Pydantic V1

The pydantic documentation desccribes two options that can be used with the .dict() method of models.

• exclude_unset: whether fields which were not explicitly set when creating the model should be excluded from the returned dictionary; default False. Prior to v1.0, exclude_unset was known as skip_defaults; use of skip_defaults is now deprecated

• exclude_defaults: whether fields which are equal to their default values (whether set or otherwise) should be excluded from the returned dictionary; default False

So you can create a model class with optional fields:

from typing import Optional
from pydantic import BaseModel


class MyModel(BaseModel):
    foo: Optional[int] = None
    bar: Optional[int] = None

And still generate a dict with fields explicitely set to None, but without default values:

baz = MyModel(foo=None)
assert baz.dict(exclude_unset=True) == {"foo": None}

baz = MyModel(bar=None)
assert baz.dict(exclude_unset=True) == {"bar": None}

Pydantic V2

Pydantic V2 is available since June 30, 2023

The .dict() method has been removed in V2. In order to get a dictionary out of a BaseModel instance, one must use the model_dump() method instead:

from __future__ import annotations
from pydantic import BaseModel


class MyModel(BaseModel):
    foo: int | None = None
    bar: int | None = None

baz = MyModel(foo=None)
assert baz.model_dump(exclude_unset=True) == {"foo": None}

baz = MyModel(bar=None)
assert baz.model_dump(exclude_unset=True) == {"bar": None}