Alias

An alias is an alternative name for a field, used when serializing and deserializing data.

You can specify an alias in the following ways:

alias on the Field
- must be a str
validation_alias on the Field
- can be an instance of str, AliasPath, or AliasChoices
serialization_alias on the Field
- must be a str
alias_generator on the Config
- can be a callable or an instance of AliasGenerator

For examples of how to use alias, validation_alias, and serialization_alias, see Field aliases.

`AliasPath` and `AliasChoices`¶

API Documentation

pydantic.aliases.AliasPath
pydantic.aliases.AliasChoices

Pydantic provides two special types for convenience when using validation_alias: AliasPath and AliasChoices.

The AliasPath is used to specify a path to a field using aliases. For example:

from pydantic import BaseModel, Field, AliasPath


class User(BaseModel):
    first_name: str = Field(validation_alias=AliasPath('names', 0))
    last_name: str = Field(validation_alias=AliasPath('names', 1))
    address: str = Field(validation_alias=AliasPath('contact', 'address'))

user = User.model_validate({  # (1)!
    'names': ['John', 'Doe'],
    'contact': {'address': '221B Baker Street'}
})
print(user)
#> first_name='John' last_name='Doe' address='221B Baker Street'

We are using model_validate() to validate a dictionary using the field aliases. Refer to documentation about validating data for more details.

In the 'first_name' field, we are using the alias 'names' and the index 0 to specify the path to the first name. In the 'last_name' field, we are using the alias 'names' and the index 1 to specify the path to the last name.

AliasChoices is used to specify a choice of aliases. For example:

from pydantic import BaseModel, Field, AliasChoices


class User(BaseModel):
    first_name: str = Field(validation_alias=AliasChoices('first_name', 'fname'))
    last_name: str = Field(validation_alias=AliasChoices('last_name', 'lname'))

user = User.model_validate({'fname': 'John', 'lname': 'Doe'})  # (1)!
print(user)
#> first_name='John' last_name='Doe'
user = User.model_validate({'first_name': 'John', 'lname': 'Doe'})  # (2)!
print(user)
#> first_name='John' last_name='Doe'

We are using the second alias choice for both fields.
We are using the first alias choice for the field 'first_name' and the second alias choice for the field 'last_name'.

You can also use AliasChoices with AliasPath:

from pydantic import BaseModel, Field, AliasPath, AliasChoices


class User(BaseModel):
    first_name: str = Field(validation_alias=AliasChoices('first_name', AliasPath('names', 0)))
    last_name: str = Field(validation_alias=AliasChoices('last_name', AliasPath('names', 1)))


user = User.model_validate({'first_name': 'John', 'last_name': 'Doe'})
print(user)
#> first_name='John' last_name='Doe'
user = User.model_validate({'names': ['John', 'Doe']})
print(user)
#> first_name='John' last_name='Doe'
user = User.model_validate({'names': ['John'], 'last_name': 'Doe'})
print(user)
#> first_name='John' last_name='Doe'

Using alias generators¶

You can use the alias_generator parameter of Config to specify a callable (or group of callables, via AliasGenerator) that will generate aliases for all fields in a model. This is useful if you want to use a consistent naming convention for all fields in a model, but do not want to specify the alias for each field individually.

Note

Pydantic offers three built-in alias generators that you can use out of the box:

to_pascal
to_camel
to_snake

Using a callable¶

Here's a basic example using a callable:

from pydantic import BaseModel, ConfigDict


class Tree(BaseModel):
    model_config = ConfigDict(
        alias_generator=lambda field_name: field_name.upper()
    )

    age: int
    height: float
    kind: str


t = Tree.model_validate({'AGE': 12, 'HEIGHT': 1.2, 'KIND': 'oak'})
print(t.model_dump(by_alias=True))
#> {'AGE': 12, 'HEIGHT': 1.2, 'KIND': 'oak'}

Using an `AliasGenerator`¶

API Documentation

pydantic.aliases.AliasGenerator

AliasGenerator is a class that allows you to specify multiple alias generators for a model. You can use an AliasGenerator to specify different alias generators for validation and serialization.

This is particularly useful if you need to use different naming conventions for loading and saving data, but you don't want to specify the validation and serialization aliases for each field individually.

For example:

from pydantic import AliasGenerator, BaseModel, ConfigDict


class Tree(BaseModel):
    model_config = ConfigDict(
        alias_generator=AliasGenerator(
            validation_alias=lambda field_name: field_name.upper(),
            serialization_alias=lambda field_name: field_name.title(),
        )
    )

    age: int
    height: float
    kind: str


t = Tree.model_validate({'AGE': 12, 'HEIGHT': 1.2, 'KIND': 'oak'})
print(t.model_dump(by_alias=True))
#> {'Age': 12, 'Height': 1.2, 'Kind': 'oak'}

Alias Precedence¶

If you specify an alias on the Field, it will take precedence over the generated alias by default:

from pydantic import BaseModel, ConfigDict, Field


def to_camel(string: str) -> str:
    return ''.join(word.capitalize() for word in string.split('_'))


class Voice(BaseModel):
    model_config = ConfigDict(alias_generator=to_camel)

    name: str
    language_code: str = Field(alias='lang')


voice = Voice(Name='Filiz', lang='tr-TR')
print(voice.language_code)
#> tr-TR
print(voice.model_dump(by_alias=True))
#> {'Name': 'Filiz', 'lang': 'tr-TR'}

Alias Priority¶

You may set alias_priority on a field to change this behavior:

alias_priority=2 the alias will not be overridden by the alias generator.
alias_priority=1 the alias will be overridden by the alias generator.
alias_priority not set:
- alias is set: the alias will not be overridden by the alias generator.
- alias is not set: the alias will be overridden by the alias generator.

