Update src/streamlit_app.py

src/streamlit_app.py

@@ -74,7 +74,8 @@ def get_player_index_brscraper():
 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.
+    Detects and renames the season column (or falls back to first column),
+    applies mapping and numeric conversion.
     """
     if not BRSCRAPER_AVAILABLE:
         return pd.DataFrame()
@@ -84,18 +85,22 @@ def get_player_career_stats_brscraper(player_name):
         if df.empty:
             return df
 
-        # 1) Detect whatever BRScraper calls its season column, rename to 'Season'
-        season_renamed = False
+        # 1) Try to autodetect a season column
+        season_col = None
         for col in df.columns:
-            if 'season' in col.lower() or 'year' in col.lower():
-                df = df.rename(columns={col: 'Season'})
-                season_renamed = True
+            low = col.lower().strip()
+            if 'season' in low or 'year' in low:
+                season_col = col
                 break
-        if not season_renamed:
-            st.error(f"Could not find a season column for {player_name}.")
-            return pd.DataFrame()
-
-        # 2) Standardize column names
+        
+        # 2) Fallback: if nothing detected, just take the first column
+        if season_col is None:
+            season_col = df.columns[0]
+        
+        # 3) Rename whatever that column is to exactly 'Season'
+        df = df.rename(columns={season_col: 'Season'})
+
+        # 4) Standard column mapping
         mapping = {
             'G':'GP','GS':'GS','MP':'MIN',
             'FG%':'FG_PCT','3P%':'FG3_PCT','FT%':'FT_PCT',
@@ -108,16 +113,16 @@ def get_player_career_stats_brscraper(player_name):
         }
         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('-', '–')
+        # 5) Clean and format the Season column
+        df['Season'] = df['Season'].astype(str).str.replace('-', '–').str.strip()
 
-        # 4) Convert all other columns to numeric where possible
+        # 6) Convert everything else numeric
         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
+        # 7) Tag with Player
         df['Player'] = player_name
 
         return df