005], [1006], [1007], [1008], [1009], [1010], [1011], [1012], [1014], [1015], [1016], [1017], [1018], [1019], [1020], [1021], [1022], [1023], [1024], [1025], [1028], [1029], [1030], [1033], [1034], [1035]]}, "program_result_list": [["t1 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing'][['stuid']]\nt3 = t1[t1['activity_name'] == 'Kayaking'][['stuid']]\nt4 = pd.merge(t2, t3, on='stuid', how='inner')\nanswer = t4['stuid']", false], ["t1 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']\nt3 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt4 = t3[t3['activity_name'] == 'Kayaking']\nt5 = pd.merge(t2, t4, on='stuid', how='inner')\nanswer = t5['stuid']", false], ["t1 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']\nt3 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt4 = t3[t3['activity_name'] == 'Kayaking']\nt5 = pd.merge(t2, t4, on='stuid', how='inner')\nanswer = t5['stuid']", false], ["t1 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']\nt3 = t1[t1['activity_name'] == 'Kayaking']\nanswer = t2[t2['stuid'].isin(t3['stuid'])]['stuid'].unique()", false], ["t1 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']\nt3 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt4 = t3[t3['activity_name'] == 'Kayaking']\nanswer = t2[t2['stuid'].isin(t4['stuid'])]['stuid']", false], ["t1 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']\nt3 = t1[t1['activity_name'] == 'Kayaking']\nanswer = t2[t2['stuid'].isin(t3['stuid'])]['stuid'].unique()", false], ["t1 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']\nt3 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt4 = t3[t3['activity_name'] == 'Kayaking']\nanswer = t2[t2['stuid'].isin(t4['stuid'])]['stuid']", false], ["t1 = pd.merge(Activity, Participates_in, left_on='actid', right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']['stuid'].to_frame()\nt3 = pd.merge(Activity, Participates_in, left_on='actid', right_on='actid')\nt4 = t3[t3['activity_name'] == 'Kayaking']['stuid'].to_frame()\nt5 = pd.merge(t2, t4, on='stuid', how='inner')\nanswer = t5['stuid'].to_frame()", false], ["t1 = pd.merge(Activity, Participates_in, left_on='actid', right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']\nt3 = pd.merge(Activity, Participates_in, left_on='actid', right_on='actid')\nt4 = t3[t3['activity_name'] == 'Kayaking']\nt5 = pd.merge(t2, t4, how='inner', on='stuid')\nanswer = t5['stuid_x'].unique()", false], ["t1 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']\nt3 = t1[t1['activity_name'] == 'Kayaking']\nt4 = pd.merge(t2, t3, on='stuid', how='inner')\nanswer = t4['stuid']", false], ["t1 = pd.merge(Participates_in, Activity, left_on='actid',right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']\nt3 = pd.merge(Participates_in, Activity, left_on='actid',right_on='actid')\nt4 = t3[t3['activity_name'] == 'Kayaking']\nt5 = pd.merge(t2, t4, on='stuid', how='inner')\nanswer = t5['stuid_x']", false], ["t1 = pd.merge(Participates_in, Activity, on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']\nt3 = pd.merge(Participates_in, Activity, on='actid')\nt4 = t3[t3['activity_name'] == 'Kayaking']\nanswer = t2[t2['stuid'].isin(t4['stuid'])]['stuid']", false], ["t1 = pd.merge(Activity, Participates_in, left_on='actid', right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']\nt3 = pd.merge(Activity, Participates_in, left_on='actid', right_on='actid')\nt4 = t3[t3['activity_name'] == 'Kayaking']\nanswer = pd.merge(t2, t4, on='stuid')['stuid'].unique()", false], ["t1 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']\nt3 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt4 = t3[t3['activity_name'] == 'Kayaking']\nt5 = t2[t2['stuid'].isin(t4['stuid'])]\nanswer = t5['stuid']", false], ["t1 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']\nt3 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt4 = t3[t3['activity_name'] == 'Kayaking']\nt5 = pd.merge(t2, t4, on='stuid')\nanswer = t5['stuid']", false], ["t1 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']\nt3 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt4 = t3[t3['activity_name'] == 'Kayaking']\nt5 = pd.merge(t2, t4, left_on='stuid', right_on='stuid')\nanswer = t5['stuid_x'].unique()", false], ["t1 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']\nt3 = t1[t1['activity_name'] == 'Kayaking']\nt4 = pd.merge(t2, t3, how='inner', left_on='stuid', right_on='stuid')\nanswer = t4['stuid_x']", false], ["t1 = pd.merge(Activity, Participates_in, left_on='actid', right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']\nt3 = pd.merge(Activity, Participates_in, left_on='actid', right_on='actid')\nt4 = t3[t3['activity_name'] == 'Kayaking']\nt5 = pd.merge(t2, t4, on='stuid')\nanswer = t5['stuid'].unique()", false], ["t1 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']\nt3 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt4 = t3[t3['activity_name'] == 'Kayaking']\nanswer = t2[t2.stuid.isin(t4.stuid)].stuid.unique()", false], ["t1 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt2 = t1[t1['activity_name'] == 'Canoeing']\nt3 = pd.merge(Participates_in, Activity, left_on='actid', right_on='actid')\nt4 = t3[t3['activity_name'] == 'Kayaking']\nanswer = t2['stuid'][t2['stuid'].isin(t4['stuid'])].unique()", false]]}
