Enhance player filtering logic in 'Manage Portfolio' section of 'app.py' by implementing conditional checks for both alpha and beta players, improving the accuracy of row selection based on user-defined criteria.

app.py

@@ -1775,48 +1775,98 @@ if selected_tab == 'Manage Portfolio':
                         st.session_state['settings_base'] = False
                         parsed_frame = st.session_state['export_base'].copy()
 
-                        # Same logic for export path
+                        # Check if we have players selected for both alpha and beta sides
                         if conditional_side_alpha and conditional_side_beta:
+                            # Create boolean mask for rows containing ALL players from alpha side
                             alpha_mask = pd.Series([True] * len(parsed_frame), index=parsed_frame.index)
                             for player in conditional_side_alpha:
-                                player_present = parsed_frame.apply(lambda row: player in row.values, axis=1)
+                                if type_var == 'Showdown':
+                                    if cpt_flex_alpha == 'Overall':
+                                        player_present = parsed_frame.apply(lambda row: player in row.values, axis=1)
+                                    elif cpt_flex_alpha == 'CPT':
+                                        player_present = parsed_frame.iloc[:, 0].apply(lambda row: player in row)
+                                    elif cpt_flex_alpha == 'FLEX':
+                                        player_present = parsed_frame.iloc[:, 1:].apply(lambda row: player in row.values, axis=1)
+                                else:
+                                    player_present = parsed_frame.apply(lambda row: player in row.values, axis=1)
                                 alpha_mask = alpha_mask & player_present
                             
                             # Only apply beta logic to rows that match alpha condition
                             rows_to_process = alpha_mask
                             
+                            # For rows that match alpha condition, check beta condition
                             if conditional_var == 'Any':
+                                # Check if row contains ANY of the beta players
                                 beta_mask = pd.Series([False] * len(parsed_frame), index=parsed_frame.index)
                                 for player in conditional_side_beta:
-                                    player_present = parsed_frame.apply(lambda row: player in row.values, axis=1)
+                                    if type_var == 'Showdown':
+                                        if cpt_flex_beta == 'Overall':
+                                            player_present = parsed_frame.apply(lambda row: player in row.values, axis=1)
+                                        elif cpt_flex_beta == 'CPT':
+                                            player_present = parsed_frame.iloc[:, 0].apply(lambda row: player in row)
+                                        elif cpt_flex_beta == 'FLEX':
+                                            player_present = parsed_frame.iloc[:, 1:].apply(lambda row: player in row.values, axis=1)
+                                    else:
+                                        player_present = parsed_frame.apply(lambda row: player in row.values, axis=1)
                                     beta_mask = beta_mask | player_present
                             elif conditional_var == 'All':
+                                # Check if row contains ALL of the beta players
                                 beta_mask = pd.Series([True] * len(parsed_frame), index=parsed_frame.index)
                                 for player in conditional_side_beta:
-                                    player_present = parsed_frame.apply(lambda row: player in row.values, axis=1)
+                                    if type_var == 'Showdown':
+                                        if cpt_flex_beta == 'Overall':
+                                            player_present = parsed_frame.apply(lambda row: player in row.values, axis=1)
+                                        elif cpt_flex_beta == 'CPT':
+                                            player_present = parsed_frame.iloc[:, 0].apply(lambda row: player in row)
+                                        elif cpt_flex_beta == 'FLEX':
+                                            player_present = parsed_frame.iloc[:, 1:].apply(lambda row: player in row.values, axis=1)
+                                    else:
+                                        player_present = parsed_frame.apply(lambda row: player in row.values, axis=1)
                                     beta_mask = beta_mask & player_present
                             elif conditional_var == 'None':
+                                # Check if row contains NONE of the beta players
                                 beta_mask = pd.Series([True] * len(parsed_frame), index=parsed_frame.index)
                                 for player in conditional_side_beta:
+                                    if type_var == 'Showdown':
+                                        if cpt_flex_beta == 'Overall':
+                                            player_present = parsed_frame.apply(lambda row: player in row.values, axis=1)
+                                        elif cpt_flex_beta == 'CPT':
+                                            player_present = parsed_frame.iloc[:, 0].apply(lambda row: player in row)
+                                        elif cpt_flex_beta == 'FLEX':
+                                            player_present = parsed_frame.iloc[:, 1:].apply(lambda row: player in row.values, axis=1)
+                                    else:
+                                        player_present = parsed_frame.apply(lambda row: player in row.values, axis=1)
                                     player_present = parsed_frame.apply(lambda row: player in row.values, axis=1)
                                     beta_mask = beta_mask & (~player_present)
                             
+                            # Combine conditions: alpha_mask AND beta_mask
                             final_condition = rows_to_process & beta_mask
                             
+                            # Apply keep or remove logic
                             if keep_remove_var == 'Keep':
                                 parsed_frame = parsed_frame[~rows_to_process | final_condition]
-                            else:
+                            else:  # Remove
                                 parsed_frame = parsed_frame[~final_condition]
                         
                         elif conditional_side_alpha:
+                            # Only alpha side specified - filter based on presence of alpha players
                             alpha_mask = pd.Series([True] * len(parsed_frame), index=parsed_frame.index)
                             for player in conditional_side_alpha:
+                                if type_var == 'Showdown':
+                                    if cpt_flex_alpha == 'Overall':
+                                        player_present = parsed_frame.apply(lambda row: player in row.values, axis=1)
+                                    elif cpt_flex_alpha == 'CPT':
+                                        player_present = parsed_frame.iloc[:, 0].apply(lambda row: player in row)
+                                    elif cpt_flex_alpha == 'FLEX':
+                                        player_present = parsed_frame.iloc[:, 1:].apply(lambda row: player in row.values, axis=1)
+                                else:
+                                    player_present = parsed_frame.apply(lambda row: player in row.values, axis=1)
                                 player_present = parsed_frame.apply(lambda row: player in row.values, axis=1)
                                 alpha_mask = alpha_mask & player_present
                             
                             if keep_remove_var == 'Keep':
                                 parsed_frame = parsed_frame[alpha_mask]
-                            else:
+                            else:  # Remove
                                 parsed_frame = parsed_frame[~alpha_mask]
                         
                         st.session_state['export_base'] = parsed_frame.sort_values(by='median', ascending=False).reset_index(drop=True)