# `dataclasses` — Data Classes

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

will add, among other things, a `__init__()` that looks like:

    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)

  This function is a decorator that is used to add generated special methods to classes, as described below.

  The `dataclass()` decorator examines the class to find `field`s. A `field` is defined as a class variable that has a type annotation. With two exceptions described below, nothing in `dataclass()` examines the type specified in the variable annotation.

  생성된 모든 메서드의 필드 순서는 클래스 정의에 나타나는 순서입니다.

  The `dataclass()` decorator will add various “dunder” methods to the class, described below. If any of the added methods already exist in the class, the behavior depends on the parameter, as documented below. The decorator returns the same class that it is called on; no new class is created.

  If `dataclass()` is used just as a simple decorator with no parameters, it acts as if it has the default values documented in this signature. That is, these three uses of `dataclass()` are equivalent:

      @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)
      class C:
         ...

  The parameters to `dataclass()` are:

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

    If the class already defines `__init__()`, this parameter is ignored.

  - `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)`.

    If the class already defines `__repr__()`, this parameter is ignored.

  - `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.

    If the class already defines `__eq__()`, this parameter is ignored.

  - `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.

    If the class already defines any of `__lt__()`, `__le__()`, `__gt__()`, or `__ge__()`, then `TypeError` is raised.

  - `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.

    By default, `dataclass()` will not implicitly add a `__hash__()` method unless it is safe to do so. Neither will it add or change an existing explicitly defined `__hash__()` method. Setting the class attribute `__hash__ = None` has a specific meaning to Python, as described in the `__hash__()` documentation.

    If `__hash__()` is not explicitly defined, or if it is set to `None`, then `dataclass()` may add an implicit `__hash__()` method. Although not recommended, you can force `dataclass()` to create a `__hash__()` method with `unsafe_hash=True`. This might be the case if your class is logically immutable but can nonetheless be mutated. This is a specialized use case and should be considered carefully.

    Here are the rules governing implicit creation of a `__hash__()` method. Note that you cannot both have an explicit `__hash__()` method in your dataclass and set `unsafe_hash=True`; this will result in a `TypeError`.

    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 affect 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.

  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)

  For common and simple use cases, no other functionality is required. There are, however, some dataclass features that require additional per-field information. To satisfy this need for additional information, you can replace the default field value with a call to the provided `field()` function. For example:

      @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.

  The parameters to `field()` are:

  - `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` objects describe each defined field. These objects are created internally, and are returned by the `fields()` module-level method (see below). Users should never instantiate a `Field` object directly. Its documented attributes are:

  > - `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)

  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`, and `slots` 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.

  Init-only variables without default values, if any exist, must be specified on the call to `replace()` so that they can be passed to `__init__()` and `__post_init__()`.

  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`

  Raised when an implicitly defined `__setattr__()` or `__delattr__()` is called on a dataclass which was defined with `frozen=True`. It is a subclass of `AttributeError`.

## 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.

다른 용도 중에서도, 하나나 그 이상의 다른 필드에 의존하는 필드 값을 초기화하는데 사용할 수 있습니다. 예를 들면:

    @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:

    @dataclass
    class Rectangle:
        height: float
        width: float

    @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.

See the section below on init-only variables for ways to pass parameters to `__post_init__()`. Also see the warning about how `replace()` handles `init=False` fields.

## 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

It is not possible to create truly immutable Python objects. However, by passing `frozen=True` to the `dataclass()` decorator you can emulate immutability. In that case, dataclasses will add `__setattr__()` and `__delattr__()` methods to the class. These methods will raise a `FrozenInstanceError` when invoked.

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

## 계승

When the dataclass is being created by the `dataclass()` decorator, it looks through all of the class’s base classes in reverse MRO (that is, starting at `object`) and, for each dataclass that it finds, adds the fields from that base class to an ordered mapping of fields. After all of the base class fields are added, it adds its own fields to the ordered mapping. All of the generated methods will use this combined, calculated ordered mapping of fields. Because the fields are in insertion order, derived classes override base classes. An example:

    @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 = []
>         def add(self, element):
>             self.x += element

> 비슷한 코드를 생성합니다:

>     class D:
>         x = []
>         def __init__(self, x=x):
>             self.x = x
>         def add(self, element):
>             self.x += 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 `TypeError` if it detects a default parameter of type `list`, `dict`, or `set`. 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

## 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, `dataclasses` will call the descriptor’s `__get__` method using its class access form (i.e. `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.