Validation Errors

Pydantic attempts to provide useful validation errors. Below are details on common validation errors users may encounter when working with pydantic, together with some suggestions on how to fix them.

`arguments_type`¶

This error is raised when an object that would be passed as arguments to a function during validation is not a tuple, list, or dict. Because NamedTuple uses function calls in its implementation, that is one way to produce this error:

from typing import NamedTuple

from pydantic import BaseModel, ValidationError


class MyNamedTuple(NamedTuple):
    x: int


class MyModel(BaseModel):
    field: MyNamedTuple


try:
    MyModel.model_validate({'field': 'invalid'})
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'arguments_type'

`assertion_error`¶

This error is raised when a failing assert statement is encountered during validation:

from pydantic import BaseModel, ValidationError, field_validator


class Model(BaseModel):
    x: int

    @field_validator('x')
    @classmethod
    def force_x_positive(cls, v):
        assert v > 0
        return v


try:
    Model(x=-1)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'assertion_error'

`bool_parsing`¶

This error is raised when the input value is a string that is not valid for coercion to a boolean:

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: bool


Model(x='true')  # OK

try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'bool_parsing'

`bool_type`¶

This error is raised when the input value's type is not valid for a bool field:

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: bool


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'bool_type'

This error is also raised for strict fields when the input value is not an instance of bool.

`bytes_too_long`¶

This error is raised when the length of a bytes value is greater than the field's max_length constraint:

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: bytes = Field(max_length=3)


try:
    Model(x=b'test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'bytes_too_long'

`bytes_too_short`¶

This error is raised when the length of a bytes value is less than the field's min_length constraint:

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: bytes = Field(min_length=3)


try:
    Model(x=b't')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'bytes_too_short'

`bytes_type`¶

This error is raised when the input value's type is not valid for a bytes field:

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: bytes


try:
    Model(x=123)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'bytes_type'

This error is also raised for strict fields when the input value is not an instance of bytes.

`callable_type`¶

This error is raised when the input value is not valid as a Callable:

from typing import Any, Callable

from pydantic import BaseModel, ImportString, ValidationError


class Model(BaseModel):
    x: ImportString[Callable[[Any], Any]]


Model(x='math:cos')  # OK

try:
    Model(x='os.path')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'callable_type'

`dataclass_exact_type`¶

This error is raised when validating a dataclass with strict=True and the input is not an instance of the dataclass:

import pydantic.dataclasses
from pydantic import TypeAdapter, ValidationError


@pydantic.dataclasses.dataclass
class MyDataclass:
    x: str


adapter = TypeAdapter(MyDataclass)

print(adapter.validate_python(MyDataclass(x='test'), strict=True))
#> MyDataclass(x='test')
print(adapter.validate_python({'x': 'test'}))
#> MyDataclass(x='test')

try:
    adapter.validate_python({'x': 'test'}, strict=True)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'dataclass_exact_type'

`dataclass_type`¶

This error is raised when the input value is not valid for a dataclass field:

from pydantic import ValidationError, dataclasses


@dataclasses.dataclass
class Inner:
    x: int


@dataclasses.dataclass
class Outer:
    y: Inner


Outer(y=Inner(x=1))  # OK

try:
    Outer(y=1)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'dataclass_type'

`date_from_datetime_inexact`¶

This error is raised when the input datetime value provided for a date field has a nonzero time component. For a timestamp to parse into a field of type date, the time components must all be zero:

from datetime import date, datetime

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: date


Model(x='2023-01-01')  # OK
Model(x=datetime(2023, 1, 1))  # OK

try:
    Model(x=datetime(2023, 1, 1, 12))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'date_from_datetime_inexact'

`date_from_datetime_parsing`¶

This error is raised when the input value is a string that cannot be parsed for a date field:

from datetime import date

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: date


try:
    Model(x='XX1494012000')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'date_from_datetime_parsing'

