Automatic generation of marshmallow schemas from dataclasses.
marshmallow-dataclass
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)
city_schema = marshmallow_dataclass.class_schema(City)()
city = city_schema.load(
{"name": "Paris", "buildings": [{"name": "Eiffel Tower", "height": 324}]}
)
# => City(name='Paris', buildings=[Building(height=324.0, name='Eiffel Tower')])
city_dict = city_schema.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
pip3 install "marshmallow-dataclass"
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)
The type of your fields must be either basic
types supported by marshmallow
(such as float, str, bytes, datetime, …), Union, or other dataclasses.
Union (de)serialization coercion
Typically the Union type; Union[X, Y] means—from a set theory perspective—either X or Y, i.e., an unordered set, howevever the order of the sub-types defines the precedence when attempting to ether deserialize or serialize the value per here.
For example,
from typing import Union
from dataclasses import dataclass
@dataclass
class Person:
name: str
age: Union[int, float]
PersonSchema = marshmallow_dataclass.class_schema(Person)
PersonSchema().load({"name": "jane", "age": 50.0})
# => Person(name="jane", age=50)
will first (sucessfully) try to coerce 50.0 to an int. If coercion is not desired the Any type can be used with the caveat that values will not be type checked without additional validation.
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.
Note that starting with version 4, marshmallow will disallow passing arbitrary arguments, so any
additional metadata should itself be put in its own metadata dict:
from dataclasses import dataclass, field
import marshmallow_dataclass
import marshmallow.validate
@dataclass
class Person:
name: str = field(
metadata=dict(
load_only=True, metadata=dict(description="The person's first name")
)
)
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, List: CustomListField}
class Sample:
my_custom: CustomType
my_custom_list: List[int]
SampleSchema = marshmallow_dataclass.class_schema(Sample, base_schema=BaseSchema)
# SampleSchema now serializes my_custom using the CustomField marshmallow field
# and serializes my_custom_list using the CustomListField 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 type aliases
This library allows you to specify customized marshmallow fields using python’s Annoted type PEP-593.
from typing import Annotated
import marshmallow.fields as mf
import marshmallow.validate as mv
IPv4 = Annotated[str, mf.String(validate=mv.Regexp(r"^([0-9]{1,3}\\.){3}[0-9]{1,3}$"))]
You can also pass a marshmallow field class.
from typing import Annotated
import marshmallow
from marshmallow_dataclass import NewType
Email = Annotated[str, marshmallow.fields.Email]
For convenience, some custom types are provided:
from marshmallow_dataclass.typing import Email, Url
When using Python 3.8, you must import Annotated from the typing_extensions package
# Version agnostic import code:
if sys.version_info >= (3, 9):
from typing import Annotated
else:
from typing_extensions import Annotated
Custom NewType declarations [deprecated]
NewType is deprecated in favor or type aliases using Annotated, as described above.
This library exports a NewType function to create types that generate customized marshmallow fields.
Keyword arguments to NewType are passed to the marshmallow field constructor.
import marshmallow.validate
from marshmallow_dataclass import NewType
IPv4 = NewType(
"IPv4", str, validate=marshmallow.validate.Regexp(r"^([0-9]{1,3}\\.){3}[0-9]{1,3}$")
)
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/
Contributing
To install this project and make changes to it locally, follow the instructions in CONTRIBUTING.md.