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Pydantic Types

The types module contains custom types used by pydantic.

StrictBool module-attribute 

StrictBool = Annotated[bool, Strict()]

A boolean that must be either True or False.

PositiveInt module-attribute 

PositiveInt = Annotated[int, annotated_types.Gt(0)]

An integer that must be greater than zero.

from pydantic import BaseModel, PositiveInt, ValidationError

class Model(BaseModel):
    positive_int: PositiveInt

m = Model(positive_int=1)
print(repr(m))
#> Model(positive_int=1)

try:
    Model(positive_int=-1)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than',
            'loc': ('positive_int',),
            'msg': 'Input should be greater than 0',
            'input': -1,
            'ctx': {'gt': 0},
            'url': 'https://errors.pydantic.dev/2/v/greater_than',
        }
    ]
    '''

NegativeInt module-attribute 

NegativeInt = Annotated[int, annotated_types.Lt(0)]

An integer that must be less than zero.

from pydantic import BaseModel, NegativeInt, ValidationError

class Model(BaseModel):
    negative_int: NegativeInt

m = Model(negative_int=-1)
print(repr(m))
#> Model(negative_int=-1)

try:
    Model(negative_int=1)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'less_than',
            'loc': ('negative_int',),
            'msg': 'Input should be less than 0',
            'input': 1,
            'ctx': {'lt': 0},
            'url': 'https://errors.pydantic.dev/2/v/less_than',
        }
    ]
    '''

NonPositiveInt module-attribute 

NonPositiveInt = Annotated[int, annotated_types.Le(0)]

An integer that must be less than or equal to zero.

from pydantic import BaseModel, NonPositiveInt, ValidationError

class Model(BaseModel):
    non_positive_int: NonPositiveInt

m = Model(non_positive_int=0)
print(repr(m))
#> Model(non_positive_int=0)

try:
    Model(non_positive_int=1)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'less_than_equal',
            'loc': ('non_positive_int',),
            'msg': 'Input should be less than or equal to 0',
            'input': 1,
            'ctx': {'le': 0},
            'url': 'https://errors.pydantic.dev/2/v/less_than_equal',
        }
    ]
    '''

NonNegativeInt module-attribute 

NonNegativeInt = Annotated[int, annotated_types.Ge(0)]

An integer that must be greater than or equal to zero.

from pydantic import BaseModel, NonNegativeInt, ValidationError

class Model(BaseModel):
    non_negative_int: NonNegativeInt

m = Model(non_negative_int=0)
print(repr(m))
#> Model(non_negative_int=0)

try:
    Model(non_negative_int=-1)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than_equal',
            'loc': ('non_negative_int',),
            'msg': 'Input should be greater than or equal to 0',
            'input': -1,
            'ctx': {'ge': 0},
            'url': 'https://errors.pydantic.dev/2/v/greater_than_equal',
        }
    ]
    '''

StrictInt module-attribute 

StrictInt = Annotated[int, Strict()]

An integer that must be validated in strict mode.

from pydantic import BaseModel, StrictInt, ValidationError

class StrictIntModel(BaseModel):
    strict_int: StrictInt

try:
    StrictIntModel(strict_int=3.14159)
except ValidationError as e:
    print(e)
    '''
    1 validation error for StrictIntModel
    strict_int
      Input should be a valid integer [type=int_type, input_value=3.14159, input_type=float]
    '''

PositiveFloat module-attribute 

PositiveFloat = Annotated[float, annotated_types.Gt(0)]

A float that must be greater than zero.

from pydantic import BaseModel, PositiveFloat, ValidationError

class Model(BaseModel):
    positive_float: PositiveFloat

m = Model(positive_float=1.0)
print(repr(m))
#> Model(positive_float=1.0)

try:
    Model(positive_float=-1.0)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than',
            'loc': ('positive_float',),
            'msg': 'Input should be greater than 0',
            'input': -1.0,
            'ctx': {'gt': 0.0},
            'url': 'https://errors.pydantic.dev/2/v/greater_than',
        }
    ]
    '''

NegativeFloat module-attribute 

NegativeFloat = Annotated[float, annotated_types.Lt(0)]

A float that must be less than zero.

from pydantic import BaseModel, NegativeFloat, ValidationError

class Model(BaseModel):
    negative_float: NegativeFloat

m = Model(negative_float=-1.0)
print(repr(m))
#> Model(negative_float=-1.0)

try:
    Model(negative_float=1.0)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'less_than',
            'loc': ('negative_float',),
            'msg': 'Input should be less than 0',
            'input': 1.0,
            'ctx': {'lt': 0.0},
            'url': 'https://errors.pydantic.dev/2/v/less_than',
        }
    ]
    '''

NonPositiveFloat module-attribute 

NonPositiveFloat = Annotated[float, annotated_types.Le(0)]

A float that must be less than or equal to zero.

from pydantic import BaseModel, NonPositiveFloat, ValidationError

class Model(BaseModel):
    non_positive_float: NonPositiveFloat

m = Model(non_positive_float=0.0)
print(repr(m))
#> Model(non_positive_float=0.0)

try:
    Model(non_positive_float=1.0)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'less_than_equal',
            'loc': ('non_positive_float',),
            'msg': 'Input should be less than or equal to 0',
            'input': 1.0,
            'ctx': {'le': 0.0},
            'url': 'https://errors.pydantic.dev/2/v/less_than_equal',
        }
    ]
    '''

NonNegativeFloat module-attribute 

NonNegativeFloat = Annotated[float, annotated_types.Ge(0)]

A float that must be greater than or equal to zero.

from pydantic import BaseModel, NonNegativeFloat, ValidationError

class Model(BaseModel):
    non_negative_float: NonNegativeFloat

m = Model(non_negative_float=0.0)
print(repr(m))
#> Model(non_negative_float=0.0)

try:
    Model(non_negative_float=-1.0)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than_equal',
            'loc': ('non_negative_float',),
            'msg': 'Input should be greater than or equal to 0',
            'input': -1.0,
            'ctx': {'ge': 0.0},
            'url': 'https://errors.pydantic.dev/2/v/greater_than_equal',
        }
    ]
    '''

StrictFloat module-attribute 

StrictFloat = Annotated[float, Strict(True)]

A float that must be validated in strict mode.

from pydantic import BaseModel, StrictFloat, ValidationError

class StrictFloatModel(BaseModel):
    strict_float: StrictFloat

try:
    StrictFloatModel(strict_float='1.0')
except ValidationError as e:
    print(e)
    '''
    1 validation error for StrictFloatModel
    strict_float
      Input should be a valid number [type=float_type, input_value='1.0', input_type=str]
    '''

FiniteFloat module-attribute 

FiniteFloat = Annotated[float, AllowInfNan(False)]

A float that must be finite (not -inf, inf, or nan).

from pydantic import BaseModel, FiniteFloat

class Model(BaseModel):
    finite: FiniteFloat

m = Model(finite=1.0)
print(m)
#> finite=1.0

StrictBytes module-attribute 

StrictBytes = Annotated[bytes, Strict()]

A bytes that must be validated in strict mode.

StrictStr module-attribute 

StrictStr = Annotated[str, Strict()]

A string that must be validated in strict mode.

