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Classes Without Boilerplate

Project description


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.


attrs would not be possible without our amazing sponsors. Especially those generously supporting us at the The Organization tier and higher:

Please consider joining them to help make attrs’s maintenance more sustainable!


attrs 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)
>>> sc == SomeClass(1, [1, 2, 3])
>>> sc != SomeClass(2, [3, 2, 1])

>>> 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__,
  • 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, allows more ways to plug into the initialization process, and allows for stepping through the generated methods using a debugger.

For more details, please refer to our comparison page.

Project Information

attrs for Enterprise

Available as part of the Tidelift Subscription.

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Release Information


  • The type annotation for attrs.resolve_types() is now correct. #1141
  • Type stubs now use typing.dataclass_transform to decorate dataclass-like decorators, instead of the non-standard __dataclass_transform__ special form, which is only supported by Pyright. #1158
  • Fixed serialization of namedtuple fields using attrs.asdict/astuple() with retain_collection_types=True. #1165
  • attrs.AttrsInstance is now a typing.Protocol in both type hints and code. This allows you to subclass it along with another Protocol. #1172
  • If attrs detects that __attrs_pre_init__ accepts more than just self, it will call it with the same arguments as __init__ was called. This allows you to, for example, pass arguments to super().__init__(). #1187
  • Slotted classes now transform functools.cached_property decorated methods to support equivalent semantics. #1200
  • Added class_body argument to attrs.make_class() to provide additional attributes for newly created classes. It is, for example, now possible to attach methods. #1203

Full changelog

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