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Models

The primary means of defining objects in pydantic is via models (models are simply classes which inherit from BaseModel).

You can think of models as similar to types in strictly typed languages, or as the requirements of a single endpoint in an API.

Untrusted data can be passed to a model, and after parsing and validation pydantic guarantees that the fields of the resultant model instance will conform to the field types defined on the model.

Basic model usage

from pydantic import BaseModel

class User(BaseModel):
    id: int
    name = 'Jane Doe'

User here is a model with two fields id which is an integer and is required, and name which is a string and is not required (it has a default value). The type of name is inferred from the default value, and so a type annotation is not required (however note this warning about field order when some fields do not have type annotations).

user = User(id='123')
user_x = User(id='123.45')

user here is an instance of User. Initialisation of the object will perform all parsing and validation, if no ValidationError is raised, you know the resulting model instance is valid.

assert user.id == 123
assert user_x.id == 123
assert isinstance(user_x.id, int)  # Note that 123.45 was casted to an int and its value is 123

More details on the casting in the case of user_x can be found in Data Conversion. Fields of a model can be accessed as normal attributes of the user object. The string ‘123’ has been cast to an int as per the field type

assert user.name == 'Jane Doe'

name wasn’t set when user was initialised, so it has the default value

assert user.__fields_set__ == {'id'}

The fields which were supplied when user was initialised.

assert user.dict() == dict(user) == {'id': 123, 'name': 'Jane Doe'}

Either .dict() or dict(user) will provide a dict of fields, but .dict() can take numerous other arguments.

user.id = 321
assert user.id == 321

This model is mutable so field values can be changed.

Model properties

The example above only shows the tip of the iceberg of what models can do. Models possess the following methods and attributes:

dict() : returns a dictionary of the model’s fields and values; cf. exporting models

json() : returns a JSON string representation dict(); cf. exporting models

copy() : returns a copy (by default, shallow copy) of the model; cf. exporting models

parse_obj() : a utility for loading any object into a model with error handling if the object is not a dictionary; cf. helper functions

parse_raw() : a utility for loading strings of numerous formats; cf. helper functions

parse_file() : like parse_raw() but for file paths; cf. helper functions

from_orm() : loads data into a model from an arbitrary class; cf. ORM mode

schema() : returns a dictionary representing the model as JSON Schema; cf. schema

schema_json() : returns a JSON string representation of schema(); cf. schema

construct() : a class method for creating models without running validation; cf. Creating models without validation

__fields_set__ : Set of names of fields which were set when the model instance was initialised

__fields__ : a dictionary of the model’s fields

__config__ : the configuration class for the model, cf. model config

Recursive Models

More complex hierarchical data structures can be defined using models themselves as types in annotations.

from typing import List, Optional
from pydantic import BaseModel


class Foo(BaseModel):
    count: int
    size: Optional[float] = None


class Bar(BaseModel):
    apple = 'x'
    banana = 'y'


class Spam(BaseModel):
    foo: Foo
    bars: List[Bar]


m = Spam(foo={'count': 4}, bars=[{'apple': 'x1'}, {'apple': 'x2'}])
print(m)
#> foo=Foo(count=4, size=None) bars=[Bar(apple='x1', banana='y'),
#> Bar(apple='x2', banana='y')]
print(m.dict())
"""
{
    'foo': {'count': 4, 'size': None},
    'bars': [
        {'apple': 'x1', 'banana': 'y'},
        {'apple': 'x2', 'banana': 'y'},
    ],
}
"""

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For self-referencing models, see postponed annotations.

ORM Mode (aka Arbitrary Class Instances)

Pydantic models can be created from arbitrary class instances to support models that map to ORM objects.

To do this:

  1. The Config property orm_mode must be set to True.
  2. The special constructor from_orm must be used to create the model instance.

