Metadata-Version: 2.1
Name: dataclass-wizard
Version: 0.21.0
Summary: Marshal dataclasses to/from JSON. Use field properties with initial values. Construct a dataclass schema with JSON input.
Home-page: https://github.com/rnag/dataclass-wizard
Author: Ritvik Nag
Author-email: rv.kvetch@gmail.com
License: Apache 2.0
Project-URL: Documentation, https://dataclass-wizard.readthedocs.io
Project-URL: Source, https://github.com/rnag/dataclass-wizard
Description: ================
        Dataclass Wizard
        ================
        
        Full documentation is available at `Read The Docs`_. (`Installation`_)
        
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        This library provides a set of simple, yet elegant *wizarding* tools for
        interacting with the Python ``dataclasses`` module.
        
            The primary use is as a fast serialization framework that enables dataclass instances to
            be converted to/from JSON; this works well in particular with a *nested dataclass* model.
        
        -------------------
        
        **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
        
        ---
        
        .. contents:: Contents
           :depth: 1
           :local:
           :backlinks: none
        
        
        Installation
        ------------
        
        The Dataclass Wizard library is available `on PyPI`_, and can be installed with ``pip``:
        
        .. code-block:: shell
        
            $ pip install dataclass-wizard
        
        The ``dataclass-wizard`` library officially supports **Python 3.6** or higher.
        
        
        Features
        --------
        
        Here are the supported features that ``dataclass-wizard`` currently provides:
        
        -  *JSON/YAML (de)serialization*: marshal dataclasses to/from JSON, YAML, and Python
           ``dict`` objects.
        -  *Field properties*: support for using properties with default
           values in dataclass instances.
        -  *JSON to Dataclass generation*: construct a dataclass schema with a JSON file
           or string input.
        
        Wizard Mixins
        -------------
        
        In addition to the ``JSONWizard``, here a few extra Mixin_ classes that might prove incredibly convenient to use.
        
        * `JSONListWizard`_ -- Extends ``JSONWizard`` to return `Container`_ -- instead of *list* -- objects where possible.
        * `JSONFileWizard`_ -- Makes it easier to convert dataclass instances from/to JSON files on a local drive.
        * `YAMLWizard`_ -- Provides support to convert dataclass instances to/from YAML, using the default ``PyYAML`` parser.
        
        Supported Types
        ---------------
        
        The Dataclass Wizard library provides inherent support for standard Python collections
        such as ``list``, ``dict`` and ``set``, as well as most Generics from the typing
        module, such as ``Union`` and ``Any``. Other commonly used types such as ``Enum``,
        ``defaultdict``, and date and time objects such as ``datetime`` are also natively
        supported.
        
        For a complete list of the supported Python types, including info on the
        load/dump process for special types, check out the `Supported Types`_ section
        in the docs.
        
        
        Usage and Examples
        ------------------
        
        Using the built-in JSON marshalling support for dataclasses:
        
            Note: The following example should work in **Python 3.7+** with the included ``__future__``
            import.
        
        .. code:: python3
        
            from __future__ import annotations  # This can be removed in Python 3.10+
        
            from dataclasses import dataclass, field
            from datetime import date
            from enum import Enum
        
            from dataclass_wizard import JSONWizard
        
        
            @dataclass
            class Data(JSONWizard):
        
                class _(JSONWizard.Meta):
                    # Sets the target key transform to use for serialization;
                    # defaults to `camelCase` if not specified.
                    key_transform_with_dump = 'LISP'
        
                a_sample_bool: bool
                values: list[Inner] = field(default_factory=list)
        
        
            @dataclass
            class Inner:
                vehicle: Car | None
                my_dates: dict[int, date]
        
        
            class Car(Enum):
                SEDAN = 'BMW Coupe'
                SUV = 'Toyota 4Runner'
                JEEP = 'Jeep Cherokee'
        
        
            def main():
        
                my_dict = {
                    'values': [
                        {
                            'vehicle': 'Toyota 4Runner',
                            'My-Dates': {'123': '2023-01-31'}
                        },
                        {
                            'vehicle': None,
                            'my_dates': {}
                        }
                    ],
                    'aSampleBool': 'TRUE'
                }
        
                # De-serialize (a JSON string or dictionary data) into a `Data` instance.
                data = Data.from_dict(my_dict)
        
                print(repr(data))
                # > Data(a_sample_bool=True, values=[Inner(vehicle=<Car.SUV: 'Toyota 4Runner'>, ...)])
        
