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# `dataclasses` — Classes de Données

Code source : Lib/dataclasses.py

Ce module fournit un décorateur et des fonctions pour générer automatiquement les méthodes spéciales comme `__init__()` et `__repr__()` dans les Classes de Données définies par l’utilisateur. Ces classes ont été décrites dans la PEP 557.

Les variables membres à utiliser dans ces méthodes générées sont définies en utilisant les annotations de type PEP 526. Par exemple, ce code :

    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

Ajoute, entre autres choses, une méthode `__init__()` qui ressemble à :

    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

Il est important de noter que cette méthode est ajoutée automatiquement dans la classe : elle n’est pas à écrire dans la définition de `InventoryItem` ci-dessus.

Nouveau dans la version 3.7.

## Décorateurs, classes et fonctions au niveau du module

`@``dataclasses.``dataclass`(*, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)

  Cette fonction est un décorateur qui est utilisé pour ajouter les méthodes spéciales générées aux classes, comme décrit ci-dessous.

  Le décorateur `dataclass()` examine la classe pour trouver des `champs`s. Un `champ` est défini comme une variable de classe qui possède une annotation de type. À deux exceptions près décrites plus bas, il n’y a rien dans `dataclass()` qui examine le type spécifié dans l’annotation de variable.

  L’ordre des paramètres des méthodes générées est celui d’apparition des champs dans la définition de la classe.

  Le décorateur `dataclass()` ajoute diverses méthodes « spéciales » à la classe, décrites ci-après. Si l’une des méthodes ajoutées existe déjà dans la classe, le comportement dépend des paramètres, comme documenté ci-dessous. Le décorateur renvoie la classe sur laquelle il est appelé ; il n’y a pas de nouvelle classe créée.

  Si `dataclass()` est utilisé comme simple décorateur sans paramètres, il se comporte comme si on l’avait appelé avec les valeurs par défaut présentes en signature. Ainsi, les trois usages suivants de `dataclass()` sont équivalents :

      @dataclass
      class C:
          ...

      @dataclass()
      class C:
          ...

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

  Les paramètres de `dataclass()` sont :

  - `init`: Si vrai (par défaut), une méthode `__init__()` est générée.

    Si la classe définit déjà une méthode `__init__()`, ce paramètre est ignoré.

  - `repr`: Si vrai (par défaut), une méthode `__repr__()` sera générée. La chaîne de représentation comportera le nom de la classe et le nom ainsi que la représentation de chaque champ, suivant leur ordre de définition. Les champs marqués comme exclus (voir `Field` ci-dessous) de la représentation ne sont pas inclus. Par exemple : `InventoryItem(name='widget', unit_price=3.0, quantity_on_hand=10)`.

    Si la classe définit déjà une méthode `__repr__()`, ce paramètre est ignoré.

  - `eq`: Si vrai (par défaut), une méthode `__eq__()` est générée. Cette méthode permet de comparer les instances de la classe comme s’il s’agissait d’un tuple de ses champs, dans l’ordre. Les deux instances dans la comparaison doivent être de même type.

    Si la classe définit déjà une méthode `__eq__()`, ce paramètre est ignoré.

  - `order`: Si vrai (`False` par défaut), les méthodes `__lt__()`, `__le__()`, `__gt__()`, et `__ge__()` sont générées. Elles permettent de comparer les instances de la classe en les considérant comme des tuples, dans l’ordre de définition des champs. Chaque instance dans la comparaison doit être de même type. Si `order` est vrai mais que `eq` est faux, une `ValueError` est levée.

    Si la classe définit déjà l’une des méthodes `__lt__()`, `__le__()`, `__gt__()`, ou `__ge__()`, alors une `TypeError` est levée.

  - `unsafe_hash`: Si `False` (par défaut), une méthode `__hash__()` est générée et son comportement dépend des valeurs de `eq` et `frozen`.

    `__hash__()` est utilisée par la fonction native `hash()`, ainsi que lorsqu’un objet est inséré dans une collection utilisant du hachage, tel qu’un dictionnaire ou un ensemble. Avoir une méthode `__hash__()` implique que les instances de la classe sont immuables. La muabilité est une propriété complexe qui dépends des intentions du programmeur, de l’existence et du comportement de la méthode `__eq__()`, et des valeurs des options `eq` et `frozen` dans l’appel au décorateur `dataclass()`.

    Par défaut, `dataclass()` n’ajoute pas de méthode implicite `__hash__()`, sauf s’il n’existe aucun risque sous-jacent. Il n’ajoute ou ne modifie pas non plus la méthode `__hash__()` si elle a est définie explicitement. Définir l’attribut de classe `__hash__ = None` a une signification particulière en Python, comme précisé dans la documentation de `__hash__()`.

