cattrs 1.10.0

cattrs is an open source Python library for structuring and unstructuring data. cattrs works best with attrs classes, dataclasses and the usual Python collections, but 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 or 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 (by your network, file system, database…), 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.

Here’s a simple taste. The list containing a float, an int and a string gets converted into a tuple of three ints.

>>> import cattr
>>>
>>> cattr.structure([1.0, 2, "3"], tuple[int, int, int])
(1, 2, 3)

cattrs works well with attrs classes out of the box.

>>> from attr import frozen
>>> import cattr
>>>
>>> @frozen  # It works with normal classes too.
... class C:
...     a: int
...     b: str
...
>>> instance = C(1, 'a')
>>> cattr.unstructure(instance)
{'a': 1, 'b': 'a'}
>>> cattr.structure({'a': 1, 'b': 'a'}, C)
C(a=1, b='a')

Here’s a much more complex example, involving attrs classes with type metadata.

>>> from enum import unique, Enum
>>> from typing import Optional, Sequence, Union
>>> from cattr import structure, unstructure
>>> from attr import define, field
>>>
>>> @unique
... class CatBreed(Enum):
...     SIAMESE = "siamese"
...     MAINE_COON = "maine_coon"
...     SACRED_BIRMAN = "birman"
...
>>> @define
... class Cat:
...     breed: CatBreed
...     names: Sequence[str]
...
>>> @define
... class DogMicrochip:
...     chip_id = field()  # Type annotations are optional, but recommended
...     time_chipped: float = field()
...
>>> @define
... class Dog:
...     cuteness: int
...     chip: Optional[DogMicrochip]
...
>>> p = unstructure([Dog(cuteness=1, chip=DogMicrochip(chip_id=1, time_chipped=10.0)),
...                  Cat(breed=CatBreed.MAINE_COON, names=('Fluffly', 'Fluffer'))])
...
>>> print(p)
[{'cuteness': 1, 'chip': {'chip_id': 1, 'time_chipped': 10.0}}, {'breed': 'maine_coon', 'names': ('Fluffly', 'Fluffer')}]
>>> print(structure(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'])]

Consider unstructured data a low-level representation that needs to be converted to structured data to be handled, and use structure. When you’re done, unstructure the data to its unstructured form and pass it along to another library or module. Use attrs type metadata to add type metadata to attributes, so cattrs will know how to structure and destructure them.

• Free software: MIT license

• Documentation: https://cattrs.readthedocs.io.

• Python versions supported: 3.7 and up. (Older Python versions, like 2.7, 3.5 and 3.6 are supported by older versions; see the changelog.)