UUID1 module-attribute 

UUID1 = Annotated[UUID, UuidVersion(1)]

A UUID that must be version 1.

import uuid

from pydantic import UUID1, BaseModel

class Model(BaseModel):
    uuid1: UUID1

Model(uuid1=uuid.uuid1())

UUID3 module-attribute 

UUID3 = Annotated[UUID, UuidVersion(3)]

A UUID that must be version 3.

import uuid

from pydantic import UUID3, BaseModel

class Model(BaseModel):
    uuid3: UUID3

Model(uuid3=uuid.uuid3(uuid.NAMESPACE_DNS, 'pydantic.org'))

UUID4 module-attribute 

UUID4 = Annotated[UUID, UuidVersion(4)]

A UUID that must be version 4.

import uuid

from pydantic import UUID4, BaseModel

class Model(BaseModel):
    uuid4: UUID4

Model(uuid4=uuid.uuid4())

UUID5 module-attribute 

UUID5 = Annotated[UUID, UuidVersion(5)]

A UUID that must be version 5.

import uuid

from pydantic import UUID5, BaseModel

class Model(BaseModel):
    uuid5: UUID5

Model(uuid5=uuid.uuid5(uuid.NAMESPACE_DNS, 'pydantic.org'))

FilePath module-attribute 

FilePath = Annotated[Path, PathType('file')]

A path that must point to a file.

from pathlib import Path

from pydantic import BaseModel, FilePath, ValidationError

class Model(BaseModel):
    f: FilePath

path = Path('text.txt')
path.touch()
m = Model(f='text.txt')
print(m.model_dump())
#> {'f': PosixPath('text.txt')}
path.unlink()

path = Path('directory')
path.mkdir(exist_ok=True)
try:
    Model(f='directory')  # directory
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    f
      Path does not point to a file [type=path_not_file, input_value='directory', input_type=str]
    '''
path.rmdir()

try:
    Model(f='not-exists-file')
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    f
      Path does not point to a file [type=path_not_file, input_value='not-exists-file', input_type=str]
    '''

DirectoryPath module-attribute 

DirectoryPath = Annotated[Path, PathType('dir')]

A path that must point to a directory.

from pathlib import Path

from pydantic import BaseModel, DirectoryPath, ValidationError

class Model(BaseModel):
    f: DirectoryPath

path = Path('directory/')
path.mkdir()
m = Model(f='directory/')
print(m.model_dump())
#> {'f': PosixPath('directory')}
path.rmdir()

path = Path('file.txt')
path.touch()
try:
    Model(f='file.txt')  # file
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    f
      Path does not point to a directory [type=path_not_directory, input_value='file.txt', input_type=str]
    '''
path.unlink()

try:
    Model(f='not-exists-directory')
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    f
      Path does not point to a directory [type=path_not_directory, input_value='not-exists-directory', input_type=str]
    '''

NewPath module-attribute 

NewPath = Annotated[Path, PathType('new')]

A path for a new file or directory that must not already exist.

Base64Bytes module-attribute 

Base64Bytes = Annotated[
    bytes, EncodedBytes(encoder=Base64Encoder)
]

A bytes type that is encoded and decoded using the standard (non-URL-safe) base64 encoder.

Note

Under the hood, Base64Bytes use standard library base64.encodebytes and base64.decodebytes functions.

As a result, attempting to decode url-safe base64 data using the Base64Bytes type may fail or produce an incorrect decoding.

from pydantic import Base64Bytes, BaseModel, ValidationError

class Model(BaseModel):
    base64_bytes: Base64Bytes

# Initialize the model with base64 data
m = Model(base64_bytes=b'VGhpcyBpcyB0aGUgd2F5')

# Access decoded value
print(m.base64_bytes)
#> b'This is the way'

# Serialize into the base64 form
print(m.model_dump())
#> {'base64_bytes': b'VGhpcyBpcyB0aGUgd2F5
'}

# Validate base64 data
try:
    print(Model(base64_bytes=b'undecodable').base64_bytes)
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    base64_bytes
      Base64 decoding error: 'Incorrect padding' [type=base64_decode, input_value=b'undecodable', input_type=bytes]
    '''

Base64Str module-attribute 

Base64Str = Annotated[
    str, EncodedStr(encoder=Base64Encoder)
]

A str type that is encoded and decoded using the standard (non-URL-safe) base64 encoder.

Note

Under the hood, Base64Bytes use standard library base64.encodebytes and base64.decodebytes functions.

As a result, attempting to decode url-safe base64 data using the Base64Str type may fail or produce an incorrect decoding.

from pydantic import Base64Str, BaseModel, ValidationError

class Model(BaseModel):
    base64_str: Base64Str

# Initialize the model with base64 data
m = Model(base64_str='VGhlc2UgYXJlbid0IHRoZSBkcm9pZHMgeW91J3JlIGxvb2tpbmcgZm9y')

# Access decoded value
print(m.base64_str)
#> These aren't the droids you're looking for

# Serialize into the base64 form
print(m.model_dump())
#> {'base64_str': 'VGhlc2UgYXJlbid0IHRoZSBkcm9pZHMgeW91J3JlIGxvb2tpbmcgZm9y
'}

# Validate base64 data
try:
    print(Model(base64_str='undecodable').base64_str)
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    base64_str
      Base64 decoding error: 'Incorrect padding' [type=base64_decode, input_value='undecodable', input_type=str]
    '''

Base64UrlBytes module-attribute 

Base64UrlBytes = Annotated[
    bytes, EncodedBytes(encoder=Base64UrlEncoder)
]

A bytes type that is encoded and decoded using the URL-safe base64 encoder.

Note

Under the hood, Base64UrlBytes use standard library base64.urlsafe_b64encode and base64.urlsafe_b64decode functions.

As a result, the Base64UrlBytes type can be used to faithfully decode "vanilla" base64 data (using '+' and '/').

from pydantic import Base64UrlBytes, BaseModel

class Model(BaseModel):
    base64url_bytes: Base64UrlBytes

# Initialize the model with base64 data
m = Model(base64url_bytes=b'SHc_dHc-TXc==')
print(m)
#> base64url_bytes=b'Hw?tw>Mw'

Base64UrlStr module-attribute 

Base64UrlStr = Annotated[
    str, EncodedStr(encoder=Base64UrlEncoder)
]

A str type that is encoded and decoded using the URL-safe base64 encoder.

Note

Under the hood, Base64UrlStr use standard library base64.urlsafe_b64encode and base64.urlsafe_b64decode functions.

As a result, the Base64UrlStr type can be used to faithfully decode "vanilla" base64 data (using '+' and '/').

from pydantic import Base64UrlStr, BaseModel

class Model(BaseModel):
    base64url_str: Base64UrlStr

# Initialize the model with base64 data
m = Model(base64url_str='SHc_dHc-TXc==')
print(m)
#> base64url_str='Hw?tw>Mw'

JsonValue module-attribute 

JsonValue: TypeAlias = Union[
    List["JsonValue"],
    Dict[str, "JsonValue"],
    str,
    bool,
    int,
    float,
    None,
]

A JsonValue is used to represent a value that can be serialized to JSON.

It may be one of:

  • List['JsonValue']
  • Dict[str, 'JsonValue']
  • str
  • bool
  • int
  • float
  • None

The following example demonstrates how to use JsonValue to validate JSON data, and what kind of errors to expect when input data is not json serializable.

import json

from pydantic import BaseModel, JsonValue, ValidationError

class Model(BaseModel):
    j: JsonValue

valid_json_data = {'j': {'a': {'b': {'c': 1, 'd': [2, None]}}}}
invalid_json_data = {'j': {'a': {'b': ...}}}

print(repr(Model.model_validate(valid_json_data)))
#> Model(j={'a': {'b': {'c': 1, 'd': [2, None]}}})
print(repr(Model.model_validate_json(json.dumps(valid_json_data))))
#> Model(j={'a': {'b': {'c': 1, 'd': [2, None]}}})

try:
    Model.model_validate(invalid_json_data)
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    j.dict.a.dict.b
      input was not a valid JSON value [type=invalid-json-value, input_value=Ellipsis, input_type=ellipsis]
    '''

Strict dataclass

Bases: _fields.PydanticMetadata, BaseMetadata

Usage Documentation

Strict mode with Annotated[..., Strict()]

A field metadata class to indicate that a field should be validated in strict mode.

Attributes:

Name Type Description
strict bool

Whether to validate the field in strict mode.
Example 
from typing_extensions import Annotated

from pydantic.types import Strict

StrictBool = Annotated[bool, Strict()]

AllowInfNan dataclass

Bases: _fields.PydanticMetadata

A field metadata class to indicate that a field should allow -inf, inf, and nan.

StringConstraints dataclass

Bases: annotated_types.GroupedMetadata

Usage Documentation

String Constraints

Apply constraints to str types.

Attributes:

Name Type Description
strip_whitespace bool | None

Whether to strip whitespace from the string.
to_upper bool | None

Whether to convert the string to uppercase.
to_lower bool | None

Whether to convert the string to lowercase.
strict bool | None

Whether to validate the string in strict mode.
min_length int | None

The minimum length of the string.
max_length int | None

The maximum length of the string.
pattern str | None

A regex pattern that the string must match.

ImportString

A type that can be used to import a type from a string.