The example here uses SQLAlchemy, but the same approach should work for any ORM.

from typing import List
from sqlalchemy import Column, Integer, String
from sqlalchemy.dialects.postgresql import ARRAY
from sqlalchemy.ext.declarative import declarative_base
from pydantic import BaseModel, constr

Base = declarative_base()


class CompanyOrm(Base):
    __tablename__ = 'companies'
    id = Column(Integer, primary_key=True, nullable=False)
    public_key = Column(String(20), index=True, nullable=False, unique=True)
    name = Column(String(63), unique=True)
    domains = Column(ARRAY(String(255)))


class CompanyModel(BaseModel):
    id: int
    public_key: constr(max_length=20)
    name: constr(max_length=63)
    domains: List[constr(max_length=255)]

    class Config:
        orm_mode = True


co_orm = CompanyOrm(
    id=123,
    public_key='foobar',
    name='Testing',
    domains=['example.com', 'foobar.com'],
)
print(co_orm)
#> <models_orm_mode.CompanyOrm object at 0x10832f4d0>
co_model = CompanyModel.from_orm(co_orm)
print(co_model)
#> id=123 public_key='foobar' name='Testing' domains=['example.com',
#> 'foobar.com']

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Reserved names

You may want to name a Column after a reserved SQLAlchemy field. In that case, Field aliases will be convenient:

import typing

from pydantic import BaseModel, Field
import sqlalchemy as sa
from sqlalchemy.ext.declarative import declarative_base


class MyModel(BaseModel):
    metadata: typing.Dict[str, str] = Field(alias='metadata_')

    class Config:
        orm_mode = True


Base = declarative_base()


class SQLModel(Base):
    __tablename__ = 'my_table'
    id = sa.Column('id', sa.Integer, primary_key=True)
    # 'metadata' is reserved by SQLAlchemy, hence the '_'
    metadata_ = sa.Column('metadata', sa.JSON)


sql_model = SQLModel(metadata_={'key': 'val'}, id=1)

pydantic_model = MyModel.from_orm(sql_model)

print(pydantic_model.dict())
#> {'metadata': {'key': 'val'}}
print(pydantic_model.dict(by_alias=True))
#> {'metadata_': {'key': 'val'}}

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Recursive ORM models

ORM instances will be parsed with from_orm recursively as well as at the top level.

Here a vanilla class is used to demonstrate the principle, but any ORM class could be used instead.

from typing import List
from pydantic import BaseModel


class PetCls:
    def __init__(self, *, name: str, species: str):
        self.name = name
        self.species = species


class PersonCls:
    def __init__(self, *, name: str, age: float = None, pets: List[PetCls]):
        self.name = name
        self.age = age
        self.pets = pets


class Pet(BaseModel):
    name: str
    species: str

    class Config:
        orm_mode = True


class Person(BaseModel):
    name: str
    age: float = None
    pets: List[Pet]

    class Config:
        orm_mode = True


bones = PetCls(name='Bones', species='dog')
orion = PetCls(name='Orion', species='cat')
anna = PersonCls(name='Anna', age=20, pets=[bones, orion])
anna_model = Person.from_orm(anna)
print(anna_model)
#> name='Anna' age=20.0 pets=[Pet(name='Bones', species='dog'),
#> Pet(name='Orion', species='cat')]

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Data binding

Arbitrary classes are processed by pydantic using the GetterDict class (see utils.py), which attempts to provide a dictionary-like interface to any class. You can customise how this works by setting your own sub-class of GetterDict as the value of Config.getter_dict (see config).

You can also customise class validation using root_validators with pre=True. In this case your validator function will be passed a GetterDict instance which you may copy and modify.

The GetterDict instance will be called for each field with a sentinel as a fallback (if no other default value is set). Returning this sentinel means that the field is missing. Any other value will be interpreted as the value of the field.

from pydantic import BaseModel
from typing import Any, Optional
from pydantic.utils import GetterDict
from xml.etree.ElementTree import fromstring


xmlstring = """
<User Id="2138">
    <FirstName />
    <LoggedIn Value="true" />
</User>
"""


class UserGetter(GetterDict):

    def get(self, key: str, default: Any) -> Any:

        # element attributes
        if key in {'Id', 'Status'}:
            return self._obj.attrib.get(key, default)

        # element children
        else:
            try:
                return self._obj.find(key).attrib['Value']
            except (AttributeError, KeyError):
                return default


class User(BaseModel):
    Id: int
    Status: Optional[str]
    FirstName: Optional[str]
    LastName: Optional[str]
    LoggedIn: bool

    class Config:
        orm_mode = True
        getter_dict = UserGetter


user = User.from_orm(fromstring(xmlstring))

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Error Handling

pydantic will raise ValidationError whenever it finds an error in the data it’s validating.

One exception will be raised regardless of the number of errors found, that ValidationError will contain information about all the errors and how they happened.

You can access these errors in several ways:

e.errors() : method will return list of errors found in the input data.

e.json() : method will return a JSON representation of errors.

str(e) : method will return a human readable representation of the errors.

