Standard Library Types

This section enumerates the supported built-in and standard library types: the allowed values, the possible constraints, and whether strictness can be configured.

See also the conversion table for a summary of the allowed values for each type.

Built-in type: bool

• A valid bool instance, i.e. True or False.
• The integers 0 or 1.
• A string, which when converted to lowercase is one of '0', 'off', 'f', 'false', 'n', 'no', '1', 'on' 't', 'true', 'y', 'yes'.
• bytes objects that are valid per the previous rule when decoded to a string.

In strict mode, only boolean values are valid. Pydantic provides the StrictBool type as a convenience to using the Strict() metadata class.

from pydantic import BaseModel, ValidationError


class BooleanModel(BaseModel):
    bool_value: bool


print(BooleanModel(bool_value=False))
#> bool_value=False
print(BooleanModel(bool_value='False'))
#> bool_value=False
print(BooleanModel(bool_value=1))
#> bool_value=True
try:
    BooleanModel(bool_value=[])
except ValidationError as e:
    print(str(e))
    """
    1 validation error for BooleanModel
    bool_value
      Input should be a valid boolean [type=bool_type, input_value=[], input_type=list]
    """

Built-in type: str

• Strings are accepted as-is.
• bytes and bytearray are decoded to UTF-8 strings.
• Enums are converted using the value attribute, by calling str() on it.
• If coerce_numbers_to_str is set, any number type (int, float and Decimal) will be coerced to a string and accepted as-is.

Strings support the following constraints:

These constraints can be provided using the StringConstraints metadata type, or using the Field() function (except for strip_whitespace, to_upper, to_lower and ascii_only).

The annotated-types library also provides the MinLen, MaxLen and Len metadata types, as well as the LowerCase, UpperCase, IsDigit and IsAscii predicates (must be parameterized with str, e.g. LowerCase[str]).

In strict mode, only string values are valid. Pydantic provides the StrictStr type as a convenience to using the Strict() metadata class.

from typing import Annotated

from pydantic import BaseModel, StringConstraints


class StringModel(BaseModel):
    str_value: str = ""
    constrained_str_value: Annotated[str, StringConstraints(to_lower=True)] = ""


print(StringModel(str_value="test").str_value)
#> test
print(StringModel(constrained_str_value='TEST').constrained_str_value)
#> test

Built-in type: bytes.

See also: ByteSize.

• bytes instances are validated as is.
• Strings and bytearray instances are converted as bytes, following the val_json_bytes configuration value (despite its name, it applies to both Python and JSON modes).

Strings support the following constraints:

The MinLen and MaxLen metadata types from the annotated-types library can also be used.

In strict mode, only bytes instances are valid. Pydantic provides the StrictBytes type as a convenience to using the Strict() metadata class.

In JSON mode, strict mode has no effect.

Pydantic supports the following numeric types from the Python standard library:

Built-in type: int.

• Integers are validated as-is.
• Strings and bytes are attempted to be converted to integers and validated as-is (see the jiter implementation for details).
• Floats are validated as integers, provided the float input is not infinite or a NaN (not-a-number) and the fractional part is 0.
• Decimal instances, provided they are finite and the denominator is 1.
• Fraction instances, provided they are integers.
• Enums are converted using the value attribute.

Integers support the following constraints (numbers must be coercible to integers):

These constraints can be provided using the Field() function. The Le, Ge, Lt, Gt and MultipleOf metadata types from the annotated-types library can also be used.

Pydantic also provides the following types to further constrain the allowed integer values:

• PositiveInt: Requires the input to be greater than zero.
• NegativeInt: Requires the input to be less than zero.
• NonPositiveInt: Requires the input to be less than or equal to zero.
• NonNegativeInt: Requires the input to be greater than or equal to zero.

In strict mode, only integer values are valid. Pydantic provides the StrictInt type as a convenience to using the Strict() metadata class.

Built-in type: float.

• Floats are validated as-is.
• String and bytes are attempted to be converted to floats and validated as-is. (see the Rust implementation for details).
• If the input has a __float__() method, it will be called to convert the input into a float. If __float__() is not defined, it falls back to __index__(). This includes (but not limited to) the Decimal and Fraction types.

Floats support the following constraints:

These constraints can be provided using the Field() function.