+{"example": {"db_id": "music_1", "query": "SELECT DISTINCT artist_name FROM artist WHERE country  =  \"Bangladesh\" EXCEPT SELECT DISTINCT artist_name FROM song WHERE rating  >  7", "query_toks": ["SELECT", "DISTINCT", "artist_name", "FROM", "artist", "WHERE", "country", "=", "``", "Bangladesh", "''", "EXCEPT", "SELECT", "DISTINCT", "artist_name", "FROM", "song", "WHERE", "rating", ">", "7"], "query_toks_no_value": ["select", "distinct", "artist_name", "from", "artist", "where", "country", "=", "value", "except", "select", "distinct", "artist_name", "from", "song", "where", "rating", ">", "value"], "question": "What are the names of the different artists from Bangladesh who never received a rating higher than a 7?", "question_toks": ["What", "are", "the", "names", "of", "the", "different", "artists", "from", "Bangladesh", "who", "never", "received", "a", "rating", "higher", "than", "a", "7", "?"], "sql": {"from": {"table_units": [["table_unit", 1]], "conds": []}, "select": [true, [[0, [0, [0, 4, false], null]]]], "where": [[false, 2, [0, [0, 5, false], null], "\"Bangladesh\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": {"from": {"table_units": [["table_unit", 3]], "conds": []}, "select": [true, [[0, [0, [0, 14, false], null]]]], "where": [[false, 3, [0, [0, 18, false], null], 7.0, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}}, "db_table_headers": {"genre": ["g_name", "rating", "most_popular_in"], "artist": ["artist_name", "country", "gender", "preferred_genre"], "files": ["f_id", "artist_name", "file_size", "duration", "formats"], "song": ["song_name", "artist_name", "country", "f_id", "genre_is", "rating", "languages", "releasedate", "resolution"]}, "answer": [["Farida"], ["Prity"]]}, "program_result_list": [["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = song[song['rating'] > 7]\nanswer = t1[~t1['artist_name'].isin(t2['artist_name'])]", false], ["t1 = artist[artist['country'] == 'Bangladesh'].rename(columns={'artist_name': 'artist_name_1'})\nt2 = song[song['rating'] > 7].rename(columns={'artist_name': 'artist_name_2'})\nt3 = pd.merge(t1, t2, left_on='artist_name_1', right_on='artist_name_2', how='left')\nt4 = t3[t3['artist_name_2'].isnull()][['artist_name_1']]\nanswer = t4['artist_name_1'].unique()", false], ["t1 = artist[artist['country'] == 'Bangladesh']['artist_name'].unique()\nt2 = song[song['rating'] > 7]['artist_name'].unique()\nanswer = np.setdiff1d(t1, t2)", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = song[song['rating'] > 7]\nanswer = t1[~t1['artist_name'].isin(t2['artist_name'])]['artist_name'].unique()", false], ["t1 = artist[artist['country'] == 'Bangladesh'].drop_duplicates(['artist_name'])\nt2 = song[song['rating'] > 7].drop_duplicates(['artist_name'])\nanswer = pd.concat([t1, t2, t2]).drop_duplicates(keep=False)['artist_name']", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = song[song['rating'] > 7]\nt3 = pd.merge(t1, t2, left_on='artist_name', right_on='artist_name', how='outer', indicator=True)\nt4 = t3[t3['_merge'] == 'left_only']\nanswer = t4['artist_name']", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = song[song['rating'] > 7]\nt3 = pd.merge(t1, t2, on='artist_name', how='outer', indicator=True)\nanswer = t3[t3['_merge'] == 'left_only']['artist_name'].unique()", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = song[song['rating'] > 7]\nanswer = t1[~t1.isin(t2)].dropna()['artist_name']", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = song[song['rating'] > 7]\nanswer = t1[~t1['artist_name'].isin(t2['artist_name'])]['artist_name'].unique()", false], ["t1 = artist[artist['country'] == 'Bangladesh']['artist_name'].unique()\nt2 = song[song['rating'] > 7]['artist_name'].unique()\nanswer = pd.DataFrame(set(t1) - set(t2), columns=['artist_name'])", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = song[song['rating'] > 7]\nt3 = t1.merge(t2, on='artist_name', how='outer')\nt4 = t3[t3['artist_name_x'].isna()]\nanswer = t4['artist_name_y'].unique()", false], ["t1 = artist[artist['country'] == 'bangladesh']\nt2 = song[song['rating'] > 7]\nanswer = t1.merge(t2, on='artist_name', how='left', indicator=True).query(\"_merge == 'left_only'\")['artist_name']", false], ["t1 = artist[artist['country'] == 'Bangladesh']['artist_name'].unique()\nt2 = song[song['rating'] > 7]['artist_name'].unique()\nanswer = np.setdiff1d(t1, t2)", false], ["t1 = artist[artist['country'] == 'bangladesh']\nt2 = song[song['rating'] > 7]\nanswer = t1[~t1['artist_name'].isin(t2['artist_name'])]['artist_name'].unique()", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = song[song['rating'] > 7]\nanswer = t1[~t1['artist_name'].isin(t2['artist_name'])]['artist_name'].unique()", false], ["t1 = artist[artist['country'] == 'Bangladesh']['artist_name'].unique()\nt2 = song[song['rating'] > 7]['artist_name'].unique()\nanswer = np.setdiff1d(t1, t2)", false], ["t1 = artist[artist['country'] == 'Bangladesh']\nt2 = song[song['rating'] > 7]\nanswer = t1.merge(t2, on='artist_name', how='left', indicator=True).query('_merge == \"left_only\"')['artist_name']", false], ["t1 = artist[artist['country'] == 'Bangladesh'].drop_duplicates()\nt2 = song[song['rating'] > 7].drop_duplicates()\nanswer = t1[~t1.isin(t2)].dropna()", false], ["t1 = artist[artist['country'] == 'Bangladesh']['artist_name'].unique()\nt2 = song[song['rating'] > 7]['artist_name'].unique()\nt3 = t1[~t1.isin(t2)]\nanswer = t3", false], ["t1 = artist[artist['country'] == 'Bangladesh']['artist_name'].unique()\nt2 = song[song['rating'] > 7]['artist_name'].unique()\nanswer = np.setdiff1d(t1, t2)", false]]}
+{"example": {"db_id": "department_store", "query": "SELECT avg(total_amount_purchased) ,  avg(total_value_purchased) FROM Product_Suppliers WHERE supplier_id  =  (SELECT supplier_id FROM Product_Suppliers GROUP BY supplier_id ORDER BY count(*) DESC LIMIT 1)", "query_toks": ["SELECT", "avg", "(", "total_amount_purchased", ")", ",", "avg", "(", "total_value_purchased", ")", "FROM", "Product_Suppliers", "WHERE", "supplier_id", "=", "(", "SELECT", "supplier_id", "FROM", "Product_Suppliers", "GROUP", "BY", "supplier_id", "ORDER", "BY", "count", "(", "*", ")", "DESC", "LIMIT", "1", ")"], "query_toks_no_value": ["select", "avg", "(", "total_amount_purchased", ")", ",", "avg", "(", "total_value_purchased", ")", "from", "product_suppliers", "where", "supplier_id", "=", "(", "select", "supplier_id", "from", "product_suppliers", "group", "by", "supplier_id", "order", "by", "count", "(", "*", ")", "desc", "limit", "value", ")"], "question": "Return the average total amount purchased and total value purchased for the supplier who supplies the greatest number of products.", "question_toks": ["Return", "the", "average", "total", "amount", "purchased", "and", "total", "value", "purchased", "for", "the", "supplier", "who", "supplies", "the", "greatest", "number", "of", "products", "."], "sql": {"from": {"table_units": [["table_unit", 12]], "conds": []}, "select": [false, [[5, [0, [0, 50, false], null]], [5, [0, [0, 