Simple, elegant wizarding tools for Python’s `dataclasses`.

📘 Docs → dcw.ritviknag.com

Dataclass Wizard is a fast, well-tested serialization library for Python dataclasses.

Behold, the power of Dataclass Wizard:

    >>> from __future__ import annotations
    >>> from dataclasses import field
    >>> from dataclass_wizard import DataclassWizard
    ...
    >>> class MyClass(DataclassWizard, load_case='AUTO', dump_case='CAMEL'):
    ...     my_str: str | None
    ...     is_active_tuple: tuple[bool, ...]
    ...     list_of_int: list[int] = field(default_factory=list)
    ...
    >>> MyClass.from_json(
    ...     '{"my_str": 20, "ListOfInt": ["1", "2", 3], "isActiveTuple": [true, false, 1]}'
    ... )
    MyClass(my_str='20', is_active_tuple=(True, False, True), list_of_int=[1, 2, 3])

Note

The example above demonstrates automatic type coercion, key casing transforms, and support for nested dataclasses. `DataclassWizard` also auto-applies `@dataclass` to subclasses.

Tip

A new v1 engine is available as an opt-in, offering explicit environment precedence, nested dataclass support, and improved performance.

`EnvWizard` v1 adds field-level configuration on top of these improvements.

See the Field Guide to V1 Opt-in and the hands-on quickstart for details.

# Why Dataclass Wizard?

Dataclass Wizard helps you turn Python dataclasses into robust, high-performance serialization schemas with minimal effort.

- 🚀 Fast — code-generated loaders and dumpers

- 🪶 Lightweight — pure Python, minimal dependencies

- 🧠 Typed — powered by Python type hints

- 🧙 Flexible — JSON, YAML, TOML, and environment variables

- 🧪 Reliable — battle-tested with extensive test coverage

# Quick Examples

JSON De/Serialization

    from dataclasses import dataclass
    from dataclass_wizard import JSONWizard

    @dataclass
    class Data(JSONWizard):
        value: int

    data = Data.from_dict({"value": "123"})
    assert data.value == 123
    print(data.to_json())

Environment Variables

    import os
    from dataclass_wizard import EnvWizard

    os.environ['DEBUG'] = 'true'

    class Config(EnvWizard):
        debug: bool

    cfg = Config()
    assert cfg.debug is True

EnvWizard v1 (Opt-in)

    import os
    from dataclass_wizard.v1 import EnvWizard, Env

    os.environ['DEBUG_MODE'] = 'true'

    class Config(EnvWizard):
        debug: bool = Env('DEBUG_MODE')

    cfg = Config()
    assert cfg.debug is True

Tip

EnvWizard v1 introduces explicit environment precedence and nested dataclass support. See Field Guide to V1 Opt-in for full details.

# Installation

Install from PyPI:

    pip install dataclass-wizard

Or via conda-forge:

    conda install -c conda-forge dataclass-wizard

Dataclass Wizard supports Python 3.9+.

Note

Python 3.6–3.8 users should install the last supported release, `dataclass-wizard==0.26.1`. See PyPI for historical versions and the Changelog for details.

# Wizard Mixins ✨

In addition to `JSONWizard`, Dataclass Wizard provides a set of focused Mixin classes to simplify common serialization tasks:

- 🪄 EnvWizard — Load environment variables and `.env` files into typed schemas, including support for secret directories.

- 🎩 JSONPyWizard — A helper for `JSONWizard` that preserves keys as-is (no case transformations).

- 🔮 JSONListWizard — Extend `JSONWizard` to convert lists into container objects.

- 💼 JSONFileWizard — Load and save dataclass instances from local JSON files.

Optional format support:

- 🌳 TOMLWizard — Map dataclasses to and from TOML.

- 🧙‍♂️ YAMLWizard — Convert between YAML and dataclasses 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!

# Custom Type Hooks 🔌

Dataclass Wizard allows you to extend serialization and deserialization to support custom or unsupported types via type hooks.

