# `dataclasses` — 데이터 클래스

소스 코드: Lib/dataclasses.py

This module provides a decorator and functions for automatically adding generated special methods such as `__init__()` and `__repr__()` to user-defined classes. It was originally described in PEP 557.

생성된 메서드에서 사용할 멤버 변수는 PEP 526 형 어노테이션을 사용하여 정의됩니다. 예를 들어, 이 코드는:

    from dataclasses import dataclass

    @dataclass
    class InventoryItem:
        """Class for keeping track of an item in inventory."""
        name: str
        unit_price: float
        quantity_on_hand: int = 0

        def total_cost(self) -> float:
            return self.unit_price * self.quantity_on_hand

다른 것 중에서도, 다음과 같은 `__init__()` 를 추가합니다:

    def __init__(self, name: str, unit_price: float, quantity_on_hand: int = 0):
        self.name = name
        self.unit_price = unit_price
        self.quantity_on_hand = quantity_on_hand

Note that this method is automatically added to the class: it is not directly specified in the `InventoryItem` definition shown above.

버전 3.7에 추가.

## Module contents

@dataclasses.dataclass(*, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False, match_args=True, kw_only=False, slots=False, weakref_slot=False)

  이 함수는 (아래에서 설명하는) 생성된 특수 메서드를 클래스에 추가하는데 사용되는 데코레이터 입니다.

  `@dataclass` 데코레이터는 클래스를 검사하여 필드를 찾습니다. 필드는 형 어노테이션을 가진 클래스 변수로 정의됩니다. 아래에 설명된 두 가지 예외를 제외하고는, `@dataclass` 는 변수 어노테이션에 지정된 형을 검사하지 않습니다.

  생성된 모든 메서드의 필드 순서는 클래스 정의에 나타나는 순서입니다.

  `@dataclass` 데코레이터는 여러 “던더(dunder)” 메서드들을 클래스에 추가하는데, 아래에서 설명합니다. 추가할 메서드가 클래스에 이미 존재하면, 동작은 아래에 설명된 대로 매개변수에 따라 다릅니다. 데코레이터는 호출된 클래스와 같은 클래스를 반환합니다; 새 클래스가 만들어지지 않습니다.

  `@dataclass` 가 매개변수 없는 단순한 데코레이터로 사용되면, 이 서명에 문서화 된 기본값들이 제공된 것처럼 행동합니다. 즉, 다음 `@dataclass` 의 세 가지 용법은 동등합니다:

      @dataclass
      class C:
          ...

      @dataclass()
      class C:
          ...

      @dataclass(init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False,
                 match_args=True, kw_only=False, slots=False, weakref_slot=False)
      class C:
          ...

  `@dataclass` 의 매개변수는 다음과 같습니다:

  - `init`: If true (the default), a `__init__()` method will be generated.

    클래스가 이미 `__init__()` 를 정의했으면, 이 매개변수는 무시됩니다.

  - `repr`: If true (the default), a `__repr__()` method will be generated. The generated repr string will have the class name and the name and repr of each field, in the order they are defined in the class. Fields that are marked as being excluded from the repr are not included. For example: `InventoryItem(name='widget', unit_price=3.0, quantity_on_hand=10)`.

    클래스가 이미 `__repr__()` 을 정의했으면, 이 매개변수는 무시됩니다.

  - `eq`: If true (the default), an `__eq__()` method will be generated. This method compares the class as if it were a tuple of its fields, in order. Both instances in the comparison must be of the identical type.

    클래스가 이미 `__eq__()` 를 정의했으면, 이 매개변수는 무시됩니다.

  - `order`: If true (the default is `False`), `__lt__()`, `__le__()`, `__gt__()`, and `__ge__()` methods will be generated. These compare the class as if it were a tuple of its fields, in order. Both instances in the comparison must be of the identical type. If `order` is true and `eq` is false, a `ValueError` is raised.

    클래스가 이미 `__lt__()`, `__le__()`, `__gt__()`, `__ge__()` 중 하나를 정의하고 있다면 `TypeError` 가 발생합니다.