51, false], null]]]], "where": [[false, 2, [0, [0, 47, false], null], {"from": {"table_units": [["table_unit", 12]], "conds": []}, "select": [false, [[0, [0, [0, 47, false], null]]]], "where": [], "groupBy": [[0, 47, false]], "having": [], "orderBy": ["desc", [[0, [3, 0, false], null]]], "limit": 1, "intersect": null, "union": null, "except": null}, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Addresses": ["address_id", "address_details"], "Staff": ["staff_id", "staff_gender", "staff_name"], "Suppliers": ["supplier_id", "supplier_name", "supplier_phone"], "Department_Store_Chain": ["dept_store_chain_id", "dept_store_chain_name"], "Customers": ["customer_id", "payment_method_code", "customer_code", "customer_name", "customer_address", "customer_phone", "customer_email"], "Products": ["product_id", "product_type_code", "product_name", "product_price"], "Supplier_Addresses": ["supplier_id", "address_id", "date_from", "date_to"], "Customer_Addresses": ["customer_id", "address_id", "date_from", "date_to"], "Customer_Orders": ["order_id", "customer_id", "order_status_code", "order_date"], "Department_Stores": ["dept_store_id", "dept_store_chain_id", "store_name", "store_address", "store_phone", "store_email"], "Departments": ["department_id", "dept_store_id", "department_name"], "Order_Items": ["order_item_id", "order_id", "product_id"], "Product_Suppliers": ["product_id", "supplier_id", "date_supplied_from", "date_supplied_to", "total_amount_purchased", "total_value_purchased"], "Staff_Department_Assignments": ["staff_id", "department_id", "date_assigned_from", "job_title_code", "date_assigned_to"]}, "answer": [[37602.4675, 56499.6425]]}, "program_result_list": [["t1 = Product_Suppliers.groupby('supplier_id').size().rename('count').sort_values(ascending=False).head(1).to_frame().reset_index()\nt2 = pd.merge(Product_Suppliers, t1, on='supplier_id')\nanswer = t2[['total_amount_purchased', 'total_value_purchased']].mean()", false], ["t1 = Product_Suppliers.groupby('supplier_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = pd.merge(Product_Suppliers, t2, on='supplier_id')\nanswer = t3[['total_amount_purchased', 'total_value_purchased']].mean()", false], ["t1 = Product_Suppliers.groupby('supplier_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = Product_Suppliers[Product_Suppliers['supplier_id'] == t2['supplier_id'].values[0]]\nanswer = t3[['total_amount_purchased', 'total_value_purchased']].mean().to_frame().T", false], ["t1 = Product_Suppliers.groupby('supplier_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = pd.merge(Product_Suppliers, t2, on='supplier_id')\nanswer = t3[['total_amount_purchased', 'total_value_purchased']].mean()", false], ["t1 = Product_Suppliers.groupby('supplier_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).reset_index()\nt3 = t2['supplier_id']\nt4 = Product_Suppliers[Product_Suppliers['supplier_id'].isin(t3)]\nanswer = t4[['total_amount_purchased', 'total_value_purchased']].mean()", false], ["t1 = Product_Suppliers.groupby('supplier_id').size().rename('count').to_frame().reset_index()\nt2 = t1.sort_values('count', ascending=False).head(1).to_frame().reset_index()\nt3 = pd.merge(Product_Suppliers, t2, left_on='supplier_id', right_on='supplier_id')\nanswer = t3[['total_amount_purchased', 'total_value_purchased']].mean()", false], ["t1 = Product_Suppliers.groupby('supplier_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = Product_Suppliers[Product_Suppliers['supplier_id'] == t2.iloc[0, 0]]\nanswer = t3[['total_amount_purchased', 'total_value_purchased']].mean()", false], ["t1 = Product_Suppliers.groupby('supplier_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = pd.merge(Product_Suppliers, t2, on='supplier_id')\nanswer = t3[['avg(total_amount_purchased)', 'avg(total_value_purchased)']].head(1)", false], ["t1 = Product_Suppliers.groupby('supplier_id').size().rename('count').sort_values(ascending=False).head(1).to_frame().reset_index()\nt2 = Product_Suppliers[Product_Suppliers['supplier_id'] == t1['supplier_id'].iloc[0]]\nanswer = t2[['total_amount_purchased', 'total_value_purchased']].agg(['mean'])", false], ["t1 = Product_Suppliers.groupby('supplier_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = Product_Suppliers[Product_Suppliers['supplier_id'] == t2['supplier_id'].iloc[0]]\nt4 = t3.agg({'total_amount_purchased': 'mean', 'total_value_purchased': 'mean'}).to_frame().reset_index()\nanswer = t4.iloc[0]", false], ["t1 = Product_Suppliers.groupby('supplier_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = pd.merge(t2, Product_Suppliers, on='supplier_id')\nanswer = t3[['total_amount_purchased', 'total_value_purchased']].mean()", false], ["t1 = Product_Suppliers.groupby('supplier_id').size()\nt2 = t1.sort_values(ascending=False).head(1)\nt3 = Product_Suppliers[Product_Suppliers['supplier_id'] == t2.index[0]]\nanswer = pd.DataFrame({'total_amount_purchased': [t3['total_amount_purchased'].mean()], 'total_value_purchased': [t3['total_value_purchased'].mean()]})", false], ["t1 = Product_Suppliers.groupby('supplier_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = pd.merge(Product_Suppliers, t2, on='supplier_id')\nanswer = t3[['total_amount_purchased', 'total_value_purchased']].mean()", false], ["t1 = Product_Suppliers.groupby('supplier_id').size().to_frame().reset_index().sort_values(by=0, ascending=False).head(1)\nt2 = Product_Suppliers[Product_Suppliers['supplier_id'] == t1['supplier_id'].values[0]]\nanswer = t2[['total_amount_purchased', 'total_value_purchased']].mean()", false], ["t1 = Product_Suppliers.groupby('supplier_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = pd.merge(Product_Suppliers, t2, on='supplier_id')\nanswer = t3[['total_amount_purchased', 'total_value_purchased']].mean().to_frame().reset_index()\nanswer.columns = ['total_amount_purchased', 'total_value_purchased']", false], ["t1 = Product_Suppliers.groupby('supplier_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = pd.merge(Product_Suppliers, t2, left_on='supplier_id', right_on='supplier_id')\nanswer = t3[['avg(total_amount_purchased)', 'avg(total_value_purchased)']].mean()\n \n# Question: Return the average total amount purchased and total value purchased for the supplier who supplies the greatest number of products.