:mod:`dataclasses` --- Data Classes

.. module:: dataclasses
 :synopsis: Generate special methods on user-defined classes.

.. moduleauthor:: Eric V. Smith <eric@trueblade.com>

.. sectionauthor:: Eric V. Smith <eric@trueblade.com>

Source code: :source:`Lib/dataclasses.py`

This module provides a decorator and functions for automatically adding generated :term:`special method`s such as :meth:`__init__` and :meth:`__repr__` to user-defined classes. It was originally described in PEP 526 type annotations. For example this 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

Will add, among other things, a :meth:`__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.

.. versionadded:: 3.7

Module-level decorators, classes, and functions

.. decorator:: dataclass(*, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)

 This function is a :term:`decorator` that is used to add generated
 :term:`special method`\s to classes, as described below.

 The :func:`dataclass` decorator examines the class to find
 ``field``\s. A ``field`` is defined as class variable that has a
 :term:`type annotation <variable annotation>`. With two
 exceptions described below, nothing in :func:`dataclass`
 examines the type specified in the variable annotation.

 The order of the fields in all of the generated methods is the
 order in which they appear in the class definition.

 The :func:`dataclass` decorator will add various "dunder" methods to
 the class, described below. If any of the added methods already
 exist on the class, the behavior depends on the parameter, as documented
 below. The decorator returns the same class that is called on; no new
 class is created.

 If :func:`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 :func:`dataclass` are
 equivalent::

 @dataclass
 class C:
 ...

 @dataclass()
 class C:
 ...

 @dataclass(init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False)
 class C:
 ...

 The parameters to :func:`dataclass` are:

 - ``init``: If true (the default), a :meth:`__init__` method will be
 generated.

 If the class already defines :meth:`__init__`, this parameter is
 ignored.

 - ``repr``: If true (the default), a :meth:`__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 :meth:`__repr__`, this parameter is
 ignored.

 - ``eq``: If true (the default), an :meth:`__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 :meth:`__eq__`, this parameter is
 ignored.

 - ``order``: If true (the default is ``False``), :meth:`__lt__`,
 :meth:`__le__`, :meth:`__gt__`, and :meth:`__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
 :exc:`ValueError` is raised.

 If the class already defines any of :meth:`__lt__`,
 :meth:`__le__`, :meth:`__gt__`, or :meth:`__ge__`, then
 :exc:`TypeError` is raised.

 - ``unsafe_hash``: If ``False`` (the default), a :meth:`__hash__` method
 is generated according to how ``eq`` and ``frozen`` are set.

 :meth:`__hash__` is used by built-in :meth:`hash()`, and when objects are
 added to hashed collections such as dictionaries and sets. Having a
 :meth:`__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 :meth:`__eq__`, and the values of
 the ``eq`` and ``frozen`` flags in the :func:`dataclass` decorator.

 By default, :func:`dataclass` will not implicitly add a :meth:`__hash__`
 method unless it is safe to do so. Neither will it add or change an
 existing explicitly defined :meth:`__hash__` method. Setting the class
 attribute ``__hash__ = None`` has a specific meaning to Python, as
 described in the :meth:`__hash__` documentation.

 If :meth:`__hash__` is not explicit defined, or if it is set to ``None``,
 then :func:`dataclass` *may* add an implicit :meth:`__hash__` method.
 Although not recommended, you can force :func:`dataclass` to create a
 :meth:`__hash__` method with ``unsafe_hash=True``. This might be the case
 if your class is logically immutable but can nonetheless be mutated.
 This is a specialized use case and should be considered carefully.

 Here are the rules governing implicit creation of a :meth:`__hash__`
 method. Note that you cannot both have an explicit :meth:`__hash__`
 method in your dataclass and set ``unsafe_hash=True``; this will result
 in a :exc:`TypeError`.

 If ``eq`` and ``frozen`` are both true, by default :func:`dataclass` will
 generate a :meth:`__hash__` method for you. If ``eq`` is true and
 ``frozen`` is false, :meth:`__hash__` will be set to ``None``, marking it
 unhashable (which it is, since it is mutable). If ``eq`` is false,
 :meth:`__hash__` will be left untouched meaning the :meth:`__hash__`
 method of the superclass will be used (if the superclass is
 :class:`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
 :meth:`__setattr__` or :meth:`__delattr__` is defined in the class, then
 :exc:`TypeError` is raised. See the discussion below.

