def METHOD_NAME():
    """Bring population prognosis from DemandRegio to Zensus grid"""
    cfg = egon.data.config.datasets()["society_prognosis"]
    local_engine = db.engine()
    # Input: Zensus2011 population data including the NUTS3-Code
    zensus_district = db.select_dataframe(
        f"""SELECT zensus_population_id, vg250_nuts3
        FROM {cfg['soucres']['map_zensus_vg250']['schema']}.
        {cfg['soucres']['map_zensus_vg250']['table']}
        WHERE zensus_population_id IN (
            SELECT id
        FROM {cfg['soucres']['zensus_population']['schema']}.
        {cfg['soucres']['zensus_population']['table']})""",
        index_col="zensus_population_id",
    )
    zensus = db.select_dataframe(
        f"""SELECT id, population
        FROM {cfg['soucres']['zensus_population']['schema']}.
        {cfg['soucres']['zensus_population']['table']}
        WHERE population > 0""",
        index_col="id",
    )
    zensus["nuts3"] = zensus_district.vg250_nuts3
    # Rename index
    zensus.index = zensus.index.rename("zensus_population_id")
    # Replace population value of uninhabited cells for calculation
    zensus.population = zensus.population.replace(-1, 0)
    # Calculate share of population per cell in nuts3-region
    zensus["share"] = (
        zensus.groupby(zensus.nuts3)
        .population.apply(lambda grp: grp / grp.sum())
        .fillna(0)
    )
    db.execute_sql(
        f"""DELETE FROM {cfg['target']['population_prognosis']['schema']}.
        {cfg['target']['population_prognosis']['table']}"""
    )
    # Scale to pogosis values from demandregio
    for year in [2035, 2050]:
        # Input: dataset on population prognosis on district-level (NUTS3)
        prognosis = db.select_dataframe(
            f"""SELECT nuts3, population
            FROM {cfg['soucres']['demandregio_population']['schema']}.
            {cfg['soucres']['demandregio_population']['table']}
            WHERE year={year}""",
            index_col="nuts3",
        )
        df = pd.DataFrame(
            zensus["share"]
            .mul(prognosis.population[zensus["nuts3"]].values)
            .replace(0, -1)
        ).rename({"share": "population"}, axis=1)
        df["year"] = year
        # Insert to database
        df.to_sql(
            cfg["target"]["population_prognosis"]["table"],
            schema=cfg["target"]["population_prognosis"]["schema"],
            con=local_engine,
            if_exists="append",
        )