Refactor player data handling in init_team_data function of app.py. Updated results_dict assignment to drop NaN values, ensuring cleaner data output for each game iteration. Adjusted playername indexing to maintain clarity in player statistics during simulations. This change enhances the overall quality and usability of the player summary data.

app.py

@@ -343,7 +343,7 @@ def init_team_data(team, opponent, win_loss_settings, kill_predictions, death_pr
                     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
+            results_dict[f'game {game + 1}'] = team_data.dropna()
             team_data['playername'] = team_data['playername'] + f' game {game + 1}'
 
         overall_team_data = pd.concat([overall_team_data, team_data])
@@ -366,7 +366,7 @@ if st.button("Run"):
             player_summary.update(clean_df)  # Update teamname and position if needed
             for col in ['Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']:
                 player_summary[col] += clean_df[col]
-        player_summary = player_summary.set_index('playername')
+    player_summary = player_summary.set_index('playername')
 
     # Create simulated percentiles
     sim_results = []