Enhance seed frame initialization in app.py by adding name mapping for player positions in Draftkings and Fanduel functions, ensuring consistent data representation across all seed frames.

app.py

@@ -66,6 +66,9 @@ 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))
         DK_seed = raw_display.to_numpy()
 
         return DK_seed
@@ -88,6 +91,9 @@ 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))
         
         # Remove any remaining NaN values
         raw_display = raw_display.dropna()
@@ -113,6 +119,9 @@ 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']
+        # Map names
+        raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
         
         # Remove any remaining NaN values
         raw_display = raw_display.dropna()
@@ -138,6 +147,9 @@ 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']
+        # Map names
+        raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
         
         # Remove any remaining NaN values
         raw_display = raw_display.dropna()