Each error object contains:

loc : the error’s location as a list. The first item in the list will be the field where the error occurred, and if the field is a sub-model, subsequent items will be present to indicate the nested location of the error.

type : a computer-readable identifier of the error type.

msg : a human readable explanation of the error.

ctx : an optional object which contains values required to render the error message.

As a demonstration:

from typing import List
from pydantic import BaseModel, ValidationError, conint


class Location(BaseModel):
    lat = 0.1
    lng = 10.1


class Model(BaseModel):
    is_required: float
    gt_int: conint(gt=42)
    list_of_ints: List[int] = None
    a_float: float = None
    recursive_model: Location = None


data = dict(
    list_of_ints=['1', 2, 'bad'],
    a_float='not a float',
    recursive_model={'lat': 4.2, 'lng': 'New York'},
    gt_int=21,
)

try:
    Model(**data)
except ValidationError as e:
    print(e)
    """
    5 validation errors for Model
    is_required
      field required (type=value_error.missing)
    gt_int
      ensure this value is greater than 42 (type=value_error.number.not_gt;
    limit_value=42)
    list_of_ints -> 2
      value is not a valid integer (type=type_error.integer)
    a_float
      value is not a valid float (type=type_error.float)
    recursive_model -> lng
      value is not a valid float (type=type_error.float)
    """

try:
    Model(**data)
except ValidationError as e:
    print(e.json())
    """
    [
      {
        "loc": [
          "is_required"
        ],
        "msg": "field required",
        "type": "value_error.missing"
      },
      {
        "loc": [
          "gt_int"
        ],
        "msg": "ensure this value is greater than 42",
        "type": "value_error.number.not_gt",
        "ctx": {
          "limit_value": 42
        }
      },
      {
        "loc": [
          "list_of_ints",
          2
        ],
        "msg": "value is not a valid integer",
        "type": "type_error.integer"
      },
      {
        "loc": [
          "a_float"
        ],
        "msg": "value is not a valid float",
        "type": "type_error.float"
      },
      {
        "loc": [
          "recursive_model",
          "lng"
        ],
        "msg": "value is not a valid float",
        "type": "type_error.float"
      }
    ]
    """

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Custom Errors

In your custom data types or validators you should use ValueError, TypeError or AssertionError to raise errors.

See validators for more details on use of the @validator decorator.

from pydantic import BaseModel, ValidationError, validator


class Model(BaseModel):
    foo: str

    @validator('foo')
    def value_must_equal_bar(cls, v):
        if v != 'bar':
            raise ValueError('value must be "bar"')

        return v


try:
    Model(foo='ber')
except ValidationError as e:
    print(e.errors())
    """
    [
        {
            'loc': ('foo',),
            'msg': 'value must be "bar"',
            'type': 'value_error',
        },
    ]
    """

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You can also define your own error classes, which can specify a custom error code, message template, and context:

from pydantic import BaseModel, PydanticValueError, ValidationError, validator


class NotABarError(PydanticValueError):
    code = 'not_a_bar'
    msg_template = 'value is not "bar", got "{wrong_value}"'


class Model(BaseModel):
    foo: str

    @validator('foo')
    def value_must_equal_bar(cls, v):
        if v != 'bar':
            raise NotABarError(wrong_value=v)
        return v


try:
    Model(foo='ber')
except ValidationError as e:
    print(e.json())
    """
    [
      {
        "loc": [
          "foo"
        ],
        "msg": "value is not \"bar\", got \"ber\"",
        "type": "value_error.not_a_bar",
        "ctx": {
          "wrong_value": "ber"
        }
      }
    ]
    """

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Helper Functions

Pydantic provides three classmethod helper functions on models for parsing data:

  • parse_obj: this is very similar to the __init__ method of the model, except it takes a dict rather than keyword arguments. If the object passed is not a dict a ValidationError will be raised.
  • parse_raw: this takes a str or bytes and parses it as json, then passes the result to parse_obj. Parsing pickle data is also supported by setting the content_type argument appropriately.
  • parse_file: this takes in a file path, reads the file and passes the contents to parse_raw. If content_type is omitted, it is inferred from the file’s extension.
import pickle
from datetime import datetime
from pathlib import Path

from pydantic import BaseModel, ValidationError


class User(BaseModel):
    id: int
    name = 'John Doe'
    signup_ts: datetime = None


m = User.parse_obj({'id': 123, 'name': 'James'})
print(m)
#> id=123 signup_ts=None name='James'

try:
    User.parse_obj(['not', 'a', 'dict'])
except ValidationError as e:
    print(e)
    """
    1 validation error for User
    __root__
      User expected dict not list (type=type_error)
    """