`date_future`¶

This error is raised when the input value provided for a FutureDate field is not in the future:

from datetime import date

from pydantic import BaseModel, FutureDate, ValidationError


class Model(BaseModel):
    x: FutureDate


try:
    Model(x=date(2000, 1, 1))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'date_future'

`date_parsing`¶

This error is raised when validating JSON where the input value is string that cannot be parsed for a date field:

import json
from datetime import date

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: date = Field(strict=True)


try:
    Model.model_validate_json(json.dumps({'x': '1'}))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'date_parsing'

`date_past`¶

This error is raised when the value provided for a PastDate field is not in the past:

from datetime import date, timedelta

from pydantic import BaseModel, PastDate, ValidationError


class Model(BaseModel):
    x: PastDate


try:
    Model(x=date.today() + timedelta(1))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'date_past'

`date_type`¶

This error is raised when the input value's type is not valid for a date field:

from datetime import date

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: date


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'date_type'

This error is also raised for strict fields when the input value is not an instance of date.

`datetime_from_date_parsing`¶

Note

Support for this error, along with support for parsing datetimes from yyyy-MM-DD dates will be added in v2.6.0

This error is raised when the input value is a string that cannot be parsed for a datetime field:

from datetime import datetime

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: datetime


try:
    # there is no 13th month
    Model(x='2023-13-01')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'datetime_from_date_parsing'

`datetime_future`¶

This error is raised when the value provided for a FutureDatetime field is not in the future:

from datetime import datetime

from pydantic import BaseModel, FutureDatetime, ValidationError


class Model(BaseModel):
    x: FutureDatetime


try:
    Model(x=datetime(2000, 1, 1))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'datetime_future'

`datetime_object_invalid`¶

This error is raised when something about the datetime object is not valid:

from datetime import datetime, tzinfo

from pydantic import AwareDatetime, BaseModel, ValidationError


class CustomTz(tzinfo):
    # utcoffset is not implemented!

    def tzname(self, _dt):
        return 'CustomTZ'


class Model(BaseModel):
    x: AwareDatetime


try:
    Model(x=datetime(2023, 1, 1, tzinfo=CustomTz()))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'datetime_object_invalid'

`datetime_parsing`¶

This error is raised when the value is a string that cannot be parsed for a datetime field:

import json
from datetime import datetime

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: datetime = Field(strict=True)


try:
    Model.model_validate_json(json.dumps({'x': 'not a datetime'}))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'datetime_parsing'

`datetime_past`¶

This error is raised when the value provided for a PastDatetime field is not in the past:

from datetime import datetime, timedelta

from pydantic import BaseModel, PastDatetime, ValidationError


class Model(BaseModel):
    x: PastDatetime


try:
    Model(x=datetime.now() + timedelta(100))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'datetime_past'

`datetime_type`¶

This error is raised when the input value's type is not valid for a datetime field:

from datetime import datetime

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: datetime


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'datetime_type'

This error is also raised for strict fields when the input value is not an instance of datetime.

`decimal_max_digits`¶

This error is raised when the value provided for a Decimal has too many digits:

from decimal import Decimal

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: Decimal = Field(max_digits=3)


try:
    Model(x='42.1234')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'decimal_max_digits'

`decimal_max_places`¶

This error is raised when the value provided for a Decimal has too many digits after the decimal point:

from decimal import Decimal

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: Decimal = Field(decimal_places=3)


try:
    Model(x='42.1234')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'decimal_max_places'

`decimal_parsing`¶

This error is raised when the value provided for a Decimal could not be parsed as a decimal number:

from decimal import Decimal

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: Decimal = Field(decimal_places=3)


try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'decimal_parsing'

`decimal_type`¶

This error is raised when the value provided for a Decimal is of the wrong type:

from decimal import Decimal

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: Decimal = Field(decimal_places=3)


try:
    Model(x=[1, 2, 3])
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'decimal_type'

This error is also raised for strict fields when the input value is not an instance of Decimal.