ImportString expects a string and loads the Python object importable at that dotted path. Attributes of modules may be separated from the module by : or ., e.g. if 'math:cos' was provided, the resulting field value would be the functioncos. If a . is used and both an attribute and submodule are present at the same path, the module will be preferred.

On model instantiation, pointers will be evaluated and imported. There is some nuance to this behavior, demonstrated in the examples below.

A known limitation: setting a default value to a string won't result in validation (thus evaluation). This is actively being worked on.

Good behavior: 

from math import cos

from pydantic import BaseModel, ImportString, ValidationError


class ImportThings(BaseModel):
    obj: ImportString


# A string value will cause an automatic import
my_cos = ImportThings(obj='math.cos')

# You can use the imported function as you would expect
cos_of_0 = my_cos.obj(0)
assert cos_of_0 == 1


# A string whose value cannot be imported will raise an error
try:
    ImportThings(obj='foo.bar')
except ValidationError as e:
    print(e)
    '''
    1 validation error for ImportThings
    obj
    Invalid python path: No module named 'foo.bar' [type=import_error, input_value='foo.bar', input_type=str]
    '''


# Actual python objects can be assigned as well
my_cos = ImportThings(obj=cos)
my_cos_2 = ImportThings(obj='math.cos')
assert my_cos == my_cos_2

Serializing an ImportString type to json is also possible.

from pydantic import BaseModel, ImportString


class ImportThings(BaseModel):
    obj: ImportString


# Create an instance
m = ImportThings(obj='math:cos')
print(m)
#> obj=<built-in function cos>
print(m.model_dump_json())
#> {"obj":"math.cos"}

UuidVersion dataclass

A field metadata class to indicate a UUID version.

Json

A special type wrapper which loads JSON before parsing.

You can use the Json data type to make Pydantic first load a raw JSON string before validating the loaded data into the parametrized type:

from typing import Any, List

from pydantic import BaseModel, Json, ValidationError


class AnyJsonModel(BaseModel):
    json_obj: Json[Any]


class ConstrainedJsonModel(BaseModel):
    json_obj: Json[List[int]]


print(AnyJsonModel(json_obj='{"b": 1}'))
#> json_obj={'b': 1}
print(ConstrainedJsonModel(json_obj='[1, 2, 3]'))
#> json_obj=[1, 2, 3]

try:
    ConstrainedJsonModel(json_obj=12)
except ValidationError as e:
    print(e)
    '''
    1 validation error for ConstrainedJsonModel
    json_obj
    JSON input should be string, bytes or bytearray [type=json_type, input_value=12, input_type=int]
    '''

try:
    ConstrainedJsonModel(json_obj='[a, b]')
except ValidationError as e:
    print(e)
    '''
    1 validation error for ConstrainedJsonModel
    json_obj
    Invalid JSON: expected value at line 1 column 2 [type=json_invalid, input_value='[a, b]', input_type=str]
    '''

try:
    ConstrainedJsonModel(json_obj='["a", "b"]')
except ValidationError as e:
    print(e)
    '''
    2 validation errors for ConstrainedJsonModel
    json_obj.0
    Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str]
    json_obj.1
    Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='b', input_type=str]
    '''

When you dump the model using model_dump or model_dump_json, the dumped value will be the result of validation, not the original JSON string. However, you can use the argument round_trip=True to get the original JSON string back:

from typing import List

from pydantic import BaseModel, Json


class ConstrainedJsonModel(BaseModel):
    json_obj: Json[List[int]]


print(ConstrainedJsonModel(json_obj='[1, 2, 3]').model_dump_json())
#> {"json_obj":[1,2,3]}
print(
    ConstrainedJsonModel(json_obj='[1, 2, 3]').model_dump_json(round_trip=True)
)
#> {"json_obj":"[1,2,3]"}

SecretStr

Bases: _SecretField[str]

A string used for storing sensitive information that you do not want to be visible in logging or tracebacks.

It displays '**********' instead of the string value on repr() and str() calls.

from pydantic import BaseModel, SecretStr

class User(BaseModel):
    username: str
    password: SecretStr

user = User(username='scolvin', password='password1')

print(user)
#> username='scolvin' password=SecretStr('**********')
print(user.password.get_secret_value())
#> password1

SecretBytes

Bases: _SecretField[bytes]

A bytes used for storing sensitive information that you do not want to be visible in logging or tracebacks.

It displays b'**********' instead of the string value on repr() and str() calls.

from pydantic import BaseModel, SecretBytes

class User(BaseModel):
    username: str
    password: SecretBytes

user = User(username='scolvin', password=b'password1')
#> username='scolvin' password=SecretBytes(b'**********')
print(user.password.get_secret_value())
#> b'password1'

PaymentCardNumber 

PaymentCardNumber(card_number)

Bases: str

Based on: https://en.wikipedia.org/wiki/Payment_card_number.

Source code in pydantic/types.py 
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def __init__(self, card_number: str):
    self.validate_digits(card_number)

    card_number = self.validate_luhn_check_digit(card_number)

    self.bin = card_number[:6]
    self.last4 = card_number[-4:]
    self.brand = self.validate_brand(card_number)

masked property 

masked: str

Mask all but the last 4 digits of the card number.

Returns:

Type Description
str

A masked card number string.

validate classmethod 

validate(__input_value, _)

Validate the card number and return a PaymentCardNumber instance.

Source code in pydantic/types.py 
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@classmethod
def validate(cls, __input_value: str, _: core_schema.ValidationInfo) -> PaymentCardNumber:
    """Validate the card number and return a `PaymentCardNumber` instance."""
    return cls(__input_value)

validate_digits classmethod 

validate_digits(card_number)

Validate that the card number is all digits.

Source code in pydantic/types.py 
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@classmethod
def validate_digits(cls, card_number: str) -> None:
    """Validate that the card number is all digits."""
    if not card_number.isdigit():
        raise PydanticCustomError('payment_card_number_digits', 'Card number is not all digits')

validate_luhn_check_digit classmethod 

validate_luhn_check_digit(card_number)

Based on: https://en.wikipedia.org/wiki/Luhn_algorithm.

Source code in pydantic/types.py 
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@classmethod
def validate_luhn_check_digit(cls, card_number: str) -> str:
    """Based on: https://en.wikipedia.org/wiki/Luhn_algorithm."""
    sum_ = int(card_number[-1])
    length = len(card_number)
    parity = length % 2
    for i in range(length - 1):
        digit = int(card_number[i])
        if i % 2 == parity:
            digit *= 2
        if digit > 9:
            digit -= 9
        sum_ += digit
    valid = sum_ % 10 == 0
    if not valid:
        raise PydanticCustomError('payment_card_number_luhn', 'Card number is not luhn valid')
    return card_number

validate_brand staticmethod 

validate_brand(card_number)

Validate length based on BIN for major brands: https://en.wikipedia.org/wiki/Payment_card_number#Issuer_identification_number_(IIN).

Source code in pydantic/types.py 
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@staticmethod
def validate_brand(card_number: str) -> PaymentCardBrand:
    """Validate length based on BIN for major brands:
    https://en.wikipedia.org/wiki/Payment_card_number#Issuer_identification_number_(IIN).
    """
    if card_number[0] == '4':
        brand = PaymentCardBrand.visa
    elif 51 <= int(card_number[:2]) <= 55:
        brand = PaymentCardBrand.mastercard
    elif card_number[:2] in {'34', '37'}:
        brand = PaymentCardBrand.amex
    else:
        brand = PaymentCardBrand.other

    required_length: None | int | str = None
    if brand in PaymentCardBrand.mastercard:
        required_length = 16
        valid = len(card_number) == required_length
    elif brand == PaymentCardBrand.visa:
        required_length = '13, 16 or 19'
        valid = len(card_number) in {13, 16, 19}
    elif brand == PaymentCardBrand.amex:
        required_length = 15
        valid = len(card_number) == required_length
    else:
        valid = True

    if not valid:
        raise PydanticCustomError(
            'payment_card_number_brand',
            'Length for a {brand} card must be {required_length}',
            {'brand': brand, 'required_length': required_length},
        )
    return brand

ByteSize

Bases: int

Converts a string representing a number of bytes with units (such as '1KB' or '11.5MiB') into an integer.

