Update handbuilder lineup to use '4x%' instead of '2x%' for player statistics and adjust related calculations accordingly.

src/streamlit_app.py

@@ -638,7 +638,7 @@ if selected_tab == 'Handbuilder':
 
     # --- LINEUP STATE ---
     if 'handbuilder_lineup' not in st.session_state:
-        st.session_state['handbuilder_lineup'] = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Median', '2x%', 'Own'])
+        st.session_state['handbuilder_lineup'] = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Median', '4x%', 'Own'])
 
     # Count positions in the current lineup
     lineup = st.session_state['handbuilder_lineup']
@@ -673,14 +673,14 @@ if selected_tab == 'Handbuilder':
             handbuild_roo['Team'].isin(selected_teams)
         ][['Player', 'Position', 'Team', 'Salary', 'Median', '2x%', 'Own']].drop_duplicates(subset=['Player', 'Team']).copy()
     else:
-        st.session_state['player_select_df'] = handbuild_roo[['Player', 'Position', 'Team', 'Salary', 'Median', '2x%', 'Own']].drop_duplicates(subset=['Player', 'Team']).copy()
+        st.session_state['player_select_df'] = handbuild_roo[['Player', 'Position', 'Team', 'Salary', 'Median', '4x%', 'Own']].drop_duplicates(subset=['Player', 'Team']).copy()
     
     # If no teams selected, show all teams
     if pos_select3:
         position_mask_2 = handbuild_roo['Position'].apply(lambda x: any(pos in x for pos in pos_select3))
-        st.session_state['player_select_df'] = st.session_state['player_select_df'][position_mask_2][['Player', 'Position', 'Team', 'Salary', 'Median', '2x%', 'Own']].drop_duplicates(subset=['Player', 'Team']).copy()
+        st.session_state['player_select_df'] = st.session_state['player_select_df'][position_mask_2][['Player', 'Position', 'Team', 'Salary', 'Median', '4x%', 'Own']].drop_duplicates(subset=['Player', 'Team']).copy()
     else:
-        st.session_state['player_select_df'] = st.session_state['player_select_df'][['Player', 'Position', 'Team', 'Salary', 'Median', '2x%', 'Own']].drop_duplicates(subset=['Player', 'Team']).copy()
+        st.session_state['player_select_df'] = st.session_state['player_select_df'][['Player', 'Position', 'Team', 'Salary', 'Median', '4x%', 'Own']].drop_duplicates(subset=['Player', 'Team']).copy()
     
     st.session_state['player_select_df'] = st.session_state['player_select_df'][st.session_state['player_select_df']['Salary'] <= salary_var]
 
@@ -757,7 +757,7 @@ if selected_tab == 'Handbuilder':
                         # Add the slot info
                         player_row = player_row.assign(Slot=slot_to_fill)
                         st.session_state['handbuilder_lineup'] = pd.concat(
-                            [st.session_state['handbuilder_lineup'], player_row[['Player', 'Position', 'Team', 'Salary', 'Median', '2x%', 'Own', 'Slot']]],
+                            [st.session_state['handbuilder_lineup'], player_row[['Player', 'Position', 'Team', 'Salary', 'Median', '4x%', 'Own', 'Slot']]],
                             ignore_index=True
                         )
                         st.success(f"Added {player_row['Player'].iloc[0]} to {slot_to_fill} slot")
@@ -794,7 +794,7 @@ if selected_tab == 'Handbuilder':
                         'Team': row['Team'],
                         'Salary': row['Salary'],
                         'Median': row['Median'],
-                        '2x%': row['2x%'],
+                        '4x%': row['4x%'],
                         'Own': row['Own']
                     })
                 else:
@@ -805,7 +805,7 @@ if selected_tab == 'Handbuilder':
                         'Team': '',
                         'Salary': np.nan,
                         'Median': np.nan,
-                        '2x%': np.nan,
+                        '4x%': np.nan,
                         'Own': np.nan
                     })
 
@@ -822,7 +822,7 @@ if selected_tab == 'Handbuilder':
         if not filled_lineup.empty:
             total_salary = filled_lineup['Salary'].sum()
             total_median = filled_lineup['Median'].sum()
-            avg_2x = filled_lineup['2x%'].mean()
+            avg_4x = filled_lineup['4x%'].mean()
             total_own = filled_lineup['Own'].sum()
             most_common_team = filled_lineup['Team'].mode()[0] if not filled_lineup['Team'].mode().empty else ""
 
