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__slots__的用法?

问题:__slots__的用法?

__slots__Python 的目的是什么-尤其是关于何时要使用它,何时不使用它?

What is the purpose of __slots__ in Python — especially with respect to when I would want to use it, and when not?


回答 0

在Python中,目的是__slots__什么?在什么情况下应该避免这种情况?

TLDR:

特殊属性__slots__允许您显式说明您希望对象实例具有哪些实例属性,并具有预期的结果:

  1. 更快的属性访问。
  2. 节省内存空间

节省的空间来自

  1. 将值引用存储在插槽中而不是中__dict__
  2. 如果父类拒绝它们并且您声明,则拒绝__dict____weakref__创建__slots__

快速警告

请注意,您只应在继承树中一次声明一个特定的插槽。例如:

class Base:
    __slots__ = 'foo', 'bar'

class Right(Base):
    __slots__ = 'baz', 

class Wrong(Base):
    __slots__ = 'foo', 'bar', 'baz'        # redundant foo and bar

遇到错误时,Python不会反对(它应该会),否则问题可能不会显现出来,但是您的对象将比原先占用更多的空间。Python 3.8:

>>> from sys import getsizeof
>>> getsizeof(Right()), getsizeof(Wrong())
(56, 72)

这是因为基准站的插槽描述符的插槽与错误的插槽分开。通常不应该这样,但是可以:

>>> w = Wrong()
>>> w.foo = 'foo'
>>> Base.foo.__get__(w)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: foo
>>> Wrong.foo.__get__(w)
'foo'

最大的警告是多重继承-无法将多个“具有非空插槽的父类”组合在一起。

为适应此限制,请遵循最佳做法:排除其父母(或他们的具体类)将共同继承的除一个或所有父代之外的所有抽象-给这些抽象留出空位(就像在父类中的抽象基类一样)标准库)。

有关示例,请参见下面有关多重继承的部分。

要求:

  • 要使名为in的属性__slots__实际上存储在插槽中而不是存储在插槽中__dict__,则类必须从继承object

  • 为防止创建__dict__,您必须继承,object并且继承中的所有类都必须声明,__slots__并且它们都不能具有'__dict__'条目。

如果您想继续阅读,有很多细节。

为什么使用__slots__:更快的属性访问。

Python的创建者Guido van Rossum 指出,他实际上是__slots__为了更快地访问属性而创建的。

证明可观的显着更快访问是微不足道的:

import timeit

class Foo(object): __slots__ = 'foo',

class Bar(object): pass

slotted = Foo()
not_slotted = Bar()

def get_set_delete_fn(obj):
    def get_set_delete():
        obj.foo = 'foo'
        obj.foo
        del obj.foo
    return get_set_delete

>>> min(timeit.repeat(get_set_delete_fn(slotted)))
0.2846834529991611
>>> min(timeit.repeat(get_set_delete_fn(not_slotted)))
0.3664822799983085

在Ubuntu 3.5上的Python 3.5中,插槽式访问的速度几乎快了30%。

>>> 0.3664822799983085 / 0.2846834529991611
1.2873325658284342

在Windows上的Python 2中,我测得的速度要快15%。

为何使用__slots__:内存节省

的另一个目的__slots__是减少每个对象实例占用的内存空间。

我自己对文档的贡献清楚地说明了背后的原因

通过使用节省的空间__dict__可能很大。

SQLAlchemy将大量内存节省归因__slots__

为了验证这一点,请在Ubuntu Linux上使用Python 2.7的Anaconda发行版(带有guppy.hpy(又是堆的)和)sys.getsizeof,不__slots__声明且没有其他声明的类实例的大小为64字节。但这包括__dict__。再次感谢Python的惰性求值,在__dict__引用它之前,显然不会调用,但是没有数据的类通常是无用的。当存在时,该__dict__属性另外至少为280个字节。

相反,__slots__声明为()(无数据)的类实例只有16个字节,插槽中有一项的总字节数为56个,插槽中有两项的总数为64个字节。

对于64位Python,我说明了dict在3.6中增长的每个点(0、1和2属性除外)的for __slots____dict__(未定义插槽)在Python 2.7和3.6中以字节为单位的内存消耗:

       Python 2.7             Python 3.6
attrs  __slots__  __dict__*   __slots__  __dict__* | *(no slots defined)
none   16         56 + 272   16         56 + 112 | if __dict__ referenced
one    48         56 + 272    48         56 + 112
two    56         56 + 272    56         56 + 112
six    88         56 + 1040   88         56 + 152
11     128        56 + 1040   128        56 + 240
22     216        56 + 3344   216        56 + 408     
43     384        56 + 3344   384        56 + 752

因此,尽管Python 3中的指令较小,但我们仍然可以看到__slots__实例可以很好地扩展以节省内存,这是您要使用的主要原因__slots__

只是为了完整起见,请注意,在类的命名空间中,每个插槽的一次性成本为Python 2中64字节,而在Python 3中为72字节,因为插槽使用数据描述符(如属性)称为“成员”。

>>> Foo.foo
<member 'foo' of 'Foo' objects>
>>> type(Foo.foo)
<class 'member_descriptor'>
>>> getsizeof(Foo.foo)
72

演示__slots__

要拒绝创建__dict__,必须子类化object

class Base(object): 
    __slots__ = ()

现在:

>>> b = Base()
>>> b.a = 'a'
Traceback (most recent call last):
  File "<pyshell#38>", line 1, in <module>
    b.a = 'a'
AttributeError: 'Base' object has no attribute 'a'

或子类化另一个定义的类 __slots__

class Child(Base):
    __slots__ = ('a',)

现在:

c = Child()
c.a = 'a'

但:

>>> c.b = 'b'
Traceback (most recent call last):
  File "<pyshell#42>", line 1, in <module>
    c.b = 'b'
AttributeError: 'Child' object has no attribute 'b'

要在对有槽位的__dict__对象进行子类化时允许创建,只需添加'__dict__'__slots__(请注意,槽位是有序的,并且您不应重复父类中已经存在的槽位):

class SlottedWithDict(Child): 
    __slots__ = ('__dict__', 'b')

swd = SlottedWithDict()
swd.a = 'a'
swd.b = 'b'
swd.c = 'c'

>>> swd.__dict__
{'c': 'c'}

或者甚至不需要__slots__在子类中声明,并且仍将使用父级的插槽,但不限制创建__dict__

class NoSlots(Child): pass
ns = NoSlots()
ns.a = 'a'
ns.b = 'b'

和:

>>> ns.__dict__
{'b': 'b'}

但是,__slots__可能会导致多重继承问题:

class BaseA(object): 
    __slots__ = ('a',)

class BaseB(object): 
    __slots__ = ('b',)

由于从具有两个非空插槽的父母创建子类失败:

>>> class Child(BaseA, BaseB): __slots__ = ()
Traceback (most recent call last):
  File "<pyshell#68>", line 1, in <module>
    class Child(BaseA, BaseB): __slots__ = ()
TypeError: Error when calling the metaclass bases
    multiple bases have instance lay-out conflict

如果遇到此问题,则可以将其__slots__从父级中移除,或者如果您可以控制父级,则给他们留空的空位,或重构为抽象:

from abc import ABC

class AbstractA(ABC):
    __slots__ = ()

class BaseA(AbstractA): 
    __slots__ = ('a',)

class AbstractB(ABC):
    __slots__ = ()

class BaseB(AbstractB): 
    __slots__ = ('b',)

class Child(AbstractA, AbstractB): 
    __slots__ = ('a', 'b')

c = Child() # no problem!

添加'__dict__'__slots__以获得动态分配:

class Foo(object):
    __slots__ = 'bar', 'baz', '__dict__'

现在:

>>> foo = Foo()
>>> foo.boink = 'boink'

因此,'__dict__'在具有插槽的情况下,我们将失去一些尺寸上的好处,因为它具有动态分配的优势,并且仍具有我们确实期望的名称的插槽。

当您从未插入槽的对象继承时,使用时会得到相同的语义__slots____slots__指向插入槽的值的名称,而其他所有值都放在实例的中__dict__

避免这样做__slots__是因为您不希望出现这种情况,因为它实际上并不是一个很好的理由- 如果需要,只需添加"__dict__"您的属性即可__slots__

如果需要该功能,可以类似地将__weakref____slots__显式添加。

子类化namedtuple时,设置为空tuple:

内置namedtuple使不可变的实例非常轻巧(本质上是元组的大小),但是要获得好处,如果您将它们子类化,则需要自己做:

from collections import namedtuple
class MyNT(namedtuple('MyNT', 'bar baz')):
    """MyNT is an immutable and lightweight object"""
    __slots__ = ()

用法:

>>> nt = MyNT('bar', 'baz')
>>> nt.bar
'bar'
>>> nt.baz
'baz'

尝试分配意外属性会引发,AttributeError因为我们已阻止创建__dict__

>>> nt.quux = 'quux'
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'MyNT' object has no attribute 'quux'

可以__dict__通过设置off 允许创建__slots__ = (),但是不能__slots__对元组的子类型使用非空。

最大的警告:多重继承

即使多个父级的非空插槽相同,也不能一起使用:

class Foo(object): 
    __slots__ = 'foo', 'bar'
class Bar(object):
    __slots__ = 'foo', 'bar' # alas, would work if empty, i.e. ()

>>> class Baz(Foo, Bar): pass
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: Error when calling the metaclass bases
    multiple bases have instance lay-out conflict

使用空__slots__父似乎提供了最大的灵活性,允许孩子选择阻止或允许(通过增加'__dict__'获得动态分配,见上面部分)创建的__dict__

class Foo(object): __slots__ = ()
class Bar(object): __slots__ = ()
class Baz(Foo, Bar): __slots__ = ('foo', 'bar')
b = Baz()
b.foo, b.bar = 'foo', 'bar'

你不具备有槽-因此,如果您添加它们,后来删除它们,它不应引起任何问题。

走出放在这里肢体:如果您撰写的混入或使用抽象基类,它不打算被实例化,空__slots__在那些父母似乎是在灵活性作为子类方面最好的一段路要走。

为了演示,首先,让我们创建一个我们希望在多重继承下使用的代码的类。

class AbstractBase:
    __slots__ = ()
    def __init__(self, a, b):
        self.a = a
        self.b = b
    def __repr__(self):
        return f'{type(self).__name__}({repr(self.a)}, {repr(self.b)})'

我们可以通过继承并声明预期的位置来直接使用以上内容:

class Foo(AbstractBase):
    __slots__ = 'a', 'b'

但是我们对此并不在意,这是微不足道的单一继承,我们需要另一个我们也可能继承的类,也许带有嘈杂的属性:

class AbstractBaseC:
    __slots__ = ()
    @property
    def c(self):
        print('getting c!')
        return self._c
    @c.setter
    def c(self, arg):
        print('setting c!')
        self._c = arg

现在,如果两个基地都有非空插槽,我们将无法进行以下操作。(实际上,如果我们愿意,我们可以给AbstractBase非空槽a和b,并将它们排除在下面的声明之外-将它们留在里面是错误的):

class Concretion(AbstractBase, AbstractBaseC):
    __slots__ = 'a b _c'.split()

现在,我们具有通过多重继承的功能,并且仍然可以拒绝__dict____weakref__实例化:

>>> c = Concretion('a', 'b')
>>> c.c = c
setting c!
>>> c.c
getting c!
Concretion('a', 'b')
>>> c.d = 'd'
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'Concretion' object has no attribute 'd'

其他避免插槽的情况:

  • __class__除非插槽布局相同,否则要在不具有它们的另一个类(并且不能添加它们)上执行分配时,请避免使用它们。(我对了解谁在做什么以及为什么这样做很感兴趣。)
  • 如果您想将诸如long,tuple或str之类的可变长度内建子类化,并想为其添加属性,请避免使用它们。
  • 如果您坚持通过实例变量的类属性提供默认值,请避免使用它们。

您也许可以从__slots__ 文档的其余部分(最新的3.7 dev文档)中找出更多的警告,我最近做出了很大的贡献。

对其他答案的批评

当前的最佳答案引用了过时的信息,而且非常容易波动,并且在某些重要方面未达到要求。

不要“仅__slots__在实例化许多对象时使用”

我引用:

__slots__如果要实例化大量(数百个,数千个)同一类的对象,则需要使用。”

例如,来自collections模块的抽象基类未实例化,但__slots__已为其声明。

为什么?

