average duplicate measurements. This requires that IDs and norrec labels
    were assigned using the *assign_norrec_to_df* function.

    Parameters
    ----------
    df
        DataFrame
    keys_mean: list
        list of keys to average. For all other keys the first entry will be
        used.

def average_repetitions(df, keys_mean):
    """average duplicate measurements. This requires that IDs and norrec labels
    were assigned using the *assign_norrec_to_df* function.

    Parameters
    ----------
    df
        DataFrame
    keys_mean: list
        list of keys to average. For all other keys the first entry will be
        used.
    """
    if 'norrec' not in df.columns:
        raise Exception(
            'The "norrec" column is required for this function to work!'
        )

    # Get column order to restore later
    cols = list(df.columns.values)

    keys_keep = list(set(df.columns.tolist()) - set(keys_mean))
    agg_dict = {x: _first for x in keys_keep}
    agg_dict.update({x: np.mean for x in keys_mean})
    for key in ('id', 'timestep', 'frequency', 'norrec'):
        if key in agg_dict:
            del(agg_dict[key])
    # print(agg_dict)

    # average over duplicate measurements
    extra_dimensions_raw = ['id', 'norrec', 'frequency', 'timestep']
    extra_dimensions = [x for x in extra_dimensions_raw if x in df.columns]
    df = df.groupby(extra_dimensions).agg(agg_dict)
    df.reset_index(inplace=True)
    return df[cols]