dataclasses
--- Data Classes¶
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.
在这些生成的方法中使用的成员变量是使用 PEP 526 类型标注来定义的。 例如以下代码:
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
请注意,此方法会自动添加到类中:它不会在上面显示的 InventoryItem
定义中直接指定。
在 3.7 版本新加入.
模块内容¶
- @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)¶
这个函数是 decorator ,用于将生成的 special method 添加到类中,如下所述。
dataclass()
装饰器会检查类以查找field
。field
被定义为具有 类型标注 的类变量。 除了下面描述的两个例外,在dataclass()
中没有什么东西会去检查在变量标注中所指定的类型。所有生成的方法中的字段顺序是它们在类定义中出现的顺序。
dataclass()
装饰器将向类中添加各种“dunder”方法,如下所述。 如果所添加的方法已存在于类中,则行为将取决于下面所列出的参数。 装饰器会返回调用它的类本身;不会创建新的类。如果
dataclass()
仅用作没有参数的简单装饰器,它就像它具有此签名中记录的默认值一样。也就是说,这三种dataclass()
用法是等价的:@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: ...
dataclass()
的参数有: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 isFalse
),__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. Iforder
is true andeq
is false, aValueError
is raised.If the class already defines any of
__lt__()
,__le__()
,__gt__()
, or__ge__()
, thenTypeError
is raised.unsafe_hash
: IfFalse
(the default), a__hash__()
method is generated according to howeq
andfrozen
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 theeq
andfrozen
flags in thedataclass()
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
, thendataclass()
may add an implicit__hash__()
method. Although not recommended, you can forcedataclass()
to create a__hash__()
method withunsafe_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
__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
andfrozen
are both true, by defaultdataclass()
will generate a__hash__()
method for you. Ifeq
is true andfrozen
is false,__hash__()
will be set toNone
, marking it unhashable (which it is, since it is mutable). Ifeq
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 isFalse
), 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 isTrue
), 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.
在 3.10 版本新加入.
kw_only
: If true (the default value isFalse
), 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.
在 3.10 版本新加入.
slots
: If true (the default isFalse
),__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.
在 3.10 版本新加入.
在 3.11 版本變更: 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 isFalse
), add a slot named "__weakref__", which is required to make an instance weakref-able. It is an error to specifyweakref_slot=True
without also specifyingslots=True
.
在 3.11 版本新加入.
field
s 可以选择使用普通的 Python 语法指定默认值:@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):
如果具有默认值的字段之后存在没有默认值的字段,将会引发
TypeError
。 无论此情况是发生在单个类中还是作为类继承的结果,都是如此。
- dataclasses.field(*, default=MISSING, default_factory=MISSING, init=True, repr=True, hash=None, compare=True, metadata=None, kw_only=MISSING)¶
对于常见和简单的用例,不需要其他功能。但是,有些数据类功能需要额外的每字段信息。为了满足这种对附加信息的需求,你可以通过调用提供的
field()
函数来替换默认字段值。例如:@dataclass class C: mylist: list[int] = field(default_factory=list) c = C() c.mylist += [1, 2, 3]
如上所示,
MISSING
值是一个哨兵对象,用于检测一些参数是否由用户提供。这个哨兵对象的使用是因为``None``是一些具有独特意义的参数的有效值。 任何代码都不应该直接使用MISSING
值。field()
参数有:default
:如果提供,这将是该字段的默认值。这是必需的,因为field()
调用本身会替换一般的默认值。default_factory
:如果提供,它必须是一个零参数可调用对象,当该字段需要一个默认值时,它将被调用。除了其他目的之外,这可以用于指定具有可变默认值的字段,如下所述。 同时指定default
和default_factory
将产生错误。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 orNone
. If true, this field is included in the generated__hash__()
method. IfNone
(the default), use the value ofcompare
: 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.设置
hash=False
但compare=True
的一个可能原因是,如果一个计算 hash 的代价很高的字段是检验等价性需要的,但还有其他字段可以计算类型的 hash 。 即使从 hash 中排除某个字段,它仍将用于比较。compare
: If true (the default), this field is included in the generated equality and comparison methods (__eq__()
,__gt__()
, et al.).metadata
:这可以是映射或 None 。 None 被视为一个空的字典。这个值包含在MappingProxyType()
中,使其成为只读,并暴露在Field
对象上。数据类根本不使用它,它是作为第三方扩展机制提供的。多个第三方可以各自拥有自己的键值,以用作元数据中的命名空间。kw_only
: If true, this field will be marked as keyword-only. This is used when the generated__init__()
method's parameters are computed.
在 3.10 版本新加入.
