Dataclass Wizard

Release v0.33.0 | 📚 Full docs on Read the Docs (Installation).

Dataclass Wizard 🪄 Simple, elegant wizarding tools for Python’s dataclasses.

Lightning-fast ⚡, pure Python, and lightweight — effortlessly convert dataclass instances to/from JSON, perfect for complex and nested dataclass models!

---

Behold, the power of the Dataclass Wizard:

>>> from __future__ import annotations
>>> 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)
>>> instance
MyClass(my_str='20', is_active_tuple=(True, False, True), list_of_int=[1, 2, 3])
>>> instance.to_json()
'{"myStr": "20", "isActiveTuple": [true, false, true], "listOfInt": [1, 2, 3]}'
>>> instance == MyClass.from_dict(instance.to_dict())
True

---

v1 Opt-In 🚀

Early access to V1 is available! To opt in, simply enable v1=True in the Meta settings:

from dataclasses import dataclass
from dataclass_wizard import JSONPyWizard
from dataclass_wizard.v1 import Alias

@dataclass
class A(JSONPyWizard):
    class _(JSONPyWizard.Meta):
        v1 = True

    my_str: str
    version_info: float = Alias(load='v-info')

# Alternatively, for simple dataclasses that don't subclass `JSONPyWizard`:
# LoadMeta(v1=True).bind_to(A)

a = A.from_dict({'my_str': 'test', 'v-info': '1.0'})
assert a.version_info == 1.0
assert a.to_dict() == {'my_str': 'test', 'version_info': 1.0}

For more information, see the Field Guide to V1 Opt-in.

Performance Improvements

The upcoming V1 release brings significant performance improvements in de/serialization. Personal benchmarks show that V1 can make Dataclass Wizard approximately 2x faster than pydantic!

While some features are still being refined and fully supported, v1 positions Dataclass Wizard alongside other high-performance serialization libraries in Python.

Why Use Dataclass Wizard?

Effortlessly handle complex data with one of the fastest and lightweight libraries available! Perfect for APIs, JSON wrangling, and more.

• 🚀 Blazing Fast — One of the fastest libraries out there!
• 🪶 Lightweight — Pure Python, minimal dependencies
• 👶 Easy Setup — Intuitive, hassle-free
• ☝️ Battle-Tested — Proven reliability with solid test coverage
• ⚙️ Highly Customizable — Endless de/serialization options to fit your needs
• 🎉 Built-in Support — JSON, YAML, TOML, and environment/settings management
• 📦 Full Python Type Support — Powered by type hints with full support for native types and typing-extensions
• 📝 Auto-Generate Schemas — JSON to Dataclass made easy

Key Features

• 🔄 Flexible (de)serialization — Marshal dataclasses to/from JSON, TOML, YAML, or dict with ease.
• 🌿 Environment Magic — Map env vars and .env files to strongly-typed class fields effortlessly.
• 🧑‍💻 Field Properties Made Simple — Add properties with default values to your dataclasses.
• 🧙‍♂️ JSON-to-Dataclass Wizardry — Auto-generate a dataclass schema from any JSON file or string instantly.

Installation

Dataclass Wizard is available on PyPI. You can install it with pip:

$ pip install dataclass-wizard

Also available on conda via conda-forge. To install via conda:

$ conda install dataclass-wizard -c conda-forge

This library supports Python 3.9+. Support for Python 3.6 – 3.8 was available in earlier releases but is no longer maintained, as those versions no longer receive security updates.

For convenience, the table below outlines the last compatible version of Dataclass Wizard for unsupported Python versions (3.6 – 3.8):

Python Version	Last Version of dataclass-wizard	Python EOL
3.6	0.26.1	2021-12-23
3.7	0.26.1	2023-06-27
3.8	0.26.1	2024-10-07

See the package on PyPI and the Changelog in the docs for the latest version details.