Type hooks define how a value is:

- loaded (parsed) from JSON or a dictionary

- dumped (serialized) back into JSON-compatible data

This makes it easy to support standard-library types such as ipaddress.IPv4Address as well as user-defined classes.

Type hooks work with or without inheritance, and integrate seamlessly with the v1 code generation engine.

See Type Hooks for details and examples.

# 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:

Note

As of v0.18.0, the Meta config for the main dataclass will cascade down and be merged with the Meta config (if specified) of each nested dataclass. To disable this behavior, you can pass in `recursive=False` to the Meta config.

    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

Note

Important: The functionality for custom key mappings (such as JSON-to-dataclass field mappings) is being re-imagined with the introduction of V1 Opt-in. Enhanced support for these features is now available, improving the user experience for working with custom mappings.

For more details, see the Field Guide to V1 Opt-in and the V1 Alias documentation.

This change is part of the ongoing improvements in version `v0.35.0+`, and the old functionality will no longer be maintained in future releases.

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

Note

Important: The current “nested path” functionality is being re-imagined. Please refer to the new docs for V1 Opt-in features, which introduce enhanced support for these use cases. For more details, see the Field Guide to V1 Opt-in and the V1 Alias documentation.

This change is part of the ongoing improvements in version `v0.35.0+`, and the old functionality will no longer be maintained in future releases.

The `dataclass-wizard` library allows you to 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 now 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.

Note

As of v0.18.0, the Meta config for the main dataclass will cascade down and be merged with the Meta config (if specified) of each nested dataclass. To disable this behavior, you can pass in `recursive=False` to the Meta config.

    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

Added in v0.28.0

There is now Easier 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.

Note

There is a minor performance impact when DEBUG mode is enabled; for that reason, I would personally advise against enabling this in a production environment.

### 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]}

Note

- When using a “catch-all” field, it is strongly recommended to define exactly one field of type `CatchAll` in the dataclass.

- `LetterCase` transformations do not apply to keys stored in the `CatchAll` field; the keys remain as they are provided.

- If you specify a default (or a default factory) for the `CatchAll` field, such as `unknown_things: CatchAll = None`, the default value will be used instead of an empty dictionary when no undefined parameters are present.

- The `CatchAll` functionality is guaranteed only when using `from_dict` or `from_json`. Currently, unknown keyword arguments passed to `__init__` will not be written to a `CatchAll` field.

# Date and Time with Custom Patterns

Tip

As of v0.35.0 with V1 Opt-in, Dataclass Wizard now supports timezone-aware and UTC `datetime` and `time` patterns, as well as multiple pattern strings (i.e. multiple custom formats) for greater flexibility in pattern matching. These features are not available in the current `v0.*` versions.

The new features include:

- Timezone-aware `datetime` and `time` patterns.

- UTC `datetime` and `time` patterns.

- Multiple custom formats for a single field, providing more control over pattern matching.

For more details and examples on how to use these new features, refer to the V1 Opt-in documentation for Patterned Date and Time.

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

Internally, these annotations use `datetime.strptime` with the specified format and the `fromisoformat()` method for ISO-8601 formatted strings. All date and time values are still serialized to ISO format strings by default. For more information, refer to the Patterned Date and Time section in the documentation.

Here is an example demonstrating how to use these annotations:

    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:
        # Custom format for date (Month-Year)
        date_field: DatePattern['%m-%Y']
        # Custom format for datetime (Month/Day/Year Hour.Minute.Second)
        dt_field: Annotated[datetime, Pattern('%m/%d/%y %H.%M.%S')]
        # Custom format for time (Hour:Minute)
        time_field1: TimePattern['%H:%M']
        # Custom format for a list of times (12-hour format with AM/PM)
        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 to date, time, or datetime objects, 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']}

    # The patterned date/times can be de-serialized back after serialization, which will be faster than
    # re-parsing the custom patterns!
    assert class_obj == fromdict(MyClass, asdict(class_obj))

# Recursive Types and Dataclasses with Cyclic References

Prior to version 0.27.0, 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, but starting with `v0.27.0`, Dataclass Wizard now supports recursive dataclasses, including cyclic references.