You can use the ByteSize data type to (case-insensitively) convert a string representation of a number of bytes into an integer, and also to print out human-readable strings representing a number of bytes.

In conformance with IEC 80000-13 Standard we interpret '1KB' to mean 1000 bytes, and '1KiB' to mean 1024 bytes. In general, including a middle 'i' will cause the unit to be interpreted as a power of 2, rather than a power of 10 (so, for example, '1 MB' is treated as 1_000_000 bytes, whereas '1 MiB' is treated as 1_048_576 bytes).

Info

Note that 1b will be parsed as "1 byte" and not "1 bit".

from pydantic import BaseModel, ByteSize

class MyModel(BaseModel):
    size: ByteSize

print(MyModel(size=52000).size)
#> 52000
print(MyModel(size='3000 KiB').size)
#> 3072000

m = MyModel(size='50 PB')
print(m.size.human_readable())
#> 44.4PiB
print(m.size.human_readable(decimal=True))
#> 50.0PB

print(m.size.to('TiB'))
#> 45474.73508864641

human_readable 

human_readable(decimal=False)

Converts a byte size to a human readable string.

Parameters:

Name Type Description Default
decimal bool

If True, use decimal units (e.g. 1000 bytes per KB). If False, use binary units (e.g. 1024 bytes per KiB).

 False

Returns:

Type Description
str

A human readable string representation of the byte size.
Source code in pydantic/types.py 
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def human_readable(self, decimal: bool = False) -> str:
    """Converts a byte size to a human readable string.

    Args:
        decimal: If True, use decimal units (e.g. 1000 bytes per KB). If False, use binary units
            (e.g. 1024 bytes per KiB).

    Returns:
        A human readable string representation of the byte size.
    """
    if decimal:
        divisor = 1000
        units = 'B', 'KB', 'MB', 'GB', 'TB', 'PB'
        final_unit = 'EB'
    else:
        divisor = 1024
        units = 'B', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB'
        final_unit = 'EiB'

    num = float(self)
    for unit in units:
        if abs(num) < divisor:
            if unit == 'B':
                return f'{num:0.0f}{unit}'
            else:
                return f'{num:0.1f}{unit}'
        num /= divisor

    return f'{num:0.1f}{final_unit}'

to 

to(unit)

Converts a byte size to another unit.

Parameters:

Name Type Description Default
unit str

The unit to convert to. Must be one of the following: B, KB, MB, GB, TB, PB, EiB, KiB, MiB, GiB, TiB, PiB, EiB.

 required

Returns:

Type Description
float

The byte size in the new unit.
Source code in pydantic/types.py 
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def to(self, unit: str) -> float:
    """Converts a byte size to another unit.

    Args:
        unit: The unit to convert to. Must be one of the following: B, KB, MB, GB, TB, PB, EiB,
            KiB, MiB, GiB, TiB, PiB, EiB.

    Returns:
        The byte size in the new unit.
    """
    try:
        unit_div = BYTE_SIZES[unit.lower()]
    except KeyError:
        raise PydanticCustomError('byte_size_unit', 'Could not interpret byte unit: {unit}', {'unit': unit})

    return self / unit_div

PastDate

A date in the past.

FutureDate

A date in the future.

AwareDatetime

A datetime that requires timezone info.

NaiveDatetime

A datetime that doesn't require timezone info.

PastDatetime

A datetime that must be in the past.

FutureDatetime

A datetime that must be in the future.

EncoderProtocol

Bases: Protocol

Protocol for encoding and decoding data to and from bytes.

decode classmethod 

decode(data)

Decode the data using the encoder.

Parameters:

Name Type Description Default
data bytes

The data to decode.

 required

Returns:

Type Description
bytes

The decoded data.
Source code in pydantic/types.py 
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@classmethod
def decode(cls, data: bytes) -> bytes:
    """Decode the data using the encoder.

    Args:
        data: The data to decode.

    Returns:
        The decoded data.
    """
    ...

encode classmethod 

encode(value)

Encode the data using the encoder.

Parameters:

Name Type Description Default
value bytes

The data to encode.

 required

Returns:

Type Description
bytes

The encoded data.
Source code in pydantic/types.py 
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@classmethod
def encode(cls, value: bytes) -> bytes:
    """Encode the data using the encoder.

    Args:
        value: The data to encode.

    Returns:
        The encoded data.
    """
    ...

get_json_format classmethod 

get_json_format()

Get the JSON format for the encoded data.

Returns:

Type Description
str

The JSON format for the encoded data.
Source code in pydantic/types.py 
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@classmethod
def get_json_format(cls) -> str:
    """Get the JSON format for the encoded data.

    Returns:
        The JSON format for the encoded data.
    """
    ...

Base64Encoder

Bases: EncoderProtocol

Standard (non-URL-safe) Base64 encoder.

decode classmethod 

decode(data)

Decode the data from base64 encoded bytes to original bytes data.

Parameters:

Name Type Description Default
data bytes

The data to decode.

 required

Returns:

Type Description
bytes

The decoded data.
Source code in pydantic/types.py 
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@classmethod
def decode(cls, data: bytes) -> bytes:
    """Decode the data from base64 encoded bytes to original bytes data.

    Args:
        data: The data to decode.

    Returns:
        The decoded data.
    """
    try:
        return base64.decodebytes(data)
    except ValueError as e:
        raise PydanticCustomError('base64_decode', "Base64 decoding error: '{error}'", {'error': str(e)})

encode classmethod 

encode(value)

Encode the data from bytes to a base64 encoded bytes.

Parameters:

Name Type Description Default
value bytes

The data to encode.

 required

Returns:

Type Description
bytes

The encoded data.
Source code in pydantic/types.py 
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@classmethod
def encode(cls, value: bytes) -> bytes:
    """Encode the data from bytes to a base64 encoded bytes.

    Args:
        value: The data to encode.

    Returns:
        The encoded data.
    """
    return base64.encodebytes(value)

get_json_format classmethod 

get_json_format()

Get the JSON format for the encoded data.

Returns:

Type Description
Literal['base64']

The JSON format for the encoded data.
Source code in pydantic/types.py 
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@classmethod
def get_json_format(cls) -> Literal['base64']:
    """Get the JSON format for the encoded data.

    Returns:
        The JSON format for the encoded data.
    """
    return 'base64'

Base64UrlEncoder

Bases: EncoderProtocol

URL-safe Base64 encoder.

decode classmethod 

decode(data)

Decode the data from base64 encoded bytes to original bytes data.

Parameters:

Name Type Description Default
data bytes

The data to decode.

 required

Returns:

Type Description
bytes

The decoded data.
Source code in pydantic/types.py 
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@classmethod
def decode(cls, data: bytes) -> bytes:
    """Decode the data from base64 encoded bytes to original bytes data.

    Args:
        data: The data to decode.

    Returns:
        The decoded data.
    """
    try:
        return base64.urlsafe_b64decode(data)
    except ValueError as e:
        raise PydanticCustomError('base64_decode', "Base64 decoding error: '{error}'", {'error': str(e)})

encode classmethod 

encode(value)

Encode the data from bytes to a base64 encoded bytes.

Parameters:

Name Type Description Default
value bytes

The data to encode.

 required

Returns:

Type Description
bytes

The encoded data.
Source code in pydantic/types.py 
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@classmethod
def encode(cls, value: bytes) -> bytes:
    """Encode the data from bytes to a base64 encoded bytes.

    Args:
        value: The data to encode.

    Returns:
        The encoded data.
    """
    return base64.urlsafe_b64encode(value)

get_json_format classmethod 

get_json_format()

Get the JSON format for the encoded data.

Returns:

Type Description
Literal['base64url']

The JSON format for the encoded data.
Source code in pydantic/types.py 
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@classmethod
def get_json_format(cls) -> Literal['base64url']:
    """Get the JSON format for the encoded data.

    Returns:
        The JSON format for the encoded data.
    """
    return 'base64url'

EncodedBytes dataclass

A bytes type that is encoded and decoded using the specified encoder.