如果用户希望拒绝__dict____weakref__创建,则这些内容在父类中必须不可用。

__slots__ 创建接口或混入时有助于重用。

的确,许多Python用户并不是为可重用性而编写的,但是当您这样做时,可以选择拒绝不必要的空间使用是很有价值的。

__slots__ 不会破坏酸洗

腌制开槽的物体时,您可能会发现它带有误导性的抱怨TypeError

>>> pickle.loads(pickle.dumps(f))
TypeError: a class that defines __slots__ without defining __getstate__ cannot be pickled

这实际上是不正确的。此消息来自最早的协议,这是默认协议。您可以使用-1参数选择最新的协议。在Python 2.7中为2(在2.3中引入),在3.6中为4

>>> pickle.loads(pickle.dumps(f, -1))
<__main__.Foo object at 0x1129C770>

在Python 2.7中:

>>> pickle.loads(pickle.dumps(f, 2))
<__main__.Foo object at 0x1129C770>

在Python 3.6中

>>> pickle.loads(pickle.dumps(f, 4))
<__main__.Foo object at 0x1129C770>

所以我会牢记这一点,因为这是一个已解决的问题。

评论(至2016年10月2日)被接受

第一段是一半简短的解释,一半是预测的。这是真正回答问题的唯一部分

正确的用法__slots__是节省对象空间。静态结构不允许创建后添加任何内容,而不是具有允许随时向对象添加属性的动态命令。这样可以为使用插槽的每个对象节省一个指令的开销

后半部分是一厢情愿的想法,并且超出了预期:

尽管有时这是一个有用的优化,但是如果Python解释器足够动态,则仅在实际向对象添加内容时才需要dict,就完全没有必要了。

Python实际上做了类似的事情,只在__dict__访问时创建,但是创建许多没有数据的对象是相当荒谬的。

第二段过分简化,错过了避免的实际原因__slots__。以下不是避免使用插槽的真正原因(出于实际原因,请参阅上面我的回答的其余部分。):

它们以一种可被控制怪胎和静态类型临时表滥用的方式更改具有插槽的对象的行为。

然后,它继续讨论了使用Python实现该有害目标的其他方法,而不是讨论与之相关的任何方法__slots__

第三段是更多的如意算盘。答案者甚至根本没有写过这些杂乱的内容,而是为该网站的批评者弹药。

内存使用证据

创建一些普通对象和带槽对象:

>>> class Foo(object): pass
>>> class Bar(object): __slots__ = ()

实例化其中的一百万:

>>> foos = [Foo() for f in xrange(1000000)]
>>> bars = [Bar() for b in xrange(1000000)]

检查guppy.hpy().heap()

>>> guppy.hpy().heap()
Partition of a set of 2028259 objects. Total size = 99763360 bytes.
 Index  Count   %     Size   % Cumulative  % Kind (class / dict of class)
     0 1000000  49 64000000  64  64000000  64 __main__.Foo
     1     169   0 16281480  16  80281480  80 list
     2 1000000  49 16000000  16  96281480  97 __main__.Bar
     3   12284   1   987472   1  97268952  97 str
...

访问常规对象及其对象,__dict__然后再次检查:

>>> for f in foos:
...     f.__dict__
>>> guppy.hpy().heap()
Partition of a set of 3028258 objects. Total size = 379763480 bytes.
 Index  Count   %      Size    % Cumulative  % Kind (class / dict of class)
     0 1000000  33 280000000  74 280000000  74 dict of __main__.Foo
     1 1000000  33  64000000  17 344000000  91 __main__.Foo
     2     169   0  16281480   4 360281480  95 list
     3 1000000  33  16000000   4 376281480  99 __main__.Bar
     4   12284   0    987472   0 377268952  99 str
...

这与Python历史一致,来自Python 2.2中的Unifying类型和类。

如果您将内置类型作为子类,则多余的空间会自动添加到实例中以容纳__dict____weakrefs__。(__dict__尽管直到使用完,它才会被初始化,因此您不必担心空字典为您创建的每个实例所占用的空间。)如果不需要此多余的空间,则可以在短语中添加“ __slots__ = []”你的班。

In Python, what is the purpose of __slots__ and what are the cases one should avoid this?

TLDR:

The special attribute __slots__ allows you to explicitly state which instance attributes you expect your object instances to have, with the expected results:

  1. faster attribute access.
  2. space savings in memory.

The space savings is from

  1. Storing value references in slots instead of __dict__.
  2. Denying __dict__ and __weakref__ creation if parent classes deny them and you declare __slots__.

Quick Caveats

Small caveat, you should only declare a particular slot one time in an inheritance tree. For example:

class Base:
    __slots__ = 'foo', 'bar'

class Right(Base):
    __slots__ = 'baz', 

class Wrong(Base):
    __slots__ = 'foo', 'bar', 'baz'        # redundant foo and bar

Python doesn’t object when you get this wrong (it probably should), problems might not otherwise manifest, but your objects will take up more space than they otherwise should. Python 3.8:

>>> from sys import getsizeof
>>> getsizeof(Right()), getsizeof(Wrong())
(56, 72)

This is because the Base’s slot descriptor has a slot separate from the Wrong’s. This shouldn’t usually come up, but it could:

>>> w = Wrong()
>>> w.foo = 'foo'
>>> Base.foo.__get__(w)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: foo
>>> Wrong.foo.__get__(w)
'foo'

The biggest caveat is for multiple inheritance – multiple “parent classes with nonempty slots” cannot be combined.

To accommodate this restriction, follow best practices: Factor out all but one or all parents’ abstraction which their concrete class respectively and your new concrete class collectively will inherit from – giving the abstraction(s) empty slots (just like abstract base classes in the standard library).

See section on multiple inheritance below for an example.

Requirements:

  • To have attributes named in __slots__ to actually be stored in slots instead of a __dict__, a class must inherit from object.

  • To prevent the creation of a __dict__, you must inherit from object and all classes in the inheritance must declare __slots__ and none of them can have a '__dict__' entry.

There are a lot of details if you wish to keep reading.

Why use __slots__: Faster attribute access.

The creator of Python, Guido van Rossum, states that he actually created __slots__ for faster attribute access.

It is trivial to demonstrate measurably significant faster access:

import timeit

class Foo(object): __slots__ = 'foo',

class Bar(object): pass

slotted = Foo()
not_slotted = Bar()

def get_set_delete_fn(obj):
    def get_set_delete():
        obj.foo = 'foo'
        obj.foo
        del obj.foo
    return get_set_delete

and

>>> min(timeit.repeat(get_set_delete_fn(slotted)))
0.2846834529991611
>>> min(timeit.repeat(get_set_delete_fn(not_slotted)))
0.3664822799983085

The slotted access is almost 30% faster in Python 3.5 on Ubuntu.

>>> 0.3664822799983085 / 0.2846834529991611
1.2873325658284342

In Python 2 on Windows I have measured it about 15% faster.

Why use __slots__: Memory Savings

Another purpose of __slots__ is to reduce the space in memory that each object instance takes up.

My own contribution to the documentation clearly states the reasons behind this:

The space saved over using __dict__ can be significant.

SQLAlchemy attributes a lot of memory savings to __slots__.

To verify this, using the Anaconda distribution of Python 2.7 on Ubuntu Linux, with guppy.hpy (aka heapy) and sys.getsizeof, the size of a class instance without __slots__ declared, and nothing else, is 64 bytes. That does not include the __dict__. Thank you Python for lazy evaluation again, the __dict__ is apparently not called into existence until it is referenced, but classes without data are usually useless. When called into existence, the __dict__ attribute is a minimum of 280 bytes additionally.

In contrast, a class instance with __slots__ declared to be () (no data) is only 16 bytes, and 56 total bytes with one item in slots, 64 with two.

For 64 bit Python, I illustrate the memory consumption in bytes in Python 2.7 and 3.6, for __slots__ and __dict__ (no slots defined) for each point where the dict grows in 3.6 (except for 0, 1, and 2 attributes):

       Python 2.7             Python 3.6
attrs  __slots__  __dict__*   __slots__  __dict__* | *(no slots defined)
none   16         56 + 272†   16         56 + 112† | †if __dict__ referenced
one    48         56 + 272    48         56 + 112
two    56         56 + 272    56         56 + 112
six    88         56 + 1040   88         56 + 152
11     128        56 + 1040   128        56 + 240
22     216        56 + 3344   216        56 + 408     
43     384        56 + 3344   384        56 + 752

So, in spite of smaller dicts in Python 3, we see how nicely __slots__ scale for instances to save us memory, and that is a major reason you would want to use __slots__.

Just for completeness of my notes, note that there is a one-time cost per slot in the class’s namespace of 64 bytes in Python 2, and 72 bytes in Python 3, because slots use data descriptors like properties, called “members”.

>>> Foo.foo
<member 'foo' of 'Foo' objects>
>>> type(Foo.foo)
<class 'member_descriptor'>
>>> getsizeof(Foo.foo)
72

Demonstration of __slots__:

To deny the creation of a __dict__, you must subclass object:

class Base(object): 
    __slots__ = ()

now:

>>> b = Base()
>>> b.a = 'a'
Traceback (most recent call last):
  File "<pyshell#38>", line 1, in <module>
    b.a = 'a'
AttributeError: 'Base' object has no attribute 'a'

Or subclass another class that defines __slots__

class Child(Base):
    __slots__ = ('a',)

and now:

c = Child()
c.a = 'a'

but:

>>> c.b = 'b'
Traceback (most recent call last):
  File "<pyshell#42>", line 1, in <module>
    c.b = 'b'
AttributeError: 'Child' object has no attribute 'b'

To allow __dict__ creation while subclassing slotted objects, just add '__dict__' to the __slots__ (note that slots are ordered, and you shouldn’t repeat slots that are already in parent classes):

class SlottedWithDict(Child): 
    __slots__ = ('__dict__', 'b')

swd = SlottedWithDict()
swd.a = 'a'
swd.b = 'b'
swd.c = 'c'

and

>>> swd.__dict__
{'c': 'c'}

Or you don’t even need to declare __slots__ in your subclass, and you will still use slots from the parents, but not restrict the creation of a __dict__:

class NoSlots(Child): pass
ns = NoSlots()
ns.a = 'a'
ns.b = 'b'

And:

>>> ns.__dict__
{'b': 'b'}

However, __slots__ may cause problems for multiple inheritance:

class BaseA(object): 
    __slots__ = ('a',)

class BaseB(object): 
    __slots__ = ('b',)

Because creating a child class from parents with both non-empty slots fails:

>>> class Child(BaseA, BaseB): __slots__ = ()
Traceback (most recent call last):
  File "<pyshell#68>", line 1, in <module>
    class Child(BaseA, BaseB): __slots__ = ()
TypeError: Error when calling the metaclass bases
    multiple bases have instance lay-out conflict

If you run into this problem, You could just remove __slots__ from the parents, or if you have control of the parents, give them empty slots, or refactor to abstractions:

from abc import ABC

class AbstractA(ABC):
    __slots__ = ()

class BaseA(AbstractA): 
    __slots__ = ('a',)

class AbstractB(ABC):
    __slots__ = ()

class BaseB(AbstractB): 
    __slots__ = ('b',)

class Child(AbstractA, AbstractB): 
    __slots__ = ('a', 'b')

c = Child() # no problem!

Add '__dict__' to __slots__ to get dynamic assignment:

class Foo(object):
    __slots__ = 'bar', 'baz', '__dict__'

and now:

>>> foo = Foo()
>>> foo.boink = 'boink'

So with '__dict__' in slots we lose some of the size benefits with the upside of having dynamic assignment and still having slots for the names we do expect.

When you inherit from an object that isn’t slotted, you get the same sort of semantics when you use __slots__ – names that are in __slots__ point to slotted values, while any other values are put in the instance’s __dict__.

Avoiding __slots__ because you want to be able to add attributes on the fly is actually not a good reason – just add "__dict__" to your __slots__ if this is required.

You can similarly add __weakref__ to __slots__ explicitly if you need that feature.

