Dataclasses JSON

This library provides a simple API for encoding and decoding dataclasses to and from JSON.

It's very easy to get started.

README / Documentation website . Features a navigation bar and search functionality, and should mirror this README exactly -- take a look!

pip install dataclasses-json

from dataclasses import dataclass
from dataclasses_json import dataclass_json
@dataclass_json
@dataclass
class Person:
    name: str
person = Person(name='lidatong')
person.to_json()  # '{"name": "lidatong"}' <- this is a string
person.to_dict()  # {'name': 'lidatong'} <- this is a dict
Person.from_json('{"name": "lidatong"}')  # Person(1)
Person.from_dict({'name': 'lidatong'})  # Person(1)
# You can also apply _schema validation_ using an alternative API
# This can be useful for "typed" Python code
Person.from_json('{"name": 42}')  # This is ok. 42 is not a `str`, but
                                  # dataclass creation does not validate types
Person.schema().loads('{"name": 42}')  # Error! Raises `ValidationError`

What if you want to work with camelCase JSON?

# same imports as above, with the additional `LetterCase` import
from dataclasses import dataclass
from dataclasses_json import dataclass_json, LetterCase
@dataclass_json(letter_case=LetterCase.CAMEL)  # now all fields are encoded/decoded from camelCase
@dataclass
class ConfiguredSimpleExample:
    int_field: int
ConfiguredSimpleExample(1).to_json()  # {"intField": 1}
ConfiguredSimpleExample.from_json('{"intField": 1}')  # ConfiguredSimpleExample(1)

It's recursive (see caveats below), so you can easily work with nested dataclasses. In addition to the supported types in the py to JSON table , this library supports the following:

any arbitrary Collection type is supported. Mapping types are encoded as JSON objects and str types as JSON strings. Any other Collection types are encoded into JSON arrays, but decoded into the original collection types.

datetime objects. datetime objects are encoded to float (JSON number) using timestamp . As specified in the datetime docs, if your datetime object is naive, it will assume your system local timezone when calling .timestamp() . JSON numbers corresponding to a datetime field in your dataclass are decoded into a datetime-aware object, with tzinfo set to your system local timezone. Thus, if you encode a datetime-naive object, you will decode into a datetime-aware object. This is important, because encoding and decoding won't strictly be inverses. See this section if you want to override this default behavior (for example, if you want to use ISO).

UUID objects. They are encoded as str (JSON string).

Decimal objects. They are also encoded as str .

The latest release is compatible with both Python 3.7 and Python 3.6 (with the dataclasses backport).

from dataclasses import dataclass
from dataclasses_json import dataclass_json
@dataclass_json
@dataclass
class Person:
    name: str
lidatong = Person('lidatong')
# Encoding to JSON
lidatong.to_json()  # '{"name": "lidatong"}'
# Decoding from JSON
Person.from_json('{"name": "lidatong"}')  # Person(name='lidatong')

Note that the @dataclass_json decorator must be stacked above the @dataclass decorator (order matters!)

from dataclasses import dataclass
from dataclasses_json import DataClassJsonMixin
@dataclass
class Person(DataClassJsonMixin):
    name: str
lidatong = Person('lidatong')
# A different example from Approach 1 above, but usage is the exact same
assert Person.from_json(lidatong.to_json()) == lidatong

Pick whichever approach suits your taste. Note that there is better support for the mixin approach when using static analysis tools (e.g. linting, typing), but the differences in implementation will be invisible in runtime usage.

from dataclasses import dataclass
from dataclasses_json import dataclass_json
@dataclass_json
@dataclass
class Person:
    name: str

Encode into a JSON array containing instances of my Data Class

people_json = [Person('lidatong')]
Person.schema().dumps(people_json, many=True)  # '[{"name": "lidatong"}]'

Decode a JSON array containing instances of my Data Class

people_json = '[{"name": "lidatong"}]'
Person.schema().loads(people_json, many=True)  # [Person(name='lidatong')]

Encode as part of a larger JSON object containing my Data Class (e.g. an HTTP request/response)

import json
response_dict = {
    'response': {
        'person': Person('lidatong').to_dict()
response_json = json.dumps(response_dict)

In this case, we do two steps. First, we encode the dataclass into a python dictionary rather than a JSON string, using .to_dict .

Second, we leverage the built-in json.dumps to serialize our dataclass into a JSON string.