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())))

Starting with version 0.34.0, recursive types are supported out of the box (OOTB) with `v1` opt-in, removing the need for any `Meta` settings like `recursive_classes = True`.

This makes working with recursive dataclasses even easier and more streamlined. In addition, recursive types are now supported for the following Python type constructs:

- NamedTuple

- TypedDict

- Union

- Literal

- Nested dataclasses

- Type aliases (introduced in Python 3.12+)

## Example Usage

Recursive types allow handling complex nested data structures, such as deeply nested JSON objects or lists. With `v0.34.0` of Dataclass Wizard, de/serializing these structures becomes seamless and more intuitive.

### Recursive `Union`

    from dataclasses import dataclass
    from dataclass_wizard import JSONWizard

    # For Python 3.9, use this `Union` approach:
    from typing_extensions import TypeAlias
    JSON: TypeAlias = 'str | int | float | bool | dict[str, JSON] | list[JSON] | None'

    # For Python 3.10 and above, use this simpler approach:
    # JSON = str | int | float | bool | dict[str, 'JSON'] | list['JSON'] | None

    # For Python 3.12+, you can use the `type` statement:
    # type JSON = str | int | float | bool | dict[str, JSON] | list[JSON] | None

    @dataclass
    class MyTestClass(JSONWizard):

        class _(JSONWizard.Meta):
            v1 = True

        name: str
        meta: str
        msg: JSON

    x = MyTestClass.from_dict(
        {
            "name": "name",
            "meta": "meta",
            "msg": [{"x": {"x": [{"x": ["x", 1, 1.0, True, None]}]}}],
        }
    )
    assert x == MyTestClass(
        name="name",
        meta="meta",
        msg=[{"x": {"x": [{"x": ["x", 1, 1.0, True, None]}]}}],
    )

Note

The `type` statement in Python 3.12+ simplifies type alias definitions by avoiding string annotations for recursive references.

### Recursive `Union` with Nested `dataclasses`

    from dataclasses import dataclass, field
    from dataclass_wizard import JSONWizard

    @dataclass
    class A(JSONWizard):

        class _(JSONWizard.Meta):
            v1 = True

        value: int
        nested: 'B'
        next: 'A | None' = None

    @dataclass
    class B:
        items: list[A] = field(default_factory=list)

    x = A.from_dict(
        {
            "value": 1,
            "next": {"value": 2, "next": None, "nested": {}},
            "nested": {"items": [{"value": 3, "nested": {}}]},
        }
    )
    assert x == A(
        value=1,
        next=A(value=2, next=None, nested=B(items=[])),
        nested=B(items=[A(value=3, nested=B())]),
    )

Note

Nested `dataclasses` are particularly useful for representing hierarchical structures, such as trees or graphs, in a readable and maintainable way.

## Official References

For more information, see:

- Typing in Python

- PEP 695: Type Syntax

These examples illustrate the power of recursive types in simplifying complex data structures while leveraging the functionality of `dataclass-wizard`.

# 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

Added in v0.30.0

Dataclass Wizard introduces conditional skipping to omit fields during JSON serialization based on user-defined conditions. This feature works seamlessly with:

- Global rules via `Meta` settings.

- Per-field controls using `SkipIf()` annotations.

- Field wrappers for maximum flexibility.

## Quick Examples

1. Globally Skip Fields Matching a Condition

> Define a global skip rule using `Meta.skip_if`:

>     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}

2. Skip Defaults Based on a Condition

> Skip fields with default values matching a specific condition using `Meta.skip_defaults_if`:

>     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}

> Note

> Setting `skip_defaults_if` also enables `skip_defaults=True` automatically.

3. Per-Field Conditional Skipping

> Apply skip rules to specific fields with annotations or `skip_if_field`:

>     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: {}

4. 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())
       #> {}

Note

Special Cases

- SkipIfNone: Alias for `SkipIf(IS(None))`, skips fields with a value of `None`.

- Condition Helpers:

  - `IS`, `IS_NOT`: Identity checks.

