Fields

??? api "API Documentation" [pydantic.fields.Field][pydantic.fields.Field]

In this section, we will go through the available mechanisms to customize Pydantic model fields: default values, JSON Schema metadata, constraints, etc.

To do so, the [Field()][pydantic.fields.Field] function is used a lot, and behaves the same way as the standard library [field()][dataclasses.field] function for dataclasses – by assigning to the annotated attribute:

python

from pydantic import BaseModel, Field


class Model(BaseModel):
    name: str = Field(frozen=True)

!!! note Even though name is assigned a value, it is still required and has no default value. If you want to emphasize on the fact that a value must be provided, you can use the [ellipsis][Ellipsis]:

```python {lint="skip" test="skip"}
class Model(BaseModel):
    name: str = Field(..., frozen=True)
```

However, its usage is discouraged as it doesn't play well with static type checkers.

The annotated pattern

To apply constraints or attach [Field()][pydantic.fields.Field] functions to a model field, Pydantic also supports the [Annotated][typing.Annotated] typing construct to attach metadata to an annotation:

python

from typing import Annotated

from pydantic import BaseModel, Field, WithJsonSchema


class Model(BaseModel):
    name: Annotated[str, Field(strict=True), WithJsonSchema({'extra': 'data'})]

As far as static type checkers are concerned, name is still typed as str, but Pydantic leverages the available metadata to add validation logic, type constraints, etc.

Using this pattern has some advantages:

Using the f: <type> = Field(...) form can be confusing and might trick users into thinking f has a default value, while in reality it is still required.
You can provide an arbitrary amount of metadata elements for a field. As shown in the example above, the [Field()][pydantic.fields.Field] function only supports a limited set of constraints/metadata, and you may have to use different Pydantic utilities such as [WithJsonSchema][pydantic.WithJsonSchema] in some cases.
Types can be made reusable (see the documentation on custom types using this pattern).

However, note that certain arguments to the [Field()][pydantic.fields.Field] function (namely, default, default_factory, and alias) are taken into account by static type checkers to synthesize a correct __init__() method. The annotated pattern is not understood by them, so you should use the normal assignment form instead.

!!! tip The annotated pattern can also be used to add metadata to specific parts of the type. For instance, validation constraints can be added this way:

```python
from typing import Annotated

from pydantic import BaseModel, Field


class Model(BaseModel):
    int_list: list[Annotated[int, Field(gt=0)]]
    # Valid: [1, 3]
    # Invalid: [-1, 2]
```

Be careful not mixing *field* and *type* metadata:

```python {test="skip" lint="skip"}
class Model(BaseModel):
    field_bad: Annotated[int, Field(deprecated=True)] | None = None  # (1)!
    field_ok: Annotated[int | None, Field(deprecated=True)] = None  # (2)!
```

  1. The [`Field()`][pydantic.fields.Field] function is applied to `int` type, hence the
     `deprecated` flag won't have any effect. While this may be confusing given that the name of
     the [`Field()`][pydantic.fields.Field] function would imply it should apply to the field,
     the API was designed when this function was the only way to provide metadata. You can
     alternatively make use of the [`annotated_types`](https://github.com/annotated-types/annotated-types)
     library which is now supported by Pydantic.

  2. The [`Field()`][pydantic.fields.Field] function is applied to the "top-level" union type,
     hence the `deprecated` flag will be applied to the field.

Inspecting model fields

The fields of a model can be inspected using the [model_fields][pydantic.main.BaseModel.model_fields] class attribute (or the __pydantic_fields__ attribute for Pydantic dataclasses). It is a mapping of field names to their definition (represented as [FieldInfo][pydantic.fields.FieldInfo] instances).

python

from typing import Annotated

from pydantic import BaseModel, Field, WithJsonSchema


class Model(BaseModel):
    a: Annotated[
        int, Field(gt=1), WithJsonSchema({'extra': 'data'}), Field(alias='b')
    ] = 1


field_info = Model.model_fields['a']
print(field_info.annotation)
#> <class 'int'>
print(field_info.alias)
#> b
print(field_info.metadata)
#> [Gt(gt=1), WithJsonSchema(json_schema={'extra': 'data'}, mode=None)]

/// deprecated-removed | v2.11 v3 [model_fields][pydantic.main.BaseModel.model_fields] can only be accessed from the class object, not the instance. ///

Default values

Default values for fields can be provided using the normal assignment syntax or by providing a value to the default argument:

python

from pydantic import BaseModel, Field


class User(BaseModel):
    # Both fields aren't required:
    name: str = 'John Doe'
    age: int = Field(default=20)

