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https://github.com/team23/pydantic-partial

Create partial models from pydantic models
https://github.com/team23/pydantic-partial

model partial pydantic python

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Create partial models from pydantic models

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# pydantic-partial



## Installation



Just use `pip install pydantic-partial` to install the library.



**Note:** `pydantic-partial` is compatible with `pydantic` versions `1.9`, `1.10` and even `2.x` (🥳) on

Python `3.9`, `3.10`, `3.11`, `3.12` and `3.13`. This is also ensured running all tests on all those versions

using `tox`.



## About



Create partial models from your normal pydantic models. Partial models will allow

some or all fields to be optional and thus not be required when creating the model

instance.



Partial models can be used to support PATCH HTTP requests where the user only wants

to update some fields of the model and normal validation for required fields is not

required. It may also be used to have partial response DTOs where you want to skip

certain fields, this can be useful in combination with `exclude_none`. It is - like

shown in these examples - intended to be used with API use cases, so when using

pydantic with for example FastAPI.



**Disclaimer:** This is still an early release of `pydantic-partial`. Things might

change in the future. PR welcome. ;-)



### Usage example



`pydantic-partial` provides a mixin to generate partial model classes. The mixin can

be used like this:



```python

import pydantic

from pydantic_partial import PartialModelMixin



# Something model, then can be used as a partial, too:

class Something(PartialModelMixin, pydantic.BaseModel):

    name: str

    age: int



# Create a full partial model

FullSomethingPartial = Something.model_as_partial()

FullSomethingPartial()  # Same as FullSomethingPartial(name=None, age=None)

```



### Without using the mixin



You also may create partial models without using the mixin:



```python

import pydantic

from pydantic_partial import create_partial_model



# Something model, without the mixin:

class Something(pydantic.BaseModel):

    name: str

    age: int



# Create a full partial model

FullSomethingPartial = create_partial_model(Something)

FullSomethingPartial()  # Same as FullSomethingPartial(name=None, age=None)

```



### Only changing some fields to being optional



`pydantic-partial` can be used to create partial models that only change some

of the fields to being optional. Just pass the list of fields to be optional to

the `as_partial()` or `create_partial_model()` function.



```python

import pydantic

from pydantic_partial import create_partial_model



class Something(pydantic.BaseModel):

    name: str

    age: int



# Create a partial model only for the name attribute

FullSomethingPartial = create_partial_model(Something, 'name')

FullSomethingPartial(age=40)  # Same as FullSomethingPartial(name=None, age=40)

# This would still raise an error: FullSomethingPartial(age=None, ...)

```



### Recursive partials



Partial models can be created changing the field of all nested models to being

optional, too.



```python

from typing import List



import pydantic

from pydantic_partial import PartialModelMixin, create_partial_model



class InnerSomething(PartialModelMixin, pydantic.BaseModel):

    name: str



class OuterSomething(pydantic.BaseModel):

    name: str

    things: List[InnerSomething]



# Create a full partial model

RecursiveOuterSomethingPartial = create_partial_model(OuterSomething, recursive=True)

RecursiveOuterSomethingPartial(things=[

    {},

])

```



**Note:** The inner model MUST extend the `PartialModelMixin` mixin. Otherwise

`pydantic-partial` will not be able to detect which fields may allow to being

converted to partial models.



**Also note:** My recommendation would be to always create such recursive

partials by creating partials for all the required models and then override

the fields on you outer partial model class. This is way more explicit.



## Known limitations



`pydantic-partial` cannot generate new class types that actually are supported by the

Python typing system rules. This means that the partial models will only be recognized

as the same as their original model classes - type checkers will not know about the partial

model changes and thus will think all those partial fields are still required.



This is due to the fact that Python itself has no concept of partials. `pydantic-partial`

could (in theory) provide plugins for `mypy` for example to "patch" this in, but this would

be a massive amount of work while being kind of a bad hack. The real solution would be to

have a partial type in Python itself, but this is not planned for the near future as far

as I know.



My recommendation is to use `pydantic-partial` only for API use cases where you do not

need to work with the partial aspects of the models - they are just the DTOs (data transfer

objects) you are using. If you need to use partial models in other cases you might get

errors by your type checker - if you use one. Please be aware of this.



**Note:** Not having a good solution in Python itself for this is the reason `pydantic` does

not support partial models in the first place. `pydantic-partial` is just a really good

workaround for this issue.  

See [issue 2](https://github.com/team23/pydantic-partial/issues/2) in this project and

[issue 1673](https://github.com/pydantic/pydantic/issues/1673#issuecomment-1557267229)

in the `pydantic` project for reference.



Having that all said: If anyone wants to get a working plugin for `mypy` or others ready,

I'm going to very much support this.



# Contributing



If you want to contribute to this project, feel free to just fork the project,

create a dev branch in your fork and then create a pull request (PR). If you

are unsure about whether your changes really suit the project please create an

issue first, to talk about this.