Wizard Mixins ✨

In addition to JSONWizard, these Mixin classes simplify common tasks and make your data handling spellbindingly efficient:

• 🪄 EnvWizard — Load environment variables and .env files into typed schemas, even supporting secret files (keys as file names).
• 🎩 JSONPyWizard — A helper for JSONWizard that preserves your keys as-is (no camelCase changes).
• 🔮 JSONListWizard — Extend JSONWizard to convert lists into Container objects.
• 💼 JSONFileWizard — Convert dataclass instances to/from local JSON files with ease.
• 🌳 TOMLWizard — Map your dataclasses to/from TOML format.
• 🧙‍♂️ YAMLWizard — Convert between YAML and dataclass instances using PyYAML.

Supported Types 🧑‍💻

Dataclass Wizard supports:

• 📋 Collections: Handle list, dict, and set effortlessly.
• 🔢 Typing Generics: Manage Union, Any, and other types from the typing module.
• 🌟 Advanced Types: Work with Enum, defaultdict, and datetime with ease.

For more info, check out the Supported Types section in the docs for detailed insights into each type and the load/dump process!

Usage and Examples

Seamless JSON De/Serialization with JSONWizard

from __future__ import annotations  # Optional in Python 3.10+

from dataclasses import dataclass, field
from enum import Enum
from datetime import date

from dataclass_wizard import JSONWizard


@dataclass
class Data(JSONWizard):
    # Use Meta to customize JSON de/serialization
    class _(JSONWizard.Meta):
        key_transform_with_dump = 'LISP'  # Transform keys to LISP-case during dump

    a_sample_bool: bool
    values: list[Inner] = field(default_factory=list)


@dataclass
class Inner:
    # Nested data with optional enums and typed dictionaries
    vehicle: Car | None
    my_dates: dict[int, date]


class Car(Enum):
    SEDAN = 'BMW Coupe'
    SUV = 'Toyota 4Runner'


# Input JSON-like dictionary
my_dict = {
    'values': [{'vehicle': 'Toyota 4Runner', 'My-Dates': {'123': '2023-01-31'}}],
    'aSampleBool': 'TRUE'
}

# Deserialize into strongly-typed dataclass instances
data = Data.from_dict(my_dict)
print((v := data.values[0]).vehicle)  # Prints: 
assert v.my_dates[123] == date(2023, 1, 31)  # > True

# Serialize back into pretty-printed JSON
print(data.to_json(indent=2))

Map Environment Variables with EnvWizard

Easily map environment variables to Python dataclasses:

import os
from dataclass_wizard import EnvWizard

os.environ.update({
    'APP_NAME': 'My App',
    'MAX_CONNECTIONS': '10',
    'DEBUG_MODE': 'true'
})

class AppConfig(EnvWizard):
    app_name: str
    max_connections: int
    debug_mode: bool

config = AppConfig()
print(config.app_name)    # My App
print(config.debug_mode)  # True

📖 See more on EnvWizard in the full documentation.

Dataclass Properties with property_wizard

Add field properties to your dataclasses with default values using property_wizard:

from __future__ import annotations  # This can be removed in Python 3.10+

from dataclasses import dataclass, field
from typing_extensions import Annotated

from dataclass_wizard import property_wizard


@dataclass
class Vehicle(metaclass=property_wizard):
    wheels: Annotated[int | str, field(default=4)]
    # or, alternatively:
    #   _wheels: int | str = 4

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

    @wheels.setter
    def wheels(self, value: int | str):
        self._wheels = int(value)


v = Vehicle()
print(v.wheels)  # 4
v.wheels = '6'
print(v.wheels)  # 6

assert v.wheels == 6, 'Setter correctly handles type conversion'

📖 For a deeper dive, visit the documentation on field properties.

Generate Dataclass Schemas with CLI

Quickly generate Python dataclasses from JSON input using the wiz-cli tool:

$ echo '{"myFloat": "1.23", "Items": [{"created": "2021-01-01"}]}' | wiz gs - output.py

from dataclasses import dataclass
from datetime import date
from typing import List, Union

from dataclass_wizard import JSONWizard

@dataclass
class Data(JSONWizard):
    my_float: Union[float, str]
    items: List['Item']

@dataclass
class Item:
    created: date

📖 Check out the full CLI documentation at wiz-cli.