如果通过调用
field()
指定字段的默认值,则该字段的类属性将替换为指定的default
值。如果没有提供default
,那么将删除类属性。目的是在dataclass()
装饰器运行之后,类属性将包含字段的默认值,就像指定了默认值一样。例如,之后:@dataclass class C: x: int y: int = field(repr=False) z: int = field(repr=False, default=10) t: int = 20
类属性
C.z
将是10
,类属性C.t
将是20
,类属性C.x
和C.y
将不设置。
- class dataclasses.Field¶
Field
对象描述每个定义的字段。这些对象在内部创建,并由fields()
模块级方法返回(见下文)。用户永远不应该直接实例化Field
对象。 其有文档的属性是:name
:字段的名字。type
:字段的类型。default
,default_factory
,init
,repr
,hash
,compare
,metadata
和kw_only
具有与field()
函数中对应参数相同的含义和值。
可能存在其他属性,但它们是私有的,不能被审查或依赖。
- dataclasses.fields(class_or_instance)¶
返回
Field
对象的元组,用于定义此数据类的字段。 接受数据类或数据类的实例。如果没有传递一个数据类或实例将引发TypeError
。 不返回ClassVar
或InitVar
的伪字段。
- dataclasses.asdict(obj, *, dict_factory=dict)¶
将数据类``obj``转换为一个字典(通过使用工厂函数``dict_factory``)。 每个数据类被转换为其字段的字典,作为``name: value``键值对。数据类、字典、列表和元组被递归到。 其他对象用
copy.deepcopy()
来复制。在嵌套的数据类上使用
asdict()
的例子:@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}]}
要创建一个浅拷贝,可以使用以下方法:
dict((field.name, getattr(obj, field.name)) for field in fields(obj))
- dataclasses.astuple(obj, *, tuple_factory=tuple)¶
将数据类``obj``转换为一个元组(通过使用工厂函数``tuple_factory``)。 每个数据类被转换为其字段值的元组。数据类、字典、列表和元组被递归到。其他对象用
copy.deepcopy()
来复制。继续前一个例子:
assert astuple(p) == (10, 20) assert astuple(c) == ([(0, 0), (10, 4)],)
要创建一个浅拷贝,可以使用以下方法:
tuple(getattr(obj, field.name) for field in dataclasses.fields(obj))
- 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 infields
, base classes as given inbases
, and initialized with a namespace as given innamespace
.fields
is an iterable whose elements are each eithername
,(name, type)
, or(name, type, Field)
. If justname
is supplied,typing.Any
is used fortype
. The values ofinit
,repr
,eq
,order
,unsafe_hash
,frozen
,match_args
,kw_only
,slots
, andweakref_slot
have the same meaning as they do indataclass()
.此函数不是严格要求的,因为用于任何创建带有
__annotations__
的新类的 Python 机制都可以应用dataclass()
函数将该类转换为数据类。提供此功能是为了方便。例如:C = make_dataclass('C', [('x', int), 'y', ('z', int, field(default=5))], namespace={'add_one': lambda self: self.x + 1})
等价于
@dataclass class C: x: int y: 'typing.Any' z: int = 5 def add_one(self): return self.x + 1
- dataclasses.replace(obj, /, **changes)¶
创建一个与``obj``类型相同的新对象,将字段替换为来自``changes``的值。如果``obj``不是数据类,则引发
TypeError
。如果``changes``里面的值没有指定字段,引发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__.changes
包含任何定义为init=False
的字段是错误的。在这种情况下会引发ValueError
。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.
- dataclasses.is_dataclass(obj)¶
如果其形参为 dataclass 或其实例则返回
True
,否则返回False
。如果你需要知道一个类是否是一个数据类的实例(而不是一个数据类本身),那么再添加一个
not isinstance(obj, type)
检查:def is_dataclass_instance(obj): return is_dataclass(obj) and not isinstance(obj, type)
- dataclasses.MISSING¶
一个表示缺失 default 或 default_factory 的监视值。
- 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.在这个例子中,字段
y
和z
将被标记为仅限关键字字段:@dataclass class Point: x: float _: KW_ONLY y: float z: float p = Point(0, y=1.5, z=2.0)
在单个数据类中,指定一个以上
KW_ONLY
类型的字段将导致错误。在 3.10 版本新加入.
- 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
.
初始化后处理¶
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.
在其他用途中,这允许初始化依赖于一个或多个其他字段的字段值。例如:
@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:
@dataclass
class Rectangle:
height: float
width: float
@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.
类变量¶
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.
仅初始化变量¶
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.
例如,假设在创建类时没有为某个字段提供值,初始化时将从数据库中取值:
@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)
冻结的实例¶
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__()
.
继承¶
当数组由 dataclass()
装饰器创建时,它会查看反向 MRO 中的所有类的基类(即从 object
开始 ),并且对于它找到的每个数据类, 将该基类中的字段添加到字段的有序映射中。添加完所有基类字段后,它会将自己的字段添加到有序映射中。所有生成的方法都将使用这种组合的,计算的有序字段映射。由于字段是按插入顺序排列的,因此派生类会重载基类。一个例子:
@dataclass
class Base:
x: Any = 15.0
y: int = 0
@dataclass
class C(Base):
z: int = 10
x: int = 15
最后的字段列表依次是 x
、 y
、 z
。 x
的最终类型是 int
,如类 C
中所指定的那样。
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.
在这个例子中,Base.y
, Base.w
, and D.t
是仅限关键字字段,而 Base.x
和 D.z
是常规字段:
@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):
请注意形参原来在字段列表中出现的位置已被重新排序:前面是来自常规字段的形参而后面是来自仅限关键字字段的形参。
The relative ordering of keyword-only parameters is maintained in the
re-ordered __init__()
parameter list.
默认工厂函数¶
如果一个 field()
指定了一个 default_factory
,当需要该字段的默认值时,将使用零参数调用它。例如,要创建列表的新实例,请使用:
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.
可变的默认值¶
Python 在类属性中存储默认成员变量值。思考这个例子,不使用数据类:
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
请注意,类 C
的两个实例共享相同的类变量 x
,如预期的那样。
使用数据类, 如果 此代码有效:
@dataclass
class D:
x: list = [] # This code raises ValueError
def add(self, element):
self.x += element
它會生成類似的程式碼:
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, the
dataclass()
decorator will raise a TypeError
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.
使用默认工厂函数是一种创建可变类型新实例的方法,并将其作为字段的默认值:
@dataclass
class D:
x: list = field(default_factory=list)
assert D().x is not D().x
在 3.11 版本變更: 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.
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,
dataclasses
will call the descriptor's__get__
method using its class access form (i.e.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.