If you don't want to use pydantic's BaseModel you can instead get the same data validation on standard dataclasses (introduced in python 3.7).

Dataclasses work in python 3.6 using the dataclasses backport package.

from datetime import datetime
from pydantic.dataclasses import dataclass

class User:
    id: int
    name: str = 'John Doe'
    signup_ts: datetime = None

user = User(id='42', signup_ts='2032-06-21T12:00')
#> User(id=42, name='John Doe', signup_ts=datetime.datetime(2032, 6, 21, 12, 0))

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Keep in mind that pydantic.dataclasses.dataclass is a drop-in replacement for dataclasses.dataclass with validation, not a replacement for pydantic.BaseModel (with a small difference in how initialization hooks work). There are cases where subclassing pydantic.BaseModel is the better choice.

For more information and discussion see samuelcolvin/pydantic#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.

import dataclasses
from typing import List
from pydantic.dataclasses import dataclass

class User:
    id: int
    name: str = 'John Doe'
    friends: List[int] = dataclasses.field(default_factory=lambda: [0])

user = User(id='42')
    'title': 'User',
    'type': 'object',
    'properties': {
        'id': {'title': 'Id', 'type': 'integer'},
        'name': {
            'title': 'Name',
            'default': 'John Doe',
            'type': 'string',
        'friends': {
            'title': 'Friends',
            'default': [0],
            'type': 'array',
            'items': {'type': 'integer'},
    'required': ['id'],

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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.


After v1.2, The Mypy plugin must be installed to type check pydantic dataclasses.

For more information about combining validators with dataclasses, see dataclass validators.

Nested dataclasses🔗

Nested dataclasses are supported both in dataclasses and normal models.

from pydantic import AnyUrl
from pydantic.dataclasses import dataclass

class NavbarButton:
    href: AnyUrl

class Navbar:
    button: NavbarButton

navbar = Navbar(button=('',))
#> Navbar(button=NavbarButton(href=AnyUrl('', scheme='https',
#> host='', tld='com', host_type='domain')))

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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.

from datetime import datetime
from pydantic.dataclasses import dataclass

class Birth:
    year: int
    month: int
    day: int

class User:
    birth: Birth

    def __post_init__(self):
#> {'year': 1995, 'month': 3, 'day': 2}

    def __post_init_post_parse__(self):
#> Birth(year=1995, month=3, day=2)

user = User(**{'birth': {'year': 1995, 'month': 3, 'day': 2}})

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Since version v1.0, any fields annotated with dataclasses.InitVar are passed to both __post_init__ and __post_init_post_parse__.

from dataclasses import InitVar
from pathlib import Path
from typing import Optional

from pydantic.dataclasses import dataclass

class PathData:
    path: Path
    base_path: InitVar[Optional[Path]]

    def __post_init__(self, base_path):
        print(f"Received path={self.path!r}, base_path={base_path!r}")
#> Received path='world', base_path='/hello'

    def __post_init_post_parse__(self, base_path):
        if base_path is not None:
            self.path = base_path / self.path

path_data = PathData('world', base_path="/hello")
# Received path='world', base_path='/hello'
assert path_data.path == Path('/hello/world')

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Difference with stdlib dataclasses🔗

Note that the dataclasses.dataclass from python stdlib implements only the __post_init__ method since it doesn't run a validation step.

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.