Decode as part of a larger JSON object containing my Data Class (e.g. an HTTP response)

import json
response_dict = json.loads('{"response": {"person": {"name": "lidatong"}}}')
person_dict = response_dict['response']
person = Person.from_dict(person_dict)

In a similar vein to encoding above, we leverage the built-in json module.

First, call json.loads to read the entire JSON object into a dictionary. We then access the key of the value containing the encoded dict of our Person that we want to decode ( response_dict['response'] ).

Second, we load in the dictionary using Person.from_dict .

This can be by calling .schema() and then using the corresponding encoder/decoder methods, ie. .load(...) / .dump(...) .

Encode into a single Python dictionary

person = Person('lidatong')
person.to_dict()  # {'name': 'lidatong'}

Encode into a list of Python dictionaries

people = [Person('lidatong')]
Person.schema().dump(people, many=True)  # [{'name': 'lidatong'}]

Decode a dictionary into a single dataclass instance

person_dict = {'name': 'lidatong'}
Person.from_dict(person_dict)  # Person(name='lidatong')

Decode a list of dictionaries into a list of dataclass instances

people_dicts = [{"name": "lidatong"}]
Person.schema().load(people_dicts, many=True)  # [Person(name='lidatong')]

JSON letter case by convention is camelCase, in Python members are by convention snake_case.

You can configure it to encode/decode from other casing schemes at both the class level and the field level.

from dataclasses import dataclass, field
from dataclasses_json import LetterCase, config, dataclass_json
# changing casing at the class level
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass
class Person:
    given_name: str
    family_name: str
Person('Alice', 'Liddell').to_json()  # '{"givenName": "Alice"}'
Person.from_json('{"givenName": "Alice", "familyName": "Liddell"}')  # Person('Alice', 'Liddell')
# at the field level
@dataclass_json
@dataclass
class Person:
    given_name: str = field(metadata=config(letter_case=LetterCase.CAMEL))
    family_name: str
Person('Alice', 'Liddell').to_json()  # '{"givenName": "Alice"}'
# notice how the `family_name` field is still snake_case, because it wasn't configured above
Person.from_json('{"givenName": "Alice", "family_name": "Liddell"}')  # Person('Alice', 'Liddell')

This library assumes your field follows the Python convention of snake_case naming. If your field is not snake_case to begin with and you attempt to parameterize LetterCase , the behavior of encoding/decoding is undefined (most likely it will result in subtle bugs).

from dataclasses import dataclass, field
from dataclasses_json import config, dataclass_json
@dataclass_json
@dataclass
class Person:
    given_name: str = field(metadata=config(field_name="overriddenGivenName"))
Person(given_name="Alice")  # Person('Alice')
Person.from_json('{"overriddenGivenName": "Alice"}')  # Person('Alice')
Person('Alice').to_json()  # {"overriddenGivenName": "Alice"}

By default, any fields in your dataclass that use default or default_factory will have the values filled with the provided default, if the corresponding field is missing from the JSON you're decoding.

Decode JSON with missing field

@dataclass_json
@dataclass
class Student:
    id: int
    name: str = 'student'
Student.from_json('{"id": 1}')  # Student(id=1, name='student')

Notice from_json filled the field name with the specified default 'student' when it was missing from the JSON.

Sometimes you have fields that are typed as Optional , but you don't necessarily want to assign a default. In that case, you can use the infer_missing kwarg to make from_json infer the missing field value as None .

Decode optional field without default

@dataclass_json
@dataclass
class Tutor:
    id: int
    student: Optional[Student] = None
Tutor.from_json('{"id": 1}')  # Tutor(id=1, student=None)

Personally I recommend you leverage dataclass defaults rather than using infer_missing , but if for some reason you need to decouple the behavior of JSON decoding from the field's default value, this will allow you to do so.

By default, it is up to the implementation what happens when a json_dataclass receives input parameters that are not defined. (the from_dict method ignores them, when loading using schema() a ValidationError is raised.) There are three ways to customize this behavior.

