dataclass-wizard 0.4.0

Marshal dataclasses to/from JSON and Python dict objects, and add support for field properties with initial values.

This library provides a set of simple, yet elegant wizarding tools for interacting with the Python dataclasses module.

Full documentation is at:

• https://dataclass-wizard.readthedocs.io

Features

Here are the supported features that dataclass-wizard currently provides:

• JSON (de)serialization: marshal dataclasses to/from JSON and Python dict objects.

• Field properties: support for using properties with default values in dataclass instances.

Usage

Using the built-in JSON marshalling support for dataclasses:

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

from dataclass_wizard import JSONSerializable


@dataclass
class MyClass(JSONSerializable):

    my_str: Optional[str]
    list_of_int: List[int] = field(default_factory=list)
    is_active: bool = False


string = """{"my_str": 20, "ListOfInt": ["1", "2", 3], "isActive": "true"}"""
c = MyClass.from_json(string)
print(repr(c))
# prints:
#   MySampleClass(my_str='20', list_of_int=[1, 2, 3], is_active=True)

print(c.to_json())
# prints:
#   {"myStr": "20", "listOfInt": [1, 2, 3], "isActive": true}

… and with the property_wizard, which provides supports for field properties with default values in dataclasses:

from dataclasses import dataclass
from typing import Union

from dataclass_wizard import property_wizard


@dataclass
class Vehicle(metaclass=property_wizard):

    # Note: The example below uses the default value for the annotated type
    # (0 here, because `int` appears first). The right-hand value assigned to
    # `wheels` is ignored, as it is simply re-declared by the property. To
    # specify a default value of 4, comment out the `wheels` field and
    # replace it with the `_wheels` declaration below.
    #   _wheels: Union[int, str] = 4
    wheels: Union[int, str] = 0

    @property
    def wheels(self) -> int:
        return self._wheels

    @wheels.setter
    def wheels(self, wheels: Union[int, str]):
        self._wheels = int(wheels)


if __name__ == '__main__':
    v = Vehicle()
    print(v)
    # prints:
    #   Vehicle(wheels=0)

    v = Vehicle(wheels=3)
    print(v)
    # prints:
    #   Vehicle(wheels=3)

    v = Vehicle('6')
    print(v)
    # prints:
    #   Vehicle(wheels=6)

    assert v.wheels == 6, 'The constructor should use our setter method'

    # Confirm that we go through our setter method
    v.wheels = '123'
    assert v.wheels == 123

Installing Dataclass Wizard and Supported Versions

The Dataclass Wizard library is available on PyPI:

$ python -m pip install dataclass-wizard

The dataclass-wizard library officially supports Python 3.6 or higher.

JSON Marshalling

JSONSerializable is a Mixin class which provides the following helper methods that are useful for serializing (and loading) a dataclass instance to/from JSON, as defined by the AbstractJSONWizard interface.

Method	Example	Description
from_json	item = Product.from_json(string)	Converts a JSON string to an instance of the dataclass, or a list of the dataclass instances.
from_list	list_of_item = Product.from_list(l)	Converts a Python list object to a list of the dataclass instances.
from_dict	item = Product.from_dict(d)	Converts a Python dict object to an instance of the dataclass.
to_dict	d = item.to_dict()	Converts the dataclass instance to a Python dict object that is JSON serializable.
to_json	string = item.to_json()	Converts the dataclass instance to a JSON string representation.

Additionally, it adds a default __str__ method to subclasses, which will pretty print the JSON representation of an object; this is quite useful for debugging purposes. Whenever you invoke print(obj) or str(obj), for example, it’ll call this method which will format the dataclass object as a prettified JSON string. If you prefer a __str__ method to not be added, you can pass in str=False when extending from the Mixin class as mentioned here.

Note that the __repr__ method, which is implemented by the dataclass decorator, is also available. To invoke the Python object representation of the dataclass instance, you can instead use repr(obj) or f'{obj!r}'.

To mark a dataclass as being JSON serializable (and de-serializable), simply sub-class from JSONSerializable as shown below. You can also extend from the class alias JSONWizard, if you prefer to use that instead.