JSON Marshalling

JSONSerializable (aliased to JSONWizard) 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.
list_to_json	string = Product.list_to_json(list_of_item)	Converts a list of dataclass instances 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 aliased name JSONWizard, if you prefer to use that instead.

Check out a more complete example of using the JSONSerializable Mixin class.

No Inheritance Needed

It is important to note that the main purpose of sub-classing from JSONWizard Mixin class is to provide helper methods like from_dict and to_dict, which makes it much more convenient and easier to load or dump your data class from and to JSON.

That is, it's meant to complement the usage of the dataclass decorator, rather than to serve as a drop-in replacement for data classes, or to provide type validation for example; there are already excellent libraries like pydantic that provide these features if so desired.

However, there may be use cases where we prefer to do away with the class inheritance model introduced by the Mixin class. In the interests of convenience and also so that data classes can be used as is, the Dataclass Wizard library provides the helper functions fromlist and fromdict for de-serialization, and asdict for serialization. These functions also work recursively, so there is full support for nested dataclasses -- just as with the class inheritance approach.

Here is an example to demonstrate the usage of these helper functions:

from __future__ import annotations

from dataclasses import dataclass, field
from datetime import datetime, date

from dataclass_wizard import fromdict, asdict, DumpMeta


@dataclass
class A:
    created_at: datetime
    list_of_b: list[B] = field(default_factory=list)


@dataclass
class B:
    my_status: int | str
    my_date: date | None = None


source_dict = {'createdAt': '2010-06-10 15:50:00Z',
               'List-Of-B': [
                   {'MyStatus': '200', 'my_date': '2021-12-31'}
               ]}

# De-serialize the JSON dictionary object into an `A` instance.
a = fromdict(A, source_dict)

print(repr(a))
# A(created_at=datetime.datetime(2010, 6, 10, 15, 50, tzinfo=datetime.timezone.utc),
#   list_of_b=[B(my_status='200', my_date=datetime.date(2021, 12, 31))])

# Set an optional dump config for the main dataclass, for example one which
# converts converts date and datetime objects to a unix timestamp (as an int)
#
# Note that `recursive=True` is the default, so this Meta config will be
# merged with the Meta config (if specified) of each nested dataclass.
DumpMeta(marshal_date_time_as='TIMESTAMP',
         key_transform='SNAKE',
         # Finally, apply the Meta config to the main dataclass.
         ).bind_to(A)

# Serialize the `A` instance to a Python dict object.
json_dict = asdict(a)

expected_dict = {'created_at': 1276185000, 'list_of_b': [{'my_status': '200', 'my_date': 1640926800}]}

print(json_dict)
# Assert that we get the expected dictionary object.
assert json_dict == expected_dict

Custom Key Mappings

If you ever find the need to add a custom mapping of a JSON key to a dataclass field (or vice versa), the helper function json_field -- which can be considered an alias to dataclasses.field() -- is one approach that can resolve this.

Example below:

from dataclasses import dataclass

from dataclass_wizard import JSONSerializable, json_field


@dataclass
class MyClass(JSONSerializable):

    my_str: str = json_field('myString1', all=True)


# De-serialize a dictionary object with the newly mapped JSON key.
d = {'myString1': 'Testing'}
c = MyClass.from_dict(d)

print(repr(c))
# prints:
#   MyClass(my_str='Testing')

# Assert we get the same dictionary object when serializing the instance.
assert c.to_dict() == d

Mapping Nested JSON Keys

The dataclass-wizard library lets you map deeply nested JSON keys to dataclass fields using custom path notation. This is ideal for handling complex or non-standard JSON structures.

