Refactor stacking logic in app.py to streamline team assignment calculations for player lineups. This update removes legacy stacking code and optimizes the application of team data, enhancing performance and maintainability.

app.py

@@ -310,48 +310,6 @@ def calculate_lineup_metrics(df, player_columns, map_dict, type_var, sport_var,
     df['median'] = calculate_median_vectorized(df[player_columns], player_columns, map_dict, type_var, sport_var)
     df['Own'] = calculate_ownership_vectorized(df[player_columns], player_columns, map_dict, type_var, sport_var)
     
-    # Handle stacking for specific sports
-    if projections_df is not None and 'team' in projections_df.columns:
-        team_dict = dict(zip(projections_df['player_names'], projections_df['team']))
-        
-        if type_var == 'Classic' and sport_var not in ['CS2', 'LOL', 'GOLF']:
-            # Stack calculation for classic sports (excluding first 2 columns for pitchers)
-            stack_columns = player_columns[2:] if len(player_columns) > 2 else player_columns
-            df['Stack'] = df[stack_columns].apply(
-                lambda row: Counter(
-                    team_dict.get(player, '') for player in row
-                    if team_dict.get(player, '') != ''
-                ).most_common(1)[0][0] if any(team_dict.get(player, '') for player in row) else '',
-                axis=1
-            )
-            df['Size'] = df[stack_columns].apply(
-                lambda row: Counter(
-                    team_dict.get(player, '') for player in row
-                    if team_dict.get(player, '') != ''
-                ).most_common(1)[0][1] if any(team_dict.get(player, '') for player in row) else 0,
-                axis=1
-            )
-        elif sport_var == 'LOL':
-            # LOL uses all player columns for stacking
-            df['Stack'] = df[player_columns].apply(
-                lambda row: Counter(
-                    team_dict.get(player, '') for player in row
-                    if team_dict.get(player, '') != ''
-                ).most_common(1)[0][0] if any(team_dict.get(player, '') for player in row) else '',
-                axis=1
-            )
-            df['Size'] = df[player_columns].apply(
-                lambda row: Counter(
-                    team_dict.get(player, '') for player in row
-                    if team_dict.get(player, '') != ''
-                ).most_common(1)[0][1] if any(team_dict.get(player, '') for player in row) else 0,
-                axis=1
-            )
-    
-    # Optimize data types
-    df['salary'] = df['salary'].astype('uint16')
-    df['median'] = df['median'].astype('float32')
-    
     return df
 
 def create_team_filter_mask(df, player_columns, team_map, teams_to_filter, focus_type='Overall', type_var='Classic'):
@@ -615,14 +573,6 @@ if selected_tab == 'Data Load':
 
                     st.session_state['portfolio'] = st.session_state['portfolio'].dropna(how='all')
                     st.session_state['portfolio'] = st.session_state['portfolio'].reset_index(drop=True)
-                # Check if Stack column exists in the portfolio
-                if 'Stack' in st.session_state['portfolio'].columns:
-                    # Create dictionary mapping index to Stack values
-                    stack_dict = dict(zip(st.session_state['portfolio'].index, st.session_state['portfolio']['Stack']))
-                    st.write(f"Found {len(stack_dict)} stack assignments")
-                    st.session_state['portfolio'] = st.session_state['portfolio'].drop(columns=['Stack'])
-                else:
-                    stack_dict = None
                 if st.session_state['portfolio'] is not None:
 
                     # Optimize data types early for memory efficiency
@@ -2102,6 +2052,21 @@ if selected_tab == 'Manage Portfolio':
                         
                         # st.session_state['working_frame'] = predict_dupes(st.session_state['working_frame'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var)
                         st.session_state['working_frame'] = reassess_edge(st.session_state['working_frame'], st.session_state['base_frame'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
+                        team_dict = dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team']))
+                        st.session_state['working_frame']['Stack'] = st.session_state['working_frame'].apply(
+                            lambda row: Counter(
+                                team_dict.get(player, '') for player in row[stack_column_dict[site_var][type_var][sport_var]]
+                                if team_dict.get(player, '') != ''
+                            ).most_common(1)[0][0] if any(team_dict.get(player, '') for player in row[stack_column_dict[site_var][type_var][sport_var]]) else '',
+                            axis=1
+                        )
+                        st.session_state['working_frame']['Size'] = st.session_state['working_frame'].apply(
+                            lambda row: Counter(
+                                team_dict.get(player, '') for player in row[stack_column_dict[site_var][type_var][sport_var]]
+                                if team_dict.get(player, '') != ''
+                            ).most_common(1)[0][1] if any(team_dict.get(player, '') for player in row[stack_column_dict[site_var][type_var][sport_var]]) else 0,
+                            axis=1
+                        )
                         st.session_state['export_merge'] = st.session_state['working_frame'].copy()
                     elif exp_submitted:
                         st.session_state['settings_base'] = False
@@ -2124,6 +2089,21 @@ if selected_tab == 'Manage Portfolio':
                         
                         # st.session_state['export_base'] = predict_dupes(st.session_state['export_base'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var)
                         st.session_state['export_base'] = reassess_edge(st.session_state['export_base'], st.session_state['base_frame'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
+                        team_dict = dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team']))
+                        st.session_state['working_frame']['Stack'] = st.session_state['working_frame'].apply(
+                            lambda row: Counter(
+                                team_dict.get(player, '') for player in row[stack_column_dict[site_var][type_var][sport_var]]
+                                if team_dict.get(player, '') != ''
+                            ).most_common(1)[0][0] if any(team_dict.get(player, '') for player in row[stack_column_dict[site_var][type_var][sport_var]]) else '',
+                            axis=1
+                        )
+                        st.session_state['working_frame']['Size'] = st.session_state['working_frame'].apply(
+                            lambda row: Counter(
+                                team_dict.get(player, '') for player in row[stack_column_dict[site_var][type_var][sport_var]]
+                                if team_dict.get(player, '') != ''
+                            ).most_common(1)[0][1] if any(team_dict.get(player, '') for player in row[stack_column_dict[site_var][type_var][sport_var]]) else 0,
+                            axis=1
+                        )
                         st.session_state['export_merge'] = st.session_state['export_base'].copy()
                         
         with st.container():