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Project Description

cattrs

cattrs is an experimental open source Python 3 library providing composable complex class conversion support for attrs classes. Other kinds of classes are supported by manually registering converters.

Python has a rich set of powerful, easy to use, built-in data types like dictionaries, lists and tuples. These data types are also the lingua franca of most data serialization libraries, for formats like json, msgpack, yaml or toml.

Data types like this, and mappings like dict s in particular, represent unstructured data. Your data is, in all likelihood, structured: not all combinations of field names are values are valid inputs to your programs. In Python, structured data is better represented with classes and enumerations. attrs is an excellent library for declaratively describing the structure of your data, and validating it.

When you’re handed unstructured data, cattrs helps to convert this data into structured data. When you have to convert your structured data into data types other libraries can handle, cattrs turns your classes and enumerations into dictionaries, integers and strings.

A taste:

>>> from enum import unique, Enum
>>> from typing import List, Sequence, Union
>>> from cattr import loads, dumps
>>> import attr
>>> from attr.validators import instance_of, optional
>>>
>>> @unique
... class CatBreed(Enum):
...     SIAMESE = "siamese"
...     MAINE_COON = "maine_coon"
...     SACRED_BIRMAN = "birman"
...
>>> @attr.s
... class Cat:
...     breed = attr.ib(validator=instance_of(CatBreed))
...     names = attr.ib(validator=instance_of(Sequence[str]))
...
>>> @attr.s
... class DogMicrochip:
...     chip_id = attr.ib()
...     time_chipped = attr.ib(validator=instance_of(float))
...
>>> @attr.s
... class Dog:
...     cuteness = attr.ib(validator=instance_of(int))
...     chip = attr.ib(validator=optional(instance_of(DogMicrochip)))
...
>>> p = dumps([Dog(cuteness=1, chip=DogMicrochip(chip_id=1, time_chipped=10.0)),
...            Cat(breed=CatBreed.MAINE_COON, names=('Fluffly', 'Fluffer'))])
...
>>> print(p)
[{'chip': {'chip_id': 1, 'time_chipped': 10.0}, 'cuteness': 1}, {'names': ('Fluffly', 'Fluffer'), 'breed': 'maine_coon'}]
>>> print(loads(p, List[Union[Dog, Cat]]))
[Dog(cuteness=1, chip=DogMicrochip(chip_id=1, time_chipped=10.0)), Cat(breed=<CatBreed.MAINE_COON: 'maine_coon'>, names=['Fluffly', 'Fluffer'])]

dumps and loads were chosen for their similarity to the functionality of modules like marshal, pickle and json. Consider unstructured data a low-level representation that needs to be converted to structured data to be handled, and use loads. When you’re done, dumps the data to its unstructured form and pass it along to another library or module.

Features

  • Converts structured data into unstructured data, recursively:
    • attrs classes are converted into dictionaries, in a way similar to attrs.asdict.
    • Enumeration instances are converted to their values.
    • Other types are let through without conversion. This includes types such as integers, dictionaries, lists and instances of non-attrs classes.
    • Custom converters for any type can be registered using register_dumps_hook.
  • Converts unstructured data into structured data, recursively, according to your specification given as a type. The following types are supported:
    • typing.Optional[T].
    • typing.List[T], typing.MutableSequence[T], typing.Sequence[T] (converts to a list).
    • typing.Tuple (both variants, Tuple[T, ...] and Tuple[X, Y, Z]).
    • typing.MutableSet[T], typing.Set[T] (converts to a set).
    • typing.FrozenSet[T] (converts to a frozenset).
    • typing.Dict[K, V], typing.MutableMapping[K, V], typing.Mapping[K, V] (converts to a dict).
    • attrs classes with simple attributes and the usual __init__.
      • Simple attributes are attributes that can be assigned unstructured data, like numbers, strings, and collections of unstructured data.
    • All attrs classes with the usual __init__, if their complex attributes have type metadata.
    • typing.Union s of supported attrs classes, given that all of the classes have a unique required field.
    • typing.Union s of anything, given that you provide a disambiguation function for it.
    • Custom converters for any type can be registered using register_loads_hook.

Credits

Major credits to Hynek Schlawack for creating attrs and its predecessor, characteristic.

cattrs is tested with Hypothesis, by David R. MacIver.

cattrs is benchmarked using perf, by Victor Stinner.

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.1.0 (2016-08-13)

  • First release on PyPI.
Release History

Release History

0.2.0

This version

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0.1.0

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
cattrs-0.2.0-py2.py3-none-any.whl (11.5 kB) Copy SHA256 Checksum SHA256 3.5 Wheel Oct 2, 2016
cattrs-0.2.0.tar.gz (33.0 kB) Copy SHA256 Checksum SHA256 Source Oct 2, 2016

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