# assumes json as no content type passed
m = User.parse_raw('{"id": 123, "name": "James"}')
print(m)
#> id=123 signup_ts=None name='James'

pickle_data = pickle.dumps({
    'id': 123,
    'name': 'James',
    'signup_ts': datetime(2017, 7, 14)
})
m = User.parse_raw(
    pickle_data, content_type='application/pickle', allow_pickle=True
)
print(m)
#> id=123 signup_ts=datetime.datetime(2017, 7, 14, 0, 0) name='James'

path = Path('data.json')
path.write_text('{"id": 123, "name": "James"}')
m = User.parse_file(path)
print(m)
#> id=123 signup_ts=None name='James'

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Creating models without validation

pydantic also provides the construct() method which allows models to be created without validation this can be useful when data has already been validated or comes from a trusted source and you want to create a model as efficiently as possible (construct() is generally around 30x faster than creating a model with full validation).

from pydantic import BaseModel


class User(BaseModel):
    id: int
    age: int
    name: str = 'John Doe'


original_user = User(id=123, age=32)

user_data = original_user.dict()
print(user_data)
#> {'id': 123, 'age': 32, 'name': 'John Doe'}
fields_set = original_user.__fields_set__
print(fields_set)
#> {'age', 'id'}

# ...
# pass user_data and fields_set to RPC or save to the database etc.
# ...

# you can then create a new instance of User without
# re-running validation which would be unnecessary at this point:
new_user = User.construct(_fields_set=fields_set, **user_data)
print(repr(new_user))
#> User(id=123, age=32, name='John Doe')
print(new_user.__fields_set__)
#> {'age', 'id'}

# construct can be dangerous, only use it with validated data!:
bad_user = User.construct(id='dog')
print(repr(bad_user))
#> User(id='dog', name='John Doe')

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The _fields_set keyword argument to construct() is optional, but allows you to be more precise about which fields were originally set and which weren’t. If it’s omitted __fields_set__ will just be the keys of the data provided.

For example, in the example above, if _fields_set was not provided, new_user.__fields_set__ would be \{'id', 'age', 'name'\}.

Generic Models

Pydantic supports the creation of generic models to make it easier to reuse a common model structure.

In order to declare a generic model, you perform the following steps:

  • Declare one or more typing.TypeVar instances to use to parameterize your model.
  • Declare a pydantic model that inherits from pydantic.generics.GenericModel and typing.Generic, where you pass the TypeVar instances as parameters to typing.Generic.
  • Use the TypeVar instances as annotations where you will want to replace them with other types or pydantic models.

Here is an example using GenericModel to create an easily-reused HTTP response payload wrapper:

from typing import Generic, TypeVar, Optional, List

from pydantic import BaseModel, validator, ValidationError
from pydantic.generics import GenericModel

DataT = TypeVar('DataT')


class Error(BaseModel):
    code: int
    message: str


class DataModel(BaseModel):
    numbers: List[int]
    people: List[str]


class Response(GenericModel, Generic[DataT]):
    data: Optional[DataT]
    error: Optional[Error]

    @validator('error', always=True)
    def check_consistency(cls, v, values):
        if v is not None and values['data'] is not None:
            raise ValueError('must not provide both data and error')
        if v is None and values.get('data') is None:
            raise ValueError('must provide data or error')
        return v


data = DataModel(numbers=[1, 2, 3], people=[])
error = Error(code=404, message='Not found')

print(Response[int](data=1))
#> data=1 error=None
print(Response[str](data='value'))
#> data='value' error=None
print(Response[str](data='value').dict())
#> {'data': 'value', 'error': None}
print(Response[DataModel](data=data).dict())
"""
{
    'data': {'numbers': [1, 2, 3], 'people': []},
    'error': None,
}
"""
print(Response[DataModel](error=error).dict())
"""
{
    'data': None,
    'error': {'code': 404, 'message': 'Not found'},
}
"""
try:
    Response[int](data='value')
except ValidationError as e:
    print(e)
    """
    2 validation errors for Response[int]
    data
      value is not a valid integer (type=type_error.integer)
    error
      must provide data or error (type=value_error)
    """

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If you set Config or make use of validator in your generic model definition, it is applied to concrete subclasses in the same way as when inheriting from BaseModel. Any methods defined on your generic class will also be inherited.

