Optimize ownership calculation in reassess_edge function by directly applying calculate_weighted_ownership_wrapper to selected lineups, improving performance and readability.

global_func/reassess_edge.py

@@ -89,7 +89,7 @@ def reassess_edge(refactored_frame: pd.DataFrame, original_frame: pd.DataFrame,
         refactored_df.loc[lineups, 'Finish_percentile'] = refactored_df.loc[lineups, 'Finish_percentile']
         refactored_df.loc[lineups, 'Win%'] = refactored_df.loc[lineups, 'Win%']
         refactored_df.loc[lineups, 'Edge'] = reassess_lineup_edge(refactored_df.loc[lineups, :], Contest_Size)
-        refactored_df.loc[lineups, 'Weighted Own'] = refactored_df[own_columns].apply(calculate_weighted_ownership_wrapper, axis=1)
+        refactored_df.loc[lineups, 'Weighted Own'] = calculate_weighted_ownership_wrapper(refactored_df.loc[lineups, own_columns])
         refactored_df.loc[lineups, 'Geomean'] = np.power((refactored_df.loc[lineups, own_columns] * 100).product(axis=1), 1 / len(own_columns))
 
     return refactored_df