mirror of
https://github.com/kennethreitz/pydantic.git
synced 2026-06-05 23:00:18 +00:00
Samuel Colvin
33b7d52d31
* moving docs to mkdocs * transfering readme to md and more * fixing build * splitting usage.md * improving schema.md and index.md * fix make_history.rst * models intro * working on data conversation and required fields * more fixes to models.md * list all standard types supported * list of pydantic types * tweaks * update links in code * Apply suggestions from code review incorporate @dmontagu's suggestions. Co-Authored-By: dmontagu <35119617+dmontagu@users.noreply.github.com> * Apply suggestions from code review more missed suggestions. Co-Authored-By: dmontagu <35119617+dmontagu@users.noreply.github.com> * Apply suggestions from code review more corrects. * cleanup * Field order warning * fix and regenerate benchmarks * format examples better, cleanup * improve schema mapping table * correct highlighting file types in schema.md * add redirects in javascript * add logo
63 lines
2.4 KiB
Markdown
63 lines
2.4 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
|
|
{!./examples/ex_dataclasses.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.
|
|
|
|
`pydantic.dataclasses.dataclass`'s arguments are the same as the standard decorator, except one extra
|
|
key word argument `config` which has the same meaning as [Config](model_config.md).
|
|
|
|
!!! note
|
|
As a side effect of getting pydantic dataclasses to play nicely with mypy the `config` argument will show
|
|
as invalid in IDEs and mypy, use `@dataclass(..., config=Config) # type: ignore` as a workaround.
|
|
|
|
See [python/mypy#6239](https://github.com/python/mypy/issues/6239) for an explanation of why this is.
|
|
|
|
For information on validators with dataclasses see [dataclass validators](validators.md#dataclass-validators)
|
|
|
|
## Nested dataclasses
|
|
|
|
Nested dataclasses are supported both in dataclasses and normal models.
|
|
|
|
```py
|
|
{!./examples/ex_nested_dataclasses.py!}
|
|
```
|
|
_(This script is complete, it should run "as is")_
|
|
|
|
Dataclasses attributes can be populated by tuples, dictionaries or instances of that dataclass.
|
|
|
|
## 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
|
|
{!./examples/ex_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
|
|
{!./examples/ex_post_init_post_parse_initvars.py!}
|
|
```
|
|
_(This script is complete, it should run "as is")_
|
|
|