/// version-changed | v2 In Pydantic V1, a type annotated as [Any][typing.Any] or wrapped by [Optional][typing.Optional] would be given an implicit default of None even if no default was explicitly specified. This is no longer the case in Pydantic V2. ///

You can also pass a callable to the default_factory argument that will be called to generate a default value:

python

from uuid import uuid4

from pydantic import BaseModel, Field


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

{#default-factory-validated-data}

The default factory can also take a single required argument, in which case the already validated data will be passed as a dictionary.

python

from pydantic import BaseModel, EmailStr, Field


class User(BaseModel):
    email: EmailStr
    username: str = Field(default_factory=lambda data: data['email'])


user = User(email='[email protected]')
print(user.username)
#> [email protected]

The data argument will only contain the already validated data, based on the order of model fields (the above example would fail if username were to be defined before email).

/// version-added | v2.10 Default factories can take already validated data as an argument. ///

/// version-added | v2.13 Default factories for private attributes can take the validated data as an argument. ///

Validate default values

By default, Pydantic will not validate default values. The validate_default field parameter (or the [validate_default][pydantic.ConfigDict.validate_default] configuration value) can be used to enable this behavior:

python

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]
    """

Mutable default values

A common source of bugs in Python is to use a mutable object as a default value for a function or method argument, as the same instance ends up being reused in each call.

The [dataclasses][dataclasses] module actually raises an error in this case, indicating that you should use a default factory instead.

While the same thing can be done in Pydantic, it is not required. In the event that the default value is not hashable, Pydantic will create a deep copy of the default value when creating each instance of the model:

python

from pydantic import BaseModel


class Model(BaseModel):
    item_counts: list[dict[str, int]] = [{}]


m1 = Model()
m1.item_counts[0]['a'] = 1
print(m1.item_counts)
#> [{'a': 1}]

m2 = Model()
print(m2.item_counts)
#> [{}]

Field aliases

!!! tip Read more about aliases in the dedicated section.

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 the validation_alias and serialization_alias parameters, which will apply only in their respective use cases.

Here is an example of using the alias parameter:

python

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()][pydantic.main.BaseModel.model_dump] to convert the model into a serializable format.

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

You can also use [ConfigDict.serialize_by_alias][pydantic.config.ConfigDict.serialize_by_alias] to configure this behavior at the model level.

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

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

python

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:

python

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.

!!! note "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 provide a value for the [`alias_generator`][pydantic.config.ConfigDict.alias_generator] model setting, you can control the order of precedence for field alias and generated aliases via the `alias_priority` field parameter. You can read more about alias precedence [here](../concepts/alias.md#alias-precedence).

??? tip "Static type checking/IDE support" If you provide a value for the alias field parameter, static type checkers will use this alias instead of the actual field name to synthesize the __init__ method:

```python
from pydantic import BaseModel, Field


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


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

1. Accepted by type checkers.

This means that when using the [`validate_by_name`][pydantic.config.ConfigDict.validate_by_name] model setting (which allows both the field name and alias to be used during model validation), type checkers will error when the actual field name is used:

```python
from pydantic import BaseModel, ConfigDict, Field


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

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


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

1. *Not* accepted by type checkers.

If you still want type checkers to use the field name and not the alias, the [annotated pattern](#the-annotated-pattern)
can be used (which is only understood by Pydantic):

```python
from typing import Annotated

from pydantic import BaseModel, ConfigDict, Field


class User(BaseModel):
    model_config = ConfigDict(validate_by_name=True, validate_by_alias=True)

    name: Annotated[str, Field(alias='username')]


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

1. Accepted by type checkers.
2. *Not* accepted by type checkers.

<h3>Validation Alias</h3>

Even though Pydantic treats `alias` and `validation_alias` the same when creating model instances, type checkers
only understand the `alias` field parameter. As a workaround, you can instead specify both an `alias` and
`serialization_alias` (identical to the field name), as the `serialization_alias` will override the `alias` during
serialization:

```python
from pydantic import BaseModel, Field


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

with:

```python
from pydantic import BaseModel, Field


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


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

{#numeric-constraints}

{#string-constraints}

{#decimal-constraints}

Field constraints

The [Field()][pydantic.Field] function can also be used to add constraints to specific types:

python

from decimal import Decimal

from pydantic import BaseModel, Field


class Model(BaseModel):
    positive: int = Field(gt=0)
    short_str: str = Field(max_length=3)
    precise_decimal: Decimal = Field(max_digits=5, decimal_places=2)

The available constraints for each type (and the way they affect the JSON Schema) are described in the standard library types documentation.

!!! note When adding constraints to a union type, if a member of the union is None or the MISSING sentinel, the constraints will be automatically applied to the remaining type(s) of the union:

