Refactor WNBA lineup column names in load_contest_file.py

- Updated the column names for WNBA lineups to use shorter identifiers ('G1', 'G2', 'F1', 'F2', 'F3') instead of longer names, improving readability and consistency in the DataFrame.
- Adjusted the regex replacement logic to ensure accurate parsing of player positions, contributing to better data integrity during lineup processing.

global_func/load_contest_file.py

@@ -114,7 +114,7 @@ def load_contest_file(upload, type, helper = None, sport = None):
                 elif sport == 'GOLF':
                     cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' P ', ' C ', '1B ', ' 2B ', ' 3B ', ' SS ', ' OF ', ' G ', 'G '], value=',', regex=True)
                 elif sport == 'WNBA':
-                    cleaned_df['Lineup'] = cleaned_df['Lineup'].replace(['F ', ' UTIL ', ' G ', ' F ', 'G '], value=',', regex=True)
+                    cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' G ', 'F ', ' F ', ' UTIL '], value=',', regex=True)
                 print(sport)
                 check_lineups = cleaned_df.copy()
                 if sport == 'MLB':
@@ -124,7 +124,7 @@ def load_contest_file(upload, type, helper = None, sport = None):
                 elif sport == 'GOLF':
                     cleaned_df[['Remove', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief', 'Buddy']] = cleaned_df['Lineup'].str.split(',', expand=True)
                 elif sport == 'WNBA':
-                    cleaned_df[['Remove', 'Forward1', 'Forward2', 'Forward3', 'Guard1', 'Guard2', 'UTIL']] = cleaned_df['Lineup'].str.split(',', expand=True)
+                    cleaned_df[['Remove', 'G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']] = cleaned_df['Lineup'].str.split(',', expand=True)
                 cleaned_df = cleaned_df.drop(columns=['Lineup', 'Remove'])
                 entry_counts = cleaned_df['BaseName'].value_counts()
                 cleaned_df['EntryCount'] = cleaned_df['BaseName'].map(entry_counts)
@@ -135,7 +135,7 @@ def load_contest_file(upload, type, helper = None, sport = None):
                 elif sport == 'GOLF':
                     cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief', 'Buddy']]
                 elif sport == 'WNBA':
-                    cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'Forward1', 'Forward2', 'Forward3', 'Guard1', 'Guard2', 'UTIL']]
+                    cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']]
             elif type == 'Showdown':
                 if sport == 'NHL':
                     cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' FLEX ', 'CPT '], value=',', regex=True)