The same precedence applies to validation_alias and serialization_alias. See more about the different field aliases under field aliases.

Alias Configuration¶

You can use ConfigDict settings or runtime validation/serialization settings to control whether or not aliases are used.

`ConfigDict` Settings¶

You can use configuration settings to control, at the model level, whether or not aliases are used for validation and serialization. If you would like to control this behavior for nested models/surpassing the config-model boundary, use runtime settings.

Validation¶

When validating data, you can enable population of attributes by attribute name, alias, or both. By default, Pydantic uses aliases for validation. Further configuration is available via:

ConfigDict.validate_by_alias: True by default
ConfigDict.validate_by_name: False by default

validate_by_aliasvalidate_by_namevalidate_by_alias and validate_by_name

from pydantic import BaseModel, ConfigDict, Field


class Model(BaseModel):
    my_field: str = Field(validation_alias='my_alias')

    model_config = ConfigDict(validate_by_alias=True, validate_by_name=False)


print(repr(Model(my_alias='foo')))  # (1)!
#> Model(my_field='foo')

The alias my_alias is used for validation.

from pydantic import BaseModel, ConfigDict, Field


class Model(BaseModel):
    my_field: str = Field(validation_alias='my_alias')

    model_config = ConfigDict(validate_by_alias=False, validate_by_name=True)


print(repr(Model(my_field='foo')))  # (1)!
#> Model(my_field='foo')

the attribute identifier my_field is used for validation.

from pydantic import BaseModel, ConfigDict, Field


class Model(BaseModel):
    my_field: str = Field(validation_alias='my_alias')

    model_config = ConfigDict(validate_by_alias=True, validate_by_name=True)


print(repr(Model(my_alias='foo')))  # (1)!
#> Model(my_field='foo')

print(repr(Model(my_field='foo')))  # (2)!
#> Model(my_field='foo')

The alias my_alias is used for validation.
the attribute identifier my_field is used for validation.

Warning

You cannot set both validate_by_alias and validate_by_name to False. A user error is raised in this case.

Serialization¶

When serializing data, you can enable serialization by alias, which is disabled by default. See the ConfigDict.serialize_by_alias API documentation for more details.

from pydantic import BaseModel, ConfigDict, Field


class Model(BaseModel):
    my_field: str = Field(serialization_alias='my_alias')

    model_config = ConfigDict(serialize_by_alias=True)


m = Model(my_field='foo')
print(m.model_dump())  # (1)!
#> {'my_alias': 'foo'}

The alias my_alias is used for serialization.

Note

The fact that serialization by alias is disabled by default is notably inconsistent with the default for validation (where aliases are used by default). We anticipate changing this default in V3.

Runtime Settings¶

You can use runtime alias flags to control alias use for validation and serialization on a per-call basis. If you would like to control this behavior on a model level, use ConfigDict settings.

Validation¶

When validating data, you can enable population of attributes by attribute name, alias, or both.

The by_alias and by_name flags are available on the model_validate(), model_validate_json(), and model_validate_strings() methods, as well as the TypeAdapter validation methods.

By default:

by_alias is True
by_name is False

by_aliasby_namevalidate_by_alias and validate_by_name

from pydantic import BaseModel, Field


class Model(BaseModel):
    my_field: str = Field(validation_alias='my_alias')


m = Model.model_validate(
    {'my_alias': 'foo'},  # (1)!
    by_alias=True,
    by_name=False,
)
print(repr(m))
#> Model(my_field='foo')

The alias my_alias is used for validation.

from pydantic import BaseModel, Field


class Model(BaseModel):
    my_field: str = Field(validation_alias='my_alias')


m = Model.model_validate(
    {'my_field': 'foo'}, by_alias=False, by_name=True  # (1)!
)
print(repr(m))
#> Model(my_field='foo')

The attribute name my_field is used for validation.

from pydantic import BaseModel, Field


class Model(BaseModel):
    my_field: str = Field(validation_alias='my_alias')


m = Model.model_validate(
    {'my_alias': 'foo'}, by_alias=True, by_name=True  # (1)!
)
print(repr(m))
#> Model(my_field='foo')

m = Model.model_validate(
    {'my_field': 'foo'}, by_alias=True, by_name=True  # (2)!
)
print(repr(m))
#> Model(my_field='foo')

The alias my_alias is used for validation.
The attribute name my_field is used for validation.

Warning

You cannot set both by_alias and by_name to False. A user error is raised in this case.

Serialization¶

When serializing data, you can enable serialization by alias via the by_alias flag which is available on the model_dump() and model_dump_json() methods, as well as the TypeAdapter ones.

By default, by_alias is False.

from pydantic import BaseModel, Field


class Model(BaseModel):
    my_field: str = Field(serialization_alias='my_alias')


m = Model(my_field='foo')
print(m.model_dump(by_alias=True))  # (1)!
#> {'my_alias': 'foo'}

The alias my_alias is used for serialization.

Note

The fact that serialization by alias is disabled by default is notably inconsistent with the default for validation (where aliases are used by default). We anticipate changing this default in V3.

Alias

AliasPath and AliasChoices¶

Using alias generators¶

Using a callable¶

Using an AliasGenerator¶

Alias Precedence¶

Alias Priority¶

Alias Configuration¶

ConfigDict Settings¶

Validation¶

Serialization¶

Runtime Settings¶

Validation¶

Serialization¶

`AliasPath` and `AliasChoices`¶

Using an `AliasGenerator`¶

`ConfigDict` Settings¶