  - `unsafe_hash`: If `False` (the default), a `__hash__()` method is generated according to how `eq` and `frozen` are set.

    `__hash__()` is used by built-in `hash()`, and when objects are added to hashed collections such as dictionaries and sets. Having a `__hash__()` implies that instances of the class are immutable. Mutability is a complicated property that depends on the programmer’s intent, the existence and behavior of `__eq__()`, and the values of the `eq` and `frozen` flags in the `@dataclass` decorator.

    기본적으로, `@dataclass` 는 안전하지 않다면 `__hash__()` 메서드를 묵시적으로 추가하지 않습니다. 기존에 명시적으로 정의된 `__hash__()` 메서드를 추가하거나 변경하지도 않습니다. `__hash__()` 문서에서 설명된 대로, 클래스 어트리뷰트를 `__hash__ = None` 로 설정하는 것은 파이썬에 특별한 의미가 있습니다.

    `__hash__()` 가 명시적으로 정의되어 있지 않거나 `None` 으로 설정된 경우, `@dataclass` 는 묵시적 `__hash__()` 메서드를 추가할 수 있습니다. 권장하지는 않지만, `unsafe_hash=True` 로 `@dataclass` 가 `__hash__()` 메서드를 만들도록 강제할 수 있습니다. 이것은 당신의 클래스가 논리적으로 불변이지만, 여전히 변경될 수 있는 경우 일 수 있습니다. 이는 특수한 사용 사례이므로 신중하게 고려해야 합니다.

    다음은 `__hash__()` 메서드의 묵시적 생성을 관장하는 규칙입니다. 데이터 클래스에 명시적 `__hash__()` 메서드를 가지면서 `unsafe_hash=True` 를 설정할 수는 없습니다; 그러면 `TypeError` 가 발생합니다.

    If `eq` and `frozen` are both true, by default `@dataclass` will generate a `__hash__()` method for you. If `eq` is true and `frozen` is false, `__hash__()` will be set to `None`, marking it unhashable (which it is, since it is mutable). If `eq` is false, `__hash__()` will be left untouched meaning the `__hash__()` method of the superclass will be used (if the superclass is `object`, this means it will fall back to id-based hashing).

  - `frozen`: If true (the default is `False`), assigning to fields will generate an exception. This emulates read-only frozen instances. If `__setattr__()` or `__delattr__()` is defined in the class, then `TypeError` is raised. See the discussion below.

  - `match_args`: If true (the default is `True`), the `__match_args__` tuple will be created from the list of parameters to the generated `__init__()` method (even if `__init__()` is not generated, see above). If false, or if `__match_args__` is already defined in the class, then `__match_args__` will not be generated.

  > 버전 3.10에 추가.

  - `kw_only`: If true (the default value is `False`), then all fields will be marked as keyword-only. If a field is marked as keyword-only, then the only effect is that the `__init__()` parameter generated from a keyword-only field must be specified with a keyword when `__init__()` is called. There is no effect on any other aspect of dataclasses. See the parameter glossary entry for details. Also see the `KW_ONLY` section.

  > 버전 3.10에 추가.

  - `slots`: If true (the default is `False`), `__slots__` attribute will be generated and new class will be returned instead of the original one. If `__slots__` is already defined in the class, then `TypeError` is raised.

  > 버전 3.10에 추가.

  > 버전 3.11에서 변경: If a field name is already included in the `__slots__` of a base class, it will not be included in the generated `__slots__` to prevent overriding them. Therefore, do not use `__slots__` to retrieve the field names of a dataclass. Use `fields()` instead. To be able to determine inherited slots, base class `__slots__` may be any iterable, but not an iterator.

  - `weakref_slot`: If true (the default is `False`), add a slot named “__weakref__”, which is required to make an instance weakref-able. It is an error to specify `weakref_slot=True` without also specifying `slots=True`.

  > 버전 3.11에 추가.

