Computed Fields

API Documentation

pydantic.fields.computed_field

Computed fields allow property and cached_property to be included when serializing models or dataclasses. This is useful for fields that are computed from other fields, or for fields that are expensive to compute and should be cached.

from pydantic import BaseModel, computed_field


class Rectangle(BaseModel):
    width: int
    length: int

    @computed_field
    @property
    def area(self) -> int:
        return self.width * self.length


print(Rectangle(width=3, length=2).model_dump())
#> {'width': 3, 'length': 2, 'area': 6}

If the computed_field decorator is applied to a bare function (e.g. a function without the @property or @cached_property decorator) it will wrap the function in property itself. Although this is more concise, you will lose IntelliSense in your IDE, and confuse static type checkers, thus explicit use of @property is recommended.

Mypy Warning

Even with the @property or @cached_property applied to your function before @computed_field, mypy may throw a Decorated property not supported error. See mypy issue #1362, for more information. To avoid this error message, add # type: ignore[misc] to the @computed_field line.

pyright supports @computed_field without error.

import random
from functools import cached_property

from pydantic import BaseModel, computed_field


class Square(BaseModel):
    width: float

    @computed_field
    def area(self) -> float:  # converted to a `property` by `computed_field`
        return round(self.width**2, 2)

    @area.setter
    def area(self, new_area: float) -> None:
        self.width = new_area**0.5

    @computed_field(alias='the magic number', repr=False)
    @cached_property
    def random_number(self) -> int:
        return random.randint(0, 1_000)


square = Square(width=1.3)

# `random_number` does not appear in representation
print(repr(square))
#> Square(width=1.3, area=1.69)

print(square.random_number)
#> 3

# same random number as before (cached as expected)
print(square.model_dump())
#> {'width': 1.3, 'area': 1.69, 'random_number': 3}

square.area = 4

print(square.model_dump_json(by_alias=True))
#> {"width":2.0,"area":4.0,"the magic number":3}