mirror of
https://github.com/kennethreitz/pydantic.git
synced 2026-06-05 23:00:18 +00:00
Koudai Aono
33b3dc1825
* mypy plugin support for dataclassesv * fix styles and types * - change type-hint for `Config` - change name of an expected file - update documents * fix broken a reference of a document. * - update unittest - update documents * fix a document link * Update docs/mypy_plugin.md Co-Authored-By: Samuel Colvin <samcolvin@gmail.com> * Update docs/mypy_plugin.md Co-Authored-By: Samuel Colvin <samcolvin@gmail.com> * Update docs/mypy_plugin.md Co-Authored-By: Samuel Colvin <samcolvin@gmail.com> * remove extra whitespaces on mypy test results * fix output file name of mypy test * Update docs/usage/dataclasses.md Co-Authored-By: Samuel Colvin <samcolvin@gmail.com> * use TypeVar for DataclassType
68 lines
2.5 KiB
Markdown
68 lines
2.5 KiB
Markdown
If you don't want to use pydantic's `BaseModel` you can instead get the same data validation on standard
|
|
[dataclasses](https://docs.python.org/3/library/dataclasses.html) (introduced in python 3.7).
|
|
|
|
Dataclasses work in python 3.6 using the [dataclasses backport package](https://github.com/ericvsmith/dataclasses).
|
|
|
|
```py
|
|
{!.tmp_examples/dataclasses_main.py!}
|
|
```
|
|
_(This script is complete, it should run "as is")_
|
|
|
|
!!! note
|
|
Keep in mind that `pydantic.dataclasses.dataclass` is a drop-in replacement for `dataclasses.dataclass`
|
|
with validation, **not** a replacement for `pydantic.BaseModel`. There are cases where subclassing
|
|
`pydantic.BaseModel` is the better choice.
|
|
|
|
For more information and discussion see
|
|
[samuelcolvin/pydantic#710](https://github.com/samuelcolvin/pydantic/issues/710).
|
|
|
|
You can use all the standard pydantic field types, and the resulting dataclass will be identical to the one
|
|
created by the standard library `dataclass` decorator.
|
|
|
|
The underlying model and its schema can be accessed through `__pydantic_model__`.
|
|
Also, fields that require a `default_factory` can be specified by a `dataclasses.field`.
|
|
|
|
```py
|
|
{!.tmp_examples/dataclasses_default_schema.py!}
|
|
```
|
|
_(This script is complete, it should run "as is")_
|
|
|
|
`pydantic.dataclasses.dataclass`'s arguments are the same as the standard decorator, except one extra
|
|
keyword argument `config` which has the same meaning as [Config](model_config.md).
|
|
|
|
!!! warning
|
|
After v1.2, [The Mypy plugin](/mypy_plugin.md) must be installed to type check pydantic dataclasses.
|
|
|
|
For more information about combining validators with dataclasses, see
|
|
[dataclass validators](validators.md#dataclass-validators).
|
|
|
|
## Nested dataclasses
|
|
|
|
Nested dataclasses are supported both in dataclasses and normal models.
|
|
|
|
```py
|
|
{!.tmp_examples/dataclasses_nested.py!}
|
|
```
|
|
_(This script is complete, it should run "as is")_
|
|
|
|
Dataclasses attributes can be populated by tuples, dictionaries or instances of the dataclass itself.
|
|
|
|
## Initialize hooks
|
|
|
|
When you initialize a dataclass, it is possible to execute code *after* validation
|
|
with the help of `__post_init_post_parse__`. This is not the same as `__post_init__`, which executes
|
|
code *before* validation.
|
|
|
|
```py
|
|
{!.tmp_examples/dataclasses_post_init_post_parse.py!}
|
|
```
|
|
_(This script is complete, it should run "as is")_
|
|
|
|
Since version **v1.0**, any fields annotated with `dataclasses.InitVar` are passed to both `__post_init__` *and*
|
|
`__post_init_post_parse__`.
|
|
|
|
```py
|
|
{!.tmp_examples/dataclasses_initvars.py!}
|
|
```
|
|
_(This script is complete, it should run "as is")_
|