EncodedBytes needs an encoder that implements EncoderProtocol to operate.

from typing_extensions import Annotated

from pydantic import BaseModel, EncodedBytes, EncoderProtocol, ValidationError

class MyEncoder(EncoderProtocol):
    @classmethod
    def decode(cls, data: bytes) -> bytes:
        if data == b'**undecodable**':
            raise ValueError('Cannot decode data')
        return data[13:]

    @classmethod
    def encode(cls, value: bytes) -> bytes:
        return b'**encoded**: ' + value

    @classmethod
    def get_json_format(cls) -> str:
        return 'my-encoder'

MyEncodedBytes = Annotated[bytes, EncodedBytes(encoder=MyEncoder)]

class Model(BaseModel):
    my_encoded_bytes: MyEncodedBytes

# Initialize the model with encoded data
m = Model(my_encoded_bytes=b'**encoded**: some bytes')

# Access decoded value
print(m.my_encoded_bytes)
#> b'some bytes'

# Serialize into the encoded form
print(m.model_dump())
#> {'my_encoded_bytes': b'**encoded**: some bytes'}

# Validate encoded data
try:
    Model(my_encoded_bytes=b'**undecodable**')
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    my_encoded_bytes
      Value error, Cannot decode data [type=value_error, input_value=b'**undecodable**', input_type=bytes]
    '''

decode 

decode(data, _)

Decode the data using the specified encoder.

Parameters:

Name Type Description Default
data bytes

The data to decode.

 required

Returns:

Type Description
bytes

The decoded data.
Source code in pydantic/types.py 
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def decode(self, data: bytes, _: core_schema.ValidationInfo) -> bytes:
    """Decode the data using the specified encoder.

    Args:
        data: The data to decode.

    Returns:
        The decoded data.
    """
    return self.encoder.decode(data)

encode 

encode(value)

Encode the data using the specified encoder.

Parameters:

Name Type Description Default
value bytes

The data to encode.

 required

Returns:

Type Description
bytes

The encoded data.
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def encode(self, value: bytes) -> bytes:
    """Encode the data using the specified encoder.

    Args:
        value: The data to encode.

    Returns:
        The encoded data.
    """
    return self.encoder.encode(value)

EncodedStr dataclass

Bases: EncodedBytes

A str type that is encoded and decoded using the specified encoder.

EncodedStr needs an encoder that implements EncoderProtocol to operate.

from typing_extensions import Annotated

from pydantic import BaseModel, EncodedStr, EncoderProtocol, ValidationError

class MyEncoder(EncoderProtocol):
    @classmethod
    def decode(cls, data: bytes) -> bytes:
        if data == b'**undecodable**':
            raise ValueError('Cannot decode data')
        return data[13:]

    @classmethod
    def encode(cls, value: bytes) -> bytes:
        return b'**encoded**: ' + value

    @classmethod
    def get_json_format(cls) -> str:
        return 'my-encoder'

MyEncodedStr = Annotated[str, EncodedStr(encoder=MyEncoder)]

class Model(BaseModel):
    my_encoded_str: MyEncodedStr

# Initialize the model with encoded data
m = Model(my_encoded_str='**encoded**: some str')

# Access decoded value
print(m.my_encoded_str)
#> some str

# Serialize into the encoded form
print(m.model_dump())
#> {'my_encoded_str': '**encoded**: some str'}

# Validate encoded data
try:
    Model(my_encoded_str='**undecodable**')
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    my_encoded_str
      Value error, Cannot decode data [type=value_error, input_value='**undecodable**', input_type=str]
    '''

decode_str 

decode_str(data, _)

Decode the data using the specified encoder.

Parameters:

Name Type Description Default
data bytes

The data to decode.

 required

Returns:

Type Description
str

The decoded data.
Source code in pydantic/types.py 
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def decode_str(self, data: bytes, _: core_schema.ValidationInfo) -> str:
    """Decode the data using the specified encoder.

    Args:
        data: The data to decode.

    Returns:
        The decoded data.
    """
    return data.decode()

encode_str 

encode_str(value)

Encode the data using the specified encoder.

Parameters:

Name Type Description Default
value str

The data to encode.

 required

Returns:

Type Description
str

The encoded data.
Source code in pydantic/types.py 
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def encode_str(self, value: str) -> str:
    """Encode the data using the specified encoder.

    Args:
        value: The data to encode.

    Returns:
        The encoded data.
    """
    return super(EncodedStr, self).encode(value=value.encode()).decode()  # noqa: UP008

GetPydanticSchema dataclass

Usage Documentation

Using GetPydanticSchema to reduce boilerplate

A convenience class for creating an annotation that provides pydantic custom type hooks.

This class is intended to eliminate the need to create a custom "marker" which defines the __get_pydantic_core_schema__ and __get_pydantic_json_schema__ custom hook methods.

For example, to have a field treated by type checkers as int, but by pydantic as Any, you can do: 

from typing import Any

from typing_extensions import Annotated

from pydantic import BaseModel, GetPydanticSchema

HandleAsAny = GetPydanticSchema(lambda _s, h: h(Any))

class Model(BaseModel):
    x: Annotated[int, HandleAsAny]  # pydantic sees `x: Any`

print(repr(Model(x='abc').x))
#> 'abc'

Tag dataclass

Provides a way to specify the expected tag to use for a case of a (callable) discriminated union.

Also provides a way to label a union case in error messages.

When using a callable Discriminator, attach a Tag to each case in the Union to specify the tag that should be used to identify that case. For example, in the below example, the Tag is used to specify that if get_discriminator_value returns 'apple', the input should be validated as an ApplePie, and if it returns 'pumpkin', the input should be validated as a PumpkinPie.

The primary role of the Tag here is to map the return value from the callable Discriminator function to the appropriate member of the Union in question.

from typing import Any, Union

from typing_extensions import Annotated, Literal

from pydantic import BaseModel, Discriminator, Tag

class Pie(BaseModel):
    time_to_cook: int
    num_ingredients: int

class ApplePie(Pie):
    fruit: Literal['apple'] = 'apple'

class PumpkinPie(Pie):
    filling: Literal['pumpkin'] = 'pumpkin'

def get_discriminator_value(v: Any) -> str:
    if isinstance(v, dict):
        return v.get('fruit', v.get('filling'))
    return getattr(v, 'fruit', getattr(v, 'filling', None))

class ThanksgivingDinner(BaseModel):
    dessert: Annotated[
        Union[
            Annotated[ApplePie, Tag('apple')],
            Annotated[PumpkinPie, Tag('pumpkin')],
        ],
        Discriminator(get_discriminator_value),
    ]

apple_variation = ThanksgivingDinner.model_validate(
    {'dessert': {'fruit': 'apple', 'time_to_cook': 60, 'num_ingredients': 8}}
)
print(repr(apple_variation))
'''
ThanksgivingDinner(dessert=ApplePie(time_to_cook=60, num_ingredients=8, fruit='apple'))
'''

pumpkin_variation = ThanksgivingDinner.model_validate(
    {
        'dessert': {
            'filling': 'pumpkin',
            'time_to_cook': 40,
            'num_ingredients': 6,
        }
    }
)
print(repr(pumpkin_variation))
'''
ThanksgivingDinner(dessert=PumpkinPie(time_to_cook=40, num_ingredients=6, filling='pumpkin'))
'''

Note

You must specify a Tag for every case in a Tag that is associated with a callable Discriminator. Failing to do so will result in a PydanticUserError with code callable-discriminator-no-tag.

See the Discriminated Unions concepts docs for more details on how to use Tags.

Discriminator dataclass

Usage Documentation

Discriminated Unions with callable Discriminator

Provides a way to use a custom callable as the way to extract the value of a union discriminator.

This allows you to get validation behavior like you'd get from Field(discriminator=<field_name>), but without needing to have a single shared field across all the union choices. This also makes it possible to handle unions of models and primitive types with discriminated-union-style validation errors. Finally, this allows you to use a custom callable as the way to identify which member of a union a value belongs to, while still seeing all the performance benefits of a discriminated union.

