Update src/streamlit_app.py

src/streamlit_app.py

@@ -243,6 +243,20 @@ def player_vs_player():
     seasons = get_available_seasons()
     selected_seasons = st.multiselect("Select Seasons", seasons, default=[seasons[0]] if seasons else [])
 
+    # ─── DEBUG PREVIEW ──────────────────────────────────────────────────────
+    if selected_players:
+        st.markdown("### 🧪 Raw Data Preview (Before Filtering)")
+        for player in selected_players:
+            try:
+                raw_df = get_player_career_stats_brscraper(player)
+                st.subheader(f"{player} — Raw Data")
+                st.write("Columns:", raw_df.columns.tolist())
+                st.dataframe(raw_df, use_container_width=True)
+            except Exception as e:
+                st.error(f"❌ Error fetching data for {player}: {e}")
+        st.markdown("---")
+    # ───────────────────────────────────────────────────────────────────────
+
     if st.button("Run Comparison"):
         if not selected_players:
             st.warning("Please select at least one player.")
@@ -251,14 +265,18 @@ def player_vs_player():
         all_data, no_data = [], []
         with st.spinner("Fetching player data..."):
             for p in selected_players:
-                df = get_player_career_stats_brscraper(p)
-                if not df.empty:
-                    filt = df[df['Season'].isin(selected_seasons)]
-                    if not filt.empty:
-                        all_data.append(filt)
+                try:
+                    df = get_player_career_stats_brscraper(p)
+                    if not df.empty:
+                        filt = df[df['Season'].isin(selected_seasons)]
+                        if not filt.empty:
+                            all_data.append(filt)
+                        else:
+                            no_data.append(p)
                     else:
                         no_data.append(p)
-                else:
+                except Exception as e:
+                    st.error(f"Error fetching stats for {p}: {e}")
                     no_data.append(p)
 
         if no_data:
@@ -272,7 +290,7 @@ def player_vs_player():
 
         with tabs[0]:
             st.subheader("Basic Statistics")
-            if len(selected_seasons)>1:
+            if len(selected_seasons) > 1:
                 df_b = comp_df.groupby('Player').mean(numeric_only=True).reset_index()
                 cols = ['Player','GP','MIN','PTS','REB','AST','STL','BLK','FG_PCT','FT_PCT','FG3_PCT']
             else:
@@ -283,19 +301,19 @@ def player_vs_player():
         with tabs[1]:
             st.subheader("Advanced Statistics")
             df_a = comp_df.copy()
-            df_a['FGA'] = pd.to_numeric(df_a.get('FGA',0), errors='coerce').fillna(0)
-            df_a['FTA'] = pd.to_numeric(df_a.get('FTA',0), errors='coerce').fillna(0)
-            df_a['PTS'] = pd.to_numeric(df_a.get('PTS',0), errors='coerce').fillna(0)
+            df_a['FGA'] = pd.to_numeric(df_a.get('FGA', 0), errors='coerce').fillna(0)
+            df_a['FTA'] = pd.to_numeric(df_a.get('FTA', 0), errors='coerce').fillna(0)
+            df_a['PTS'] = pd.to_numeric(df_a.get('PTS', 0), errors='coerce').fillna(0)
             df_a['TS_PCT'] = df_a.apply(
-                lambda r: r['PTS']/(2*(r['FGA']+0.44*r['FTA'])) if (r['FGA']+0.44*r['FTA']) else 0,
+                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:
+            if len(selected_seasons) > 1:
                 df_adv = df_a.groupby('Player').mean(numeric_only=True).reset_index()
-                cols = ['Player','PTS','REB','AST','FG_PCT','TS_PCT']
+                cols = ['Player', 'PTS', 'REB', 'AST', 'FG_PCT', 'TS_PCT']
             else:
                 df_adv = df_a.copy()
-                cols = ['Player','Season','PTS','REB','AST','FG_PCT','TS_PCT']
+                cols = ['Player', 'Season', 'PTS', 'REB', 'AST', 'FG_PCT', 'TS_PCT']
             st.dataframe(df_adv[[c for c in cols if c in df_adv.columns]].round(3), use_container_width=True)
 
         with tabs[2]:
@@ -303,8 +321,8 @@ def player_vs_player():
             metrics = [m for m in ['PTS','REB','AST','FG_PCT','FG3_PCT','FT_PCT','STL','BLK'] if m in comp_df.columns]
             if metrics:
                 m = st.selectbox("Select Metric", metrics)
-                if len(selected_players)==1 and len(selected_seasons)>1:
-                    trend = comp_df[comp_df['Player']==selected_players[0]].sort_values('Season')
+                if len(selected_players) == 1 and len(selected_seasons) > 1:
+                    trend = comp_df[comp_df['Player'] == selected_players[0]].sort_values('Season')
                     fig = px.line(trend, x='Season', y=m, markers=True,
                                   title=f"{selected_players[0]} – {m} Trend")
                 else:
@@ -312,16 +330,15 @@ def player_vs_player():
                     fig = px.bar(avg, x='Player', y=m, title=f"Average {m} Comparison")
                 st.plotly_chart(fig, use_container_width=True)
 
-                # Radar
                 cats = [c for c in ['PTS','REB','AST','STL','BLK'] if c in comp_df.columns]
-                if len(cats)>=3:
+                if len(cats) >= 3:
                     src = (comp_df.groupby('Player')[cats].mean(numeric_only=True).reset_index()
-                           if len(selected_seasons)>1 else comp_df.copy())
+                           if len(selected_seasons) > 1 else comp_df.copy())
                     scaled = src.copy()
                     for c in cats:
                         mn, mx = scaled[c].min(), scaled[c].max()
-                        scaled[c] = ((scaled[c]-mn)/(mx-mn)*100) if mx>mn else 0
-                    radar = {r['Player']:{c:r[c] for c in cats} for _,r in scaled.iterrows()}
+                        scaled[c] = ((scaled[c] - mn) / (mx - mn) * 100) if mx > mn else 0
+                    radar = {r['Player']: {c: r[c] for c in cats} for _, r in scaled.iterrows()}
                     st.plotly_chart(create_radar_chart(radar, cats), use_container_width=True)
                 else:
                     st.info("Need ≥3 metrics for radar chart.")