Add team splits retrieval from baseball reference

docs/split_stats.md

@@ -30,3 +30,57 @@ df, player_info_dict = get_splits('troutmi01', player_info=True)
 #find the pitching split stats for Jon Lester
 df = get_splits('lestejo01', pitching_splits=True)
 ```
+
+# Get Team Splits Stats
+
+`get_team_splits(team, year, pitching_splits = False)`
+Look up a team's split stats from baseball-reference. Either batting or pitching splits can be returned and splits can be provided for any season.
+Split stats are returned as a multi-index dataframe by split category and split. 
+## Arguments
+
+`team:` String. The team's baseball-reference code.
+|Team Name|Team Code|
+|:--:|:--:|
+|Angels|ANA|
+|Astros|HOU|
+|Athletics|OAK|
+|Blue Jays|TOR|
+|Braves|ATL|
+|Brewers|MIL|
+|Cardinals|STL|
+|Cubs|CHC|
+|Rays|TBD|
+|Diamondbacks|ARI|
+|Dodgers|LAD|
+|Giants|SFG|
+|Indians (Guardians)|CLE|
+|Mariners|SEA|
+|Marlins|FLA|
+|Mets|NYM|
+|Nationals|WSN|
+|Orioles|BAL|
+|Padres|SDP|
+|Phillies|PHI|
+|Pirates|PIT|
+|Rangers|TEX|
+|Red Sox|BOS|
+|Reds|CIN|
+|Rockies|COL|
+|Royals|KCR|
+|Tigers|DET|
+|Twins|MIN|
+|White Sox|CHW|
+|Yankees|NYY|
+
+`year:` Integer. The year to get split stats for.
+
+`pitching_splits:` Boolean. Optional. If set to True, the get_splits function will return pitching splits. Otherwise, get_splits will return batting splits.
+
+## Examples of valid queries
+
+```python
+from pybaseball import get_splits
+
+# find the split stats for the Braves for 2023
+df = get_team_splits('ATL', 2023)
+```

pybaseball/__init__.py

@@ -100,4 +100,5 @@
 from .datasources.fangraphs import (fg_batting_data, fg_pitching_data, fg_team_batting_data, fg_team_fielding_data,
                                     fg_team_pitching_data)
 from .split_stats import get_splits
+from .split_stats import get_team_splits
 from .version import __version__

pybaseball/split_stats.py

@@ -1,4 +1,4 @@
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import bs4 as bs
 import pandas as pd
@@ -8,6 +8,16 @@
 
 session = BRefSession()
 
+def get_team_split_soup(team: str, year: int, pitching_splits: bool = False) -> bs.BeautifulSoup:
+    """
+    gets soup for the team splits.
+    """
+    pitch_or_bat = 'p' if pitching_splits else 'b'
+    url = f"https://www.baseball-reference.com/teams/split.cgi?t={pitch_or_bat}&team={team}&year={year}"
+    html = session.get(url).content
+    soup = bs.BeautifulSoup(html, 'lxml')
+    return soup
+
 
 def get_split_soup(playerid: str, year: Optional[int] = None, pitching_splits: bool = False) -> bs.BeautifulSoup:
     """
@@ -57,13 +67,10 @@ def get_player_info(playerid: str, soup: bs.BeautifulSoup = None) -> Dict:
     return player_info_data
 
 
-def get_splits(playerid: str, year: Optional[int] = None, player_info: bool = False, pitching_splits: bool = False) -> Union[pd.DataFrame, Tuple[pd.DataFrame, Dict]]:
+def soup_to_data(soup: bs.BeautifulSoup, year: int, identifier: str, identifier_label: str):
     """
-    Returns a dataframe of all split stats for a given player.