Consider this example, which is much more performant with the use of Discriminator and thus a TaggedUnion than it would be as a normal Union.

from typing import Any, Union

from typing_extensions import Annotated, Literal

from pydantic import BaseModel, Discriminator, Tag

class Pie(BaseModel):
    time_to_cook: int
    num_ingredients: int

class ApplePie(Pie):
    fruit: Literal['apple'] = 'apple'

class PumpkinPie(Pie):
    filling: Literal['pumpkin'] = 'pumpkin'

def get_discriminator_value(v: Any) -> str:
    if isinstance(v, dict):
        return v.get('fruit', v.get('filling'))
    return getattr(v, 'fruit', getattr(v, 'filling', None))

class ThanksgivingDinner(BaseModel):
    dessert: Annotated[
        Union[
            Annotated[ApplePie, Tag('apple')],
            Annotated[PumpkinPie, Tag('pumpkin')],
        ],
        Discriminator(get_discriminator_value),
    ]

apple_variation = ThanksgivingDinner.model_validate(
    {'dessert': {'fruit': 'apple', 'time_to_cook': 60, 'num_ingredients': 8}}
)
print(repr(apple_variation))
'''
ThanksgivingDinner(dessert=ApplePie(time_to_cook=60, num_ingredients=8, fruit='apple'))
'''

pumpkin_variation = ThanksgivingDinner.model_validate(
    {
        'dessert': {
            'filling': 'pumpkin',
            'time_to_cook': 40,
            'num_ingredients': 6,
        }
    }
)
print(repr(pumpkin_variation))
'''
ThanksgivingDinner(dessert=PumpkinPie(time_to_cook=40, num_ingredients=6, filling='pumpkin'))
'''

See the Discriminated Unions concepts docs for more details on how to use Discriminators.

discriminator instance-attribute 

discriminator: str | Callable[[Any], Hashable]

The callable or field name for discriminating the type in a tagged union.

A Callable discriminator must extract the value of the discriminator from the input. A str discriminator must be the name of a field to discriminate against.

custom_error_type instance-attribute class-attribute 

custom_error_type: str | None = None

Type to use in custom errors replacing the standard discriminated union validation errors.

custom_error_message instance-attribute class-attribute 

custom_error_message: str | None = None

Message to use in custom errors.

custom_error_context instance-attribute class-attribute 

custom_error_context: dict[
    str, int | str | float
] | None = None

Context to use in custom errors.

conint 

conint(
    *,
    strict=None,
    gt=None,
    ge=None,
    lt=None,
    le=None,
    multiple_of=None
)

Discouraged

This function is discouraged in favor of using Annotated with Field instead.

This function will be deprecated in Pydantic 3.0.

The reason is that conint returns a type, which doesn't play well with static analysis tools.

from pydantic import BaseModel, conint

class Foo(BaseModel):
    bar: conint(strict=True, gt=0)

from typing_extensions import Annotated

from pydantic import BaseModel, Field

class Foo(BaseModel):
    bar: Annotated[int, Field(strict=True, gt=0)]

A wrapper around int that allows for additional constraints.

Parameters:

Name Type Description Default
strict bool | None

Whether to validate the integer in strict mode. Defaults to None.

 None
gt int | None

The value must be greater than this.

 None
ge int | None

The value must be greater than or equal to this.

 None
lt int | None

The value must be less than this.

 None
le int | None

The value must be less than or equal to this.

 None
multiple_of int | None

The value must be a multiple of this.

 None

Returns:

Type Description
type[int]

The wrapped integer type.

 
from pydantic import BaseModel, ValidationError, conint

class ConstrainedExample(BaseModel):
    constrained_int: conint(gt=1)

m = ConstrainedExample(constrained_int=2)
print(repr(m))
#> ConstrainedExample(constrained_int=2)

try:
    ConstrainedExample(constrained_int=0)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than',
            'loc': ('constrained_int',),
            'msg': 'Input should be greater than 1',
            'input': 0,
            'ctx': {'gt': 1},
            'url': 'https://errors.pydantic.dev/2/v/greater_than',
        }
    ]
    '''
Source code in pydantic/types.py 
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def conint(
    *,
    strict: bool | None = None,
    gt: int | None = None,
    ge: int | None = None,
    lt: int | None = None,
    le: int | None = None,
    multiple_of: int | None = None,
) -> type[int]:
    """
    !!! warning "Discouraged"
        This function is **discouraged** in favor of using
        [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated) with
        [`Field`][pydantic.fields.Field] instead.

        This function will be **deprecated** in Pydantic 3.0.

        The reason is that `conint` returns a type, which doesn't play well with static analysis tools.

        === ":x: Don't do this"
            ```py
            from pydantic import BaseModel, conint

            class Foo(BaseModel):
                bar: conint(strict=True, gt=0)
            ```

        === ":white_check_mark: Do this"
            ```py
            from typing_extensions import Annotated

            from pydantic import BaseModel, Field

            class Foo(BaseModel):
                bar: Annotated[int, Field(strict=True, gt=0)]
            ```

    A wrapper around `int` that allows for additional constraints.

    Args:
        strict: Whether to validate the integer in strict mode. Defaults to `None`.
        gt: The value must be greater than this.
        ge: The value must be greater than or equal to this.
        lt: The value must be less than this.
        le: The value must be less than or equal to this.
        multiple_of: The value must be a multiple of this.

    Returns:
        The wrapped integer type.

    ```py
    from pydantic import BaseModel, ValidationError, conint

    class ConstrainedExample(BaseModel):
        constrained_int: conint(gt=1)

    m = ConstrainedExample(constrained_int=2)
    print(repr(m))
    #> ConstrainedExample(constrained_int=2)

    try:
        ConstrainedExample(constrained_int=0)
    except ValidationError as e:
        print(e.errors())
        '''
        [
            {
                'type': 'greater_than',
                'loc': ('constrained_int',),
                'msg': 'Input should be greater than 1',
                'input': 0,
                'ctx': {'gt': 1},
                'url': 'https://errors.pydantic.dev/2/v/greater_than',
            }
        ]
        '''
    ```

    """  # noqa: D212
    return Annotated[
        int,
        Strict(strict) if strict is not None else None,
        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
        annotated_types.MultipleOf(multiple_of) if multiple_of is not None else None,
    ]

confloat 

confloat(
    *,
    strict=None,
    gt=None,
    ge=None,
    lt=None,
    le=None,
    multiple_of=None,
    allow_inf_nan=None
)

Discouraged

This function is discouraged in favor of using Annotated with Field instead.

This function will be deprecated in Pydantic 3.0.

The reason is that confloat returns a type, which doesn't play well with static analysis tools.

from pydantic import BaseModel, confloat

class Foo(BaseModel):
    bar: confloat(strict=True, gt=0)

from typing_extensions import Annotated

from pydantic import BaseModel, Field

class Foo(BaseModel):
    bar: Annotated[float, Field(strict=True, gt=0)]

A wrapper around float that allows for additional constraints.

Parameters:

Name Type Description Default
strict bool | None

Whether to validate the float in strict mode.

 None
gt float | None

The value must be greater than this.

 None
ge float | None

The value must be greater than or equal to this.

 None
lt float | None

The value must be less than this.

 None
le float | None

The value must be less than or equal to this.

 None
multiple_of float | None

The value must be a multiple of this.

 None
allow_inf_nan bool | None

Whether to allow -inf, inf, and nan.

 None

Returns:

Type Description
type[float]

The wrapped float type.

 
from pydantic import BaseModel, ValidationError, confloat

class ConstrainedExample(BaseModel):
    constrained_float: confloat(gt=1.0)

m = ConstrainedExample(constrained_float=1.1)
print(repr(m))
#> ConstrainedExample(constrained_float=1.1)

try:
    ConstrainedExample(constrained_float=0.9)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than',
            'loc': ('constrained_float',),
            'msg': 'Input should be greater than 1',
            'input': 0.9,
            'ctx': {'gt': 1.0},
            'url': 'https://errors.pydantic.dev/2/v/greater_than',
        }
    ]
    '''
Source code in pydantic/types.py 
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def confloat(
    *,
    strict: bool | None = None,
    gt: float | None = None,
    ge: float | None = None,
    lt: float | None = None,
    le: float | None = None,
    multiple_of: float | None = None,
    allow_inf_nan: bool | None = None,
) -> type[float]:
    """
    !!! warning "Discouraged"
        This function is **discouraged** in favor of using
        [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated) with
        [`Field`][pydantic.fields.Field] instead.

        This function will be **deprecated** in Pydantic 3.0.

        The reason is that `confloat` returns a type, which doesn't play well with static analysis tools.

