"dataclasses" --- Data Classes#

Código fuente: 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.

Las variables miembro a utilizar en estos métodos generados son definidas teniendo en cuenta anotaciones de tipo PEP 526. Por ejemplo, en este código:

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.

Added in version 3.7.

Contenidos del módulo#

@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 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.

El orden de los campos en los métodos generados es el mismo en el que se encuentran en la definición de la clase.

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 by comparing each field in order. Both instances in the comparison must be of the identical type.

  If the class already defines "eq()", this parameter is ignored.

  Distinto en la versión 3.13: The generated "eq" method now compares each field individually (for example, "self.a == other.a and self.b == other.b"), rather than comparing tuples of fields as in previous versions.This change makes the comparison faster but it may alter results in cases where attributes compare equal by identity but not by value (such as "float('nan')").In Python 3.12 and earlier, the comparison was performed by creating tuples of the fields and comparing them (for example, "(self.a, self.b) == (other.a, other.b)").

• 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 true, force "dataclasses" to create a "hash()" method, even though it may not be safe to do so. Otherwise, generate a "hash()" method according to how eq and frozen are set. The default value is "False".

  "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 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 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. See the discussion below.

  If "setattr()" or "delattr()" is defined in the class and frozen is true, then "TypeError" is raised.

• match_args: If true (the default is "True"), the "match_args" tuple will be created from the list of non keyword-only 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.

  Added in 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. See the parameter glossary entry for details. Also see the "KW_ONLY" section.

  Keyword-only fields are not included in "match_args".

  Added in 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, then "TypeError" is raised.

  Advertencia:

  Passing parameters to a base class "__init_subclass__()" when
  using "slots=True" will result in a "TypeError". Either use
  "__init_subclass__" with no parameters or use default values
  as a workaround. See gh-91126 for full details.
  

  Added in version 3.10.

  Distinto en la versión 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. Use "fields()" 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 instance "weakref-able". It is an error to specify "weakref_slot=True" without also specifying "slots=True".

  Added in version 3.11.

Los "fields" pueden especificar un valor por defecto opcionalmente, simplemente usando la sintaxis normal de Python:

  @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):

Si, en la definición de una clase, a un campo con valor por defecto le sigue un campo sin valor por defecto será lanzada una excepción "TypeError". Esto se aplica también a la implementación de una clase única o como resultado de herencia de clases.

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]

Como se muestra arriba, el valor "MISSING" es un objeto centinela que se usa para detectar si el usuario proporciona algunos parámetros. Este centinela se utiliza porque "None" es un valor válido para algunos parámetros con un significado distinto. Ningún código debe utilizar directamente el valor "MISSING".

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 false, this field is excluded from the generated "hash()". If "None" (the default), use the value of compare: this would normally be the expected behavior, since a field should be included in the hash if it's used for comparisons. Setting this value to anything other than "None" is discouraged.

  Una posible razón para definir "hash=False" y "compare=True" podría ser el caso en el que computar el valor hash para dicho campo es costoso pero el campo es necesario para los métodos de comparación, siempre que existan otros campos que contribuyen al valor hash del tipo. Incluso si un campo se excluye del hash, se seguirá utilizando a la hora de comparar.

• 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.

  Keyword-only fields are also not included in "match_args".

  Added in version 3.10.

• doc: optional docstring for this field.

  Added in version 3.14.

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 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.

Pueden existir otros atributos, pero son privados y no deberían ser considerados ni depender de ellos.

class dataclasses.InitVar

"InitVar[T]" type annotations describe variables that are init- only. Fields annotated with "InitVar" are considered pseudo-fields, and thus are neither returned by the "fields()" function nor used in any way except adding them as parameters to "init()" and an optional "post_init()".

dataclasses.fields(class_or_instance)

Retorna una tupla de objetos "Field" que definen los campos para esta clase de datos. Acepta tanto una clase de datos como una instancia de esta. Lanza una excepción "TypeError" si se le pasa cualquier otro objeto. No retorna pseudocampos, que son "ClassVar" o "InitVar".

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:

  {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()".

Continuando con el ejemplo anterior:

  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, 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 just "name" is supplied, "typing.Any" is used for "type". 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})

Es equivalente a:

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

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

Added in 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, 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.

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, but not including generic aliases) or an instance of one, otherwise return "False".

Si se necesita conocer si una clase es una instancia de dataclass (y no una clase de datos en si misma), se debe agregar una verificación adicional para "not isinstance(obj, type)":

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

dataclasses.MISSING

Un valor centinela que significa que falta un default o 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.

En este ejemplo, los campos "y" y "z" se marcarán como campos de solo palabras clave:

  @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".

Added in version 3.10.

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".

Procesamiento posterior a la inicialización#

dataclasses.post_init()

When defined on the class, it will be called by the generated "init()", normally 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.

Entre otros usos, esto permite inicializar valores de campo que dependen de uno o más campos. Por ejemplo:

  @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 clase#

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.

Variable de solo inicialización#

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 "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.

Por ejemplo, supongamos que se va a inicializar un campo desde una base de datos, de no proporcionarse un valor al crear la clase:

@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".

Instancias congeladas#

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()".

Herencia#

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):

Tenga en cuenta que los parámetros se han reordenado a partir de cómo aparecen en la lista de campos: los parámetros derivados de los campos regulares son seguidos por los parámetros derivados de los campos de solo palabras clave.

The relative ordering of keyword-only parameters is maintained in the re-ordered "init()" parameter list.

Funciones fábrica por defecto#

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.

Valores por defecto mutables#

Python almacena los valores miembros por defecto en atributos de clase. Considera este ejemplo, sin usar clases de datos:

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.

Usando clases de datos, si este código fuera válido:

@dataclass class D: x: list = [] # This code raises ValueError def add(self, element): self.x.append(element)

generaría un código similar a:

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.

Usar las funciones de fábrica por defecto es una forma de crear nuevas instancias de tipos mutables como valores por defecto para campos:

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

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

Distinto en la versión 3.11: Instead of looking for and disallowing objects of type "list", "dict", or "set", unhashable objects are now not allowed as default values. Unhashability is used to approximate mutability.

Campos tipo descriptor#

Los campos a los que se asigna objetos descriptor como valor por defecto tienen los siguientes comportamientos especiales:

• 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 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

Tenga en cuenta que si un campo está anotado con un tipo de descriptor, pero no se le asigna un objeto descriptor como valor por defecto, el campo actuará como un campo normal.