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}'

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

Standard 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.

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.

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__)

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)

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.

pydantic

With pydantic models you get a dataclasses-like experience and full support for dict and Json conversions (and much more).

Note: Everything below is for Pydantic V1. for Pydantic V2 please see the Migration Guide.

Python 3.9 and above:

from typing import Optional
from pydantic import BaseModel, parse_obj_as, parse_raw_as


class Foo(BaseModel):
    count: int
    size: Optional[float] = None


f1 = Foo(count=10)

# Parse to dict
print(f1.dict())
# OUT: {'count': 10, 'size': None}

# Load from dict
f2 = Foo.parse_obj({"count": 20})

# Parse to json
print(f2.json())
# OUT: {"count": 20, "size": null}

More options:

# Load from json string
f3 = Foo.parse_raw('{"count": 30}')

# Load from json file
f4 = Foo.parse_file("path/to/data.json")

# Load from list of dicts
f_list1 = parse_obj_as(list[Foo], [{"count": 110}, {"count": 120}])
print(f_list1)
# OUT: [Foo(count=110, size=None), Foo(count=120, size=None)]

# Load from list in json string
f_list2 = parse_raw_as(list[Foo], '[{"count": 130}, {"count": 140}]')
print(f_list2)
# OUT: [Foo(count=130, size=None), Foo(count=140, size=None)]

Complex hierarchical data structures

class Bar(BaseModel):
    apple = "x"
    banana = "y"


class Spam(BaseModel):
    foo: Foo
    bars: list[Bar]


m = Spam(foo={"count": 4}, bars=[{"apple": "x1"}, {"apple": "x2"}])
print(m)
# OUT: foo=Foo(count=4, size=None) bars=[Bar(apple='x1', banana='y'), Bar(apple='x2', banana='y')]

print(m.dict())
# OUT:
# {
#     'foo': {'count': 4, 'size': None},
#     'bars': [
#         {'apple': 'x1', 'banana': 'y'},
#         {'apple': 'x2', 'banana': 'y'},
#     ],
# }

Pydantic supports many standard types (like datetime) and special commonly used types (like EmailStr and HttpUrl):

from datetime import datetime
from pydantic import HttpUrl


class User(BaseModel):
    name = "John Doe"
    signup_ts: datetime = None
    url: HttpUrl = None


u1 = User(signup_ts="2017-07-14 00:00:00")
print(u1)
# OUT: signup_ts=datetime.datetime(2017, 7, 14, 0, 0) url=None name='John Doe'

u2 = User(url="http://example.com")
print(u2)
# OUT: signup_ts=None url=HttpUrl('http://example.com', ) name='John Doe'

u3 = User(url="ht://example.com")
# OUT:
#  ValidationError: 1 validation error for User
#  url
#    URL scheme not permitted (type=value_error.url.scheme; allowed_schemes={'http', 'https'})

If you really need to use json.dumps, write a Custom Encoder:

import json


class EnhancedJSONEncoder(json.JSONEncoder):
    def default(self, o):
        if isinstance(o, BaseModel):
            return o.dict()
        return super().default(o)


foo = Foo(count=20)
json.dumps([{"foo": foo}], cls=EnhancedJSONEncoder)
# OUT: '[{"foo": {"count": 20, "size": null}}]'

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.

Simply use orjson.

import orjson
foo = Foo(x="bar")
orjson.dumps(foo).decode('utf-8')

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)

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 !!

A dataclass providing json formating method

import json
from dataclasses import dataclass

@dataclass
class Foo:
    x: str
   
    def to_json(self):
      return json.dumps(self.__dict__)

Foo("bar").to_json()
>>> '{"x":"bar"}'

I find serde really useful for this.

pip install pyserde

from serde import serde
from serde.json to_json

@serde
class Foo:
    i: int
    s: str
    f: float
    b: bool

to_json(Foo(i=10, s='foo', f=100.0, b=True))

Reference: https://pypi.org/project/pyserde/