code: def summary_df_from_list(results_list, names, **kwargs):
      
      for arr in results_list:
          assert arr.shape == (len(names),)
      df = pd.DataFrame(np.stack(results_list, axis=0))
      df.columns = names
      return summary_df(df, **kwargs)
docs: Make a panda data frame of the mean and std devs of each element of a
      list of 1d arrays, including the uncertainties on the values.
  
      This just converts the array to a DataFrame and calls summary_df on it.
  
      Parameters
      ----------
      results_list: list of 1d numpy arrays
          Must have same length as names.
      names: list of strs
          Names for the output df's columns.
      kwargs: dict, optional
          Keyword arguments to pass to summary_df.
  
      Returns
      -------
      df: MultiIndex DataFrame
          See summary_df docstring for more details.