def METHOD_NAME(*, df_list, **_):
    """
    Combine, parse, and format the provided dataframes
    :param df_list: list of dataframes to concat and format
    :return: df, parsed and partially formatted to flowbyactivity
        specifications
    """
    # concat dataframes
    df = pd.concat(df_list, sort=False)
    # drop columns
    df = df.drop(columns=['COORDINATE', 'DECIMALS', 'DGUID', 'SYMBOL',
                          'TERMINATED', 'UOM_ID', 'SCALAR_ID', 'VECTOR'])
    # rename columns
    df = df.rename(columns={'GEO': 'Location',
                            'North American Industry Classification System '
                            '(NAICS)': 'Description',
                            'REF_DATE': 'Year',
                            'STATUS': 'Spread',
                            'VALUE': "FlowAmount",
                            'Labour force characteristics': 'FlowName'})
    # limit data to pull
    df = df[df['Location'] == 'Canada']
    df = df[df['Sex'] == 'Both sexes']
    df = df[df['Age group'] == '15 years and over']
    df = df[df['FlowName'] == 'Employment'].reset_index(drop=True)
    df.loc[:, 'FlowAmount'] = df['FlowAmount'] * 10000
    df.loc[:, 'Unit'] = 'p'
    df = df.drop(columns=['Sex', 'Age group', 'UOM', 'SCALAR_FACTOR'])
    # extract NAICS as activity column. rename activity based on flowname
    df['Activity'] = df['Description'].str.extract('.*\[(.*)\].*')
    df.loc[df['Description'] == 'Total, all industries', 'Activity'] = '31-33'
    df.loc[df['Description'] ==
           'Other manufacturing industries', 'Activity'] = 'Other'
    df['FlowName'] = df['FlowName'].str.strip()
    df.loc[df['FlowName'] == 'Water intake',
           'ActivityConsumedBy'] = df['Activity']
    df.loc[df['FlowName'].isin(['Water discharge', "Water recirculation"]),
           'ActivityProducedBy'] = df['Activity']
    # drop columns used to create unit and activity columns
    df = df.drop(columns=['Activity'])
    # Modify the assigned RSD letter values to numeric value
    df.loc[df['Spread'] == 'A', 'Spread'] = 2.5  # given range: 0.01 - 4.99%
    df.loc[df['Spread'] == 'B', 'Spread'] = 7.5  # given range: 5 - 9.99%
    df.loc[df['Spread'] == 'C', 'Spread'] = 12.5  # given range: 10 - 14.99%
    df.loc[df['Spread'] == 'D', 'Spread'] = 20  # given range: 15 - 24.99%
    df.loc[df['Spread'] == 'E', 'Spread'] = 37.5  # given range:25 - 49.99%
    df.loc[df['Spread'] == 'F', 'Spread'] = 75  # given range: > 49.99%
    df.loc[df['Spread'] == 'x', 'Spread'] = WITHDRAWN_KEYWORD
    # hard code data
    df['Class'] = 'Employment'
    df['SourceName'] = 'StatCan_LFS'
    # temp hardcode canada iso code
    df['Location'] = call_country_code('Canada')
    df['LocationSystem'] = "ISO"
    df["MeasureofSpread"] = 'RSD'
    df["DataReliability"] = 3
    df["DataCollection"] = 4
    return df