Types/number types

Pydantic supports the following numeric types from the Python standard library:

• int
• float
• enum.IntEnum
• decimal.Decimal

Validation of numeric types

int : Pydantic uses int(v) to coerce types to an int; see Data conversion for details on loss of information during data conversion.

float : similarly, float(v) is used to coerce values to floats.

enum.IntEnum : checks that the value is a valid IntEnum instance.

subclass of enum.IntEnum : checks that the value is a valid member of the integer enum; see Enums and Choices for more details.

decimal.Decimal : Pydantic attempts to convert the value to a string, then passes the string to Decimal(v).

Constrained types

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.

Those functions accept the following arguments:

• gt (greater than)
• ge (greater than or equal to)
• lt (less than)
• le (less than or equal to)
• multiple_of (multiple of)
• strict (whether to allow coercion from compatible types)

Some functions accept additional arguments, which you can see in the API reference of each function. For example, confloat accepts an allow_inf_nan, which specifies whether to allow -inf, inf, and nan.

Constrained integers

There are also types that can be used to constrain integers:

• PositiveInt: Constrain an int to be positive.
• NegativeInt: Constrain an int to be negative.
• NonPositiveInt: Constrain an int to be non-positive.
• NonNegativeInt: Constrain an int to be non-negative.

from pydantic import (
    BaseModel,
    NegativeInt,
    NonNegativeInt,
    NonPositiveInt,
    PositiveInt,
)


class Model(BaseModel):
    positive: PositiveInt
    negative: NegativeInt
    non_positive: NonPositiveInt
    non_negative: NonNegativeInt


m = Model(positive=1, negative=-1, non_positive=0, non_negative=0)
print(m)
#> positive=1 negative=-1 non_positive=0 non_negative=0

Constrained floats

There are also types that can be used to constrain floats:

• PositiveFloat: Constrain a float to be positive.
• NegativeFloat: Constrain a float to be negative.
• NonPositiveFloat: Constrain a float to be non-positive.
• NonNegativeFloat: Constrain a float to be non-negative.

from pydantic import (
    BaseModel,
    NegativeFloat,
    NonNegativeFloat,
    NonPositiveFloat,
    PositiveFloat,
)


class Model(BaseModel):
    positive: PositiveFloat
    negative: NegativeFloat
    non_positive: NonPositiveFloat
    non_negative: NonNegativeFloat


m = Model(positive=1.0, negative=-1.0, non_positive=0.0, non_negative=0.0)
print(m)
#> positive=1.0 negative=-1.0 non_positive=0.0 non_negative=0.0

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 import BaseModel, FiniteFloat


class Model(BaseModel):
    finite: FiniteFloat


m = Model(finite=1.0)
print(m)
#> finite=1.0