from __future__ import print_function
import argparse
from .random_csv import RandCSV
[docs]def parse_args(*args):
"""Argument parser.
"""
# Create the parser.
mkcsv_parser = argparse.ArgumentParser(
description="Generate random CSVs."
)
# Add arguments
mkcsv_parser.add_argument(
'--rows',
'-m',
action='store',
type=int,
required=True,
help='Number of rows the desired CSV file contains.'
)
mkcsv_parser.add_argument(
'--cols',
'-n',
action='store',
type=int,
required=True,
help='Number of columns the desired CSV file contains.'
)
mkcsv_parser.add_argument(
'--output',
'-o',
action='store',
type=str,
required=False,
default="rand.csv",
help='Output file name.'
)
mkcsv_parser.add_argument(
'--data-types',
'-d',
action='store',
nargs='+',
required=False,
default=['integer'],
help='Data types present in the desired CSV file. Supported data types are: integer, float, token.'
)
mkcsv_parser.add_argument(
'--nan-freq',
'-a',
action='store',
type=float,
required=False,
default=.0,
help='Approx. frequency of NaN values contained in desired CSV file.'
)
mkcsv_parser.add_argument(
'--empty-freq',
'-e',
action='store',
type=float,
required=False,
default=.0,
help='Approx. frequency of empty values contained in desired CSV file.'
)
mkcsv_parser.add_argument(
'--index-col',
'-i',
action='store_true',
dest='index_col',
help='Flag, the left most column should be a row index (ascending integer).'
)
mkcsv_parser.set_defaults(index=False)
mkcsv_parser.add_argument(
'--title-row',
'-t',
action='store_true',
dest='title_row',
help='Flag, the top most row should be a column index (ascending integer).'
)
mkcsv_parser.set_defaults(title=False)
mkcsv_parser.add_argument(
'--byte-size',
'-b',
action='store',
type=int,
required=False,
default=8,
help='Character length of the individual random values.'
)
return mkcsv_parser.parse_args(*args)
[docs]def cli(*args):
"""CLI entry point.
"""
if len(args):
args = parse_args(*args)
else:
# pipx returns here
args = parse_args()
csv = RandCSV(
rows=args.rows,
cols=args.cols,
byte_size=args.byte_size,
data_types=args.data_types,
nan_freq=args.nan_freq,
empty_freq=args.empty_freq,
index_col=args.index_col,
title_row=args.title_row,
)
csv.to_file(args.output)
print(f'generated CSV file: {args.output}')
return None
