Pydantic Validators

Custom validation and complex relationships between objects can be achieved using the validator decorator.

from pydantic import BaseModel, ValidationError, validator


class UserModel(BaseModel):
    name: str
    username: str
    password1: str
    password2: str

    @validator('name')
    def name_must_contain_space(cls, v):
        if ' ' not in v:
            raise ValueError('must contain a space')
        return v.title()

    @validator('password2')
    def passwords_match(cls, v, values, **kwargs):
        if 'password1' in values and v != values['password1']:
            raise ValueError('passwords do not match')
        return v

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


user = UserModel(
    name='samuel colvin',
    username='scolvin',
    password1='zxcvbn',
    password2='zxcvbn',
)
print(user)
#> name='Samuel Colvin' username='scolvin' password1='zxcvbn' password2='zxcvbn'

try:
    UserModel(
        name='samuel',
        username='scolvin',
        password1='zxcvbn',
        password2='zxcvbn2',
    )
except ValidationError as e:
    print(e)
    """
    2 validation errors for UserModel
    name
      must contain a space (type=value_error)
    password2
      passwords do not match (type=value_error)
    """

You need to be aware of these validator behaviours.

Validators are "class methods", so the first argument value they receive is the UserModel class, not an instance of UserModel.
The second argument is always the field value to validate; it can be named as you please.
You can also add any subset of the following arguments to the signature (the names must match):
- values: a dict containing the name-to-value mapping of any previously-validated fields.
- config: the model config.
- field: the field being validated.
- **kwargs: if provided, this will include the arguments above not explicitly listed in the signature.
Validators should either return the parsed value or raise a ValueError, TypeError, or AssertionError (assert statements may be used).
Where validators rely on other values, you should be aware that:
- Validation is done in the order fields are defined.
- If validation fails on another field (or that field is missing) it will not be included in values, hence if 'password1' in values and ... in this example.

Pre and per-item validators ⚑

Validators can do a few more complex things:

A single validator can be applied to multiple fields by passing it multiple field names.
A single validator can also be called on all fields by passing the special value '*'.
The keyword argument pre will cause the validator to be called prior to other validation.
Passing each_item=True will result in the validator being applied to individual values (e.g. of List, Dict, Set, etc.), rather than the whole object.

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


class DemoModel(BaseModel):
    square_numbers: List[int] = []
    cube_numbers: List[int] = []

    # '*' is the same as 'cube_numbers', 'square_numbers' here:
    @validator('*', pre=True)
    def split_str(cls, v):
        if isinstance(v, str):
            return v.split('|')
        return v

    @validator('cube_numbers', 'square_numbers')
    def check_sum(cls, v):
        if sum(v) > 42:
            raise ValueError('sum of numbers greater than 42')
        return v

    @validator('square_numbers', each_item=True)
    def check_squares(cls, v):
        assert v ** 0.5 % 1 == 0, f'{v} is not a square number'
        return v

    @validator('cube_numbers', each_item=True)
    def check_cubes(cls, v):
        # 64 ** (1 / 3) == 3.9999999999999996 (!)
        # this is not a good way of checking cubes
        assert v ** (1 / 3) % 1 == 0, f'{v} is not a cubed number'
        return v


print(DemoModel(square_numbers=[1, 4, 9]))
#> square_numbers=[1, 4, 9] cube_numbers=[]
print(DemoModel(square_numbers='1|4|16'))
#> square_numbers=[1, 4, 16] cube_numbers=[]
print(DemoModel(square_numbers=[16], cube_numbers=[8, 27]))
#> square_numbers=[16] cube_numbers=[8, 27]
try:
    DemoModel(square_numbers=[1, 4, 2])
except ValidationError as e:
    print(e)
    """
    1 validation error for DemoModel
    square_numbers -> 2
      2 is not a square number (type=assertion_error)
    """

try:
    DemoModel(cube_numbers=[27, 27])
except ValidationError as e:
    print(e)
    """
    1 validation error for DemoModel
    cube_numbers
      sum of numbers greater than 42 (type=value_error)
    """

Subclass Validators and `each_item`⚑

If using a validator with a subclass that references a List type field on a parent class, using each_item=True will cause the validator not to run; instead, the list must be iterated over programatically.

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


class ParentModel(BaseModel):
    names: List[str]


class ChildModel(ParentModel):
    @validator('names', each_item=True)
    def check_names_not_empty(cls, v):
        assert v != '', 'Empty strings are not allowed.'
        return v


# This will NOT raise a ValidationError because the validator was not called
try:
    child = ChildModel(names=['Alice', 'Bob', 'Eve', ''])
except ValidationError as e:
    print(e)
else:
    print('No ValidationError caught.')
    #> No ValidationError caught.


class ChildModel2(ParentModel):
    @validator('names')
    def check_names_not_empty(cls, v):
        for name in v:
            assert name != '', 'Empty strings are not allowed.'
        return v


try:
    child = ChildModel2(names=['Alice', 'Bob', 'Eve', ''])
except ValidationError as e:
    print(e)
    """
    1 validation error for ChildModel2
    names
      Empty strings are not allowed. (type=assertion_error)
    """