Set to empty tuple when subclassing a namedtuple:

The namedtuple builtin make immutable instances that are very lightweight (essentially, the size of tuples) but to get the benefits, you need to do it yourself if you subclass them:

from collections import namedtuple
class MyNT(namedtuple('MyNT', 'bar baz')):
    """MyNT is an immutable and lightweight object"""
    __slots__ = ()

usage:

>>> nt = MyNT('bar', 'baz')
>>> nt.bar
'bar'
>>> nt.baz
'baz'

And trying to assign an unexpected attribute raises an AttributeError because we have prevented the creation of __dict__:

>>> nt.quux = 'quux'
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'MyNT' object has no attribute 'quux'

You can allow __dict__ creation by leaving off __slots__ = (), but you can’t use non-empty __slots__ with subtypes of tuple.

Biggest Caveat: Multiple inheritance

Even when non-empty slots are the same for multiple parents, they cannot be used together:

class Foo(object): 
    __slots__ = 'foo', 'bar'
class Bar(object):
    __slots__ = 'foo', 'bar' # alas, would work if empty, i.e. ()

>>> class Baz(Foo, Bar): pass
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: Error when calling the metaclass bases
    multiple bases have instance lay-out conflict

Using an empty __slots__ in the parent seems to provide the most flexibility, allowing the child to choose to prevent or allow (by adding '__dict__' to get dynamic assignment, see section above) the creation of a __dict__:

class Foo(object): __slots__ = ()
class Bar(object): __slots__ = ()
class Baz(Foo, Bar): __slots__ = ('foo', 'bar')
b = Baz()
b.foo, b.bar = 'foo', 'bar'

You don’t have to have slots – so if you add them, and remove them later, it shouldn’t cause any problems.

Going out on a limb here: If you’re composing mixins or using abstract base classes, which aren’t intended to be instantiated, an empty __slots__ in those parents seems to be the best way to go in terms of flexibility for subclassers.

To demonstrate, first, let’s create a class with code we’d like to use under multiple inheritance

class AbstractBase:
    __slots__ = ()
    def __init__(self, a, b):
        self.a = a
        self.b = b
    def __repr__(self):
        return f'{type(self).__name__}({repr(self.a)}, {repr(self.b)})'

We could use the above directly by inheriting and declaring the expected slots:

class Foo(AbstractBase):
    __slots__ = 'a', 'b'

But we don’t care about that, that’s trivial single inheritance, we need another class we might also inherit from, maybe with a noisy attribute:

class AbstractBaseC:
    __slots__ = ()
    @property
    def c(self):
        print('getting c!')
        return self._c
    @c.setter
    def c(self, arg):
        print('setting c!')
        self._c = arg

Now if both bases had nonempty slots, we couldn’t do the below. (In fact, if we wanted, we could have given AbstractBase nonempty slots a and b, and left them out of the below declaration – leaving them in would be wrong):

class Concretion(AbstractBase, AbstractBaseC):
    __slots__ = 'a b _c'.split()

And now we have functionality from both via multiple inheritance, and can still deny __dict__ and __weakref__ instantiation:

>>> c = Concretion('a', 'b')
>>> c.c = c
setting c!
>>> c.c
getting c!
Concretion('a', 'b')
>>> c.d = 'd'
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'Concretion' object has no attribute 'd'

Other cases to avoid slots:

  • Avoid them when you want to perform __class__ assignment with another class that doesn’t have them (and you can’t add them) unless the slot layouts are identical. (I am very interested in learning who is doing this and why.)
  • Avoid them if you want to subclass variable length builtins like long, tuple, or str, and you want to add attributes to them.
  • Avoid them if you insist on providing default values via class attributes for instance variables.

You may be able to tease out further caveats from the rest of the __slots__ documentation (the 3.7 dev docs are the most current), which I have made significant recent contributions to.

Critiques of other answers

The current top answers cite outdated information and are quite hand-wavy and miss the mark in some important ways.

Do not “only use __slots__ when instantiating lots of objects”

I quote:

“You would want to use __slots__ if you are going to instantiate a lot (hundreds, thousands) of objects of the same class.”

Abstract Base Classes, for example, from the collections module, are not instantiated, yet __slots__ are declared for them.

Why?

If a user wishes to deny __dict__ or __weakref__ creation, those things must not be available in the parent classes.

__slots__ contributes to reusability when creating interfaces or mixins.

It is true that many Python users aren’t writing for reusability, but when you are, having the option to deny unnecessary space usage is valuable.

__slots__ doesn’t break pickling

When pickling a slotted object, you may find it complains with a misleading TypeError:

>>> pickle.loads(pickle.dumps(f))
TypeError: a class that defines __slots__ without defining __getstate__ cannot be pickled

This is actually incorrect. This message comes from the oldest protocol, which is the default. You can select the latest protocol with the -1 argument. In Python 2.7 this would be 2 (which was introduced in 2.3), and in 3.6 it is 4.

>>> pickle.loads(pickle.dumps(f, -1))
<__main__.Foo object at 0x1129C770>

in Python 2.7:

>>> pickle.loads(pickle.dumps(f, 2))
<__main__.Foo object at 0x1129C770>

in Python 3.6

>>> pickle.loads(pickle.dumps(f, 4))
<__main__.Foo object at 0x1129C770>

So I would keep this in mind, as it is a solved problem.

Critique of the (until Oct 2, 2016) accepted answer

The first paragraph is half short explanation, half predictive. Here’s the only part that actually answers the question

The proper use of __slots__ is to save space in objects. Instead of having a dynamic dict that allows adding attributes to objects at anytime, there is a static structure which does not allow additions after creation. This saves the overhead of one dict for every object that uses slots

The second half is wishful thinking, and off the mark:

While this is sometimes a useful optimization, it would be completely unnecessary if the Python interpreter was dynamic enough so that it would only require the dict when there actually were additions to the object.

Python actually does something similar to this, only creating the __dict__ when it is accessed, but creating lots of objects with no data is fairly ridiculous.

The second paragraph oversimplifies and misses actual reasons to avoid __slots__. The below is not a real reason to avoid slots (for actual reasons, see the rest of my answer above.):

They change the behavior of the objects that have slots in a way that can be abused by control freaks and static typing weenies.

It then goes on to discuss other ways of accomplishing that perverse goal with Python, not discussing anything to do with __slots__.

The third paragraph is more wishful thinking. Together it is mostly off-the-mark content that the answerer didn’t even author and contributes to ammunition for critics of the site.

Memory usage evidence

Create some normal objects and slotted objects:

>>> class Foo(object): pass
>>> class Bar(object): __slots__ = ()

Instantiate a million of them:

>>> foos = [Foo() for f in xrange(1000000)]
>>> bars = [Bar() for b in xrange(1000000)]

Inspect with guppy.hpy().heap():

>>> guppy.hpy().heap()
Partition of a set of 2028259 objects. Total size = 99763360 bytes.
 Index  Count   %     Size   % Cumulative  % Kind (class / dict of class)
     0 1000000  49 64000000  64  64000000  64 __main__.Foo
     1     169   0 16281480  16  80281480  80 list
     2 1000000  49 16000000  16  96281480  97 __main__.Bar
     3   12284   1   987472   1  97268952  97 str
...

Access the regular objects and their __dict__ and inspect again:

>>> for f in foos:
...     f.__dict__
>>> guppy.hpy().heap()
Partition of a set of 3028258 objects. Total size = 379763480 bytes.
 Index  Count   %      Size    % Cumulative  % Kind (class / dict of class)
     0 1000000  33 280000000  74 280000000  74 dict of __main__.Foo
     1 1000000  33  64000000  17 344000000  91 __main__.Foo
     2     169   0  16281480   4 360281480  95 list
     3 1000000  33  16000000   4 376281480  99 __main__.Bar
     4   12284   0    987472   0 377268952  99 str
...

This is consistent with the history of Python, from Unifying types and classes in Python 2.2

If you subclass a built-in type, extra space is automatically added to the instances to accomodate __dict__ and __weakrefs__. (The __dict__ is not initialized until you use it though, so you shouldn’t worry about the space occupied by an empty dictionary for each instance you create.) If you don’t need this extra space, you can add the phrase “__slots__ = []” to your class.


回答 1

引用雅各布·哈伦Jacob Hallen)的话

正确的用法__slots__是节省对象空间。静态结构不允许创建后添加任何内容,而不是具有允许随时向对象添加属性的动态命令。[这种使用__slots__消除了每个对象一个字典的开销。]尽管这有时是有用的优化,但是如果Python解释器足够动态,以至仅在实际添加了dict时才需要该字典,则完全没有必要。宾语。

不幸的是,插槽有副作用。它们以一种可被控制怪胎和静态类型临时表滥用的方式更改具有插槽的对象的行为。这是不好的,因为控制怪胎应该滥用元类,而静态类型之间应该滥用装饰器,因为在Python中,应该只有一种明显的方法。

使CPython足够智能来处理节省的空间__slots__是一项艰巨的任务,这可能就是为什么它不在P3k更改列表中的原因(至今)。

Quoting Jacob Hallen:

The proper use of __slots__ is to save space in objects. Instead of having a dynamic dict that allows adding attributes to objects at anytime, there is a static structure which does not allow additions after creation. [This use of __slots__ eliminates the overhead of one dict for every object.] While this is sometimes a useful optimization, it would be completely unnecessary if the Python interpreter was dynamic enough so that it would only require the dict when there actually were additions to the object.

Unfortunately there is a side effect to slots. They change the behavior of the objects that have slots in a way that can be abused by control freaks and static typing weenies. This is bad, because the control freaks should be abusing the metaclasses and the static typing weenies should be abusing decorators, since in Python, there should be only one obvious way of doing something.

Making CPython smart enough to handle saving space without __slots__ is a major undertaking, which is probably why it is not on the list of changes for P3k (yet).


回答 2

你会想使用__slots__,如果你要实例化同一个类的对象很多(几百,几千)。__slots__仅作为内存优化工具存在。

不建议将其__slots__用于约束属性创建。

__slots__使用默认的(最旧的)泡菜协议将不能使用酸洗对象。有必要指定一个更高的版本。

python的其他一些自省功能也可能受到不利影响。

You would want to use __slots__ if you are going to instantiate a lot (hundreds, thousands) of objects of the same class. __slots__ only exists as a memory optimization tool.

It’s highly discouraged to use __slots__ for constraining attribute creation.

Pickling objects with __slots__ won’t work with the default (oldest) pickle protocol; it’s necessary to specify a later version.

Some other introspection features of python may also be adversely affected.


回答 3

每个python对象都有一个__dict__属性,该属性是包含所有其他属性的字典。例如,当您键入self.attrpython时实际上正在执行self.__dict__['attr']。您可以想象使用字典存储属性会花费一些额外的空间和时间来访问它。

但是,当您使用 __slots__,为该类创建的任何对象将没有__dict__属性。相反,所有属性访问都直接通过指针进行。

因此,如果要使用C样式结构而不是完整的类,则可以使用它__slots__来压缩对象的大小并减少属性访问时间。一个很好的例子是一个包含属性x&y的Point类。如果您有很多要点,可以尝试使用__slots__以节省一些内存。

Each python object has a __dict__ atttribute which is a dictionary containing all other attributes. e.g. when you type self.attr python is actually doing self.__dict__['attr']. As you can imagine using a dictionary to store attribute takes some extra space & time for accessing it.

However, when you use __slots__, any object created for that class won’t have a __dict__ attribute. Instead, all attribute access is done directly via pointers.

So if want a C style structure rather than a full fledged class you can use __slots__ for compacting size of the objects & reducing attribute access time. A good example is a Point class containing attributes x & y. If you are going to have a lot of points, you can try using __slots__ in order to conserve some memory.


