# `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

Es importante observar que este método es añadido a la clase automáticamente; está implícito en la definición de `InventoryItem` implementada arriba.

Nuevo en la versión 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 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, a `ValueError` is raised.

    If the class already defines any of `__lt__()`, `__le__()`, `__gt__()`, or `__ge__()`, then `TypeError` 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-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. If `__setattr__()` or `__delattr__()` is defined in the class, then `TypeError` 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.

  > Nuevo en la versión 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 the `KW_ONLY` section.

  > Nuevo en la versión 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.

  > Nuevo en la versión 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`: Si es verdadero (por defecto es `False`), añade una ranura llamada «__weakref__», que es necesaria para generar una instancia referenciable de forma débil. Es un error especificar `weakref_slot=True` sin especificar también `slots=True`.

  > Nuevo en la versión 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)

  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`: Si es provisto, debe ser un objeto invocable sin argumentos, el cual será llamado cuando el valor por defecto de este campo sea necesario. Además de otros propósitos, puede ser utilizado para especificar campos con valores por defecto mutables, como se explica a continuación. Especificar tanto `default` como `default_factory` resulta en un error.

  - `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 `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.

    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`: Puede ser un mapeo o None. None es tratado como un diccionario vacío. Este valor es envuelto en `MappingProxyType()` para que sea de sólo lectura y visible en el objeto `Field`. No es utilizado por las clases de datos, mas bien es provisto como un mecanismo de extensión de terceros. Varios terceros pueden tener su propia clave para utilizar como espacio de nombres en 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.

  > Nuevo en la versión 3.10.

  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

  El atributo de clase `C.z` será `10`, el atributo de clase `C.t` será `20` y los atributos de clase `C.x` y `C.y` no serán definidos.

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`: El nombre del campo.

  - `type`: El tipo del campo.

  - `default`, `default_factory`, `init`, `repr`, `hash`, `compare` y `metadata` tienen los mismos valores y significados respecto a la declaración de `field()` (ver arriba).

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

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:

      dict((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)

  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`.

  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

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 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__()`.

  Es un error que `changes` contenga cualquier campo que esté definido como `init=False`. Una excepción `ValueError` se lanzará en este caso.

  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(obj)

  Retorna `True` si su parámetro es una clase de datos o una instancia de una, en caso contrario retorna `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`.

  Nuevo en la versión 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

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.

> 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.

> > @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 `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.

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)

En este caso, `fields()` retornará objetos `Field` para `i` y `j`, pero no para `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 `__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

La lista final de campos es, en orden, `x`, `y`, `z`. El tipo final de `x` es `int`, como se especifica en la clase `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.

En este ejemplo, `Base.y`, `Base.w` y `D.t` son campos de solo palabras clave, y `Base.x` y `D.z` son campos regulares:

    @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

Si un `field()` especifica una `default_factory`, se llama sin argumentos cuando se necesita un valor predeterminado para el campo. Por ejemplo, para crear una nueva instancia de una lista, debe usarse:

    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

Tenga en cuenta que, tal como cabe esperar, las dos instancias de la clase `C` comparten la misma variable de clase `x`.

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.