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Description:

drfpydantic 2.5.1

Use pydantic with Django REST framework

Introduction

Performance

Installation
Usage

General
Existing Models
Nested Models
Manual Serializer Configuration

Per-Field Configuration
Custom Serializer

Additional Properties

Introduction
Pydantic is a Python library used to perform
data serialization and validation.
Django REST framework is a framework built
on top of Django used to write REST APIs.
If you develop DRF APIs and rely on pydantic for data validation/(de)serialization ,
then drf-pydantic is for you 😍.

ℹ️ INFO > drf_pydantic supports pydantic v2. Due to breaking API changes in pydantic
v2 support for pydantic v1 is available only in drf_pydantic 1.*.*.

Performance
Translation between pydantic models and DRF serializers is done during class
creation (e.g., when you first import the model). This means that there will be
zero impact on the performance of your application
(server instance or serverless session)
when using drf_pydantic while your application is running.
Installation
pip install drf-pydantic

Usage
General
Use drf_pydantic.BaseModel instead of pydantic.BaseModel when creating your
models:
from drf_pydantic import BaseModel

class MyModel(BaseModel):
name: str
addresses: list[str]

MyModel.drf_serializer would be equvalent to the following DRF Serializer class:
class MyModelSerializer:
name = CharField(allow_null=False, required=True)
addresses = ListField(
allow_empty=True,
allow_null=False,
child=CharField(allow_null=False),
required=True,
)

Whenever you need a DRF serializer you can get it from the model like this:
my_value = MyModel.drf_serializer(data={"name": "Van", addresses: ["Gym"]})
my_value.is_valid(raise_exception=True)

ℹ️ INFO
Models created using drf_pydantic are fully idenditcal to those created by
pydantic. The only change is the addition of the drf_serializer attribute.

Existing Models
If you have an existing code base and you would like to add the drf_serializer
attribute only to some of your models, then I have great news 🥳 - you can easily
extend your existing pydantic models by adding drf_pydantic.BaseModel to the list
of parent classes of the model you want to extend.
Your existing pydantic models:
from pydantic import BaseModel

class Pet(BaseModel):
name: str

class Dog(Pet):
breed: str

Update your Dog model and get serializer via the drf_serializer:
from drf_pydantic import BaseModel as DRFBaseModel
from pydantic import BaseModel

class Pet(BaseModel):
name: str

class Dog(DRFBaseModel, Pet):
breed: str

Dog.drf_serializer

⚠️ ATTENTION
Inheritance order is important: drf_pydantic.BaseModel must always go before
the pydantic.BaseModel class.

Nested Models
If you have nested models and you want to generate serializer only from one of them,
you don't have to update all models - only update the model you need, drf_pydantic
will generate serializers for all normal nested pydantic models for free 🥷.
from drf_pydantic import BaseModel as DRFBaseModel
from pydantic import BaseModel

class Apartment(BaseModel):
floor: int
tenant: str

class Building(BaseModel):
address: str
aparments: list[Apartment]

class Block(DRFBaseModel):
buildings: list[Buildind]

Block.drf_serializer

Manual Serializer Configuration
If drf_pydantic does not generate the serializer you need, you can either granularly
configure which DRF serializer fields to use for each pydantic field, or you can
create a custom serializer for the model altogether.

⚠️ WARNING
When manually configuring the serializer you are responsible for setting all
properties of the fields (e.g., allow_null, required, default, etc.).
drf_pydantic does not perform any introspection for fields that are manually
configured or for any fields if a custom serializer is used.

Per-Field Configuration
from typing import Annotated

from drf_pydantic import BaseModel
from rest_framework.serializers import IntegerField

class Person(BaseModel):
name: str
age: Annotated[float, IntegerField(min_value=0, max_value=100)]

Custom Serializer
In example below, Person will use MyCustomSerializer as its drf serializer.
Employee will have its own serializer generated by drf_pydantic because it
does not have a user-defined drf_serializer attribute (it's never inherited).
Company will have its own serializer generated by drf_pydantic and it will use
Person's manually-defined serializer for its ceo field.
from drf_pydantic import BaseModel
from rest_framework.serializers import Serializer

class MyCustomSerializer(Serializer):
name = CharField(allow_null=False, required=True)
age = IntegerField(allow_null=False, required=True)

class Person(BaseModel):
name: str
age: float

drf_serializer = MyCustomSerializer

class Employee(Person):
salary: float

class Company(BaseModel):
ceo: Person

Additional Properties
Additional field properties are set according to the following mapping (pydantic -> drf):

description -> help_text
title -> label
StringConstraints -> min_length and max_length attributes are set
pattern -> uses special serializer field RegexField
max_digits and decimal_places attributes are carried over as is
(used for Decimal type). By default uses current decimal context precision.
ge / gt -> min_value
le / lt -> max_value