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marshmallow-dataclass

Build Status PyPI version marshmallow 3 compatible

Automatic generation of marshmallow schemas from dataclasses.

from dataclasses import dataclass, field
from typing import List, Optional

import marshmallow_dataclass
import marshmallow.validate


@dataclass
class Building:
    # 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=list)


CitySchema = marshmallow_dataclass.class_schema(City)

city = CitySchema().load(
    {"name": "Paris", "buildings": [{"name": "Eiffel Tower", "height": 324}]}
)
# => City(name='Paris', buildings=[Building(height=324.0, name='Eiffel Tower')])

city_dict = CitySchema().dump(city)
# => {'name': 'Paris', 'buildings': [{'name': 'Eiffel Tower', 'height': 324.0}]}

Why

Using schemas in Python often means having both a class to represent your data and a class to represent its schema, which results in duplicated code that could fall out of sync. As of Python 3.6, types can be defined for class members, which allows libraries to generate schemas automatically.

Therefore, you can document your APIs in a way that allows you to statically check that the code matches the documentation.

Installation

This package is hosted on PyPI.

pip3 install marshmallow-dataclass

You may optionally install the following extras:

pip3 install "marshmallow-dataclass[enum,union]"

marshmallow 2 support

marshmallow-dataclass no longer supports marshmallow 2. Install marshmallow_dataclass<6.0 if you need marshmallow 2 compatibility.

Usage

Use the class_schema function to generate a marshmallow Schema class from a dataclass.

from dataclasses import dataclass
from datetime import date

import marshmallow_dataclass


@dataclass
class Person:
    name: str
    birth: date


PersonSchema = marshmallow_dataclass.class_schema(Person)

Customizing generated fields

To pass arguments to the generated marshmallow fields (e.g., validate, load_only, dump_only, etc.), pass them to the metadata argument of the field function.

from dataclasses import dataclass, field
import marshmallow_dataclass
import marshmallow.validate


@dataclass
class Person:
    name: str = field(
        metadata=dict(description="The person's first name", load_only=True)
    )
    height: float = field(metadata=dict(validate=marshmallow.validate.Range(min=0)))


PersonSchema = marshmallow_dataclass.class_schema(Person)

@dataclass shortcut

marshmallow_dataclass provides a @dataclass decorator that behaves like the standard library's @dataclasses.dataclass and adds a Schema attribute with the generated marshmallow Schema.

# Use marshmallow_dataclass's @dataclass shortcut
from marshmallow_dataclass import dataclass


@dataclass
class Point:
    x: float
    y: float


Point.Schema().dump(Point(4, 2))
# => {'x': 4, 'y': 2}

Note: Since the .Schema property is added dynamically, it can confuse type checkers. To avoid that, you can declare Schema as a ClassVar.

from typing import ClassVar, Type

from marshmallow_dataclass import dataclass
from marshmallow import Schema


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

Customizing the base Schema

It is also possible to derive all schemas from your own base Schema class (see marshmallow's documentation about extending Schema). This allows you to implement custom (de)serialization behavior, for instance specifying a custom mapping between your classes and marshmallow fields, or renaming fields on serialization.

Custom mapping between classes and fields

class BaseSchema(marshmallow.Schema):
    TYPE_MAPPING = {CustomType: CustomField}


class Sample:
    my_custom: CustomType


SampleSchema = marshmallow_dataclass.class_schema(Sample, base_schema=BaseSchema)
# SampleSchema now serializes my_custom using the CustomField marshmallow field

Renaming fields on serialization

import marshmallow
import marshmallow_dataclass


class UppercaseSchema(marshmallow.Schema):
    """A Schema that marshals data with uppercased keys."""

    def on_bind_field(self, field_name, field_obj):
        field_obj.data_key = (field_obj.data_key or field_name).upper()


class Sample:
    my_text: str
    my_int: int


SampleSchema = marshmallow_dataclass.class_schema(Sample, base_schema=UppercaseSchema)

SampleSchema().dump(Sample(my_text="warm words", my_int=1))
# -> {"MY_TEXT": "warm words", "MY_INT": 1}

You can also pass base_schema to marshmallow_dataclass.dataclass.

@marshmallow_dataclass.dataclass(base_schema=UppercaseSchema)
class Sample:
    my_text: str
    my_int: int

See marshmallow's documentation about extending Schema.

Custom NewType declarations

This library exports a NewType function to create types that generate customized marshmallow fields.

Keyword arguments to NewType are passed to the marshmallow field constructor.

import marshmallow.validate
from marshmallow_dataclass import NewType

IPv4 = NewType(
    "IPv4", str, validate=marshmallow.validate.Regexp(r"^([0-9]{1,3}\\.){3}[0-9]{1,3}$")
)

You can also pass a marshmallow field to NewType.

import marshmallow
from marshmallow_dataclass import NewType

Email = NewType("Email", str, field=marshmallow.fields.Email)

Note: if you are using mypy, you will notice that mypy throws an error if a variable defined with NewType is used in a type annotation. To resolve this, add the marshmallow_dataclass.mypy plugin to your mypy configuration, e.g.:

[mypy]
plugins = marshmallow_dataclass.mypy
# ...

Meta options

Meta options are set the same way as a marshmallow Schema.

from marshmallow_dataclass import dataclass


@dataclass
class Point:
    x: float
    y: float

    class Meta:
        ordered = True

Documentation

The project documentation is hosted on GitHub Pages: https://lovasoa.github.io/marshmallow_dataclass/

Usage warning

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

Contributing

python3 -m venv venv
. venv/bin/activate
pip install '.[dev]'
# Make your changes
git commit # Pre-commit hooks should be run, checking your code

Every commit is checked with pre-commit hooks for :