Refactor player data processing in app.py

- Updated the logic for calculating 'stack', 'stack_size', 'salary', 'actual_fpts', and 'actual_own' to utilize the player columns defined in session state, ensuring accurate data aggregation.
- Enhanced the application’s ability to handle player data by applying functions specifically to the relevant columns, improving overall data integrity and performance.

app.py

@@ -168,21 +168,21 @@ with tab2:
         if type_var == 'Classic':
             working_df['stack'] = working_df.apply(
                 lambda row: Counter(
-                    st.session_state['map_dict']['team_map'].get(player, '') for player in row[4:]
+                    st.session_state['map_dict']['team_map'].get(player, '') for player in row[st.session_state['player_columns']]
                     if st.session_state['map_dict']['team_map'].get(player, '') != ''
-                ).most_common(1)[0][0] if any(st.session_state['map_dict']['team_map'].get(player, '') for player in row[4:]) else '',
+                ).most_common(1)[0][0] if any(st.session_state['map_dict']['team_map'].get(player, '') for player in row[st.session_state['player_columns']]) else '',
                 axis=1
             )
             working_df['stack_size'] = working_df.apply(
                 lambda row: Counter(
-                    st.session_state['map_dict']['team_map'].get(player, '') for player in row[4:]
+                    st.session_state['map_dict']['team_map'].get(player, '') for player in row[st.session_state['player_columns']]
                     if st.session_state['map_dict']['team_map'].get(player, '') != ''
-                ).most_common(1)[0][1] if any(st.session_state['map_dict']['team_map'].get(player, '') for player in row[4:]) else '',
+                ).most_common(1)[0][1] if any(st.session_state['map_dict']['team_map'].get(player, '') for player in row[st.session_state['player_columns']]) else '',
                 axis=1
             )
-            working_df['salary'] = working_df.apply(lambda row: sum(st.session_state['map_dict']['salary_map'].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['actual_own'] = working_df.apply(lambda row: sum(st.session_state['ownership_dict'].get(player, 0) for player in row), axis=1)
+            working_df['salary'] = working_df.apply(lambda row: sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row[st.session_state['player_columns']]), axis=1)
+            working_df['actual_fpts'] = working_df.apply(lambda row: sum(st.session_state['actual_dict'].get(player, 0) for player in row[st.session_state['player_columns']]), axis=1)
+            working_df['actual_own'] = working_df.apply(lambda row: sum(st.session_state['ownership_dict'].get(player, 0) for player in row[st.session_state['player_columns']]), axis=1)
             working_df['sorted'] = working_df[st.session_state['player_columns']].apply(
                 lambda row: ','.join(sorted(row.values)),
                 axis=1