You can specify paths to JSON keys with the KeyPath or path_field helpers. For example, the deeply nested key data.items.myJSONKey can be mapped to a dataclass field, such as my_str:

from dataclasses import dataclass
from dataclass_wizard import path_field, JSONWizard

@dataclass
class MyData(JSONWizard):
    my_str: str = path_field('data.items.myJSONKey', default="default_value")

input_dict = {'data': {'items': {'myJSONKey': 'Some value'}}}
data_instance = MyData.from_dict(input_dict)
print(data_instance.my_str)  # Output: 'Some value'

Custom Paths for Complex JSON

You can use custom paths to access nested keys and map them to specific fields, even when keys contain special characters or follow non-standard conventions.

Example with nested and complex keys:

from dataclasses import dataclass
from typing import Annotated
from dataclass_wizard import JSONWizard, path_field, KeyPath


@dataclass
class NestedData(JSONWizard):
    my_str: str = path_field('data[0].details["key with space"]', default="default_value")
    my_int: Annotated[int, KeyPath('data[0].items[3.14].True')] = 0


input_dict = {
    'data': [
        {
            'details': {'key with space': 'Another value'},
            'items': {3.14: {True: "42"}}
        }
    ]
}

# Deserialize JSON to dataclass
data = NestedData.from_dict(input_dict)
print(data.my_str)  # Output: 'Another value'

# Serialize back to JSON
output_dict = data.to_dict()
print(output_dict)  # {'data': {0: {'details': {'key with space': 'Another value'}, 'items': {3.14: {True: 42}}}}}

# Verify data consistency
assert data == NestedData.from_dict(output_dict)

# Handle empty input gracefully
data = NestedData.from_dict({'data': []})
print(repr(data))  # NestedData(my_str='default_value', my_int=0)

Extending from Meta

Looking to change how date and datetime objects are serialized to JSON? Or prefer that field names appear in snake case when a dataclass instance is serialized?

The inner Meta class allows easy configuration of such settings, as shown below; and as a nice bonus, IDEs should be able to assist with code completion along the way.

from dataclasses import dataclass
from datetime import date

from dataclass_wizard import JSONWizard
from dataclass_wizard.enums import DateTimeTo


@dataclass
class MyClass(JSONWizard):

    class _(JSONWizard.Meta):
        marshal_date_time_as = DateTimeTo.TIMESTAMP
        key_transform_with_dump = 'SNAKE'

    my_str: str
    my_date: date


data = {'my_str': 'test', 'myDATE': '2010-12-30'}

c = MyClass.from_dict(data)

print(repr(c))
# prints:
#   MyClass(my_str='test', my_date=datetime.date(2010, 12, 30))

string = c.to_json()
print(string)
# prints:
#   {"my_str": "test", "my_date": 1293685200}

Other Uses for Meta

Here are a few additional use cases for the inner Meta class. Note that a full list of available settings can be found in the Meta section in the docs.

Debug Mode

Enables additional (more verbose) log output. For example, a message can be logged whenever an unknown JSON key is encountered when from_dict or from_json is called.

This also results in more helpful error messages during the JSON load (de-serialization) process, such as when values are an invalid type --i.e. they don't match the annotation for the field. This can be particularly useful for debugging purposes.

Handle Unknown JSON Keys

The default behavior is to ignore any unknown or extraneous JSON keys that are encountered when from_dict or from_json is called, and emit a "warning" which is visible when debug mode is enabled (and logging is properly configured). An unknown key is one that does not have a known mapping to a dataclass field.

However, we can also raise an error in such cases if desired. The below example demonstrates a use case where we want to raise an error when an unknown JSON key is encountered in the load (de-serialization) process.

import logging
from dataclasses import dataclass

from dataclass_wizard import JSONWizard
from dataclass_wizard.errors import UnknownJSONKey

# Sets up application logging if we haven't already done so
logging.basicConfig(level='DEBUG')


@dataclass
class Container(JSONWizard):

    class _(JSONWizard.Meta):
        # True to enable Debug mode for additional (more verbose) log output.
        #
        # Pass in a `str` to `int` to set the minimum log level:
        #   logging.getLogger('dataclass_wizard').setLevel('INFO')
        debug_enabled = logging.INFO
        # True to raise an class:`UnknownJSONKey` when an unmapped JSON key is
        # encountered when `from_dict` or `from_json` is called. Note that by
        # default, this is also recursively applied to any nested dataclasses.
        raise_on_unknown_json_key = True

    element: 'MyElement'


