================
Dataclass Wizard
================
Full documentation is available at `Read The Docs`_. (`Installation`_)
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**Dataclass Wizard** offers simple, elegant, *wizarding* 🪄 tools for
interacting with Python's ``dataclasses``.
It excels at ⚡️ lightning-fast de/serialization, effortlessly
converting dataclass instances to/from JSON -- perfect for
*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
---
.. contents:: Contents
:depth: 1
:local:
:backlinks: none
Installation
------------
Dataclass Wizard is available on `PyPI`_. Install with ``pip``:
.. code-block:: console
$ pip install dataclass-wizard
Also available on `conda`_ via `conda-forge`_. Install with ``conda``:
.. code-block:: console
$ conda install dataclass-wizard -c conda-forge
This library supports **Python 3.9** or higher.
.. _PyPI: https://pypi.org/project/dataclass-wizard/
.. _conda: https://anaconda.org/conda-forge/dataclass-wizard
.. _conda-forge: https://conda-forge.org/
Features
--------
Unlock the full potential of your `dataclasses`_ with these key features:
- *Flexible (de)serialization*: Marshal dataclasses to/from JSON, TOML, YAML, or ``dict`` with ease.
- *Environment magic*: Map env vars and ``dotenv`` 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.
Wizard Mixins
-------------
In addition to ``JSONWizard``, these handy Mixin_ classes simplify your workflow:
* `EnvWizard`_ — Seamlessly load env variables and ``.env`` files into typed schemas. Supports secret files (file names as keys).
* `JSONPyWizard`_ — A ``JSONWizard`` helper to skip *camelCase* and preserve keys as-is.
* `JSONListWizard`_ — Extends ``JSONWizard`` to return `Container`_ objects instead of *lists* when possible.
* `JSONFileWizard`_ — Effortlessly convert dataclass instances from/to JSON files on your local drive.
* `TOMLWizard`_ — Easily map dataclass instances to/from TOML format.
* `YAMLWizard`_ — Instantly convert dataclass instances to/from YAML, using the default ``PyYAML`` parser.
Supported Types
---------------
The Dataclass Wizard library natively supports standard Python
collections like ``list``, ``dict``, and ``set``, along with
popular `typing`_ module Generics such as ``Union`` and ``Any``.
Additionally, it handles commonly used types like ``Enum``,
``defaultdict``, and date/time objects (e.g., ``datetime``)
with ease.
For a detailed list of supported types and insights into the
load/dump process for special types, visit the
`Supported Types`_ section of the docs.
Usage and Examples
------------------
.. rubric:: Seamless JSON De/Serialization with ``JSONWizard``
.. code-block:: python3
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: <Car.SUV: 'Toyota 4Runner'>
assert v.my_dates[123] == date(2023, 1, 31) # > True
# Serialize back into pretty-printed JSON
print(data.to_json(indent=2))
.. rubric:: Map Environment Variables with ``EnvWizard``
Easily map environment variables to Python dataclasses:
.. code-block:: python3
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.
.. rubric:: Dataclass Properties with ``property_wizard``
Add field properties to your dataclasses with default values using ``property_wizard``:
.. code-block:: 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):
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`_.
.. rubric:: Generate Dataclass Schemas with CLI
Quickly generate Python dataclasses from JSON input using the ``wiz-cli`` tool:
.. code-block:: console
$ echo '{"myFloat": "1.23", "Items": [{"created": "2021-01-01"}]}' | wiz gs - output.py
.. code-block:: python3
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.
.. 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
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``:
.. code:: python3
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:
.. code:: python3
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.
.. 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
##########
.. admonition:: **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.
.. 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='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.
.. code:: python
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
----------------------------------
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 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:
.. code:: python3
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.
.. code:: python3
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 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())
Conditional Field Skipping
--------------------------
.. admonition:: **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``:
.. code-block:: 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}
2. **Skip Defaults Based on a Condition**
Skip fields with default values matching a specific condition using ``Meta.skip_defaults_if``:
.. code-block:: 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}
.. 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``:
.. code-block:: 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: {}
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:
.. code-block:: python3
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!
.. _conditional skipping: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/serialization_options.html#skip-if-functionality
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 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'}
``Environ`` Magic
-----------------
Easily map environment variables to Python dataclasses with ``EnvWizard``:
.. code-block:: python3
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 <https://dataclass-wizard.readthedocs.io/en/latest/env_magic.html>`_
Advanced Example: Dynamic Prefix Handling
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
``EnvWizard`` supports dynamic prefix application, ideal for customizable environments:
.. code-block:: python3
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
------------------
.. warning::
- **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*: ``key_transform='NONE'`` will be the standard setting.
*How to Prepare*: You can enforce this future behavior right now by using the ``JSONPyWizard`` helper:
.. code-block:: python3
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'}
- **Float to Int Conversion Change**
Starting in ``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 with no fractional parts (e.g., ``3.0``
or ``"3.0"``) will still convert to integers as before.
*How to Prepare*: To ensure compatibility with the new behavior:
- Use ``float`` annotations for fields that may include fractional values.
- Review your data and avoid passing fractional values (e.g., ``123.4``) to fields annotated as ``int``.
- Update tests or logic that rely on the current rounding behavior.