回答 4

除了其他答案,这是使用的示例__slots__

>>> class Test(object):   #Must be new-style class!
...  __slots__ = ['x', 'y']
... 
>>> pt = Test()
>>> dir(pt)
['__class__', '__delattr__', '__doc__', '__getattribute__', '__hash__', 
 '__init__', '__module__', '__new__', '__reduce__', '__reduce_ex__', 
 '__repr__', '__setattr__', '__slots__', '__str__', 'x', 'y']
>>> pt.x
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: x
>>> pt.x = 1
>>> pt.x
1
>>> pt.z = 2
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'Test' object has no attribute 'z'
>>> pt.__dict__
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'Test' object has no attribute '__dict__'
>>> pt.__slots__
['x', 'y']

因此,要实现__slots__,只需要多花一行(如果还没有,请将您的类变成新样式的类)。这样,您可以将这些类的内存占用量减少5倍,而在必要时以及必须编写自定义的pickle代码的代价是。

In addition to the other answers, here is an example of using __slots__:

>>> class Test(object):   #Must be new-style class!
...  __slots__ = ['x', 'y']
... 
>>> pt = Test()
>>> dir(pt)
['__class__', '__delattr__', '__doc__', '__getattribute__', '__hash__', 
 '__init__', '__module__', '__new__', '__reduce__', '__reduce_ex__', 
 '__repr__', '__setattr__', '__slots__', '__str__', 'x', 'y']
>>> pt.x
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: x
>>> pt.x = 1
>>> pt.x
1
>>> pt.z = 2
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'Test' object has no attribute 'z'
>>> pt.__dict__
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'Test' object has no attribute '__dict__'
>>> pt.__slots__
['x', 'y']

So, to implement __slots__, it only takes an extra line (and making your class a new-style class if it isn’t already). This way you can reduce the memory footprint of those classes 5-fold, at the expense of having to write custom pickle code, if and when that becomes necessary.


回答 5

插槽对于库调用非常有用,以消除进行函数调用时的“命名方法分派”。SWIG 文档中提到了这一点。对于想要减少使用插槽的通常称为函数的函数开销的高性能库,速度要快得多。

现在,这可能与OP问题没有直接关系。它与构建扩展有关,而不是与在对象上使用slot语法有关。但这确实有助于完整了解插槽的使用情况以及它们背后的一些原因。

Slots are very useful for library calls to eliminate the “named method dispatch” when making function calls. This is mentioned in the SWIG documentation. For high performance libraries that want to reduce function overhead for commonly called functions using slots is much faster.

Now this may not be directly related to the OPs question. It is related more to building extensions than it does to using the slots syntax on an object. But it does help complete the picture for the usage of slots and some of the reasoning behind them.


回答 6

类实例的属性具有3个属性:实例,属性名称和属性值。

常规属性访问中,实例充当字典,而属性名称充当该字典中查找值的键。

instance(attribute)->值

__slots__访问,属性名称充当字典,实例充当字典中查找值的键。

属性(实例)->值

flyweight模式中,属性的名称充当字典,而值充当该字典中查找实例的键。

attribute(value)->实例

An attribute of a class instance has 3 properties: the instance, the name of the attribute, and the value of the attribute.

In regular attribute access, the instance acts as a dictionary and the name of the attribute acts as the key in that dictionary looking up value.

instance(attribute) –> value

In __slots__ access, the name of the attribute acts as the dictionary and the instance acts as the key in the dictionary looking up value.

attribute(instance) –> value

In flyweight pattern, the name of the attribute acts as the dictionary and the value acts as the key in that dictionary looking up the instance.

attribute(value) –> instance


回答 7

一个非常简单的__slot__属性示例。

问题:无 __slots__

如果__slot__我的Class中没有属性,则可以向对象添加新属性。

class Test:
    pass

obj1=Test()
obj2=Test()

print(obj1.__dict__)  #--> {}
obj1.x=12
print(obj1.__dict__)  # --> {'x': 12}
obj1.y=20
print(obj1.__dict__)  # --> {'x': 12, 'y': 20}

obj2.x=99
print(obj2.__dict__)  # --> {'x': 99}

如果您看上面的示例,可以看到obj1obj2都有自己的xy属性,而python还dict为每个对象创建了一个属性(obj1obj2)。

假设我的类Test是否有成千上万个此类对象?dict为每个对象创建一个附加属性将导致我的代码中大量开销(内存,计算能力等)。

解决方案: __slots__

现在,在下面的示例中,我的类Test包含__slots__属性。现在,我无法向对象添加新属性(attribute除外x),而python不再创建dict属性。这消除了每个对象的开销,如果您有很多对象,开销可能会变得很大。

class Test:
    __slots__=("x")

obj1=Test()
obj2=Test()
obj1.x=12
print(obj1.x)  # --> 12
obj2.x=99
print(obj2.x)  # --> 99

obj1.y=28
print(obj1.y)  # --> AttributeError: 'Test' object has no attribute 'y'

A very simple example of __slot__ attribute.

Problem: Without __slots__

If I don’t have __slot__ attribute in my class, I can add new attributes to my objects.

class Test:
    pass

obj1=Test()
obj2=Test()

print(obj1.__dict__)  #--> {}
obj1.x=12
print(obj1.__dict__)  # --> {'x': 12}
obj1.y=20
print(obj1.__dict__)  # --> {'x': 12, 'y': 20}

obj2.x=99
print(obj2.__dict__)  # --> {'x': 99}

If you look at example above, you can see that obj1 and obj2 have their own x and y attributes and python has also created a dict attribute for each object (obj1 and obj2).

Suppose if my class Test has thousands of such objects? Creating an additional attribute dict for each object will cause lot of overhead (memory, computing power etc.) in my code.

Solution: With __slots__

Now in the following example my class Test contains __slots__ attribute. Now I can’t add new attributes to my objects (except attribute x) and python doesn’t create a dict attribute anymore. This eliminates overhead for each object, which can become significant if you have many objects.

class Test:
    __slots__=("x")

obj1=Test()
obj2=Test()
obj1.x=12
print(obj1.x)  # --> 12
obj2.x=99
print(obj2.x)  # --> 99

obj1.y=28
print(obj1.y)  # --> AttributeError: 'Test' object has no attribute 'y'

回答 8

另一个晦涩的用法__slots__是从ProxyTypes包(以前是PEAK项目的一部分)中向对象代理添加属性。它ObjectWrapper允许您代理另一个对象,但拦截与代理对象的所有交互。它不是很常用(并且没有Python 3支持),但是我们已经使用它来实现基于龙卷风的异步实现周围的线程安全的阻塞包装,该龙卷风使用线程安全来反弹通过ioloop对代理对象的所有访问concurrent.Future对象以同步并返回结果。

默认情况下,对代理对象的任何属性访问都将为您提供代理对象的结果。如果需要在代理对象上添加属性,__slots__则可以使用。

from peak.util.proxies import ObjectWrapper

class Original(object):
    def __init__(self):
        self.name = 'The Original'

class ProxyOriginal(ObjectWrapper):

    __slots__ = ['proxy_name']

    def __init__(self, subject, proxy_name):
        # proxy_info attributed added directly to the
        # Original instance, not the ProxyOriginal instance
        self.proxy_info = 'You are proxied by {}'.format(proxy_name)

        # proxy_name added to ProxyOriginal instance, since it is
        # defined in __slots__
        self.proxy_name = proxy_name

        super(ProxyOriginal, self).__init__(subject)

if __name__ == "__main__":
    original = Original()
    proxy = ProxyOriginal(original, 'Proxy Overlord')

    # Both statements print "The Original"
    print "original.name: ", original.name
    print "proxy.name: ", proxy.name

    # Both statements below print 
    # "You are proxied by Proxy Overlord", since the ProxyOriginal
    # __init__ sets it to the original object 
    print "original.proxy_info: ", original.proxy_info
    print "proxy.proxy_info: ", proxy.proxy_info

    # prints "Proxy Overlord"
    print "proxy.proxy_name: ", proxy.proxy_name
    # Raises AttributeError since proxy_name is only set on 
    # the proxy object
    print "original.proxy_name: ", proxy.proxy_name

Another somewhat obscure use of __slots__ is to add attributes to an object proxy from the ProxyTypes package, formerly part of the PEAK project. Its ObjectWrapper allows you to proxy another object, but intercept all interactions with the proxied object. It is not very commonly used (and no Python 3 support), but we have used it to implement a thread-safe blocking wrapper around an async implementation based on tornado that bounces all access to the proxied object through the ioloop, using thread-safe concurrent.Future objects to synchronise and return results.

By default any attribute access to the proxy object will give you the result from the proxied object. If you need to add an attribute on the proxy object, __slots__ can be used.

from peak.util.proxies import ObjectWrapper

class Original(object):
    def __init__(self):
        self.name = 'The Original'

class ProxyOriginal(ObjectWrapper):

    __slots__ = ['proxy_name']

    def __init__(self, subject, proxy_name):
        # proxy_info attributed added directly to the
        # Original instance, not the ProxyOriginal instance
        self.proxy_info = 'You are proxied by {}'.format(proxy_name)

        # proxy_name added to ProxyOriginal instance, since it is
        # defined in __slots__
        self.proxy_name = proxy_name

        super(ProxyOriginal, self).__init__(subject)

if __name__ == "__main__":
    original = Original()
    proxy = ProxyOriginal(original, 'Proxy Overlord')

    # Both statements print "The Original"
    print "original.name: ", original.name
    print "proxy.name: ", proxy.name

    # Both statements below print 
    # "You are proxied by Proxy Overlord", since the ProxyOriginal
    # __init__ sets it to the original object 
    print "original.proxy_info: ", original.proxy_info
    print "proxy.proxy_info: ", proxy.proxy_info

    # prints "Proxy Overlord"
    print "proxy.proxy_name: ", proxy.proxy_name
    # Raises AttributeError since proxy_name is only set on 
    # the proxy object
    print "original.proxy_name: ", proxy.proxy_name

回答 9

您基本上没有用__slots__

在您认为自己可能需要的时间里__slots__,您实际上想使用轻量级轻量级设计模式。在这些情况下,您不再希望使用纯Python对象。相反,您希望在数组,结构或numpy数组周围使用类似Python对象的包装器。

class Flyweight(object):

    def get(self, theData, index):
        return theData[index]

    def set(self, theData, index, value):
        theData[index]= value

类似于类的包装器没有属性-它仅提供对基础数据起作用的方法。这些方法可以简化为类方法。实际上,可以将其简化为仅对底层数据数组进行操作的函数。

You have — essentially — no use for __slots__.

For the time when you think you might need __slots__, you actually want to use Lightweight or Flyweight design patterns. These are cases when you no longer want to use purely Python objects. Instead, you want a Python object-like wrapper around an array, struct, or numpy array.

class Flyweight(object):

    def get(self, theData, index):
        return theData[index]

    def set(self, theData, index, value):
        theData[index]= value

The class-like wrapper has no attributes — it just provides methods that act on the underlying data. The methods can be reduced to class methods. Indeed, it could be reduced to just functions operating on the underlying array of data.


回答 10

最初的问题是关于一般用例,而不仅仅是内存。因此,在这里应该提到的是,在实例化大量对象时,您也会获得更好的性能 -有趣的是,例如,将大型文档解析为对象或从数据库中解析时。

这是使用插槽和不使用插槽创建具有一百万个条目的对象树的比较。作为参考,在对树使用普通字典时的性能(在OSX上为Py2.7.10):

********** RUN 1 **********
1.96036410332 <class 'css_tree_select.element.Element'>
3.02922606468 <class 'css_tree_select.element.ElementNoSlots'>
2.90828204155 dict
********** RUN 2 **********
1.77050495148 <class 'css_tree_select.element.Element'>
3.10655999184 <class 'css_tree_select.element.ElementNoSlots'>
2.84120798111 dict
********** RUN 3 **********
1.84069895744 <class 'css_tree_select.element.Element'>
3.21540498734 <class 'css_tree_select.element.ElementNoSlots'>
2.59615707397 dict
********** RUN 4 **********
1.75041103363 <class 'css_tree_select.element.Element'>
3.17366290092 <class 'css_tree_select.element.ElementNoSlots'>
2.70941114426 dict

测试类(ident,来自插槽的appart):

class Element(object):
    __slots__ = ['_typ', 'id', 'parent', 'childs']
    def __init__(self, typ, id, parent=None):
        self._typ = typ
        self.id = id
        self.childs = []
        if parent:
            self.parent = parent
            parent.childs.append(self)

class ElementNoSlots(object): (same, w/o slots)

测试代码,详细模式:

na, nb, nc = 100, 100, 100
for i in (1, 2, 3, 4):
    print '*' * 10, 'RUN', i, '*' * 10
    # tree with slot and no slot:
    for cls in Element, ElementNoSlots:
        t1 = time.time()
        root = cls('root', 'root')
        for i in xrange(na):
            ela = cls(typ='a', id=i, parent=root)
            for j in xrange(nb):
                elb = cls(typ='b', id=(i, j), parent=ela)
                for k in xrange(nc):
                    elc = cls(typ='c', id=(i, j, k), parent=elb)
        to =  time.time() - t1
        print to, cls
        del root

    # ref: tree with dicts only:
    t1 = time.time()
    droot = {'childs': []}
    for i in xrange(na):
        ela =  {'typ': 'a', id: i, 'childs': []}
        droot['childs'].append(ela)
        for j in xrange(nb):
            elb =  {'typ': 'b', id: (i, j), 'childs': []}
            ela['childs'].append(elb)
            for k in xrange(nc):
                elc =  {'typ': 'c', id: (i, j, k), 'childs': []}
                elb['childs'].append(elc)
    td = time.time() - t1
    print td, 'dict'
    del droot

The original question was about general use cases not only about memory. So it should be mentioned here that you also get better performance when instantiating large amounts of objects – interesting e.g. when parsing large documents into objects or from a database.