 ``field``\s may optionally specify a default value, using normal
 Python syntax::

 @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
 :meth:`__init__` method, which will be defined as::

 def __init__(self, a: int, b: int = 0):

 :exc:`TypeError` will be raised if a field without a default value
 follows a field with a default value. This is true either when this
 occurs in a single class, or as a result of class inheritance.

.. function:: 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 :func:`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 :func:`field` are:

 - ``default``: If provided, this will be the default value for this
 field. This is needed because the :meth:`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 :meth:`__init__` method.

 - ``repr``: If true (the default), this field is included in the
 string returned by the generated :meth:`__repr__` method.

 - ``compare``: If true (the default), this field is included in the
 generated equality and comparison methods (:meth:`__eq__`,
 :meth:`__gt__`, et al.).

 - ``hash``: This can be a bool or ``None``. If true, this field is
 included in the generated :meth:`__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
 :func:`~types.MappingProxyType` to make it read-only, and exposed
 on the :class:`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
 :func:`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 :func:`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:`Field` objects describe each defined field. These objects are created internally, and are returned by the :func:`fields` module-level method (see below). Users should never instantiate a :class:`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 :func:`field` declaration.

Other attributes may exist, but they are private and must not be inspected or relied on.

.. function:: fields(class_or_instance)

 Returns a tuple of :class:`Field` objects that define the fields for this
 dataclass. Accepts either a dataclass, or an instance of a dataclass.
 Raises :exc:`TypeError` if not passed a dataclass or instance of one.
 Does not return pseudo-fields which are ``ClassVar`` or ``InitVar``.

.. function:: 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 :exc:`TypeError` if ``instance`` is not a dataclass instance.

.. function:: 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 :exc:`TypeError` if ``instance`` is not a dataclass instance.

.. function:: 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 :func:`dataclass`.

 This function is not strictly required, because any Python
 mechanism for creating a new class with ``__annotations__`` can
 then apply the :func:`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

.. function:: 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 :exc:`TypeError`. If values in ``changes`` do not
 specify fields, raises :exc:`TypeError`.

 The newly returned object is created by calling the :meth:`__init__`
 method of the dataclass. This ensures that
 :meth:`__post_init__`, if present, is also called.

 Init-only variables without default values, if any exist, must be
 specified on the call to :func:`replace` so that they can be passed to
 :meth:`__init__` and :meth:`__post_init__`.

 It is an error for ``changes`` to contain any fields that are
 defined as having ``init=False``. A :exc:`ValueError` will be raised
 in this case.

 Be forewarned about how ``init=False`` fields work during a call to
 :func:`replace`. They are not copied from the source object, but
 rather are initialized in :meth:`__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.

.. function:: 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 :meth:`__init__` code will call a method named :meth:`__post_init__`, if :meth:`__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 :meth:`__post_init__` in the order they were defined in the class. If no :meth:`__init__` method is generated, then :meth:`__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 :meth:`__post_init__`. Also see the warning about how :func:`replace` handles init=False fields.

Class variables

One of two places where :func:`dataclass` actually inspects the type of a field is to determine if a field is a class variable as defined in :func:`fields` function.

Init-only variables

The other place where :func:`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 :func:`fields` function. Init-only fields are added as parameters to the generated :meth:`__init__` method, and are passed to the optional :meth:`__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, :func:`fields` will return :class:`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 :meth:`dataclass` decorator you can emulate immutability. In that case, dataclasses will add :meth:`__setattr__` and :meth:`__delattr__` methods to the class. These methods will raise a :exc:`FrozenInstanceError` when invoked.

There is a tiny performance penalty when using frozen=True: :meth:`__init__` cannot use simple assignment to initialize fields, and must use :meth:`object.__setattr__`.

Inheritance

When the dataclass is being created by the :meth:`dataclass` decorator, it looks through all of the class's base classes in reverse MRO (that is, starting at :class:`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 :meth:`__init__` method for C will look like:

def __init__(self, x: int = 15, y: int = 0, z: int = 10):

Default factory functions

If a :func:`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 :meth:`__init__` (using init=False) and the field also specifies default_factory, then the default factory function will always be called from the generated :meth:`__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 :exc:`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:: FrozenInstanceError

 Raised when an implicitly defined :meth:`__setattr__` or
 :meth:`__delattr__` is called on a dataclass which was defined with
 ``frozen=True``.

此处可能存在不合适展示的内容,页面不予展示。您可通过相关编辑功能自查并修改。

如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容,可点击提交进行申诉,我们将尽快为您处理。