The annotated-types library also provides the Le, Ge, Lt, Gt and MultipleOf metadata types, as well as the IsFinite, IsNotFinite, IsNan, IsNotNan, IsAscii, IsInfinite and IsNotInfinite predicates (must be parameterized with float, e.g. IsFinite[float]). The AllowInfNan type can also be used.

Pydantic also provides the following types as convenience aliases:

• PositiveFloat: Requires the input to be greater than zero.
• NegativeFloat: Requires the input to be less than zero.
• NonPositiveFloat: Requires the input to be less than or equal to zero.
• NonNegativeFloat: Requires the input to be greater than or equal to zero.
• FiniteFloat: Prevents NaN (not-a-number) and infinite values.

In strict mode, only float values and inputs having a __float__() or __index__() method are valid. Pydantic provides the StrictFloat type as a convenience to using the Strict() metadata class.

Standard library type: enum.IntEnum.

• If the enum.IntEnum type is used directly, any enum.IntEnum instance is validated as-is
• If an enum.IntEnum subclass is used as a type, any enum member or value that correspond to the enum members values is validated as-is.

See Enums for more details.

Standard library type: decimal.Decimal.

• Decimal instances are validated as is.
• Any value accepted by the Decimal constructor.

Decimals support the following constraints (numbers must be coercible to decimals):

These constraints can be provided using the Field() function. The Le, Ge, Lt, Gt and MultipleOf metadata types from the annotated-types library and the AllowInfNan type can also be used.

In strict mode, only decimal.Decimal instances are accepted. In JSON mode, strict mode has no effect.

In Python mode, Decimal instances are serialized as is.

In JSON mode, they are serialized as strings. A serializer can be used to override this behavior:

from decimal import Decimal
from typing import Annotated

from pydantic import BaseModel, PlainSerializer


class Model(BaseModel):
    f: Annotated[Decimal, PlainSerializer(float, when_used='json')]


my_model = Model(f=Decimal('2.1'))

print(my_model.model_dump())  # (1)!
#> {'f': Decimal('2.1')}
print(my_model.model_dump_json())  # (2)!
#> {"f":2.1}

1. In Python mode, fremains a Decimal instance.
2. In JSON mode, f is serialized as a float.

Built-in type: complex.

• complex instances are validated as-is.
• In Python mode, data is validated using the complex() constructor.
• In JSON mode, string are validated using the complex() constructor, numbers (integers and floats) are used as the real part.

In strict mode, only complex instances are accepted. In JSON mode, only strings that are accepted by the complex() constructor are allowed.

In Python mode, complex instances are serialized as is.

In JSON mode, they are serialized as strings.

Standard library type: fractions.Fraction.

• Fraction instances are validated as is.
• Floats, strings and decimal.Decimal instances are validated using the Fraction() constructor.

In strict mode, only Fraction instances are accepted. In JSON mode, strict mode has no effect.

Fractions are serialized as strings, both in Python and JSON modes.

Pydantic supports the following date and time types from the datetime standard library:

Standard library type: datetime.datetime.

• datetime instances are validated as is.
• Strings and bytes are validated in two ways:
  • Strings complying to the RFC 3339 format (both datetime and date). See the speedate documentation for more details.
  • Unix timestamps, both as seconds or milliseconds since the epoch. See the val_temporal_unit configuration value for more details.
• Integers and floats (or types that can be coerced as integers or floats) are validated as unix timestamps, following the same semantics as strings.
• datetime.date instances are accepted, and converted to a datetime instance by setting the hour, minute, second and microsecond attributes to 0, and the tzinfo attribute to None.

In Python mode, datetime instances are serialized as is.

In JSON mode, they are serialized as strings.

Datetimes support the following constraints (constraint values must be coercible to a datetime instance):

These constraints can be provided using the Field() function. The Le, Ge, Lt and Gt metadata types from the annotated-types library can also be used.

Pydantic also provides the following types to further constrain the allowed datetime values:

• AwareDatetime: Requires the input to have a timezone.
• NaiveDatetime: Requires the input to not have a timezone.
• PastDatetime: Requires the input to be in the past when validated.
• FutureDatetime: Requires the input to be in the future when validated.

