def METHOD_NAME(left_df, left_column_names, right_df, db_column_name, db_table_name, quiet=False):
    """
    :param left_df:
    :param left_column_names:
    :param right_df:
    :param db_column_name:
    :param db_table_name:
    :param quiet:
    :return:
    """
    result_df = left_df.copy()
    if not quiet and db_column_name in left_df.columns:
        logger.debug('WARNING | BLENDING: column "%s" already exists in orginal df' % db_column_name)
    unique_slug = '_EDITNAME'
    unique_original_column_names = [("%s%s" % (column_name, unique_slug)) for column_name in left_column_names]
    result_df.rename(index=str, columns=dict(zip(left_column_names, unique_original_column_names)),
                     inplace=True)
    not_existent_proteins = []
    for i in range(0, len(unique_original_column_names)):
        result_df = Repository._blend_column(result_df, right_df, unique_original_column_names[i],
                                             db_column_name,
                                             db_table_name, i + 1)
        not_existent_proteins = not_existent_proteins + \
                                result_df[result_df['_merge_%s' % (i + 1)] == 'left_only'][
                                    unique_original_column_names[i]].drop_duplicates().tolist()
    not_existent_proteins = list(set(not_existent_proteins))
    for i in range(1, len(unique_original_column_names) + 1):
        result_df = result_df[(result_df['_merge_%s' % i] == 'both')]
    result_df.drop(['_merge_%s' % merge_column for merge_column in
                    range(1, len(unique_original_column_names) + 1)] + unique_original_column_names, axis=1,
                   inplace=True)
    if len(left_column_names) == 1:
        result_df.rename(index=str, columns={'%s_1' % db_column_name: db_column_name,
                                             '%s_1_id' % db_table_name: '%s_id' % db_table_name}, inplace=True)
    if not quiet and not_existent_proteins:
        logger.warning('WARNING | BLENDING: THIS %s DIDNT EXIST IN %s' % (db_column_name, db_table_name))
        logger.warning(not_existent_proteins)
    return result_df