"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".