In strict mode, only datetime instances are accepted. In JSON mode, only strings complying to the RFC 3339 format (only datetime) or as unix timestamps are accepted.

from datetime import datetime
from typing import Annotated

from pydantic import AwareDatetime, BaseModel, Field


class Event(BaseModel):
    dt: Annotated[AwareDatetime, Field(gt=datetime(2000, 1, 1))]


event = Event(dt='2032-04-23T10:20:30.400+02:30')

print(event.model_dump())
"""
{'dt': datetime.datetime(2032, 4, 23, 10, 20, 30, 400000, tzinfo=TzInfo(9000))}
"""
print(event.model_dump_json())
#> {"dt":"2032-04-23T10:20:30.400000+02:30"}

Standard library type: datetime.date.

• date instances are validated as is.
• Strings and bytes are validated in two ways:
  • Strings complying to the RFC 3339 date format. See the speedate documentation for more details.
  • Unix timestamps, both as seconds or milliseconds since the epoch. See the val_temporal_unit configuration value for more details.
• If the validation fails, the input can be validated as a datetime (including as numbers), provided that the time component is 0 and that it is naive.

In Python mode, date instances are serialized as is.

In JSON mode, they are serialized as strings.

Dates support the following constraints (constraint values must be coercible to a date instance):

These constraints can be provided using the Field() function. The Le, Ge, Lt and Gt metadata types from the annotated-types library can also be used.

Pydantic also provides the following types to further constrain the allowed date values:

• PastDate: Requires the input to be in the past when validated.
• FutureDate: Requires the input to be in the future when validated.

In strict mode, only date instances are accepted. In JSON mode, only strings complying to the RFC 3339 format (only date) or as unix timestamps are accepted.

from datetime import date

from pydantic import BaseModel


class Birthday(BaseModel):
    d: date


my_birthday = Birthday(d=1679616000.0)

print(my_birthday.model_dump())
#> {'d': datetime.date(2023, 3, 24)}
print(my_birthday.model_dump_json())
#> {"d":"2023-03-24"}

Standard library type: datetime.time.

• time instances are validated as is.
• Strings and bytes are validated according to the RFC 3339 time format.
• Integers and floats (or values that can be coerced to such numbers) are validated as seconds. The value should not exceed 86 399.

In Python mode, time instances are serialized as is.

In JSON mode, they are serialized as strings.

Time support the following constraints (constraint values must be coercible to a time instance):

These constraints can be provided using the Field() function. The Le, Ge, Lt and Gt metadata types from the annotated-types library can also be used.

In strict mode, only time instances are accepted. In JSON mode, only strings complying to the RFC 3339 format are accepted.

from datetime import time

from pydantic import BaseModel


class Meeting(BaseModel):
    t: time


m = Meeting(t=time(4, 8, 16))

print(m.model_dump())
#> {'t': datetime.time(4, 8, 16)}
print(m.model_dump_json())
#> {"t":"04:08:16"}

Standard library type: datetime.timedelta.

• timedelta instances are validated as is.
• Strings and bytes are validated according to the RFC 3339 time format.
• Integers and floats (or values that can be coerced to such numbers) are validated as seconds.

Timedeltas support the following constraints (constraint values must be coercible to a timedata instance):

| Constraint | Description | JSON Schema | | ---------- | ---------------------------------------------------- -----| ----------- | | le | The value must be less than or equal to this timedelta | N/A | | ge | The value must be greater than or equal to this timedelta | N/A | | lt | The value must be strictly less than this timedelta | N/A | | gt | The value must be strictly greater than this timedelta | N/A |

These constraints can be provided using the Field() function. The Le, Ge, Lt and Gt metadata types from the annotated-types library can also be used.

In Python mode, timedelta instances are serialized as is.

In JSON mode, they are serialized as strings.

In strict mode, only timedelta instances are accepted. In JSON mode, only strings complying to the RFC 3339 format are accepted.

from datetime import timedelta

from pydantic import BaseModel


class Model(BaseModel):
    td: timedelta


m = Model(td='P3DT12H30M5S')

print(m.model_dump())
#> {'td': datetime.timedelta(days=3, seconds=45005)}
print(m.model_dump_json())
#> {"td":"P3DT12H30M5S"}

Standard library type: enum.Enum.

• If the enum.Enum type is used directly, any enum.Enum instance is validated as-is.
• If an enum.Enum subclass is used as a type, any enum member or value that correspond to the enum members values is validated as-is.

In Python mode, enum instances are serialized as is. The use_enum_values configuration value can be set to use the enum value during validation (so that it is also used during serialization).

