Pydantic¶

Documentation for version:v2.11.1.

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 3.9+; validate it with Pydantic.

fromdatetimeimportdatetimeimportlogfirefrompydanticimportBaseModellogfire.configure()logfire.instrument_pydantic()# (1)!classDelivery(BaseModel):timestamp:datetimedimensions:tuple[int,int]# this will record details of a successful validation to logfirem=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)Delivery(timestamp='2020-01-02T03:04:05Z',dimensions=['10'])# (2)!

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 more…
• Speed — Pydantic's core validation logic is written in Rust. As a result, Pydantic is among the fastest data validation libraries for Python.Learn more…
• JSON Schema — Pydantic models can emit JSON Schema, allowing for easy integration with other tools.Learn more…
• Strict andLax mode — Pydantic can run in either strict mode (where data is not converted) or lax mode where Pydantic tries to coerce data to the correct type where appropriate.Learn more…
• Dataclasses,TypedDicts and more — Pydantic supports validation of many standard library types includingdataclass andTypedDict.Learn more…
• Customisation — Pydantic allows custom validators and serializers to alter how data is processed in many powerful ways.Learn more…
• Ecosystem — around 8,000 packages on PyPI use Pydantic, including massively popular libraries likeFastAPI,huggingface,Django Ninja,SQLModel, &LangChain.Learn more…
• 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 more…

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 fromBaseModel:

fromdatetimeimportdatetimefrompydanticimportBaseModel,PositiveIntclassUser(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)!#> 123print(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 typeint; the annotation-only declaration tells Pydantic that this field is required. Strings, bytes, or floats will be coerced to integers 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 adatetime field that is required, but the valueNone may be provided; Pydantic will process either aUnix timestamp integer (e.g.1496498400) or a string representing the date and time.
4. tastes is a dictionary with string keys and positive integer values. ThePositiveInt type is shorthand forAnnotated[int, annotated_types.Gt(0)].
5. The input here is anISO 8601 formatted datetime, but Pydantic will convert it to adatetime object.
6. The key here isbytes, but Pydantic will take care of coercing it to a string.
7. Similarly, Pydantic will coerce the string'1' to the integer1.
8. We create instance ofUser by passing our external data toUser as keyword arguments.
9. We can access fields as attributes of the model.
10. We can convert the model to a dictionary withmodel_dump().

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

# continuing the above example...fromdatetimeimportdatetimefrompydanticimportBaseModel,PositiveInt,ValidationErrorclassUser(BaseModel):id:intname:str='John Doe'signup_ts:datetime|Nonetastes:dict[str,PositiveInt]external_data={'id':'not an int','tastes':{}}# (1)!try:User(**external_data)# (2)!exceptValidationErrorase: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, andsignup_ts is missing.
2. Trying to instantiateUser will raise aValidationError 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 thelist of dependents on github, or you can find some awesome projects using Pydantic inawesome-pydantic.