Fields

API Documentation

pydantic.fields.Field

The Field function is used to customize and add metadata to fields of models.

Default values¶

The default parameter is used to define a default value for a field.

from pydantic import BaseModel, Field


class User(BaseModel):
    name: str = Field(default='John Doe')


user = User()
print(user)
#> name='John Doe'

You can also use default_factory to define a callable that will be called to generate a default value.

from uuid import uuid4

from pydantic import BaseModel, Field


class User(BaseModel):
    id: str = Field(default_factory=lambda: uuid4().hex)

Info

The default and default_factory parameters are mutually exclusive.

Note

If you use typing.Optional, it doesn't mean that the field has a default value of None!

Using `Annotated`¶

The Field function can also be used together with Annotated.

from uuid import uuid4

from typing_extensions import Annotated

from pydantic import BaseModel, Field


class User(BaseModel):
    id: Annotated[str, Field(default_factory=lambda: uuid4().hex)]

Note

Defaults can be set outside Annotated as the assigned value or with Field.default_factory inside Annotated. The Field.default argument is not supported inside Annotated.

Field aliases¶

For validation and serialization, you can define an alias for a field.

There are three ways to define an alias:

Field(..., alias='foo')
Field(..., validation_alias='foo')
Field(..., serialization_alias='foo')

The alias parameter is used for both validation and serialization. If you want to use different aliases for validation and serialization respectively, you can use thevalidation_alias and serialization_alias parameters, which will apply only in their respective use cases.

Here is an example of using the alias parameter:

from pydantic import BaseModel, Field


class User(BaseModel):
    name: str = Field(..., alias='username')


user = User(username='johndoe')  # (1)!
print(user)
#> name='johndoe'
print(user.model_dump(by_alias=True))  # (2)!
#> {'username': 'johndoe'}

The alias 'username' is used for instance creation and validation.
We are using model_dump to convert the model into a serializable format.

You can see more details about model_dump in the API reference.

Note that the by_alias keyword argument defaults to False, and must be specified explicitly to dump models using the field (serialization) aliases.

When by_alias=True, the alias 'username' is also used during serialization.

If you want to use an alias only for validation, you can use the validation_alias parameter:

from pydantic import BaseModel, Field


class User(BaseModel):
    name: str = Field(..., validation_alias='username')


user = User(username='johndoe')  # (1)!
print(user)
#> name='johndoe'
print(user.model_dump(by_alias=True))  # (2)!
#> {'name': 'johndoe'}

The validation alias 'username' is used during validation.
The field name 'name' is used during serialization.

If you only want to define an alias for serialization, you can use the serialization_alias parameter:

from pydantic import BaseModel, Field


class User(BaseModel):
    name: str = Field(..., serialization_alias='username')


user = User(name='johndoe')  # (1)!
print(user)
#> name='johndoe'
print(user.model_dump(by_alias=True))  # (2)!
#> {'username': 'johndoe'}

The field name 'name' is used for validation.
The serialization alias 'username' is used for serialization.

Alias precedence and priority

In case you use alias together with validation_alias or serialization_alias at the same time, the validation_alias will have priority over alias for validation, and serialization_alias will have priority over alias for serialization.

If you use an alias_generator in the Model Config, you can control the order of precedence for specified field vs generated aliases via the alias_priority setting. You can read more about alias precedence here.

VSCode and Pyright users

In VSCode, if you use the Pylance extension, you won't see a warning when instantiating a model using a field's alias:

from pydantic import BaseModel, Field


class User(BaseModel):
    name: str = Field(..., alias='username')


user = User(username='johndoe')  # (1)!

VSCode will NOT show a warning here.

When the 'alias' keyword argument is specified, even if you set populate_by_name to True in the Model Config, VSCode will show a warning when instantiating a model using the field name (though it will work at runtime) — in this case, 'name':

from pydantic import BaseModel, ConfigDict, Field


class User(BaseModel):
    model_config = ConfigDict(populate_by_name=True)

    name: str = Field(..., alias='username')


user = User(name='johndoe')  # (1)!

