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 a
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 great
list for existing solutions and differences.
I could also add to that list the libraries
Python fire
and
dargparser
, just to give
few examples.
Features and benefits
Use this project if you want particular added features, such as:
- More control over the interface display
- More control over the argument flag creation
- Support for argument groups and mutually exclusive argument groups
- More simplicity
The simplicity is mentioned because it is just a simple module
dataparsers.py
that doesn't have 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 200 lines
IPython code cell region
(which 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 and Class
(a TypeVar
)
Additionally, this project also provides a stub file [dataparsers.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 current documentation for convenience. The stub
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 pythondataclasses
) - Enums
- Complex types (Sequences, Optionals, and Literals)
- Sub Commands (subparsers)
If you want any of these features, use the datargs
package. If you need the
added features of dataparsers
, use this module instead.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file dataparsers-1.0.1.tar.gz
.
File metadata
- Download URL: dataparsers-1.0.1.tar.gz
- Upload date:
- Size: 23.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.7.1 CPython/3.11.7 Windows/10
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | c8ade7317504e246215c5ab1a2d50473d17a6ac12280bd6dbf7f9a4ef4ea783e |
|
MD5 | 6efc969c030b9f7a7763c01b7c66e4dc |
|
BLAKE2b-256 | 8401be634c47610cb536a838a80b52ea29f43d4907356c78939451304b9b8a0f |
File details
Details for the file dataparsers-1.0.1-py3-none-any.whl
.
File metadata
- Download URL: dataparsers-1.0.1-py3-none-any.whl
- Upload date:
- Size: 23.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.7.1 CPython/3.11.7 Windows/10
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 546a11efa63406981c6eeffa4bbdddbd09ce9565bf4556a3aa0788c511905c55 |
|
MD5 | c140d758dfe4b6324b8e88ff851d70b6 |
|
BLAKE2b-256 | f29137687e578ec076b67a97fbb309fb959bf07ceed0e15b9968951f5d0bd376 |