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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def summary_df_from_list(results_list, names, **kwargs):
 """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. """

    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)