Player stats fix

main.py

@@ -1204,47 +1204,22 @@ def get_player_stats():
         all_player_season_data = []
         players_with_no_data = []
 
-        # Handle individual player stats (1 player, 1 season)
-        if len(selected_players) == 1 and len(selected_seasons) == 1:
-            player_name = selected_players[0]
-            season_str = selected_seasons[0]
-            df_player_data = get_player_career_stats_brscraper(player_name, seasons_to_fetch=[season_str], playoffs=False)
+        for player_name in selected_players:
+            df_player_career = get_player_career_stats_brscraper(player_name, playoffs=False)
             
-            if not df_player_data.empty:
-                all_player_season_data.append(df_player_data)
-                logging.info(f"Successfully retrieved data for {player_name} in {season_str}.")
-            else:
+            if df_player_career.empty:
+                logging.info(f"No career data found for {player_name}. Adding to no_data list.")
                 players_with_no_data.append(player_name)
-                logging.info(f"No data found for {player_name} in {season_str}.")
-
-        # Handle comparison (2 players, 2 seasons)
-        elif len(selected_players) == 2 and len(selected_seasons) == 2:
-            player1_name = selected_players[0]
-            player1_season = selected_seasons[0]
-            player2_name = selected_players[1]
-            player2_season = selected_seasons[1]
-
-            df_player1_data = get_player_career_stats_brscraper(player1_name, seasons_to_fetch=[player1_season], playoffs=False)
-            if not df_player1_data.empty:
-                all_player_season_data.append(df_player1_data)
-                logging.info(f"Successfully retrieved data for {player1_name} in {player1_season}.")
-            else:
-                players_with_no_data.append(player1_name)
-                logging.info(f"No data found for {player1_name} in {player1_season}.")
-
-            # Add a delay between fetching data for player 1 and player 2
-            time.sleep(2) # Introduce a 2-second delay
+                continue
 
-            df_player2_data = get_player_career_stats_brscraper(player2_name, seasons_to_fetch=[player2_season], playoffs=False)
-            if not df_player2_data.empty:
-                all_player_season_data.append(df_player2_data)
-                logging.info(f"Successfully retrieved data for {player2_name} in {player2_season}.")
+            filtered_df = df_player_career[df_player_career['Season'].isin(selected_seasons)].copy()
+            
+            if not filtered_df.empty:
+                all_player_season_data.append(filtered_df)
+                logging.info(f"Successfully filtered data for {player_name} in requested seasons.")
             else:
-                players_with_no_data.append(player2_name)
-                logging.info(f"No data found for {player2_name} in {player2_season}.")
-        else:
-            return jsonify({'error': 'Invalid combination of players and seasons. Expected 1 player/1 season or 2 players/2 seasons.'}), 400
-
+                logging.info(f"No data found for {player_name} in the specific requested seasons: {selected_seasons}. Adding to no_data list.")
+                players_with_no_data.append(player_name)
 
         if not all_player_season_data:
             logging.warning("After processing all players, 'all_player_season_data' is empty. Returning 404.")
@@ -1255,15 +1230,15 @@ def get_player_stats():
 
         comparison_df_raw = pd.concat(all_player_season_data, ignore_index=True)
 
-        # Basic stats for display
-        basic_display_df = comparison_df_raw.copy()
+        if len(selected_seasons) > 1:
+            basic_display_df = comparison_df_raw.groupby('Player').mean(numeric_only=True).reset_index()
+        else:
+            basic_display_df = comparison_df_raw.copy()
+        
         basic_cols = ['Player', 'Season', 'GP', 'MIN', 'PTS', 'REB', 'AST', 'STL', 'BLK', 'FG_PCT', 'FT_PCT', 'FG3_PCT']
-        # Ensure only existing columns are selected and then round
         basic_display_df = basic_display_df[[c for c in basic_cols if c in basic_display_df.columns]].round(2)
 
-        # Advanced stats calculation (e.g., TS%)
         advanced_df = comparison_df_raw.copy()
-        # Ensure necessary columns for TS% are numeric and handle potential missing columns
         advanced_df['FGA'] = pd.to_numeric(advanced_df.get('FGA', 0), errors='coerce').fillna(0)
         advanced_df['FTA'] = pd.to_numeric(advanced_df.get('FTA', 0), errors='coerce').fillna(0)
         advanced_df['PTS'] = pd.to_numeric(advanced_df.get('PTS', 0), errors='coerce').fillna(0)
@@ -1271,8 +1246,13 @@ def get_player_stats():
             lambda r: r['PTS'] / (2 * (r['FGA'] + 0.44 * r['FTA'])) if (r['FGA'] + 0.44 * r['FTA']) else 0,
             axis=1
         )
-        advanced_cols = ['Player', 'Season', 'PTS', 'REB', 'AST', 'FG_PCT', 'TS_PCT'] # Example advanced stats
-        advanced_display_df = advanced_df[[c for c in advanced_cols if c in advanced_df.columns]].round(3)
+        if len(selected_seasons) > 1:
+            advanced_display_df = advanced_df.groupby('Player').mean(numeric_only=True).reset_index()
+        else:
+            advanced_display_df = advanced_df.copy()
+        
+        advanced_cols = ['Player', 'Season', 'PTS', 'REB', 'AST', 'FG_PCT', 'TS_PCT']
+        advanced_display_df = advanced_display_df[[c for c in advanced_cols if c in advanced_display_df.columns]].round(3)
 
         return jsonify({
             'basic_stats': basic_display_df.to_dict(orient='records'),
@@ -1297,46 +1277,21 @@ def get_player_playoff_stats():
         all_player_season_data = []
         players_with_no_data = []
 