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
.. _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
.. _`JSONPyWizard`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/wizard_mixins.html#jsonpywizard
.. _`EnvWizard`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/wizard_mixins.html#envwizard
.. _`on EnvWizard`: https://dataclass-wizard.readthedocs.io/en/latest/env_magic.html
.. _`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
.. _`TOMLWizard`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/wizard_mixins.html#tomlwizard
.. _`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
.. _`Cyclic or "Recursive" Dataclasses`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/cyclic_or_recursive_dataclasses.html
.. _as milestones: https://github.com/rnag/dataclass-wizard/milestones
.. _longstanding issue: https://github.com/rnag/dataclass-wizard/issues/62
.. _Easier Debug Mode: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/easier_debug_mode.html
.. _Handling Unknown JSON Keys: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/handling_unknown_json_keys.html
.. _custom paths to access nested keys: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/nested_key_paths.html
.. _annotations: https://docs.python.org/3/library/typing.html#typing.Annotated
.. _typing: https://docs.python.org/3/library/typing.html
.. _dataclasses: https://docs.python.org/3/library/dataclasses.html
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"description": "================\nDataclass Wizard\n================\n\nFull documentation is available at `Read The Docs`_. (`Installation`_)\n\n.. image:: https://img.shields.io/pypi/v/dataclass-wizard.svg\n :target: https://pypi.org/project/dataclass-wizard\n\n.. image:: https://img.shields.io/conda/vn/conda-forge/dataclass-wizard.svg\n :target: https://anaconda.org/conda-forge/dataclass-wizard\n\n.. image:: https://img.shields.io/pypi/pyversions/dataclass-wizard.svg\n :target: https://pypi.org/project/dataclass-wizard\n\n.. image:: https://github.com/rnag/dataclass-wizard/actions/workflows/dev.yml/badge.svg\n :target: https://github.com/rnag/dataclass-wizard/actions/workflows/dev.yml\n\n.. image:: https://readthedocs.org/projects/dataclass-wizard/badge/?version=latest\n :target: https://dataclass-wizard.readthedocs.io/en/latest/?version=latest\n :alt: Documentation Status\n\n\n.. image:: https://pyup.io/repos/github/rnag/dataclass-wizard/shield.svg\n :target: https://pyup.io/repos/github/rnag/dataclass-wizard/\n :alt: Updates\n\n\n\n**Dataclass Wizard** offers simple, elegant, *wizarding* \ud83e\ude84 tools for\ninteracting with Python's ``dataclasses``.\n\n It excels at \u26a1\ufe0f lightning-fast de/serialization, effortlessly\n converting dataclass instances to/from JSON -- perfect for\n *nested dataclass* models!\n\n-------------------\n\n**Behold, the power of the Dataclass Wizard**::\n\n >>> from __future__ import annotations\n >>> from dataclasses import dataclass, field\n >>> from dataclass_wizard import JSONWizard\n ...\n >>> @dataclass\n ... class MyClass(JSONWizard):\n ... my_str: str | None\n ... is_active_tuple: tuple[bool, ...]\n ... list_of_int: list[int] = field(default_factory=list)\n ...\n >>> string = \"\"\"\n ... {\n ... \"my_str\": 20,\n ... \"ListOfInt\": [\"1\", \"2\", 3],\n ... \"isActiveTuple\": [\"true\", false, 1]\n ... }\n ... \"\"\"\n ...\n >>> instance = MyClass.from_json(string)\n >>> instance\n MyClass(my_str='20', is_active_tuple=(True, False, True), list_of_int=[1, 2, 3])\n >>> instance.to_json()\n '{\"myStr\": \"20\", \"isActiveTuple\": [true, false, true], \"listOfInt\": [1, 2, 3]}'\n >>> instance == MyClass.from_dict(instance.to_dict())\n True\n\n---\n\n.. contents:: Contents\n :depth: 1\n :local:\n :backlinks: none\n\n\nInstallation\n------------\n\nDataclass Wizard is available on `PyPI`_. Install with ``pip``:\n\n.. code-block:: console\n\n $ pip install dataclass-wizard\n\nAlso available on `conda`_ via `conda-forge`_. Install with ``conda``:\n\n.. code-block:: console\n\n $ conda install dataclass-wizard -c conda-forge\n\nThis library supports **Python 3.9** or higher.\n\n.. _PyPI: https://pypi.org/project/dataclass-wizard/\n.. _conda: https://anaconda.org/conda-forge/dataclass-wizard\n.. _conda-forge: https://conda-forge.org/\n\nFeatures\n--------\n\nUnlock the full potential of your `dataclasses`_ with these key features:\n\n- *Flexible (de)serialization*: Marshal dataclasses to/from JSON, TOML, YAML, or ``dict`` with ease.\n- *Environment magic*: Map env vars and ``dotenv`` files to strongly-typed class fields effortlessly.\n- *Field properties made simple*: Add properties with default values to your dataclasses.\n- *JSON-to-Dataclass wizardry*: Auto-generate a dataclass schema from any JSON file or string instantly.\n\nWizard Mixins\n-------------\n\nIn addition to ``JSONWizard``, these handy Mixin_ classes simplify your workflow:\n\n* `EnvWizard`_ \u2014 Seamlessly load env variables and ``.env`` files into typed schemas. Supports secret files (file names as keys).\n* `JSONPyWizard`_ \u2014 A ``JSONWizard`` helper to skip *camelCase* and preserve keys as-is.\n* `JSONListWizard`_ \u2014 Extends ``JSONWizard`` to return `Container`_ objects instead of *lists* when possible.\n* `JSONFileWizard`_ \u2014 Effortlessly convert dataclass instances from/to JSON files on your local drive.\n* `TOMLWizard`_ \u2014 Easily map dataclass instances to/from TOML format.\n* `YAMLWizard`_ \u2014 Instantly convert dataclass instances to/from YAML, using the default ``PyYAML`` parser.\n\nSupported Types\n---------------\n\nThe Dataclass Wizard library natively supports standard Python\ncollections like ``list``, ``dict``, and ``set``, along with\npopular `typing`_ module Generics such as ``Union`` and ``Any``.\nAdditionally, it handles commonly used types like ``Enum``,\n``defaultdict``, and date/time objects (e.g., ``datetime``)\nwith ease.\n\nFor a detailed list of supported types and insights into the\nload/dump process for special types, visit the\n`Supported Types`_ section of the docs.