VSCode will show a warning here.

To "trick" VSCode into preferring the field name, you can use the str function to wrap the alias value. With this approach, though, a warning is shown when instantiating a model using the alias for the field:

from pydantic import BaseModel, ConfigDict, Field


class User(BaseModel):
    model_config = ConfigDict(populate_by_name=True)

    name: str = Field(..., alias=str('username'))  # noqa: UP018


user = User(name='johndoe')  # (1)!
user = User(username='johndoe')  # (2)!

Now VSCode will NOT show a warning
VSCode will show a warning here, though

This is discussed in more detail in this issue.

Validation Alias¶

Even though Pydantic treats alias and validation_alias the same when creating model instances, VSCode will not use the validation_alias in the class initializer signature. If you want VSCode to use the validation_alias in the class initializer, you can instead specify both an alias and serialization_alias, as the serialization_alias will override the alias during serialization:

from pydantic import BaseModel, Field


class MyModel(BaseModel):
    my_field: int = Field(..., validation_alias='myValidationAlias')

with:

from pydantic import BaseModel, Field


class MyModel(BaseModel):
    my_field: int = Field(
        ...,
        alias='myValidationAlias',
        serialization_alias='my_serialization_alias',
    )


m = MyModel(myValidationAlias=1)
print(m.model_dump(by_alias=True))
#> {'my_serialization_alias': 1}

All of the above will likely also apply to other tools that respect the @typing.dataclass_transform decorator, such as Pyright.

For more information on alias usage, see the Alias concepts page.

Numeric Constraints¶

There are some keyword arguments that can be used to constrain numeric values:

gt - greater than
lt - less than
ge - greater than or equal to
le - less than or equal to
multiple_of - a multiple of the given number
allow_inf_nan - allow 'inf', '-inf', 'nan' values

Here's an example:

from pydantic import BaseModel, Field


class Foo(BaseModel):
    positive: int = Field(gt=0)
    non_negative: int = Field(ge=0)
    negative: int = Field(lt=0)
    non_positive: int = Field(le=0)
    even: int = Field(multiple_of=2)
    love_for_pydantic: float = Field(allow_inf_nan=True)


foo = Foo(
    positive=1,
    non_negative=0,
    negative=-1,
    non_positive=0,
    even=2,
    love_for_pydantic=float('inf'),
)
print(foo)
"""
positive=1 non_negative=0 negative=-1 non_positive=0 even=2 love_for_pydantic=inf
"""

JSON Schema

In the generated JSON schema:

gt and lt constraints will be translated to exclusiveMinimum and exclusiveMaximum.
ge and le constraints will be translated to minimum and maximum.
multiple_of constraint will be translated to multipleOf.

The above snippet will generate the following JSON Schema:

{
  "title": "Foo",
  "type": "object",
  "properties": {
    "positive": {
      "title": "Positive",
      "type": "integer",
      "exclusiveMinimum": 0
    },
    "non_negative": {
      "title": "Non Negative",
      "type": "integer",
      "minimum": 0
    },
    "negative": {
      "title": "Negative",
      "type": "integer",
      "exclusiveMaximum": 0
    },
    "non_positive": {
      "title": "Non Positive",
      "type": "integer",
      "maximum": 0
    },
    "even": {
      "title": "Even",
      "type": "integer",
      "multipleOf": 2
    },
    "love_for_pydantic": {
      "title": "Love For Pydantic",
      "type": "number"
    }
  },
  "required": [
    "positive",
    "non_negative",
    "negative",
    "non_positive",
    "even",
    "love_for_pydantic"
  ]
}

See the JSON Schema Draft 2020-12 for more details.

Constraints on compound types

In case you use field constraints with compound types, an error can happen in some cases. To avoid potential issues, you can use Annotated:

from typing import Optional

from typing_extensions import Annotated

from pydantic import BaseModel, Field


class Foo(BaseModel):
    positive: Optional[Annotated[int, Field(gt=0)]]
    # Can error in some cases, not recommended:
    non_negative: Optional[int] = Field(ge=0)

String Constraints¶

API Documentation

pydantic.types.StringConstraints

There are fields that can be used to constrain strings:

min_length: Minimum length of the string.
max_length: Maximum length of the string.
pattern: A regular expression that the string must match.