`decimal_whole_digits`¶

This error is raised when the value provided for a Decimal has more digits before the decimal point than max_digits - decimal_places (as long as both are specified):

from decimal import Decimal

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: Decimal = Field(max_digits=6, decimal_places=3)


try:
    Model(x='12345.6')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'decimal_whole_digits'

This error is also raised for strict fields when the input value is not an instance of Decimal.

`dict_type`¶

This error is raised when the input value's type is not dict for a dict field:

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: dict


try:
    Model(x=['1', '2'])
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'dict_type'

`enum`¶

This error is raised when the input value does not exist in an enum field members:

from enum import Enum

from pydantic import BaseModel, ValidationError


class MyEnum(str, Enum):
    option = 'option'


class Model(BaseModel):
    x: MyEnum


try:
    Model(x='other_option')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'enum'

`extra_forbidden`¶

This error is raised when the input value contains extra fields, but model_config['extra'] == 'forbid':

from pydantic import BaseModel, ConfigDict, ValidationError


class Model(BaseModel):
    x: str

    model_config = ConfigDict(extra='forbid')


try:
    Model(x='test', y='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'extra_forbidden'

You can read more about the extra configuration in the Extra Attributes section.

`finite_number`¶

This error is raised when the value is infinite, or too large to be represented as a 64-bit floating point number during validation:

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: int


try:
    Model(x=2.2250738585072011e308)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'finite_number'

`float_parsing`¶

This error is raised when the value is a string that can't be parsed as a float:

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: float


try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'float_parsing'

`float_type`¶

This error is raised when the input value's type is not valid for a float field:

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: float


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'float_type'

`frozen_field`¶

This error is raised when you attempt to assign a value to a field with frozen=True, or to delete such a field:

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: str = Field('test', frozen=True)


model = Model()

try:
    model.x = 'test1'
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'frozen_field'

try:
    del model.x
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'frozen_field'

`frozen_instance`¶

This error is raised when model_config['frozen] == True and you attempt to delete or assign a new value to any of the fields:

from pydantic import BaseModel, ConfigDict, ValidationError


class Model(BaseModel):
    x: int

    model_config = ConfigDict(frozen=True)


m = Model(x=1)

try:
    m.x = 2
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'frozen_instance'

try:
    del m.x
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'frozen_instance'

`frozen_set_type`¶

This error is raised when the input value's type is not valid for a frozenset field:

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: frozenset


try:
    model = Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'frozen_set_type'

`get_attribute_error`¶

This error is raised when model_config['from_attributes'] == True and an error is raised while reading the attributes:

from pydantic import BaseModel, ConfigDict, ValidationError


class Foobar:
    def __init__(self):
        self.x = 1

    @property
    def y(self):
        raise RuntimeError('intentional error')


class Model(BaseModel):
    x: int
    y: str

    model_config = ConfigDict(from_attributes=True)


try:
    Model.model_validate(Foobar())
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'get_attribute_error'

`greater_than`¶

This error is raised when the value is not greater than the field's gt constraint:

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: int = Field(gt=10)


try:
    Model(x=10)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'greater_than'

`greater_than_equal`¶

This error is raised when the value is not greater than or equal to the field's ge constraint:

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: int = Field(ge=10)


try:
    Model(x=9)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'greater_than_equal'

`int_from_float`¶

This error is raised when you provide a float value for an int field:

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: int


try:
    Model(x=0.5)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'int_from_float'

`int_parsing`¶

This error is raised when the value can't be parsed as int:

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: int


try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'int_parsing'

`int_parsing_size`¶

This error is raised when attempting to parse a python or JSON value from a string outside the maximum range that Python str to int parsing permits:

import json

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: int


# from Python
assert Model(x='1' * 4_300).x == int('1' * 4_300)  # OK

too_long = '1' * 4_301
try:
    Model(x=too_long)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'int_parsing_size'

# from JSON
try:
    Model.model_validate_json(json.dumps({'x': too_long}))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'int_parsing_size'

`int_type`¶

This error is raised when the input value's type is not valid for an int field:

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: int


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'int_type'

