Pydantic Types
The types module contains custom types used by pydantic.
Bases: PydanticMetadata, BaseMetadata
A field metadata class to indicate that a field should be validated in strict mode.
Whether to validate the field in strict mode.
Type: bool
Bases: PydanticMetadata
A field metadata class to indicate that a field should allow -inf, inf, and nan.
Bases: GroupedMetadata
Apply constraints to str types.
Whether to strip whitespace from the string.
Type: bool | None
Whether to convert the string to uppercase.
Type: bool | None
Whether to convert the string to lowercase.
Type: bool | None
Whether to validate the string in strict mode.
Type: bool | None
The minimum length of the string.
Type: int | None
The maximum length of the string.
Type: int | None
A regex pattern that the string must match.
Type: str | None
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"}
A field metadata class to indicate a UUID version.
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]"}
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
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'
Bases: str
Based on: https://en.wikipedia.org/wiki/Payment_card_number.
Mask all but the last 4 digits of the card number.
Type: str
@classmethod
def validate(
cls,
__input_value: str,
_: core_schema.ValidationInfo,
) -> PaymentCardNumber
Validate the card number and return a PaymentCardNumber instance.
@classmethod
def validate_digits(cls, card_number: str) -> None
Validate that the card number is all digits.
None
@classmethod
def validate_luhn_check_digit(cls, card_number: str) -> str
Based on: https://en.wikipedia.org/wiki/Luhn_algorithm.
str
@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).
PaymentCardBrand
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).
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
def human_readable(decimal: bool = False) -> str
Converts a byte size to a human readable string.
str — A human readable string representation of the byte size.
If True, use decimal units (e.g. 1000 bytes per KB). If False, use binary units (e.g. 1024 bytes per KiB).
def to(unit: str) -> float
Converts a byte size to another unit.
float — The byte size in the new 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.
A date in the past.
A date in the future.
A datetime that requires timezone info.
A datetime that doesn’t require timezone info.
A datetime that must be in the past.
A datetime that must be in the future.
Bases: Protocol
Protocol for encoding and decoding data to and from bytes.
@classmethod
def decode(cls, data: bytes) -> bytes
Decode the data using the encoder.
bytes — The decoded data.
The data to decode.
@classmethod
def encode(cls, value: bytes) -> bytes
Encode the data using the encoder.
bytes — The encoded data.
The data to encode.
@classmethod
def get_json_format(cls) -> str
Get the JSON format for the encoded data.
str — The JSON format for the encoded data.
Bases: EncoderProtocol
Standard (non-URL-safe) Base64 encoder.
@classmethod
def decode(cls, data: bytes) -> bytes
Decode the data from base64 encoded bytes to original bytes data.
bytes — The decoded data.
The data to decode.
@classmethod
def encode(cls, value: bytes) -> bytes
Encode the data from bytes to a base64 encoded bytes.
bytes — The encoded data.
The data to encode.
@classmethod
def get_json_format(cls) -> Literal['base64']
Get the JSON format for the encoded data.
Literal[‘base64’] — The JSON format for the encoded data.
Bases: EncoderProtocol
URL-safe Base64 encoder.
@classmethod
def decode(cls, data: bytes) -> bytes
Decode the data from base64 encoded bytes to original bytes data.
bytes — The decoded data.
The data to decode.
@classmethod
def encode(cls, value: bytes) -> bytes
Encode the data from bytes to a base64 encoded bytes.
bytes — The encoded data.
The data to encode.
@classmethod
def get_json_format(cls) -> Literal['base64url']
Get the JSON format for the encoded data.
Literal[‘base64url’] — The JSON format for the encoded data.
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]
'''
def decode(data: bytes, _: core_schema.ValidationInfo) -> bytes
Decode the data using the specified encoder.
bytes — The decoded data.
The data to decode.
def encode(value: bytes) -> bytes
Encode the data using the specified encoder.
bytes — The encoded data.
The data to encode.
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]
'''
def decode_str(data: bytes, _: core_schema.ValidationInfo) -> str
Decode the data using the specified encoder.
str — The decoded data.
The data to decode.
def encode_str(value: str) -> str
Encode the data using the specified encoder.
str — The encoded data.
The data to encode.
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'
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]
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.
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',
}
]
'''
type[int] — The wrapped integer type.
Whether to validate the integer in strict mode. Defaults to None.
The value must be greater than this.
The value must be greater than or equal to this.
The value must be less than this.
The value must be less than or equal to this.
The value must be a multiple of this.
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]
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.
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',
}
]
'''
type[float] — The wrapped float type.
Whether to validate the float in strict mode.
The value must be greater than this.
The value must be greater than or equal to this.
The value must be less than this.
The value must be less than or equal to this.
The value must be a multiple of this.
Whether to allow -inf, inf, and nan.
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.
type[bytes] — The wrapped bytes type.
The minimum length of the bytes.
The maximum length of the bytes.