        === ":x: Don't do this"
            ```py
            from pydantic import BaseModel, confloat

            class Foo(BaseModel):
                bar: confloat(strict=True, gt=0)
            ```

        === ":white_check_mark: Do this"
            ```py
            from typing_extensions import Annotated

            from pydantic import BaseModel, Field

            class Foo(BaseModel):
                bar: Annotated[float, Field(strict=True, gt=0)]
            ```

    A wrapper around `float` that allows for additional constraints.

    Args:
        strict: Whether to validate the float in strict mode.
        gt: The value must be greater than this.
        ge: The value must be greater than or equal to this.
        lt: The value must be less than this.
        le: The value must be less than or equal to this.
        multiple_of: The value must be a multiple of this.
        allow_inf_nan: Whether to allow `-inf`, `inf`, and `nan`.

    Returns:
        The wrapped float type.

    ```py
    from pydantic import BaseModel, ValidationError, confloat

    class ConstrainedExample(BaseModel):
        constrained_float: confloat(gt=1.0)

    m = ConstrainedExample(constrained_float=1.1)
    print(repr(m))
    #> ConstrainedExample(constrained_float=1.1)

    try:
        ConstrainedExample(constrained_float=0.9)
    except ValidationError as e:
        print(e.errors())
        '''
        [
            {
                'type': 'greater_than',
                'loc': ('constrained_float',),
                'msg': 'Input should be greater than 1',
                'input': 0.9,
                'ctx': {'gt': 1.0},
                'url': 'https://errors.pydantic.dev/2/v/greater_than',
            }
        ]
        '''
    ```
    """  # noqa: D212
    return Annotated[
        float,
        Strict(strict) if strict is not None else None,
        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
        annotated_types.MultipleOf(multiple_of) if multiple_of is not None else None,
        AllowInfNan(allow_inf_nan) if allow_inf_nan is not None else None,
    ]

conbytes 

conbytes(*, min_length=None, max_length=None, strict=None)

A wrapper around bytes that allows for additional constraints.

Parameters:

Name Type Description Default
min_length int | None

The minimum length of the bytes.

 None
max_length int | None

The maximum length of the bytes.

 None
strict bool | None

Whether to validate the bytes in strict mode.

 None

Returns:

Type Description
type[bytes]

The wrapped bytes type.
Source code in pydantic/types.py 
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def conbytes(
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
) -> type[bytes]:
    """A wrapper around `bytes` that allows for additional constraints.

    Args:
        min_length: The minimum length of the bytes.
        max_length: The maximum length of the bytes.
        strict: Whether to validate the bytes in strict mode.

    Returns:
        The wrapped bytes type.
    """
    return Annotated[
        bytes,
        Strict(strict) if strict is not None else None,
        annotated_types.Len(min_length or 0, max_length),
    ]

constr 

constr(
    *,
    strip_whitespace=None,
    to_upper=None,
    to_lower=None,
    strict=None,
    min_length=None,
    max_length=None,
    pattern=None
)

Discouraged

This function is discouraged in favor of using Annotated with StringConstraints instead.

This function will be deprecated in Pydantic 3.0.

The reason is that constr returns a type, which doesn't play well with static analysis tools.

from pydantic import BaseModel, constr

class Foo(BaseModel):
    bar: constr(strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$')

from typing_extensions import Annotated

from pydantic import BaseModel, StringConstraints

class Foo(BaseModel):
    bar: Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$')]

A wrapper around str that allows for additional constraints.

from pydantic import BaseModel, constr

class Foo(BaseModel):
    bar: constr(strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$')


foo = Foo(bar='  hello  ')
print(foo)
#> bar='HELLO'

Parameters:

Name Type Description Default
strip_whitespace bool | None

Whether to remove leading and trailing whitespace.

 None
to_upper bool | None

Whether to turn all characters to uppercase.

 None
to_lower bool | None

Whether to turn all characters to lowercase.

 None
strict bool | None

Whether to validate the string in strict mode.

 None
min_length int | None

The minimum length of the string.

 None
max_length int | None

The maximum length of the string.

 None
pattern str | None

A regex pattern to validate the string against.

 None

Returns:

Type Description
type[str]

The wrapped string type.
Source code in pydantic/types.py 
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def constr(
    *,
    strip_whitespace: bool | None = None,
    to_upper: bool | None = None,
    to_lower: bool | None = None,
    strict: bool | None = None,
    min_length: int | None = None,
    max_length: int | None = None,
    pattern: str | None = None,
) -> type[str]:
    """
    !!! warning "Discouraged"
        This function is **discouraged** in favor of using
        [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated) with
        [`StringConstraints`][pydantic.types.StringConstraints] instead.

        This function will be **deprecated** in Pydantic 3.0.

        The reason is that `constr` returns a type, which doesn't play well with static analysis tools.

        === ":x: Don't do this"
            ```py
            from pydantic import BaseModel, constr

            class Foo(BaseModel):
                bar: constr(strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$')
            ```

        === ":white_check_mark: Do this"
            ```py
            from typing_extensions import Annotated

            from pydantic import BaseModel, StringConstraints

            class Foo(BaseModel):
                bar: Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$')]
            ```

    A wrapper around `str` that allows for additional constraints.

    ```py
    from pydantic import BaseModel, constr

    class Foo(BaseModel):
        bar: constr(strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$')


    foo = Foo(bar='  hello  ')
    print(foo)
    #> bar='HELLO'
    ```

    Args:
        strip_whitespace: Whether to remove leading and trailing whitespace.
        to_upper: Whether to turn all characters to uppercase.
        to_lower: Whether to turn all characters to lowercase.
        strict: Whether to validate the string in strict mode.
        min_length: The minimum length of the string.
        max_length: The maximum length of the string.
        pattern: A regex pattern to validate the string against.

    Returns:
        The wrapped string type.
    """  # noqa: D212
    return Annotated[
        str,
        StringConstraints(
            strip_whitespace=strip_whitespace,
            to_upper=to_upper,
            to_lower=to_lower,
            strict=strict,
            min_length=min_length,
            max_length=max_length,
            pattern=pattern,
        ),
    ]

conset 

conset(item_type, *, min_length=None, max_length=None)

A wrapper around typing.Set that allows for additional constraints.

Parameters:

Name Type Description Default
item_type type[HashableItemType]

The type of the items in the set.

 required
min_length int | None

The minimum length of the set.

 None
max_length int | None

The maximum length of the set.

 None

Returns:

Type Description
type[set[HashableItemType]]

The wrapped set type.
Source code in pydantic/types.py 
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def conset(
    item_type: type[HashableItemType], *, min_length: int | None = None, max_length: int | None = None
) -> type[set[HashableItemType]]:
    """A wrapper around `typing.Set` that allows for additional constraints.

    Args:
        item_type: The type of the items in the set.
        min_length: The minimum length of the set.
        max_length: The maximum length of the set.

    Returns:
        The wrapped set type.
    """
    return Annotated[Set[item_type], annotated_types.Len(min_length or 0, max_length)]

confrozenset 

confrozenset(
    item_type, *, min_length=None, max_length=None
)

A wrapper around typing.FrozenSet that allows for additional constraints.

Parameters:

Name Type Description Default
item_type type[HashableItemType]

The type of the items in the frozenset.

 required
min_length int | None

The minimum length of the frozenset.

 None
max_length int | None

The maximum length of the frozenset.

 None

Returns:

Type Description
type[frozenset[HashableItemType]]

The wrapped frozenset type.
Source code in pydantic/types.py 
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def confrozenset(
    item_type: type[HashableItemType], *, min_length: int | None = None, max_length: int | None = None
) -> type[frozenset[HashableItemType]]:
    """A wrapper around `typing.FrozenSet` that allows for additional constraints.

    Args:
        item_type: The type of the items in the frozenset.
        min_length: The minimum length of the frozenset.
        max_length: The maximum length of the frozenset.

    Returns:
        The wrapped frozenset type.
    """
    return Annotated[FrozenSet[item_type], annotated_types.Len(min_length or 0, max_length)]

conlist 

conlist(
    item_type,
    *,
    min_length=None,
    max_length=None,
    unique_items=None
)

A wrapper around typing.List that adds validation.