`invalid_key`¶

This error is raised when attempting to validate a dict that has a key that is not an instance of str:

from pydantic import BaseModel, ConfigDict, ValidationError


class Model(BaseModel):
    x: int

    model_config = ConfigDict(extra='allow')


try:
    Model.model_validate({'x': 1, b'y': 2})
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'invalid_key'

`is_instance_of`¶

This error is raised when the input value is not an instance of the expected type:

from pydantic import BaseModel, ConfigDict, ValidationError


class Nested:
    x: str


class Model(BaseModel):
    y: Nested

    model_config = ConfigDict(arbitrary_types_allowed=True)


try:
    Model(y='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'is_instance_of'

`is_subclass_of`¶

This error is raised when the input value is not a subclass of the expected type:

from typing import Type

from pydantic import BaseModel, ValidationError


class Nested:
    x: str


class Model(BaseModel):
    y: Type[Nested]


try:
    Model(y='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'is_subclass_of'

`iterable_type`¶

This error is raised when the input value is not valid as an Iterable:

from typing import Iterable

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    y: Iterable


try:
    Model(y=123)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'iterable_type'

`iteration_error`¶

This error is raised when an error occurs during iteration:

from typing import List

from pydantic import BaseModel, ValidationError


def gen():
    yield 1
    raise RuntimeError('error')


class Model(BaseModel):
    x: List[int]


try:
    Model(x=gen())
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'iteration_error'

`json_invalid`¶

This error is raised when the input value is not a valid JSON string:

from pydantic import BaseModel, Json, ValidationError


class Model(BaseModel):
    x: Json


try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'json_invalid'

`json_type`¶

This error is raised when the input value is of a type that cannot be parsed as JSON:

from pydantic import BaseModel, Json, ValidationError


class Model(BaseModel):
    x: Json


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'json_type'

`less_than`¶

This error is raised when the input value is not less than the field's lt constraint:

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: int = Field(lt=10)


try:
    Model(x=10)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'less_than'

`less_than_equal`¶

This error is raised when the input value is not less than or equal to the field's le constraint:

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: int = Field(le=10)


try:
    Model(x=11)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'less_than_equal'

`list_type`¶

This error is raised when the input value's type is not valid for a list field:

from typing import List

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: List[int]


try:
    Model(x=1)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'list_type'

`literal_error`¶

This error is raised when the input value is not one of the expected literal values:

from typing_extensions import Literal

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: Literal['a', 'b']


Model(x='a')  # OK

try:
    Model(x='c')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'literal_error'

`mapping_type`¶

This error is raised when a problem occurs during validation due to a failure in a call to the methods from the Mapping protocol, such as .items():

from collections.abc import Mapping
from typing import Dict

from pydantic import BaseModel, ValidationError


class BadMapping(Mapping):
    def items(self):
        raise ValueError()

    def __iter__(self):
        raise ValueError()

    def __getitem__(self, key):
        raise ValueError()

    def __len__(self):
        return 1


class Model(BaseModel):
    x: Dict[str, str]


try:
    Model(x=BadMapping())
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'mapping_type'

`missing`¶

This error is raised when there are required fields missing from the input value:

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: str


try:
    Model()
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'missing'

`missing_argument`¶

This error is raised when a required positional-or-keyword argument is not passed to a function decorated with validate_call:

from pydantic import ValidationError, validate_call


@validate_call
def foo(a: int):
    return a


try:
    foo()
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'missing_argument'

`missing_keyword_only_argument`¶

This error is raised when a required keyword-only argument is not passed to a function decorated with validate_call:

from pydantic import ValidationError, validate_call


@validate_call
def foo(*, a: int):
    return a


try:
    foo()
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'missing_keyword_only_argument'

`missing_positional_only_argument`¶

This error is raised when a required positional-only argument is not passed to a function decorated with validate_call:

from pydantic import ValidationError, validate_call


@validate_call
def foo(a: int, /):
    return a


try:
    foo()
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'missing_positional_only_argument'