@dataclass
class MyElement:
    my_str: str
    my_float: float


d = {
    'element': {
        'myStr': 'string',
        'my_float': '1.23',
        # Notice how this key is not mapped to a known dataclass field!
        'my_bool': 'Testing'
    }
}

# Try to de-serialize the dictionary object into a `MyClass` object.
try:
    c = Container.from_dict(d)
except UnknownJSONKey as e:
    print('Received error:', type(e).__name__)
    print('Class:', e.class_name)
    print('Unknown JSON key:', e.json_key)
    print('JSON object:', e.obj)
    print('Known Fields:', e.fields)
else:
    print('Successfully de-serialized the JSON object.')
    print(repr(c))

See the section on Handling Unknown JSON Keys for more info.

Save or "Catch-All" Unknown JSON Keys

When calling from_dict or from_json, any unknown or extraneous JSON keys that are not mapped to fields in the dataclass are typically ignored or raise an error. However, you can capture these undefined keys in a catch-all field of type CatchAll, allowing you to handle them as needed later.

For example, suppose you have the following dictionary:

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

You can save the undefined keys in a catch-all field and process them later. Simply define a field of type CatchAll in your dataclass. This field will act as a dictionary to store any unmapped keys and their values. If there are no undefined keys, the field will default to an empty dictionary.

from dataclasses import dataclass
from typing import Any
from dataclass_wizard import CatchAll, JSONWizard

@dataclass
class UnknownAPIDump(JSONWizard):
    endpoint: str
    data: dict[str, Any]
    unknown_things: CatchAll

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

dump = UnknownAPIDump.from_dict(dump_dict)
print(f'{dump!r}')
# > UnknownAPIDump(endpoint='some_api_endpoint', data={'foo': 1, 'bar': '2'},
#       unknown_things={'undefined_field_name': [1, 2, 3]})

print(dump.to_dict())
# > {'endpoint': 'some_api_endpoint', 'data': {'foo': 1, 'bar': '2'}, 'undefined_field_name': [1, 2, 3]}

Date and Time with Custom Patterns

As of v0.20.0, date and time strings in a custom format can be de-serialized using the DatePattern, TimePattern, and DateTimePattern type annotations, representing patterned date, time, and datetime objects respectively.

This will internally call datetime.strptime with the format specified in the annotation, and also use the fromisoformat() method in case the date string is in ISO-8601 format. All dates and times will continue to be serialized as ISO format strings by default. For more info, check out the Patterned Date and Time section in the docs.

A brief example of the intended usage is shown below:

from dataclasses import dataclass
from datetime import time, datetime
from typing import Annotated

from dataclass_wizard import fromdict, asdict, DatePattern, TimePattern, Pattern


@dataclass
class MyClass:
    date_field: DatePattern['%m-%Y']
    dt_field: Annotated[datetime, Pattern('%m/%d/%y %H.%M.%S')]
    time_field1: TimePattern['%H:%M']
    time_field2: Annotated[list[time], Pattern('%I:%M %p')]


data = {'date_field': '12-2022',
        'time_field1': '15:20',
        'dt_field': '1/02/23 02.03.52',
        'time_field2': ['1:20 PM', '12:30 am']}

class_obj = fromdict(MyClass, data)

# All annotated fields de-serialize as just date, time, or datetime, as shown.
print(class_obj)
# MyClass(date_field=datetime.date(2022, 12, 1), dt_field=datetime.datetime(2023, 1, 2, 2, 3, 52),
#         time_field1=datetime.time(15, 20), time_field2=[datetime.time(13, 20), datetime.time(0, 30)])