                # assert enums values are as expected
                assert data.values[0].vehicle is Car.SUV
        
                print(data.to_json(indent=2))
                # {
                #   "a-sample-bool": true,
                #   "values": [
                #     {
                #       "vehicle": "Toyota 4Runner",
                #       "my-dates": {
                #         "123": "2023-01-31"
                #       },
                #   ...
        
                # True
                assert data == data.from_json(data.to_json())
        
            if __name__ == '__main__':
                main()
        
        ... and with the ``property_wizard``, which provides support for
        `field properties`_ with default values in dataclasses:
        
        .. code:: python3
        
            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):
                # Note: The example below uses the default value from the `field` extra in
                # the `Annotated` definition; if `wheels` were annotated as `int | str`,
                # it would default to 0, because `int` appears as the first type argument.
                #
                # Any right-hand value assigned to `wheels` is ignored as it is simply
                # re-declared by the property; here it is simply omitted for brevity.
                wheels: Annotated[int | str, field(default=4)]
        
                # This is a shorthand version of the above; here an IDE suggests
                # `_wheels` as a keyword argument to the constructor method, though
                # it will actually be named as `wheels`.
                # _wheels: int | str = 4
        
                @property
                def wheels(self) -> int:
                    return self._wheels
        
                @wheels.setter
                def wheels(self, wheels: int | str):
                    self._wheels = int(wheels)
        
        
            if __name__ == '__main__':
                v = Vehicle()
                print(v)
                # prints:
                #   Vehicle(wheels=4)
        
                v = Vehicle(wheels=3)
                print(v)
        
                v = Vehicle('6')
                print(v)
        
                assert v.wheels == 6, 'The constructor should use our setter method'
        
                # Confirm that we go through our setter method
                v.wheels = '123'
                assert v.wheels == 123
        
        ... or generate a dataclass schema for JSON input, via the `wiz-cli`_ tool:
        
        .. code:: shell
        
            $ echo '{"myFloat": "1.23", "Products": [{"created_at": "2021-11-17"}]}' | wiz gs - my_file
        
            # Contents of my_file.py
            from dataclasses import dataclass
            from datetime import date
            from typing import List, Union
        
            from dataclass_wizard import JSONWizard
        
        
            @dataclass
            class Data(JSONWizard):
                """
                Data dataclass
        
                """
                my_float: Union[float, str]
                products: List['Product']
        
        
            @dataclass
            class Product:
                """
                Product dataclass
        
                """
                created_at: date
        
        
        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.
        
        .. list-table::
           :widths: 10 40 35
           :header-rows: 1
        
           * - 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 <https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/skip_the_str.html>`_.
        
        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.
        
        .. code:: python3
        
            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:
        
        .. code:: python3
        
            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
        
        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.
        
        .. code:: python3
        
            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.
        
        .. code:: python3
        
            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='INFO')
        
        
            @dataclass
            class Container(JSONWizard):
        
                class _(JSONWizard.Meta):
                    # True to enable Debug mode for additional (more verbose) log output.
                    debug_enabled = True
                    # 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))
        
        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:
        
        .. code:: python3
        
            from dataclasses import dataclass
            from datetime import time, datetime
            from typing import List
            # Note: in Python 3.9+, you can import this from `typing` instead
            from typing_extensions 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))
        
        Dataclasses in ``Union`` Types
        ------------------------------
        
        The ``dataclass-wizard`` library fully supports declaring dataclass models in
        `Union`_ types as field annotations, such as ``list[Wizard | Archer | Barbarian]``.
        
