solution of problem 1

01-GroupSoldProductsByTheDate.py

@@ -0,0 +1,46 @@
+# Problem 1 : Group Sold Products by the Date	(https://leetcode.com/problems/group-sold-products-by-the-date/)
+
+import pandas as pd
+
+# solution 1: using a for loop
+def categorize_products(activities: pd.DataFrame) -> pd.DataFrame:
+    dict = {}
+    for i in range(len(activities)):
+        s_date = activities['sell_date'][i]
+        pdt = activities['product'][i]
+        if s_date not in dict:
+            dict[s_date] = set()
+        dict[s_date].add(pdt)
+    result = []
+    for key, value in dict.items():
+        temp = [p for p in value]
+        temp.sort()
+        # temp = sorted(value)  # this would also work and sorted returns a list
+        s = ','.join(temp)
+        result.append([key, len(value), s])
+
+    return pd.DataFrame(result, columns=['sell_date', 'num_sold', 'products']).sort_values('sell_date')
+
+# solution 2: using pandas functions
+def categorize_products(activities: pd.DataFrame) -> pd.DataFrame:
+    df = activities.groupby('sell_date').agg(
+        num_sold = ('product', 'nunique'),
+        products = ('product', lambda x: ','.join(sorted(set(x))))
+    ).reset_index()
+    return df.sort_values('sell_date')
+
+
+"""
+- 'nunique', 'count', 'sum' etc. are Pandas-specific string method names, so they must be in quotes.
+- min, max, len, etc. are built-in Python functions, so they can be used with or without quotes, depending on context.
+- reset_index() would return my column back and 0,1,2... becaomes a new index.
+
+- if we mention a single column in the agg or any other function, then we get back series. 
+if we use the full df then, df is returned
+
+eg: df = activities.groupby('sell_date')['product'].agg(max) -> this returns a series with index as sell_date and values as max of product
+    df = activities.groupby('sell_date').agg(max) -> this returns a df
+
+    df = activities.groupby('sell_date')['product']max() -> series
+    df = activities.groupby('sell_date').max() -> df
+"""