```python
from typing import Annotated, Union

from pydantic import BaseModel, Field


class Model(BaseModel):
    positive: Union[int, None] = Field(gt=0)
    # Also works with the annotated pattern:
    negative: Annotated[Union[int, None], Field(lt=0)]
```

{#strict-mode}

Strict fields

The strict parameter of the [Field()][pydantic.Field] function specifies whether the field should be validated in strict mode.

python

from pydantic import BaseModel, Field


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


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

This is the default value.
The age field is validated in lax mode. Therefore, it can be assigned a string.

The standard library types documentation describes the strict behavior for each type.

{#dataclass-constraints}

Dataclass fields

Some parameters of the [Field()][pydantic.Field] function can be used on dataclasses:

init: Whether the field should be included in the synthesized __init__() method of the dataclass.
init_var: Whether the field should be [init-only][dataclasses-init-only-variables] in the dataclass.
kw_only: Whether the field should be a keyword-only argument in the constructor of the dataclass.

Here is an example:

python

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 serialized output, since it is an init-only field.

Field Representation

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

python

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

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 model_validate() in the Validating data documentation.

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

python

from typing import Annotated, Literal, Union

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 documentation for more details.

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.

python

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}

Excluding fields

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

See the following example:

python

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()][pydantic.BaseModel.model_dump] output, since it is excluded.

See the dedicated serialization section for more details.

/// version-added | v2.12 The exclude_if parameter. ///

Deprecated fields

/// version-added | v2.7.0 ///

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.
The deprecated keyword being set in the generated JSON schema.

This parameter accepts different types, described below.

`deprecated` as a string

The value will be used as the deprecation message.

python

from typing 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

The [@warnings.deprecated][warnings.deprecated] decorator (or the [typing_extensions backport][typing_extensions.deprecated] on Python 3.12 and lower) can be used as an instance.

=== "Python 3.9 and above"

```python
from typing import Annotated

from typing_extensions import deprecated

from pydantic import BaseModel, Field


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'))]
```

=== "Python 3.13 and above"

```python {requires="3.13"}
from typing import Annotated
from warnings import deprecated

from pydantic import BaseModel, Field


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'))]
```

!!! note "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.

`deprecated` as a boolean

python

from typing 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'}

!!! warning "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][warnings.catch_warnings] to explicitly ignore it:

```python
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 "API Documentation" [@computed_field][pydantic.fields.computed_field]

/// version-added | v2.13 Computed fields can be conditionally excluded from the serialization output by using the exclude_if parameter of the decorator. ///

The [@computed_field][pydantic.fields.computed_field] decorator can be used to include [properties][property] (or [cached properties][functools.cached_property]) when serializing a model or dataclass. The property will also be included in the JSON Schema (in serialization mode).

!!! note Properties can be useful for fields that are computed from other fields, or for fields that are expensive to be computed (and thus, are cached if using [@cached_property][functools.cached_property]).

However, note that Pydantic will *not* perform any additional logic on the wrapped property
(validation, cache invalidation, etc.).

Here's an example of the JSON schema (in serialization mode) generated for a model with a computed field:

python

from pydantic import BaseModel, computed_field


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

    @computed_field
    @property  # (1)!
    def volume(self) -> float:
        return self.width * self.height * self.depth


print(Box.model_json_schema(mode='serialization'))
"""
{
    'properties': {
        'width': {'title': 'Width', 'type': 'number'},
        'height': {'title': 'Height', 'type': 'number'},
        'depth': {'title': 'Depth', 'type': 'number'},
        'volume': {'readOnly': True, 'title': 'Volume', 'type': 'number'},
    },
    'required': ['width', 'height', 'depth', 'volume'],
    'title': 'Box',
    'type': 'object',
}
"""

If not specified, [@computed_field][pydantic.fields.computed_field] will implicitly convert the method to a [@property][property]. However, it is preferable to explicitly use the [@property][property] decorator for type checking purposes.

Here's an example using the [model_dump()][pydantic.BaseModel.model_dump] method with a computed field:

python

from pydantic import BaseModel, computed_field


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

    @computed_field
    @property
    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:

python

from typing_extensions import deprecated

from pydantic import BaseModel, computed_field


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

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

The annotated pattern

Inspecting model fields

Default values

Validate default values

Mutable default values

Field aliases

Field constraints

Strict fields

Dataclass fields

Field Representation

Discriminator

Immutability

Excluding fields

Deprecated fields

deprecated as a string

deprecated via the @warnings.deprecated decorator

deprecated as a boolean

Customizing JSON Schema

The computed_field decorator

`deprecated` as a string

`deprecated` via the `@warnings.deprecated` decorator

`deprecated` as a boolean

The `computed_field` decorator