Pydantic’s generics also integrate properly with mypy, so you get all the type checking you would expect mypy to provide if you were to declare the type without using GenericModel.

To inherit from a GenericModel without replacing the TypeVar instance, a class must also inherit from typing.Generic:

from typing import TypeVar, Generic
from pydantic.generics import GenericModel

TypeX = TypeVar('TypeX')


class BaseClass(GenericModel, Generic[TypeX]):
    X: TypeX


class ChildClass(BaseClass[TypeX], Generic[TypeX]):
    # Inherit from Generic[TypeX]
    pass


# Replace TypeX by int
print(ChildClass[int](X=1))
#> X=1

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You can also create a generic subclass of a GenericModel that partially or fully replaces the type parameters in the superclass.

from typing import TypeVar, Generic
from pydantic.generics import GenericModel

TypeX = TypeVar('TypeX')
TypeY = TypeVar('TypeY')
TypeZ = TypeVar('TypeZ')


class BaseClass(GenericModel, Generic[TypeX, TypeY]):
    x: TypeX
    y: TypeY


class ChildClass(BaseClass[int, TypeY], Generic[TypeY, TypeZ]):
    z: TypeZ


# Replace TypeY by str
print(ChildClass[str, int](x=1, y='y', z=3))
#> x=1 y='y' z=3

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If the name of the concrete subclasses is important, you can also override the default behavior:

from typing import Generic, TypeVar, Type, Any, Tuple

from pydantic.generics import GenericModel

DataT = TypeVar('DataT')


class Response(GenericModel, Generic[DataT]):
    data: DataT

    @classmethod
    def __concrete_name__(cls: Type[Any], params: Tuple[Type[Any], ...]) -> str:
        return f'{params[0].__name__.title()}Response'


print(repr(Response[int](data=1)))
#> IntResponse(data=1)
print(repr(Response[str](data='a')))
#> StrResponse(data='a')

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Using the same TypeVar in nested models allows you to enforce typing relationships at different points in your model:

from typing import Generic, TypeVar

from pydantic import ValidationError
from pydantic.generics import GenericModel

T = TypeVar('T')


class InnerT(GenericModel, Generic[T]):
    inner: T


class OuterT(GenericModel, Generic[T]):
    outer: T
    nested: InnerT[T]


nested = InnerT[int](inner=1)
print(OuterT[int](outer=1, nested=nested))
#> outer=1 nested=InnerT[int](inner=1)
try:
    nested = InnerT[str](inner='a')
    print(OuterT[int](outer='a', nested=nested))
except ValidationError as e:
    print(e)
    """
    2 validation errors for OuterT[int]
    outer
      value is not a valid integer (type=type_error.integer)
    nested -> inner
      value is not a valid integer (type=type_error.integer)
    """

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Pydantic also treats GenericModel similarly to how it treats built-in generic types like List and Dict when it comes to leaving them unparameterized, or using bounded TypeVar instances:

  • If you don’t specify parameters before instantiating the generic model, they will be treated as Any
  • You can parametrize models with one or more bounded parameters to add subclass checks

Also, like List and Dict, any parameters specified using a TypeVar can later be substituted with concrete types.

from typing import Generic, TypeVar

from pydantic import ValidationError
from pydantic.generics import GenericModel

AT = TypeVar('AT')
BT = TypeVar('BT')


class Model(GenericModel, Generic[AT, BT]):
    a: AT
    b: BT


print(Model(a='a', b='a'))
#> a='a' b='a'

IntT = TypeVar('IntT', bound=int)
typevar_model = Model[int, IntT]
print(typevar_model(a=1, b=1))
#> a=1 b=1
try:
    typevar_model(a='a', b='a')
except ValidationError as exc:
    print(exc)
    """
    2 validation errors for Model[int, TypeVar]
    a
      value is not a valid integer (type=type_error.integer)
    b
      value is not a valid integer (type=type_error.integer)
    """

concrete_model = typevar_model[int]
print(concrete_model(a=1, b=1))
#> a=1 b=1

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Dynamic model creation

There are some occasions where the shape of a model is not known until runtime. For this pydantic provides the create_model method to allow models to be created on the fly.

from pydantic import BaseModel, create_model

DynamicFoobarModel = create_model('DynamicFoobarModel', foo=(str, ...), bar=123)


class StaticFoobarModel(BaseModel):
    foo: str
    bar: int = 123

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Here StaticFoobarModel and DynamicFoobarModel are identical.