Here's an example:

from pydantic import BaseModel, Field


class Foo(BaseModel):
    short: str = Field(min_length=3)
    long: str = Field(max_length=10)
    regex: str = Field(pattern=r'^\d*$')  # (1)!


foo = Foo(short='foo', long='foobarbaz', regex='123')
print(foo)
#> short='foo' long='foobarbaz' regex='123'

Only digits are allowed.

JSON Schema

In the generated JSON schema:

min_length constraint will be translated to minLength.
max_length constraint will be translated to maxLength.
pattern constraint will be translated to pattern.

The above snippet will generate the following JSON Schema:

{
  "title": "Foo",
  "type": "object",
  "properties": {
    "short": {
      "title": "Short",
      "type": "string",
      "minLength": 3
    },
    "long": {
      "title": "Long",
      "type": "string",
      "maxLength": 10
    },
    "regex": {
      "title": "Regex",
      "type": "string",
      "pattern": "^\\d*$"
    }
  },
  "required": [
    "short",
    "long",
    "regex"
  ]
}

Decimal Constraints¶

There are fields that can be used to constrain decimals:

max_digits: Maximum number of digits within the Decimal. It does not include a zero before the decimal point or trailing decimal zeroes.
decimal_places: Maximum number of decimal places allowed. It does not include trailing decimal zeroes.

Here's an example:

from decimal import Decimal

from pydantic import BaseModel, Field


class Foo(BaseModel):
    precise: Decimal = Field(max_digits=5, decimal_places=2)


foo = Foo(precise=Decimal('123.45'))
print(foo)
#> precise=Decimal('123.45')

Dataclass Constraints¶

There are fields that can be used to constrain dataclasses:

init: Whether the field should be included in the __init__ of the dataclass.
init_var: Whether the field should be seen as an init-only field in the dataclass.
kw_only: Whether the field should be a keyword-only argument in the constructor of the dataclass.

Here's an example:

from pydantic import BaseModel, Field
from pydantic.dataclasses import dataclass


@dataclass
class Foo:
    bar: str
    baz: str = Field(init_var=True)
    qux: str = Field(kw_only=True)


class Model(BaseModel):
    foo: Foo


model = Model(foo=Foo('bar', baz='baz', qux='qux'))
print(model.model_dump())  # (1)!
#> {'foo': {'bar': 'bar', 'qux': 'qux'}}

The baz field is not included in the model_dump() output, since it is an init-only field.

Validate Default Values¶

The parameter validate_default can be used to control whether the default value of the field should be validated.

By default, the default value of the field is not validated.

from pydantic import BaseModel, Field, ValidationError


class User(BaseModel):
    age: int = Field(default='twelve', validate_default=True)


try:
    user = User()
except ValidationError as e:
    print(e)
    """
    1 validation error for User
    age
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='twelve', input_type=str]
    """

Field Representation¶

The parameter repr can be used to control whether the field should be included in the string representation of the model.

from pydantic import BaseModel, Field


class User(BaseModel):
    name: str = Field(repr=True)  # (1)!
    age: int = Field(repr=False)


user = User(name='John', age=42)
print(user)
#> name='John'

This is the default value.

Discriminator¶

The parameter discriminator can be used to control the field that will be used to discriminate between different models in a union. It takes either the name of a field or a Discriminator instance. The Discriminator approach can be useful when the discriminator fields aren't the same for all the models in the Union.

