Refactor player data processing in app.py for improved accuracy

- Updated logic to correctly reference player data starting from the fifth column, ensuring accurate calculations for stack and stack size.
- Renamed the 'actual' column to 'actual_fpts' for clarity, and added a new 'actual_own' column to track ownership data more effectively.
- Enhanced data integrity by ensuring all calculations are based on the correct player data, improving overall functionality.

app.py

@@ -120,22 +120,23 @@ with tab2:
         if type_var == 'Classic':
             working_df['stack'] = working_df.apply(
                 lambda row: Counter(
-                    map_dict['team_map'].get(player, '') for player in row
+                    map_dict['team_map'].get(player, '') for player in row[4:]
                     if map_dict['team_map'].get(player, '') != ''
-                ).most_common(1)[0][0] if any(map_dict['team_map'].get(player, '') for player in row) else '',
+                ).most_common(1)[0][0] if any(map_dict['team_map'].get(player, '') for player in row[4:]) else '',
                 axis=1
             )
             working_df['stack_size'] = working_df.apply(
                 lambda row: Counter(
-                    map_dict['team_map'].get(player, '') for player in row
+                    map_dict['team_map'].get(player, '') for player in row[4:]
                     if map_dict['team_map'].get(player, '') != ''
-                ).most_common(1)[0][1] if any(map_dict['team_map'].get(player, '') for player in row) else '',
+                ).most_common(1)[0][1] if any(map_dict['team_map'].get(player, '') for player in row[4:]) else '',
                 axis=1
             )
             working_df['salary'] = working_df.apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
             working_df['median'] = working_df.apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
-            working_df['actual'] = working_df.apply(lambda row: sum(st.session_state['actual_dict'].get(player, 0) for player in row), axis=1)
+            working_df['actual_fpts'] = working_df.apply(lambda row: sum(st.session_state['actual_dict'].get(player, 0) for player in row), axis=1)
             working_df['Own'] = working_df.apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
+            working_df['actual_own'] = working_df.apply(lambda row: sum(st.session_state['ownership_dict'].get(player, 0) for player in row), axis=1)
             working_df['sorted'] = working_df[player_columns].apply(
                 lambda row: ','.join(sorted(row.values)),
                 axis=1