Here is a comparison of creating object trees with a million entries, using slots and without slots. As a reference also the performance when using plain dicts for the trees (Py2.7.10 on OSX):

********** RUN 1 **********
1.96036410332 <class 'css_tree_select.element.Element'>
3.02922606468 <class 'css_tree_select.element.ElementNoSlots'>
2.90828204155 dict
********** RUN 2 **********
1.77050495148 <class 'css_tree_select.element.Element'>
3.10655999184 <class 'css_tree_select.element.ElementNoSlots'>
2.84120798111 dict
********** RUN 3 **********
1.84069895744 <class 'css_tree_select.element.Element'>
3.21540498734 <class 'css_tree_select.element.ElementNoSlots'>
2.59615707397 dict
********** RUN 4 **********
1.75041103363 <class 'css_tree_select.element.Element'>
3.17366290092 <class 'css_tree_select.element.ElementNoSlots'>
2.70941114426 dict

Test classes (ident, appart from slots):

class Element(object):
    __slots__ = ['_typ', 'id', 'parent', 'childs']
    def __init__(self, typ, id, parent=None):
        self._typ = typ
        self.id = id
        self.childs = []
        if parent:
            self.parent = parent
            parent.childs.append(self)

class ElementNoSlots(object): (same, w/o slots)

testcode, verbose mode:

na, nb, nc = 100, 100, 100
for i in (1, 2, 3, 4):
    print '*' * 10, 'RUN', i, '*' * 10
    # tree with slot and no slot:
    for cls in Element, ElementNoSlots:
        t1 = time.time()
        root = cls('root', 'root')
        for i in xrange(na):
            ela = cls(typ='a', id=i, parent=root)
            for j in xrange(nb):
                elb = cls(typ='b', id=(i, j), parent=ela)
                for k in xrange(nc):
                    elc = cls(typ='c', id=(i, j, k), parent=elb)
        to =  time.time() - t1
        print to, cls
        del root

    # ref: tree with dicts only:
    t1 = time.time()
    droot = {'childs': []}
    for i in xrange(na):
        ela =  {'typ': 'a', id: i, 'childs': []}
        droot['childs'].append(ela)
        for j in xrange(nb):
            elb =  {'typ': 'b', id: (i, j), 'childs': []}
            ela['childs'].append(elb)
            for k in xrange(nc):
                elc =  {'typ': 'c', id: (i, j, k), 'childs': []}
                elb['childs'].append(elc)
    td = time.time() - t1
    print td, 'dict'
    del droot

Python中可以使用静态类变量吗?

问题:Python中可以使用静态类变量吗?

Python中是否可以有静态类变量或方法?为此需要什么语法?

Is it possible to have static class variables or methods in Python? What syntax is required to do this?


回答 0

在类定义中声明但在方法内部声明的变量是类或静态变量:

>>> class MyClass:
...     i = 3
...
>>> MyClass.i
3 

正如@ millerdev指出的那样,这将创建一个类级别的i变量,但这不同于任何实例级别的i变量,因此您可以

>>> m = MyClass()
>>> m.i = 4
>>> MyClass.i, m.i
>>> (3, 4)

这与C ++和Java不同,但与C#并没有太大区别,在C#中,无法使用对实例的引用来访问静态成员。

了解有关类和类对象的Python教程必须说些什么

@Steve Johnson已经回答了有关静态方法的问题,该方法也记录在Python Library Reference中的“内置函数”下

class C:
    @staticmethod
    def f(arg1, arg2, ...): ...

@beidy建议使用classmethod而不是staticmethod,因为该方法随后将类类型作为第一个参数,但是对于这种方法相对于staticmethod的优势,我还是有些模糊。如果您也是,那可能没关系。

Variables declared inside the class definition, but not inside a method are class or static variables:

>>> class MyClass:
...     i = 3
...
>>> MyClass.i
3 

As @millerdev points out, this creates a class-level i variable, but this is distinct from any instance-level i variable, so you could have

>>> m = MyClass()
>>> m.i = 4
>>> MyClass.i, m.i
>>> (3, 4)

This is different from C++ and Java, but not so different from C#, where a static member can’t be accessed using a reference to an instance.

See what the Python tutorial has to say on the subject of classes and class objects.

@Steve Johnson has already answered regarding static methods, also documented under “Built-in Functions” in the Python Library Reference.

class C:
    @staticmethod
    def f(arg1, arg2, ...): ...

@beidy recommends classmethods over staticmethod, as the method then receives the class type as the first argument, but I’m still a little fuzzy on the advantages of this approach over staticmethod. If you are too, then it probably doesn’t matter.


回答 1

@Blair Conrad说,在类定义中声明但在方法内部声明的静态变量是类或“静态”变量:

>>> class Test(object):
...     i = 3
...
>>> Test.i
3

这里有一些陷阱。从上面的示例继续进行:

>>> t = Test()
>>> t.i     # "static" variable accessed via instance
3
>>> t.i = 5 # but if we assign to the instance ...
>>> Test.i  # we have not changed the "static" variable
3
>>> t.i     # we have overwritten Test.i on t by creating a new attribute t.i
5
>>> Test.i = 6 # to change the "static" variable we do it by assigning to the class
>>> t.i
5
>>> Test.i
6
>>> u = Test()
>>> u.i
6           # changes to t do not affect new instances of Test

# Namespaces are one honking great idea -- let's do more of those!
>>> Test.__dict__
{'i': 6, ...}
>>> t.__dict__
{'i': 5}
>>> u.__dict__
{}

请注意,直接t.i将属性i设置为时,实例变量如何与“静态”类变量不同步t。这是因为i已在t命名空间中重新绑定,这与Test命名空间不同。如果要更改“静态”变量的值,则必须在其最初定义的范围(或对象)内进行更改。我将“ static”用引号引起来,因为Python实际上没有C ++和Java所具有的静态变量。

尽管它没有对静态变量或方法进行任何具体说明,但是Python教程提供了有关类和类对象的一些相关信息。

@Steve Johnson还回答了有关静态方法的问题,该方法也记录在Python库参考的“内置函数”下。

class Test(object):
    @staticmethod
    def f(arg1, arg2, ...):
        ...

@beid还提到了classmethod,它与staticmethod相似。类方法的第一个参数是类对象。例:

class Test(object):
    i = 3 # class (or static) variable
    @classmethod
    def g(cls, arg):
        # here we can use 'cls' instead of the class name (Test)
        if arg > cls.i:
            cls.i = arg # would be the same as Test.i = arg1

以上示例的图形表示

@Blair Conrad said static variables declared inside the class definition, but not inside a method are class or “static” variables:

>>> class Test(object):
...     i = 3
...
>>> Test.i
3

There are a few gotcha’s here. Carrying on from the example above:

>>> t = Test()
>>> t.i     # "static" variable accessed via instance
3
>>> t.i = 5 # but if we assign to the instance ...
>>> Test.i  # we have not changed the "static" variable
3
>>> t.i     # we have overwritten Test.i on t by creating a new attribute t.i
5
>>> Test.i = 6 # to change the "static" variable we do it by assigning to the class
>>> t.i
5
>>> Test.i
6
>>> u = Test()
>>> u.i
6           # changes to t do not affect new instances of Test

# Namespaces are one honking great idea -- let's do more of those!
>>> Test.__dict__
{'i': 6, ...}
>>> t.__dict__
{'i': 5}
>>> u.__dict__
{}

Notice how the instance variable t.i got out of sync with the “static” class variable when the attribute i was set directly on t. This is because i was re-bound within the t namespace, which is distinct from the Test namespace. If you want to change the value of a “static” variable, you must change it within the scope (or object) where it was originally defined. I put “static” in quotes because Python does not really have static variables in the sense that C++ and Java do.

Although it doesn’t say anything specific about static variables or methods, the Python tutorial has some relevant information on classes and class objects.

@Steve Johnson also answered regarding static methods, also documented under “Built-in Functions” in the Python Library Reference.

class Test(object):
    @staticmethod
    def f(arg1, arg2, ...):
        ...

@beid also mentioned classmethod, which is similar to staticmethod. A classmethod’s first argument is the class object. Example:

class Test(object):
    i = 3 # class (or static) variable
    @classmethod
    def g(cls, arg):
        # here we can use 'cls' instead of the class name (Test)
        if arg > cls.i:
            cls.i = arg # would be the same as Test.i = arg1

Pictorial Representation Of Above Example


回答 2

静态和类方法

正如其他答案所指出的,使用内置装饰器可以轻松实现静态和类方法:

class Test(object):

    # regular instance method:
    def MyMethod(self):
        pass

    # class method:
    @classmethod
    def MyClassMethod(klass):
        pass

    # static method:
    @staticmethod
    def MyStaticMethod():
        pass

通常,第一个参数to MyMethod()绑定到类实例对象。与此相反,第一个参数MyClassMethod()绑定到类对象本身(例如,在这种情况下,Test)。对于MyStaticMethod(),没有参数绑定,并且完全没有参数是可选的。

“静态变量”

然而,实现“静态变量”(无论如何,可变静态变量,如果这不是一个矛盾的话……)并不是那么简单。正如millerdev 在回答中指出的那样,问题在于Python的类属性并不是真正的“静态变量”。考虑:

class Test(object):
    i = 3  # This is a class attribute

x = Test()
x.i = 12   # Attempt to change the value of the class attribute using x instance
assert x.i == Test.i  # ERROR
assert Test.i == 3    # Test.i was not affected
assert x.i == 12      # x.i is a different object than Test.i

这是因为该行x.i = 12向其中添加了新的实例属性ix而不是更改Testclass i属性的值。

可以通过将class属性变成属性来实现部分预期的静态变量行为,即,多个实例之间的属性同步(但与类本身同步;请参见下面的“陷阱”):

class Test(object):

    _i = 3

    @property
    def i(self):
        return type(self)._i

    @i.setter
    def i(self,val):
        type(self)._i = val

## ALTERNATIVE IMPLEMENTATION - FUNCTIONALLY EQUIVALENT TO ABOVE ##
## (except with separate methods for getting and setting i) ##

class Test(object):

    _i = 3

    def get_i(self):
        return type(self)._i

    def set_i(self,val):
        type(self)._i = val

    i = property(get_i, set_i)

现在您可以执行以下操作:

x1 = Test()
x2 = Test()
x1.i = 50
assert x2.i == x1.i  # no error
assert x2.i == 50    # the property is synced

现在,静态变量将在所有类实例之间保持同步。

(注意:也就是说,除非类实例决定定义其自己的版本_i!但是,如果有人决定执行该操作,那么他们应得的是什么,不是吗???)

