Update src/streamlit_app.py

src/streamlit_app.py

@@ -71,65 +71,56 @@ def get_player_index_brscraper():
         return pd.DataFrame({'name': common})
 
 @st.cache_data(ttl=300)
-def get_player_career_stats_brscraper(player_name):
+def get_player_career_stats_brscraper(player_name, seasons_to_check=10):
     """
-    Uses BRScraper to get a player's career stats.
-    Detects and renames the season column (or falls back to first column),
-    applies mapping and numeric conversion.
+    Build a player's career stats by fetching per-game data season-by-season
+    via nba.get_stats, instead of nba.get_player_stats (which was returning empty).
     """
     if not BRSCRAPER_AVAILABLE:
         return pd.DataFrame()
-    
-    try:
-        df = nba.get_player_stats(player_name)
-        if df.empty:
-            return df
-
-        # 1) Try to autodetect a season column
-        season_col = None
-        for col in df.columns:
-            low = col.lower().strip()
-            if 'season' in low or 'year' in low:
-                season_col = col
-                break
-        
-        # 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',
-            'TRB':'REB','AST':'AST','STL':'STL','BLK':'BLK','TOV':'TO',
-            'PF':'PF','PTS':'PTS','Age':'AGE','Tm':'TEAM_ABBREVIATION',
-            'Lg':'LEAGUE_ID','Pos':'POSITION',
-            'FG':'FGM','FGA':'FGA','3P':'FG3M','3PA':'FG3A',
-            '2P':'FGM2','2PA':'FGA2','2P%':'FG2_PCT','eFG%':'EFG_PCT',
-            '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})
-
-        # 5) Clean and format the Season column
-        df['Season'] = df['Season'].astype(str).str.replace('-', '–').str.strip()
 
-        # 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')
+    all_rows = []
+    seasons = get_available_seasons(seasons_to_check)
+    for season_str in seasons:
+        end_year = int(season_str.split('–')[1])
+        try:
+            df_season = nba.get_stats(end_year, info='per_game', playoffs=False, rename=False)
+            if 'Player' in df_season.columns:
+                row = df_season[df_season['Player'] == player_name]
+                if not row.empty:
+                    row = row.copy()
+                    # normalize the season label
+                    row['Season'] = season_str
+                    all_rows.append(row)
+        except Exception as e:
+            # skip any failures
+            st.warning(f"Could not fetch {season_str} for {player_name}: {e}")
+
+    if not all_rows:
+        return pd.DataFrame()
 
-        # 7) Tag with Player
-        df['Player'] = player_name
+    df = pd.concat(all_rows, ignore_index=True)
+
+    # Standardize columns
+    mapping = {
+        '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','ORB':'OREB','DRB':'DREB',
+        'FG':'FGM','FGA':'FGA','3P':'FG3M','3PA':'FG3A',
+        '2P':'FGM2','2PA':'FGA2','2P%':'FG2_PCT','eFG%':'EFG_PCT',
+        'FT':'FTM','FTA':'FTA'
+    }
+    df = df.rename(columns={o:n for o,n in mapping.items() if o in df.columns})
 
-        return df
+    # Convert numeric columns
+    non_num = {'Season','Player','Tm','Lg','Pos'}
+    for col in df.columns:
+        if col not in non_num:
+            df[col] = pd.to_numeric(df[col], errors='coerce')
 
-    except Exception as e:
-        st.error(f"Error fetching stats for {player_name}: {e}")
-        return pd.DataFrame()
+    df['Player'] = player_name
+    return df
 
 @st.cache_data(ttl=300)
 def get_team_season_stats_brscraper(year):
@@ -243,19 +234,19 @@ def player_vs_player():
     seasons = get_available_seasons()
     selected_seasons = st.multiselect("Select Seasons", seasons, default=[seasons[0]] if seasons else [])
 
-    # ─── DEBUG PREVIEW ──────────────────────────────────────────────────────
+    # ─── RAW DEBUG PREVIEW ──────────────────────────────────────────────────────
     if selected_players:
-        st.markdown("### 🧪 Raw Data Preview (Before Filtering)")
+        st.markdown("### 🧪 Raw Seasonal Data Preview")
         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.subheader(player)
+            raw = get_player_career_stats_brscraper(player, seasons_to_check=6)
+            if raw.empty:
+                st.write("❌ No rows returned—player may be named differently or data unavailable.")
+            else:
+                st.write("Columns:", raw.columns.tolist())
+                st.dataframe(raw, use_container_width=True)
         st.markdown("---")
-    # ───────────────────────────────────────────────────────────────────────
+    # ────────────────────────────────────────────────────────────────────────────
 
     if st.button("Run Comparison"):
         if not selected_players:
@@ -263,20 +254,16 @@ def player_vs_player():
             return
 
         all_data, no_data = [], []
-        with st.spinner("Fetching player data..."):
+        with st.spinner("Fetching filtered data..."):
             for p in selected_players:
-                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)
+                df = get_player_career_stats_brscraper(p, seasons_to_check=6)
+                if not df.empty:
+                    filt = df[df['Season'].isin(selected_seasons)]
+                    if not filt.empty:
+                        all_data.append(filt)
                     else:
                         no_data.append(p)
-                except Exception as e:
-                    st.error(f"Error fetching stats for {p}: {e}")
+                else:
                     no_data.append(p)
 
         if no_data:
@@ -286,7 +273,7 @@ def player_vs_player():
             return
 
         comp_df = pd.concat(all_data, ignore_index=True)
-        tabs = st.tabs(["Basic Stats","Advanced Stats","Visualizations"])
+        tabs = st.tabs(["Basic Stats", "Advanced Stats", "Visualizations"])
 
         with tabs[0]:
             st.subheader("Basic Statistics")
@@ -310,10 +297,10 @@ def player_vs_player():
             )
             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]:
@@ -323,8 +310,7 @@ def player_vs_player():
                 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')
-                    fig = px.line(trend, x='Season', y=m, markers=True,
-                                  title=f"{selected_players[0]} – {m} Trend")
+                    fig = px.line(trend, x='Season', y=m, markers=True, title=f"{selected_players[0]} – {m} Trend")
                 else:
                     avg = comp_df.groupby('Player')[metrics].mean(numeric_only=True).reset_index()
                     fig = px.bar(avg, x='Player', y=m, title=f"Average {m} Comparison")