marshmallow-dataclass 6.0.0b2

Python library to convert dataclasses into marshmallow schemas.

marshmallow_dataclass

Automatic generation of marshmallow schemas from dataclasses.

How to use

You simply import marshmallow_dataclass.dataclass instead of dataclasses.dataclass. It adds a Schema property to the generated class, containing a marshmallow Schema class.

If you need to specify custom properties on your marshmallow fields (such as attribute, error, validate, required, dump_only, error_messages, description ...) you can add them using the metadata argument of the field function.

from dataclasses import field
from marshmallow_dataclass import dataclass # Importing from marshmallow_dataclass instead of dataclasses
import marshmallow.validate
from typing import List, Optional

@dataclass
class Building:
  # The field metadata is used to instantiate the marshmallow field
  height: float = field(metadata={'validate': marshmallow.validate.Range(min=0)})
  name: str = field(default="anonymous")


@dataclass
class City:
  name: Optional[str]
  buildings: List[Building] = field(default_factory=lambda: [])

# City.Schema contains a marshmallow schema class
city = City.Schema().load({
    "name": "Paris",
    "buildings": [
        {"name": "Eiffel Tower", "height":324}
    ]
})

# Serializing city as a json string
city_json = City.Schema().dumps(city)

The previous syntax is very convenient, as the only change you have to apply to your existing code is update the dataclass import.

However, as the .Schema property is added dynamically, it can confuse type checkers. If you want to avoid that, you can also use the standard dataclass decorator, and generate the schema manually using class_schema :

from dataclasses import dataclass
from datetime import datetime
import marshmallow_dataclass

@dataclass
class Person:
    name: str
    birth: datetime

PersonSchema = marshmallow_dataclass.class_schema(Person)

You can also declare the schema as a ClassVar:

from marshmallow_dataclass import dataclass
from marshmallow import Schema
from typing import ClassVar, Type

@dataclass
class Point:
  x:float
  y:float
  Schema: ClassVar[Type[Schema]] = Schema

You can specify the Meta just as you would in a marshmallow Schema:

from marshmallow_dataclass import dataclass

@dataclass
class Point:
  x:float
  y:float
  class Meta:
    ordered = True

installation

This package is hosted on pypi :

pipenv install marshmallow-dataclass

Documentation

The project documentation is hosted on github pages:

• documentation.

Usage warning

This library depends on python's standard typing library, which is provisional.