The following example shows how to use discriminator with a field name:

Python 3.8 and abovePython 3.10 and above

from typing import Literal, Union

from pydantic import BaseModel, Field


class Cat(BaseModel):
    pet_type: Literal['cat']
    age: int


class Dog(BaseModel):
    pet_type: Literal['dog']
    age: int


class Model(BaseModel):
    pet: Union[Cat, Dog] = Field(discriminator='pet_type')


print(Model.model_validate({'pet': {'pet_type': 'cat', 'age': 12}}))  # (1)!
#> pet=Cat(pet_type='cat', age=12)

See more about Helper Functions in the Models page.

from typing import Literal

from pydantic import BaseModel, Field


class Cat(BaseModel):
    pet_type: Literal['cat']
    age: int


class Dog(BaseModel):
    pet_type: Literal['dog']
    age: int


class Model(BaseModel):
    pet: Cat | Dog = Field(discriminator='pet_type')


print(Model.model_validate({'pet': {'pet_type': 'cat', 'age': 12}}))  # (1)!
#> pet=Cat(pet_type='cat', age=12)

See more about Helper Functions in the Models page.

The following example shows how to use the discriminator keyword argument with a Discriminator instance:

from typing import Literal, Union

from typing_extensions import Annotated

from pydantic import BaseModel, Discriminator, Field, Tag


class Cat(BaseModel):
    pet_type: Literal['cat']
    age: int


class Dog(BaseModel):
    pet_kind: Literal['dog']
    age: int


def pet_discriminator(v):
    if isinstance(v, dict):
        return v.get('pet_type', v.get('pet_kind'))
    return getattr(v, 'pet_type', getattr(v, 'pet_kind', None))


class Model(BaseModel):
    pet: Union[Annotated[Cat, Tag('cat')], Annotated[Dog, Tag('dog')]] = Field(
        discriminator=Discriminator(pet_discriminator)
    )


print(repr(Model.model_validate({'pet': {'pet_type': 'cat', 'age': 12}})))
#> Model(pet=Cat(pet_type='cat', age=12))

print(repr(Model.model_validate({'pet': {'pet_kind': 'dog', 'age': 12}})))
#> Model(pet=Dog(pet_kind='dog', age=12))

You can also take advantage of Annotated to define your discriminated unions. See the Discriminated Unions docs for more details.

Strict Mode¶

The strict parameter on a Field specifies whether the field should be validated in "strict mode". In strict mode, Pydantic throws an error during validation instead of coercing data on the field where strict=True.

from pydantic import BaseModel, Field


class User(BaseModel):
    name: str = Field(strict=True)  # (1)!
    age: int = Field(strict=False)


user = User(name='John', age='42')  # (2)!
print(user)
#> name='John' age=42

This is the default value.
The age field is not validated in the strict mode. Therefore, it can be assigned a string.

See Strict Mode for more details.

See Conversion Table for more details on how Pydantic converts data in both strict and lax modes.

Immutability¶

The parameter frozen is used to emulate the frozen dataclass behaviour. It is used to prevent the field from being assigned a new value after the model is created (immutability).

See the frozen dataclass documentation for more details.

from pydantic import BaseModel, Field, ValidationError


class User(BaseModel):
    name: str = Field(frozen=True)
    age: int


user = User(name='John', age=42)

try:
    user.name = 'Jane'  # (1)!
except ValidationError as e:
    print(e)
    """
    1 validation error for User
    name
      Field is frozen [type=frozen_field, input_value='Jane', input_type=str]
    """

Since name field is frozen, the assignment is not allowed.

Exclude¶

The exclude parameter can be used to control which fields should be excluded from the model when exporting the model.

See the following example:

from pydantic import BaseModel, Field


class User(BaseModel):
    name: str
    age: int = Field(exclude=True)


user = User(name='John', age=42)
print(user.model_dump())  # (1)!
#> {'name': 'John'}

The age field is not included in the model_dump() output, since it is excluded.

See the Serialization section for more details.

Deprecated fields¶

The deprecated parameter can be used to mark a field as being deprecated. Doing so will result in:

a runtime deprecation warning emitted when accessing the field.
"deprecated": true being set in the generated JSON schema.

You can set the deprecated parameter as one of:

A string, which will be used as the deprecation message.
An instance of the warnings.deprecated decorator (or the typing_extensions backport).
A boolean, which will be used to mark the field as deprecated with a default 'deprecated' deprecation message.