Validate Always ⚑

For performance reasons, by default validators are not called for fields when a value is not supplied. However there are situations where it may be useful or required to always call the validator, e.g. to set a dynamic default value.

from datetime import datetime

from pydantic import BaseModel, validator


class DemoModel(BaseModel):
    ts: datetime = None

    @validator('ts', pre=True, always=True)
    def set_ts_now(cls, v):
        return v or datetime.now()


print(DemoModel())
#> ts=datetime.datetime(2020, 7, 15, 20, 1, 48, 966302)
print(DemoModel(ts='2017-11-08T14:00'))
#> ts=datetime.datetime(2017, 11, 8, 14, 0)

You'll often want to use this together with pre, since otherwise with always=True pydantic would try to validate the default None which would cause an error.

Reuse validators ⚑

Occasionally, you will want to use the same validator on multiple fields/models (e.g. to normalize some input data). The "naive" approach would be to write a separate function, then call it from multiple decorators. Obviously, this entails a lot of repetition and boiler plate code. To circumvent this, the allow_reuse parameter has been added to pydantic.validator in v1.2 (False by default):

from pydantic import BaseModel, validator


def normalize(name: str) -> str:
    return ' '.join((word.capitalize()) for word in name.split(' '))


class Producer(BaseModel):
    name: str

    # validators
    _normalize_name = validator('name', allow_reuse=True)(normalize)


class Consumer(BaseModel):
    name: str

    # validators
    _normalize_name = validator('name', allow_reuse=True)(normalize)


jane_doe = Producer(name='JaNe DOE')
john_doe = Consumer(name='joHN dOe')
assert jane_doe.name == 'Jane Doe'
assert john_doe.name == 'John Doe'

As it is obvious, repetition has been reduced and the models become again almost declarative.

Tip

If you have a lot of fields that you want to validate, it usually makes sense to define a help function with which you will avoid setting allow_reuse=True over and over again.

Root Validators ⚑

Validation can also be performed on the entire model's data.

from pydantic import BaseModel, ValidationError, root_validator


class UserModel(BaseModel):
    username: str
    password1: str
    password2: str

    @root_validator(pre=True)
    def check_card_number_omitted(cls, values):
        assert 'card_number' not in values, 'card_number should not be included'
        return values

    @root_validator
    def check_passwords_match(cls, values):
        pw1, pw2 = values.get('password1'), values.get('password2')
        if pw1 is not None and pw2 is not None and pw1 != pw2:
            raise ValueError('passwords do not match')
        return values


print(UserModel(username='scolvin', password1='zxcvbn', password2='zxcvbn'))
#> username='scolvin' password1='zxcvbn' password2='zxcvbn'
try:
    UserModel(username='scolvin', password1='zxcvbn', password2='zxcvbn2')
except ValidationError as e:
    print(e)
    """
    1 validation error for UserModel
    __root__
      passwords do not match (type=value_error)
    """

try:
    UserModel(
        username='scolvin',
        password1='zxcvbn',
        password2='zxcvbn',
        card_number='1234',
    )
except ValidationError as e:
    print(e)
    """
    1 validation error for UserModel
    __root__
      card_number should not be included (type=assertion_error)
    """

As with field validators, root validators can have pre=True, in which case they're called before field validation occurs (and are provided with the raw input data), or pre=False (the default), in which case they're called after field validation.

Field validation will not occur if pre=True root validators raise an error. As with field validators, "post" (i.e. pre=False) root validators by default will be called even if prior validators fail; this behaviour can be changed by setting the skip_on_failure=True keyword argument to the validator. The values argument will be a dict containing the values which passed field validation and field defaults where applicable.

Field Checks ⚑

On class creation, validators are checked to confirm that the fields they specify actually exist on the model.

Occasionally however this is undesirable: e.g. if you define a validator to validate fields on inheriting models. In this case you should set check_fields=False on the validator.

Dataclass Validators ⚑

Validators also work with pydantic dataclasses.

from datetime import datetime

from pydantic import validator
from pydantic.dataclasses import dataclass


@dataclass
class DemoDataclass:
    ts: datetime = None

    @validator('ts', pre=True, always=True)
    def set_ts_now(cls, v):
        return v or datetime.now()


print(DemoDataclass())
#> DemoDataclass(ts=datetime.datetime(2020, 7, 15, 20, 1, 48, 969037))
print(DemoDataclass(ts='2017-11-08T14:00'))
#> DemoDataclass(ts=datetime.datetime(2017, 11, 8, 14, 0))

Troubleshooting validators⚑

pylint complains on the validators ⚑

Pylint complains that R0201: Method could be a function and N805: first argument of a method should be named 'self'. Seems to be an error of pylint, people have solved it by specifying @classmethod between the definition and the validator decorator.

References⚑

Pydantic validators