请注意,从技术上讲,i它仍然根本不是“静态变量”。它是property,这是一种特殊类型的描述符。但是,该property行为现在等同于跨所有类实例同步的(可变)静态变量。

不变的“静态变量”

对于不可变的静态变量行为,只需省略propertysetter:

class Test(object):

    _i = 3

    @property
    def i(self):
        return type(self)._i

## ALTERNATIVE IMPLEMENTATION - FUNCTIONALLY EQUIVALENT TO ABOVE ##
## (except with separate methods for getting i) ##

class Test(object):

    _i = 3

    def get_i(self):
        return type(self)._i

    i = property(get_i)

现在尝试设置实例i属性将返回AttributeError

x = Test()
assert x.i == 3  # success
x.i = 12         # ERROR

要意识到的一个陷阱

请注意,上述方法只能用工作实例类的-他们会工作使用类本身时。因此,例如:

x = Test()
assert x.i == Test.i  # ERROR

# x.i and Test.i are two different objects:
type(Test.i)  # class 'property'
type(x.i)     # class 'int'

assert Test.i == x.i产生一个错误,这是因为i的属性Testx是两个不同的对象。

许多人会发现这令人惊讶。但是,事实并非如此。如果我们返回并检查Test类定义(第二个版本),请注意以下这一行:

    i = property(get_i) 

显然,部件iTest必须是一个property对象,该对象是对象的从返回的类型property的功能。

如果您发现上述混淆,您很可能仍会从其他语言(例如Java或c ++)的角度考虑它。您应该研究property对象,有关返回Python属性的顺序,描述符协议和方法解析顺序(MRO)。

我在下面提出了上述“陷阱”的解决方案;但是,我建议-努力-除非您完全理解为什么assert Test.i = x.i会导致错误,否则不要尝试执行以下操作。

REAL,ACTUAL静态变量-Test.i == x.i

我仅在下面提供(Python 3)解决方案,仅供参考。我不赞成将其作为“好的解决方案”。我对是否真的有必要在Python中模拟其他语言的静态变量行为感到怀疑。但是,不管它是否真的有用,下面的内容应有助于进一步了解Python的工作方式。

更新:这种尝试确实非常糟糕;如果您坚持要做这样的事情(提示:请不要; Python是一种非常优雅的语言,并且不需要像其他语言那样勉强地表现出来),请改用Ethan Furman的答案中的代码。

使用元类模拟其他语言的静态变量行为

元类是类的类。Python中所有类的默认元类(即,我认为Python 2.3之后的“新样式”类)是type。例如:

type(int)  # class 'type'
type(str)  # class 'type'
class Test(): pass
type(Test) # class 'type'

但是,您可以这样定义自己的元类:

class MyMeta(type): pass

并将其应用于您自己的类(仅适用于Python 3):

class MyClass(metaclass = MyMeta):
    pass

type(MyClass)  # class MyMeta

下面是我创建的元类,它试图模仿其他语言的“静态变量”行为。它基本上是通过将默认的getter,setter和deleter替换为版本来工作的,该版本检查以查看所请求的属性是否为“静态变量”。

“静态变量”的目录存储在StaticVarMeta.statics属性中。最初尝试使用替代解决顺序解决所有属性请求。我将其称为“静态解决方案命令”或“ SRO”。这是通过在给定类(或其父类)的“静态变量”集中查找请求的属性来完成的。如果该属性未出现在“ SRO”中,则该类将回退到默认属性的“获取/设置/删除”行为(即“ MRO”)。

from functools import wraps

class StaticVarsMeta(type):
    '''A metaclass for creating classes that emulate the "static variable" behavior
    of other languages. I do not advise actually using this for anything!!!

    Behavior is intended to be similar to classes that use __slots__. However, "normal"
    attributes and __statics___ can coexist (unlike with __slots__). 

    Example usage: 

        class MyBaseClass(metaclass = StaticVarsMeta):
            __statics__ = {'a','b','c'}
            i = 0  # regular attribute
            a = 1  # static var defined (optional)

        class MyParentClass(MyBaseClass):
            __statics__ = {'d','e','f'}
            j = 2              # regular attribute
            d, e, f = 3, 4, 5  # Static vars
            a, b, c = 6, 7, 8  # Static vars (inherited from MyBaseClass, defined/re-defined here)

        class MyChildClass(MyParentClass):
            __statics__ = {'a','b','c'}
            j = 2  # regular attribute (redefines j from MyParentClass)
            d, e, f = 9, 10, 11   # Static vars (inherited from MyParentClass, redefined here)
            a, b, c = 12, 13, 14  # Static vars (overriding previous definition in MyParentClass here)'''
    statics = {}
    def __new__(mcls, name, bases, namespace):
        # Get the class object
        cls = super().__new__(mcls, name, bases, namespace)
        # Establish the "statics resolution order"
        cls.__sro__ = tuple(c for c in cls.__mro__ if isinstance(c,mcls))

        # Replace class getter, setter, and deleter for instance attributes
        cls.__getattribute__ = StaticVarsMeta.__inst_getattribute__(cls, cls.__getattribute__)
        cls.__setattr__ = StaticVarsMeta.__inst_setattr__(cls, cls.__setattr__)
        cls.__delattr__ = StaticVarsMeta.__inst_delattr__(cls, cls.__delattr__)
        # Store the list of static variables for the class object
        # This list is permanent and cannot be changed, similar to __slots__
        try:
            mcls.statics[cls] = getattr(cls,'__statics__')
        except AttributeError:
            mcls.statics[cls] = namespace['__statics__'] = set() # No static vars provided
        # Check and make sure the statics var names are strings
        if any(not isinstance(static,str) for static in mcls.statics[cls]):
            typ = dict(zip((not isinstance(static,str) for static in mcls.statics[cls]), map(type,mcls.statics[cls])))[True].__name__
            raise TypeError('__statics__ items must be strings, not {0}'.format(typ))
        # Move any previously existing, not overridden statics to the static var parent class(es)
        if len(cls.__sro__) > 1:
            for attr,value in namespace.items():
                if attr not in StaticVarsMeta.statics[cls] and attr != ['__statics__']:
                    for c in cls.__sro__[1:]:
                        if attr in StaticVarsMeta.statics[c]:
                            setattr(c,attr,value)
                            delattr(cls,attr)
        return cls
    def __inst_getattribute__(self, orig_getattribute):
        '''Replaces the class __getattribute__'''
        @wraps(orig_getattribute)
        def wrapper(self, attr):
            if StaticVarsMeta.is_static(type(self),attr):
                return StaticVarsMeta.__getstatic__(type(self),attr)
            else:
                return orig_getattribute(self, attr)
        return wrapper
    def __inst_setattr__(self, orig_setattribute):
        '''Replaces the class __setattr__'''
        @wraps(orig_setattribute)
        def wrapper(self, attr, value):
            if StaticVarsMeta.is_static(type(self),attr):
                StaticVarsMeta.__setstatic__(type(self),attr, value)
            else:
                orig_setattribute(self, attr, value)
        return wrapper
    def __inst_delattr__(self, orig_delattribute):
        '''Replaces the class __delattr__'''
        @wraps(orig_delattribute)
        def wrapper(self, attr):
            if StaticVarsMeta.is_static(type(self),attr):
                StaticVarsMeta.__delstatic__(type(self),attr)
            else:
                orig_delattribute(self, attr)
        return wrapper
    def __getstatic__(cls,attr):
        '''Static variable getter'''
        for c in cls.__sro__:
            if attr in StaticVarsMeta.statics[c]:
                try:
                    return getattr(c,attr)
                except AttributeError:
                    pass
        raise AttributeError(cls.__name__ + " object has no attribute '{0}'".format(attr))
    def __setstatic__(cls,attr,value):
        '''Static variable setter'''
        for c in cls.__sro__:
            if attr in StaticVarsMeta.statics[c]:
                setattr(c,attr,value)
                break
    def __delstatic__(cls,attr):
        '''Static variable deleter'''
        for c in cls.__sro__:
            if attr in StaticVarsMeta.statics[c]:
                try:
                    delattr(c,attr)
                    break
                except AttributeError:
                    pass
        raise AttributeError(cls.__name__ + " object has no attribute '{0}'".format(attr))
    def __delattr__(cls,attr):
        '''Prevent __sro__ attribute from deletion'''
        if attr == '__sro__':
            raise AttributeError('readonly attribute')
        super().__delattr__(attr)
    def is_static(cls,attr):
        '''Returns True if an attribute is a static variable of any class in the __sro__'''
        if any(attr in StaticVarsMeta.statics[c] for c in cls.__sro__):
            return True
        return False

Static and Class Methods

As the other answers have noted, static and class methods are easily accomplished using the built-in decorators:

class Test(object):

    # regular instance method:
    def MyMethod(self):
        pass

    # class method:
    @classmethod
    def MyClassMethod(klass):
        pass

    # static method:
    @staticmethod
    def MyStaticMethod():
        pass

As usual, the first argument to MyMethod() is bound to the class instance object. In contrast, the first argument to MyClassMethod() is bound to the class object itself (e.g., in this case, Test). For MyStaticMethod(), none of the arguments are bound, and having arguments at all is optional.

“Static Variables”

However, implementing “static variables” (well, mutable static variables, anyway, if that’s not a contradiction in terms…) is not as straight forward. As millerdev pointed out in his answer, the problem is that Python’s class attributes are not truly “static variables”. Consider:

class Test(object):
    i = 3  # This is a class attribute

x = Test()
x.i = 12   # Attempt to change the value of the class attribute using x instance
assert x.i == Test.i  # ERROR
assert Test.i == 3    # Test.i was not affected
assert x.i == 12      # x.i is a different object than Test.i

This is because the line x.i = 12 has added a new instance attribute i to x instead of changing the value of the Test class i attribute.

Partial expected static variable behavior, i.e., syncing of the attribute between multiple instances (but not with the class itself; see “gotcha” below), can be achieved by turning the class attribute into a property:

class Test(object):

    _i = 3

    @property
    def i(self):
        return type(self)._i

    @i.setter
    def i(self,val):
        type(self)._i = val

## ALTERNATIVE IMPLEMENTATION - FUNCTIONALLY EQUIVALENT TO ABOVE ##
## (except with separate methods for getting and setting i) ##

class Test(object):

    _i = 3

    def get_i(self):
        return type(self)._i

    def set_i(self,val):
        type(self)._i = val

    i = property(get_i, set_i)

Now you can do:

x1 = Test()
x2 = Test()
x1.i = 50
assert x2.i == x1.i  # no error
assert x2.i == 50    # the property is synced

The static variable will now remain in sync between all class instances.

(NOTE: That is, unless a class instance decides to define its own version of _i! But if someone decides to do THAT, they deserve what they get, don’t they???)

Note that technically speaking, i is still not a ‘static variable’ at all; it is a property, which is a special type of descriptor. However, the property behavior is now equivalent to a (mutable) static variable synced across all class instances.

Immutable “Static Variables”

For immutable static variable behavior, simply omit the property setter:

class Test(object):

    _i = 3

    @property
    def i(self):
        return type(self)._i

## ALTERNATIVE IMPLEMENTATION - FUNCTIONALLY EQUIVALENT TO ABOVE ##
## (except with separate methods for getting i) ##

class Test(object):

    _i = 3

    def get_i(self):
        return type(self)._i

    i = property(get_i)

Now attempting to set the instance i attribute will return an AttributeError:

x = Test()
assert x.i == 3  # success
x.i = 12         # ERROR

One Gotcha to be Aware of

Note that the above methods only work with instances of your class – they will not work when using the class itself. So for example:

x = Test()
assert x.i == Test.i  # ERROR

# x.i and Test.i are two different objects:
type(Test.i)  # class 'property'
type(x.i)     # class 'int'

The line assert Test.i == x.i produces an error, because the i attribute of Test and x are two different objects.

Many people will find this surprising. However, it should not be. If we go back and inspect our Test class definition (the second version), we take note of this line:

    i = property(get_i) 

Clearly, the member i of Test must be a property object, which is the type of object returned from the property function.

If you find the above confusing, you are most likely still thinking about it from the perspective of other languages (e.g. Java or c++). You should go study the property object, about the order in which Python attributes are returned, the descriptor protocol, and the method resolution order (MRO).

I present a solution to the above ‘gotcha’ below; however I would suggest – strenuously – that you do not try to do something like the following until – at minimum – you thoroughly understand why assert Test.i = x.i causes an error.

REAL, ACTUAL Static Variables – Test.i == x.i

I present the (Python 3) solution below for informational purposes only. I am not endorsing it as a “good solution”. I have my doubts as to whether emulating the static variable behavior of other languages in Python is ever actually necessary. However, regardless as to whether it is actually useful, the below should help further understanding of how Python works.

UPDATE: this attempt is really pretty awful; if you insist on doing something like this (hint: please don’t; Python is a very elegant language and shoe-horning it into behaving like another language is just not necessary), use the code in Ethan Furman’s answer instead.

