Skip to main content

A wrapper around argparse to get command line argument parsers from dataclasses

Project description

dataparsers

A simple module to wrap around argparse to get command line argument parsers from dataclasses.

Installation

pip install dataparsers

Basic usage

Create a dataclass describing your command line interface, and call parse() with the class:

# prog.py
from dataclasses import dataclass
from dataparsers import parse

@dataclass
class Args:
    foo: str
    bar: int = 42

args = parse(Args)
print("Printing `args`:")
print(args)

The dataclass fields that have a "default" value are turned into optional arguments, while the non default fields will be positional arguments.

The script can then be used in the same way as used with argparse:

$ python prog.py -h
usage: prog.py [-h] [--bar BAR] foo

positional arguments:
  foo

options:
  -h, --help  show this help message and exit
  --bar BAR

And the resulting type of args is Args (recognized by type checkers and autocompletes):

$ python prog.py test --bar 12
Printing `args`:
Args(foo='test', bar=12)

Argument specification

To specify detailed information about each argument, call the arg() function on the dataclass fields:

# prog.py
from dataclasses import dataclass
from dataparsers import parse, arg

@dataclass
class Args:
    foo: str = arg(help="foo help")
    bar: int = arg(default=42, help="bar help")

args = parse(Args)

It allows to customize the interface:

$ python prog.py -h
usage: prog.py [-h] [--bar BAR] foo

positional arguments:
  foo         foo help

options:
  -h, --help  show this help message and exit
  --bar BAR   bar help

In general, the arg() function accepts all parameters that are used in the original add_argument() method (with few exceptions) and some additional parameters. The default keyword argument used above makes the argument optional (i.e., passed with flags like --bar) except in some specific situations.

For more information, see the documentation.

Formalities, features, benefits and drawbacks

This project basically consists of a simple module dataparsers.py with few functions that allows to define typed arguments parsers in a single place, based on dataclasses.

Formalities

The main strategy of the module is based on the same approach of the package datargs, which consists in using the metadata attribute of the dataclass fields to store argument parameters. Some additional features of this project have already been contributed back upstream.

There are a lot of alternative libraries out there that do similar things. The README file of the datargs repository provides a good list for existing solutions and differences. I could also add to that list the libraries Python fire and the package dargparser, just to give few examples.

Features and benefits

Use this project if you want particular added features, such as:

  • Support for argument groups and mutually exclusive argument groups
  • Define all interface in one single place
  • More control over the interface display
  • More control over the argument flag -- creation
  • More similarity with argparse module
  • More simplicity

The simplicity is mentioned because it is just a simple module dataparsers.py that doesn't have any additional dependencies (it is pure Python) which can be downloaded directly and placed in your CLI scripts folder to import from.

In deed, the module consists of a 320 lines IPython code cell region (i.e., starts and ends with a #%% line comment block), that can also be placed on top of your "single file" CLI script to directly distribute. The used names are just the few provided functions, the stdlib imports, Class (a TypeVar) and SubParser (a frozen dataclass).

Additionally, this project also provides a stub file (.pyi) that can be used by type checkers but, moreover, may be used by some code editors to give helper documentation including the related docs of argparse methods, which are also provided in this project's documentation, for convenience. The stub file can be downloaded directly but it is installed with the module by default.

Drawbacks

Unlike the datargs package, dataparsers doesn't support:

  • The library attrs (only works with pure python dataclasses)
  • Enums classes
  • Complex types (Sequences, Optionals, and Literals)

If you want any of these features, use the package datargs. If you need the added features of dataparsers, use this module instead.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dataparsers-2.3.2.tar.gz (34.3 kB view details)

Uploaded Source

Built Distribution

dataparsers-2.3.2-py3-none-any.whl (31.2 kB view details)

Uploaded Python 3

File details

Details for the file dataparsers-2.3.2.tar.gz.

File metadata

  • Download URL: dataparsers-2.3.2.tar.gz
  • Upload date:
  • Size: 34.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-httpx/0.27.0

File hashes

Hashes for dataparsers-2.3.2.tar.gz
Algorithm Hash digest
SHA256 9b039881f1c9bf113b3c624c81734689b14f36fa06de59f5e6f01375ee423d73
MD5 628db69d27a7badff3200dc92b67921c
BLAKE2b-256 8ae7edabf9992b185e4e9593cd2fed26de048d8c8c8765b9dbdc0c4fa5526aee

See more details on using hashes here.

File details

Details for the file dataparsers-2.3.2-py3-none-any.whl.

File metadata

File hashes

Hashes for dataparsers-2.3.2-py3-none-any.whl
Algorithm Hash digest
SHA256 6308fc12c7c1536b8f349a25363d3742c148716ec13bf7c854c473c0d1dfecb8
MD5 8b35b19e4c9ee44c9221841995f0e63e
BLAKE2b-256 5fd026cf8d8446e1c91807882e660b8a07e31023c30bda3c1c5f67126c04f025

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page