61 lines
2.1 KiB
ReStructuredText
61 lines
2.1 KiB
ReStructuredText
=============================================
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attrs: Python attributes without boilerplate.
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=============================================
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.. image:: https://pypip.in/version/attrs/badge.svg
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:target: https://pypi.python.org/pypi/attrs/
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:alt: Latest Version
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.. image:: https://travis-ci.org/hynek/attrs.svg
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:target: https://travis-ci.org/hynek/attrs
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:alt: CI status
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.. image:: https://coveralls.io/repos/hynek/attrs/badge.png?branch=master
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:target: https://coveralls.io/r/hynek/attrs?branch=master
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:alt: Current coverage
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.. teaser-begin
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``attrs`` is an `MIT <http://choosealicense.com/licenses/mit/>`_-licensed Python package with class decorators that ease the chores of implementing the most common attribute-related object protocols:
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.. code-block:: pycon
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>>> import attr
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>>> @attr.s
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... class C(object):
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... x = attr.ib(default_value=42)
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... y = attr.ib(default_factory=list)
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>>> i = C(x=1, y=2)
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>>> i
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C(x=1, y=2)
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>>> i == C(1, 2)
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True
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>>> i != C(2, 1)
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True
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>>> attr.to_dict(i)
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{'y': 2, 'x': 1}
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>>> C()
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C(x=42, y=[])
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(If you don’t like the playful ``attr.s`` and ``attr.ib``, you can also use their no-nonsense aliases ``attr.attributes`` and ``attr.attr``).
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You just specify the attributes to work with and ``attrs`` gives you:
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- a nice human-readable ``__repr__``,
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- a complete set of comparison methods,
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- an initializer,
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- and much more
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*without* writing dull boilerplate code again and again.
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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.
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So put down that type-less data structures and welcome some class into your life!
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.. note::
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I wrote an `explanation <https://attrs.readthedocs.org/en/latest/why.html#characteristic>`_ on why I forked my own ``characteristic``.
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It's not dead but ``attrs`` will have more new features.
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``attrs``\ ’s documentation lives at `Read the Docs <https://attrs.readthedocs.org/>`_, the code on `GitHub <https://github.com/hynek/attrs>`_.
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It’s rigorously tested on Python 2.6, 2.7, 3.3+, and PyPy.
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