In JSON mode, enum instances are serialized using their value.

from enum import Enum, IntEnum

from pydantic import BaseModel, ValidationError


class FruitEnum(str, Enum):
    PEAR = 'pear'
    BANANA = 'banana'


class ToolEnum(IntEnum):
    SPANNER = 1
    WRENCH = 2


class CookingModel(BaseModel):
    fruit: FruitEnum = FruitEnum.PEAR
    tool: ToolEnum = ToolEnum.SPANNER


print(CookingModel())
#> fruit=<FruitEnum.PEAR: 'pear'> tool=<ToolEnum.SPANNER: 1>
print(CookingModel(tool=2, fruit='banana'))
#> fruit=<FruitEnum.BANANA: 'banana'> tool=<ToolEnum.WRENCH: 2>
try:
    CookingModel(fruit='other')
except ValidationError as e:
    print(e)
    """
    1 validation error for CookingModel
    fruit
      Input should be 'pear' or 'banana' [type=enum, input_value='other', input_type=str]
    """

Supported types: None, NoneType or Literal[None] (they are equivalent).

Allows only None as a value.

Pydantic supports a wide variety of generic collection types, both built-ins (such as list) and abstract base classes from the collections.abc module (such as Sequence).

In most cases, it is recommended to make use of the built-in types over the abstract ones. Due to data coercion, using list or tuple will allow most other iterables as input, with better performance.

Built-in type: list (deprecated alias: typing.List).

• Allows list, tuple, set and frozenset instances, or any iterable that is not a string, bytes, bytearray, dict or mapping. Produces a list instance.
• If a generic parameter is provided, the appropriate validation is applied to all items of the list.

Lists support the following constraints:

These constraints can be provided using the Field() function. The MinLen and MaxLen metadata types from the annotated-types library can also be used.

In strict mode, only list instances are valid. Strict mode does not apply to the items of the list. The strict constraint must be applied to the parameter type for this to work.

from pydantic import BaseModel, Field


class Model(BaseModel):
    simple_list: list[object] | None = None
    list_of_ints: list[int] | None = Field(default=None, strict=True)


print(Model(simple_list=('1', '2', '3')).simple_list)
#> ['1', '2', '3']
print(Model(list_of_ints=['1', 2, 3]).list_of_ints)
#> [1, 2, 3]

Built-in type: tuple (deprecated alias: typing.Tuple).

• Allows tuple, list, set and frozenset instances, or any iterable that is not a string, bytes, bytearray, dict or mapping. Produces a tuple instance.
• Appropriate validation is applied to items of the tuple, if element types are specified.

Lists support the following constraints:

These constraints can be provided using the Field() function. The MinLen and MaxLen metadata types from the annotated-types library can also be used.

Additionally, the prefixItems JSON Schema keyword may be used depending on the tuple shape.

In strict mode, only tuple instances are valid. Strict mode does not apply to the items of the tuple. The strict constraint must be applied to the parameter types for this to work.

from pydantic import BaseModel


class Model(BaseModel):
    simple_tuple: tuple | None = None
    tuple_of_different_types: tuple[int, float, bool] | None = None


print(Model(simple_tuple=[1, 2, 3, 4]).simple_tuple)
#> (1, 2, 3, 4)
print(Model(tuple_of_different_types=[3, 2, 1]).tuple_of_different_types)
#> (3, 2.0, True)

Standard library type: typing.NamedTuple (and types created by the collections.namedtuple() factory function – each field will implicitly have the type Any).

• Allows tuple and list instances. Validate each item according to the field definition.
• Allows dict instances. Keys must match the named tuple field names, and values are validated according to the field definition.
• Allows instances of the named tuple class (fields are revalidated).

In Python mode, named tuples are serialized as tuples. In JSON mode, they are serialized as arrays.

from typing import NamedTuple

from pydantic import BaseModel


class Point(NamedTuple):
    x: int
    y: int


class Model(BaseModel):
    p: Point


model = Model(p=('1', 2))

print(model.model_dump())
#> {'p': (1, 2)}

Types: set (or collections.abc.MutableSet) and frozenset (or collections.abc.Set) (deprecated aliases: typing.Set and typing.FrozenSet).

• Allows set, frozenset, tuple and list instances, or any iterable that is not a string, bytes, bytearray, dict or mapping. Produces a set or frozenset instance.
• If a generic parameter is provided, the appropriate validation is applied to all items of the set/frozenset.

Sets support the following constraints:

These constraints can be provided using the Field() function. The MinLen and MaxLen metadata types from the annotated-types library can also be used.