\n\nUsage and Examples\n------------------\n\n.. rubric:: Seamless JSON De/Serialization with ``JSONWizard``\n\n.. code-block:: python3\n\n from __future__ import annotations # Optional in Python 3.10+\n\n from dataclasses import dataclass, field\n from enum import Enum\n from datetime import date\n\n from dataclass_wizard import JSONWizard\n\n\n @dataclass\n class Data(JSONWizard):\n # Use Meta to customize JSON de/serialization\n class _(JSONWizard.Meta):\n key_transform_with_dump = 'LISP' # Transform keys to LISP-case during dump\n\n a_sample_bool: bool\n values: list[Inner] = field(default_factory=list)\n\n\n @dataclass\n class Inner:\n # Nested data with optional enums and typed dictionaries\n vehicle: Car | None\n my_dates: dict[int, date]\n\n\n class Car(Enum):\n SEDAN = 'BMW Coupe'\n SUV = 'Toyota 4Runner'\n\n\n # Input JSON-like dictionary\n my_dict = {\n 'values': [{'vehicle': 'Toyota 4Runner', 'My-Dates': {'123': '2023-01-31'}}],\n 'aSampleBool': 'TRUE'\n }\n\n # Deserialize into strongly-typed dataclass instances\n data = Data.from_dict(my_dict)\n print((v := data.values[0]).vehicle) # Prints: <Car.SUV: 'Toyota 4Runner'>\n assert v.my_dates[123] == date(2023, 1, 31) # > True\n\n # Serialize back into pretty-printed JSON\n print(data.to_json(indent=2))\n\n.. rubric:: Map Environment Variables with ``EnvWizard``\n\nEasily map environment variables to Python dataclasses:\n\n.. code-block:: python3\n\n import os\n from dataclass_wizard import EnvWizard\n\n os.environ.update({\n 'APP_NAME': 'My App',\n 'MAX_CONNECTIONS': '10',\n 'DEBUG_MODE': 'true'\n })\n\n class AppConfig(EnvWizard):\n app_name: str\n max_connections: int\n debug_mode: bool\n\n config = AppConfig()\n print(config.app_name) # My App\n print(config.debug_mode) # True\n\n\ud83d\udcd6 See more `on EnvWizard`_ in the full documentation.\n\n.. rubric:: Dataclass Properties with ``property_wizard``\n\nAdd field properties to your dataclasses with default values using ``property_wizard``:\n\n.. code-block:: python3\n\n from __future__ import annotations # This can be removed in Python 3.10+\n\n from dataclasses import dataclass, field\n from typing_extensions import Annotated\n\n from dataclass_wizard import property_wizard\n\n\n @dataclass\n class Vehicle(metaclass=property_wizard):\n wheels: Annotated[int | str, field(default=4)]\n # or, alternatively:\n # _wheels: int | str = 4\n\n @property\n def wheels(self) -> int:\n return self._wheels\n\n @wheels.setter\n def wheels(self, value: int | str):\n self._wheels = int(value)\n\n\n v = Vehicle()\n print(v.wheels) # 4\n v.wheels = '6'\n print(v.wheels) # 6\n\n assert v.wheels == 6, 'Setter correctly handles type conversion'\n\n\ud83d\udcd6 For a deeper dive, visit the documentation on `field properties`_.\n\n.. rubric:: Generate Dataclass Schemas with CLI\n\nQuickly generate Python dataclasses from JSON input using the ``wiz-cli`` tool:\n\n.. code-block:: console\n\n $ echo '{\"myFloat\": \"1.23\", \"Items\": [{\"created\": \"2021-01-01\"}]}' | wiz gs - output.py\n\n.. code-block:: python3\n\n from dataclasses import dataclass\n from datetime import date\n from typing import List, Union\n\n from dataclass_wizard import JSONWizard\n\n @dataclass\n class Data(JSONWizard):\n my_float: Union[float, str]\n items: List['Item']\n\n @dataclass\n class Item:\n created: date\n\n\ud83d\udcd6 Check out the full CLI documentation at wiz-cli_.\n\nJSON Marshalling\n----------------\n\n``JSONSerializable`` (aliased to ``JSONWizard``) is a Mixin_ class which\nprovides the following helper methods that are useful for serializing (and loading)\na dataclass instance to/from JSON, as defined by the ``AbstractJSONWizard``\ninterface.\n\n.. list-table::\n :widths: 10 40 35\n :header-rows: 1\n\n * - Method\n - Example\n - Description\n * - ``from_json``\n - `item = Product.from_json(string)`\n - Converts a JSON string to an instance of the\n dataclass, or a list of the dataclass instances.\n * - ``from_list``\n - `list_of_item = Product.from_list(l)`\n - Converts a Python ``list`` object to a list of the\n dataclass instances.\n * - ``from_dict``\n - `item = Product.from_dict(d)`\n - Converts a Python ``dict`` object to an instance\n of the dataclass.\n * - ``to_dict``\n - `d = item.to_dict()`\n - Converts the dataclass instance to a Python ``dict``\n object that is JSON serializable.\n * - ``to_json``\n - `string = item.to_json()`\n - Converts the dataclass instance to a JSON string\n representation.\n * - ``list_to_json``\n - `string = Product.list_to_json(list_of_item)`\n - Converts a list of dataclass instances to a JSON string\n representation.\n\nAdditionally, it adds a default ``__str__`` method to subclasses, which will\npretty print the JSON representation of an object; this is quite useful for\ndebugging purposes. Whenever you invoke ``print(obj)`` or ``str(obj)``, for\nexample, it'll call this method which will format the dataclass object as\na prettified JSON string. If you prefer a ``__str__`` method to not be\nadded, you can pass in ``str=False`` when extending from the Mixin class\nas mentioned `here <https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/skip_the_str.html>`_.\n\nNote that the ``__repr__`` method, which is implemented by the\n``dataclass`` decorator, is also available. To invoke the Python object\nrepresentation of the dataclass instance, you can instead use\n``repr(obj)`` or ``f'{obj!r}'``.\n\nTo mark a dataclass as being JSON serializable (and\nde-serializable), simply sub-class from ``JSONSerializable`` as shown\nbelow. You can also extend from the aliased name ``JSONWizard``, if you\nprefer to use that instead.\n\nCheck out a `more complete example`_ of using the ``JSONSerializable``\nMixin class.\n\nNo Inheritance Needed\n---------------------\n\nIt is important to note that the main purpose of sub-classing from\n``JSONWizard`` Mixin class is to provide helper methods like ``from_dict``\nand ``to_dict``, which makes it much more convenient and easier to load or\ndump your data class from and to JSON.\n\nThat is, it's meant to *complement* the usage of the ``dataclass`` decorator,\nrather than to serve as a drop-in replacement for data classes, or to provide type\nvalidation for example; there are already excellent libraries like `pydantic`_ that\nprovide these features if so desired.\n\nHowever, there may be use cases where we prefer to do away with the class\ninheritance model introduced by the Mixin class. In the interests of convenience\nand also so that data classes can be used *as is*, the Dataclass\nWizard library provides the helper functions ``fromlist`` and ``fromdict``\nfor de-serialization, and ``asdict`` for serialization. These functions also\nwork recursively, so there is full support for nested dataclasses -- just as with\nthe class inheritance approach.