Assume you want to instantiate a dataclass with the following dictionary:

dump_dict = {"endpoint": "some_api_endpoint", "data": {"foo": 1, "bar": "2"}, "undefined_field_name": [1, 2, 3]}

from dataclasses_json import Undefined
@dataclass_json(undefined=Undefined.RAISE)
@dataclass()
class ExactAPIDump:
    endpoint: str
    data: Dict[str, Any]
dump = ExactAPIDump.from_dict(dump_dict)  # raises UndefinedParameterError

from dataclasses_json import Undefined
@dataclass_json(undefined=Undefined.EXCLUDE)
@dataclass()
class DontCareAPIDump:
    endpoint: str
    data: Dict[str, Any]
dump = DontCareAPIDump.from_dict(dump_dict)  # DontCareAPIDump(endpoint='some_api_endpoint', data={'foo': 1, 'bar': '2'})
dump.to_dict()  # {"endpoint": "some_api_endpoint", "data": {"foo": 1, "bar": "2"}}

from dataclasses_json import Undefined, CatchAll
@dataclass_json(undefined=Undefined.INCLUDE)
@dataclass()
class UnknownAPIDump:
    endpoint: str
    data: Dict[str, Any]
    unknown_things: CatchAll
dump = UnknownAPIDump.from_dict(dump_dict)  # UnknownAPIDump(endpoint='some_api_endpoint', data={'foo': 1, 'bar': '2'}, unknown_things={'undefined_field_name': [1, 2, 3]})
dump.to_dict()  # {'endpoint': 'some_api_endpoint', 'data': {'foo': 1, 'bar': '2'}, 'undefined_field_name': [1, 2, 3]}

Notes:

All 3 options work as well using schema().loads and schema().dumps , as long as you don't overwrite it by specifying schema(unknown=<a marshmallow value>) . marshmallow uses the same 3 keywords 'include', 'exclude', 'raise' .

All 3 operations work as well using __init__ , e.g. UnknownAPIDump(**dump_dict) will not raise a TypeError , but write all unknown values to the field tagged as CatchAll . Classes tagged with EXCLUDE will also simply ignore unknown parameters. Note that classes tagged as RAISE still raise a TypeError , and not a UndefinedParameterError if supplied with unknown keywords.

See Overriding

Object hierarchies where fields are of the type that they are declared within require a small type hinting trick to declare the forward reference.

from typing import Optional
from dataclasses import dataclass
from dataclasses_json import dataclass_json
@dataclass_json
@dataclass
class Tree():
    value: str
    left: Optional['Tree']
    right: Optional['Tree']

Avoid using

from __future__ import annotations

as it will cause problems with the way dataclasses_json accesses the type annotations.

Data types specific to libraries commonly used in data analysis and machine learning like numpy and pandas are not supported by default, but you can easily enable them by using custom decoders and encoders. Below are two examples for numpy and pandas types.

from dataclasses import field, dataclass
from dataclasses_json import config, dataclass_json
import numpy as np
import pandas as pd
@dataclass_json
@dataclass
class DataWithNumpy:
    my_int: np.int64 = field(metadata=config(decoder=np.int64))
    my_float: np.float64 = field(metadata=config(decoder=np.float64))
    my_array: np.ndarray = field(metadata=config(decoder=np.asarray))
DataWithNumpy.from_json("{\"my_int\": 42, \"my_float\": 13.37, \"my_array\": [1,2,3]}")
@dataclass_json
@dataclass
class DataWithPandas:
    my_df: pd.DataFrame = field(metadata=config(decoder=pd.DataFrame.from_records, encoder=lambda x: x.to_dict(orient="records")))
data = DataWithPandas.from_dict({"my_df": [{"col1": 1, "col2": 2}, {"col1": 3, "col2": 4}]})
# my_df results in:
# col1  col2
# 1    2    
# 3    4
data.to_dict()
# {"my_df": [{"col1": 1, "col2": 2}, {"col1": 3, "col2": 4}]}

Using the dataclass_json decorator or mixing in DataClassJsonMixin will provide you with an additional method .schema() .

.schema() generates a schema exactly equivalent to manually creating a marshmallow schema for your dataclass. You can reference the marshmallow API docs to learn other ways you can use the schema returned by .schema() .

You can pass in the exact same arguments to .schema() that you would when constructing a PersonSchema instance, e.g. .schema(many=True) , and they will get passed through to the marshmallow schema.

from dataclasses import dataclass
from dataclasses_json import dataclass_json
@dataclass_json
@dataclass
class Person:
    name: str
# You don't need to do this - it's generated for you by `.schema()`!
from marshmallow import Schema, fields
class PersonSchema(Schema):
    name = fields.Str()

Briefly, on what's going on under the hood in the above examples: calling .schema() will have this library generate a marshmallow schema for you. It also fills in the corresponding object hook, so that marshmallow will create an instance of your Data Class on load (e.g. Person.schema().load returns a Person ) rather than a dict , which it does by default in marshmallow.

