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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.isalpha(), 'must be alphanumeric'
        return v

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

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A few things to note on validators:

• 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).

Warning

If you make use of assert statements, keep in mind that running Python with the -O optimization flag disables assert statements, and validators will stop working.

• where validators rely on other values, you should be aware that:

  • Validation is done in the order fields are defined. E.g. in the example above, password2 has access to password1 (and name), but password1 does not have access to password2. See Field Ordering for more information on how fields are ordered

  • 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:

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(f'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)
"""

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A few more things to note:

• 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

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(2019, 11, 28, 16, 4, 35, 479774)
print(DemoModel(ts='2017-11-08T14:00'))
#> ts=datetime.datetime(2017, 11, 8, 14, 0)

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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.

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)
"""

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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 will be called even if field validation fails; 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(2019, 11, 28, 16, 4, 35, 482105))
print(DemoDataclass(ts='2017-11-08T14:00'))
#> DemoDataclass(ts=datetime.datetime(2017, 11, 8, 14, 0))

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