Remove redundant mapping and validation steps in DK and FD seed frame initialization functions to streamline data processing.

app.py

@@ -66,15 +66,6 @@ def init_DK_seed_frames(sharp_split):
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-        dict_columns = ['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']
-        # Map names
-        raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
-        
-        # Validate lineups against valid players
-        raw_display = validate_lineup_players(raw_display, valid_players, dict_columns)
-        
-        # Remove any remaining NaN values
-        raw_display = raw_display.dropna()
         DK_seed = raw_display.to_numpy()
 
         return DK_seed
@@ -97,12 +88,6 @@ def init_DK_secondary_seed_frames(sharp_split):
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-        dict_columns = ['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']
-        # Map names
-        raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
-        
-        # Validate lineups against valid players
-        raw_display = validate_lineup_players(raw_display, valid_players, dict_columns)
         
         # Remove any remaining NaN values
         raw_display = raw_display.dropna()
@@ -128,12 +113,6 @@ def init_FD_seed_frames(sharp_split):
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-        dict_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL']
-        # Map names
-        raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
-        
-        # Validate lineups against valid players
-        raw_display = validate_lineup_players(raw_display, valid_players, dict_columns)
         
         # Remove any remaining NaN values
         raw_display = raw_display.dropna()
@@ -159,12 +138,6 @@ def init_FD_secondary_seed_frames(sharp_split):
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-        dict_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL']
-        # Map names
-        raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
-        
-        # Validate lineups against valid players
-        raw_display = validate_lineup_players(raw_display, valid_players, dict_columns)
         
         # Remove any remaining NaN values
         raw_display = raw_display.dropna()