\n# SQL: select avg(total_amount_purchased) ,  avg(total_value_purchased) from product_suppliers where supplier_id  =  (select supplier_id from product_suppliers group by supplier_id order by count(*) desc limit 1)\nt1 = Product_Suppliers.groupby('supplier_id').size().rename('count')\nt2 = t1.sort_values(ascending=", false], ["t1 = Product_Suppliers.groupby(['supplier_id']).size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = Product_Suppliers[Product_Suppliers['supplier_id'] == t2['supplier_id'].values[0]]\nanswer = t3[['total_amount_purchased', 'total_value_purchased']].mean()", false], ["t1 = Product_Suppliers.groupby('supplier_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = pd.merge(Product_Suppliers, t2, on='supplier_id')\nanswer = t3[['total_amount_purchased', 'total_value_purchased']].mean().to_frame().reset_index()\nanswer.columns = ['avg_total_amount', 'avg_total_value']", false], ["t1 = Product_Suppliers.groupby('supplier_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = pd.merge(Product_Suppliers, t2, on='supplier_id')\nanswer = t3[['total_amount_purchased', 'total_value_purchased']].mean()", false], ["t1 = Product_Suppliers.groupby('supplier_id').size().rename('count')\nt2 = t1.sort_values(ascending=False).head(1).to_frame().reset_index()\nt3 = pd.merge(t2, Product_Suppliers, on='supplier_id')\nanswer = t3[['total_amount_purchased', 'total_value_purchased']].mean().to_frame().reset_index().rename(columns={'index': 'metric'})", false]]}
+{"example": {"db_id": "gas_company", "query": "SELECT headquarters FROM company EXCEPT SELECT headquarters FROM company WHERE main_industry  =  'Banking'", "query_toks": ["SELECT", "headquarters", "FROM", "company", "EXCEPT", "SELECT", "headquarters", "FROM", "company", "WHERE", "main_industry", "=", "'Banking", "'"], "query_toks_no_value": ["select", "headquarters", "from", "company", "except", "select", "headquarters", "from", "company", "where", "main_industry", "=", "value"], "question": "Show all headquarters without a company in banking industry.", "question_toks": ["Show", "all", "headquarters", "without", "a", "company", "in", "banking", "industry", "."], "sql": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[0, [0, [0, 4, false], null]]]], "where": [], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": {"from": {"table_units": [["table_unit", 0]], "conds": []}, "select": [false, [[0, [0, [0, 4, false], null]]]], "where": [[false, 2, [0, [0, 5, false], null], "\"Banking\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}}, "db_table_headers": {"company": ["Company_ID", "Rank", "Company", "Headquarters", "Main_Industry", "Sales_billion", "Profits_billion", "Assets_billion", "Market_Value"], "gas_station": ["Station_ID", "Open_Year", "Location", "Manager_Name", "Vice_Manager_Name", "Representative_Name"], "station_company": ["Station_ID", "Company_ID", "Rank_of_the_Year"]}, "answer": [["Brazil"], ["Netherlands"]]}, "program_result_list": [["t1 = company[company['main_industry'] == 'banking']\nt2 = company[company['main_industry'] != 'banking']\nanswer = t2['headquarters'].unique()", false], ["t1 = company[company['main_industry'] == 'banking']\nanswer = company[~company['headquarters'].isin(t1['headquarters'])]['headquarters']", false], ["t1 = company[company['main_industry'] == 'Banking']\nt2 = company.drop_duplicates('headquarters')\nt3 = t2[~t2['headquarters'].isin(t1['headquarters'])]\nanswer = t3['headquarters']", false], ["t1 = company[company['main_industry'] == 'banking'][['headquarters']]\nt2 = company[['headquarters']]\nanswer = t2[~t2.isin(t1)].dropna()", false], ["t1 = company[company['main_industry'] == 'banking']['headquarters']\nt2 = company['headquarters']\nanswer = t2[~t2.isin(t1)].drop_duplicates()", false], ["t1 = company[company['main_industry'] == 'banking']['headquarters']\nt2 = company['headquarters']\nanswer = t2[~t2.isin(t1)]", false], ["t1 = company[company['main_industry'] == 'banking']\nanswer = company[~company['headquarters'].isin(t1['headquarters'])]['headquarters'].unique()", false], ["t1 = company[company['main_industry'] == 'banking']['headquarters']\nanswer = company[~company['headquarters'].isin(t1)]['headquarters']", false], ["t1 = company[company['main_industry'] == 'banking']\nt2 = company[company['main_industry'] != 'banking']\nt3 = t1['headquarters'].unique()\nanswer = t2['headquarters'].unique()", false], ["t1 = company[company['main_industry'] == 'banking']['headquarters'].rename('headquarters')\nt2 = company[company['main_industry'] != 'banking']['headquarters'].rename('headquarters')\nt3 = t2[~t2.isin(t1)]\nanswer = t3.drop_duplicates()", false], ["t1 = company[company['main_industry'] == 'banking']['headquarters']\nt2 = company['headquarters']\nanswer = t2[~t2.isin(t1)]", false], ["t1 = company.copy()\nt1 = t1[t1['main_industry'] != 'banking']\nt2 = company.copy()\nt2 = t2[t2['main_industry'] != 'banking']\nanswer = t2.merge(t1, on='headquarters', how='left', indicator=True).query('_merge == \"left_only\"')['headquarters']", false], ["t1 = company[company['main_industry'] == 'banking']['headquarters'].to_frame().reset_index(drop=True)\nt2 = company[['headquarters']].reset_index(drop=True)\nanswer = t2.loc[t2.index.difference(t1.index)]", false], ["t1 = company[company['main_industry'] == 'banking']\nt2 = company.drop(t1.index)\nanswer = t2['headquarters']", false], ["t1 = company[company['main_industry'] == 'banking']\nt2 = company[['headquarters']].drop_duplicates()\nanswer = pd.merge(t1, t2, how='outer', indicator=True).query('_merge == \"left_only\"')['headquarters']", false], ["t1 = company[company['main_industry'] == 'banking']['headquarters'].unique()\nt2 = company[~company['headquarters'].isin(t1)]['headquarters'].unique()\nanswer = t2", false], ["t1 = company[company['main_industry'] == 'banking']\nt2 = company.drop_duplicates('headquarters')\nanswer = t2[~t2['headquarters'].isin(t1['headquarters'])]", false], ["t1 = company[company['main_industry'] == 'banking']\nt2 = company.drop_duplicates('headquarters')\nanswer = pd.concat([t2, t1], ignore_index=True).drop_duplicates(subset='headquarters', keep=False)['headquarters']", false], ["t1 = company[company['main_industry'] == 'banking']\nt2 = company[~(company['headquarters'].isin(t1['headquarters']))]\nanswer = t2['headquarters']", false], ["t1 = company[company['main_industry'] == 'banking']\nt2 = company.loc[~company['headquarters'].isin(t1['headquarters']), 'headquarters'].unique()\nanswer = t2", false]]}
+{"example": {"db_id": "college_2", "query": "SELECT title FROM course WHERE course_id IN (SELECT T1.course_id FROM prereq AS T1 JOIN course AS T2 ON T1.prereq_id  =  T2.course_id WHERE T2.title  =  'Differential Geometry')", "query_toks": ["SELECT", "title", "FROM", "course", "WHERE", "course_id", "IN", "(", "SELECT", "T1.course_id", "FROM", "prereq", "AS", "T1", "JOIN", "course", "AS", "T2", "ON", "T1.prereq_id", "=", "T2.course_id", "WHERE", "T2.title", "=", "'Differential", "Geometry", "'", ")"], "query_toks_no_value": ["select", "title", "from", "course", "where", "course_id", "in", "(", "select", "t1", ".", "course_id", "from", "prereq", "as", "t1", "join", "course", "as", "t2", "on", "t1", ".", "prereq_id", "=", "t2", ".", "course_id", "where", "t2", ".", "title", "=", "value", ")"], "question": "Find the title of course whose prerequisite is course Differential Geometry.", "question_toks": ["Find", "the", "title", "of", "course", "whose", "prerequisite", "is", "course", "Differential", "Geometry", "."], "sql": {"from": {"table_units": [["table_unit", 2]], "conds": []}, "select": [false, [[0, [0, [0, 8, false], null]]]], "where": [[false, 8, [0, [0, 7, false], null], {"from": {"table_units": [["table_unit", 10], ["table_unit", 2]], "conds": [[false, 2, [0, [0, 46, false], null], [0, 7, false], null]]}, "select": [false, [[0, [0, [0, 45, false], null]]]], "where": [[false, 2, [0, [0, 8, false], null], "\"Differential Geometry\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"classroom": ["building", "room_number", "capacity"], "department": ["dept_name", "building", "budget"], "course": ["course_id", "title", "dept_name", "credits"], "instructor": ["ID", "name", "dept_name", "salary"], "section": ["course_id", "sec_id", "semester", "year", "building", "room_number", "time_slot_id"], "teaches": ["ID", "course_id", "sec_id", "semester", "year"], "student": ["ID", "name", "dept_name", "tot_cred"], "takes": ["ID", "course_id", "sec_id", "semester", "year", "grade"], "advisor": ["s_ID", "i_ID"], "time_slot": ["time_slot_id", "day", "start_hr", "start_min", "end_hr", "end_min"], "prereq": ["course_id", "prereq_id"]}, "answer": [["Cost Accounting"], ["Systems Software"], ["Environmental Law"], ["International Practicum"], ["Existentialism"]]}, "program_result_list": [["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nt2 = t1[t1['title_y'] == 'Differential Geometry']\nt3 = pd.merge(t2, course, left_on='course_id_x', right_on='course_id')\nanswer = t3['title_x']", false], ["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nt2 = t1[t1['title_y'] == 'Differential Geometry']\nt3 = pd.merge(course, t2, left_on='course_id', right_on='course_id_y')\nanswer = t3['title_x']", false], ["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nt2 = t1[t1['title_y'] == 'Differential Geometry']\nt3 = pd.merge(course, t2, left_on='course_id', right_on='course_id_x')\nanswer = t3['title_x']", false], ["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nanswer = course[course['course_id'].isin(t1['course_id'])]['title']", false], ["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nt2 = t1[t1['title_y'] == 'Differential Geometry']\nt3 = pd.merge(course, t2, left_on='course_id', right_on='course_id_x')\nanswer = t3['title_x']", false], ["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nt2 = t1[t1['title_y'] == 'Differential Geometry']\nt3 = pd.merge(t2, course, left_on='course_id_x', right_on='course_id')\nanswer = t3['title_x']", false], ["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nt2 = t1[t1['title_y'] == 'Differential Geometry']\nt3 = pd.merge(t2, course, left_on='course_id_x', right_on='course_id')\nanswer = t3['title_x']", false], ["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nt2 = t1[t1['title_x'] == 'Differential Geometry']\nt3 = pd.merge(t2, course, left_on='course_id_x', right_on='course_id')\nanswer = t3['title_y']", false], ["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nt2 = t1[t1['title_y'] == 'Differential Geometry']\nanswer = course[course['course_id'].isin(t2['course_id_x'])]", false], ["t1 = pd.merge(prereq, course, on='prereq_id')\nanswer = t1[t1['title_y'] == 'Differential Geometry']['title_x']", false], ["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nt2 = t1[t1['title_x'] == 'Differential Geometry']\nt3 = pd.merge(t2, course, left_on='course_id_y', right_on='course_id')\nanswer = t3['title_y']", false], ["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nt2 = t1[t1['title_y'] == 'Differential Geometry']\nt3 = pd.merge(t2, course, left_on='course_id_x', right_on='course_id')\nanswer = t3['title_x']", false], ["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nt2 = t1[t1['title_y'] == 'Differential Geometry'][['course_id_x', 'title_x']]\nanswer = t2['title_x']", false], ["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nt2 = t1[t1['title_x'] == 'Differential Geometry']\nt3 = pd.merge(t2, course, left_on='course_id', right_on='course_id')\nanswer = t3['title_y']", false], ["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nt2 = t1[t1['title_y'] == 'Differential Geometry']\nt3 = pd.merge(t2, course, left_on='course_id_x', right_on='course_id')\nanswer = t3['title_x']", false], ["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nt2 = t1[t1['title_y'] == 'Differential Geometry']\nt3 = pd.merge(course, t2, left_on='course_id', right_on='course_id_x')\nanswer = t3['title_x']", false], ["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nanswer = t1[t1['title_y'] == 'Differential Geometry']['title_x']", false], ["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nt2 = t1[t1['title_y'] == 'Differential Geometry']\nt3 = pd.merge(t2, course, left_on='course_id_x', right_on='course_id')\nanswer = t3['title_x']", false], ["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nt2 = t1[t1['title_y'] == 'Differential Geometry']\nanswer = course[course['course_id'].isin(t2['course_id_x'])]['title']", false], ["t1 = pd.merge(prereq, course, left_on='prereq_id', right_on='course_id')\nt2 = t1[t1['title_y'] == 'Differential Geometry']\nt3 = pd.merge(t2, course, left_on='course_id_x', right_on='course_id')\nanswer = t3['title_x']", false]]}