Here is a (more) complete example of using the JSONSerializable Mixin class:

from dataclasses import dataclass
from datetime import datetime
from typing import Optional, List, Literal, Union, Dict, Any, NamedTuple

from dataclass_wizard import JSONSerializable


@dataclass
class MyTestClass(JSONSerializable):
    my_ledger: Dict[str, Any]
    the_answer_to_life: Optional[int]
    people: List['Person']
    is_enabled: bool = True


@dataclass
class Person:
    name: 'Name'
    age: int
    birthdate: datetime
    gender: Literal['M', 'F', 'N/A']
    occupation: Union[str, List[str]]
    details: Optional[str] = None


class Name(NamedTuple):
    """A person's name"""
    first: str
    last: str
    salutation: Optional[Literal['Mr.', 'Mrs.', 'Ms.', 'Dr.']] = 'Mr.'


data = {
    'myLedger': {
        'Day 1': 'some details',
        'Day 17': ['a', 'sample', 'list']
    },
    'theAnswerTOLife': '42',
    'People': [
        {
            'name': ('Roberto', 'Fuirron'),
            'age': 21,
            'birthdate': '1950-02-28T17:35:20Z',
            'gender': 'M',
            'occupation': ['sailor', 'fisher'],
            'details': 'My sample details here'
        },
        {
            'name': ('Janice', 'Darr', 'Dr.'),
            'age': 45,
            'birthdate': '1971-11-05 05:10:59',
            'gender': 'F',
            'occupation': 'Dentist'
        }
    ]
}

c = MyTestClass.from_dict(data)

print(repr(c))
# prints the following result on a single line:
#   MyTestClass(
#       my_ledger={'Day 1': 'some details', 'Day 17': ['a', 'sample', 'list']},
#       the_answer_to_life=42,
#       people=[
#           Person(
#               name=Name(first='Roberto', last='Fuirron', salutation='Mr.'),
#               age=21, birthdate=datetime.datetime(1950, 2, 28, 17, 35, 20, tzinfo=datetime.timezone.utc),
#               gender='M', occupation=['sailor', 'fisher'], details='My sample details here'
#           ),
#           Person(
#               name=Name(first='Janice', last='Darr', salutation='Dr.'),
#               age=45, birthdate=datetime.datetime(1971, 11, 5, 5, 10, 59),
#               gender='F', occupation='Dentist', details=None
#           )
#       ], is_enabled=True)

# calling `print` on the object invokes the `__str__` method, which will
# pretty-print the JSON representation of the object by default. You can
# also call the `to_json` method to print the JSON string on a single line.

print(c)
# prints:
#     {
#       "myLedger": {
#         "Day 1": "some details",
#         "Day 17": [
#           "a",
#           "sample",
#           "list"
#         ]
#       },
#       "theAnswerToLife": 42,
#       "people": [
#         {
#           "name": [
#             "Roberto",
#             "Fuirron",
#             "Mr."
#           ],
#           "age": 21,
#           "birthdate": "1950-02-28T17:35:20Z",
#           "gender": "M",
#           "occupation": [
#             "sailor",
#             "fisher"
#           ],
#           "details": "My sample details here"
#         },
#         {
#           "name": [
#             "Janice",
#             "Darr",
#             "Dr."
#           ],
#           "age": 45,
#           "birthdate": "1971-11-05T05:10:59",
#           "gender": "F",
#           "occupation": "Dentist",
#           "details": null
#         }
#       ],
#       "isEnabled": true
#     }

Field Properties

The Python dataclasses library has some key limitations with how it currently handles properties and default values.

The dataclass-wizard package natively provides support for using field properties with default values in dataclasses. The main use case here is to assign an initial value to the field property, if one is not explicitly passed in via the constructor method.

To use it, simply import the property_wizard helper function, and add it as a metaclass on any dataclass where you would benefit from using field properties with default values. The metaclass also pairs well with the JSONSerializable mixin class.

For more examples and important how-to’s on properties with default values, refer to the Using Field Properties section in the documentation.

Credits

This package was created with Cookiecutter and the rnag/cookiecutter-pypackage project template.