\n\nHere is an example to demonstrate the usage of these helper functions:\n\n.. note::\n As of *v0.18.0*, the Meta config for the main dataclass will cascade down\n and be merged with the Meta config (if specified) of each nested dataclass. To\n disable this behavior, you can pass in ``recursive=False`` to the Meta config.\n\n.. code:: python3\n\n from __future__ import annotations\n\n from dataclasses import dataclass, field\n from datetime import datetime, date\n\n from dataclass_wizard import fromdict, asdict, DumpMeta\n\n\n @dataclass\n class A:\n created_at: datetime\n list_of_b: list[B] = field(default_factory=list)\n\n\n @dataclass\n class B:\n my_status: int | str\n my_date: date | None = None\n\n\n source_dict = {'createdAt': '2010-06-10 15:50:00Z',\n 'List-Of-B': [\n {'MyStatus': '200', 'my_date': '2021-12-31'}\n ]}\n\n # De-serialize the JSON dictionary object into an `A` instance.\n a = fromdict(A, source_dict)\n\n print(repr(a))\n # A(created_at=datetime.datetime(2010, 6, 10, 15, 50, tzinfo=datetime.timezone.utc),\n # list_of_b=[B(my_status='200', my_date=datetime.date(2021, 12, 31))])\n\n # Set an optional dump config for the main dataclass, for example one which\n # converts converts date and datetime objects to a unix timestamp (as an int)\n #\n # Note that `recursive=True` is the default, so this Meta config will be\n # merged with the Meta config (if specified) of each nested dataclass.\n DumpMeta(marshal_date_time_as='TIMESTAMP',\n key_transform='SNAKE',\n # Finally, apply the Meta config to the main dataclass.\n ).bind_to(A)\n\n # Serialize the `A` instance to a Python dict object.\n json_dict = asdict(a)\n\n expected_dict = {'created_at': 1276185000, 'list_of_b': [{'my_status': '200', 'my_date': 1640926800}]}\n\n print(json_dict)\n # Assert that we get the expected dictionary object.\n assert json_dict == expected_dict\n\nCustom Key Mappings\n-------------------\n\nIf you ever find the need to add a `custom mapping`_ of a JSON key to a dataclass\nfield (or vice versa), the helper function ``json_field`` -- which can be\nconsidered an alias to ``dataclasses.field()`` -- is one approach that can\nresolve this.\n\nExample below:\n\n.. code:: python3\n\n from dataclasses import dataclass\n\n from dataclass_wizard import JSONSerializable, json_field\n\n\n @dataclass\n class MyClass(JSONSerializable):\n\n my_str: str = json_field('myString1', all=True)\n\n\n # De-serialize a dictionary object with the newly mapped JSON key.\n d = {'myString1': 'Testing'}\n c = MyClass.from_dict(d)\n\n print(repr(c))\n # prints:\n # MyClass(my_str='Testing')\n\n # Assert we get the same dictionary object when serializing the instance.\n assert c.to_dict() == d\n\nMapping Nested JSON Keys\n------------------------\n\nThe ``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.\n\nYou 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``:\n\n.. code:: python3\n\n from dataclasses import dataclass\n from dataclass_wizard import path_field, JSONWizard\n\n @dataclass\n class MyData(JSONWizard):\n my_str: str = path_field('data.items.myJSONKey', default=\"default_value\")\n\n input_dict = {'data': {'items': {'myJSONKey': 'Some value'}}}\n data_instance = MyData.from_dict(input_dict)\n print(data_instance.my_str) # Output: 'Some value'\n\nCustom Paths for Complex JSON\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nYou 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.\n\nExample with nested and complex keys:\n\n.. code:: python3\n\n from dataclasses import dataclass\n from typing import Annotated\n from dataclass_wizard import JSONWizard, path_field, KeyPath\n\n\n @dataclass\n class NestedData(JSONWizard):\n my_str: str = path_field('data[0].details[\"key with space\"]', default=\"default_value\")\n my_int: Annotated[int, KeyPath('data[0].items[3.14].True')] = 0\n\n\n input_dict = {\n 'data': [\n {\n 'details': {'key with space': 'Another value'},\n 'items': {3.14: {True: \"42\"}}\n }\n ]\n }\n\n # Deserialize JSON to dataclass\n data = NestedData.from_dict(input_dict)\n print(data.my_str) # Output: 'Another value'\n\n # Serialize back to JSON\n output_dict = data.to_dict()\n print(output_dict) # {'data': {0: {'details': {'key with space': 'Another value'}, 'items': {3.14: {True: 42}}}}}\n\n # Verify data consistency\n assert data == NestedData.from_dict(output_dict)\n\n # Handle empty input gracefully\n data = NestedData.from_dict({'data': []})\n print(repr(data)) # NestedData(my_str='default_value', my_int=0)\n\nExtending from ``Meta``\n-----------------------\n\nLooking to change how ``date`` and ``datetime`` objects are serialized to JSON? Or\nprefer that field names appear in *snake case* when a dataclass instance is serialized?\n\nThe inner ``Meta`` class allows easy configuration of such settings, as\nshown below; and as a nice bonus, IDEs should be able to assist with code completion\nalong the way.\n\n.. note::\n As of *v0.18.0*, the Meta config for the main dataclass will cascade down\n and be merged with the Meta config (if specified) of each nested dataclass. To\n disable this behavior, you can pass in ``recursive=False`` to the Meta config.\n\n.. code:: python3\n\n from dataclasses import dataclass\n from datetime import date\n\n from dataclass_wizard import JSONWizard\n from dataclass_wizard.enums import DateTimeTo\n\n\n @dataclass\n class MyClass(JSONWizard):\n\n class _(JSONWizard.Meta):\n marshal_date_time_as = DateTimeTo.TIMESTAMP\n key_transform_with_dump = 'SNAKE'\n\n my_str: str\n my_date: date\n\n\n data = {'my_str': 'test', 'myDATE': '2010-12-30'}\n\n c = MyClass.from_dict(data)\n\n print(repr(c))\n # prints:\n # MyClass(my_str='test', my_date=datetime.date(2010, 12, 30))\n\n string = c.to_json()\n print(string)\n # prints:\n # {\"my_str\": \"test\", \"my_date\": 1293685200}\n\nOther Uses for ``Meta``\n~~~~~~~~~~~~~~~~~~~~~~~\n\nHere are a few additional use cases for the inner ``Meta`` class. Note that\na full list of available settings can be found in the `Meta`_ section in the docs.\n\nDebug Mode\n##########\n\n.. admonition:: **Added in v0.28.0**\n\n There is now `Easier Debug Mode`_.\n\nEnables additional (more verbose) log output. For example, a message can be\nlogged whenever an unknown JSON key is encountered when\n``from_dict`` or ``from_json`` is called.