Make the Python json encoder support Python's new dataclasses

Question

Starting with Python 3.7, there is something called a dataclass:

from dataclasses import dataclass

@dataclass
class Foo:
    x: str

However, the following fails:

>>> import json
>>> foo = Foo(x="bar")
>>> json.dumps(foo)
TypeError: Object of type Foo is not JSON serializable

How can I make json.dumps() encode instances of Foo into json objects?

Answer 1

Much like you can add support to the JSON encoder for datetime objects or Decimals, you can also provide a custom encoder subclass to serialize dataclasses:

import dataclasses, json

class EnhancedJSONEncoder(json.JSONEncoder):
        def default(self, o):
            if dataclasses.is_dataclass(o):
                return dataclasses.asdict(o)
            return super().default(o)

json.dumps(foo, cls=EnhancedJSONEncoder)

Answer 2

Can't you just use the dataclasses.asdict() function to convert the dataclass to a dict? Something like:

>>> @dataclass
... class Foo:
...     a: int
...     b: int
...     
>>> x = Foo(1,2)
>>> json.dumps(dataclasses.asdict(x))
'{"a": 1, "b": 2}'

Answer 3

Ways of getting JSONified dataclass instance

There are couple of options to accomplish that goal, selection of each imply analyze on which approach suits best for your needs:

Standart library: dataclass.asdict

import dataclasses
import json


@dataclass.dataclass
class Foo:
    x: str

foo = Foo(x='1')
json_foo = json.dumps(dataclasses.asdict(foo)) # '{"x": "1"}'

Picking it back to dataclass instance isn't trivial, so you may want to visit that answer https://stackoverflow.com/a/53498623/2067976

Marshmallow Dataclass

from dataclasses import field
from marshmallow_dataclass import dataclass


@dataclass
class Foo:
    x: int = field(metadata={"required": True})

foo = Foo(x='1') # Foo(x='1')
json_foo = foo.Schema().dumps(foo) # '{"x": "1"}'

# Back to class instance.
Foo.Schema().loads(json_foo) # Foo(x=1)

As a bonus for marshmallow_dataclass you may use validation on the field itself, that validation will be used when someone deserialize the object from json using that schema.

Dataclasses Json

from dataclasses import dataclass
from dataclasses_json import dataclass_json


@dataclass_json
@dataclass
class Foo:
    x: int

foo = Foo(x='1')
json_foo = foo.to_json() # Foo(x='1')
# Back to class instance
Foo.from_json(json_foo) # Foo(x='1')

Also, in addition to that notice that marshmallow dataclass did type conversion for you whereas dataclassses-json(ver.: 0.5.1) ignores that.

Write Custom Encoder

Follow accepted miracle2k answer and reuse custom json encoder.

Answer 4

If you are ok with using a library for that, you can use dataclasses-json. Here is an example:

from dataclasses import dataclass

from dataclasses_json import dataclass_json


@dataclass_json
@dataclass
class Foo:
    x: str


foo = Foo(x="some-string")
foo_json = foo.to_json()

It also supports embedded dataclasses - if your dataclass has a field typed as another dataclass - if all dataclasses envolved have the @dataclass_json decorator.

Answer 5

I'd suggest creating a parent class for your dataclasses with a to_json() method:

import json
from dataclasses import dataclass, asdict

@dataclass
class Dataclass:
    def to_json(self) -> str:
        return json.dumps(asdict(self))

@dataclass
class YourDataclass(Dataclass):
    a: int
    b: int

x = YourDataclass(a=1, b=2)
x.to_json()  # '{"a": 1, "b": 2}'

This is especially useful if you have other functionality to add to all your dataclasses.

