def METHOD_NAME():
    pivot_df = pp.pivot(
        df=multiple_metrics_df,
        index=["dttm"],
        columns=["country"],
        aggregates={
            "sum_metric": {"operator": "sum"},
            "count_metric": {"operator": "sum"},
        },
    )
    """
                   count_metric    sum_metric
    country              UK US         UK US
    dttm
    2019-01-01            1  2          5  6
    2019-01-02            3  4          7  8
    """
    compared_df = pp.compare(
        pivot_df,
        source_columns=["count_metric"],
        compare_columns=["sum_metric"],
        compare_type=PPC.DIFF,
        drop_original_columns=True,
    )
    """
               difference__count_metric__sum_metric
    country                                      UK US
    dttm
    2019-01-01                                   -4 -4
    2019-01-02                                   -4 -4
    """
    flat_df = pp.flatten(compared_df)
    """
            dttm  difference__count_metric__sum_metric, UK  difference__count_metric__sum_metric, US
    0 2019-01-01                                        -4                                        -4
    1 2019-01-02                                        -4                                        -4
    """
    assert flat_df.equals(
        pd.DataFrame(
            data={
                "dttm": pd.to_datetime(["2019-01-01", "2019-01-02"]),
                FLAT_COLUMN_SEPARATOR.join(
                    ["difference__count_metric__sum_metric", "UK"]
                ): [-4, -4],
                FLAT_COLUMN_SEPARATOR.join(
                    ["difference__count_metric__sum_metric", "US"]
                ): [-4, -4],
            }
        )
    )