Metadata-Version: 2.1
Name: pydantic-django
Version: 0.0.6
Summary: Pydantic model support for Django ORM
Home-page: https://github.com/jordaneremieff/pydantic-django/
Author: Jordan Eremieff
Author-email: jordan@eremieff.com
License: MIT
Description: # Pydantic-Django
        
        An attempt to implement a [Pydantic](https://pydantic-docs.helpmanual.io/) model interface for [Django](https://www.djangoproject.com/) ORM. The goal of this project is to support all of Pydantic's features to provide as many (useful) conveniences for Django models as possible.
        
        **Important**: this project should be considered an ***experimental*** work-in-progress. The current API design and behaviour is not finalised, specific version support is not yet determined, and there is still a lot of things to test yet.
        
        Also, I typically haven't used metaclasses/classes like this previously, so there may be some details in the implementation to refine. 
        
        Seems to work okay so far. :)
        
        ## Installation
        
        ```
        pip install pydantic-django
        ```
        
        ## Usage
        
        **Requirements**: Python 3.7+, Django 2+
        
        An example of basic [schema](https://pydantic-docs.helpmanual.io/usage/schema/) usage:
        
        ```python
        class UserSchema(PydanticDjangoModel):
            class Config:
                model = User
        ```
        
        The schema call above would return something like this:
        
        ```python
        {
            "title": "UserSchema",
            "description": "A user of the application.",
            "type": "object",
            "properties": {
                "profile": {"title": "Profile", "type": "integer"},
                "id": {"title": "Id", "type": "integer"},
                "first_name": {"title": "First Name", "maxLength": 50, "type": "string"},
                "last_name": {"title": "Last Name", "maxLength": 50, "type": "string"},
                "email": {"title": "Email", "maxLength": 254, "type": "string"},
                "created_at": {
                    "title": "Created At",
                    "type": "string",
                    "format": "date-time",
                },
                "updated_at": {
                    "title": "Updated At",
                    "type": "string",
                    "format": "date-time",
                },
            },
            "required": ["first_name", "email", "created_at", "updated_at"],
        }
        ```
        
        There are a few ways to populate the models with values, the first is using the `from_django` method:
        
        ```python
        user = User.objects.create(
            first_name="Jordan", 
            last_name="Eremieff", 
            email="jordan@eremieff.com"
        )
        
        user_schema = UserSchema.from_django(user)
        ```
        
        Alternatively, the Pydantic model can be used to create a new object:
        
        ```python
        user_schema = UserSchema.create(
            first_name="Jordan", 
            last_name="Eremieff", 
            email="jordan@eremieff.com"
        )
        ```
        
        Or retrieve an existing one:
        
        ```python
        user_schema = UserSchema.get(id=user.id)
        ```
        
        The object in each case can be validated and [export](https://pydantic-docs.helpmanual.io/usage/exporting_models/) the values in the same way:
        
        ```python
        user_json = user_schema.json()
        ```
        
        To produce a result such as:
        
        ```json
        {
            "profile": null,
            "id": 1,
            "first_name": "Jordan",
            "last_name": "Eremieff",
            "email": "jordan@eremieff.com",
            "created_at": "2020-08-15T16:50:30.606345+00:00",
            "updated_at": "2020-08-15T16:50:30.606452+00:00"
        }
        ```
        
        It can also use standard Python type annotations in conjunction with the fields retrieved automatically from the database, and the configuration class supports `exclude` and `include` options:
        
        ```python
        class UserSchema(PydanticDjangoModel):
            first_name: Optional[str]
            last_name: str
        
            class Config:
                model = User
                include = ["first_name", "last_name"]
        ```
        
        In this example, the first name and last name annotations override the fields that would normally be picked up from the Django model automatically, and the `include` list filters out the other fields from the schema definition.
        
        The `first_name` field here is required in the database and the `last_name` field is optional, but using the type annotations this can be determined for the specific schema:
        
        ```python
        {
            'description': 'A user of the application.',
            'properties': {
                    'first_name': {'title': 'First Name', 'type': 'string'},
                    'last_name': {'title': 'Last Name', 'type': 'string'}
                    },
            'required': ['last_name'],
            'title': 'UserSchema',
            'type': 'object'
        }
        ```
                
        It can do a bit more than this, but you'll have to check out the testing application and test cases as a reference for now.
        
        ## Roadmap
        
        - [x] Automatic schema generation from Django models
        - [x] Basic queryset interface for CRUD operations
        - [x] Include & exclude field filtering
        - [x] Default factory support
        - [x] Support basic field types
        - [x] Sub-model support for forward and reverse relations
        - [x] JSON field types
        - [x] Support for multi-object querysets
        - [x] Support generic relations
        - [ ] Postgres field types
        - [ ] Look into custom validators and configurations
        - [ ] More comprehensive support for Django features
        - [ ] HTML schema generation
        - [ ] Create a complete application example
        - [ ] Look into performance & benchmarking
        - [ ] More test coverage & type annotations
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.7
Description-Content-Type: text/markdown
