Update functions/pitch_summary_functions.py

functions/pitch_summary_functions.py

@@ -1086,8 +1086,8 @@ def stat_summary_table(df: pl.DataFrame,
 
     
     # Format start and end dates
-    start_date_format = str(pd.to_datetime(df['game_date'][0]))#.strftime('%m/%d/%Y'))
-    end_date_format = str(pd.to_datetime(df['game_date'][-1]))#.strftime('%m/%d/%Y'))
+    start_date_format = str(pd.to_datetime(df['game_date'][0]).strftime('%m/%d/%Y'))
+    end_date_format = str(pd.to_datetime(df['game_date'][-1]).strftime('%m/%d/%Y'))
 
     # Determine app context based on sport ID
     appContext = 'majorLeague' if sport_id == 1 else 'minorLeague'
@@ -1098,6 +1098,7 @@ def stat_summary_table(df: pl.DataFrame,
     pitcher_stats_call = requests.get(
         f'https://statsapi.mlb.com/api/v1/people/{player_input}?appContext={appContext}&hydrate=stats(group=[pitching],type=[byDateRange],sportId={sport_id},startDate={start_date_format},endDate={end_date_format},gameType=[{game_type_str}])'
     ).json()
+    print('HERE')
     print(f'https://statsapi.mlb.com/api/v1/people/{player_input}?appContext={appContext}&hydrate=stats(group=[pitching],type=[byDateRange],sportId={sport_id},startDate={start_date_format},endDate={end_date_format},gameType=[{game_type_str}])')
     # Extract stats and create DataFrame
     pitcher_stats_call_header = [x for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][-1]['stat']]