Aiming to remedy the missing players from the projections in exposure management functions

app.py

@@ -1655,6 +1655,16 @@ if selected_tab == 'Data Load':
             buffer = io.BytesIO()
             st.session_state['portfolio'].to_parquet(buffer, compression='snappy')
             st.session_state['origin_portfolio'] = buffer.getvalue()
+
+            portfolio_inc_proj = pd.DataFrame()
+            portfolio_inc_proj['player_names'] = get_portfolio_names(st.session_state['portfolio'])
+            portfolio_inc_proj['position'] = portfolio_inc_proj['player_names'].map(lambda x: st.session_state['map_dict']['pos_map'].get(x, 'FLEX'))
+            portfolio_inc_proj['team'] = portfolio_inc_proj['player_names'].map(lambda x: st.session_state['map_dict']['team_map'].get(x, 'Unknown'))
+            portfolio_inc_proj['salary'] = portfolio_inc_proj['player_names'].map(lambda x: st.session_state['map_dict']['salary_map'].get(x, 0))
+            portfolio_inc_proj['median'] = portfolio_inc_proj['player_names'].map(lambda x: st.session_state['map_dict']['proj_map'].get(x, 0.0))
+            portfolio_inc_proj['ownership'] = portfolio_inc_proj['player_names'].map(lambda x: st.session_state['map_dict']['own_map'].get(x, 0.0))
+            portfolio_inc_proj['captain ownership'] = portfolio_inc_proj['player_names'].map(lambda x: st.session_state['map_dict']['own_map'].get(x, 0.0))
+            st.session_state['portfolio_inc_proj'] = portfolio_inc_proj.reset_index(drop=True)
             
             del st.session_state['portfolio'], st.session_state['export_portfolio']
 
@@ -2165,7 +2175,7 @@ if selected_tab == 'Manage Portfolio':
                         elif preset_choice == 'Volatility (Heavy Lineup Edge)':
                             parsed_frame = volatility_preset(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var)
                         elif preset_choice == 'Hedge Chalk (Manage Leverage)':
-                            parsed_frame = hedging_preset(st.session_state['working_frame'], lineup_target, st.session_state['projections_df'], sport_var)
+                            parsed_frame = hedging_preset(st.session_state['working_frame'], lineup_target, st.session_state['portfolio_inc_proj'], sport_var)
                         elif preset_choice == 'Reduce Volatility (Manage Own)':
                             parsed_frame = reduce_volatility_preset(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var)
 
@@ -2181,7 +2191,7 @@ if selected_tab == 'Manage Portfolio':
                         elif preset_choice == 'Volatility (Heavy Lineup Edge)':
                             parsed_frame = volatility_preset(st.session_state['export_base'], lineup_target, excluded_cols, sport_var)
                         elif preset_choice == 'Hedge Chalk (Manage Leverage)':
-                            parsed_frame = hedging_preset(st.session_state['export_base'], lineup_target, st.session_state['projections_df'], sport_var)
+                            parsed_frame = hedging_preset(st.session_state['export_base'], lineup_target, st.session_state['portfolio_inc_proj'], sport_var)
                         elif preset_choice == 'Reduce Volatility (Manage Own)':
                             parsed_frame = reduce_volatility_preset(st.session_state['export_base'], lineup_target, excluded_cols, sport_var)
 
@@ -2453,7 +2463,7 @@ if selected_tab == 'Manage Portfolio':
                         st.session_state['settings_base'] = False
                         # Prepare DataFrame for exposure_spread to avoid categorical issues
                         working_frame_prepared = prepare_dataframe_for_exposure_spread(st.session_state['working_frame'], st.session_state['player_columns'])
-                        parsed_frame = exposure_spread(working_frame_prepared, st.session_state['exposure_player'], exposure_target, comp_salary_below, comp_salary_above, ignore_stacks, remove_teams_exposure, specific_replacements, specific_columns, st.session_state['projections_df'], sport_var, type_var, salary_max, stacking_sports)
+                        parsed_frame = exposure_spread(working_frame_prepared, st.session_state['exposure_player'], exposure_target, comp_salary_below, comp_salary_above, ignore_stacks, remove_teams_exposure, specific_replacements, specific_columns, st.session_state['portfolio_inc_proj'], sport_var, type_var, salary_max, stacking_sports)
                         
                         # Use consolidated calculation function
                         parsed_frame = calculate_lineup_metrics(
@@ -2462,14 +2472,14 @@ if selected_tab == 'Manage Portfolio':
                             st.session_state['map_dict'], 
                             type_var, 
                             sport_var, 
-                            st.session_state['projections_df']
+                            st.session_state['portfolio_inc_proj']
                         )
                         
                         st.session_state['working_frame'] = parsed_frame.reset_index(drop=True)
                         
                         # 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']))
+                        team_dict = dict(zip(st.session_state['portfolio_inc_proj']['player_names'], st.session_state['portfolio_inc_proj']['team']))
                         if 'Stack' in st.session_state['working_frame'].columns:
                             st.session_state['working_frame']['Stack'] = st.session_state['working_frame'].apply(
                                 lambda row: Counter(
@@ -2490,7 +2500,7 @@ if selected_tab == 'Manage Portfolio':
                         st.session_state['settings_base'] = False
                         # Prepare DataFrame for exposure_spread to avoid categorical issues
                         export_base_prepared = prepare_dataframe_for_exposure_spread(st.session_state['export_base'], st.session_state['player_columns'])
-                        parsed_frame = exposure_spread(export_base_prepared, st.session_state['exposure_player'], exposure_target, comp_salary_below, comp_salary_above, ignore_stacks, remove_teams_exposure, specific_replacements, specific_columns, st.session_state['projections_df'], sport_var, type_var, salary_max, stacking_sports)
+                        parsed_frame = exposure_spread(export_base_prepared, st.session_state['exposure_player'], exposure_target, comp_salary_below, comp_salary_above, ignore_stacks, remove_teams_exposure, specific_replacements, specific_columns, st.session_state['portfolio_inc_proj'], sport_var, type_var, salary_max, stacking_sports)
                         
                         # Use consolidated calculation function for export
                         parsed_frame = calculate_lineup_metrics(
@@ -2499,7 +2509,7 @@ if selected_tab == 'Manage Portfolio':
                             st.session_state['map_dict'], 
                             type_var, 
                             sport_var, 
-                            st.session_state['projections_df']
+                            st.session_state['portfolio_inc_proj']
                         )
 
                         
@@ -2507,7 +2517,7 @@ 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']))
+                        team_dict = dict(zip(st.session_state['portfolio_inc_proj']['player_names'], st.session_state['portfolio_inc_proj']['team']))
                         if 'Stack' in st.session_state['export_base'].columns:
                             st.session_state['export_base']['Stack'] = st.session_state['export_base'].apply(
                                 lambda row: Counter(