  필드는 선택적으로 일반적인 파이썬 문법을 사용하여 기본값을 지정할 수 있습니다:

      @dataclass
      class C:
          a: int       # 'a' has no default value
          b: int = 0   # assign a default value for 'b'

  In this example, both `a` and `b` will be included in the added `__init__()` method, which will be defined as:

      def __init__(self, a: int, b: int = 0):

  기본값이 없는 필드가 기본값이 있는 필드 뒤에 오는 경우 `TypeError` 가 발생합니다. 이것은 단일 클래스 내에서 발생하든, 클래스 상속의 결과로 발생하든 마찬가지입니다.

dataclasses.field(*, default=MISSING, default_factory=MISSING, init=True, repr=True, hash=None, compare=True, metadata=None, kw_only=MISSING)

  일반적이고 간단한 사용 사례의 경우 다른 기능은 필요하지 않습니다. 그러나 필드별로 추가 정보가 필요한 일부 데이터 클래스 기능이 있습니다. 추가 정보에 대한 필요성을 충족시키기 위해, 기본 필드 값을 제공된 `field()` 함수 호출로 바꿀 수 있습니다. 예를 들면:

      @dataclass
      class C:
          mylist: list[int] = field(default_factory=list)

      c = C()
      c.mylist += [1, 2, 3]

  위에서 보인 것처럼, `MISSING` 값은 사용자가 일부 매개변수를 제공했는지를 탐지하는데 사용되는 표지 객체입니다. 일부 매개변수에 대해서는 `None` 이 별개의 의미를 갖는 유효한 값이기 때문에 이 표지가 사용됩니다. 어떤 코드도 `MISSING` 값을 직접 사용해서는 안 됩니다.

  `field()` 의 매개변수는 다음과 같습니다:

  - `default`: If provided, this will be the default value for this field. This is needed because the `field()` call itself replaces the normal position of the default value.

  - `default_factory`: If provided, it must be a zero-argument callable that will be called when a default value is needed for this field. Among other purposes, this can be used to specify fields with mutable default values, as discussed below. It is an error to specify both `default` and `default_factory`.

  - `init`: If true (the default), this field is included as a parameter to the generated `__init__()` method.

  - `repr`: If true (the default), this field is included in the string returned by the generated `__repr__()` method.

  - `hash`: This can be a bool or `None`. If true, this field is included in the generated `__hash__()` method. If `None` (the default), use the value of `compare`: this would normally be the expected behavior. A field should be considered in the hash if it’s used for comparisons. Setting this value to anything other than `None` is discouraged.

    `hash=False` 이지만 `compare=True` 로 설정하는 한 가지 가능한 이유는, 동등 비교에 포함되는 필드가 해시값을 계산하는 데 비용이 많이 들고, 형의 해시값에 이바지하는 다른 필드가 있는 경우입니다. 필드가 해시에서 제외된 경우에도 비교에는 계속 사용됩니다.

  - `compare`: If true (the default), this field is included in the generated equality and comparison methods (`__eq__()`, `__gt__()`, et al.).

  - `metadata`: This can be a mapping or None. None is treated as an empty dict. This value is wrapped in `MappingProxyType()` to make it read-only, and exposed on the `Field` object. It is not used at all by Data Classes, and is provided as a third-party extension mechanism. Multiple third-parties can each have their own key, to use as a namespace in the metadata.

  - `kw_only`: If true, this field will be marked as keyword-only. This is used when the generated `__init__()` method’s parameters are computed.

  > 버전 3.10에 추가.

  If the default value of a field is specified by a call to `field()`, then the class attribute for this field will be replaced by the specified `default` value. If no `default` is provided, then the class attribute will be deleted. The intent is that after the `@dataclass` decorator runs, the class attributes will all contain the default values for the fields, just as if the default value itself were specified. For example, after:

      @dataclass
      class C:
          x: int
          y: int = field(repr=False)
          z: int = field(repr=False, default=10)
          t: int = 20

  The class attribute `C.z` will be `10`, the class attribute `C.t` will be `20`, and the class attributes `C.x` and `C.y` will not be set.

class dataclasses.Field

  `Field` 객체는 정의된 각 필드를 설명합니다. 이 객체는 내부적으로 생성되며 `fields()` 모듈 수준 메서드(아래 참조)가 돌려줍니다. 사용자는 직접 `Field` 인스턴스 객체를 만들어서는 안 됩니다. 문서화 된 어트리뷰트는 다음과 같습니다:

  - `name`: The name of the field.