  - `EQ`, `NE`, `LT`, `LE`, `GT`, `GE`: Comparison operators.

  - `IS_TRUTHY`, `IS_FALSY`: Skip fields based on truthy or falsy values.

Combine these helpers for flexible serialization rules!

# 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

Note

`EnvWizard` simplifies environment variable mapping with type validation, `.env` file support, and secret file handling (file names become keys).

Key Features:

- Auto Parsing: Supports complex types and nested structures.

- Configurable: Customize variable names, prefixes, and dotenv files.

- Validation: Errors for missing or malformed variables.

📖 Full Documentation

## 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

Opt-in for v1 Now Available

The early opt-in for v1 is now available with enhanced features, including intuitive `Union` parsing and optimized performance. To enable this, set `v1=True` in your `Meta` settings.

For more details and migration guidance, see the Field Guide to V1 Opt-in.

Warning

Important Changes in v1.0

- Default Key Transformation Update

  Starting with v1.0.0, the default key transformation for JSON serialization will change to keep keys as-is instead of converting them to `camelCase`.

  New Default Behavior: The default setting for key transformation will be `key_transform='NONE'`.

  How to Prepare: You can enforce this behavior immediately by using the `JSONPyWizard` helper, as shown below:

      from dataclasses import dataclass
      from dataclass_wizard import JSONPyWizard

      @dataclass
      class MyModel(JSONPyWizard):
          my_field: str

      print(MyModel(my_field="value").to_dict())
      # Output: {'my_field': 'value'}

- String Coercion Change (None Handling)

  Starting with v1.0, `None` values for fields annotated as `str` are converted using `str(None)` (i.e. `'None'`) instead of being silently coerced to the empty string.

  `Optional[str]` fields continue to preserve `None` by default.

  To restore the previous behavior and coerce `None` to `''`, set:

      class _(Meta):
          v1 = True
          v1_coerce_none_to_empty_str = True

- Default __str__() Behavior Change

  > > Starting with v1.0.0, we no longer pretty-print the serialized JSON value with keys in `camelCase`. Instead, we now use the `pprint` module to handle serialization formatting.

  > > New Default Behavior: The `__str__()` method in the `JSONWizard` class will use `pprint` by default.

  > > How to Prepare: You can immediately test this new behavior using the `JSONPyWizard` helper, as demonstrated below:

  > >     from dataclasses import dataclass
  > >     from dataclass_wizard import JSONWizard, JSONPyWizard

  > >     @dataclass
  > >     class CurrentModel(JSONWizard):
  > >         my_field: str

  > >     @dataclass
  > >     class NewModel(JSONPyWizard):
  > >         my_field: str

  > >     print(CurrentModel(my_field="value"))
  > >     #> {
  > >     #   "myField": "value"
  > >     # }

  > >     print(NewModel(my_field="value"))
  > >     #> NewModel(my_field='value')

  > - Float to Int Conversion Change Starting with v1.0, floats or float strings with fractional parts (e.g., `123.4` or `"123.4"`) will no longer be silently converted to integers. Instead, they will raise an error. However, floats without fractional parts (e.g., `3.0` or `"3.0"`) will continue to convert to integers as before.

  >   How to Prepare: You can opt in to v1 via `v1=True` to test this behavior right now. Additionally, to ensure compatibility with the new behavior:

  >   - Use `float` annotations for fields that may include fractional values.

  >   - Review your data to avoid passing fractional values (e.g., `123.4`) to fields annotated as `int`.

  >   - Update tests or logic that depend on the current rounding behavior.

  >       from dataclasses import dataclass
  >       from dataclass_wizard import JSONPyWizard

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

  >           list_of_int: list[int]

  >       input_dict = {'list_of_int': [1, '2.0', '3.', -4, '-5.00', '6', '-7']}
  >       t = Test.from_dict(input_dict)
  >       print(t)  #> Test(list_of_int=[1, 2, 3, -4, -5, 6, -7])

  >       # ERROR!
  >       _ = Test.from_dict({'list_of_int': [123.4]})

# 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.