Refactor data export logic in app.py: streamline the export process by consolidating column drop operations for Portfolio Manager exports and enhancing the mapping of player position columns. This update improves data clarity and ensures accurate filtering based on salary ranges.

app.py

@@ -627,9 +627,11 @@ 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
+
+                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)
                 reg_opt_col, pm_opt_col = st.columns(2)
+
                 with reg_opt_col:
                     st.download_button(
                         label="Export optimals set (IDs)",
@@ -644,28 +646,6 @@ with tab2:
                         mime='text/csv',
                     )
                 with pm_opt_col:
-                    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:
-                            data_export[col_idx] = data_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:
-                            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(pm_data_export),
@@ -690,11 +670,6 @@ with tab2:
                             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:
-                        if slate_type_var1 == 'Regular':
-                            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(dk_id_dict_sd)
                 elif site_var2 == 'Fanduel':
                     if slate_type_var1 == 'Regular':
                         if league_var == 'NBA':
@@ -703,19 +678,19 @@ with tab2:
                             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:
-                        if slate_type_var1 == 'Regular':
-                            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)
+                for col_idx in map_columns:
+                    if slate_type_var1 == 'Regular':
+                        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)
                 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]
-
-                data_export = data_export
-                name_export = name_export
+                
+                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)
 
                 reg_opt_col, pm_opt_col = st.columns(2)
                 with reg_opt_col:
@@ -732,32 +707,6 @@ with tab2:
                         mime='text/csv',
                     )
                 with pm_opt_col:
-                    if site_var2 == 'Draftkings':
-                        if slate_type_var1 == 'Regular':
-                            if league_var == 'NBA':
-                                data_export = data_export.drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
-                            elif league_var == 'WNBA':
-                                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)
-                    elif site_var2 == 'Fanduel':
-                        if slate_type_var1 == 'Regular':
-                            if league_var == 'NBA':
-                                data_export = data_export.drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
-                            elif league_var == 'WNBA':
-                                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(pm_data_export),