Refactor position filtering logic in create_position_export_dict to use DataFrame indexing for improved clarity and performance in filtering positions across various sports.

app.py

@@ -53,13 +53,13 @@ def create_position_export_dict(column_name, csv_file, site_var, type_var, sport
                     filtered_df = csv_file.copy()
                 elif position_filter == 'FLEX' or position_filter == 'UTIL':
                     if sport_var == 'NFL':
-                        filtered_df = csv_file['Position'].isin(['RB', 'WR', 'TE'])
+                        filtered_df = csv_file[csv_file['Position'].isin(['RB', 'WR', 'TE'])]
                     elif sport_var == 'SOC':
-                        filtered_df = csv_file['Position'].str.contains(['D', 'M', 'F'], na=False, regex=False)
+                        filtered_df = csv_file[csv_file['Position'].str.contains('D|M|F', na=False, regex=True)]
                     elif sport_var == 'NCAAF':
-                        filtered_df = csv_file['Position'].str.contains(['RB', 'WR'])
+                        filtered_df = csv_file[csv_file['Position'].str.contains('RB|WR', na=False, regex=True)]
                     elif sport_var == 'NHL':
-                        filtered_df = csv_file['Position'].str.contains(['C', 'W', 'D'], na=False, regex=False)
+                        filtered_df = csv_file[csv_file['Position'].str.contains('C|W|D', na=False, regex=True)]
                     else:
                         filtered_df = csv_file.copy()
                 elif position_filter == 'SFLEX':