def METHOD_NAME(*, df_list, year, config, **_):
    """
    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, ignore_index=True)
    fips = get_all_state_FIPS_2().reset_index(drop=True)
    # ensure capitalization of state names
    fips['State'] = fips['State'].apply(lambda x: x.title())
    fips['StateAbbrev'] = fips['State'].map(us_state_abbrev)
    # pad zeroes
    fips['FIPS_2'] = fips['FIPS_2'].apply(lambda x: x.ljust(3 + len(x), '0'))
    df = pd.merge(
        df, fips, how='left', left_on='State', right_on='StateAbbrev')
    # set us location code
    df.loc[df['State_x'] == 'US', 'FIPS_2'] = US_FIPS
    df = df.rename(columns={'FIPS_2': "Location"})
    assign_fips_location_system(df, year)
    df = df.drop(columns=['StateAbbrev', 'State_x', 'State_y'])
    ## Extract information for SEDS codes
    units = pd.read_excel(config['url']['activities_url'],
                          sheet_name='Codes_and_Descriptions',
                          header=10, usecols='B:D')
    units['FuelCode'] = units['MSN'].str[0:2]
    units['SectorCode'] = units['MSN'].str[2:4]
    units['UnitCode'] = units['MSN'].str[4:5]
    units = units.query("UnitCode not in ['D', 'K']")
    # get fuel names from Total Consumption and Industrial Consumption
    fuels = (units.query("SectorCode.isin(['TC', 'IC'])")
             .reset_index(drop=True))
    fuels['Fuel'] = (fuels.query(
        "Description.str.contains('total consumption')")
        .Description.str.split(' total consumption', expand=True)[0])
    fuels['FuelName2'] = (fuels.query(
        "Description.str.contains('consumed by')")
        .Description.str.split(' consumed by', expand=True)[0])
    fuels['Fuel'] = fuels['Fuel'].fillna(fuels['FuelName2'])
    fuels['Fuel'] = fuels['Fuel'].str.rstrip(',')
    fuels = (fuels[['Fuel','FuelCode']].dropna().sort_values(by='Fuel')
             .drop_duplicates(subset='FuelCode'))
    # get sector names
    sectors = units.copy()
    sectors['ActivityConsumedBy'] = (units.query(
        "Description.str.contains('consumed by')")
        .Description.str.split('consumed by the ', expand=True)[1]
        .str.strip())
    sectors = (sectors[['SectorCode', 'ActivityConsumedBy']].dropna()
               .sort_values(by='ActivityConsumedBy')
               .drop_duplicates(subset='SectorCode'))
    units = units.merge(fuels, how='left', on='FuelCode')
    units = units.merge(sectors, how='left', on='SectorCode')
    units = units.drop(columns=['FuelCode','SectorCode','UnitCode'])
    units['Description'] = units['MSN'] + ': ' + units['Description']
    df = df.merge(units, how='left', on='MSN')
    df = (df.rename(columns={year: "FlowAmount",
                            "Fuel": "FlowName"})
          .drop(columns=['Data_Status'])
          .dropna())
    # hard code data
    df['Class'] = np.where(df['Unit'].str.contains('Btu') |
                           df['Unit'].str.contains('watt'),
                           'Energy', 'Other')
    df['SourceName'] = 'EIA_SEDS'
    df['ActivityProducedBy'] = 'None'
    df['Year'] = year
    df['FlowType'] = 'TECHNOSPHERE_FLOW'
    # Fill in the rest of the Flow by fields so they show
    # "None" instead of nan.
    df['Compartment'] = 'None'
    df['MeasureofSpread'] = 'None'
    df['DistributionType'] = 'None'
    # Add DQ scores
    df['DataReliability'] = 5  # tmp
    df['DataCollection'] = 5  # tmp
    return df