Refactor player and stack statistics display in app.py: streamline player summary calculations and add stack summary section for enhanced data visualization and user experience.

app.py

@@ -1208,45 +1208,13 @@ with tab2:
                 height=1000,
                 use_container_width=True
             )
+        player_stats_col, stack_stats_col = st.columns(2)
+        with player_stats_col:
+
+            player_stats = []
+            player_columns = [col for col in display_frame.columns if col not in excluded_cols]
             
-        # Create player summary dataframe
-        player_stats = []
-        player_columns = [col for col in display_frame.columns if col not in excluded_cols]
-        
-        if type_var == 'Showdown':
-            for player in player_names:
-                # Create mask for lineups where this player is Captain (first column)
-                cpt_mask = display_frame[player_columns[0]] == player
-                
-                if cpt_mask.any():
-                    player_stats.append({
-                        'Player': f"{player} (CPT)",
-                        'Lineup Count': cpt_mask.sum(),
-                        'Avg Median': display_frame[cpt_mask]['median'].mean(),
-                        'Avg Own': display_frame[cpt_mask]['Own'].mean(),
-                        'Avg Dupes': display_frame[cpt_mask]['Dupes'].mean(),
-                        'Avg Finish %': display_frame[cpt_mask]['Finish_percentile'].mean(),
-                        'Avg Lineup Edge': display_frame[cpt_mask]['Lineup Edge'].mean(),
-                    })
-                
-                # Create mask for lineups where this player is FLEX (other columns)
-                flex_mask = display_frame[player_columns[1:]].apply(
-                    lambda row: player in list(row), axis=1
-                )
-                
-                if flex_mask.any():
-                    player_stats.append({
-                        'Player': f"{player} (FLEX)",
-                        'Lineup Count': flex_mask.sum(),
-                        'Avg Median': display_frame[flex_mask]['median'].mean(),
-                        'Avg Own': display_frame[flex_mask]['Own'].mean(),
-                        'Avg Dupes': display_frame[flex_mask]['Dupes'].mean(),
-                        'Avg Finish %': display_frame[flex_mask]['Finish_percentile'].mean(),
-                        'Avg Lineup Edge': display_frame[flex_mask]['Lineup Edge'].mean(),
-                    })
-        else:
-            if sport_var == 'CS2':
-                # Handle Captain positions
+            if type_var == 'Showdown':
                 for player in player_names:
                     # Create mask for lineups where this player is Captain (first column)
                     cpt_mask = display_frame[player_columns[0]] == player
@@ -1277,38 +1245,106 @@ with tab2:
                             'Avg Finish %': display_frame[flex_mask]['Finish_percentile'].mean(),
                             'Avg Lineup Edge': display_frame[flex_mask]['Lineup Edge'].mean(),
                         })
-            elif sport_var != 'CS2':
-                # Original Classic format processing
-                for player in player_names:
-                    player_mask = display_frame[player_columns].apply(
-                        lambda row: player in list(row), axis=1
-                    )
-                    
-                    if player_mask.any():
-                        player_stats.append({
-                            'Player': player,
-                            'Lineup Count': player_mask.sum(),
-                            'Avg Median': display_frame[player_mask]['median'].mean(),
-                            'Avg Own': display_frame[player_mask]['Own'].mean(),
-                            'Avg Dupes': display_frame[player_mask]['Dupes'].mean(),
-                            'Avg Finish %': display_frame[player_mask]['Finish_percentile'].mean(),
-                            'Avg Lineup Edge': display_frame[player_mask]['Lineup Edge'].mean(),
-                        })
-        
-        player_summary = pd.DataFrame(player_stats)
-        player_summary = player_summary.sort_values('Lineup Count', ascending=False)
+            else:
+                if sport_var == 'CS2':
+                    # Handle Captain positions
+                    for player in player_names:
+                        # Create mask for lineups where this player is Captain (first column)
+                        cpt_mask = display_frame[player_columns[0]] == player
+                        
+                        if cpt_mask.any():
+                            player_stats.append({
+                                'Player': f"{player} (CPT)",
+                                'Lineup Count': cpt_mask.sum(),
+                                'Avg Median': display_frame[cpt_mask]['median'].mean(),
+                                'Avg Own': display_frame[cpt_mask]['Own'].mean(),
+                                'Avg Dupes': display_frame[cpt_mask]['Dupes'].mean(),
+                                'Avg Finish %': display_frame[cpt_mask]['Finish_percentile'].mean(),
+                                'Avg Lineup Edge': display_frame[cpt_mask]['Lineup Edge'].mean(),
+                            })
+                        
+                        # Create mask for lineups where this player is FLEX (other columns)
+                        flex_mask = display_frame[player_columns[1:]].apply(
+                            lambda row: player in list(row), axis=1
+                        )
+                        
+                        if flex_mask.any():
+                            player_stats.append({
+                                'Player': f"{player} (FLEX)",
+                                'Lineup Count': flex_mask.sum(),
+                                'Avg Median': display_frame[flex_mask]['median'].mean(),
+                                'Avg Own': display_frame[flex_mask]['Own'].mean(),
+                                'Avg Dupes': display_frame[flex_mask]['Dupes'].mean(),
+                                'Avg Finish %': display_frame[flex_mask]['Finish_percentile'].mean(),
+                                'Avg Lineup Edge': display_frame[flex_mask]['Lineup Edge'].mean(),
+                            })
+                elif sport_var != 'CS2':
+                    # Original Classic format processing
+                    for player in player_names:
+                        player_mask = display_frame[player_columns].apply(
+                            lambda row: player in list(row), axis=1
+                        )
+                        
+                        if player_mask.any():
+                            player_stats.append({
+                                'Player': player,
+                                'Lineup Count': player_mask.sum(),
+                                'Avg Median': display_frame[player_mask]['median'].mean(),
+                                'Avg Own': display_frame[player_mask]['Own'].mean(),
+                                'Avg Dupes': display_frame[player_mask]['Dupes'].mean(),
+                                'Avg Finish %': display_frame[player_mask]['Finish_percentile'].mean(),
+                                'Avg Lineup Edge': display_frame[player_mask]['Lineup Edge'].mean(),
+                            })
+            
+            player_summary = pd.DataFrame(player_stats)
+            player_summary = player_summary.sort_values('Lineup Count', ascending=False)
+            
+            st.subheader("Player Summary")
+            st.dataframe(
+                player_summary.style
+                .background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Avg Finish %', 'Avg Own', 'Avg Dupes'])
+                .format({
+                    'Avg Median': '{:.2f}',
+                    'Avg Own': '{:.2f}',
+                    'Avg Dupes': '{:.2f}',
+                    'Avg Finish %': '{:.2%}',
+                    'Avg Lineup Edge': '{:.2%}'
+                }),
+                height=400,
+                use_container_width=True
+            )
         
