def METHOD_NAME(*, df_list, year, **_):
    """
    Combine, parse, and format the provided dataframes
    :param df_list: list of dataframes to concat and format
    :param args: dictionary, used to run flowbyactivity.py
        ('year' and 'source')
    :return: df, parsed and partially formatted to flowbyactivity
        specifications
    """
    # concat dataframes
    df = pd.concat(df_list, sort=False)
    # Add year
    df['Year'] = year
    # drop puerto rico
    df = df[df['state'] != '72']
    if "county" in df.columns:
        # replace null county cells with '000'
        df['county'] = df['county'].fillna('000')
        # Make FIPS as a combo of state and county codes
        df['Location'] = df['state'] + df['county']
        # replace the null value representing the US with US fips
        if "us" in df.columns:
            df.loc[df['us'] == '1', 'Location'] = US_FIPS
            # drop columns
            df = df.drop(columns=['state', 'county', 'us'])
        else:
            df = df.drop(columns=['state', 'county'])
    else:
        df['Location'] = df['state'] + '000'
        if "us" in df.columns:
            df.loc[df['us'] == '1', 'Location'] = US_FIPS
            # drop columns
            df = df.drop(columns=['state', 'us'])
        else:
            df = df.drop(columns=['state'])
    # rename columns
    if year == "2020":
        df = df.rename(columns={"POP_2020": "FlowAmount"})
    elif year == "2021":
        df = df.rename(columns={"POP_2021": "FlowAmount"})
    else:
        df = df.rename(columns={"POP": "FlowAmount"})
    # add location system based on year of data
    df = assign_fips_location_system(df, year)
    # hardcode dta
    df['Class'] = 'Other'
    df['SourceName'] = 'Census_PEP_Population'
    df['FlowName'] = 'Population'
    df['Unit'] = 'p'
    df['ActivityConsumedBy'] = 'All'
    # temporary data quality scores
    df['DataReliability'] = 5  # tmp
    df['DataCollection'] = 5  # tmp
    # sort df
    df = df.sort_values(['Location'])
    # reset index
    df.reset_index(drop=True, inplace=True)
    return df