creating the main file with git StatsBomb example

IntroToStatsBomb/main.py

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+# May 2022 - Aloma Blanch Granada
+# Practicing StatsBomb, copied from https://github.com/cjmayes123/IntroToStatsBomb/blob/main/main.py
+
+from statsbombpy import sb
+from pandas import json_normalize
+
+import numpy as np
+import pandas as pd
+
+# Get competitions
+comp = sb.competitions()
+comp.to_csv('competitions.csv', index=False)
+
+# Get Matches from WSL - 2020/21
+df = sb.matches(competition_id=37, season_id=90)
+df.to_csv('matches.csv', index=False)
+
+# Find a match_id required
+match = 3764235
+match_events = sb.events(match_id=match)
+
+# split locations into x and y components
+match_events[['location_x', 'location_y']] = match_events['location'].apply(pd.Series)
+match_events[['pass_end_location_x', 'pass_end_location_y']] = match_events['pass_end_location'].apply(pd.Series)
+
+# split the shot_end_locations into x,y and z - some z missing
+match_events['shot_end_location_x'], match_events['shot_end_location_y'], \
+    match_events['shot_end_location_z'] = np.nan, np.nan, np.nan
+
+end_locations = np.vstack(match_events.loc[match_events.type == 'Shot'].shot_end_location.apply(
+    lambda x: x if len(x) == 3 else x + [np.nan]).values)
+
+match_events.loc[match_events.type == 'Shot', 'shot_end_location_x'] = end_locations[:, 0]
+match_events.loc[match_events.type == 'Shot', 'shot_end_location_y'] = end_locations[:, 1]
+match_events.loc[match_events.type == 'Shot', 'shot_end_location_z'] = end_locations[:, 2]
+
+# clean up and choose the columns wanted
+events_df = match_events[[
+    'index', 'id', 'duration', 'match_id', 'minute', 'pass_angle', 'pass_height', 'pass_length', 'pass_outcome',
+    'pass_recipient', 'pass_shot_assist', 'period', 'play_pattern', 'player', 'player_id', 'position', 'possession',
+    'possession_team', 'possession_team_id', 'shot_type', 'shot_outcome', 'team', 'timestamp', 'type', 'location_x',
+    'location_y', 'pass_end_location_x', 'pass_end_location_y', 'shot_end_location_x', 'shot_end_location_y',
+    'shot_end_location_z'
+]]
+events_df.to_csv('match_events.csv', index=False)
+
+# create separate data sheet with freeze frame
+shots = match_events.loc[match_events['type'] == 'Shot'].set_index('id', 'match')
+
+shot_freeze_frame = match_events['shot_freeze_frame']
+
+ffs = {}
+
+for id_, row in shots.iterrows():
+    try:
+        ff = json_normalize(row.shot_freeze_frame, sep="_")
+        ff = ff.assign(x=ff.apply(lambda x: x.location[0], axis=1)). \
+            assign(y=ff.apply(lambda x: x.location[1], axis=1)). \
+            drop('location', axis=1). \
+            assign(id=id_)
+        ffs[id_] = ff
+    except:
+        pass
+
+# concatenate all the Freeze Frame dataframes
+ff_df = pd.concat(ffs)
+ff_df.to_csv('match_shot_freeze_frames.csv', index=False)