Refactor overall_team_data structure in app.py by removing the 'Opponent' column and updating the 'playername' assignment to include game iteration. This change streamlines the data representation and enhances clarity in player statistics during simulations.

app.py

@@ -151,7 +151,7 @@ def simulate_stats(row, num_sims=1000):
 @st.cache_data(ttl = 60)
 def init_team_data(team, opponent, win_loss_settings, kill_predictions, death_predictions, start_date, end_date):  
         game_count = len(kill_predictions)
-        overall_team_data = pd.DataFrame(columns = ['playername', 'teamname', 'position', 'league', 'Opponent', 'result', 'Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj'])
+        overall_team_data = pd.DataFrame(columns = ['playername', 'teamname', 'position', 'Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj'])
         # Convert date objects to datetime strings in the correct format
         start_datetime = datetime.combine(start_date, datetime.min.time()).strftime("%Y-%m-%d %H:%M:%S")
         end_datetime = datetime.combine(end_date, datetime.max.time()).strftime("%Y-%m-%d %H:%M:%S")
@@ -300,7 +300,6 @@ def init_team_data(team, opponent, win_loss_settings, kill_predictions, death_pr
                 }).set_index(pd.Index(list(opp_pos_kills_boost_win.keys()), name='position'))
 
         for game in range(game_count):
-            st.write(f'Game {game + 1}')
             if kill_predictions[game] > 0:
                 player_tables = player_tables[['playername', 'teamname', 'position', 'playername_avg_kill_share_win', 'playername_avg_death_share_win','playername_avg_assist_share_win',
                                         'playername_avg_total_cs_win', 'playername_avg_kill_share_loss', 'playername_avg_death_share_loss', 'playername_avg_assist_share_loss', 'playername_avg_total_cs_loss']]
@@ -341,8 +340,7 @@ 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']]
-            team_data['playername'] = f'playername {game + 1}'
-            st.write(team_data)
+            team_data['playername'] = team_data['playername'] + f'game {game + 1}'
 
             overall_team_data = pd.concat([overall_team_data, team_data])