In strict mode, only set/frozenset instances are valid. Strict mode does not apply to the items of the set. The strict constraint must be applied to the parameter type for this to work.

In Python mode, sets are serialized as is. In JSON mode, they are serialized as arrays.

from pydantic import BaseModel


class Model(BaseModel):
    simple_set: set | None = None
    set_of_ints: frozenset[int] | None = None


print(Model(simple_set=['1', '2', '3']).simple_set)
#> {'1', '2', '3'}
print(Model(set_of_ints=['1', '2', '3']).set_of_ints)
#> frozenset({1, 2, 3})

Pydantic does best effort to sort default values that are collections.abc.Set instances.

Standard library type: collections.deque (deprecated alias: typing.Deque).

Any iterable (except strings, bytes and mappings) is accepted and converted to a deque, with each item validated against the parameter type. If the input is already a deque instance, its maxlen attribute is preserved.

Deques support the following constraints:

These constraints can be provided using the Field() function. The MinLen and MaxLen metadata types from the annotated-types library can also be used.

In strict mode, only deque instances are valid. Strict mode does not apply to the items of the deque. The strict constraint must be applied to the parameter type for this to work.

In Python mode, deques are serialized as is. In JSON mode, they are serialized as arrays.

from collections import deque

from pydantic import BaseModel


class Model(BaseModel):
    deque: deque[int]


print(Model(deque=[1, 2, 3]).deque)
#> deque([1, 2, 3])

Standard library type: collections.abc.Sequence (deprecated alias: typing.Sequence).

In most cases, you will want to use the built-in types (such as list or tuple) as type coercion will apply. The Sequence type can be used when you want to preserve the input type during serialization.

Any collections.abc.Sequence instance (expect strings and bytes) is accepted. It is converted to a list using the list() constructor, and then converted back to the original input type.

Sequences support the following constraints:

These constraints can be provided using the Field() function. The MinLen and MaxLen metadata types from the annotated-types library can also be used.

In Python mode, sequences are serialized as is. In JSON mode, they are serialized as arrays.

from collections.abc import Sequence

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    sequence_of_strs: Sequence[str]


print(Model(sequence_of_strs=['a', 'bc']).sequence_of_strs)
#> ['a', 'bc']
print(Model(sequence_of_strs=('a', 'bc')).sequence_of_strs)
#> ('a', 'bc')

try:
    Model(sequence_of_strs='abc')
except ValidationError as e:
    print(e)
    """
    1 validation error for Model
    sequence_of_strs
      'str' instances are not allowed as a Sequence value [type=sequence_str, input_value='abc', input_type=str]
    """

Built-in type: dict.

• dict instances are accepted as is.
• mappings instances are accepted and coerced to a dict.
• If generic parameters for keys and values are provided, the appropriate validation is applied.

Dictionaries support the following constraints:

These constraints can be provided using the Field() function. The MinLen and MaxLen metadata types from the annotated-types library can also be used.

In strict mode, only dict instances are valid. Strict mode does not apply to the keys and values of the dictionaries. The strict constraint must be applied to the parameter types for this to work.

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: dict[str, int]


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

try:
    Model(x='test')
except ValidationError as e:
    print(e)
    """
    1 validation error for Model
    x
      Input should be a valid dictionary [type=dict_type, input_value='test', input_type=str]
    """

Built-in type: frozendict.

• frozendict instances are accepted as is.
• dict and mappings instances are accepted and coerced to a frozendict.
• If generic parameters for keys and values are provided, the appropriate validation is applied.

As with dictionaries, frozen dictionaries support the following constraints:

These constraints can be provided using the Field() function. The MinLen and MaxLen metadata types from the annotated-types library can also be used.

In strict mode, only frozendict instances are valid. Strict mode does not apply to the keys and values of the frozen dictionaries. The strict constraint must be applied to the parameter types for this to work.

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: frozendict[str, int]


m = Model(x={'foo': 1})
print(m.model_dump())
#> {'x': frozendict({'foo': 1})}

try:
    Model(x='test')
except ValidationError as e:
    print(e)
    """
    1 validation error for Model
    x
      Input should be a valid frozendict [type=frozen_dict_type, input_value='test', input_type=str]
    """

Standard library type: collections.OrderedDict (deprecated alias: typing.OrderedDict).