Fields are defined by either a tuple of the form (<type>, <default value>) or just a default value. The special key word arguments __config__ and __base__ can be used to customise the new model. This includes extending a base model with extra fields.

from pydantic import BaseModel, create_model


class FooModel(BaseModel):
    foo: str
    bar: int = 123


BarModel = create_model(
    'BarModel',
    apple='russet',
    banana='yellow',
    __base__=FooModel,
)
print(BarModel)
#> <class 'pydantic.main.BarModel'>
print(BarModel.__fields__.keys())
#> dict_keys(['foo', 'bar', 'apple', 'banana'])

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You can also add validators by passing a dict to the __validators__ argument.

from pydantic import create_model, ValidationError, validator


def username_alphanumeric(cls, v):
    assert v.isalnum(), 'must be alphanumeric'
    return v


validators = {
    'username_validator':
    validator('username')(username_alphanumeric)
}

UserModel = create_model(
    'UserModel',
    username=(str, ...),
    __validators__=validators
)

user = UserModel(username='scolvin')
print(user)
#> username='scolvin'

try:
    UserModel(username='scolvi%n')
except ValidationError as e:
    print(e)
    """
    1 validation error for UserModel
    username
      must be alphanumeric (type=assertion_error)
    """

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Model creation from NamedTuple or TypedDict

Sometimes you already use in your application classes that inherit from NamedTuple or TypedDict and you don’t want to duplicate all your information to have a BaseModel. For this pydantic provides create_model_from_namedtuple and create_model_from_typeddict methods. Those methods have the exact same keyword arguments as create_model.

from typing_extensions import TypedDict

from pydantic import ValidationError, create_model_from_typeddict


class User(TypedDict):
    name: str
    id: int


class Config:
    extra = 'forbid'


UserM = create_model_from_typeddict(User, __config__=Config)
print(repr(UserM(name=123, id='3')))
#> User(name='123', id=3)

try:
    UserM(name=123, id='3', other='no')
except ValidationError as e:
    print(e)
    """
    1 validation error for User
    other
      extra fields not permitted (type=value_error.extra)
    """

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Custom Root Types

Pydantic models can be defined with a custom root type by declaring the __root__ field.

The root type can be any type supported by pydantic, and is specified by the type hint on the __root__ field. The root value can be passed to the model __init__ via the __root__ keyword argument, or as the first and only argument to parse_obj.

from typing import List
import json
from pydantic import BaseModel
from pydantic.schema import schema


class Pets(BaseModel):
    __root__: List[str]


print(Pets(__root__=['dog', 'cat']))
#> __root__=['dog', 'cat']
print(Pets(__root__=['dog', 'cat']).json())
#> ["dog", "cat"]
print(Pets.parse_obj(['dog', 'cat']))
#> __root__=['dog', 'cat']
print(Pets.schema())
"""
{
    'title': 'Pets',
    'type': 'array',
    'items': {'type': 'string'},
}
"""
pets_schema = schema([Pets])
print(json.dumps(pets_schema, indent=2))
"""
{
  "definitions": {
    "Pets": {
      "title": "Pets",
      "type": "array",
      "items": {
        "type": "string"
      }
    }
  }
}
"""

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If you call the parse_obj method for a model with a custom root type with a dict as the first argument, the following logic is used:

  • If the custom root type is a mapping type (eg., Dict or Mapping), the argument itself is always validated against the custom root type.
  • For other custom root types, if the dict has precisely one key with the value __root__, the corresponding value will be validated against the custom root type.
  • Otherwise, the dict itself is validated against the custom root type.

This is demonstrated in the following example:

from typing import List, Dict
from pydantic import BaseModel, ValidationError


class Pets(BaseModel):
    __root__: List[str]


print(Pets.parse_obj(['dog', 'cat']))
#> __root__=['dog', 'cat']
print(Pets.parse_obj({'__root__': ['dog', 'cat']}))  # not recommended
#> __root__=['dog', 'cat']


class PetsByName(BaseModel):
    __root__: Dict[str, str]


print(PetsByName.parse_obj({'Otis': 'dog', 'Milo': 'cat'}))
#> __root__={'Otis': 'dog', 'Milo': 'cat'}
try:
    PetsByName.parse_obj({'__root__': {'Otis': 'dog', 'Milo': 'cat'}})
except ValidationError as e:
    print(e)
    """
    1 validation error for PetsByName
    __root__ -> __root__
      str type expected (type=type_error.str)
    """

