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).
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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 typing import List
>>> from attr 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 complete set of comparison methods (equality and ordering),
- an initializer,
- and much more,
without writing dull boilerplate code again and again and without runtime performance penalties.
This gives you the power to use actual classes with actual types in your code instead of confusing tuples or confusingly behaving namedtuples.
Which in turn encourages you to write small classes that do one thing well.
Never again violate the single responsibility principle just because implementing __init__ et al is a painful drag.
In case you're wondering: this example uses attrs's modern APIs that have been introduced in version 20.1.0.
The classic APIs (@attr.s, attr.ib, @attr.attrs, attr.attrib, and attr.dataclass) will remain indefinitely.
Type annotations will also stay entirely optional forever.
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 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
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