Pydantic uses types to define how validation and serialization should be performed. Built-in and standard library types (such as int, str, date) can be used as is. Strictness can be controlled and constraints can be applied on them · On top ...

from decimal import Decimal from pydantic import ( BaseModel, NegativeFloat, NegativeInt, PositiveFloat, PositiveInt, NonNegativeFloat, NonNegativeInt, NonPositiveFloat, NonPositiveInt, conbytes, condecimal, confloat, conint, conlist, conset, constr, Field, ) class Model(BaseModel): upper_bytes: conbytes(to_upper=True) lower_bytes: conbytes(to_lower=True) short_bytes: conbytes(min_length=2, max_length=10) strip_bytes: conbytes(strip_whitespace=True) upper_str: constr(to_upper=True) lower_str: constr(to_lower=True) short_str: constr(min_length=2, max_length=10) regex_str: constr(regex=r'^apple

from pydantic import BaseModel, conint class Apples(BaseModel): count: conint(le=50, gt=0) class Bananas(BaseModel): count: conint(le=100, gt=0) The only difference is the max value of count. create_model should be the solution: from pydantic import conint, create_model def fruits_model_factory(max_count=200): return create_model('Fruit', count=conint(gt=0, lt=max_count)) but ·

from pydantic import conint, BaseModel class Post(BaseModel): age: conint(gt=0) name: str

Pydantic provides functions that can be used to constrain numbers: conint: Add constraints to an int type. confloat: Add constraints to a float type. condecimal: Add constraints to a decimal.Decimal type.

July 17, 2024 - This conint and constr helps a lot when you are working with API · from pydantic import BaseModel, conint, constr class Product(BaseModel): name: constr(min_length=2, max_length=50) quantity: conint(gt=0, le=1000) price: float product = Product(name="Laptop", quantity=5, price=999.99)

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', } ] '''

from decimal import Decimal from pydantic import ( BaseModel, NegativeFloat, NegativeInt, PositiveFloat, PositiveInt, conbytes, condecimal, confloat, conint, conlist, constr, Field, ) class Model(BaseModel): short_bytes: conbytes(min_length=2, max_length=10) strip_bytes: conbytes(strip_whitespace=True) short_str: constr(min_length=2, max_length=10) regex_str: constr(regex='apple (pie|tart|sandwich)') strip_str: constr(strip_whitespace=True) big_int: conint(gt=1000, lt=1024) mod_int: conint(multiple_of=5) pos_int: PositiveInt neg_int: NegativeInt big_float: confloat(gt=1000, lt=1024) unit_inter

You can following arguments to the conint function. ... from pydantic import ( BaseModel, conint, ValidationError ) class Employee(BaseModel): id: int name: str age: conint(gt = 18) try: emp1 = Employee(id = 1, name = 'Krishna', age = 17) except ValidationError as e: print(e.json())

conint : type method for constraining ints; see Constrained Types.

July 16, 2024 - from pydantic import BaseModel, conint, constr class Product(BaseModel): name: constr(min_length=2, max_length=50) quantity: conint(gt=0, le=1000) price: float product = Product(name="Laptop", quantity=5, price=999.99)

StrictInt (and the strict option of conint()) will not accept bool types, even though bool is a subclass of int in Python. Other subclasses will work. StrictFloat (and the strict option of confloat()) will not accept int. Besides the above, you can also have a FiniteFloat type that will only accept finite values (i.e. not inf, -inf or nan). from pydantic ...

March 23, 2020 - import pydantic class SomeModel(pydantic.BaseModel): odd_integer: pydantic.conint(odd=True) good_model = SomeModel(odd_integer=7) bad_model = SomeModel(odd_integer=2) >>> ValidationError: 1 validation error for SomeModel odd_integer ensure this value is odd of 2 (type=value_error.number.not_odd; odd=True)

pydantic.conint · pydantic.constr · pydantic.create_model · pydantic.error_wrappers.ErrorWrapper · pydantic.errors · pydantic.errors.ConfigError · pydantic.errors.PydanticValueError · pydantic.Extra · pydantic.Field · pydantic.fields.ModelField · pydantic.PositiveInt ·

May 11, 2020 - from typing import Dict from pydantic import BaseModel, conint, validator, root_validator class InterpolationSetting(BaseModel): interpolation_factor: conint(gt=1) interpolation_method: str interpolate_on_integral: bool @validator("interpolation_method") def method_is_valid(cls, method: str) -> str: allowed_set = {"repeat", "distribute", "linear", "cubic", "akima"} if method not in allowed_set: raise ValueError(f"must be in {allowed_set}, got '{method}'") return method @root_validator() def valid_combination_of_method_and_on_integral(cls, values: Dict) -> Dict: on_integral = values.get("interpolate_on_integral") method = values.get("interpolation_method") if on_integral is False and method == "distribute": raise ValueError( f"Invalid combination of interpolation_method " f"{method} and interpolate_on_integral {on_integral}" ) return values

Source code in pydantic/types.py · conint( *, strict=None, gt=None, ge=None, lt=None, le=None, multiple_of=None ) A wrapper around int that allows for additional constraints. Parameters: Returns: Source code in pydantic/types.py · conlist( item_type, *, min_length=None, max_length=None, ...

September 13, 2023 - from pydantic import validate_call, conint @validate_call def greet_times(times: conint(gt=0, lt=11), name: str): for _ in range(times): print(f"Hello - {name}") # Valid. print("-------Valid--------") greet_times(times=3, name="John Doe") # ...