dataclasses
--- Data Classes¶
Code source : 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.
Les variables membres à utiliser dans ces méthodes générées sont définies en utilisant les annotations de type PEP 526. Par exemple :
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.
Ajouté dans la version 3.7.
Classe de données¶
- @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)¶
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 findfield
s. Afield
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.L’ordre des paramètres des méthodes générées est celui d’apparition des champs dans la définition de la classe.
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, weakref_slot=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, aValueError
is raised.If the class already defines any of
__lt__()
,__le__()
,__gt__()
, or__ge__()
, thenTypeError
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-inhash()
, 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 toNone
, then@dataclass
may add an implicit__hash__()
method. Although not recommended, you can force@dataclass
to create a__hash__()
method withunsafe_hash=True
. This might be the case if your class is logically immutable but can still 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 setunsafe_hash=True
; this will result in aTypeError
.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 toNone
, 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 isobject
, 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, thenTypeError
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.
Ajouté dans la version 3.10.
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 theKW_ONLY
section.
Ajouté dans la version 3.10.
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, thenTypeError
is raised.
Avertissement
Passing parameters to a base class
__init_subclass__()
when usingslots=True
will result in aTypeError
. Either use__init_subclass__
with no parameters or use default values as a workaround. See gh-91126 for full details.Ajouté dans la version 3.10.
Modifié dans la version 3.11: 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. Usefields()
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 instanceweakref-able
. It is an error to specifyweakref_slot=True
without also specifyingslots=True
.
Ajouté dans la version 3.11.
Les champs peuvent éventuellement préciser 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'
In this example, both
a
andb
will be included in the added__init__()
method, which will be defined as: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 ou si c’est le résultat d’un héritage de classes.
- dataclasses.field(*, default=MISSING, default_factory=MISSING, init=True, repr=True, hash=None, compare=True, metadata=None, kw_only=MISSING, doc=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]
Comme le montre la signature, la constante
MISSING
est une valeur sentinelle pour déterminer si des paramètres ont été fournis par l'utilisateur.None
ne conviendrait pas puisque c'est une valeur avec un sens qui peut être différent pour certains paramètres. La sentinelleMISSING
est interne au module et ne doit pas être utilisée dans vos programmes.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. IfNone
(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 thanNone
is discouraged.Cependant, une raison légitime de mettre hash à
False
alors que compare est àTrue
est la concourance de trois facteurs : le champ est coûteux à hacher ; il est nécessaire pour les comparaisons d'égalité ; et il y a déjà d'autres champs qui participent au hachage des instances. À ce moment, on peut alors se passer du champ dans le hachage tout en le faisant participer aux comparaisons.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 inMappingProxyType()
to make it read-only, and exposed on theField
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.
Ajouté dans la version 3.10.
doc
: optional docstring for this field.
Ajouté dans la version 3.13.
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 default is not 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 be10
, the class attributeC.t
will be20
, and the class attributesC.x
andC.y
will not be set.
- class dataclasses.Field¶
Field
objects describe each defined field. These objects are created internally, and are returned by thefields()
module-level method (see below). Users should never instantiate aField
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
, andkw_only
have the identical meaning and values as they do in thefield()
function.
D'autres attributs peuvent exister, mais ils sont privés et ne sont pas censés être inspectés. Le code ne doit jamais reposer sur eux.
- dataclasses.fields(class_or_instance)¶
Renvoie un n-uplet d'objets
Field
correspondant aux champs de l'argument, à l'exclusion des pseudo-champsClassVar
ouInitVar
. L'argument peut être soit une classe de données, soit une instance d'une telle classe ; si ce n'est pas le cas, une exceptionTypeError
est levée.