# All date/time fields are serialized as ISO-8601 format strings by default.
print(asdict(class_obj))
# {'dateField': '2022-12-01', 'dtField': '2023-01-02T02:03:52',
#  'timeField1': '15:20:00', 'timeField2': ['13:20:00', '00:30:00']}

# But, the patterned date/times can still be de-serialized back after
# serialization. In fact, it'll be faster than parsing the custom patterns!
assert class_obj == fromdict(MyClass, asdict(class_obj))

"Recursive" Dataclasses with Cyclic References

Prior to version v0.27.0, dataclasses with cyclic references or self-referential structures were not supported. This limitation is shown in the following toy example:

from dataclasses import dataclass

@dataclass
class A:
    a: 'A | None' = None

a = A(a=A(a=A(a=A())))

This was a longstanding issue.

New in v0.27.0: The Dataclass Wizard now extends its support to cyclic and self-referential dataclass models.

The example below demonstrates recursive dataclasses with cyclic dependencies, following the pattern A -> B -> A -> B. For more details, see the Cyclic or "Recursive" Dataclasses section in the documentation.

from __future__ import annotations  # This can be removed in Python 3.10+

from dataclasses import dataclass

from dataclass_wizard import JSONWizard


@dataclass
class A(JSONWizard):
    class _(JSONWizard.Meta):
        # enable support for self-referential / recursive dataclasses
        recursive_classes = True

    b: 'B | None' = None


@dataclass
class B:
    a: A | None = None


# confirm that `from_dict` with a recursive, self-referential
# input `dict` works as expected.
a = A.from_dict({'b': {'a': {'b': {'a': None}}}})

assert a == A(b=B(a=A(b=B())))

Dataclasses in Union Types

The dataclass-wizard library fully supports declaring dataclass models in Union types, such as list[Wizard | Archer | Barbarian].

Starting from v0.19.0, the library introduces two key features: - Auto-generated tags for dataclass models (based on class names). - A customizable tag key (default: __tag__) that identifies the model in JSON.

These options are controlled by the auto_assign_tags and tag_key attributes in the Meta config.

For example, if a JSON object looks like {"type": "A", ...}, you can set tag_key = "type" to automatically deserialize it into the appropriate class, like A.

Let's start out with an example, which aims to demonstrate the simplest usage of dataclasses in Union types. For more info, check out the Dataclasses in Union Types section in the docs.

from __future__ import annotations

from dataclasses import dataclass
from dataclass_wizard import JSONWizard


@dataclass
class Container(JSONWizard):

    class Meta(JSONWizard.Meta):
        tag_key = 'type'
        auto_assign_tags = True

    objects: list[A | B | C]


@dataclass
class A:
    my_int: int
    my_bool: bool = False


@dataclass
class B:
    my_int: int
    my_bool: bool = True


@dataclass
class C:
    my_str: str


data = {
    'objects': [
        {'type': 'A', 'my_int': 42},
        {'type': 'C', 'my_str': 'hello world'},
        {'type': 'B', 'my_int': 123},
        {'type': 'A', 'my_int': 321, 'myBool': True}
    ]
}

c = Container.from_dict(data)
print(repr(c))

# Output:
# Container(objects=[A(my_int=42, my_bool=False),
#                    C(my_str='hello world'),
#                    B(my_int=123, my_bool=True),
#                    A(my_int=321, my_bool=True)])

print(c.to_dict())

# True
assert c == c.from_json(c.to_json())

Supercharged Union Parsing

What about untagged dataclasses in Union types or | syntax? With the major release V1 opt-in, dataclass-wizard supercharges Union parsing, making it intuitive and flexible, even without tags.

This is especially useful for collections like list[Wizard] or when tags (discriminators) are not feasible.