  - `type`: The type of the field.

  - `default`, `default_factory`, `init`, `repr`, `hash`, `compare`, `metadata`, and `kw_only` have the identical meaning and values as they do in the `field()` function.

  다른 어트리뷰트도 있을 수 있지만, 내부적인 것이므로 검사하거나 의존해서는 안 됩니다.

dataclasses.fields(class_or_instance)

  데이터 클래스의 필드들을 정의하는 `Field` 객체들의 튜플을 돌려줍니다. 데이터 클래스나 데이터 클래스의 인스턴스를 받아들입니다. 데이터 클래스 나 데이터 클래스의 인스턴스를 전달하지 않으면 `TypeError` 를 돌려줍니다. `ClassVar` 또는 `InitVar` 인 의사 필드는 반환하지 않습니다.

dataclasses.asdict(obj, *, dict_factory=dict)

  Converts the dataclass `obj` to a dict (by using the factory function `dict_factory`). Each dataclass is converted to a dict of its fields, as `name: value` pairs. dataclasses, dicts, lists, and tuples are recursed into. Other objects are copied with `copy.deepcopy()`.

  Example of using `asdict()` on nested dataclasses:

      @dataclass
      class Point:
           x: int
           y: int

      @dataclass
      class C:
           mylist: list[Point]

      p = Point(10, 20)
      assert asdict(p) == {'x': 10, 'y': 20}

      c = C([Point(0, 0), Point(10, 4)])
      assert asdict(c) == {'mylist': [{'x': 0, 'y': 0}, {'x': 10, 'y': 4}]}

  To create a shallow copy, the following workaround may be used:

      dict((field.name, getattr(obj, field.name)) for field in fields(obj))

  `asdict()` raises `TypeError` if `obj` is not a dataclass instance.

dataclasses.astuple(obj, *, tuple_factory=tuple)

  Converts the dataclass `obj` to a tuple (by using the factory function `tuple_factory`). Each dataclass is converted to a tuple of its field values. dataclasses, dicts, lists, and tuples are recursed into. Other objects are copied with `copy.deepcopy()`.

  이전 예에서 계속하면:

      assert astuple(p) == (10, 20)
      assert astuple(c) == ([(0, 0), (10, 4)],)

  To create a shallow copy, the following workaround may be used:

      tuple(getattr(obj, field.name) for field in dataclasses.fields(obj))

  `astuple()` raises `TypeError` if `obj` is not a dataclass instance.

dataclasses.make_dataclass(cls_name, fields, *, bases=(), namespace=None, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False, match_args=True, kw_only=False, slots=False, weakref_slot=False)

  Creates a new dataclass with name `cls_name`, fields as defined in `fields`, base classes as given in `bases`, and initialized with a namespace as given in `namespace`. `fields` is an iterable whose elements are each either `name`, `(name, type)`, or `(name, type, Field)`. If just `name` is supplied, `typing.Any` is used for `type`. The values of `init`, `repr`, `eq`, `order`, `unsafe_hash`, `frozen`, `match_args`, `kw_only`, `slots`, and `weakref_slot` have the same meaning as they do in `@dataclass`.

  This function is not strictly required, because any Python mechanism for creating a new class with `__annotations__` can then apply the `@dataclass` function to convert that class to a dataclass. This function is provided as a convenience. For example:

      C = make_dataclass('C',
                         [('x', int),
                           'y',
                          ('z', int, field(default=5))],
                         namespace={'add_one': lambda self: self.x + 1})

  는 다음과 동등합니다:

      @dataclass
      class C:
          x: int
          y: 'typing.Any'
          z: int = 5

          def add_one(self):
              return self.x + 1

dataclasses.replace(obj, /, **changes)

  Creates a new object of the same type as `obj`, replacing fields with values from `changes`. If `obj` is not a Data Class, raises `TypeError`. If values in `changes` do not specify fields, raises `TypeError`.