`model_attributes_type`¶

This error is raised when the input value is not a valid dictionary, model instance, or instance that fields can be extracted from:

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    a: int
    b: int


# simply validating a dict
print(Model.model_validate({'a': 1, 'b': 2}))
#> a=1 b=2


class CustomObj:
    def __init__(self, a, b):
        self.a = a
        self.b = b


# using from attributes to extract fields from an objects
print(Model.model_validate(CustomObj(3, 4), from_attributes=True))
#> a=3 b=4

try:
    Model.model_validate('not an object', from_attributes=True)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'model_attributes_type'

`model_type`¶

This error is raised when the input to a model is not an instance of the model or dict:

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    a: int
    b: int


# simply validating a dict
m = Model.model_validate({'a': 1, 'b': 2})
print(m)
#> a=1 b=2

# validating an existing model instance
print(Model.model_validate(m))
#> a=1 b=2

try:
    Model.model_validate('not an object')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'model_type'

`multiple_argument_values`¶

This error is raised when you provide multiple values for a single argument while calling a function decorated with validate_call:

from pydantic import ValidationError, validate_call


@validate_call
def foo(a: int):
    return a


try:
    foo(1, a=2)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'multiple_argument_values'

`multiple_of`¶

This error is raised when the input is not a multiple of a field's multiple_of constraint:

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: int = Field(multiple_of=5)


try:
    Model(x=1)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'multiple_of'

`no_such_attribute`¶

This error is raised when validate_assignment=True in the config, and you attempt to assign a value to an attribute that is not an existing field:

from pydantic import ConfigDict, ValidationError, dataclasses


@dataclasses.dataclass(config=ConfigDict(validate_assignment=True))
class MyDataclass:
    x: int


m = MyDataclass(x=1)
try:
    m.y = 10
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'no_such_attribute'

`none_required`¶

This error is raised when the input value is not None for a field that requires None:

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: None


try:
    Model(x=1)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'none_required'

`recursion_loop`¶

This error is raised when a cyclic reference is detected:

from typing import List

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: List['Model']


d = {'x': []}
d['x'].append(d)
try:
    Model(**d)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'recursion_loop'

`set_type`¶

This error is raised when the value type is not valid for a set field:

from typing import Set

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: Set[int]


try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'set_type'

`string_pattern_mismatch`¶

This error is raised when the input value doesn't match the field's pattern constraint:

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: str = Field(pattern='test')


try:
    Model(x='1')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'string_pattern_mismatch'

`string_sub_type`¶

This error is raised when the value is an instance of a strict subtype of str when the field is strict:

from enum import Enum

from pydantic import BaseModel, Field, ValidationError


class MyEnum(str, Enum):
    foo = 'foo'


class Model(BaseModel):
    x: str = Field(strict=True)


try:
    Model(x=MyEnum.foo)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'string_sub_type'

`string_too_long`¶

This error is raised when the input value is a string whose length is greater than the field's max_length constraint:

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: str = Field(max_length=3)


try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'string_too_long'

`string_too_short`¶

This error is raised when the input value is a string whose length is less than the field's min_length constraint:

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: str = Field(min_length=3)


try:
    Model(x='t')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'string_too_short'

`string_type`¶

This error is raised when the input value's type is not valid for a str field:

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: str


try:
    Model(x=1)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'string_type'

This error is also raised for strict fields when the input value is not an instance of str.

`string_unicode`¶

This error is raised when the value cannot be parsed as a Unicode string:

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: str


try:
    Model(x=b'\x81')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'string_unicode'

`time_delta_parsing`¶

This error is raised when the input value is a string that cannot be parsed for a timedelta field:

from datetime import timedelta

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: timedelta


try:
    Model(x='t')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'time_delta_parsing'

`time_delta_type`¶

This error is raised when the input value's type is not valid for a timedelta field:

from datetime import timedelta

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: timedelta


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'time_delta_type'

This error is also raised for strict fields when the input value is not an instance of timedelta.