+{"example": {"db_id": "bike_1", "query": "SELECT zip_code FROM weather GROUP BY zip_code HAVING avg(mean_humidity)  <  70 INTERSECT SELECT zip_code FROM trip GROUP BY zip_code HAVING count(*)  >=  100", "query_toks": ["SELECT", "zip_code", "FROM", "weather", "GROUP", "BY", "zip_code", "HAVING", "avg", "(", "mean_humidity", ")", "<", "70", "INTERSECT", "SELECT", "zip_code", "FROM", "trip", "GROUP", "BY", "zip_code", "HAVING", "count", "(", "*", ")", ">", "=", "100"], "query_toks_no_value": ["select", "zip_code", "from", "weather", "group", "by", "zip_code", "having", "avg", "(", "mean_humidity", ")", "<", "value", "intersect", "select", "zip_code", "from", "trip", "group", "by", "zip_code", "having", "count", "(", "*", ")", ">", "=", "value"], "question": "What are the zip codes that have an average mean humidity below 70 and had at least 100 trips come through there?", "question_toks": ["What", "are", "the", "zip", "codes", "that", "have", "an", "average", "mean", "humidity", "below", "70", "and", "had", "at", "least", "100", "trips", "come", "through", "there", "?"], "sql": {"from": {"table_units": [["table_unit", 3]], "conds": []}, "select": [false, [[0, [0, [0, 46, false], null]]]], "where": [], "groupBy": [[0, 46, false]], "having": [[false, 4, [0, [5, 31, false], null], 70.0, null]], "orderBy": [], "limit": null, "intersect": {"from": {"table_units": [["table_unit", 2]], "conds": []}, "select": [false, [[0, [0, [0, 22, false], null]]]], "where": [], "groupBy": [[0, 22, false]], "having": [[false, 5, [0, [3, 0, false], null], 100.0, null]], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "union": null, "except": null}, "db_table_headers": {"station": ["id", "name", "lat", "long", "dock_count", "city", "installation_date"], "status": ["station_id", "bikes_available", "docks_available", "time"], "trip": ["id", "duration", "start_date", "start_station_name", "start_station_id", "end_date", "end_station_name", "end_station_id", "bike_id", "subscription_type", "zip_code"], "weather": ["date", "max_temperature_f", "mean_temperature_f", "min_temperature_f", "max_dew_point_f", "mean_dew_point_f", "min_dew_point_f", "max_humidity", "mean_humidity", "min_humidity", "max_sea_level_pressure_inches", "mean_sea_level_pressure_inches", "min_sea_level_pressure_inches", "max_visibility_miles", "mean_visibility_miles", "min_visibility_miles", "max_wind_Speed_mph", "mean_wind_speed_mph", "max_gust_speed_mph", "precipitation_inches", "cloud_cover", "events", "wind_dir_degrees", "zip_code"]}, "answer": [[94107]]}, "program_result_list": [["t1 = weather.groupby('zip_code')['mean_humidity'].mean().rename('avg')\nt2 = t1[t1 < 70].to_frame().reset_index()\nt3 = trip.groupby('zip_code').size().rename('count')\nt4 = t3[t3 >= 100].to_frame().reset_index()\nt5 = pd.merge(t2, t4, on='zip_code', how='inner')\nanswer = t5['zip_code']", false], ["t1 = weather.groupby('zip_code').mean()['mean_humidity']\nt2 = t1[t1 < 70].to_frame().reset_index()\nt3 = trip.groupby('zip_code').size().rename('count')\nt4 = t3[t3 >= 100].to_frame().reset_index()\nt5 = pd.merge(t2, t4, on='zip_code')\nanswer = t5['zip_code']", false], ["t1 = weather.groupby('zip_code').mean()['mean_humidity'].rename('mean_humidity')\nt2 = t1[t1 < 70].index.to_list()\nt3 = trip.groupby('zip_code').size().rename('count')\nt4 = t3[t3 >= 100].index.to_list()\nt5 = set(t2).intersection(set(t4))\nanswer = pd.Series(t5)", false], ["t1 = weather.groupby('zip_code').mean()\nt2 = t1[t1['mean_humidity'] < 70].reset_index()\nt3 = trip.groupby('zip_code').size().rename('count')\nt4 = t3[t3 >= 100].to_frame().reset_index()\nt5 = pd.merge(t2, t4, on='zip_code', how='inner')\nanswer = t5['zip_code']", false], ["t1 = weather.groupby('zip_code').mean()\nt2 = t1[t1['mean_humidity'] < 70].reset_index()\nt3 = trip.groupby('zip_code').size().rename('count')\nt4 = t3[t3 >= 100].to_frame().reset_index()\nanswer = pd.merge(t2, t4, on='zip_code', how='inner')['zip_code']", false], ["t1 = weather.groupby('zip_code').mean()\nt2 = t1[t1['mean_humidity'] < 70]\nt3 = trip.groupby('zip_code').size().rename('count')\nt4 = t3[t3 >= 100]\nanswer = pd.merge(t2, t4, left_index=True, right_index=True)", false], ["t1 = weather.groupby('zip_code').agg({'mean_humidity': 'mean'}).rename(columns={'mean_humidity': 'avg_mean_humidity'})\nt1 = t1[t1['avg_mean_humidity'] < 70].reset_index()\nt2 = trip.groupby('zip_code').size().rename('count').to_frame().reset_index()\nt2 = t2[t2['count'] >= 100].reset_index()\nt3 = pd.merge(t1, t2, on='zip_code')\nanswer = t3['zip_code']", false], ["t1 = weather.groupby('zip_code').mean()['mean_humidity'].rename('mean_humidity').to_frame()\nt2 = t1[t1['mean_humidity'] < 70].to_frame().reset_index()\nt3 = trip.groupby('zip_code').size().rename('count').to_frame()\nt4 = t3[t3['count'] >= 100].to_frame().reset_index()\nt5 = pd.merge(t2, t4, on='zip_code')\nanswer = t5['zip_code']", false], ["t1 = weather.groupby('zip_code').mean()['mean_humidity'].to_frame().reset_index()\nt1 = t1[t1['mean_humidity'] < 70]['zip_code'].unique().tolist()\nt2 = trip.groupby('zip_code').size().rename('count').to_frame().reset_index()\nt2 = t2[t2['count'] >= 100]['zip_code'].unique().tolist()\nanswer = list(set(t1) & set(t2))", false], ["t1 = weather.groupby('zip_code').agg({'mean_humidity': 'mean'}).rename(columns={'mean_humidity': 'mean'})\nt2 = t1[t1['mean'] < 70].reset_index()\nt3 = trip.groupby('zip_code').size().rename('count')\nt4 = t3[t3 >= 100].to_frame().reset_index()\nt5 = pd.merge(t2, t4, on='zip_code')\nanswer = t5['zip_code'].unique()", false], ["t1 = weather.groupby('zip_code').agg({'mean_humidity': 'mean'})\nt2 = t1[t1['mean_humidity'] < 70]\nt3 = trip.groupby('zip_code').size().rename('count')\nt4 = t3[t3 >= 100]\nanswer = t2.join(t4, on='zip_code').reset_index()['zip_code']", false], ["t1 = weather.groupby('zip_code').mean().reset_index()\nt2 = t1[t1['mean_humidity'] < 70]\nt3 = trip.groupby('zip_code').size().rename('count').to_frame().reset_index()\nt4 = t3[t3['count'] >= 100]\nanswer = pd.merge(t2, t4, on='zip_code', how='inner')['zip_code']", false], ["t1 = weather.groupby('zip_code').mean()\nt1 = t1[t1['mean_humidity'] < 70]\nt2 = trip.groupby('zip_code').size().rename('count')\nt2 = t2[t2 >= 100]\nanswer = t1.merge(t2, on='zip_code', how='inner')", false], ["t1 = weather.groupby('zip_code').mean()['mean_humidity'].to_frame().reset_index()\nt2 = trip.groupby('zip_code').size().rename('count').to_frame().reset_index()\nt3 = t1[t1['mean_humidity'] < 70]['zip_code']\nt4 = t2[t2['count'] >= 100]['zip_code']\nanswer = pd.merge(t3, t4, on='zip_code')", false], ["t1 = weather.groupby('zip_code').mean()['mean_humidity'].to_frame()\nt2 = t1[t1['mean_humidity'] < 70].reset_index()\nt3 = trip.groupby('zip_code').size().rename('count').to_frame()\nt4 = t3[t3['count'] >= 100].reset_index()\nt5 = pd.merge(t2, t4, on='zip_code')\nanswer = t5['zip_code']", false], ["t1 = weather.groupby('zip_code').agg({'mean_humidity': 'mean'}).rename(columns={'mean_humidity': 'avg_mean_humidity'})\nt2 = t1[t1['avg_mean_humidity'] < 70]\nt3 = trip.groupby('zip_code').size().rename('count')\nt4 = t3[t3 >= 100]\nt5 = pd.merge(t2, t4, on='zip_code')\nanswer = t5.index", false], ["t1 = weather.groupby('zip_code').mean()\nt2 = t1[t1['mean_humidity'] < 70].reset_index()\nt3 = trip.groupby('zip_code').size().rename('count')\nt4 = t3[t3 >= 100].to_frame().reset_index()\nt5 = pd.merge(t2, t4, on='zip_code')\nanswer = t5['zip_code']", false], ["t1 = weather.groupby('zip_code').mean()\nt2 = t1[t1['mean_humidity'] < 70].reset_index()\nt3 = trip.groupby('zip_code').size().rename('count')\nt4 = t3[t3 >= 100].to_frame().reset_index()\nt5 = pd.merge(t2, t4, on='zip_code')\nanswer = t5['zip_code']", false], ["t1 = weather.groupby('zip_code').mean()\nt2 = t1[t1['mean_humidity'] < 70].reset_index()\nt3 = trip.groupby('zip_code').size().rename('count')\nt4 = t3[t3 >= 100].to_frame().reset_index()\nt5 = pd.merge(t2, t4, on='zip_code')\nanswer = t5['zip_code']", false], ["t1 = weather.groupby('zip_code').mean().reset_index()\nt2 = t1[t1['mean_humidity'] < 70]['zip_code']\nt3 = trip.groupby('zip_code').size().rename('count').reset_index()\nt4 = t3[t3['count'] >= 100]['zip_code']\nanswer = t2.intersection(t4)", false]]}
+{"example": {"db_id": "apartment_rentals", "query": "SELECT T1.booking_start_date ,  T1.booking_start_date FROM Apartment_Bookings AS T1 JOIN Guests AS T2 ON T1.guest_id  =  T2.guest_id WHERE T2.gender_code  =  \"Female\"", "query_toks": ["SELECT", "T1.booking_start_date", ",", "T1.booking_start_date", "FROM", "Apartment_Bookings", "AS", "T1", "JOIN", "Guests", "AS", "T2", "ON", "T1.guest_id", "=", "T2.guest_id", "WHERE", "T2.gender_code", "=", "``", "Female", "''"], "query_toks_no_value": ["select", "t1", ".", "booking_start_date", ",", "t1", ".", "booking_start_date", "from", "apartment_bookings", "as", "t1", "join", "guests", "as", "t2", "on", "t1", ".", "guest_id", "=", "t2", ".", "guest_id", "where", "t2", ".", "gender_code", "=", "value"], "question": "What are the start date and end date of the apartment bookings made by female guests (gender code \"Female\")?", "question_toks": ["What", "are", "the", "start", "date", "and", "end", "date", "of", "the", "apartment", "bookings", "made", "by", "female", "guests", "(", "gender", "code", "``", "Female", "''", ")", "?"], "sql": {"from": {"table_units": [["table_unit", 4], ["table_unit", 3]], "conds": [[false, 2, [0, [0, 24, false], null], [0, 17, false], null]]}, "select": [false, [[0, [0, [0, 26, false], null]], [0, [0, [0, 26, false], null]]]], "where": [[false, 2, [0, [0, 18, false], null], "\"Female\"", null]], "groupBy": [], "having": [], "orderBy": [], "limit": null, "intersect": null, "union": null, "except": null}, "db_table_headers": {"Apartment_Buildings": ["building_id", "building_short_name", "building_full_name", "building_description", "building_address", "building_manager", "building_phone"], "Apartments": ["apt_id", "building_id", "apt_type_code", "apt_number", "bathroom_count", "bedroom_count", "room_count"], "Apartment_Facilities": ["apt_id", "facility_code"], "Guests": ["guest_id", "gender_code", "guest_first_name", "guest_last_name", "date_of_birth"], "Apartment_Bookings": ["apt_booking_id", "apt_id", "guest_id", "booking_status_code", "booking_start_date", "booking_end_date"], "View_Unit_Status": ["apt_id", "apt_booking_id", "status_date", "available_yn"]}, "answer": [["2016-04-01 06:28:08", "2016-04-01 06:28:08"], ["2017-03-13 16:20:14", "2017-03-13 16:20:14"], ["2016-08-04 10:33:00", "2016-08-04 10:33:00"], ["2016-05-24 20:09:38", "2016-05-24 20:09:38"], ["2016-11-26 05:04:31", "2016-11-26 05:04:31"], ["2017-05-13 18:17:20", "2017-05-13 18:17:20"], ["2016-06-07 05:05:18", "2016-06-07 05:05:18"], ["2016-09-28 05:00:50", "2016-09-28 05:00:50"], ["2017-07-03 14:15:56", "2017-07-03 14:15:56"]]}, "program_result_list": [["t1 = pd.merge(Apartment_Bookings, Guests, left_on='guest_id', right_on='guest_id')\nt2 = t1[t1['gender_code'] == 'Female']\nanswer = t2[['booking_start_date', 'booking_start_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, left_on='guest_id', right_on='guest_id')\nt2 = t1[t1['gender_code'] == 'Female']\nanswer = t2[['booking_start_date', 'booking_end_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, left_on='guest_id', right_on='guest_id')\nt2 = t1[t1['gender_code'] == 'Female']\nanswer = t2[['booking_start_date', 'booking_start_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, left_on='guest_id', right_on='guest_id')\nt2 = t1[t1['gender_code'] == 'Female']\nanswer = t2[['booking_start_date', 'booking_end_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, left_on='guest_id', right_on='guest_id')\nanswer = t1[t1['gender_code'] == 'Female'][['booking_start_date', 'booking_end_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, left_on='guest_id', right_on='guest_id')\nt2 = t1[t1['gender_code'] == 'Female']\nanswer = t2[['booking_start_date', 'booking_end_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, left_on='guest_id', right_on='guest_id')\nt2 = t1[t1['gender_code'] == 'Female']\nanswer = t2[['booking_start_date', 'booking_end_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, left_on='guest_id', right_on='guest_id')\nt2 = t1[t1['gender_code'] == 'Female']\nanswer = t2[['booking_start_date', 'booking_end_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, left_on='guest_id', right_on='guest_id')\nanswer = t1[t1['gender_code'] == 'Female'][['booking_start_date', 'booking_end_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, left_on='guest_id', right_on='guest_id')\nt2 = t1[t1['gender_code'] == 'Female']\nanswer = t2[['booking_start_date', 'booking_end_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, on='guest_id')\nt2 = t1[t1['gender_code'] == \"Female\"]\nanswer = t2[['booking_start_date', 'booking_end_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, left_on='guest_id', right_on='guest_id')\nt2 = t1[t1['gender_code'] == 'Female']\nanswer = t2[['booking_start_date', 'booking_end_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, left_on='guest_id', right_on='guest_id')\nt2 = t1[t1['gender_code'] == 'Female']\nanswer = t2[['booking_start_date', 'booking_end_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, on='guest_id')\nanswer = t1[t1['gender_code'] == 'Female'][['booking_start_date', 'booking_end_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, left_on='guest_id', right_on='guest_id')\nanswer = t1[t1['gender_code'] == 'Female'][['booking_start_date', 'booking_end_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, left_on='guest_id', right_on='guest_id')\nt2 = t1[t1['gender_code'] == 'Female']\nanswer = t2[['booking_start_date', 'booking_end_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, left_on='guest_id', right_on='guest_id')\nt2 = t1[t1['gender_code'] == 'Female']\nanswer = t2[['booking_start_date', 'booking_end_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, on='guest_id')\nt2 = t1[t1['gender_code'] == 'Female']\nanswer = t2[['booking_start_date', 'booking_end_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, left_on='guest_id', right_on='guest_id')\nt2 = t1[t1['gender_code'] == 'Female']\nanswer = t2[['booking_start_date', 'booking_end_date']]", false], ["t1 = pd.merge(Apartment_Bookings, Guests, left_on='guest_id', right_on='guest_id')\nt2 = t1[t1['gender_code'] == 'Female']\nanswer = t2[['booking_start_date', 'booking_end_date']]", false]]}
\ No newline at end of file
diff --git a/stephen_playground/hundred_fails_solution.jsonl b/stephen_playground/hundred_fails_solution.jsonl
new file mode 100644
index 00000000..655f1129
--- /dev/null
+++ b/stephen_playground/hundred_fails_solution.jsonl
@@ -0,0 +1,10 @@
+"airports[airports['country'] == 'iceland']['elevation'].max()" NOTE: Capitalization of iceland (df item)
+"match.winning_aircraft = match.winning_aircraft.astype('Int64')\nt1 = pd.merge(aircraft, match, left_on='aircraft_id', right_on='winning_aircraft')\nanswer = t1[['location', 'aircraft']]" NOTE: Had to change data type of column
+"t1 = pd.merge(station, trip, left_on='id', right_on='start_station_id')\nanswer = t1[['lat', 'long']].mean()" NOTE: Old python execution didn't take into account math.isclose()
+"answer = teachers[['firstname', 'lastname']].drop_duplicates()" NOTE: Need to sort the answers due to non-deterministic SQL return order
+"answer = files.groupby('formats').size().rename('count').to_frame().reset_index()[['count', 'formats']]" NOTE: Need to switch order of columns to correct order
+"t1 = pd.merge(station, status, left_on='id', right_on='station_id')\nt2 = t1.groupby(['id', 'name']).mean()\nt3 = t2[t2['bikes_available'] > 14].reset_index()\nt4 = station[station['installation_date'].str.startswith('12/')]\nt5 = pd.concat([t3, t4], ignore_index=True)\nanswer = t5[['name', 'id']].drop_duplicates()" NOTE: Made sorting in answer check convert everything to a string to fix int and str comparison failure
+"t1 = pd.merge(student, enroll, left_on='stu_num', right_on='stu_num')\nt2 = pd.merge(t1, _class, left_on='class_code', right_on='class_code')\nt3 = pd.merge(t2, course, left_on='crs_code', right_on='crs_code')\nt4 = pd.merge(t3, department, left_on='dept_code_y', right_on='dept_code')\nt5 = t4[t4['dept_name'] == 'accounting']\nt6 = t4[t4['dept_name'] == 'computer info. systems']\nanswer = pd.merge(t5, t6, left_on='stu_fname', right_on='stu_fname')['stu_fname'].unique()" NOTE: Multiple dept_code must choose correct suffix
+"t1 = artist[artist['country'] == 'bangladesh']\nt2 = song[song['rating'] > 7]\nanswer = t1[~t1['artist_name'].isin(t2['artist_name'])][['artist_name']]" NOTE: Capitalization and select only artist_name
+"t1 = pd.merge(station_company, company, left_on='company_id', right_on='company_id')\nt2 = pd.merge(t1, gas_station, left_on='station_id', right_on='station_id')\nt3 = t2[t2['market_value'] > 100]\nanswer = t3['location']" NOTE: Correct with spider_execution updates
+"answer = rooms[rooms['bedtype'] == 'king']['beds'].sum()" NOTE: Capitalization of df
diff --git a/stephen_playground/test_pandas.py b/stephen_playground/test_pandas.py
new file mode 100644
index 00000000..44edea15
--- /dev/null
+++ b/stephen_playground/test_pandas.py
@@ -0,0 +1,18 @@
+import pandas as pd
+import execution.spider_execution as e
+import json
+import numpy as np
+
+db = "inn_1"
+sol = "answer = rooms[rooms['bedtype'] == 'king']['beds'].sum()"
+actual = """[[6]]"""
+
+con = e.connect_databse("../NLP4Code-Playground/spider/database/" + db + "/" + db +".sqlite")
+df_dict = e.db_to_df_dict(con)
+
+# Comment out after done testing
+# for table_name in df_dict.keys():
+#     exec(f"{table_name} = df_dict['{table_name}']")
+
+res = e.spider_execution_py(sol, df_dict)
+e.spider_answer_eq(res, json.loads(actual), True)