Emulating static variable behavior of other languages using a metaclass

A metaclass is the class of a class. The default metaclass for all classes in Python (i.e., the “new style” classes post Python 2.3 I believe) is type. For example:

type(int)  # class 'type'
type(str)  # class 'type'
class Test(): pass
type(Test) # class 'type'

However, you can define your own metaclass like this:

class MyMeta(type): pass

And apply it to your own class like this (Python 3 only):

class MyClass(metaclass = MyMeta):
    pass

type(MyClass)  # class MyMeta

Below is a metaclass I have created which attempts to emulate “static variable” behavior of other languages. It basically works by replacing the default getter, setter, and deleter with versions which check to see if the attribute being requested is a “static variable”.

A catalog of the “static variables” is stored in the StaticVarMeta.statics attribute. All attribute requests are initially attempted to be resolved using a substitute resolution order. I have dubbed this the “static resolution order”, or “SRO”. This is done by looking for the requested attribute in the set of “static variables” for a given class (or its parent classes). If the attribute does not appear in the “SRO”, the class will fall back on the default attribute get/set/delete behavior (i.e., “MRO”).

from functools import wraps

class StaticVarsMeta(type):
    '''A metaclass for creating classes that emulate the "static variable" behavior
    of other languages. I do not advise actually using this for anything!!!

    Behavior is intended to be similar to classes that use __slots__. However, "normal"
    attributes and __statics___ can coexist (unlike with __slots__). 

    Example usage: 

        class MyBaseClass(metaclass = StaticVarsMeta):
            __statics__ = {'a','b','c'}
            i = 0  # regular attribute
            a = 1  # static var defined (optional)

        class MyParentClass(MyBaseClass):
            __statics__ = {'d','e','f'}
            j = 2              # regular attribute
            d, e, f = 3, 4, 5  # Static vars
            a, b, c = 6, 7, 8  # Static vars (inherited from MyBaseClass, defined/re-defined here)

        class MyChildClass(MyParentClass):
            __statics__ = {'a','b','c'}
            j = 2  # regular attribute (redefines j from MyParentClass)
            d, e, f = 9, 10, 11   # Static vars (inherited from MyParentClass, redefined here)
            a, b, c = 12, 13, 14  # Static vars (overriding previous definition in MyParentClass here)'''
    statics = {}
    def __new__(mcls, name, bases, namespace):
        # Get the class object
        cls = super().__new__(mcls, name, bases, namespace)
        # Establish the "statics resolution order"
        cls.__sro__ = tuple(c for c in cls.__mro__ if isinstance(c,mcls))

        # Replace class getter, setter, and deleter for instance attributes
        cls.__getattribute__ = StaticVarsMeta.__inst_getattribute__(cls, cls.__getattribute__)
        cls.__setattr__ = StaticVarsMeta.__inst_setattr__(cls, cls.__setattr__)
        cls.__delattr__ = StaticVarsMeta.__inst_delattr__(cls, cls.__delattr__)
        # Store the list of static variables for the class object
        # This list is permanent and cannot be changed, similar to __slots__
        try:
            mcls.statics[cls] = getattr(cls,'__statics__')
        except AttributeError:
            mcls.statics[cls] = namespace['__statics__'] = set() # No static vars provided
        # Check and make sure the statics var names are strings
        if any(not isinstance(static,str) for static in mcls.statics[cls]):
            typ = dict(zip((not isinstance(static,str) for static in mcls.statics[cls]), map(type,mcls.statics[cls])))[True].__name__
            raise TypeError('__statics__ items must be strings, not {0}'.format(typ))
        # Move any previously existing, not overridden statics to the static var parent class(es)
        if len(cls.__sro__) > 1:
            for attr,value in namespace.items():
                if attr not in StaticVarsMeta.statics[cls] and attr != ['__statics__']:
                    for c in cls.__sro__[1:]:
                        if attr in StaticVarsMeta.statics[c]:
                            setattr(c,attr,value)
                            delattr(cls,attr)
        return cls
    def __inst_getattribute__(self, orig_getattribute):
        '''Replaces the class __getattribute__'''
        @wraps(orig_getattribute)
        def wrapper(self, attr):
            if StaticVarsMeta.is_static(type(self),attr):
                return StaticVarsMeta.__getstatic__(type(self),attr)
            else:
                return orig_getattribute(self, attr)
        return wrapper
    def __inst_setattr__(self, orig_setattribute):
        '''Replaces the class __setattr__'''
        @wraps(orig_setattribute)
        def wrapper(self, attr, value):
            if StaticVarsMeta.is_static(type(self),attr):
                StaticVarsMeta.__setstatic__(type(self),attr, value)
            else:
                orig_setattribute(self, attr, value)
        return wrapper
    def __inst_delattr__(self, orig_delattribute):
        '''Replaces the class __delattr__'''
        @wraps(orig_delattribute)
        def wrapper(self, attr):
            if StaticVarsMeta.is_static(type(self),attr):
                StaticVarsMeta.__delstatic__(type(self),attr)
            else:
                orig_delattribute(self, attr)
        return wrapper
    def __getstatic__(cls,attr):
        '''Static variable getter'''
        for c in cls.__sro__:
            if attr in StaticVarsMeta.statics[c]:
                try:
                    return getattr(c,attr)
                except AttributeError:
                    pass
        raise AttributeError(cls.__name__ + " object has no attribute '{0}'".format(attr))
    def __setstatic__(cls,attr,value):
        '''Static variable setter'''
        for c in cls.__sro__:
            if attr in StaticVarsMeta.statics[c]:
                setattr(c,attr,value)
                break
    def __delstatic__(cls,attr):
        '''Static variable deleter'''
        for c in cls.__sro__:
            if attr in StaticVarsMeta.statics[c]:
                try:
                    delattr(c,attr)
                    break
                except AttributeError:
                    pass
        raise AttributeError(cls.__name__ + " object has no attribute '{0}'".format(attr))
    def __delattr__(cls,attr):
        '''Prevent __sro__ attribute from deletion'''
        if attr == '__sro__':
            raise AttributeError('readonly attribute')
        super().__delattr__(attr)
    def is_static(cls,attr):
        '''Returns True if an attribute is a static variable of any class in the __sro__'''
        if any(attr in StaticVarsMeta.statics[c] for c in cls.__sro__):
            return True
        return False

回答 3

您还可以随时将类变量添加到类中

>>> class X:
...     pass
... 
>>> X.bar = 0
>>> x = X()
>>> x.bar
0
>>> x.foo
Traceback (most recent call last):
  File "<interactive input>", line 1, in <module>
AttributeError: X instance has no attribute 'foo'
>>> X.foo = 1
>>> x.foo
1

类实例可以更改类变量

class X:
  l = []
  def __init__(self):
    self.l.append(1)

print X().l
print X().l

>python test.py
[1]
[1, 1]

You can also add class variables to classes on the fly

>>> class X:
...     pass
... 
>>> X.bar = 0
>>> x = X()
>>> x.bar
0
>>> x.foo
Traceback (most recent call last):
  File "<interactive input>", line 1, in <module>
AttributeError: X instance has no attribute 'foo'
>>> X.foo = 1
>>> x.foo
1

And class instances can change class variables

class X:
  l = []
  def __init__(self):
    self.l.append(1)

print X().l
print X().l

>python test.py
[1]
[1, 1]

回答 4

就个人而言,每当我需要静态方法时,我都会使用类方法。主要是因为我将类作为参数。

class myObj(object):
   def myMethod(cls)
     ...
   myMethod = classmethod(myMethod) 

或使用装饰器

class myObj(object):
   @classmethod
   def myMethod(cls)

对于静态属性..它时候您查找一些python定义..变量可以随时更改。有两种类型,它们是可变的和不可变的。此外,还有类属性和实例属性。从Java和C ++的意义上说,没有什么比静态属性更像

如果与类没有任何关系,为什么要使用pythonic意义上的静态方法!如果您是我,则可以使用classmethod或独立于类定义方法。

Personally I would use a classmethod whenever I needed a static method. Mainly because I get the class as an argument.

class myObj(object):
   def myMethod(cls)
     ...
   myMethod = classmethod(myMethod) 

or use a decorator

class myObj(object):
   @classmethod
   def myMethod(cls)

For static properties.. Its time you look up some python definition.. variable can always change. There are two types of them mutable and immutable.. Also, there are class attributes and instance attributes.. Nothing really like static attributes in the sense of java & c++

Why use static method in pythonic sense, if it has no relation whatever to the class! If I were you, I’d either use classmethod or define the method independent from the class.


回答 5

关于静态属性和实例属性的一件事要特别注意,如下面的示例所示:

class my_cls:
  my_prop = 0

#static property
print my_cls.my_prop  #--> 0

#assign value to static property
my_cls.my_prop = 1 
print my_cls.my_prop  #--> 1

#access static property thru' instance
my_inst = my_cls()
print my_inst.my_prop #--> 1

#instance property is different from static property 
#after being assigned a value
my_inst.my_prop = 2
print my_cls.my_prop  #--> 1
print my_inst.my_prop #--> 2

这意味着在将值分配给实例属性之前,如果我们尝试通过实例访问属性,则将使用静态值。python类中声明的每个属性在内存中始终具有一个静态插槽

One special thing to note about static properties & instance properties, shown in the example below:

class my_cls:
  my_prop = 0

#static property
print my_cls.my_prop  #--> 0

#assign value to static property
my_cls.my_prop = 1 
print my_cls.my_prop  #--> 1

#access static property thru' instance
my_inst = my_cls()
print my_inst.my_prop #--> 1

#instance property is different from static property 
#after being assigned a value
my_inst.my_prop = 2
print my_cls.my_prop  #--> 1
print my_inst.my_prop #--> 2

This means before assigning the value to instance property, if we try to access the property thru’ instance, the static value is used. Each property declared in python class always has a static slot in memory.


回答 6

python中的静态方法称为classmethod。看下面的代码

class MyClass:

    def myInstanceMethod(self):
        print 'output from an instance method'

    @classmethod
    def myStaticMethod(cls):
        print 'output from a static method'

>>> MyClass.myInstanceMethod()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unbound method myInstanceMethod() must be called [...]

>>> MyClass.myStaticMethod()
output from a static method

注意,当我们调用方法myInstanceMethod时,我们得到一个错误。这是因为它要求在此类的实例上调用该方法。使用装饰器@classmethod将方法myStaticMethod设置为类方法。

只是为了一笑而过,我们可以通过传入类的实例来在类上调用myInstanceMethod,如下所示:

>>> MyClass.myInstanceMethod(MyClass())
output from an instance method

Static methods in python are called classmethods. Take a look at the following code

class MyClass:

    def myInstanceMethod(self):
        print 'output from an instance method'

    @classmethod
    def myStaticMethod(cls):
        print 'output from a static method'

>>> MyClass.myInstanceMethod()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unbound method myInstanceMethod() must be called [...]

>>> MyClass.myStaticMethod()
output from a static method

Notice that when we call the method myInstanceMethod, we get an error. This is because it requires that method be called on an instance of this class. The method myStaticMethod is set as a classmethod using the decorator @classmethod.

Just for kicks and giggles, we could call myInstanceMethod on the class by passing in an instance of the class, like so:

>>> MyClass.myInstanceMethod(MyClass())
output from an instance method

回答 7

当在任何成员方法之外定义某个成员变量时,该变量可以是静态的也可以是非静态的,具体取决于变量的表示方式。

  • CLASSNAME.var是静态变量
  • INSTANCENAME.var不是静态变量。
  • 类中的self.var不是静态变量。
  • 类成员函数内部的var未定义。

例如:

#!/usr/bin/python

class A:
    var=1

    def printvar(self):
        print "self.var is %d" % self.var
        print "A.var is %d" % A.var


    a = A()
    a.var = 2
    a.printvar()

    A.var = 3
    a.printvar()

结果是

self.var is 2
A.var is 1
self.var is 2
A.var is 3

When define some member variable outside any member method, the variable can be either static or non-static depending on how the variable is expressed.

  • CLASSNAME.var is static variable
  • INSTANCENAME.var is not static variable.
  • self.var inside class is not static variable.
  • var inside the class member function is not defined.