  The newly returned object is created by calling the `__init__()` method of the dataclass. This ensures that __post_init__, if present, is also called.

  기본값을 가지지 않는 초기화 전용 변수가 존재한다면, `replace()` 호출에 반드시 지정해서 `__init__()` 와 `__post_init__()` 에 전달 될 수 있도록 해야 합니다.

  It is an error for `changes` to contain any fields that are defined as having `init=False`. A `ValueError` will be raised in this case.

  Be forewarned about how `init=False` fields work during a call to `replace()`. They are not copied from the source object, but rather are initialized in `__post_init__()`, if they’re initialized at all. It is expected that `init=False` fields will be rarely and judiciously used. If they are used, it might be wise to have alternate class constructors, or perhaps a custom `replace()` (or similarly named) method which handles instance copying.

dataclasses.is_dataclass(obj)

  Return `True` if its parameter is a dataclass or an instance of one, otherwise return `False`.

  (데이터 클래스 자체가 아니라) 데이터 클래스의 인스턴스인지 알아야 한다면 `not isinstance(obj, type)` 검사를 추가하십시오:

      def is_dataclass_instance(obj):
          return is_dataclass(obj) and not isinstance(obj, type)

dataclasses.MISSING

  A sentinel value signifying a missing default or default_factory.

dataclasses.KW_ONLY

  A sentinel value used as a type annotation. Any fields after a pseudo-field with the type of `KW_ONLY` are marked as keyword-only fields. Note that a pseudo-field of type `KW_ONLY` is otherwise completely ignored. This includes the name of such a field. By convention, a name of `_` is used for a `KW_ONLY` field. Keyword-only fields signify `__init__()` parameters that must be specified as keywords when the class is instantiated.

  In this example, the fields `y` and `z` will be marked as keyword-only fields:

      @dataclass
      class Point:
          x: float
          _: KW_ONLY
          y: float
          z: float

      p = Point(0, y=1.5, z=2.0)

  In a single dataclass, it is an error to specify more than one field whose type is `KW_ONLY`.

  버전 3.10에 추가.

exception dataclasses.FrozenInstanceError

  `frozen=True` 로 정의된 데이터 클래스에서 묵시적으로 정의된 `__setattr__()` 또는 `__delattr__()` 이 호출 될 때 발생합니다. `AttributeError`의 서브 클래스입니다.

## 초기화 후처리

The generated `__init__()` code will call a method named `__post_init__()`, if `__post_init__()` is defined on the class. It will normally be called as `self.__post_init__()`. However, if any `InitVar` fields are defined, they will also be passed to `__post_init__()` in the order they were defined in the class. If no `__init__()` method is generated, then `__post_init__()` will not automatically be called.

> When defined on the class, it will be called by the generated `__init__()`, normally as `self.__post_init__()`. However, if any `InitVar` fields are defined, they will also be passed to `__post_init__()` in the order they were defined in the class. If no `__init__()` method is generated, then `__post_init__()` will not automatically be called.

> > @dataclass class C:

> > > a: float b: float c: float = field(init=False)

> > > def __post_init__(self):

> > >   self.c = self.a + self.b

The `__init__()` method generated by `@dataclass` does not call base class `__init__()` methods. If the base class has an `__init__()` method that has to be called, it is common to call this method in a `__post_init__()` method:

    class Rectangle:
        def __init__(self, height, width):
          self.height = height
          self.width = width

    @dataclass
    class Square(Rectangle):
        side: float

        def __post_init__(self):
            super().__init__(self.side, self.side)

Note, however, that in general the dataclass-generated `__init__()` methods don’t need to be called, since the derived dataclass will take care of initializing all fields of any base class that is a dataclass itself.