• OrderedDict instances are accepted as is.
• dict and mappings instances are accepted and coerced to an OrderedDict.
• If generic parameters for keys and values are provided, the appropriate validation is applied.

As with dictionaries, ordered dictionaries support the following constraints:

These constraints can be provided using the Field() function. The MinLen and MaxLen metadata types from the annotated-types library can also be used.

In strict mode, only OrderedDict instances are valid. Strict mode does not apply to the keys and values of the ordered dictionaries. The strict constraint must be applied to the parameter types for this to work.

from collections import OrderedDict

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: OrderedDict[str, int]


m = Model(x={'foo': 1})
print(m.model_dump())
#> {'x': OrderedDict({'foo': 1})}

try:
    Model(x='test')
except ValidationError as e:
    print(e)
    """
    1 validation error for Model
    x
      Input should be a valid OrderedDict [type=ordered_dict_type, input_value='test', input_type=str]
    """

Standard library type: collections.Counter (deprecated alias: typing.Counter).

• Counter instances are accepted as is.
• dict and mappings instances are accepted and coerced to a Counter.
• If a generic parameter for keys is provided, the appropriate validation is applied. Values are always validated as integers.

As with dictionaries, counters support the following constraints:

These constraints can be provided using the Field() function. The MinLen and MaxLen metadata types from the annotated-types library can also be used.

In strict mode, only Counter instances are valid. Strict mode does not apply to the keys and values of the counters. The strict constraint must be applied to the parameter types for this to work.

from collections import Counter

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: Counter[str]


m = Model(x={'foo': '1'})
print(m.model_dump())
#> {'x': Counter({'foo': 1})}

try:
    Model(x='test')
except ValidationError as e:
    print(e)
    """
    1 validation error for Model
    x
      Input should be a valid Counter [type=counter_type, input_value='test', input_type=str]
    """

Standard library type: typing.TypedDict (see also: the typing specification).

TypedDict declares a dictionary type that expects all of its instances to have a certain set of keys where each key is associated with a value of a consistent type.

This type supports configuration.

In strict mode, only dict instances are valid (unlike mappings in lax mode). Strict mode does not apply to the values of the typed dictionary. The strict constraint must be applied to the value types for this to work.

from typing_extensions import TypedDict

from pydantic import TypeAdapter, ValidationError


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


ta = TypeAdapter(User)

print(ta.validate_python({'name': 'foo', 'id': 1}))
#> {'name': 'foo', 'id': 1}

try:
    ta.validate_python({'name': 'foo'})
except ValidationError as e:
    print(e)
    """
    1 validation error for User
    id
      Field required [type=missing, input_value={'name': 'foo'}, input_type=dict]
    """

Standard library type: collections.abc.Iterable (deprecated alias: typing.Iterable).

Iterables are lazily validated, and wrapped in an internal datastructure that can be iterated over (and will validate the items type while doing so). This means that even if you provide a concrete container such as a list, the validated type will not be of type list. However, Pydantic will ensure that the input value is iterable by getting an iterator from it (by calling iter() on the value).

It is recommended to use concrete collection types (such as lists) instead, unless you are using an infinite iterator (in which case eagerly validating the input would result in an infinite loop).

from collections.abc import Iterable

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    f: Iterable[str]


m = Model(f=[1, 2])  # Validates fine

try:
    next(m.f)
except ValidationError as e:
    print(e)
    """
    1 validation error for ValidatorIterator
    0
      Input should be a valid string [type=string_type, input_value=1, input_type=int]
    """

Standard library type: collections.abc.Callable (deprecated alias: typing.Callable).

Pydantic only validates that the input is a callable (using the callable() function). It does not validate the number of parameters or their type, nor the type of the return value.

from collections.abc import Callable

from pydantic import BaseModel


class Foo(BaseModel):
    callback: Callable[[int], int]


m = Foo(callback=lambda x: x)
print(m)
#> callback=<function <lambda> at 0x0123456789ab>

Callables are serialized as is. Callables can't be serialized in JSON mode (a PydanticSerializationError is raised).

Standard library types:

• ipaddress.IPv4Address
• ipaddress.IPv4Interface
• ipaddress.IPv4Network
• ipaddress.IPv6Address
• ipaddress.IPv6Interface
• ipaddress.IPv6Network

See also: the IPvAnyAddress, IPvAnyInterface and IPvAnyNetwork Pydantic types.

• Instances are validated as is.
• Other input values are passed to the constructor of the relevant address type.

