Refactor reassess_edge function in reassess_edge.py to improve efficiency by concatenating modified and base frames, simplifying the extraction of updated rows, and enhancing clarity in the logic for processing modified data.

global_func/reassess_edge.py

@@ -3,8 +3,8 @@ from global_func.predict_dupes import predict_dupes
 
 def reassess_edge(modified_frame: pd.DataFrame, base_frame: pd.DataFrame, maps_dict: dict, site_var: str, type_var: str, Contest_Size: int, strength_var: str, sport_var: str) -> pd.DataFrame:
     """
-    Reassess edge by re-inserting modified rows into the base frame, running predict_dupes,
-    and then extracting the updated modified rows.
+    Reassess edge by concatenating modified frame with base frame, running predict_dupes,
+    and then extracting the first N rows (where N is the length of modified_frame).
     
     Args:
         modified_frame: DataFrame with rows that were modified by exposure_spread
@@ -19,37 +19,16 @@ def reassess_edge(modified_frame: pd.DataFrame, base_frame: pd.DataFrame, maps_d
     Returns:
         DataFrame: Updated modified_frame with recalculated metrics
     """
-    # Create a copy of the base frame
-    combined_frame = base_frame.copy()
+    # Store the number of rows in the modified frame
+    num_modified_rows = len(modified_frame)
     
-    # Get the player columns (excluding salary, median, Own)
-    player_columns = [col for col in modified_frame.columns if col not in ['salary', 'median', 'Own']]
-    
-    # For each modified row, find and replace the corresponding row in the combined frame
-    for idx, modified_row in modified_frame.iterrows():
-        # Find the row in combined_frame that matches the player composition
-        for combined_idx, combined_row in combined_frame.iterrows():
-            if all(modified_row[col] == combined_row[col] for col in player_columns):
-                # Replace the row with the modified version
-                combined_frame.loc[combined_idx] = modified_row
-                break
+    # Concatenate the modified frame with the base frame
+    combined_frame = pd.concat([modified_frame, base_frame], ignore_index=True)
     
     # Run predict_dupes on the combined frame
     updated_combined_frame = predict_dupes(combined_frame, maps_dict, site_var, type_var, Contest_Size, strength_var, sport_var)
     
-    # Extract the updated modified rows
-    updated_modified_rows = []
-    for idx, modified_row in modified_frame.iterrows():
-        for combined_idx, combined_row in updated_combined_frame.iterrows():
-            if all(modified_row[col] == combined_row[col] for col in player_columns):
-                updated_modified_rows.append(combined_row)
-                break
+    # Extract the first N rows (which correspond to our modified frame)
+    result_frame = updated_combined_frame.head(num_modified_rows).copy()
     
-    # Convert back to DataFrame
-    if updated_modified_rows:
-        result_frame = pd.DataFrame(updated_modified_rows)
-        result_frame = result_frame.reset_index(drop=True)
-        return result_frame
-    else:
-        # If no matches found, return the original modified_frame
-        return modified_frame
+    return result_frame