Update src/streamlit_app.py

src/streamlit_app.py

@@ -72,14 +72,32 @@ def get_player_index_brscraper():
 
 @st.cache_data(ttl=300)
 def get_player_career_stats_brscraper(player_name):
+    """
+    Uses BRScraper to get a player's career stats.
+    Detects and renames the season column, applies mapping and numeric conversion.
+    """
     if not BRSCRAPER_AVAILABLE:
         return pd.DataFrame()
+    
     try:
         df = nba.get_player_stats(player_name)
         if df.empty:
             return df
+
+        # 1) Detect whatever BRScraper calls its season column, rename to 'Season'
+        season_renamed = False
+        for col in df.columns:
+            if 'season' in col.lower() or 'year' in col.lower():
+                df = df.rename(columns={col: 'Season'})
+                season_renamed = True
+                break
+        if not season_renamed:
+            st.error(f"Could not find a season column for {player_name}.")
+            return pd.DataFrame()
+
+        # 2) Standardize column names
         mapping = {
-            'Season':'Season','G':'GP','GS':'GS','MP':'MIN',
+            'G':'GP','GS':'GS','MP':'MIN',
             'FG%':'FG_PCT','3P%':'FG3_PCT','FT%':'FT_PCT',
             'TRB':'REB','AST':'AST','STL':'STL','BLK':'BLK','TOV':'TO',
             'PF':'PF','PTS':'PTS','Age':'AGE','Tm':'TEAM_ABBREVIATION',
@@ -89,13 +107,21 @@ def get_player_career_stats_brscraper(player_name):
             'FT':'FTM','FTA':'FTA','ORB':'OREB','DRB':'DREB'
         }
         df = df.rename(columns={o:n for o,n in mapping.items() if o in df.columns})
+
+        # 3) Clean up season formatting
         df['Season'] = df['Season'].astype(str).str.replace('-', '–')
-        non_num = {'Season','TEAM_ABBREVIATION','LEAGUE_ID','POSITION'}
-        for col in df.columns:
-            if col not in non_num:
-                df[col] = pd.to_numeric(df[col], errors='coerce')
+
+        # 4) Convert all other columns to numeric where possible
+        non_numeric = {'Season', 'TEAM_ABBREVIATION', 'LEAGUE_ID', 'POSITION'}
+        for c in df.columns:
+            if c not in non_numeric:
+                df[c] = pd.to_numeric(df[c], errors='coerce')
+
+        # 5) Tag with player name
         df['Player'] = player_name
+
         return df
+
     except Exception as e:
         st.error(f"Error fetching stats for {player_name}: {e}")
         return pd.DataFrame()