In strict mode, only the address types are accepted. In JSON mode, strict mode has no effect.

In Python mode, IP addresses are serialized as is. In JSON mode, they are serialized as strings.

Standard library type: uuid.UUID.

• UUID instances are validated as is.
• Strings and bytes are validated as UUIDs, and casted to a UUID instance.

The UUID type supports a version constraint. The UuidVersion metadata type can be used.

Pydantic also provides the following types as convenience aliases: UUID1, UUID3, UUID4, UUID5, UUID6, UUID7, UUID8.

In strict mode, only UUID instances are accepted. In JSON mode, strict mode has no effect.

In Python mode, UUIDs are serialized as is. In JSON mode, they are serialized as strings.

from typing import Annotated
from uuid import UUID

from pydantic import BaseModel
from pydantic.types import UUID7, UuidVersion


class Model(BaseModel):
    u1: UUID7
    u2: Annotated[UUID, UuidVersion(4)]


print(
    Model(
        u1='01999b2c-8353-749b-8dac-859307fae22b',
        u2=UUID('125725f3-e1b4-44e3-90c3-1a20eab12da5'),
    )
)
"""
u1=UUID('01999b2c-8353-749b-8dac-859307fae22b') u2=UUID('125725f3-e1b4-44e3-90c3-1a20eab12da5')
"""

Built-in type: type (deprecated alias: typing.Type).

Allows any type that is a subclass of the type argument. For instance, with type[str], allows the str class or any str subclass as an input. If no type argument is provided (i.e. type is used as an annotation), allow any class.

Types are serialized as is. Types can't be serialized in JSON mode (a PydanticSerializationError is raised).

from pydantic import BaseModel, ValidationError


class Foo:
    pass


class Bar(Foo):
    pass


class Other:
    pass


class SimpleModel(BaseModel):
    just_subclasses: type[Foo]


SimpleModel(just_subclasses=Foo)
SimpleModel(just_subclasses=Bar)
try:
    SimpleModel(just_subclasses=Other)
except ValidationError as e:
    print(e)
    """
    1 validation error for SimpleModel
    just_subclasses
      Input should be a subclass of Foo [type=is_subclass_of, input_value=<class '__main__.Other'>, input_type=type]
    """

Typing construct: typing.Literal (see also: the typing specification).

Literals can be used to only allow specific literal values.

Note that Pydantic applies strict mode behavior when validating literal values (see this issue).

from typing import Literal

from pydantic import BaseModel, ValidationError


class Pie(BaseModel):
    flavor: Literal['apple', 'pumpkin']
    quantity: Literal[1, 2] = 1


Pie(flavor='apple')
Pie(flavor='pumpkin')
try:
    Pie(flavor='cherry')
except ValidationError as e:
    print(str(e))
    """
    1 validation error for Pie
    flavor
      Input should be 'apple' or 'pumpkin' [type=literal_error, input_value='cherry', input_type=str]
    """

try:
    Pie(flavor='apple', quantity='1')
except ValidationError as e:
    print(str(e))
    """
    1 validation error for Pie
    quantity
      Input should be 1 or 2 [type=literal_error, input_value='1', input_type=str]
    """

Types: typing.Any or object.

Allows any value, including None.

Standard library type: collections.abc.Hashable (deprecated alias: typing.Hashable).

Any value that is hashable (using isinstance(value, Hashable)).

Standard library type: re.Pattern (deprecated alias: typing.Pattern).

• For Pattern instances, check that the pattern attribute is of the right type (str or bytes depending on the Pattern type parameter).
• If the type parameter is str or bytes, input values of type str (or bytes respectively) are attempted to be compiled using re.compile().

In Python mode, Pattern instances are serialized as is.

In JSON mode, they are serialized as strings (note that flags are currently not preserved).

Standard library types:

• pathlib.Path.
• pathlib.PurePath.
• pathlib.PosixPath.
• pathlib.WindowsPath.
• pathlib.PurePosixPath.
• pathlib.PureWindowsPath.
• os.PathLike (must be parameterized with str, bytes or Any).

• Path instances are validated as is.
• Strings are accepted and passed to the type constructor. If os.PathLike was used, bytes are accepted if it was parameterized with the bytes type.

In strict mode, only Path instances are accepted. In JSON mode, strict mode has no effect.

In Python mode, Path instances are serialized as is.

In JSON mode, they are serialized as strings.