def main():
    parser = argparse.ArgumentParser(description='A Python tool that automatically cleans data sets and readies them for analysis')

    parser.add_argument('INPUT_FILENAME', type=str, help='File name of the data file to clean')

    parser.add_argument('-cv', action='store', dest='CROSS_VAL_FILENAME', default=None,
                         type=str, help='File name for the validation data set if performing cross-validation')

    parser.add_argument('-o', action='store', dest='OUTPUT_FILENAME', default=None,
                        type=str, help='Data file to output the cleaned data set to')

    parser.add_argument('-cvo', action='store', dest='CV_OUTPUT_FILENAME', default=None,
                        type=str, help='Data file to output the cleaned cross-validation data set to')

    parser.add_argument('-is', action='store', dest='INPUT_SEPARATOR', default='\t',
                        type=str, help='Column separator for the input file(s) (default: \\t)')
                    
    parser.add_argument('-os', action='store', dest='OUTPUT_SEPARATOR', default='\t',
                        type=str, help='Column separator for the output file(s) (default: \\t)')

    parser.add_argument('--drop-nans', action='store_true', dest='DROP_NANS', default=False,
                        help='Drop all rows that have a NaN in any column (default: False)')
                        
    parser.add_argument('--ignore-update-check', action='store_true', dest='IGNORE_UPDATE_CHECK', default=False,
                        help='Do not check for the latest version of datacleaner (default: False)')

    parser.add_argument('--version', action='version', version='datacleaner v{version}'.format(version=__version__))

    args = parser.parse_args()

    input_data = pd.read_csv(args.INPUT_FILENAME, sep=args.INPUT_SEPARATOR)
    if args.CROSS_VAL_FILENAME is None:
        clean_data = autoclean(input_data, drop_nans=args.DROP_NANS, ignore_update_check=args.IGNORE_UPDATE_CHECK)
        if args.OUTPUT_FILENAME is None:
            print('Cleaned data set:')
            print(clean_data)
            print('')
            print('If you cannot view the entire data set, output it to a file instead. '
                  'Type datacleaner --help for more information.')
        else:
            clean_data.to_csv(args.OUTPUT_FILENAME, sep=args.OUTPUT_SEPARATOR, index=False)
    else:
        if args.OUTPUT_FILENAME is not None and args.CV_OUTPUT_FILENAME is None:
            print('You must specify both output file names. Type datacleaner --help for more information.')
            return
    
        cross_val_data = pd.read_csv(args.CROSS_VAL_FILENAME, sep=args.INPUT_SEPARATOR)
        clean_training_data, clean_testing_data = autoclean_cv(input_data, cross_val_data,
                                                               drop_nans=args.DROP_NANS,
                                                               ignore_update_check=args.IGNORE_UPDATE_CHECK)

        if args.OUTPUT_FILENAME is None:
            print('Cleaned training data set:')
            print(clean_training_data)
            print('')
            print('Cleaned testing data set:')
            print(clean_testing_data)
            print('')
            print('If you cannot view the entire data set, output it to a file instead. '
                  'Type datacleaner --help for more information.')
        else:
            clean_training_data.to_csv(args.OUTPUT_FILENAME, sep=args.OUTPUT_SEPARATOR, index=False)
            clean_testing_data.to_csv(args.OUTPUT_FILENAME, sep=args.OUTPUT_SEPARATOR, index=False)
Download distribute from a specified location and return its filename
dataset/ETHPy150Open rhiever/datacleaner/datacleaner/datacleaner.py/main--crossover with--dataset/ETHPy150Open aerosol/django-dilla/distribute_setup.py/download_setuptools