`time_parsing`¶

This error is raised when the input value is a string that cannot be parsed for a time field:

from datetime import time

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: time


try:
    Model(x='25:20:30.400')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'time_parsing'

`time_type`¶

This error is raised when the value type is not valid for a time field:

from datetime import time

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: time


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'time_type'

This error is also raised for strict fields when the input value is not an instance of time.

`timezone_aware`¶

This error is raised when the datetime value provided for a timezone-aware datetime field doesn't have timezone information:

from datetime import datetime

from pydantic import AwareDatetime, BaseModel, ValidationError


class Model(BaseModel):
    x: AwareDatetime


try:
    Model(x=datetime.now())
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'timezone_aware'

`timezone_naive`¶

This error is raised when the datetime value provided for a timezone-naive datetime field has timezone info:

from datetime import datetime, timezone

from pydantic import BaseModel, NaiveDatetime, ValidationError


class Model(BaseModel):
    x: NaiveDatetime


try:
    Model(x=datetime.now(tz=timezone.utc))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'timezone_naive'

`too_long`¶

This error is raised when the input value's length is greater than the field's max_length constraint:

from typing import List

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: List[int] = Field(max_length=3)


try:
    Model(x=[1, 2, 3, 4])
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'too_long'

`too_short`¶

This error is raised when the value length is less than the field's min_length constraint:

from typing import List

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: List[int] = Field(min_length=3)


try:
    Model(x=[1, 2])
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'too_short'

`tuple_type`¶

This error is raised when the input value's type is not valid for a tuple field:

from typing import Tuple

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: Tuple[int]


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'tuple_type'

This error is also raised for strict fields when the input value is not an instance of tuple.

`unexpected_keyword_argument`¶

This error is raised when you provide a value by keyword for a positional-only argument while calling a function decorated with validate_call:

from pydantic import ValidationError, validate_call


@validate_call
def foo(a: int, /):
    return a


try:
    foo(a=2)
except ValidationError as exc:
    print(repr(exc.errors()[1]['type']))
    #> 'unexpected_keyword_argument'

It is also raised when using pydantic.dataclasses and extra=forbid:

from pydantic import TypeAdapter, ValidationError
from pydantic.dataclasses import dataclass


@dataclass(config={'extra': 'forbid'})
class Foo:
    bar: int


try:
    TypeAdapter(Foo).validate_python({'bar': 1, 'foobar': 2})
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'unexpected_keyword_argument'

`unexpected_positional_argument`¶

This error is raised when you provide a positional value for a keyword-only argument while calling a function decorated with validate_call:

from pydantic import ValidationError, validate_call


@validate_call
def foo(*, a: int):
    return a


try:
    foo(2)
except ValidationError as exc:
    print(repr(exc.errors()[1]['type']))
    #> 'unexpected_positional_argument'

`union_tag_invalid`¶

This error is raised when the input's discriminator is not one of the expected values:

from typing import Union

from typing_extensions import Literal

from pydantic import BaseModel, Field, ValidationError


class BlackCat(BaseModel):
    pet_type: Literal['blackcat']


class WhiteCat(BaseModel):
    pet_type: Literal['whitecat']


class Model(BaseModel):
    cat: Union[BlackCat, WhiteCat] = Field(..., discriminator='pet_type')


try:
    Model(cat={'pet_type': 'dog'})
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'union_tag_invalid'

`union_tag_not_found`¶

This error is raised when it is not possible to extract a discriminator value from the input:

from typing import Union

from typing_extensions import Literal

from pydantic import BaseModel, Field, ValidationError


class BlackCat(BaseModel):
    pet_type: Literal['blackcat']


class WhiteCat(BaseModel):
    pet_type: Literal['whitecat']


class Model(BaseModel):
    cat: Union[BlackCat, WhiteCat] = Field(..., discriminator='pet_type')


try:
    Model(cat={'name': 'blackcat'})
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'union_tag_not_found'