To enable this feature, opt in to v1 using the Meta settings. For details, see the Field Guide to V1 Opt-in.

from __future__ import annotations  # Remove in Python 3.10+

from dataclasses import dataclass
from typing import Literal

from dataclass_wizard import JSONWizard

@dataclass
class MyClass(JSONWizard):

    class _(JSONWizard.Meta):
        v1 = True  # Enable v1 opt-in
        v1_unsafe_parse_dataclass_in_union = True

    literal_or_float: Literal['Auto'] | float
    entry: int | MoreDetails
    collection: list[MoreDetails | int]

@dataclass
class MoreDetails:
    arg: str

# OK: Union types work seamlessly
c = MyClass.from_dict({
    "literal_or_float": 1.23,
    "entry": 123,
    "collection": [{"arg": "test"}]
})
print(repr(c))
#> MyClass(literal_or_float=1.23, entry=123, collection=[MoreDetails(arg='test')])

# OK: Handles primitive and dataclass parsing
c = MyClass.from_dict({
    "literal_or_float": "Auto",
    "entry": {"arg": "example"},
    "collection": [123]
})
print(repr(c))
#> MyClass(literal_or_float='Auto', entry=MoreDetails(arg='example'), collection=[123])

Conditional Field Skipping

Quick Examples

1. Globally Skip Fields Matching a Condition

> Define a global skip rule using Meta.skip_if: > > python3 > from dataclasses import dataclass > from dataclass_wizard import JSONWizard, IS_NOT > > > @dataclass > class Example(JSONWizard): > class _(JSONWizard.Meta): > skip_if = IS_NOT(True) # Skip fields if the value is not `True` > > my_bool: bool > my_str: 'str | None' > > > print(Example(my_bool=True, my_str=None).to_dict()) > # Output: {'myBool': True} >

1. Skip Defaults Based on a Condition

> Skip fields with default values matching a specific condition using > Meta.skip_defaults_if: > > python3 > from __future__ import annotations # Can remove in PY 3.10+ > > from dataclasses import dataclass > from dataclass_wizard import JSONPyWizard, IS > > > @dataclass > class Example(JSONPyWizard): > class _(JSONPyWizard.Meta): > skip_defaults_if = IS(None) # Skip default `None` values. > > str_with_no_default: str | None > my_str: str | None = None > my_bool: bool = False > > > print(Example(str_with_no_default=None, my_str=None).to_dict()) > #> {'str_with_no_default': None, 'my_bool': False} > > >

1. Per-Field Conditional Skipping

> Apply skip rules to specific fields with > annotations > or skip_if_field: > > python3 > from __future__ import annotations # can be removed in Python 3.10+ > > from dataclasses import dataclass > from typing import Annotated > > from dataclass_wizard import JSONWizard, SkipIfNone, skip_if_field, EQ > > > @dataclass > class Example(JSONWizard): > my_str: Annotated[str | None, SkipIfNone] # Skip if `None`. > other_str: str | None = skip_if_field(EQ(''), default=None) # Skip if empty. > > print(Example(my_str=None, other_str='').to_dict()) > # Output: {} >

1. Skip Fields Based on Truthy or Falsy Values

   Use the IS_TRUTHY and IS_FALSY helpers to conditionally skip fields based on their truthiness:

   from dataclasses import dataclass, field
   from dataclass_wizard import JSONWizard, IS_FALSY
   
   
   @dataclass
   class ExampleWithFalsy(JSONWizard):
       class _(JSONWizard.Meta):
           skip_if = IS_FALSY()  # Skip fields if they evaluate as "falsy".
   
       my_bool: bool
       my_list: list = field(default_factory=list)
       my_none: None = None
   
   print(ExampleWithFalsy(my_bool=False, my_list=[], my_none=None).to_dict())
   #> {}
   

Serialization Options

The following parameters can be used to fine-tune and control how the serialization of a dataclass instance to a Python dict object or JSON string is handled.

Skip Defaults

A common use case is skipping fields with default values - based on the default or default_factory argument to dataclasses.field - in the serialization process.

