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Classes Without Boilerplate

Project description

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attrs is the Python package that will bring back the joy of writing classes by relieving you from the drudgery of implementing object protocols (aka dunder methods). Trusted by NASA for Mars missions since 2020!

Its main goal is to help you to write concise and correct software without slowing down your code.

For that, it gives you a class decorator and a way to declaratively define the attributes on that class:

>>> from attrs import asdict, define, make_class, Factory

>>> @define
... class SomeClass:
...     a_number: int = 42
...     list_of_numbers: list[int] = Factory(list)
...
...     def hard_math(self, another_number):
...         return self.a_number + sum(self.list_of_numbers) * another_number


>>> sc = SomeClass(1, [1, 2, 3])
>>> sc
SomeClass(a_number=1, list_of_numbers=[1, 2, 3])

>>> sc.hard_math(3)
19
>>> sc == SomeClass(1, [1, 2, 3])
True
>>> sc != SomeClass(2, [3, 2, 1])
True

>>> asdict(sc)
{'a_number': 1, 'list_of_numbers': [1, 2, 3]}

>>> SomeClass()
SomeClass(a_number=42, list_of_numbers=[])

>>> C = make_class("C", ["a", "b"])
>>> C("foo", "bar")
C(a='foo', b='bar')

After declaring your attributes attrs gives you:

  • a concise and explicit overview of the class’s attributes,
  • a nice human-readable __repr__,
  • a equality-checking methods,
  • an initializer,
  • and much more,

without writing dull boilerplate code again and again and without runtime performance penalties.

Hate type annotations!? No problem! Types are entirely optional with attrs. Simply assign attrs.field() to the attributes instead of annotating them with types.


This example uses attrs’s modern APIs that have been introduced in version 20.1.0, and the attrs package import name that has been added in version 21.3.0. The classic APIs (@attr.s, attr.ib, plus their serious business aliases) and the attr package import name will remain indefinitely.

Please check out On The Core API Names for a more in-depth explanation.

Data Classes

On the tin, attrs might remind you of dataclasses (and indeed, dataclasses are a descendant of attrs). In practice it does a lot more and is more flexible. For instance it allows you to define special handling of NumPy arrays for equality checks, or allows more ways to plug into the initialization process.

For more details, please refer to our comparison page.

Getting Help

Please use the python-attrs tag on Stack Overflow to get help.

Answering questions of your fellow developers is also a great way to help the project!

Project Information

attrs is released under the MIT license, its documentation lives at Read the Docs, the code on GitHub, and the latest release on PyPI. It’s rigorously tested on Python 2.7, 3.5+, and PyPy.

We collect information on third-party extensions in our wiki. Feel free to browse and add your own!

If you’d like to contribute to attrs you’re most welcome and we’ve written a little guide to get you started!

attrs for Enterprise

Available as part of the Tidelift Subscription.

The maintainers of attrs and thousands of other packages are working with Tidelift to deliver commercial support and maintenance for the open source packages you use to build your applications. Save time, reduce risk, and improve code health, while paying the maintainers of the exact packages you use. Learn more.

Release Information

21.4.0 (2021-12-29)

Changes

  • Fixed the test suite on PyPy3.8 where cloudpickle does not work. #892
  • Fixed coverage report for projects that use attrs and don’t set a --source. #895, #896

Full changelog.

Credits

attrs is written and maintained by Hynek Schlawack.

The development is kindly supported by Variomedia AG.

A full list of contributors can be found in GitHub’s overview.

It’s the spiritual successor of characteristic and aspires to fix some of it clunkiness and unfortunate decisions. Both were inspired by Twisted’s FancyEqMixin but both are implemented using class decorators because subclassing is bad for you, m’kay?

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