`url_parsing`¶

This error is raised when the input value cannot be parsed as a URL:

from pydantic import AnyUrl, BaseModel, ValidationError


class Model(BaseModel):
    x: AnyUrl


try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'url_parsing'

`url_scheme`¶

This error is raised when the URL scheme is not valid for the URL type of the field:

from pydantic import BaseModel, HttpUrl, ValidationError


class Model(BaseModel):
    x: HttpUrl


try:
    Model(x='ftp://example.com')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'url_scheme'

`url_syntax_violation`¶

This error is raised when the URL syntax is not valid:

from pydantic import BaseModel, Field, HttpUrl, ValidationError


class Model(BaseModel):
    x: HttpUrl = Field(strict=True)


try:
    Model(x='http:////example.com')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'url_syntax_violation'

`url_too_long`¶

This error is raised when the URL length is greater than 2083:

from pydantic import BaseModel, HttpUrl, ValidationError


class Model(BaseModel):
    x: HttpUrl


try:
    Model(x='x' * 2084)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'url_too_long'

`url_type`¶

This error is raised when the input value's type is not valid for a URL field:

from pydantic import BaseModel, HttpUrl, ValidationError


class Model(BaseModel):
    x: HttpUrl


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'url_type'

`uuid_parsing`¶

This error is raised when the input value's type is not valid for a UUID field:

from uuid import UUID

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    u: UUID


try:
    Model(u='12345678-124-1234-1234-567812345678')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'uuid_parsing'

`uuid_type`¶

This error is raised when the input value's type is not valid instance for a UUID field (str, bytes or UUID):

from uuid import UUID

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    u: UUID


try:
    Model(u=1234567812412341234567812345678)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'uuid_type'

`uuid_version`¶

This error is raised when the input value's type is not match UUID version:

from pydantic import UUID5, BaseModel, ValidationError


class Model(BaseModel):
    u: UUID5


try:
    Model(u='a6cc5730-2261-11ee-9c43-2eb5a363657c')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'uuid_version'

`value_error`¶

This error is raised when a ValueError is raised during validation:

from pydantic import BaseModel, ValidationError, field_validator


class Model(BaseModel):
    x: str

    @field_validator('x')
    @classmethod
    def repeat_b(cls, v):
        raise ValueError()


try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'value_error'

Validation Errors

arguments_type¶

assertion_error¶

bool_parsing¶

bool_type¶

bytes_too_long¶

bytes_too_short¶

bytes_type¶

callable_type¶

dataclass_exact_type¶

dataclass_type¶

date_from_datetime_inexact¶

date_from_datetime_parsing¶

date_future¶

date_parsing¶

date_past¶

date_type¶

datetime_from_date_parsing¶

datetime_future¶

datetime_object_invalid¶

datetime_parsing¶

datetime_past¶

datetime_type¶

decimal_max_digits¶

decimal_max_places¶

decimal_parsing¶

decimal_type¶

decimal_whole_digits¶

dict_type¶

enum¶

extra_forbidden¶

finite_number¶

float_parsing¶

float_type¶

frozen_field¶

frozen_instance¶

frozen_set_type¶

get_attribute_error¶

greater_than¶

greater_than_equal¶

int_from_float¶

int_parsing¶

int_parsing_size¶

int_type¶

invalid_key¶

is_instance_of¶

is_subclass_of¶

iterable_type¶

iteration_error¶

json_invalid¶

json_type¶

less_than¶

less_than_equal¶

list_type¶

literal_error¶

mapping_type¶

missing¶

missing_argument¶

missing_keyword_only_argument¶

missing_positional_only_argument¶

model_attributes_type¶

model_type¶

multiple_argument_values¶

multiple_of¶

no_such_attribute¶

none_required¶

recursion_loop¶

set_type¶

string_pattern_mismatch¶

string_sub_type¶

string_too_long¶

string_too_short¶

string_type¶

string_unicode¶

time_delta_parsing¶

time_delta_type¶

time_parsing¶

time_type¶

timezone_aware¶

timezone_naive¶