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Documentation for version: v2.5.2.

Pydantic is the most widely used data validation library for Python.

Fast and extensible, Pydantic plays nicely with your linters/IDE/brain. Define how data should be in pure, canonical Python 3.7+; validate it with Pydantic.

Migrating to Pydantic V2

Using Pydantic V1? See the Migration Guide for notes on upgrading to Pydantic V2 in your applications!

Pydantic Example
from datetime import datetime
from typing import Tuple

from pydantic import BaseModel


class Delivery(BaseModel):
    timestamp: datetime
    dimensions: Tuple[int, int]


m = Delivery(timestamp='2020-01-02T03:04:05Z', dimensions=['10', '20'])
print(repr(m.timestamp))
#> datetime.datetime(2020, 1, 2, 3, 4, 5, tzinfo=TzInfo(UTC))
print(m.dimensions)
#> (10, 20)

Why use Pydantic?

  • Powered by type hints — with Pydantic, schema validation and serialization are controlled by type annotations; less to learn, less code to write, and integration with your IDE and static analysis tools. Learn moreā€¦
  • Speed — Pydantic's core validation logic is written in Rust. As a result, Pydantic is among the fastest data validation libraries for Python. Learn moreā€¦
  • JSON Schema — Pydantic models can emit JSON Schema, allowing for easy integration with other tools. Learn moreā€¦
  • Strict and Lax mode — Pydantic can run in either strict=True mode (where data is not converted) or strict=False mode where Pydantic tries to coerce data to the correct type where appropriate. Learn moreā€¦
  • Dataclasses, TypedDicts and more — Pydantic supports validation of many standard library types including dataclass and TypedDict. Learn moreā€¦
  • Customisation — Pydantic allows custom validators and serializers to alter how data is processed in many powerful ways. Learn moreā€¦
  • Ecosystem — around 8,000 packages on PyPI use Pydantic, including massively popular libraries like FastAPI, huggingface, Django Ninja, SQLModel, & LangChain. Learn moreā€¦
  • Battle tested — Pydantic is downloaded over 70M times/month and is used by all FAANG companies and 20 of the 25 largest companies on NASDAQ. If you're trying to do something with Pydantic, someone else has probably already done it. Learn moreā€¦

Installing Pydantic is as simple as: pip install pydantic

Pydantic examples

To see Pydantic at work, let's start with a simple example, creating a custom class that inherits from BaseModel:

Validation Successful
from datetime import datetime

from pydantic import BaseModel, PositiveInt


class User(BaseModel):
    id: int  # (1)!
    name: str = 'John Doe'  # (2)!
    signup_ts: datetime | None  # (3)!
    tastes: dict[str, PositiveInt]  # (4)!


external_data = {
    'id': 123,
    'signup_ts': '2019-06-01 12:22',  # (5)!
    'tastes': {
        'wine': 9,
        b'cheese': 7,  # (6)!
        'cabbage': '1',  # (7)!
    },
}

user = User(**external_data)  # (8)!

print(user.id)  # (9)!
#> 123
print(user.model_dump())  # (10)!
"""
{
    'id': 123,
    'name': 'John Doe',
    'signup_ts': datetime.datetime(2019, 6, 1, 12, 22),
    'tastes': {'wine': 9, 'cheese': 7, 'cabbage': 1},
}
"""
  1. id is of type int; the annotation-only declaration tells Pydantic that this field is required. Strings, bytes, or floats will be coerced to ints if possible; otherwise an exception will be raised.
  2. name is a string; because it has a default, it is not required.
  3. signup_ts is a datetime field that is required, but the value None may be provided; Pydantic will process either a unix timestamp int (e.g. 1496498400) or a string representing the date and time.
  4. tastes is a dictionary with string keys and positive integer values. The PositiveInt type is shorthand for Annotated[int, annotated_types.Gt(0)].
  5. The input here is an ISO8601 formatted datetime, Pydantic will convert it to a datetime object.
  6. The key here is bytes, but Pydantic will take care of coercing it to a string.
  7. Similarly, Pydantic will coerce the string '1' to an integer 1.
  8. Here we create instance of User by passing our external data to User as keyword arguments
  9. We can access fields as attributes of the model
  10. We can convert the model to a dictionary with model_dump()

If validation fails, Pydantic will raise an error with a breakdown of what was wrong:

Validation Error
# continuing the above example...

from pydantic import ValidationError


class User(BaseModel):
    id: int
    name: str = 'John Doe'
    signup_ts: datetime | None
    tastes: dict[str, PositiveInt]


external_data = {'id': 'not an int', 'tastes': {}}  # (1)!

try:
    User(**external_data)  # (2)!
except ValidationError as e:
    print(e.errors())
    """
    [
        {
            'type': 'int_parsing',
            'loc': ('id',),
            'msg': 'Input should be a valid integer, unable to parse string as an integer',
            'input': 'not an int',
            'url': 'https://errors.pydantic.dev/2/v/int_parsing',
        },
        {
            'type': 'missing',
            'loc': ('signup_ts',),
            'msg': 'Field required',
            'input': {'id': 'not an int', 'tastes': {}},
            'url': 'https://errors.pydantic.dev/2/v/missing',
        },
    ]
    """
  1. The input data is wrong here — id is not a valid integer, and signup_ts is missing
  2. User(...) will raise a ValidationError with a list of errors

Who is using Pydantic?

Hundreds of organisations and packages are using Pydantic. Some of the prominent companies and organizations around the world who are using Pydantic include:

For a more comprehensive list of open-source projects using Pydantic see the list of dependents on github, or you can find some awesome projects using Pydantic in awesome-pydantic.