매개변수를 `__post_init__()` 에 전달하는 방법은 초기화 전용 변수에 대한 아래 섹션을 참조하십시오. 또한 `replace()` 가 `init=False` 필드를 처리하는 방식에 관한 경고를 보십시오.

## 클래스 변수

One of the few places where `@dataclass` actually inspects the type of a field is to determine if a field is a class variable as defined in PEP 526. It does this by checking if the type of the field is `typing.ClassVar`. If a field is a `ClassVar`, it is excluded from consideration as a field and is ignored by the dataclass mechanisms. Such `ClassVar` pseudo-fields are not returned by the module-level `fields()` function.

## 초기화 전용 변수

Another place where `@dataclass` inspects a type annotation is to determine if a field is an init-only variable. It does this by seeing if the type of a field is of type `dataclasses.InitVar`. If a field is an `InitVar`, it is considered a pseudo-field called an init-only field. As it is not a true field, it is not returned by the module-level `fields()` function. Init-only fields are added as parameters to the generated `__init__()` method, and are passed to the optional __post_init__ method. They are not otherwise used by dataclasses.

예를 들어, 클래스를 만들 때 값이 제공되지 않으면, 필드가 데이터베이스로부터 초기화된다고 가정합시다:

    @dataclass
    class C:
        i: int
        j: int | None = None
        database: InitVar[DatabaseType | None] = None

        def __post_init__(self, database):
            if self.j is None and database is not None:
                self.j = database.lookup('j')

    c = C(10, database=my_database)

In this case, `fields()` will return `Field` objects for `i` and `j`, but not for `database`.

## 고정 인스턴스

정말로 불변인 파이썬 객체를 만드는 것은 불가능합니다. 그러나, `frozen=True` 를 `@dataclass` 데코레이터에 전달함으로써 불변성을 흉내 낼 수 있습니다. 이 경우, 데이터 클래스는 `__setattr__()` 과 `__delattr__()` 메서드를 클래스에 추가합니다. 이 메서드는 호출될 때 `FrozenInstanceError` 를 발생시킵니다.

There is a tiny performance penalty when using `frozen=True`: `__init__()` cannot use simple assignment to initialize fields, and must use `__setattr__()`.

## 계승

데이터 클래스가 `@dataclass` 데코레이터에 의해 생성될 때, 클래스의 모든 베이스 클래스들을 MRO 역순(즉, `object` 에서 시작해서)으로 조사하고, 발견되는 데이터 클래스마다 그 베이스 클래스의 필드들을 순서 있는 필드 매핑에 추가합니다. 모든 생성된 메서드들은 이 합쳐지고 계산된 순서 있는 필드 매핑을 사용합니다. 필드들이 삽입 순서이기 때문에, 파생 클래스는 베이스 클래스를 재정의합니다. 예:

    @dataclass
    class Base:
        x: Any = 15.0
        y: int = 0

    @dataclass
    class C(Base):
        z: int = 10
        x: int = 15

The final list of fields is, in order, `x`, `y`, `z`. The final type of `x` is `int`, as specified in class `C`.

The generated `__init__()` method for `C` will look like:

    def __init__(self, x: int = 15, y: int = 0, z: int = 10):

## Re-ordering of keyword-only parameters in `__init__()`

After the parameters needed for `__init__()` are computed, any keyword-only parameters are moved to come after all regular (non-keyword-only) parameters. This is a requirement of how keyword-only parameters are implemented in Python: they must come after non-keyword-only parameters.

In this example, `Base.y`, `Base.w`, and `D.t` are keyword-only fields, and `Base.x` and `D.z` are regular fields:

    @dataclass
    class Base:
        x: Any = 15.0
        _: KW_ONLY
        y: int = 0
        w: int = 1

    @dataclass
    class D(Base):
        z: int = 10
        t: int = field(kw_only=True, default=0)

The generated `__init__()` method for `D` will look like:

    def __init__(self, x: Any = 15.0, z: int = 10, *, y: int = 0, w: int = 1, t: int = 0):

Note that the parameters have been re-ordered from how they appear in the list of fields: parameters derived from regular fields are followed by parameters derived from keyword-only fields.