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If you want to access items in the __root__ field directly or to iterate over the items, you can implement custom __iter__ and __getitem__ functions, as shown in the following example.

from typing import List
from pydantic import BaseModel


class Pets(BaseModel):
    __root__: List[str]

    def __iter__(self):
        return iter(self.__root__)

    def __getitem__(self, item):
        return self.__root__[item]


pets = Pets.parse_obj(['dog', 'cat'])
print(pets[0])
#> dog
print([pet for pet in pets])
#> ['dog', 'cat']

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Faux Immutability

Models can be configured to be immutable via allow_mutation = False. When this is set, attempting to change the values of instance attributes will raise errors. See model config for more details on Config.

from pydantic import BaseModel


class FooBarModel(BaseModel):
    a: str
    b: dict

    class Config:
        allow_mutation = False


foobar = FooBarModel(a='hello', b={'apple': 'pear'})

try:
    foobar.a = 'different'
except TypeError as e:
    print(e)
    #> "FooBarModel" is immutable and does not support item assignment

print(foobar.a)
#> hello
print(foobar.b)
#> {'apple': 'pear'}
foobar.b['apple'] = 'grape'
print(foobar.b)
#> {'apple': 'grape'}

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Trying to change a caused an error, and a remains unchanged. However, the dict b is mutable, and the immutability of foobar doesn’t stop b from being changed.

Abstract Base Classes

Pydantic models can be used alongside Python’s Abstract Base Classes (ABCs).

import abc
from pydantic import BaseModel


class FooBarModel(BaseModel, abc.ABC):
    a: str
    b: int

    @abc.abstractmethod
    def my_abstract_method(self):
        pass

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Field Ordering

Field order is important in models for the following reasons:

As of v1.0 all fields with annotations (whether annotation-only or with a default value) will precede all fields without an annotation. Within their respective groups, fields remain in the order they were defined.

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    a: int
    b = 2
    c: int = 1
    d = 0
    e: float


print(Model.__fields__.keys())
#> dict_keys(['a', 'c', 'e', 'b', 'd'])
m = Model(e=2, a=1)
print(m.dict())
#> {'a': 1, 'c': 1, 'e': 2.0, 'b': 2, 'd': 0}
try:
    Model(a='x', b='x', c='x', d='x', e='x')
except ValidationError as e:
    error_locations = [e['loc'] for e in e.errors()]

print(error_locations)
#> [('a',), ('c',), ('e',), ('b',), ('d',)]

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Required fields

To declare a field as required, you may declare it using just an annotation, or you may use an ellipsis (...) as the value:

from pydantic import BaseModel, Field


class Model(BaseModel):
    a: int
    b: int = ...
    c: int = Field(...)

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Where Field refers to the field function.

Here a, b and c are all required. However, use of the ellipses in b will not work well with mypy, and as of v1.0 should be avoided in most cases.

Required Optional fields

If you want to specify a field that can take a None value while still being required, you can use Optional with ...:

from typing import Optional
from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    a: Optional[int]
    b: Optional[int] = ...
    c: Optional[int] = Field(...)


print(Model(b=1, c=2))
#> a=None b=1 c=2
try:
    Model(a=1, b=2)
except ValidationError as e:
    print(e)
    """
    1 validation error for Model
    c
      field required (type=value_error.missing)
    """

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In this model, a, b, and c can take None as a value. But a is optional, while b and c are required. b and c require a value, even if the value is None.

Field with dynamic default value

When declaring a field with a default value, you may want it to be dynamic (i.e. different for each model). To do this, you may want to use a default_factory.

Example of usage:

from datetime import datetime
from uuid import UUID, uuid4
from pydantic import BaseModel, Field


class Model(BaseModel):
    uid: UUID = Field(default_factory=uuid4)
    updated: datetime = Field(default_factory=datetime.utcnow)


m1 = Model()
m2 = Model()
print(f'{m1.uid} != {m2.uid}')
#> b54aedca-f98d-4c56-88e1-d559a6e3a182 != 70a9e47a-9758-4f3a-85e9-67783900eef6
print(f'{m1.updated} != {m2.updated}')
#> 2026-03-24 10:11:30.397414 != 2026-03-24 10:11:30.397424

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Where Field refers to the field function.

Automatically excluded attributes

Class variables which begin with an underscore and attributes annotated with typing.ClassVar will be automatically excluded from the model.

