testing exposure management with specific column selections

app.py

@@ -1882,6 +1882,7 @@ if selected_tab == 'Manage Portfolio':
                         ignore_stacks = []
                     remove_teams_exposure = st.multiselect("Removed/Locked teams?", options=sorted(list(set(st.session_state['projections_df']['team'].unique()))), default=[])
                     specific_replacements = st.multiselect("Specific Replacements?", options=sorted(list(set(st.session_state['projections_df']['player_names'].unique()))), default=[])
+                    specific_columns = st.multiselect("Specific Positions?", options=sorted(list(st.session_state['working_frame'].columns)), default=[])
                     submitted_col, export_col = st.columns(2)
                     st.info("Portfolio Button applies to your overall Portfolio, Export button applies to your Custom Export")
                     with submitted_col:
@@ -1891,7 +1892,7 @@ if selected_tab == 'Manage Portfolio':
                     if reg_submitted:
                         st.session_state['settings_base'] = False
                         prior_frame = st.session_state['working_frame'].copy()
-                        parsed_frame = exposure_spread(st.session_state['working_frame'], st.session_state['exposure_player'], exposure_target, ignore_stacks, remove_teams_exposure, specific_replacements, st.session_state['projections_df'], sport_var, type_var, salary_max, stacking_sports)
+                        parsed_frame = exposure_spread(st.session_state['working_frame'], st.session_state['exposure_player'], exposure_target, ignore_stacks, remove_teams_exposure, specific_replacements, specific_columns, st.session_state['projections_df'], sport_var, type_var, salary_max, stacking_sports)
                         
                         if type_var == 'Classic':
                             if sport_var == 'CS2' or sport_var == 'LOL':
@@ -1984,7 +1985,7 @@ if selected_tab == 'Manage Portfolio':
                     elif exp_submitted:
                         st.session_state['settings_base'] = False
                         prior_frame = st.session_state['export_base'].copy()
-                        parsed_frame = exposure_spread(st.session_state['export_base'], st.session_state['exposure_player'], exposure_target, ignore_stacks, remove_teams_exposure, specific_replacements, st.session_state['projections_df'], sport_var, type_var, salary_max, stacking_sports)
+                        parsed_frame = exposure_spread(st.session_state['export_base'], st.session_state['exposure_player'], exposure_target, ignore_stacks, remove_teams_exposure, specific_replacements, specific_columns, st.session_state['projections_df'], sport_var, type_var, salary_max, stacking_sports)
                         
                         if type_var == 'Classic':
                             if sport_var == 'CS2' or sport_var == 'LOL':

global_func/exposure_spread.py

@@ -148,7 +148,7 @@ def check_position_eligibility(sport, column_name, player_positions):
         # Default fallback - assume exact position match
         return column_name in player_positions
 
-def exposure_spread(working_frame, exposure_player, exposure_target, ignore_stacks, remove_teams, specific_replacements, projections_df, sport_var, type_var, salary_max, stacking_sports):
+def exposure_spread(working_frame, exposure_player, exposure_target, ignore_stacks, remove_teams, specific_replacements, specific_columns, projections_df, sport_var, type_var, salary_max, stacking_sports):
     comparable_players = projections_df[projections_df['player_names'] == exposure_player]
     
     comparable_players = comparable_players.reset_index(drop=True)
@@ -172,12 +172,23 @@ def exposure_spread(working_frame, exposure_player, exposure_target, ignore_stac
         return any(pos in target_positions for pos in player_pos_list)
 
     # find the exposure rate of the player in the working frame
-    player_mask = working_frame[working_frame.columns].apply(
-        lambda row: exposure_player in list(row), axis=1
-    )
-    replace_mask = working_frame.apply(
-        lambda row: exposure_player not in list(row), axis=1
-    )
+    if specific_columns != []:
+        player_mask = working_frame[specific_columns].apply(
+            lambda row: exposure_player in list(row), axis=1
+        )
+    else:
+        player_mask = working_frame[working_frame.columns].apply(
+            lambda row: exposure_player in list(row), axis=1
+        )
+    
+    if specific_columns != []:
+        replace_mask = working_frame[specific_columns].apply(
+                lambda row: exposure_player not in list(row), axis=1
+            )
+    else:
+        replace_mask = working_frame[working_frame.columns].apply(
+            lambda row: exposure_player not in list(row), axis=1
+        )
 
     player_exposure = player_mask.sum() / len(working_frame)
     replace_exposure = replace_mask.sum() / len(working_frame)