\n\nThis also results in more helpful error messages during the JSON load\n(de-serialization) process, such as when values are an invalid type --\ni.e. they don't match the annotation for the field. This can be particularly\nuseful for debugging purposes.\n\n.. note::\n There is a minor performance impact when DEBUG mode is enabled;\n for that reason, I would personally advise against enabling\n this in a *production* environment.\n\nHandle Unknown JSON Keys\n########################\n\nThe default behavior is to ignore any unknown or extraneous JSON keys that are\nencountered when ``from_dict`` or ``from_json`` is called, and emit a \"warning\"\nwhich is visible when *debug* mode is enabled (and logging is properly configured).\nAn unknown key is one that does not have a known mapping to a dataclass field.\n\nHowever, we can also raise an error in such cases if desired. The below\nexample demonstrates a use case where we want to raise an error when\nan unknown JSON key is encountered in the *load* (de-serialization) process.\n\n.. code:: python3\n\n import logging\n from dataclasses import dataclass\n\n from dataclass_wizard import JSONWizard\n from dataclass_wizard.errors import UnknownJSONKey\n\n # Sets up application logging if we haven't already done so\n logging.basicConfig(level='DEBUG')\n\n\n @dataclass\n class Container(JSONWizard):\n\n class _(JSONWizard.Meta):\n # True to enable Debug mode for additional (more verbose) log output.\n #\n # Pass in a `str` to `int` to set the minimum log level:\n # logging.getLogger('dataclass_wizard').setLevel('INFO')\n debug_enabled = logging.INFO\n # True to raise an class:`UnknownJSONKey` when an unmapped JSON key is\n # encountered when `from_dict` or `from_json` is called. Note that by\n # default, this is also recursively applied to any nested dataclasses.\n raise_on_unknown_json_key = True\n\n element: 'MyElement'\n\n\n @dataclass\n class MyElement:\n my_str: str\n my_float: float\n\n\n d = {\n 'element': {\n 'myStr': 'string',\n 'my_float': '1.23',\n # Notice how this key is not mapped to a known dataclass field!\n 'my_bool': 'Testing'\n }\n }\n\n # Try to de-serialize the dictionary object into a `MyClass` object.\n try:\n c = Container.from_dict(d)\n except UnknownJSONKey as e:\n print('Received error:', type(e).__name__)\n print('Class:', e.class_name)\n print('Unknown JSON key:', e.json_key)\n print('JSON object:', e.obj)\n print('Known Fields:', e.fields)\n else:\n print('Successfully de-serialized the JSON object.')\n print(repr(c))\n\nSee the section on `Handling Unknown JSON Keys`_ for more info.\n\nSave or \"Catch-All\" Unknown JSON Keys\n######################################\n\nWhen calling ``from_dict`` or ``from_json``, any unknown or extraneous JSON keys\nthat are not mapped to fields in the dataclass are typically ignored or raise an error.\nHowever, you can capture these undefined keys in a catch-all field of type ``CatchAll``,\nallowing you to handle them as needed later.\n\nFor example, suppose you have the following dictionary::\n\n dump_dict = {\n \"endpoint\": \"some_api_endpoint\",\n \"data\": {\"foo\": 1, \"bar\": \"2\"},\n \"undefined_field_name\": [1, 2, 3]\n }\n\nYou can save the undefined keys in a catch-all field and process them later.\nSimply define a field of type ``CatchAll`` in your dataclass. This field will act\nas a dictionary to store any unmapped keys and their values. If there are no\nundefined keys, the field will default to an empty dictionary.\n\n.. code:: python\n\n from dataclasses import dataclass\n from typing import Any\n from dataclass_wizard import CatchAll, JSONWizard\n\n @dataclass\n class UnknownAPIDump(JSONWizard):\n endpoint: str\n data: dict[str, Any]\n unknown_things: CatchAll\n\n dump_dict = {\n \"endpoint\": \"some_api_endpoint\",\n \"data\": {\"foo\": 1, \"bar\": \"2\"},\n \"undefined_field_name\": [1, 2, 3]\n }\n\n dump = UnknownAPIDump.from_dict(dump_dict)\n print(f'{dump!r}')\n # > UnknownAPIDump(endpoint='some_api_endpoint', data={'foo': 1, 'bar': '2'},\n # unknown_things={'undefined_field_name': [1, 2, 3]})\n\n print(dump.to_dict())\n # > {'endpoint': 'some_api_endpoint', 'data': {'foo': 1, 'bar': '2'}, 'undefined_field_name': [1, 2, 3]}\n\n.. note::\n - When using a \"catch-all\" field, it is strongly recommended to define exactly **one** field of type ``CatchAll`` in the dataclass.\n\n - ``LetterCase`` transformations do not apply to keys stored in the ``CatchAll`` field; the keys remain as they are provided.\n\n - If you specify a default (or a default factory) for the ``CatchAll`` field, such as\n ``unknown_things: CatchAll = None``, the default value will be used instead of an\n empty dictionary when no undefined parameters are present.\n\n - The ``CatchAll`` functionality is guaranteed only when using ``from_dict`` or ``from_json``.\n Currently, unknown keyword arguments passed to ``__init__`` will not be written to a ``CatchAll`` field.\n\nDate and Time with Custom Patterns\n----------------------------------\n\nAs of *v0.20.0*, date and time strings in a `custom format`_ can be de-serialized\nusing the ``DatePattern``, ``TimePattern``, and ``DateTimePattern`` type annotations,\nrepresenting patterned `date`, `time`, and `datetime` objects respectively.\n\nThis will internally call ``datetime.strptime`` with the format specified in the annotation,\nand also use the ``fromisoformat()`` method in case the date string is in ISO-8601 format.\nAll dates and times will continue to be serialized as ISO format strings by default. For more\ninfo, check out the `Patterned Date and Time`_ section in the docs.\n\nA brief example of the intended usage is shown below:\n\n.. code:: python3\n\n from dataclasses import dataclass\n from datetime import time, datetime\n from typing import Annotated\n\n from dataclass_wizard import fromdict, asdict, DatePattern, TimePattern, Pattern\n\n\n @dataclass\n class MyClass:\n date_field: DatePattern['%m-%Y']\n dt_field: Annotated[datetime, Pattern('%m/%d/%y %H.%M.%S')]\n time_field1: TimePattern['%H:%M']\n time_field2: Annotated[list[time], Pattern('%I:%M %p')]\n\n\n data = {'date_field': '12-2022',\n 'time_field1': '15:20',\n 'dt_field': '1/02/23 02.03.52',\n 'time_field2': ['1:20 PM', '12:30 am']}\n\n class_obj = fromdict(MyClass, data)\n\n # All annotated fields de-serialize as just date, time, or datetime, as shown.