@@ -833,7 +833,7 @@ if selected_tab == 'Handbuilder':
                 'Team': [most_common_team],
                 'Salary': [total_salary],
                 'Median': [total_median],
-                '2x%': [avg_2x],
+                '4x%': [avg_4x],
                 'Own': [total_own]
             })
             summary_row = summary_row[['Salary', 'Median', 'Own']].head(max_players)
@@ -898,7 +898,7 @@ if selected_tab == 'Handbuilder':
     clear_col, save_col, export_col, clear_saved_col, blank_col = st.columns([2, 2, 2, 2, 12])
     with clear_col:
         if st.button("Clear Lineup", key='clear_lineup_button'):
-            st.session_state['handbuilder_lineup'] = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Median', '2x%', 'Own', 'Slot'])
+            st.session_state['handbuilder_lineup'] = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Median', '4x%', 'Own', 'Slot'])
             # Clear the dataframe selections by resetting the previous selection state
             st.session_state['previous_player_selection'] = []
             # Force dataframe to re-render with new key to clear selections
@@ -1134,7 +1134,10 @@ if selected_tab == 'Optimals':
                 data_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
                 if site_var == 'Draftkings':
                     if type_var == 'Regular':
-                        map_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
+                        if sport_var == 'NBA':
+                            map_columns = ['PG', 'PG', 'SG', 'SG', 'SF', 'SF', 'PF', 'PF', 'C']
+                        elif sport_var == 'WNBA':
+                            map_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
                         for col_idx in map_columns:
                             data_export[col_idx] = data_export[col_idx].map(dk_id_map)
                     elif type_var == 'Showdown':
@@ -1143,7 +1146,10 @@ if selected_tab == 'Optimals':
                             data_export[col_idx] = data_export[col_idx].map(dk_sd_id_map)
                 elif site_var == 'Fanduel':
                     if type_var == 'Regular':
-                        map_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
+                        if sport_var == 'NBA':
+                            map_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
+                        elif sport_var == 'WNBA':
+                            map_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
                         for col_idx in map_columns:
                             data_export[col_idx] = data_export[col_idx].map(fd_id_map)
                     elif type_var == 'Showdown':
@@ -1169,12 +1175,12 @@ if selected_tab == 'Optimals':
                 with pm_opt_col:
                     if site_var == 'Draftkings':
                         if type_var == 'Regular':
-                            data_export = data_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                            data_export = data_export.set_index('PG').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                         elif type_var == 'Showdown':
                             data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                     elif site_var == 'Fanduel':
                         if type_var == 'Regular':
-                            data_export = data_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                            data_export = data_export.set_index('PG1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                         elif type_var == 'Showdown':
                             data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                     st.download_button(
@@ -1187,12 +1193,12 @@ if selected_tab == 'Optimals':
                     
                     if site_var == 'Draftkings':
                         if type_var == 'Regular':
-                            name_export = name_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                            name_export = name_export.set_index('PG').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                         elif type_var == 'Showdown':
                             name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                     elif site_var == 'Fanduel':
                         if type_var == 'Regular':
-                            name_export = name_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                            name_export = name_export.set_index('PG1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                         elif type_var == 'Showdown':
                             name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                     st.download_button(
@@ -1208,7 +1214,10 @@ if selected_tab == 'Optimals':
                 data_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
                 if site_var == 'Draftkings':
                     if type_var == 'Regular':
-                        map_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
+                        if sport_var == 'NBA':
+                            map_columns = ['PG', 'PG', 'SG', 'SG', 'SF', 'SF', 'PF', 'PF', 'C']
+                        elif sport_var == 'WNBA':
+                            map_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
                         for col_idx in map_columns:
                             data_export[col_idx] = data_export[col_idx].map(dk_id_map)
                     elif type_var == 'Showdown':
@@ -1218,7 +1227,10 @@ if selected_tab == 'Optimals':
                     
                 elif site_var == 'Fanduel':
                     if type_var == 'Regular':
-                        map_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
+                        if sport_var == 'NBA':
+                            map_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
+                        elif sport_var == 'WNBA':
+                            map_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
                         for col_idx in map_columns:
                             data_export[col_idx] = data_export[col_idx].map(fd_id_map)
                     elif type_var == 'Showdown':
@@ -1255,12 +1267,12 @@ if selected_tab == 'Optimals':
                 with pm_opt_col:
                     if site_var == 'Draftkings':
                         if type_var == 'Regular':
-                            data_export = data_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                            data_export = data_export.set_index('PG').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                         elif type_var == 'Showdown':
                             data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                     elif site_var == 'Fanduel':
                         if type_var == 'Regular':
-                            data_export = data_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                            data_export = data_export.set_index('PG1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                         elif type_var == 'Showdown':
                             data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                     st.download_button(
@@ -1273,12 +1285,12 @@ if selected_tab == 'Optimals':
                     
                     if site_var == 'Draftkings':
                         if type_var == 'Regular':
-                            name_export = name_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                            name_export = name_export.set_index('PG').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                         elif type_var == 'Showdown':
                             name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                     elif site_var == 'Fanduel':
                         if type_var == 'Regular':
-                            name_export = name_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                            name_export = name_export.set_index('PG1').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                         elif type_var == 'Showdown':
                             name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                     st.download_button(
@@ -1330,7 +1342,10 @@ if selected_tab == 'Optimals':
     name_export = st.session_state.data_export_display.copy()
     if site_var == 'Draftkings':
         if type_var == 'Regular':
-            map_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
+            if sport_var == 'NBA':
+                map_columns = ['PG', 'PG', 'SG', 'SG', 'SF', 'SF', 'PF', 'PF', 'C']
+            elif sport_var == 'WNBA':
+                map_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
             for col_idx in map_columns:
                 export_file[col_idx] = export_file[col_idx].map(dk_id_map)
         elif type_var == 'Showdown':
@@ -1339,7 +1354,10 @@ if selected_tab == 'Optimals':
                 export_file[col_idx] = export_file[col_idx].map(dk_sd_id_map)
     elif site_var == 'Fanduel':
         if type_var == 'Regular':
-            map_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
+            if sport_var == 'NBA':
+                map_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']
+            elif sport_var == 'WNBA':
+                map_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
             for col_idx in map_columns:
                 export_file[col_idx] = export_file[col_idx].map(fd_id_map)
         elif type_var == 'Showdown':