Private model attributes

If you need to vary or manipulate internal attributes on instances of the model, you can declare them using PrivateAttr:

from datetime import datetime
from random import randint

from pydantic import BaseModel, PrivateAttr


class TimeAwareModel(BaseModel):
    _processed_at: datetime = PrivateAttr(default_factory=datetime.now)
    _secret_value: str = PrivateAttr()

    def __init__(self, **data):
        super().__init__(**data)
        # this could also be done with default_factory
        self._secret_value = randint(1, 5)


m = TimeAwareModel()
print(m._processed_at)
#> 2026-03-24 12:11:30.560016
print(m._secret_value)
#> 5

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Private attribute names must start with underscore to prevent conflicts with model fields: both _attr and __attr__ are supported.

If Config.underscore_attrs_are_private is True, any non-ClassVar underscore attribute will be treated as private:

from typing import ClassVar

from pydantic import BaseModel


class Model(BaseModel):
    _class_var: ClassVar[str] = 'class var value'
    _private_attr: str = 'private attr value'

    class Config:
        underscore_attrs_are_private = True


print(Model._class_var)
#> class var value
print(Model._private_attr)
#> <member '_private_attr' of 'Model' objects>
print(Model()._private_attr)
#> private attr value

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Upon class creation pydantic constructs __slots__ filled with private attributes.

Parsing data into a specified type

Pydantic includes a standalone utility function parse_obj_as that can be used to apply the parsing logic used to populate pydantic models in a more ad-hoc way. This function behaves similarly to BaseModel.parse_obj, but works with arbitrary pydantic-compatible types.

This is especially useful when you want to parse results into a type that is not a direct subclass of BaseModel. For example:

from typing import List

from pydantic import BaseModel, parse_obj_as


class Item(BaseModel):
    id: int
    name: str


# `item_data` could come from an API call, eg., via something like:
# item_data = requests.get('https://my-api.com/items').json()
item_data = [{'id': 1, 'name': 'My Item'}]

items = parse_obj_as(List[Item], item_data)
print(items)
#> [Item(id=1, name='My Item')]

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This function is capable of parsing data into any of the types pydantic can handle as fields of a BaseModel.

Pydantic also includes two similar standalone functions called parse_file_as and parse_raw_as, which are analogous to BaseModel.parse_file and BaseModel.parse_raw.

Data Conversion

pydantic may cast input data to force it to conform to model field types, and in some cases this may result in a loss of information. For example:

from pydantic import BaseModel


class Model(BaseModel):
    a: int
    b: float
    c: str


print(Model(a=3.1415, b=' 2.72 ', c=123).dict())
#> {'a': 3, 'b': 2.72, 'c': '123'}

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This is a deliberate decision of pydantic, and in general it’s the most useful approach. See here for a longer discussion on the subject.

Nevertheless, strict type checking is partially supported.

Model signature

All pydantic models will have their signature generated based on their fields:

import inspect
from pydantic import BaseModel, Field


class FooModel(BaseModel):
    id: int
    name: str = None
    description: str = 'Foo'
    apple: int = Field(..., alias='pear')


print(inspect.signature(FooModel))
#> (*, id: int, name: str = None, description: str = 'Foo', pear: int) -> None

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An accurate signature is useful for introspection purposes and libraries like FastAPI or hypothesis.

The generated signature will also respect custom __init__ functions:

import inspect

from pydantic import BaseModel


class MyModel(BaseModel):
    id: int
    info: str = 'Foo'

    def __init__(self, id: int = 1, *, bar: str, **data) -> None:
        """My custom init!"""
        super().__init__(id=id, bar=bar, **data)


print(inspect.signature(MyModel))
#> (id: int = 1, *, bar: str, info: str = 'Foo') -> None

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To be included in the signature, a field’s alias or name must be a valid Python identifier. pydantic prefers aliases over names, but may use field names if the alias is not a valid Python identifier.

If a field’s alias and name are both invalid identifiers, a **data argument will be added. In addition, the **data argument will always be present in the signature if Config.extra is Extra.allow.

Structural pattern matching

pydantic supports structural pattern matching for models, as introduced by PEP 636 in Python 3.10.


from pydantic import BaseModel


class Pet(BaseModel):
    name: str
    species: str


a = Pet(name='Bones', species='dog')

match a:
    # match `species` to 'dog', declare and initialize `dog_name`
    case Pet(species='dog', name=dog_name):
        print(f'{dog_name} is a dog')
        #> Bones is a dog
    # default case
    case _:
        print('No dog matched')

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