- 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 withcopy.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}]}
Pour créer une copie superficielle, la solution de contournement suivante peut être utilisée :
{field.name: getattr(obj, field.name) for field in fields(obj)}
asdict()
raisesTypeError
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()
.Pour continuer l'exemple précédent :
assert astuple(p) == (10, 20) assert astuple(c) == ([(0, 0), (10, 4)],)
Pour créer une copie superficielle, la solution de contournement suivante peut être utilisée :
tuple(getattr(obj, field.name) for field in dataclasses.fields(obj))
astuple()
raisesTypeError
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, module=None, decorator=dataclass)¶
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 justname
is supplied,typing.Any
is used fortype
. 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
.If module is defined, the
__module__
attribute of the dataclass is set to that value. By default, it is set to the module name of the caller.The decorator parameter is a callable that will be used to create the dataclass. It should take the class object as a first argument and the same keyword arguments as
@dataclass
. By default, the@dataclass
function is used.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})
est équivalent à :
@dataclass class C: x: int y: 'typing.Any' z: int = 5 def add_one(self): return self.x + 1
Ajouté dans la version 3.14: Added the decorator parameter.
- 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 keys in changes are not field names of the given dataclass, raisesTypeError
.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
. AValueError
will be raised in this case.Be forewarned about how
init=False
fields work during a call toreplace()
. They are not copied from the source object, but rather are initialized in__post_init__()
, if they're initialized at all. It is expected thatinit=False
fields will be rarely and judiciously used. If they are used, it might be wise to have alternate class constructors, or perhaps a customreplace()
(or similarly named) method which handles instance copying.Dataclass instances are also supported by generic function
copy.replace()
.
- dataclasses.is_dataclass(obj)¶
Return
True
if its parameter is a dataclass (including subclasses of a dataclass) or an instance of one, otherwise returnFalse
.Pour vérifier qu'un objet obj est une instance d'une classe de données, et non pas lui-même une classe de données, ajoutez le test
not isinstance(obj, type)
:def is_dataclass_instance(obj): return is_dataclass(obj) and not isinstance(obj, type)
- dataclasses.MISSING¶
Une valeur sentinelle pour dénoter l'absence de default ou 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 typeKW_ONLY
is otherwise completely ignored. This includes the name of such a field. By convention, a name of_
is used for aKW_ONLY
field. Keyword-only fields signify__init__()
parameters that must be specified as keywords when the class is instantiated.Dans cet exemple
y
etz
sont marqués comme exclusivement nommés :@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
.Ajouté dans la version 3.10.
- exception dataclasses.FrozenInstanceError¶
Raised when an implicitly defined
__setattr__()
or__delattr__()
is called on a dataclass which was defined withfrozen=True
. It is a subclass ofAttributeError
.
Post-initialisation¶
- dataclasses.__post_init__()¶
When defined on the class, it will be called by the generated
__init__()
, normally asself.__post_init__()
. However, if anyInitVar
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.Cette méthode permet, entre autres, d'initialiser des champs qui dépendent d'autres champs. Par exemple :
@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.
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.
Variables de classe¶
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.
Variables d'initialisation¶
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.
On peut par exemple imaginer un champ initialisé à partir d'une base de données s'il n'a pas reçu de valeur explicite :
@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
.
Instances figées¶
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):
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):
Les paramètres ont été réarrangés par rapport à leur ordre d'apparition dans la liste des champs : les paramètres provenant des attributs normaux sont suivis par les paramètres qui proviennent des attributs exclusivement nommés.
The relative ordering of keyword-only parameters is maintained in the
re-ordered __init__()
parameter list.
Fabriques de valeurs par défaut¶
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.
Valeurs par défaut mutables¶
En Python, les valeurs par défaut des attributs sont stockées dans des attributs de la classe. Observez cet exemple, sans classe de données :
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.
Avec les classes de données, si ce code était valide :
@dataclass
class D:
x: list = [] # This code raises ValueError
def add(self, element):
self.x.append(element)
il générerait un code équivalent à :
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.
Pour qu'un champ d'un type mutable soit par défaut initialisé à un nouvel objet pour chaque instance, utilisez une fonction de fabrique :
@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,
@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 raisesAttributeError
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.