Python Classes Without Boilerplate


attrs: Classes Without Boilerplate

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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).

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

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

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import attr @attr.s … class Point(object): … x = attr.ib(default=42) … y = attr.ib(default=attr.Factory(list)) … … def hard_math(self, z): … return self.x * self.y * z pt = Point(x=1, y=2) pt Point(x=1, y=2) pt.hard_math(3) 6 pt == Point(1, 2) True pt != Point(2, 1) True attr.asdict(pt) {‘x’: 1, ‘y’: 2} Point() Point(x=42, y=[]) C = attr.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,
  • 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 tuple\ s or confusingly behaving namedtuple\ s. 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.

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I’m looking forward to is being able to program in Python-with-attrs everywhere. It exerts a subtle, but positive, design influence in all the codebases I’ve see it used in.

– Glyph Lefkowitz, creator of Twisted <>_ and Software Developer at Rackspace in The One Python Library Everyone Needs <>_

I’m increasingly digging your attr.ocity. Good job!

– Łukasz Langa, prolific CPython core developer and Production Engineer at Facebook

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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, 3.4+, and PyPy.

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

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