Refactor load_contest_file function to enhance player data processing

- Updated the logic for splitting player entries to handle empty strings more effectively, ensuring cleaner data output.
- Adjusted the column renaming for MLB to start from index 0, improving consistency with player position assignments.
- These changes contribute to ongoing efforts to improve data integrity and user experience within the application.

global_func/load_contest_file.py

@@ -26,8 +26,6 @@ def load_contest_file(upload, sport):
             df['EntryCount'] = df['EntryCount'].fillna('1/1')  # Default to 1/1 if no entry count
             
             # Split the lineup string by replacing position indicators with commas
-            # We need to ensure we only replace position indicators that are at the start of a player entry
-            # and not those that might appear within player names
             df['Lineup'] = df['Lineup'].str.replace(r'\b(' + '|'.join(pos_values) + r')\b', r'\1,', regex=True)
             
             # Split into individual columns and remove position indicators
@@ -39,13 +37,16 @@ def load_contest_file(upload, sport):
                 return None
                 
             # Create columns for each player
-            for i in range(1, max_players):
-                df[i] = df['Lineup'].str.split(',').str[i].str.strip()
+            for i in range(max_players):
+                # Get the player at this position, or empty string if not present
+                df[i] = df['Lineup'].str.split(',').str[i].fillna('').str.strip()
                 # Remove position indicators from the end of each entry
                 df[i] = df[i].str.replace(r'\s+(' + '|'.join(pos_values) + r')$', '', regex=True)
+                # Replace empty strings with a meaningful value if desired
+                # df[i] = df[i].replace('', 'Empty')  # Uncomment if you want to use 'Empty' instead of empty strings
             
             if sport == 'MLB':
-                df = df.rename(columns={1: '1B', 2: '2B', 3: '3B', 4: 'C', 5: 'OF1', 6: 'OF2', 7: 'OF3', 8: 'SP1', 9: 'SP2', 10: 'SS'})
+                df = df.rename(columns={0: '1B', 1: '2B', 2: '3B', 3: 'C', 4: 'OF1', 5: 'OF2', 6: 'OF3', 7: 'SP1', 8: 'SP2', 9: 'SS'})
             try:
                 df['Own'] = df['Own'].str.replace('%', '').astype(float)
             except: