Add CPT/FLEX selection options in 'Manage Portfolio' section of 'app.py' to enhance player filtering capabilities based on slot type.

app.py

@@ -1660,8 +1660,10 @@ if selected_tab == 'Manage Portfolio':
                             player_names.update(st.session_state['working_frame'][col].unique())
                     keep_remove_var = st.selectbox("Conditional:", options=['Keep', 'Remove'], index=0)
                     conditional_side_alpha = st.multiselect("Lineups containing:", options=sorted(list(player_names)), default=[])
+                    cpt_flex_alpha = st.selectbox("in slot:", options=['Overall', 'CPT', 'FLEX'], index=0)
                     conditional_var = st.selectbox("where they also contain:", options=['Any', 'All', 'None'], index=0)
                     conditional_side_beta = st.multiselect("of the following player(s):", options=sorted(list(player_names)), default=[])
+                    cpt_flex_beta = st.selectbox("in slot:", options=['Overall', 'CPT', 'FLEX'], index=0)
                     
                     submitted_col, export_col = st.columns(2)
                     st.info("Portfolio Button applies to your overall Portfolio, Export button applies to your Custom Export")
@@ -1678,7 +1680,15 @@ if selected_tab == 'Manage Portfolio':
                             # 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.values)
+                                    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
@@ -1689,18 +1699,43 @@ if selected_tab == 'Manage Portfolio':
                                 # 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.values)
+                                        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.values)
+                                        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.values)
+                                        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)
                             
@@ -1717,6 +1752,15 @@ if selected_tab == 'Manage Portfolio':
                             # 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.values)
+                                    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