\n print(class_obj)\n # MyClass(date_field=datetime.date(2022, 12, 1), dt_field=datetime.datetime(2023, 1, 2, 2, 3, 52),\n # time_field1=datetime.time(15, 20), time_field2=[datetime.time(13, 20), datetime.time(0, 30)])\n\n # All date/time fields are serialized as ISO-8601 format strings by default.\n print(asdict(class_obj))\n # {'dateField': '2022-12-01', 'dtField': '2023-01-02T02:03:52',\n # 'timeField1': '15:20:00', 'timeField2': ['13:20:00', '00:30:00']}\n\n # But, the patterned date/times can still be de-serialized back after\n # serialization. In fact, it'll be faster than parsing the custom patterns!\n assert class_obj == fromdict(MyClass, asdict(class_obj))\n\n\"Recursive\" Dataclasses with Cyclic References\n----------------------------------------------\n\nPrior to version `v0.27.0`, dataclasses with cyclic references\nor self-referential structures were not supported. This\nlimitation is shown in the following toy example:\n\n.. code:: python3\n\n from dataclasses import dataclass\n\n @dataclass\n class A:\n a: 'A | None' = None\n\n a = A(a=A(a=A(a=A())))\n\nThis was a `longstanding issue`_.\n\nNew in ``v0.27.0``: The Dataclass Wizard now extends its support\nto cyclic and self-referential dataclass models.\n\nThe example below demonstrates recursive dataclasses with cyclic\ndependencies, following the pattern ``A -> B -> A -> B``. For more details, see\nthe `Cyclic or \"Recursive\" Dataclasses`_ section in the documentation.\n\n.. code:: python3\n\n from __future__ import annotations # This can be removed in Python 3.10+\n\n from dataclasses import dataclass\n\n from dataclass_wizard import JSONWizard\n\n\n @dataclass\n class A(JSONWizard):\n class _(JSONWizard.Meta):\n # enable support for self-referential / recursive dataclasses\n recursive_classes = True\n\n b: 'B | None' = None\n\n\n @dataclass\n class B:\n a: A | None = None\n\n\n # confirm that `from_dict` with a recursive, self-referential\n # input `dict` works as expected.\n a = A.from_dict({'b': {'a': {'b': {'a': None}}}})\n\n assert a == A(b=B(a=A(b=B())))\n\nDataclasses in ``Union`` Types\n------------------------------\n\nThe ``dataclass-wizard`` library fully supports declaring dataclass models in\n`Union`_ types as field annotations, such as ``list[Wizard | Archer | Barbarian]``.\n\nAs of *v0.19.0*, there is added support to *auto-generate* tags for a dataclass model\n-- based on the class name -- as well as to specify a custom *tag key* that will be\npresent in the JSON object, which defaults to a special ``__tag__`` key otherwise.\nThese two options are controlled by the ``auto_assign_tags`` and ``tag_key``\nattributes (respectively) in the ``Meta`` config.\n\nTo illustrate a specific example, a JSON object such as\n``{\"oneOf\": {\"type\": \"A\", ...}, ...}`` will now automatically map to a dataclass\ninstance ``A``, provided that the ``tag_key`` is correctly set to \"type\", and\nthe field ``one_of`` is annotated as a Union type in the ``A | B`` syntax.\n\nLet's start out with an example, which aims to demonstrate the simplest usage of\ndataclasses in ``Union`` types. For more info, check out the\n`Dataclasses in Union Types`_ section in the docs.\n\n.. code:: python3\n\n from __future__ import annotations\n\n from dataclasses import dataclass\n\n from dataclass_wizard import JSONWizard\n\n\n @dataclass\n class Container(JSONWizard):\n\n class Meta(JSONWizard.Meta):\n tag_key = 'type'\n auto_assign_tags = True\n\n objects: list[A | B | C]\n\n\n @dataclass\n class A:\n my_int: int\n my_bool: bool = False\n\n\n @dataclass\n class B:\n my_int: int\n my_bool: bool = True\n\n\n @dataclass\n class C:\n my_str: str\n\n\n data = {\n 'objects': [\n {'type': 'A', 'my_int': 42},\n {'type': 'C', 'my_str': 'hello world'},\n {'type': 'B', 'my_int': 123},\n {'type': 'A', 'my_int': 321, 'myBool': True}\n ]\n }\n\n\n c = Container.from_dict(data)\n print(f'{c!r}')\n\n # True\n assert c == Container(objects=[A(my_int=42, my_bool=False),\n C(my_str='hello world'),\n B(my_int=123, my_bool=True),\n A(my_int=321, my_bool=True)])\n\n\n print(c.to_dict())\n # prints the following on a single line:\n # {'objects': [{'myInt': 42, 'myBool': False, 'type': 'A'},\n # {'myStr': 'hello world', 'type': 'C'},\n # {'myInt': 123, 'myBool': True, 'type': 'B'},\n # {'myInt': 321, 'myBool': True, 'type': 'A'}]}\n\n # True\n assert c == c.from_json(c.to_json())\n\nConditional Field Skipping\n--------------------------\n\n.. admonition:: **Added in v0.30.0**\n\n Dataclass Wizard introduces `conditional skipping`_ to omit fields during JSON serialization based on user-defined conditions. This feature works seamlessly with:\n\n - **Global rules** via ``Meta`` settings.\n - **Per-field controls** using ``SkipIf()`` `annotations`_.\n - **Field wrappers** for maximum flexibility.\n\nQuick Examples\n~~~~~~~~~~~~~~\n\n1. **Globally Skip Fields Matching a Condition**\n\n Define a global skip rule using ``Meta.skip_if``:\n\n .. code-block:: python3\n\n from dataclasses import dataclass\n from dataclass_wizard import JSONWizard, IS_NOT\n\n\n @dataclass\n class Example(JSONWizard):\n class _(JSONWizard.Meta):\n skip_if = IS_NOT(True) # Skip fields if the value is not `True`\n\n my_bool: bool\n my_str: 'str | None'\n\n\n print(Example(my_bool=True, my_str=None).to_dict())\n # Output: {'myBool': True}\n\n2. **Skip Defaults Based on a Condition**\n\n Skip fields with default values matching a specific condition using ``Meta.skip_defaults_if``:\n\n .. code-block:: python3\n\n from __future__ import annotations # Can remove in PY 3.10+\n\n from dataclasses import dataclass\n from dataclass_wizard import JSONPyWizard, IS\n\n\n @dataclass\n class Example(JSONPyWizard):\n class _(JSONPyWizard.Meta):\n skip_defaults_if = IS(None) # Skip default `None` values.\n\n str_with_no_default: str | None\n my_str: str | None = None\n my_bool: bool = False\n\n\n print(Example(str_with_no_default=None, my_str=None).to_dict())\n #> {'str_with_no_default': None, 'my_bool': False}\n\n\n .. note::\n Setting ``skip_defaults_if`` also enables ``skip_defaults=True`` automatically.\n\n3. **Per-Field Conditional Skipping**\n\n Apply skip rules to specific fields with `annotations`_ or ``skip_if_field``:\n\n .. code-block:: python3\n\n from __future__ import annotations # can be removed in Python 3.10+\n\n from dataclasses import dataclass\n from typing import Annotated\n\n from dataclass_wizard import JSONWizard, SkipIfNone, skip_if_field, EQ\n\n\n @dataclass\n class Example(JSONWizard):\n my_str: Annotated[str | None, SkipIfNone] # Skip if `None`.