-    If player_info is True, this will also return a dictionary that includes player position, handedness, height, weight, position, and team
+    splits tables on the bbref site are all within an embedded comment. This finds all the comments
     """
-    soup = get_split_soup(playerid, year, pitching_splits)
-    # the splits tables on the bbref site are all within an embedded comment. This finds all the comments
     comment = soup.find_all(text=lambda text: isinstance(text, bs.Comment))
     raw_data = []
     raw_level_data = []
@@ -85,7 +92,7 @@ def get_splits(playerid: str, year: Optional[int] = None, player_info: bool = Fa
                     level_headings = [th.get_text()
                                       for th in splits[j].find("tr").find_all("th")][:]
                 level_headings.append('Split Type')
-                level_headings.append('Player ID')
+                level_headings.append(identifier_label)
                 # singles data isn't included in the tables so this appends the column header
                 level_headings.append('1B')
                 raw_level_data.append(level_headings)
@@ -98,7 +105,7 @@ def get_splits(playerid: str, year: Optional[int] = None, player_info: bool = Fa
                     level_cols = [ele.text.strip() for ele in level_cols]
                     if split_type != "By Inning":  # bbref added three empty columns to the by inning tables that don't match the rest of the tables. Not including this split table in results
                         level_cols.append(split_type)
-                        level_cols.append(playerid)
+                        level_cols.append(identifier)
                         raw_level_data.append([ele for ele in level_cols])
             else:
                 if year == None:  # The bbref tables for career splits have one extra preceding th column labeled 'I' that is not used and is not in the single season records
@@ -108,7 +115,7 @@ def get_splits(playerid: str, year: Optional[int] = None, player_info: bool = Fa
                     headings = [th.get_text()
                                 for th in splits[j].find("tr").find_all("th")][:]
                 headings.append('Split Type')
-                headings.append('Player ID')
+                headings.append(identifier_label)
                 # singles data isn't included in the tables so this appends the column header
                 headings.append('1B')
                 raw_data.append(headings)
@@ -121,18 +128,39 @@ def get_splits(playerid: str, year: Optional[int] = None, player_info: bool = Fa
                     cols = [ele.text.strip() for ele in cols]
                     if split_type != "By Inning":  # bbref added three empty columns to the by inning tables that don't match the rest of the tables. Not including this split table in results
                         cols.append(split_type)
-                        cols.append(playerid)
+                        cols.append(identifier)
                         raw_data.append([ele for ele in cols])
-
     data = pd.DataFrame(raw_data)
     data = data.rename(columns=data.iloc[0])
     data = data.reindex(data.index.drop(0))
-    data = data.set_index(['Player ID', 'Split Type', 'Split'])
+    data = data.set_index([identifier_label, 'Split Type', 'Split'])
     data = data.drop(index=['Split'], level=2)
     data = data.apply(pd.to_numeric, errors='coerce').convert_dtypes()
     data = data.dropna(axis=1, how='all')
     data['1B'] = data['H']-data['2B']-data['3B']-data['HR']
-    data = data.loc[playerid]
+    data = data.loc[identifier]
+    return data, raw_level_data
+
+
+def get_team_splits(team: str, year: int, pitching_splits: bool = False) -> pd.DataFrame:
+    """
+    Returns a dataframe of all split stats for a given team.
+    """
+    soup = get_team_split_soup(team, year, pitching_splits)
+
+    data, raw_level_data = soup_to_data(soup, year, team, "Team")
+
+    return data
+
+def get_splits(playerid: str, year: Optional[int] = None, player_info: bool = False, pitching_splits: bool = False) -> Union[pd.DataFrame, Tuple[pd.DataFrame, Dict]]:
+    """
+    Returns a dataframe of all split stats for a given player.
+    If player_info is True, this will also return a dictionary that includes player position, handedness, height, weight, position, and team
+    """
+    soup = get_split_soup(playerid, year, pitching_splits)
+    # the splits tables on the bbref site are all within an embedded comment. This finds all the comments
+    data, raw_level_data = soup_to_data(soup, year, playerid, 'Player ID')
+
     if pitching_splits is True: # Returns Game Level tables as a second dataframe for pitching splits
         level_data = pd.DataFrame(raw_level_data)
         level_data = level_data.rename(columns=level_data.iloc[0])