Answer 6

You can also implement the asdict and json.dumps method within the class. In this case it wouldn't be necessary to import json.dumps into other parts of your project:

from typing import List
from dataclasses import dataclass, asdict, field
from json import dumps


@dataclass
class TestDataClass:
    """
    Data Class for TestDataClass
    """
    id: int
    name: str
    tested: bool = False
    test_list: List[str] = field(default_factory=list)

    @property
    def __dict__(self):
        """
        get a python dictionary
        """
        return asdict(self)

    @property
    def json(self):
        """
        get the json formated string
        """
        return dumps(self.__dict__)


test_object_1 = TestDataClass(id=1, name="Hi")
print(test_object_1.__dict__)
print(test_object_1.json)

Output:

{'id': 1, 'name': 'Hi', 'tested': False, 'test_list': []}
{"id": 1, "name": "Hi", "tested": false, "test_list": []}

You can also create a parent class to inherit the methods:

from typing import List
from dataclasses import dataclass, asdict, field
from json import dumps


@dataclass
class SuperTestDataClass:

    @property
    def __dict__(self):
        """
        get a python dictionary
        """
        return asdict(self)

    @property
    def json(self):
        """
        get the json formated string
        """
        return dumps(self.__dict__)


@dataclass
class TestDataClass(SuperTestDataClass):
    """
    Data Class for TestDataClass
    """
    id: int
    name: str
    tested: bool = False
    test_list: List[str] = field(default_factory=list)


test_object_1 = TestDataClass(id=1, name="Hi")
print(test_object_1.__dict__)
print(test_object_1.json)

Answer 7

The simplest way to encode dataclass and SimpleNamespace objects is to provide the default function to json.dumps() that gets called for objects that can't be otherwise serialized, and return the object __dict__:

json.dumps(foo, default=lambda o: o.__dict__)

Answer 8

dataclass-wizard is a modern option that can work for you. It supports complex types such as date and time, most generics from the typing module, and also a nested dataclass structure.

The "new style" annotations introduced in PEPs 585 and 604 can be ported back to Python 3.7 via a __future__ import as shown below.

from __future__ import annotations  # This can be removed in Python 3.10
from dataclasses import dataclass, field
from dataclass_wizard import JSONWizard


@dataclass
class MyClass(JSONWizard):
    my_str: str | None
    is_active_tuple: tuple[bool, ...]
    list_of_int: list[int] = field(default_factory=list)


string = """
{
  "my_str": 20,
  "ListOfInt": ["1", "2", 3],
  "isActiveTuple": ["true", false, 1]
}
"""

instance = MyClass.from_json(string)
print(repr(instance))
# MyClass(my_str='20', is_active_tuple=(True, False, True), list_of_int=[1, 2, 3])

print(instance.to_json())
# '{"myStr": "20", "isActiveTuple": [true, false, true], "listOfInt": [1, 2, 3]}'

# True
assert instance == MyClass.from_json(instance.to_json())

You can install the Dataclass Wizard with pip:

$ pip install dataclass-wizard

---

A bit of background info:

For serialization, it uses a slightly modified (a bit more efficient) implementation of dataclasses.asdict. When de-serializing JSON to a dataclass instance, the first time it iterates over the dataclass fields and generates a parser for each annotated type, which makes it more efficient when the de-serialization process is run multiple times.

Disclaimer: I am the creator (and maintainer) of this library.

Answer 9

A much simpler answer can be found on Reddit using dictionary unpacking

>>> from dataclasses import dataclass
>>> @dataclass
... class MyData:
...   prop1: int
...   prop2: str
...   prop3: int
...
>>> d = {'prop1': 5, 'prop2': 'hi', 'prop3': 100}
>>> my_data = MyData(**d)
>>> my_data
MyData(prop1=5, prop2='hi', prop3=100)

Answer 10

Okay so here is what I did when I was in similar situation.

1. Create a custom dictionary factory that converts nested data classes into dictionary.

   def myfactory(data): return dict(x for x in data if x[1] is not None)

2. If foo is your @dataclass, then simply provide your dictionary factory to use "myfactory()" method:

   fooDict = asdict(foo, dict_factory=myfactory)

3. Convert fooDict to json

   fooJson = json.dumps(fooDict)

This should work !!