Enhance player summary generation in init_team_data function of app.py. Introduced a results dictionary to aggregate player statistics across multiple games, allowing for a comprehensive summary of performance metrics. Updated the return statement to include the new player summary DataFrame, improving the clarity and usability of simulation results for users.

app.py

@@ -299,6 +299,8 @@ def init_team_data(team, opponent, win_loss_settings, kill_predictions, death_pr
                     'opp_pos_cs_boost_loss': opp_pos_cs_boost_loss
                 }).set_index(pd.Index(list(opp_pos_kills_boost_win.keys()), name='position'))
 
+        results_dict = {}
+
         for game in range(game_count):
             if kill_predictions[game] > 0:
                 working_tables = player_tables[['playername', 'teamname', 'position', 'playername_avg_kill_share_win', 'playername_avg_death_share_win','playername_avg_assist_share_win',
@@ -340,14 +342,26 @@ def init_team_data(team, opponent, win_loss_settings, kill_predictions, death_pr
                     team_data['Assist_Proj'] = team_data.apply(lambda row: row['lAssist%'] * opp_pos_assists_boost_loss.get(row['position'], 1), axis=1)
                     team_data['CS_Proj'] = team_data.apply(lambda row: row['lCS'] * opp_pos_cs_boost_loss.get(row['position'], 1), axis=1)
                     team_data = team_data[['playername', 'teamname', 'position', 'Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']]
+            
+            results_dict[f'game {game + 1}'] = team_data
             team_data['playername'] = team_data['playername'] + f' game {game + 1}'
 
-            overall_team_data = pd.concat([overall_team_data, team_data])
+        player_summary = pd.DataFrame()
+        for game_count, game_df in results_dict.items():
+            if player_summary.empty:
+                player_summary = game_df
+            else:
+                player_summary.update(game_df)
+                for col in ['Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']:
+                    player_summary[col] += game_df[col]
+            player_summary = player_summary.set_index('playername')
+
+        overall_team_data = pd.concat([overall_team_data, team_data])
 
-        return overall_team_data.dropna().set_index('playername'), opp_boosts
+        return overall_team_data.dropna().set_index('playername'), opp_boosts, player_summary
 
 if st.button("Run"):
-    team_data, opp_boost = init_team_data(selected_team, selected_opponent, win_loss_settings, kill_predictions, death_predictions, start_date, end_date)
+    team_data, opp_boost, player_summary = init_team_data(selected_team, selected_opponent, win_loss_settings, kill_predictions, death_predictions, start_date, end_date)
 
     # Create simulated percentiles
     sim_results = []
@@ -380,6 +394,7 @@ if st.button("Run"):
 
     tab1, tab2 = st.tabs(["Team Data", "Opponent Data"])
     with tab1:
+        st.dataframe(player_summary.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(display_formats, precision=2), use_container_width = True)
         st.dataframe(team_data.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(display_formats, precision=2), use_container_width = True)
     with tab2:
         st.dataframe(opp_boost.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
@@ -390,8 +405,9 @@ if st.button("Run"):
     for player, tab in zip(unique_players, player_tabs):
         with tab:
             player_data = sim_df[sim_df['Player'] == player]
+            player_data = player_data.set_index('Stat')
             st.dataframe(
-                player_data[['Stat', '10%', '25%', '50%', '75%', '90%']]
+                player_data[['10%', '25%', '50%', '75%', '90%']]
                 .style.format(precision=2),
                 use_container_width=True
             )