Whether to validate the bytes in strict mode.
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]
from pydantic import BaseModel, constr
class Foo(BaseModel):
bar: constr(strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$')
from pydantic import BaseModel, Annotated, StringConstraints
class Foo(BaseModel):
bar: Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$')]
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'
type[str] — The wrapped string type.
Whether to remove leading and trailing whitespace.
Whether to turn all characters to uppercase.
Whether to turn all characters to lowercase.
Whether to validate the string in strict mode.
The minimum length of the string.
The maximum length of the string.
A regex pattern to validate the string against.
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.
type[set[HashableItemType]] — The wrapped set type.
The type of the items in the set.
The minimum length of the set.
The maximum length of the set.
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.
type[frozenset[HashableItemType]] — The wrapped frozenset type.
The type of the items in the frozenset.
The minimum length of the frozenset.
The maximum length of the frozenset.
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.
type[list[AnyItemType]] — The wrapped list type.
The type of the items in the list.
The minimum length of the list. Defaults to None.
The maximum length of the list. Defaults to None.
Whether the items in the list must be unique. Defaults to None.
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]
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.
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',
}
]
'''
type[Decimal]
Whether to validate the value in strict mode. Defaults to None.
The value must be greater than this. Defaults to None.
The value must be greater than or equal to this. Defaults to None.
The value must be less than this. Defaults to None.
The value must be less than or equal to this. Defaults to None.
The value must be a multiple of this. Defaults to None.
The maximum number of digits. Defaults to None.
The number of decimal places. Defaults to None.
Whether to allow infinity and NaN. Defaults to None.
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.
type[date] — A date type with the specified constraints.
Whether to validate the date value in strict mode. Defaults to None.
The value must be greater than this. Defaults to None.
The value must be greater than or equal to this. Defaults to None.
The value must be less than this. Defaults to None.
The value must be less than or equal to this. Defaults to None.
A boolean that must be either True or False.
Default: Annotated[bool, Strict()]
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',
}
]
'''
Default: Annotated[int, annotated_types.Gt(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',
}
]
'''
Default: Annotated[int, annotated_types.Lt(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',
}
]
'''
Default: Annotated[int, annotated_types.Le(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',
}
]
'''
Default: Annotated[int, annotated_types.Ge(0)]
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]
'''
Default: Annotated[int, Strict()]
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',
}
]
'''
Default: Annotated[float, annotated_types.Gt(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',
}
]
'''
Default: Annotated[float, annotated_types.Lt(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',
}
]
'''
Default: Annotated[float, annotated_types.Le(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',
}
]
'''
Default: Annotated[float, annotated_types.Ge(0)]
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]
'''
Default: Annotated[float, Strict(True)]
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
Default: Annotated[float, AllowInfNan(False)]
A bytes that must be validated in strict mode.
Default: Annotated[bytes, Strict()]
A string that must be validated in strict mode.
Default: Annotated[str, Strict()]
A UUID that must be version 1.
import uuid
from pydantic import BaseModel, UUID1
class Model(BaseModel):
uuid1: UUID1
Model(uuid1=uuid.uuid1())
Default: Annotated[UUID, UuidVersion(1)]
A UUID that must be version 3.
import uuid
from pydantic import BaseModel, UUID3
class Model(BaseModel):
uuid3: UUID3
Model(uuid3=uuid.uuid3(uuid.NAMESPACE_DNS, 'pydantic.org'))
Default: Annotated[UUID, UuidVersion(3)]
A UUID that must be version 4.
import uuid
from pydantic import BaseModel, UUID4
class Model(BaseModel):
uuid4: UUID4
Model(uuid4=uuid.uuid4())
Default: Annotated[UUID, UuidVersion(4)]
A UUID that must be version 5.
import uuid
from pydantic import BaseModel, UUID5
class Model(BaseModel):
uuid5: UUID5
Model(uuid5=uuid.uuid5(uuid.NAMESPACE_DNS, 'pydantic.org'))
Default: Annotated[UUID, UuidVersion(5)]
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]
'''
Default: Annotated[Path, PathType('file')]
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]
'''
Default: Annotated[Path, PathType('dir')]
A path for a new file or directory that must not already exist.
Default: Annotated[Path, PathType('new')]
A bytes type that is encoded and decoded using the standard (non-URL-safe) base64 encoder.
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]
'''
Default: Annotated[bytes, EncodedBytes(encoder=Base64Encoder)]
A str type that is encoded and decoded using the standard (non-URL-safe) base64 encoder.
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]
'''
Default: Annotated[str, EncodedStr(encoder=Base64Encoder)]
A bytes type that is encoded and decoded using the URL-safe base64 encoder.
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'
Default: Annotated[bytes, EncodedBytes(encoder=Base64UrlEncoder)]
A str type that is encoded and decoded using the URL-safe base64 encoder.
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'
Default: Annotated[str, EncodedStr(encoder=Base64UrlEncoder)]