\n other_str: str | None = skip_if_field(EQ(''), default=None) # Skip if empty.\n\n print(Example(my_str=None, other_str='').to_dict())\n # Output: {}\n\n4. **Skip Fields Based on Truthy or Falsy Values**\n\n Use the ``IS_TRUTHY`` and ``IS_FALSY`` helpers to conditionally skip fields based on their truthiness:\n\n .. code-block:: python3\n\n from dataclasses import dataclass, field\n from dataclass_wizard import JSONWizard, IS_FALSY\n\n\n @dataclass\n class ExampleWithFalsy(JSONWizard):\n class _(JSONWizard.Meta):\n skip_if = IS_FALSY() # Skip fields if they evaluate as \"falsy\".\n\n my_bool: bool\n my_list: list = field(default_factory=list)\n my_none: None = None\n\n print(ExampleWithFalsy(my_bool=False, my_list=[], my_none=None).to_dict())\n #> {}\n\n.. note::\n\n *Special Cases*\n\n - **SkipIfNone**: Alias for ``SkipIf(IS(None))``, skips fields with a value of ``None``.\n - **Condition Helpers**:\n\n - ``IS``, ``IS_NOT``: Identity checks.\n - ``EQ``, ``NE``, ``LT``, ``LE``, ``GT``, ``GE``: Comparison operators.\n - ``IS_TRUTHY``, ``IS_FALSY``: Skip fields based on truthy or falsy values.\n\n Combine these helpers for flexible serialization rules!\n\n.. _conditional skipping: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/serialization_options.html#skip-if-functionality\n\nSerialization Options\n---------------------\n\nThe following parameters can be used to fine-tune and control how the serialization of a\ndataclass instance to a Python ``dict`` object or JSON string is handled.\n\nSkip Defaults\n~~~~~~~~~~~~~\n\nA common use case is skipping fields with default values - based on the ``default``\nor ``default_factory`` argument to ``dataclasses.field`` - in the serialization\nprocess.\n\nThe attribute ``skip_defaults`` in the inner ``Meta`` class can be enabled, to exclude\nsuch field values from serialization.The ``to_dict`` method (or the ``asdict`` helper\nfunction) can also be passed an ``skip_defaults`` argument, which should have the same\nresult. An example of both these approaches is shown below.\n\n.. code:: python3\n\n from collections import defaultdict\n from dataclasses import field, dataclass\n\n from dataclass_wizard import JSONWizard\n\n\n @dataclass\n class MyClass(JSONWizard):\n\n class _(JSONWizard.Meta):\n skip_defaults = True\n\n my_str: str\n other_str: str = 'any value'\n optional_str: str = None\n my_list: list[str] = field(default_factory=list)\n my_dict: defaultdict[str, list[float]] = field(\n default_factory=lambda: defaultdict(list))\n\n\n print('-- Load (Deserialize)')\n c = MyClass('abc')\n print(f'Instance: {c!r}')\n\n print('-- Dump (Serialize)')\n string = c.to_json()\n print(string)\n\n assert string == '{\"myStr\": \"abc\"}'\n\n print('-- Dump (with `skip_defaults=False`)')\n print(c.to_dict(skip_defaults=False))\n\nExclude Fields\n~~~~~~~~~~~~~~\n\nYou can also exclude specific dataclass fields (and their values) from the serialization\nprocess. There are two approaches that can be used for this purpose:\n\n* The argument ``dump=False`` can be passed in to the ``json_key`` and ``json_field``\n helper functions. Note that this is a more permanent option, as opposed to the one\n below.\n\n* The ``to_dict`` method (or the ``asdict`` helper function ) can be passed\n an ``exclude`` argument, containing a list of one or more dataclass field names\n to exclude from the serialization process.\n\nAdditionally, here is an example to demonstrate usage of both these approaches:\n\n.. code:: python3\n\n from dataclasses import dataclass\n from typing import Annotated\n\n from dataclass_wizard import JSONWizard, json_key, json_field\n\n\n @dataclass\n class MyClass(JSONWizard):\n\n my_str: str\n my_int: int\n other_str: Annotated[str, json_key('AnotherStr', dump=False)]\n my_bool: bool = json_field('TestBool', dump=False)\n\n\n data = {'MyStr': 'my string',\n 'myInt': 1,\n 'AnotherStr': 'testing 123',\n 'TestBool': True}\n\n print('-- From Dict')\n c = MyClass.from_dict(data)\n print(f'Instance: {c!r}')\n\n # dynamically exclude the `my_int` field from serialization\n additional_exclude = ('my_int',)\n\n print('-- To Dict')\n out_dict = c.to_dict(exclude=additional_exclude)\n print(out_dict)\n\n assert out_dict == {'myStr': 'my string'}\n\n``Environ`` Magic\n-----------------\n\nEasily map environment variables to Python dataclasses with ``EnvWizard``:\n\n.. code-block:: python3\n\n import os\n from dataclass_wizard import EnvWizard\n\n # Set up environment variables\n os.environ.update({\n 'APP_NAME': 'Env Wizard',\n 'MAX_CONNECTIONS': '10',\n 'DEBUG_MODE': 'true'\n })\n\n # Define dataclass using EnvWizard\n class AppConfig(EnvWizard):\n app_name: str\n max_connections: int\n debug_mode: bool\n\n # Load config from environment variables\n config = AppConfig()\n print(config.app_name) #> Env Wizard\n print(config.debug_mode) #> True\n assert config.max_connections == 10\n\n # Override with keyword arguments\n config = AppConfig(app_name='Dataclass Wizard Rocks!', debug_mode='false')\n print(config.app_name) #> Dataclass Wizard Rocks!\n assert config.debug_mode is False\n\n.. note::\n ``EnvWizard`` simplifies environment variable mapping with type validation, ``.env`` file support, and secret file handling (file names become keys).\n\n *Key Features*:\n\n - **Auto Parsing**: Supports complex types and nested structures.\n - **Configurable**: Customize variable names, prefixes, and dotenv files.\n - **Validation**: Errors for missing or malformed variables.\n\n \ud83d\udcd6 `Full Documentation <https://dataclass-wizard.readthedocs.io/en/latest/env_magic.html>`_\n\nAdvanced Example: Dynamic Prefix Handling\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n``EnvWizard`` supports dynamic prefix application, ideal for customizable environments:\n\n.. code-block:: python3\n\n import os\n from dataclass_wizard import EnvWizard, env_field\n\n # Define dataclass with custom prefix support\n class AppConfig(EnvWizard):\n\n class _(EnvWizard.Meta):\n env_prefix = 'APP_' # Default prefix for env vars\n\n name: str = env_field('A_NAME') # Looks for `APP_A_NAME` by default\n debug: bool\n\n # Set environment variables\n os.environ['CUSTOM_A_NAME'] = 'Test!'