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
    """
    df = pd.concat(df_list, sort=False)
    # # specify desired data based on domain_desc
    df = df[df['domain_desc'].str.contains("INVENTORY|TOTAL")]
    df = df[~df['domain_desc'].str.contains(
        "ECONOMIC CLASS|NAICS|FARM SALES|AREA OPERATED")]
    # drop any specialized production practices
    df = df[df['prodn_practice_desc'] == 'ALL PRODUCTION PRACTICES']
    # drop specialized class descriptions
    df = df[~df['class_desc'].str.contains("BREEDING|MARKET")]
    # drop unused columns
    df = df.drop(columns=['agg_level_desc', 'location_desc', 'state_alpha',
                          'sector_desc', 'country_code', 'begin_code',
                          'watershed_code', 'reference_period_desc',
                          'asd_desc', 'county_name', 'source_desc',
                          'congr_district_code', 'asd_code', 'week_ending',
                          'freq_desc', 'load_time', 'zip_5',
                          'watershed_desc', 'region_desc', 'state_ansi',
                          'state_name', 'country_name', 'county_ansi',
                          'end_code', 'group_desc', 'util_practice_desc'])
    # create FIPS column by combining existing columns
    df.loc[df['county_code'] == '', 'county_code'] = '000'
    df['Location'] = df['state_fips_code'] + df['county_code']
    df.loc[df['Location'] == '99000', 'Location'] = US_FIPS
    # combine column information to create activity information,
    # and create two new columns for activities
    # drop this column later
    df['ActivityProducedBy'] = df['commodity_desc'] + ', ' + df['class_desc']
    # not interested in all class_desc data
    df['ActivityProducedBy'] = df['ActivityProducedBy'].str.replace(
        ", ALL CLASSES", "", regex=True)
    # rename columns to match flowbyactivity format
    df = df.rename(columns={"Value": "FlowAmount",
                            "unit_desc": "FlowName",
                            "year": "Year",
                            "CV (%)": "Spread",
                            "domaincat_desc": "Compartment",
                            "short_desc": "Description"})
    # drop remaining unused columns
    df = df.drop(columns=['class_desc', 'commodity_desc', 'state_fips_code',
                          'county_code', 'statisticcat_desc',
                          'prodn_practice_desc'])
    # modify contents of flowamount column, "D" is supressed data,
    # "z" means less than half the unit is shown
    df['FlowAmount'] = df['FlowAmount'].str.strip()  # trim whitespace
    df.loc[df['FlowAmount'] == "(D)", 'FlowAmount'] = WITHDRAWN_KEYWORD
    # df.loc[df['FlowAmount'] == "(Z)", 'FlowAmount'] = withdrawn_keyword
    df['FlowAmount'] = df['FlowAmount'].str.replace(",", "", regex=True)
    # # USDA CoA 2017 states that (H) means CV >= 99.95,
    # therefore replacing with 99.95 so can convert column to int
    # # (L) is a CV of <= 0.05
    df['Spread'] = df['Spread'].str.strip()  # trim whitespace
    df.loc[df['Spread'] == "(H)", 'Spread'] = 99.95
    df.loc[df['Spread'] == "(L)", 'Spread'] = 0.05
    df.loc[df['Spread'] == "", 'Spread'] = None
    df.loc[df['Spread'] == "(D)", 'Spread'] = WITHDRAWN_KEYWORD
    # add location system based on year of data
    df = assign_fips_location_system(df, year)
    # # Add hardcoded data
    df['Class'] = "Other"
    df['SourceName'] = "USDA_CoA_Livestock"
    df['Unit'] = "p"
    df['MeasureofSpread'] = "RSD"
    df['DataReliability'] = 5  # tmp
    df['DataCollection'] = 2
    return df