def METHOD_NAME(self, df_path: str):
    """Extend the results DataFrame with a column ``pareto_efficient`` which is ``True`` if the point is Pareto efficient.
    Args:
        df (pd.DataFrame): the input results DataFrame.
    """
    df = pd.read_csv(df_path)
    # Check if Multi-Objective Optimization was performed to save the pareto front
    objective_columns = [col for col in df.columns if col.startswith("objective")]
    if len(objective_columns) > 1:
        if pd.api.types.is_string_dtype(df[objective_columns[0]]):
            mask_no_failures = ~df[objective_columns[0]].str.startswith("F")
        else:
            mask_no_failures = np.ones(len(df), dtype=bool)
        objectives = -df.loc[mask_no_failures, objective_columns].values.astype(
            float
        )
        mask_pareto_front = non_dominated_set(objectives)
        df["pareto_efficient"] = False
        df.loc[mask_no_failures, "pareto_efficient"] = mask_pareto_front
        df.to_csv(df_path, index=False)