-        # Handle individual player stats (1 player, 1 season)
-        if len(selected_players) == 1 and len(selected_seasons) == 1:
-            player_name = selected_players[0]
-            season_str = selected_seasons[0]
-            df_player_data = get_player_career_stats_brscraper(player_name, seasons_to_fetch=[season_str], playoffs=True)
-            
-            if not df_player_data.empty:
-                all_player_season_data.append(df_player_data)
-                logging.info(f"Successfully retrieved playoff data for {player_name} in {season_str}.")
-            else:
+        for player_name in selected_players:
+            df_player_career = get_player_career_stats_brscraper(player_name, playoffs=True)
+            if df_player_career.empty:
+                logging.info(f"No career playoff data found for {player_name}. Adding to no_data list.")
                 players_with_no_data.append(player_name)
-                logging.info(f"No playoff data found for {player_name} in {season_str}.")
-
-        # Handle comparison (2 players, 2 seasons)
-        elif len(selected_players) == 2 and len(selected_seasons) == 2:
-            player1_name = selected_players[0]
-            player1_season = selected_seasons[0]
-            player2_name = selected_players[1]
-            player2_season = selected_seasons[1]
-
-            df_player1_data = get_player_career_stats_brscraper(player1_name, seasons_to_fetch=[player1_season], playoffs=True)
-            if not df_player1_data.empty:
-                all_player_season_data.append(df_player1_data)
-                logging.info(f"Successfully retrieved playoff data for {player1_name} in {player1_season}.")
-            else:
-                players_with_no_data.append(player1_name)
-                logging.info(f"No playoff data found for {player1_name} in {player1_season}.")
-
-            time.sleep(2) # Introduce a 2-second delay
+                continue
 
-            df_player2_data = get_player_career_stats_brscraper(player2_name, seasons_to_fetch=[player2_season], playoffs=True)
-            if not df_player2_data.empty:
-                all_player_season_data.append(df_player2_data)
-                logging.info(f"Successfully retrieved playoff data for {player2_name} in {player2_season}.")
+            filtered_df = df_player_career[df_player_career['Season'].isin(selected_seasons)].copy()
+            
+            if not filtered_df.empty:
+                all_player_season_data.append(filtered_df)
+                logging.info(f"Successfully filtered playoff data for {player_name} in requested seasons.")
             else:
-                players_with_no_data.append(player2_name)
-                logging.info(f"No playoff data found for {player2_name} in {player2_season}.")
-        else:
-            return jsonify({'error': 'Invalid combination of players and seasons. Expected 1 player/1 season or 2 players/2 seasons.'}), 400
-
+                logging.info(f"No playoff data found for {player_name} in the specific requested seasons: {selected_seasons}. Adding to no_data list.")
+                players_with_no_data.append(player_name)
 
         if not all_player_season_data:
             logging.warning("After processing all players, 'all_player_season_data' is empty for playoffs. Returning 404.")
@@ -1347,7 +1302,11 @@ def get_player_playoff_stats():
 
         comparison_df_raw = pd.concat(all_player_season_data, ignore_index=True)
 
-        basic_display_df = comparison_df_raw.copy()
+        if len(selected_seasons) > 1:
+            basic_display_df = comparison_df_raw.groupby('Player').mean(numeric_only=True).reset_index()
+        else:
+            basic_display_df = comparison_df_raw.copy()
+        
         basic_cols = ['Player', 'Season', 'GP', 'MIN', 'PTS', 'REB', 'AST', 'STL', 'BLK', 'FG_PCT', 'FT_PCT', 'FG3_PCT']
         basic_display_df = basic_display_df[[c for c in basic_cols if c in basic_display_df.columns]].round(2)
 
@@ -1359,8 +1318,13 @@ def get_player_playoff_stats():
             lambda r: r['PTS'] / (2 * (r['FGA'] + 0.44 * r['FTA'])) if (r['FGA'] + 0.44 * r['FTA']) else 0,
             axis=1
         )
+        if len(selected_seasons) > 1:
+            advanced_display_df = advanced_df.groupby('Player').mean(numeric_only=True).reset_index()
+        else:
+            advanced_display_df = advanced_df.copy()
+        
         advanced_cols = ['Player', 'Season', 'PTS', 'REB', 'AST', 'FG_PCT', 'TS_PCT']
-        advanced_display_df = advanced_df[[c for c in advanced_cols if c in advanced_df.columns]].round(3)
+        advanced_display_df = advanced_display_df[[c for c in advanced_cols if c in advanced_display_df.columns]].round(3)
 
         return jsonify({
             'basic_stats': basic_display_df.to_dict(orient='records'),