# 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

@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 champss. 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 :

Les fields 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)

Returns True if its parameter is a dataclass or an instance of one, otherwise returns 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.