\n os.environ['CUSTOM_DEBUG'] = 'yes'\n\n # Apply a dynamic prefix at runtime\n config = AppConfig(_env_prefix='CUSTOM_') # Looks for `CUSTOM_A_NAME` and `CUSTOM_DEBUG`\n\n print(config)\n # > AppConfig(name='Test!', debug=True)\n\nField Properties\n----------------\n\nThe Python ``dataclasses`` library has some `key limitations`_\nwith how it currently handles properties and default values.\n\nThe ``dataclass-wizard`` package natively provides support for using\nfield properties with default values in dataclasses. The main use case\nhere is to assign an initial value to the field property, if one is not\nexplicitly passed in via the constructor method.\n\nTo use it, simply import\nthe ``property_wizard`` helper function, and add it as a metaclass on\nany dataclass where you would benefit from using field properties with\ndefault values. The metaclass also pairs well with the ``JSONSerializable``\nmixin class.\n\nFor more examples and important how-to's on properties with default values,\nrefer to the `Using Field Properties`_ section in the documentation.\n\nWhat's New in v1.0\n------------------\n\n.. warning::\n\n - **Default Key Transformation Update**\n\n Starting with ``v1.0.0``, the default key transformation for JSON serialization\n will change to keep keys *as-is* instead of converting them to `camelCase`.\n\n *New Default Behavior*: ``key_transform='NONE'`` will be the standard setting.\n\n *How to Prepare*: You can enforce this future behavior right now by using the ``JSONPyWizard`` helper:\n\n .. code-block:: python3\n\n from dataclasses import dataclass\n from dataclass_wizard import JSONPyWizard\n\n @dataclass\n class MyModel(JSONPyWizard):\n my_field: str\n\n print(MyModel(my_field=\"value\").to_dict())\n # Output: {'my_field': 'value'}\n\n\n - **Float to Int Conversion Change**\n\n Starting in ``v1.0``, floats or float strings with fractional\n parts (e.g., ``123.4`` or ``\"123.4\"``) will no longer be silently\n converted to integers. Instead, they will raise an error.\n However, floats with no fractional parts (e.g., ``3.0``\n or ``\"3.0\"``) will still convert to integers as before.\n\n *How to Prepare*: To ensure compatibility with the new behavior:\n\n - Use ``float`` annotations for fields that may include fractional values.\n - Review your data and avoid passing fractional values (e.g., ``123.4``) to fields annotated as ``int``.\n - Update tests or logic that rely on the current rounding behavior.\n\nContributing\n------------\n\nContributions are welcome! Open a pull request to fix a bug, or `open an issue`_\nto discuss a new feature or change.\n\nCheck out the `Contributing`_ section in the docs for more info.\n\nTODOs\n-----\n\nAll feature ideas or suggestions for future consideration, have been currently added\n`as milestones`_ in the project's GitHub repo.\n\nCredits\n-------\n\nThis package was created with Cookiecutter_ and the `rnag/cookiecutter-pypackage`_ project template.\n\n.. _Read The Docs: https://dataclass-wizard.readthedocs.io\n.. _Installation: https://dataclass-wizard.readthedocs.io/en/latest/installation.html\n.. _Cookiecutter: https://github.com/cookiecutter/cookiecutter\n.. _`rnag/cookiecutter-pypackage`: https://github.com/rnag/cookiecutter-pypackage\n.. _`Contributing`: https://dataclass-wizard.readthedocs.io/en/latest/contributing.html\n.. _`open an issue`: https://github.com/rnag/dataclass-wizard/issues\n.. _`JSONPyWizard`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/wizard_mixins.html#jsonpywizard\n.. _`EnvWizard`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/wizard_mixins.html#envwizard\n.. _`on EnvWizard`: https://dataclass-wizard.readthedocs.io/en/latest/env_magic.html\n.. _`JSONListWizard`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/wizard_mixins.html#jsonlistwizard\n.. _`JSONFileWizard`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/wizard_mixins.html#jsonfilewizard\n.. _`TOMLWizard`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/wizard_mixins.html#tomlwizard\n.. _`YAMLWizard`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/wizard_mixins.html#yamlwizard\n.. _`Container`: https://dataclass-wizard.readthedocs.io/en/latest/dataclass_wizard.html#dataclass_wizard.Container\n.. _`Supported Types`: https://dataclass-wizard.readthedocs.io/en/latest/overview.html#supported-types\n.. _`Mixin`: https://stackoverflow.com/a/547714/10237506\n.. _`Meta`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/meta.html\n.. _`pydantic`: https://pydantic-docs.helpmanual.io/\n.. _`Using Field Properties`: https://dataclass-wizard.readthedocs.io/en/latest/using_field_properties.html\n.. _`field properties`: https://dataclass-wizard.readthedocs.io/en/latest/using_field_properties.html\n.. _`custom mapping`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/custom_key_mappings.html\n.. _`wiz-cli`: https://dataclass-wizard.readthedocs.io/en/latest/wiz_cli.html\n.. _`key limitations`: https://florimond.dev/en/posts/2018/10/reconciling-dataclasses-and-properties-in-python/\n.. _`more complete example`: https://dataclass-wizard.readthedocs.io/en/latest/examples.html#a-more-complete-example\n.. _custom format: https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes\n.. _`Patterned Date and Time`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/patterned_date_time.html\n.. _Union: https://docs.python.org/3/library/typing.html#typing.Union\n.. _`Dataclasses in Union Types`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/dataclasses_in_union_types.html\n.. _`Cyclic or \"Recursive\" Dataclasses`: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/cyclic_or_recursive_dataclasses.html\n.. _as milestones: https://github.com/rnag/dataclass-wizard/milestones\n.. _longstanding issue: https://github.com/rnag/dataclass-wizard/issues/62\n.. _Easier Debug Mode: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/easier_debug_mode.html\n.. _Handling Unknown JSON Keys: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/handling_unknown_json_keys.html\n.. _custom paths to access nested keys: https://dataclass-wizard.readthedocs.io/en/latest/common_use_cases/nested_key_paths.html\n.. _annotations: https://docs.python.org/3/library/typing.html#typing.Annotated\n.. _typing: https://docs.python.org/3/library/typing.html\n.. _dataclasses: https://docs.python.org/3/library/dataclasses.html\n",
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