Parameters:

Name Type Description Default
item_type type[AnyItemType]

The type of the items in the list.

 required
min_length int | None

The minimum length of the list. Defaults to None.

 None
max_length int | None

The maximum length of the list. Defaults to None.

 None
unique_items bool | None

Whether the items in the list must be unique. Defaults to None.

Warning

The unique_items parameter is deprecated, use Set instead. See this issue for more details.

 None

Returns:

Type Description
type[list[AnyItemType]]

The wrapped list type.
Source code in pydantic/types.py 
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def conlist(
    item_type: type[AnyItemType],
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    unique_items: bool | None = None,
) -> type[list[AnyItemType]]:
    """A wrapper around typing.List that adds validation.

    Args:
        item_type: The type of the items in the list.
        min_length: The minimum length of the list. Defaults to None.
        max_length: The maximum length of the list. Defaults to None.
        unique_items: Whether the items in the list must be unique. Defaults to None.
            !!! warning Deprecated
                The `unique_items` parameter is deprecated, use `Set` instead.
                See [this issue](https://github.com/pydantic/pydantic-core/issues/296) for more details.

    Returns:
        The wrapped list type.
    """
    if unique_items is not None:
        raise PydanticUserError(
            (
                '`unique_items` is removed, use `Set` instead'
                '(this feature is discussed in https://github.com/pydantic/pydantic-core/issues/296)'
            ),
            code='removed-kwargs',
        )
    return Annotated[List[item_type], annotated_types.Len(min_length or 0, max_length)]

condecimal 

condecimal(
    *,
    strict=None,
    gt=None,
    ge=None,
    lt=None,
    le=None,
    multiple_of=None,
    max_digits=None,
    decimal_places=None,
    allow_inf_nan=None
)

Discouraged

This function is discouraged in favor of using Annotated with Field instead.

This function will be deprecated in Pydantic 3.0.

The reason is that condecimal returns a type, which doesn't play well with static analysis tools.

from pydantic import BaseModel, condecimal

class Foo(BaseModel):
    bar: condecimal(strict=True, allow_inf_nan=True)

from decimal import Decimal

from typing_extensions import Annotated

from pydantic import BaseModel, Field

class Foo(BaseModel):
    bar: Annotated[Decimal, Field(strict=True, allow_inf_nan=True)]

A wrapper around Decimal that adds validation.

Parameters:

Name Type Description Default
strict bool | None

Whether to validate the value in strict mode. Defaults to None.

 None
gt int | Decimal | None

The value must be greater than this. Defaults to None.

 None
ge int | Decimal | None

The value must be greater than or equal to this. Defaults to None.

 None
lt int | Decimal | None

The value must be less than this. Defaults to None.

 None
le int | Decimal | None

The value must be less than or equal to this. Defaults to None.

 None
multiple_of int | Decimal | None

The value must be a multiple of this. Defaults to None.

 None
max_digits int | None

The maximum number of digits. Defaults to None.

 None
decimal_places int | None

The number of decimal places. Defaults to None.

 None
allow_inf_nan bool | None

Whether to allow infinity and NaN. Defaults to None.

 None 
from decimal import Decimal

from pydantic import BaseModel, ValidationError, condecimal

class ConstrainedExample(BaseModel):
    constrained_decimal: condecimal(gt=Decimal('1.0'))

m = ConstrainedExample(constrained_decimal=Decimal('1.1'))
print(repr(m))
#> ConstrainedExample(constrained_decimal=Decimal('1.1'))

try:
    ConstrainedExample(constrained_decimal=Decimal('0.9'))
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than',
            'loc': ('constrained_decimal',),
            'msg': 'Input should be greater than 1.0',
            'input': Decimal('0.9'),
            'ctx': {'gt': Decimal('1.0')},
            'url': 'https://errors.pydantic.dev/2/v/greater_than',
        }
    ]
    '''
Source code in pydantic/types.py 
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def condecimal(
    *,
    strict: bool | None = None,
    gt: int | Decimal | None = None,
    ge: int | Decimal | None = None,
    lt: int | Decimal | None = None,
    le: int | Decimal | None = None,
    multiple_of: int | Decimal | None = None,
    max_digits: int | None = None,
    decimal_places: int | None = None,
    allow_inf_nan: bool | None = None,
) -> type[Decimal]:
    """
    !!! warning "Discouraged"
        This function is **discouraged** in favor of using
        [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated) with
        [`Field`][pydantic.fields.Field] instead.

        This function will be **deprecated** in Pydantic 3.0.

        The reason is that `condecimal` returns a type, which doesn't play well with static analysis tools.

        === ":x: Don't do this"
            ```py
            from pydantic import BaseModel, condecimal

            class Foo(BaseModel):
                bar: condecimal(strict=True, allow_inf_nan=True)
            ```

        === ":white_check_mark: Do this"
            ```py
            from decimal import Decimal

            from typing_extensions import Annotated

            from pydantic import BaseModel, Field

            class Foo(BaseModel):
                bar: Annotated[Decimal, Field(strict=True, allow_inf_nan=True)]
            ```

    A wrapper around Decimal that adds validation.

    Args:
        strict: Whether to validate the value in strict mode. Defaults to `None`.
        gt: The value must be greater than this. Defaults to `None`.
        ge: The value must be greater than or equal to this. Defaults to `None`.
        lt: The value must be less than this. Defaults to `None`.
        le: The value must be less than or equal to this. Defaults to `None`.
        multiple_of: The value must be a multiple of this. Defaults to `None`.
        max_digits: The maximum number of digits. Defaults to `None`.
        decimal_places: The number of decimal places. Defaults to `None`.
        allow_inf_nan: Whether to allow infinity and NaN. Defaults to `None`.

    ```py
    from decimal import Decimal

    from pydantic import BaseModel, ValidationError, condecimal

    class ConstrainedExample(BaseModel):
        constrained_decimal: condecimal(gt=Decimal('1.0'))

    m = ConstrainedExample(constrained_decimal=Decimal('1.1'))
    print(repr(m))
    #> ConstrainedExample(constrained_decimal=Decimal('1.1'))

    try:
        ConstrainedExample(constrained_decimal=Decimal('0.9'))
    except ValidationError as e:
        print(e.errors())
        '''
        [
            {
                'type': 'greater_than',
                'loc': ('constrained_decimal',),
                'msg': 'Input should be greater than 1.0',
                'input': Decimal('0.9'),
                'ctx': {'gt': Decimal('1.0')},
                'url': 'https://errors.pydantic.dev/2/v/greater_than',
            }
        ]
        '''
    ```
    """  # noqa: D212
    return Annotated[
        Decimal,
        Strict(strict) if strict is not None else None,
        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
        annotated_types.MultipleOf(multiple_of) if multiple_of is not None else None,
        _fields.pydantic_general_metadata(max_digits=max_digits, decimal_places=decimal_places),
        AllowInfNan(allow_inf_nan) if allow_inf_nan is not None else None,
    ]

condate 

condate(*, strict=None, gt=None, ge=None, lt=None, le=None)

A wrapper for date that adds constraints.

Parameters:

Name Type Description Default
strict bool | None

Whether to validate the date value in strict mode. Defaults to None.

 None
gt date | None

The value must be greater than this. Defaults to None.

 None
ge date | None

The value must be greater than or equal to this. Defaults to None.

 None
lt date | None

The value must be less than this. Defaults to None.

 None
le date | None

The value must be less than or equal to this. Defaults to None.

 None

Returns:

Type Description
type[date]

A date type with the specified constraints.
Source code in pydantic/types.py 
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def condate(
    *,
    strict: bool | None = None,
    gt: date | None = None,
    ge: date | None = None,
    lt: date | None = None,
    le: date | None = None,
) -> type[date]:
    """A wrapper for date that adds constraints.

    Args:
        strict: Whether to validate the date value in strict mode. Defaults to `None`.
        gt: The value must be greater than this. Defaults to `None`.
        ge: The value must be greater than or equal to this. Defaults to `None`.
        lt: The value must be less than this. Defaults to `None`.
        le: The value must be less than or equal to this. Defaults to `None`.

    Returns:
        A date type with the specified constraints.
    """
    return Annotated[
        date,
        Strict(strict) if strict is not None else None,
        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
    ]