`deprecated` as a string¶

from typing_extensions import Annotated

from pydantic import BaseModel, Field


class Model(BaseModel):
    deprecated_field: Annotated[int, Field(deprecated='This is deprecated')]


print(Model.model_json_schema()['properties']['deprecated_field'])
#> {'deprecated': True, 'title': 'Deprecated Field', 'type': 'integer'}

`deprecated` via the `warnings.deprecated` decorator¶

Note

You can only use the deprecated decorator in this way if you have typing_extensions >= 4.9.0 installed.

import importlib.metadata

from packaging.version import Version
from typing_extensions import Annotated, deprecated

from pydantic import BaseModel, Field

if Version(importlib.metadata.version('typing_extensions')) >= Version('4.9'):

    class Model(BaseModel):
        deprecated_field: Annotated[int, deprecated('This is deprecated')]

        # Or explicitly using `Field`:
        alt_form: Annotated[
            int, Field(deprecated=deprecated('This is deprecated'))
        ]

`deprecated` as a boolean¶

from typing_extensions import Annotated

from pydantic import BaseModel, Field


class Model(BaseModel):
    deprecated_field: Annotated[int, Field(deprecated=True)]


print(Model.model_json_schema()['properties']['deprecated_field'])
#> {'deprecated': True, 'title': 'Deprecated Field', 'type': 'integer'}

Support for category and stacklevel

The current implementation of this feature does not take into account the category and stacklevel arguments to the deprecated decorator. This might land in a future version of Pydantic.

Accessing a deprecated field in validators

When accessing a deprecated field inside a validator, the deprecation warning will be emitted. You can use catch_warnings to explicitly ignore it:

import warnings

from typing_extensions import Self

from pydantic import BaseModel, Field, model_validator


class Model(BaseModel):
    deprecated_field: int = Field(deprecated='This is deprecated')

    @model_validator(mode='after')
    def validate_model(self) -> Self:
        with warnings.catch_warnings():
            warnings.simplefilter('ignore', DeprecationWarning)
            self.deprecated_field = self.deprecated_field * 2

Customizing JSON Schema¶

Some field parameters are used exclusively to customize the generated JSON schema. The parameters in question are:

title
description
examples
json_schema_extra

Read more about JSON schema customization / modification with fields in the Customizing JSON Schema section of the JSON schema docs.

The `computed_field` decorator¶

API Documentation

pydantic.fields.computed_field

The computed_field decorator can be used to include property or cached_property attributes when serializing a model or dataclass. This can be useful for fields that are computed from other fields, or for fields that are expensive to computed (and thus, are cached).

Here's an example:

from pydantic import BaseModel, computed_field


class Box(BaseModel):
    width: float
    height: float
    depth: float

    @computed_field
    def volume(self) -> float:
        return self.width * self.height * self.depth


b = Box(width=1, height=2, depth=3)
print(b.model_dump())
#> {'width': 1.0, 'height': 2.0, 'depth': 3.0, 'volume': 6.0}

As with regular fields, computed fields can be marked as being deprecated:

from typing_extensions import deprecated

from pydantic import BaseModel, computed_field


class Box(BaseModel):
    width: float
    height: float
    depth: float

    @computed_field
    @deprecated("'volume' is deprecated")
    def volume(self) -> float:
        return self.width * self.height * self.depth

Fields

Default values¶

Using Annotated¶

Field aliases¶

Validation Alias¶

Numeric Constraints¶

String Constraints¶

Decimal Constraints¶

Dataclass Constraints¶

Validate Default Values¶

Field Representation¶

Discriminator¶

Strict Mode¶

Immutability¶

Exclude¶

Deprecated fields¶

deprecated as a string¶

deprecated via the warnings.deprecated decorator¶

deprecated as a boolean¶

Customizing JSON Schema¶

The computed_field decorator¶

Using `Annotated`¶

`deprecated` as a string¶

`deprecated` via the `warnings.deprecated` decorator¶

`deprecated` as a boolean¶

The `computed_field` decorator¶