given two dataframes calculate the absolute error of mean values of the columns "NEVs" and "MWhT" grouped by the columns "time" and "Volt" and the mean absolute error of both dataframes

def calculate_errors(df_model, df_measurements):
  df_model_agg = df_model.groupby(["time", "Volt"]).agg({"MWhT": "mean", "NEVs": "mean"})
  df_measurements_agg = df_measurements.groupby(["time", "Volt"]).agg({"MWhT": "mean", "NEVs": "mean"})

  df_abs_error = df_model_agg - df_measurements_agg
  df_abs_error["abs_error"] = df_abs_error.apply(lambda x: abs(x["MWhT"]) + abs(x["NEVs"]), axis=1)
  df_abs_error_mean = df_abs_error.mean()
  mean_abs_error = df_abs_error_mean["abs_error"]

  return mean_abs_error