Refactor player filtering logic in app.py to utilize dynamic player column selection. Updated include and remove masks to improve accuracy in filtering lineups based on user-defined criteria, enhancing overall functionality and maintainability.

app.py

@@ -1315,37 +1315,53 @@ with tab2:
                     if reg_submitted:
                         st.session_state['settings_base'] = False
                         parsed_frame = st.session_state['working_frame'].copy()
+
+                        # Get player columns (excluding non-player columns)
+                        player_columns = [col for col in parsed_frame.columns if col not in excluded_cols]
                         
-                        # Filter rows that contain ALL conditional_include_players AND NONE of conditional_remove_players
+                        # Create masks similar to lines 1205-1211
                         if conditional_include_players:
-                            include_mask = parsed_frame['player_names'].apply(lambda x: all(player in x for player in conditional_include_players))
+                            include_mask = parsed_frame[player_columns].apply(
+                                lambda row: all(player in list(row) for player in conditional_include_players), axis=1
+                            )
                         else:
-                            include_mask = True
+                            include_mask = pd.Series([True] * len(parsed_frame))
                             
                         if conditional_remove_players:
-                            remove_mask = parsed_frame['player_names'].apply(lambda x: not any(player in x for player in conditional_remove_players))
+                            remove_mask = parsed_frame[player_columns].apply(
+                                lambda row: not any(player in list(row) for player in conditional_remove_players), axis=1
+                            )
                         else:
-                            remove_mask = True
+                            remove_mask = pd.Series([True] * len(parsed_frame))
                             
                         parsed_frame = parsed_frame[include_mask & remove_mask]
+
                         st.session_state['working_frame'] = parsed_frame.sort_values(by='median', ascending=False).reset_index(drop=True)
                         st.session_state['export_merge'] = st.session_state['working_frame'].copy()
                     elif exp_submitted:
                         st.session_state['settings_base'] = False
                         parsed_frame = st.session_state['export_base'].copy()
                         
-                        # Filter rows that contain ALL conditional_include_players AND NONE of conditional_remove_players
+                        # Get player columns (excluding non-player columns)
+                        player_columns = [col for col in parsed_frame.columns if col not in excluded_cols]
+                        
+                        # Create masks similar to lines 1205-1211
                         if conditional_include_players:
-                            include_mask = parsed_frame['player_names'].apply(lambda x: all(player in x for player in conditional_include_players))
+                            include_mask = parsed_frame[player_columns].apply(
+                                lambda row: all(player in list(row) for player in conditional_include_players), axis=1
+                            )
                         else:
-                            include_mask = True
+                            include_mask = pd.Series([True] * len(parsed_frame))
                             
                         if conditional_remove_players:
-                            remove_mask = parsed_frame['player_names'].apply(lambda x: not any(player in x for player in conditional_remove_players))
+                            remove_mask = parsed_frame[player_columns].apply(
+                                lambda row: not any(player in list(row) for player in conditional_remove_players), axis=1
+                            )
                         else:
-                            remove_mask = True
+                            remove_mask = pd.Series([True] * len(parsed_frame))
                             
                         parsed_frame = parsed_frame[include_mask & remove_mask]
+                        
                         st.session_state['export_base'] = parsed_frame.sort_values(by='median', ascending=False).reset_index(drop=True)
                         st.session_state['export_merge'] = st.session_state['export_base'].copy()