        As of *v0.19.0*, there is added support to  *auto-generate* tags for a dataclass model
        -- based on the class name -- as well as to specify a custom *tag key* that will be
        present in the JSON object, which defaults to a special ``__tag__`` key otherwise.
        These two options are controlled by the ``auto_assign_tags`` and ``tag_key``
        attributes (respectively) in the ``Meta`` config.
        
        To illustrate a specific example, a JSON object such as
        ``{"oneOf": {"type": "A", ...}, ...}`` will now automatically map to a dataclass
        instance ``A``, provided that the ``tag_key`` is correctly set to "type", and
        the field ``one_of`` is annotated as a Union type in the ``A | B`` syntax.
        
        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.
        
        .. code:: python3
        
            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(f'{c!r}')
        
            # True
            assert c == 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())
            # prints the following on a single line:
            # {'objects': [{'myInt': 42, 'myBool': False, 'type': 'A'},
            #              {'myStr': 'hello world', 'type': 'C'},
            #              {'myInt': 123, 'myBool': True, 'type': 'B'},
            #              {'myInt': 321, 'myBool': True, 'type': 'A'}]}
        
            # True
            assert c == c.from_json(c.to_json())
        
        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.
        
        .. code:: python3
        
            from collections import defaultdict
            from dataclasses import field, dataclass
            from typing import DefaultDict, List
        
            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:
        
        .. code:: python3
        
            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'}
        
        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.
        
        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.
        
        .. _Read The Docs: https://dataclass-wizard.readthedocs.io
        .. _Installation: https://dataclass-wizard.readthedocs.io/en/latest/installation.html
        .. _on PyPI: https://pypi.org/project/dataclass-wizard/
        .. _Cookiecutter: https://github.com/cookiecutter/cookiecutter
        .. _`rnag/cookiecutter-pypackage`: https://github.com/rnag/cookiecutter-pypackage
        .. _`Contributing`: https://dataclass-wizard.readthedocs.io/en/latest/contributing.html
        .. _`open an issue`: https://github.com/rnag/dataclass-wizard/issues
        .. _`JSONListWizard`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/wizard_mixins.html#jsonlistwizard
        .. _`JSONFileWizard`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/wizard_mixins.html#jsonfilewizard
        .. _`YAMLWizard`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/wizard_mixins.html#yamlwizard
        .. _`Container`: https://dataclass-wizard.readthedocs.io/en/latest/dataclass_wizard.html#dataclass_wizard.Container
        .. _`Supported Types`: https://dataclass-wizard.readthedocs.io/en/latest/overview.html#supported-types
        .. _`Mixin`: https://stackoverflow.com/a/547714/10237506
        .. _`Meta`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/meta.html
        .. _`pydantic`: https://pydantic-docs.helpmanual.io/
        .. _`Using Field Properties`: https://dataclass-wizard.readthedocs.io/en/latest/using_field_properties.html
        .. _`field properties`: https://dataclass-wizard.readthedocs.io/en/latest/using_field_properties.html
        .. _`custom mapping`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/custom_key_mappings.html
        .. _`wiz-cli`: https://dataclass-wizard.readthedocs.io/en/latest/wiz_cli.html
        .. _`key limitations`: https://florimond.dev/en/posts/2018/10/reconciling-dataclasses-and-properties-in-python/
        .. _`more complete example`: https://dataclass-wizard.readthedocs.io/en/latest/examples.html#a-more-complete-example
        .. _custom format: https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes
        .. _`Patterned Date and Time`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/patterned_date_time.html
        .. _Union: https://docs.python.org/3/library/typing.html#typing.Union
        .. _`Dataclasses in Union Types`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/dataclasses_in_union_types.html
        .. _as milestones: https://github.com/rnag/dataclass-wizard/milestones
        
Keywords: dataclasses,dataclass,wizard,json,marshal,json to dataclass,json2dataclass,dict to dataclass,property,field-property,serialization,deserialization
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python
Description-Content-Type: text/x-rst
Provides-Extra: timedelta
Provides-Extra: yaml
