Refactor team data retrieval in init_player_data function to use 'teamname' for filtering. Removed unnecessary st.write() calls for player and team data display to streamline output. This change enhances data integrity and improves the clarity of the application during simulations.

app.py

@@ -413,8 +413,8 @@ def init_player_data(game_count, players, opponent, win_loss_settings, kill_pred
                 for stats in calc_columns:
                     playername_win_stats[stats] = tables[tables['result'] == 1].groupby(['playername'])[stats].mean().to_dict()
                     playername_loss_stats[stats] = tables[tables['result'] == 0].groupby(['playername'])[stats].mean().to_dict()
-                    teamname_win_stats[stats] = tables[(tables['result'] == 1) & (tables['position'] == 'team')].groupby(['teamname'])[stats].mean().to_dict()
-                    teamname_loss_stats[stats] = tables[(tables['result'] == 0) & (tables['position'] == 'team')].groupby(['teamname'])[stats].mean().to_dict() 
+                    teamname_win_stats[stats] = tables[(tables['result'] == 1) & (tables['teamname'] == 'team')].groupby(['teamname'])[stats].mean().to_dict()
+                    teamname_loss_stats[stats] = tables[(tables['result'] == 0) & (tables['teamname'] == 'team')].groupby(['teamname'])[stats].mean().to_dict() 
 
                 for stat in calc_columns:
 
@@ -451,7 +451,6 @@ def init_player_data(game_count, players, opponent, win_loss_settings, kill_pred
                 tables['playername_avg_assist_share_loss'] = tables['playername_avg_assists_loss'] / tables['teamname_avg_kills_loss']
                 tables['playername_avg_cs_share_loss'] = tables['playername_avg_total_cs_loss'] / tables['teamname_avg_total_cs_loss']
                 player_tables = tables
-                st.write(player_tables)
 
             else:
 
@@ -603,10 +602,8 @@ if st.button("Load/Reset Data", key='reset1'):
 if st.button("Run"):
     if data_type == "Team":
         team_data, opp_boost, results_dict = init_team_data(game_count, selected_team, selected_opponent, win_loss_settings, kill_predictions, death_predictions, start_date, end_date)
-        st.write(team_data)
     else:
         team_data, opp_boost, results_dict = init_player_data(game_count, selected_players, selected_opponent, win_loss_settings, kill_predictions, death_predictions, start_date, end_date)
-        st.write(team_data)
 
     player_summary = pd.DataFrame()
     for game_num in range(game_count):