mirror of
https://github.com/kennethreitz/pydantic.git
synced 2026-06-05 23:00:18 +00:00
Mike Grima
82 lines
3.3 KiB
Markdown
82 lines
3.3 KiB
Markdown
If you don't want to use pydantic's `BaseModel` you can instead get the same data validation on standard
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[dataclasses](https://docs.python.org/3/library/dataclasses.html) (introduced in python 3.7).
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Dataclasses work in python 3.6 using the [dataclasses backport package](https://github.com/ericvsmith/dataclasses).
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```py
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{!.tmp_examples/dataclasses_main.py!}
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```
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_(This script is complete, it should run "as is")_
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!!! note
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Keep in mind that `pydantic.dataclasses.dataclass` is a drop-in replacement for `dataclasses.dataclass`
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with validation, **not** a replacement for `pydantic.BaseModel` (with a small difference in how [initialization hooks](#initialize-hooks) work). There are cases where subclassing
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`pydantic.BaseModel` is the better choice.
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For more information and discussion see
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[samuelcolvin/pydantic#710](https://github.com/samuelcolvin/pydantic/issues/710).
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You can use all the standard pydantic field types, and the resulting dataclass will be identical to the one
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created by the standard library `dataclass` decorator.
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The underlying model and its schema can be accessed through `__pydantic_model__`.
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Also, fields that require a `default_factory` can be specified by a `dataclasses.field`.
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```py
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{!.tmp_examples/dataclasses_default_schema.py!}
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```
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_(This script is complete, it should run "as is")_
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`pydantic.dataclasses.dataclass`'s arguments are the same as the standard decorator, except one extra
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keyword argument `config` which has the same meaning as [Config](model_config.md).
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!!! warning
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After v1.2, [The Mypy plugin](/mypy_plugin.md) must be installed to type check pydantic dataclasses.
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For more information about combining validators with dataclasses, see
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[dataclass validators](validators.md#dataclass-validators).
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## Nested dataclasses
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Nested dataclasses are supported both in dataclasses and normal models.
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```py
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{!.tmp_examples/dataclasses_nested.py!}
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```
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_(This script is complete, it should run "as is")_
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Dataclasses attributes can be populated by tuples, dictionaries or instances of the dataclass itself.
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## Initialize hooks
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When you initialize a dataclass, it is possible to execute code *after* validation
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with the help of `__post_init_post_parse__`. This is not the same as `__post_init__`, which executes
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code *before* validation.
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```py
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{!.tmp_examples/dataclasses_post_init_post_parse.py!}
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```
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_(This script is complete, it should run "as is")_
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Since version **v1.0**, any fields annotated with `dataclasses.InitVar` are passed to both `__post_init__` *and*
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`__post_init_post_parse__`.
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```py
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{!.tmp_examples/dataclasses_initvars.py!}
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```
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_(This script is complete, it should run "as is")_
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### Difference with stdlib dataclasses
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Note that the `dataclasses.dataclass` from python stdlib implements only the `__post_init__` method since it doesn't run a validation step.
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When substituting usage of `dataclasses.dataclass` with `pydantic.dataclasses.dataclass`, it is recommended to move the code executed in the `__post_init__` method to the `__post_init_post_parse__` method, and only leave behind part of code which needs to be executed before validation.
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## JSON Dumping
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Pydantic dataclasses do not feature a `.json()` function. To dump them as JSON, you will need to make use of the `pydantic_encoder` as follows:
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```py
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{!.tmp_examples/dataclasses_json_dumps.py!}
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```
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