def METHOD_NAME(df: Table) -> Table:
    """
    Pivoting the table so that we can have a time-series of the guinea worm status and how it has changed over time
    """
    df_time = df.iloc[:, 0:24].drop(df.columns[[1]], axis=1)
    df_time.columns = df_time.columns.str.replace("_", "")
    years = df_time.drop("country", axis=1).columns.values
    df_piv = pd.melt(df_time, id_vars="country", value_vars=years)
    df_piv = df_piv.replace(
        {
            "value": {
                "Countries at precertification stage": "Pre-certification",
                "Previously endemic countries certified free of dracunculiasis": "Certified disease free",
                "Certified free of dracunculiasis": "Certified disease free",
                "Countries not known to have dracunculiasis but yet to be certified": "Pending surveillance",
                "Endemic for dracunculiasis": "Endemic",
            }
        }
    )
    df_piv = df_piv.rename(columns={"variable": "year", "value": "certification_status"})
    return df_piv