The attribute skip_defaults in the inner Meta class can be enabled, to exclude such field values from serialization.The to_dict method (or the asdict helper function) can also be passed an skip_defaults argument, which should have the same result. An example of both these approaches is shown below.

from collections import defaultdict
from dataclasses import field, dataclass

from dataclass_wizard import JSONWizard


@dataclass
class MyClass(JSONWizard):

    class _(JSONWizard.Meta):
        skip_defaults = True

    my_str: str
    other_str: str = 'any value'
    optional_str: str = None
    my_list: list[str] = field(default_factory=list)
    my_dict: defaultdict[str, list[float]] = field(
        default_factory=lambda: defaultdict(list))


print('-- Load (Deserialize)')
c = MyClass('abc')
print(f'Instance: {c!r}')

print('-- Dump (Serialize)')
string = c.to_json()
print(string)

assert string == '{"myStr": "abc"}'

print('-- Dump (with `skip_defaults=False`)')
print(c.to_dict(skip_defaults=False))

Exclude Fields

You can also exclude specific dataclass fields (and their values) from the serialization process. There are two approaches that can be used for this purpose:

• The argument dump=False can be passed in to the json_key and json_field helper functions. Note that this is a more permanent option, as opposed to the one below.
• The to_dict method (or the asdict helper function ) can be passed an exclude argument, containing a list of one or more dataclass field names to exclude from the serialization process.

Additionally, here is an example to demonstrate usage of both these approaches:

from dataclasses import dataclass
from typing import Annotated

from dataclass_wizard import JSONWizard, json_key, json_field


@dataclass
class MyClass(JSONWizard):

    my_str: str
    my_int: int
    other_str: Annotated[str, json_key('AnotherStr', dump=False)]
    my_bool: bool = json_field('TestBool', dump=False)


data = {'MyStr': 'my string',
        'myInt': 1,
        'AnotherStr': 'testing 123',
        'TestBool': True}

print('-- From Dict')
c = MyClass.from_dict(data)
print(f'Instance: {c!r}')

# dynamically exclude the `my_int` field from serialization
additional_exclude = ('my_int',)

print('-- To Dict')
out_dict = c.to_dict(exclude=additional_exclude)
print(out_dict)

assert out_dict == {'myStr': 'my string'}

Environ Magic

Easily map environment variables to Python dataclasses with EnvWizard:

import os
from dataclass_wizard import EnvWizard

# Set up environment variables
os.environ.update({
    'APP_NAME': 'Env Wizard',
    'MAX_CONNECTIONS': '10',
    'DEBUG_MODE': 'true'
})

# Define dataclass using EnvWizard
class AppConfig(EnvWizard):
    app_name: str
    max_connections: int
    debug_mode: bool

# Load config from environment variables
config = AppConfig()
print(config.app_name)    #> Env Wizard
print(config.debug_mode)  #> True
assert config.max_connections == 10

# Override with keyword arguments
config = AppConfig(app_name='Dataclass Wizard Rocks!', debug_mode='false')
print(config.app_name)    #> Dataclass Wizard Rocks!
assert config.debug_mode is False

Advanced Example: Dynamic Prefix Handling

EnvWizard supports dynamic prefix application, ideal for customizable environments:

import os
from dataclass_wizard import EnvWizard, env_field

# Define dataclass with custom prefix support
class AppConfig(EnvWizard):

    class _(EnvWizard.Meta):
        env_prefix = 'APP_'  # Default prefix for env vars

    name: str = env_field('A_NAME')  # Looks for `APP_A_NAME` by default
    debug: bool

# Set environment variables
os.environ['CUSTOM_A_NAME'] = 'Test!'
os.environ['CUSTOM_DEBUG'] = 'yes'

# Apply a dynamic prefix at runtime
config = AppConfig(_env_prefix='CUSTOM_')  # Looks for `CUSTOM_A_NAME` and `CUSTOM_DEBUG`

print(config)
# > AppConfig(name='Test!', debug=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.

What's New in v1.0

Contributing

Contributions are welcome! Open a pull request to fix a bug, or open an issue to discuss a new feature or change.

Check out the Contributing section in the docs for more info.

TODOs

All feature ideas or suggestions for future consideration, have been currently added as milestones in the project's GitHub repo.

Credits

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