The relative ordering of keyword-only parameters is maintained in the re-ordered `__init__()` parameter list.

## 기본 팩토리 함수

If a `field()` specifies a `default_factory`, it is called with zero arguments when a default value for the field is needed. For example, to create a new instance of a list, use:

    mylist: list = field(default_factory=list)

If a field is excluded from `__init__()` (using `init=False`) and the field also specifies `default_factory`, then the default factory function will always be called from the generated `__init__()` function. This happens because there is no other way to give the field an initial value.

## 가변 기본값

파이썬은 기본 멤버 변숫값을 클래스 어트리뷰트에 저장합니다. 데이터 클래스를 사용하지 않는 이 예제를 생각해보세요:

    class C:
        x = []
        def add(self, element):
            self.x.append(element)

    o1 = C()
    o2 = C()
    o1.add(1)
    o2.add(2)
    assert o1.x == [1, 2]
    assert o1.x is o2.x

Note that the two instances of class `C` share the same class variable `x`, as expected.

데이터 클래스를 사용해서, 만약 이 코드가 올바르다면:

    @dataclass
    class D:
        x: list = []      # This code raises ValueError
        def add(self, element):
            self.x.append(element)

비슷한 코드를 생성합니다:

    class D:
        x = []
        def __init__(self, x=x):
            self.x = x
        def add(self, element):
            self.x.append(element)

    assert D().x is D().x

This has the same issue as the original example using class `C`. That is, two instances of class `D` that do not specify a value for `x` when creating a class instance will share the same copy of `x`. Because dataclasses just use normal Python class creation they also share this behavior. There is no general way for Data Classes to detect this condition. Instead, the `@dataclass` decorator will raise a `ValueError` if it detects an unhashable default parameter. The assumption is that if a value is unhashable, it is mutable. This is a partial solution, but it does protect against many common errors.

기본 팩토리 함수를 사용하면 필드의 기본값으로 가변형의 새 인스턴스를 만들 수 있습니다:

    @dataclass
    class D:
        x: list = field(default_factory=list)

    assert D().x is not D().x

버전 3.11에서 변경: Instead of looking for and disallowing objects of type `list`, `dict`, or `set`, unhashable objects are now not allowed as default values. Unhashability is used to approximate mutability.

## Descriptor-typed fields

Fields that are assigned descriptor objects as their default value have the following special behaviors:

- The value for the field passed to the dataclass’s `__init__()` method is passed to the descriptor’s `__set__()` method rather than overwriting the descriptor object.

- Similarly, when getting or setting the field, the descriptor’s `__get__()` or `__set__()` method is called rather than returning or overwriting the descriptor object.

- To determine whether a field contains a default value, `@dataclass` will call the descriptor’s `__get__()` method using its class access form: `descriptor.__get__(obj=None, type=cls)`. If the descriptor returns a value in this case, it will be used as the field’s default. On the other hand, if the descriptor raises `AttributeError` in this situation, no default value will be provided for the field.

    class IntConversionDescriptor:
        def __init__(self, *, default):
            self._default = default

        def __set_name__(self, owner, name):
            self._name = "_" + name

        def __get__(self, obj, type):
            if obj is None:
                return self._default

            return getattr(obj, self._name, self._default)

        def __set__(self, obj, value):
            setattr(obj, self._name, int(value))

    @dataclass
    class InventoryItem:
        quantity_on_hand: IntConversionDescriptor = IntConversionDescriptor(default=100)

    i = InventoryItem()
    print(i.quantity_on_hand)   # 100
    i.quantity_on_hand = 2.5    # calls __set__ with 2.5
    print(i.quantity_on_hand)   # 2

Note that if a field is annotated with a descriptor type, but is not assigned a descriptor object as its default value, the field will act like a normal field.