-        st.subheader("Player Summary")
-        st.dataframe(
-            player_summary.style
-            .background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Avg Finish %', 'Avg Own', 'Avg Dupes'])
-            .format({
-                'Avg Median': '{:.2f}',
-                'Avg Own': '{:.2f}',
-                'Avg Dupes': '{:.2f}',
-                'Avg Finish %': '{:.2%}',
-                'Avg Lineup Edge': '{:.2%}'
-            }),
-            height=400,
-            use_container_width=True
-        )
+        with stack_stats_col:
+            if stack_dict is not None:
+                stack_stats = []
+                stack_columns = [col for col in display_frame.columns if col.startswith('Stack')]
+                for stack in stack_dict.values():
+                    stack_mask = display_frame['Stack'] == stack
+                    if stack_mask.any():
+                        stack_stats.append({
+                            'Stack': stack,
+                            'Lineup Count': stack_mask.sum(),
+                            'Avg Median': display_frame[stack_mask]['median'].mean(),
+                            'Avg Own': display_frame[stack_mask]['Own'].mean(),
+                            'Avg Dupes': display_frame[stack_mask]['Dupes'].mean(),
+                            'Avg Finish %': display_frame[stack_mask]['Finish_percentile'].mean(),
+                            'Avg Lineup Edge': display_frame[stack_mask]['Lineup Edge'].mean(),
+                        })
+                stack_summary = pd.DataFrame(stack_stats)
+                stack_summary = stack_summary.sort_values('Lineup Count', ascending=False)
+                st.subheader("Stack Summary")
+                st.dataframe(
+                    stack_summary.style
+                    .background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Avg Finish %', 'Avg Own', 'Avg Dupes'])
+                    .format({
+                        'Avg Median': '{:.2f}',
+                        'Avg Own': '{:.2f}',
+                        'Avg Dupes': '{:.2f}',
+                        'Avg Finish %': '{:.2%}',
+                        'Avg Lineup Edge': '{:.2%}'
+                    }),
+                    height=400,
+                    use_container_width=True
+                )
+            else:
+                stack_summary = pd.DataFrame(columns=['Stack', 'Lineup Count', 'Avg Median', 'Avg Own', 'Avg Dupes', 'Avg Finish %', 'Avg Lineup Edge'])