Refactor optimal lineup export logic to apply salary and team count filters before converting player names to IDs, ensuring accurate data processing for DraftKings and FanDuel. Update column mappings for NBA and WNBA to reflect new player positions.

src/streamlit_app.py

@@ -1168,7 +1168,7 @@ if selected_tab == 'Optimals':
                 if site_var == 'Draftkings':
                     if type_var == 'Regular':
                         if sport_var == 'NBA':
-                            map_columns = ['PG', 'PG', 'SG', 'SG', 'SF', 'SF', 'PF', 'PF', 'C']
+                            map_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
                         elif sport_var == 'WNBA':
                             map_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
                         for col_idx in map_columns:
@@ -1182,7 +1182,7 @@ if selected_tab == 'Optimals':
                         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']
+                            map_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4']
                         for col_idx in map_columns:
                             data_export[col_idx] = data_export[col_idx].map(fd_id_map)
                     elif type_var == 'Showdown':
@@ -1245,10 +1245,35 @@ if selected_tab == 'Optimals':
             if st.button("Prepare full data export (Filtered)", key='data_export_filtered_button'):
                 name_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
                 data_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
+                
+                # Apply filters BEFORE converting to IDs
+                data_export = data_export[data_export['salary'] >= salary_min_var]
+                data_export = data_export[data_export['salary'] <= salary_max_var]
+                data_export = data_export[data_export['Team_count'] >= min_stacks_var]
+                data_export = data_export[data_export['Team_count'] <= max_stacks_var]
+                if site_var == 'Draftkings':
+                    data_export = map_mask_parse(data_export, dict(zip(raw_baselines['Player'], raw_baselines['Median'])), projection_var, site_var)
+                    data_export = map_mask_parse(data_export, dict(zip(raw_baselines['Player'], raw_baselines['Own'])), ownership_var, site_var)
+                elif site_var == 'Fanduel':
+                    data_export = map_mask_parse(data_export, dict(zip(raw_baselines['Player'], raw_baselines['Median'])), projection_var, site_var)
+                    data_export = map_mask_parse(data_export, dict(zip(raw_baselines['Player'], raw_baselines['Own'])), ownership_var, site_var)
+
+                name_export = name_export[name_export['salary'] >= salary_min_var]
+                name_export = name_export[name_export['salary'] <= salary_max_var]
+                name_export = name_export[name_export['Team_count'] >= min_stacks_var]
+                name_export = name_export[name_export['Team_count'] <= max_stacks_var]
+                if site_var == 'Draftkings':
+                    name_export = map_mask_parse(name_export, dict(zip(raw_baselines['Player'], raw_baselines['Median'])), projection_var, site_var)
+                    name_export = map_mask_parse(name_export, dict(zip(raw_baselines['Player'], raw_baselines['Own'])), ownership_var, site_var)
+                elif site_var == 'Fanduel':
+                    name_export = map_mask_parse(name_export, dict(zip(raw_baselines['Player'], raw_baselines['Median'])), projection_var, site_var)
+                    name_export = map_mask_parse(name_export, dict(zip(raw_baselines['Player'], raw_baselines['Own'])), ownership_var, site_var)
+                
+                # NOW convert player names to IDs after filtering
                 if site_var == 'Draftkings':
                     if type_var == 'Regular':
                         if sport_var == 'NBA':
-                            map_columns = ['PG', 'PG', 'SG', 'SG', 'SF', 'SF', 'PF', 'PF', 'C']
+                            map_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
                         elif sport_var == 'WNBA':
                             map_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
                         for col_idx in map_columns:
@@ -1263,35 +1288,13 @@ if selected_tab == 'Optimals':
                         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']
+                            map_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4']
                         for col_idx in map_columns:
                             data_export[col_idx] = data_export[col_idx].map(fd_id_map)
                     elif type_var == 'Showdown':
                         map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
                         for col_idx in map_columns:
                             data_export[col_idx] = data_export[col_idx].map(fd_sd_id_map)
-                    
-                data_export = data_export[data_export['salary'] >= salary_min_var]
-                data_export = data_export[data_export['salary'] <= salary_max_var]
-                data_export = data_export[data_export['Team_count'] >= min_stacks_var]
-                data_export = data_export[data_export['Team_count'] <= max_stacks_var]
-                if site_var == 'Draftkings':
-                    data_export = map_mask_parse(data_export, dict(zip(raw_baselines['Player'], raw_baselines['Median'])), projection_var, site_var)
-                    data_export = map_mask_parse(data_export, dict(zip(raw_baselines['Player'], raw_baselines['Own'])), ownership_var, site_var)
-                elif site_var == 'Fanduel':
-                    data_export = map_mask_parse(data_export, dict(zip(raw_baselines['Player'], raw_baselines['Median'])), projection_var, site_var)
-                    data_export = map_mask_parse(data_export, dict(zip(raw_baselines['Player'], raw_baselines['Own'])), ownership_var, site_var)
-
-                name_export = name_export[name_export['salary'] >= salary_min_var]
-                name_export = name_export[name_export['salary'] <= salary_max_var]
-                name_export = name_export[name_export['Team_count'] >= min_stacks_var]
-                name_export = name_export[name_export['Team_count'] <= max_stacks_var]
-                if site_var == 'Draftkings':
-                    name_export = map_mask_parse(name_export, dict(zip(raw_baselines['Player'], raw_baselines['Median'])), projection_var, site_var)
-                    name_export = map_mask_parse(name_export, dict(zip(raw_baselines['Player'], raw_baselines['Own'])), ownership_var, site_var)
-                elif site_var == 'Fanduel':
-                    name_export = map_mask_parse(name_export, dict(zip(raw_baselines['Player'], raw_baselines['Median'])), projection_var, site_var)
-                    name_export = map_mask_parse(name_export, dict(zip(raw_baselines['Player'], raw_baselines['Own'])), ownership_var, site_var)
                 
                 reg_opt_col, pm_opt_col = st.columns(2)
                 with reg_opt_col:
@@ -1381,7 +1384,7 @@ if selected_tab == 'Optimals':
     if site_var == 'Draftkings':
         if type_var == 'Regular':
             if sport_var == 'NBA':
-                map_columns = ['PG', 'PG', 'SG', 'SG', 'SF', 'SF', 'PF', 'PF', 'C']
+                map_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
             elif sport_var == 'WNBA':
                 map_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
             for col_idx in map_columns:
@@ -1395,7 +1398,7 @@ if selected_tab == 'Optimals':
             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']
+                map_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4']
             for col_idx in map_columns:
                 export_file[col_idx] = export_file[col_idx].map(fd_id_map)
         elif type_var == 'Showdown':