pydantic-partials 1.1.0

Pydantic partial model class, with ability to easily dynamically omit fields when serializing a model.

• Pydantic Partials
  • Documentation
  • Quick Start
    • Install
    • Introduction
    • Basic Example
    • Inheritable
    • Automatic Partials Configuration

Pydantic Partials

An easy way to add or create partials for Pydantic models.

Documentation

📄 Detailed Documentation | 🐍 PyPi

Quick Start

Install

poetry install pydantic-partials

or

pip install pydantic-partials

Introduction

You can create from scratch, or convert existing models to be Partials. The main purpose will be to add to exiting models, and hence the default behavior of making all non-default fields partials (configurable).

Let's first look at a basic example.

Basic Example

Very basic example of a simple model follows:

from pydantic_partials import PartialModel, Missing


class MyModel(PartialModel):
    some_attr: str
    another_field: str

# By default, Partial fields without any value will get set to a
# special `Missing` type. Any field that is set to Missing is
# excluded from the model_dump/model_dump_json.
obj = MyModel()
assert obj.some_attr is Missing
assert obj.model_dump() == {}

# You can set the real value at any time, and it will behave like expected.
obj.some_attr = 'hello'
assert obj.some_attr is 'hello'
assert obj.model_dump() == {'some_attr': 'hello'}

# You can always manually set a field to `Missing` directly.
obj.some_attr = Missing

# And now it's removed from the model-dump.
assert obj.model_dump() == {}

# The json dump is also affected in the same way.
assert obj.model_dump_json() == '{}'

# Any non-missing fields will be included when dumping/serializing model.
obj.another_field = 'assigned-value'

# After dumping again, we have `another_field` outputted.
# The `some_attr` field is not present since it's still `Missing`.
assert obj.model_dump() == {'another_field': 'assigned-value'}

By default, all fields without a default value will have the ability to be partial, and can be missing from both validation and serialization. This includes any inherited Pydantic fields (from a superclass).

Inheritable

You can inherit from a model to make a partial-version of the inherited fields:

from pydantic_partials import PartialModel, Missing
from pydantic import ValidationError, BaseModel

class TestModel(BaseModel):
    name: str
    value: str
    some_null_by_default_field: str | None = None

try:
    # This should produce an error because
    # `name` and `value`are required fields.
    TestModel()
except ValidationError as e:
    print(f'Pydantic will state `name` + `value` are required: {e}')
else:
    raise Exception('Field `required_decimal` should be required.')

    # We inherit from `TestModel` and add `PartialModel` to the mix.

class PartialTestModel(PartialModel, TestModel):
    pass

# `PartialTestModel` can now be allocated without the required fields.
# Any missing required fields will be marked with the `Missing` value
# and won't be serialized out.
obj = PartialTestModel(name='a-name')

assert obj.name == 'a-name'
assert obj.value is Missing
assert obj.some_null_by_default_field is None

# The `None` field value is still serialized out,
# only fields with a `Missing` value assigned are skipped.
assert obj.model_dump() == {
    'name': 'a-name', 'some_null_by_default_field': None
}

Notice that if a field has a default value, it's used instead of marking it as Missing.

Also, the Missing sentinel value is a separate value vs None, allowing one to easily know if a value is truly just missing or is None/Null.

Exclude Fields From Auto Partials

You can exclude specific fields from the automatic partials via these means:

• AutoPartialExclude[...]
  • This puts a special Annotated item on field to mark it as excluded.
• class PartialRequired(PartialModel, auto_partials_exclude={'id', 'created_at'}):
  • This way provides them via class argument auto_partials_exclude
• Or via the standard model_config
  • model_config = {'auto_partials_exclude': {'id', 'created_at'}}
  • A dict, using auto_partials_exclude as the key and a set of field names as the value.

Any of these methods are inheritable. You can override an excluded value by explicitly marking a field as Partial via some_field: Partial[str]

Here is an example using the AutoPartialExclude method, also showing how it can inherit.

from pydantic_partials import PartialModel, AutoPartialExclude, Missing
from pydantic import BaseModel, ValidationError
from datetime import datetime
import pytest

class PartialRequired(PartialModel):
    id: AutoPartialExclude[str]
    created_at: AutoPartialExclude[datetime]

class TestModel(BaseModel):
    id: str
    created_at: datetime
    name: str
    value: str
    some_null_by_default_field: str | None = None

class PartialTestModel(TestModel, PartialRequired):
    pass

# Will raise validation error for the two fields excluded from auto-partials
with pytest.raises(
    ValidationError,
    match=r'2 validation errors[\w\W]*'
          r'id[\w\W]*Field required[\w\W]*'
          r'created_at[\w\W]*Field required'
):
    PartialTestModel()

# If we give them values, we get no ValidationError
obj = PartialTestModel(id='some-value', created_at=datetime.now())

# And fields have the expected values.
assert obj.id == 'some-value'
assert obj.name is Missing

Auto Partials Configuration

You can turn off automatically applying partials to all non-defaulted fields via auto_partials class argument or modeL_config option:

from pydantic_partials import PartialModel, PartialConfigDict

class TestModel1(PartialModel, auto_partials=False):
    ...

class TestModel2(PartialModel):
    model_config = PartialConfigDict(auto_partials=False)
    ...

You can disable this automatic function. This means you have complete control of exactly which field can be partial or not. You can use either the generic Partial[...] generic or a union with MissingType to mark a field as a partial field. The generic simple makes the union to MissingType for you.

Example of disabling auto_partials:

from pydantic_partials import PartialModel, Missing, MissingType, Partial
from decimal import Decimal
from pydantic import ValidationError

class TestModel(PartialModel, auto_partials=False):
    # Can use `Partial` generic type
    partial_int: Partial[int] = Missing
    
    # Or union with `MissingType`
    partial_str: str | MissingType
    
    required_decimal: Decimal
    
try:
    TestModel()
except ValidationError as e:
    print(f'Pydantic will state `required_decimal` is required: {e}')
else:
    raise Exception('Pydantic should have required `required_decimal`.')
    
obj = TestModel(required_decimal='1.34')

# You can find out at any time if a field is missing or not:
assert obj.partial_int is Missing
assert obj.partial_str is Missing

assert obj.required_decimal == Decimal('1.34')