Performance note

.schema() is not cached (it generates the schema on every call), so if you have a nested Data Class you may want to save the result to a variable to avoid re-generation of the schema on every usage.

person_schema = Person.schema()
person_schema.dump(people, many=True)
# later in the code...
person_schema.dump(person)

For example, you might want to encode/decode datetime objects using ISO format rather than the default timestamp .

from dataclasses import dataclass, field
from dataclasses_json import dataclass_json, config
from datetime import datetime
from marshmallow import fields
@dataclass_json
@dataclass
class DataClassWithIsoDatetime:
    created_at: datetime = field(
        metadata=config(
            encoder=datetime.isoformat,
            decoder=datetime.fromisoformat,
            mm_field=fields.DateTime(format='iso')
Extending

Similarly, you might want to extend dataclasses_json to encode date objects.
from dataclasses import dataclass, field
from dataclasses_json import dataclass_json, config
from datetime import date
from marshmallow import fields
dataclasses_json.cfg.global_config.encoders[date] = date.isoformat
dataclasses_json.cfg.global_config.decoders[date] = date.fromisoformat
@dataclass_json
@dataclass
class DataClassWithIsoDatetime:
    created_at: date
    modified_at: date
    accessed_at: date
As you can see, you can override or extend the default codecs by providing a "hook" via a
callable:
encoder: a callable, which will be invoked to convert the field value when encoding to JSON
decoder: a callable, which will be invoked to convert the JSON value when decoding from JSON
mm_field: a marshmallow field, which will affect the behavior of any operations involving .schema()
Note that these hooks will be invoked regardless if you're using
.to_json/dump/dumps
and .from_json/load/loads. So apply overrides / extensions judiciously, making sure to
carefully consider whether the interaction of the encode/decode/mm_field is consistent with what you expect!
What if I have other dataclass field extensions that rely on metadata

All the dataclasses_json.config does is return a mapping, namespaced under the key 'dataclasses_json'.
Say there's another module, other_dataclass_package that uses metadata. Here's how you solve your problem:
metadata = {'other_dataclass_package': 'some metadata...'}  # pre-existing metadata for another dataclass package
dataclass_json_config = config(
            encoder=datetime.isoformat,
            decoder=datetime.fromisoformat,
            mm_field=fields.DateTime(format='iso')
metadata.update(dataclass_json_config)
@dataclass_json
@dataclass
class DataClassWithIsoDatetime:
    created_at: datetime = field(metadata=metadata)
You can also manually specify the dataclass_json configuration mapping.
@dataclass_json
@dataclass
class DataClassWithIsoDatetime:
    created_at: date = field(
        metadata={'dataclasses_json': {
            'encoder': date.isoformat,
            'decoder': date.fromisoformat,
            'mm_field': fields.DateTime(format='iso')
A larger example

from dataclasses import dataclass
from dataclasses_json import dataclass_json
from typing import List
@dataclass_json
@dataclass(frozen=True)
class Minion:
    name: str
@dataclass_json
@dataclass(frozen=True)
class Boss:
    minions: List[Minion]
boss = Boss([Minion('evil minion'), Minion('very evil minion')])
boss_json = """
    "minions": [
            "name": "evil minion"
            "name": "very evil minion"
""".strip()
assert boss.to_json(indent=4) == boss_json
assert Boss.from_json(boss_json) == boss
Performance

Take a look at this issue
Versioning

Note this library is still pre-1.0.0 (SEMVER).
The current convention is:
PATCH version upgrades for bug fixes and minor feature additions.
MINOR version upgrades for big API features and breaking changes.
Once this library is 1.0.0, it will follow standard SEMVER conventions.
Python compatibility

Any version that is not listed in the table below we do not test against, though you might still be able to install the library. For future Python versions, please open an issue and/or a pull request, adding them to the CI suite.
Python version range
Compatible dataclasses-json version
Roadmap

Currently the focus is on investigating and fixing bugs in this library, working
on performance, and finishing this issue.
That said, if you think there's a feature missing / something new needed in the
library, please see the contributing section below.
Contributing

First of all, thank you for being interested in contributing to this library.
I really appreciate you taking the time to work on this project.
If you're just interested in getting into the code, a good place to start are
issues tagged as bugs.
If introducing a new feature, especially one that modifies the public API,
consider submitting an issue for discussion before a PR. Please also take a look
at existing issues / PRs to see what you're proposing has  already been covered
before / exists.
I like to follow the commit conventions documented here
Setting up your environment

This project uses Poetry for dependency and venv management. It is quite simple to get ready for your first commit:
Install latest stable Poetry
Navigate to where you cloned dataclasses-json
Run poetry install
Create a branch and start writing code!