Refactor data export logic in app.py: streamline the export process by removing redundant mapping for player position columns and ensuring accurate filtering of entries based on salary range. Update Portfolio Manager exports to reflect these changes, enhancing data clarity and user experience.

app.py

@@ -627,6 +627,8 @@ with tab2:
                             data_export[col_idx] = data_export[col_idx].map(id_dict)
                         elif slate_type_var1 == 'Showdown':
                             data_export[col_idx] = data_export[col_idx].map(fd_id_dict_sd)
+                name_export = name_export
+                data_export = data_export
                 reg_opt_col, pm_opt_col = st.columns(2)
                 with reg_opt_col:
                     st.download_button(
@@ -662,36 +664,17 @@ with tab2:
                             map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4']
                         for col_idx in map_columns:
                             data_export[col_idx] = data_export[col_idx].map(id_dict)
+                    pm_name_export = name_export.drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    pm_data_export = data_export.drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                     st.download_button(
                         label="Portfolio Manager Export (IDs)",
-                        data=convert_pm_df(data_export),
+                        data=convert_pm_df(pm_data_export),
                         file_name='NBA_optimals_export.csv',
                         mime='text/csv',
                     )
-                    
-                    if site_var2 == 'Draftkings':
-                        if slate_type_var1 == 'Regular':
-                            if league_var == 'NBA':
-                                map_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
-                            elif league_var == 'WNBA':
-                                map_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
-                        elif slate_type_var1 == 'Showdown':
-                            map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
-                        for col_idx in map_columns:
-                            name_export[col_idx] = name_export[col_idx].map(id_dict)
-                    elif site_var2 == 'Fanduel':
-                        if slate_type_var1 == 'Regular':
-                            if league_var == 'NBA':
-                                map_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'C2', 'UTIL']
-                            elif league_var == 'WNBA':
-                                map_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4']
-                        elif slate_type_var1 == 'Showdown':
-                            map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4']
-                        for col_idx in map_columns:
-                            name_export[col_idx] = name_export[col_idx].map(id_dict)
                     st.download_button(
                         label="Portfolio Manager Export (Names)",
-                        data=convert_pm_df(name_export),
+                        data=convert_pm_df(pm_name_export),
                         file_name='NBA_optimals_export.csv',
                         mime='text/csv',
                     )
@@ -730,7 +713,10 @@ with tab2:
 
                 name_export = name_export[name_export['salary'] >= salary_min_var]
                 name_export = name_export[name_export['salary'] <= salary_max_var]
-                
+
+                data_export = data_export
+                name_export = name_export
+
                 reg_opt_col, pm_opt_col = st.columns(2)
                 with reg_opt_col:
                     st.download_button(
@@ -762,32 +748,25 @@ with tab2:
                                 data_export = data_export.drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                         elif slate_type_var1 == 'Showdown':
                             data_export = data_export.drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    
+                    data_export = data_export[data_export['salary'] >= salary_min_var]
+                    data_export = data_export[data_export['salary'] <= salary_max_var]
+
+                    name_export = name_export[name_export['salary'] >= salary_min_var]
+                    name_export = name_export[name_export['salary'] <= salary_max_var]
+
+                    pm_name_export = name_export.drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    pm_data_export = data_export.drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+
                     st.download_button(
                         label="Portfolio Manager Export (IDs)",
-                        data=convert_pm_df(data_export),
+                        data=convert_pm_df(pm_data_export),
                         file_name='NBA_optimals_export.csv',
                         mime='text/csv',
                     )
-                    
-                    if site_var2 == 'Draftkings':
-                        if slate_type_var1 == 'Regular':
-                            if league_var == 'NBA':
-                                name_export = name_export.drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
-                            elif league_var == 'WNBA':
-                                name_export = name_export.drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
-                        elif slate_type_var1 == 'Showdown':
-                            name_export = name_export.drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
-                    elif site_var2 == 'Fanduel':
-                        if slate_type_var1 == 'Regular':
-                            if league_var == 'NBA':
-                                name_export = name_export.drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
-                            elif league_var == 'WNBA':
-                                name_export = name_export.drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
-                        elif slate_type_var1 == 'Showdown':
-                            name_export = name_export.drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                     st.download_button(
                         label="Portfolio Manager Export (Names)",
-                        data=convert_pm_df(name_export),
+                        data=convert_pm_df(pm_name_export),
                         file_name='NBA_optimals_export.csv',
                         mime='text/csv',
                     )