    Si `__hash__()` n’est pas défini explicitement, ou s’il a pour valeur `None`, alors `dataclass()` peut ajouter une méthode `__hash__()` implicite. Bien que ce ne soit pas recommandé,, vous pouvez forcer `dataclass()` à créer une méthode `__hash__()` en utilisant `unsafe_hash=True`. Cela pourrait être nécessaire si votre classe est logiquement immuable mais qu’une mutation est tout de même possible. C’est un cas très particulier qui doit être considéré avec la plus grande prudence.

    Ce sont les règles autour de la création implicite de la méthode `__hash__()`. Il faut noter que vous ne pouvez pas avoir à la fois une méthode `__hash__()` explicite dans votre dataclass et définir `unsafe_hash=True`; cela lèvera une `TypeError`.

    Si `eq` et `frozen` sont tous deux vrais, `dataclass()` génère par défaut une méthode `__hash__()` pour vous. Si `eq` est vrai mais que `frozen` est faux, `__hash__()` est prends la valeur `None`, marquant la classe comme non-hachable (et c’est le cas, puisqu’elle est modifiable). Si `eq` est faux, la méthode `__hash__()` est laissée intacte, ce qui veut dire que la méthode `__hash__()` de la classe parente sera utilisée (si la classe parente est `object`, le comportement est un hachage basé sur les id).

  - `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.

  Les `field`s peuvent éventuellement spécifier une valeur par défaut, en utilisant la syntaxe Python normale :

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

  Dans cet exemple, `a` et `b` sont tous deux inclus dans la signature de la méthode générée `__init__()`, qui est définie comme suit :

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

  Une `TypeError` est levée si un champ sans valeur par défaut est défini après un champ avec une valeur par défaut. C’est le cas que ce soit dans une seule classe, mais également si c’est le résultat d’un héritage de classes.

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

  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]

  As shown above, the `MISSING` value is a sentinel object used to detect if the `default` and `default_factory` parameters are provided. This sentinel is used because `None` is a valid value for `default`. No code should directly use the `MISSING` value.

  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.

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

  - `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.

    One possible reason to set `hash=False` but `compare=True` would be if a field is expensive to compute a hash value for, that field is needed for equality testing, and there are other fields that contribute to the type's hash value. Even if a field is excluded from the hash, it will still be used for comparisons.

  - `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.

  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`, and `metadata` have the identical meaning and values as they do in the `field()` declaration.

  Other attributes may exist, but they are private and must not be inspected or relied on.

`dataclasses.``fields`(class_or_instance)

  Returns a tuple of `Field` objects that define the fields for this dataclass. Accepts either a dataclass, or an instance of a dataclass. Raises `TypeError` if not passed a dataclass or instance of one. Does not return pseudo-fields which are `ClassVar` or `InitVar`.

`dataclasses.``asdict`(instance, *, dict_factory=dict)

  Converts the dataclass `instance` 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. For example:

      @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}]}

  Raises `TypeError` if `instance` is not a dataclass instance.

`dataclasses.``astuple`(instance, *, tuple_factory=tuple)

  Converts the dataclass `instance` 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.

  Continuing from the previous example:

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

  Raises `TypeError` if `instance` 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)

  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`, and `frozen` 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})

  Is equivalent to:

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

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

`dataclasses.``replace`(instance, **changes)

  Creates a new object of the same type of `instance`, replacing fields with values from `changes`. If `instance` 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`(class_or_instance)

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

  If you need to know if a class is an instance of a dataclass (and not a dataclass itself), then add a further check for `not isinstance(obj, type)`:

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

## Post-init processing

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.

Among other uses, this allows for initializing field values that depend on one or more other fields. For example:

    @dataclass
    class C:
        a: float
        b: float
        c: float = field(init=False)

        def __post_init__(self):
            self.c = self.a + self.b

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.

## Class variables

One of two 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.

## Init-only variables

The other 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.

For example, suppose a field will be initialized from a database, if a value is not provided when creating the class:

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

## Frozen instances

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__()`.

## Héritage

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):

## Default factory functions

> 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.

## Mutable default values

> Python stores default member variable values in class attributes. Consider this example, not using dataclasses:

>     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.

> Using dataclasses, if this code was valid:

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

> it would generate code similar to:

>     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, dataclasses 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.

> Using default factory functions is a way to create new instances of mutable types as default values for fields:

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

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

## Exceptions

exception `dataclasses.``FrozenInstanceError`

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