For example:

#!/usr/bin/python

class A:
    var=1

    def printvar(self):
        print "self.var is %d" % self.var
        print "A.var is %d" % A.var


    a = A()
    a.var = 2
    a.printvar()

    A.var = 3
    a.printvar()

The results are

self.var is 2
A.var is 1
self.var is 2
A.var is 3

回答 8

可能有static类变量,但可能不值得。

这是用Python 3编写的概念验证-如果任何确切的细节有误,则可以对代码进行调整以使其与您所表达的含义完全匹配static variable


class Static:
    def __init__(self, value, doc=None):
        self.deleted = False
        self.value = value
        self.__doc__ = doc
    def __get__(self, inst, cls=None):
        if self.deleted:
            raise AttributeError('Attribute not set')
        return self.value
    def __set__(self, inst, value):
        self.deleted = False
        self.value = value
    def __delete__(self, inst):
        self.deleted = True

class StaticType(type):
    def __delattr__(cls, name):
        obj = cls.__dict__.get(name)
        if isinstance(obj, Static):
            obj.__delete__(name)
        else:
            super(StaticType, cls).__delattr__(name)
    def __getattribute__(cls, *args):
        obj = super(StaticType, cls).__getattribute__(*args)
        if isinstance(obj, Static):
            obj = obj.__get__(cls, cls.__class__)
        return obj
    def __setattr__(cls, name, val):
        # check if object already exists
        obj = cls.__dict__.get(name)
        if isinstance(obj, Static):
            obj.__set__(name, val)
        else:
            super(StaticType, cls).__setattr__(name, val)

并在使用中:

class MyStatic(metaclass=StaticType):
    """
    Testing static vars
    """
    a = Static(9)
    b = Static(12)
    c = 3

class YourStatic(MyStatic):
    d = Static('woo hoo')
    e = Static('doo wop')

和一些测试:

ms1 = MyStatic()
ms2 = MyStatic()
ms3 = MyStatic()
assert ms1.a == ms2.a == ms3.a == MyStatic.a
assert ms1.b == ms2.b == ms3.b == MyStatic.b
assert ms1.c == ms2.c == ms3.c == MyStatic.c
ms1.a = 77
assert ms1.a == ms2.a == ms3.a == MyStatic.a
ms2.b = 99
assert ms1.b == ms2.b == ms3.b == MyStatic.b
MyStatic.a = 101
assert ms1.a == ms2.a == ms3.a == MyStatic.a
MyStatic.b = 139
assert ms1.b == ms2.b == ms3.b == MyStatic.b
del MyStatic.b
for inst in (ms1, ms2, ms3):
    try:
        getattr(inst, 'b')
    except AttributeError:
        pass
    else:
        print('AttributeError not raised on %r' % attr)
ms1.c = 13
ms2.c = 17
ms3.c = 19
assert ms1.c == 13
assert ms2.c == 17
assert ms3.c == 19
MyStatic.c = 43
assert ms1.c == 13
assert ms2.c == 17
assert ms3.c == 19

ys1 = YourStatic()
ys2 = YourStatic()
ys3 = YourStatic()
MyStatic.b = 'burgler'
assert ys1.a == ys2.a == ys3.a == YourStatic.a == MyStatic.a
assert ys1.b == ys2.b == ys3.b == YourStatic.b == MyStatic.b
assert ys1.d == ys2.d == ys3.d == YourStatic.d
assert ys1.e == ys2.e == ys3.e == YourStatic.e
ys1.a = 'blah'
assert ys1.a == ys2.a == ys3.a == YourStatic.a == MyStatic.a
ys2.b = 'kelp'
assert ys1.b == ys2.b == ys3.b == YourStatic.b == MyStatic.b
ys1.d = 'fee'
assert ys1.d == ys2.d == ys3.d == YourStatic.d
ys2.e = 'fie'
assert ys1.e == ys2.e == ys3.e == YourStatic.e
MyStatic.a = 'aargh'
assert ys1.a == ys2.a == ys3.a == YourStatic.a == MyStatic.a

It is possible to have static class variables, but probably not worth the effort.

Here’s a proof-of-concept written in Python 3 — if any of the exact details are wrong the code can be tweaked to match just about whatever you mean by a static variable:


class Static:
    def __init__(self, value, doc=None):
        self.deleted = False
        self.value = value
        self.__doc__ = doc
    def __get__(self, inst, cls=None):
        if self.deleted:
            raise AttributeError('Attribute not set')
        return self.value
    def __set__(self, inst, value):
        self.deleted = False
        self.value = value
    def __delete__(self, inst):
        self.deleted = True

class StaticType(type):
    def __delattr__(cls, name):
        obj = cls.__dict__.get(name)
        if isinstance(obj, Static):
            obj.__delete__(name)
        else:
            super(StaticType, cls).__delattr__(name)
    def __getattribute__(cls, *args):
        obj = super(StaticType, cls).__getattribute__(*args)
        if isinstance(obj, Static):
            obj = obj.__get__(cls, cls.__class__)
        return obj
    def __setattr__(cls, name, val):
        # check if object already exists
        obj = cls.__dict__.get(name)
        if isinstance(obj, Static):
            obj.__set__(name, val)
        else:
            super(StaticType, cls).__setattr__(name, val)

and in use:

class MyStatic(metaclass=StaticType):
    """
    Testing static vars
    """
    a = Static(9)
    b = Static(12)
    c = 3

class YourStatic(MyStatic):
    d = Static('woo hoo')
    e = Static('doo wop')

and some tests:

ms1 = MyStatic()
ms2 = MyStatic()
ms3 = MyStatic()
assert ms1.a == ms2.a == ms3.a == MyStatic.a
assert ms1.b == ms2.b == ms3.b == MyStatic.b
assert ms1.c == ms2.c == ms3.c == MyStatic.c
ms1.a = 77
assert ms1.a == ms2.a == ms3.a == MyStatic.a
ms2.b = 99
assert ms1.b == ms2.b == ms3.b == MyStatic.b
MyStatic.a = 101
assert ms1.a == ms2.a == ms3.a == MyStatic.a
MyStatic.b = 139
assert ms1.b == ms2.b == ms3.b == MyStatic.b
del MyStatic.b
for inst in (ms1, ms2, ms3):
    try:
        getattr(inst, 'b')
    except AttributeError:
        pass
    else:
        print('AttributeError not raised on %r' % attr)
ms1.c = 13
ms2.c = 17
ms3.c = 19
assert ms1.c == 13
assert ms2.c == 17
assert ms3.c == 19
MyStatic.c = 43
assert ms1.c == 13
assert ms2.c == 17
assert ms3.c == 19

ys1 = YourStatic()
ys2 = YourStatic()
ys3 = YourStatic()
MyStatic.b = 'burgler'
assert ys1.a == ys2.a == ys3.a == YourStatic.a == MyStatic.a
assert ys1.b == ys2.b == ys3.b == YourStatic.b == MyStatic.b
assert ys1.d == ys2.d == ys3.d == YourStatic.d
assert ys1.e == ys2.e == ys3.e == YourStatic.e
ys1.a = 'blah'
assert ys1.a == ys2.a == ys3.a == YourStatic.a == MyStatic.a
ys2.b = 'kelp'
assert ys1.b == ys2.b == ys3.b == YourStatic.b == MyStatic.b
ys1.d = 'fee'
assert ys1.d == ys2.d == ys3.d == YourStatic.d
ys2.e = 'fie'
assert ys1.e == ys2.e == ys3.e == YourStatic.e
MyStatic.a = 'aargh'
assert ys1.a == ys2.a == ys3.a == YourStatic.a == MyStatic.a

回答 9

您还可以使用元类将类强制为静态。

class StaticClassError(Exception):
    pass


class StaticClass:
    __metaclass__ = abc.ABCMeta

    def __new__(cls, *args, **kw):
        raise StaticClassError("%s is a static class and cannot be initiated."
                                % cls)

class MyClass(StaticClass):
    a = 1
    b = 3

    @staticmethod
    def add(x, y):
        return x+y

然后,每当您偶然尝试初始化MyClass时,都会收到一个StaticClassError。

You could also enforce a class to be static using metaclass.

class StaticClassError(Exception):
    pass


class StaticClass:
    __metaclass__ = abc.ABCMeta

    def __new__(cls, *args, **kw):
        raise StaticClassError("%s is a static class and cannot be initiated."
                                % cls)

class MyClass(StaticClass):
    a = 1
    b = 3

    @staticmethod
    def add(x, y):
        return x+y

Then whenever by accident you try to initialize MyClass you’ll get an StaticClassError.


回答 10

关于Python属性查找的一个非常有趣的观点是,它可以用于创建“ 虚拟变量”:

class A(object):

  label="Amazing"

  def __init__(self,d): 
      self.data=d

  def say(self): 
      print("%s %s!"%(self.label,self.data))

class B(A):
  label="Bold"  # overrides A.label

A(5).say()      # Amazing 5!
B(3).say()      # Bold 3!

通常,在创建它们之后,没有任何分配。请注意,使用查找是self因为,尽管label在不与特定实例关联的意义上说它是静态的,但该值仍取决于实例的(类)。

One very interesting point about Python’s attribute lookup is that it can be used to create “virtual variables”:

class A(object):

  label="Amazing"

  def __init__(self,d): 
      self.data=d

  def say(self): 
      print("%s %s!"%(self.label,self.data))

class B(A):
  label="Bold"  # overrides A.label

A(5).say()      # Amazing 5!
B(3).say()      # Bold 3!

Normally there aren’t any assignments to these after they are created. Note that the lookup uses self because, although label is static in the sense of not being associated with a particular instance, the value still depends on the (class of the) instance.


回答 11

关于此答案,对于常量静态变量,可以使用描述符。这是一个例子:

class ConstantAttribute(object):
    '''You can initialize my value but not change it.'''
    def __init__(self, value):
        self.value = value

    def __get__(self, obj, type=None):
        return self.value

    def __set__(self, obj, val):
        pass


class Demo(object):
    x = ConstantAttribute(10)


class SubDemo(Demo):
    x = 10


demo = Demo()
subdemo = SubDemo()
# should not change
demo.x = 100
# should change
subdemo.x = 100
print "small demo", demo.x
print "small subdemo", subdemo.x
print "big demo", Demo.x
print "big subdemo", SubDemo.x

导致 …

small demo 10
small subdemo 100
big demo 10
big subdemo 10

如果您pass不想静默地忽略设置值(以上),则总是可以引发异常。如果要查找C ++ Java样式静态类变量:

class StaticAttribute(object):
    def __init__(self, value):
        self.value = value

    def __get__(self, obj, type=None):
        return self.value

    def __set__(self, obj, val):
        self.value = val

请查看此答案和官方文档HOWTO,以获取有关描述符的更多信息。

In regards to this answer, for a constant static variable, you can use a descriptor. Here’s an example:

class ConstantAttribute(object):
    '''You can initialize my value but not change it.'''
    def __init__(self, value):
        self.value = value

    def __get__(self, obj, type=None):
        return self.value

    def __set__(self, obj, val):
        pass


class Demo(object):
    x = ConstantAttribute(10)


class SubDemo(Demo):
    x = 10


demo = Demo()
subdemo = SubDemo()
# should not change
demo.x = 100
# should change
subdemo.x = 100
print "small demo", demo.x
print "small subdemo", subdemo.x
print "big demo", Demo.x
print "big subdemo", SubDemo.x

resulting in …

small demo 10
small subdemo 100
big demo 10
big subdemo 10

You can always raise an exception if quietly ignoring setting value (pass above) is not your thing. If you’re looking for a C++, Java style static class variable:

class StaticAttribute(object):
    def __init__(self, value):
        self.value = value

    def __get__(self, obj, type=None):
        return self.value

    def __set__(self, obj, val):
        self.value = val

Have a look at this answer and the official docs HOWTO for more information about descriptors.


回答 12

绝对可以,Python本身没有明确的静态数据成员,但是我们可以这样做

class A:
    counter =0
    def callme (self):
        A.counter +=1
    def getcount (self):
        return self.counter  
>>> x=A()
>>> y=A()
>>> print(x.getcount())
>>> print(y.getcount())
>>> x.callme() 
>>> print(x.getcount())
>>> print(y.getcount())

输出

0
0
1
1

说明

here object (x) alone increment the counter variable
from 0 to 1 by not object y. But result it as "static counter"

Absolutely Yes, Python by itself don’t have any static data member explicitly, but We can have by doing so

class A:
    counter =0
    def callme (self):
        A.counter +=1
    def getcount (self):
        return self.counter  
>>> x=A()
>>> y=A()
>>> print(x.getcount())
>>> print(y.getcount())
>>> x.callme() 
>>> print(x.getcount())
>>> print(y.getcount())

output

0
0
1
1

explanation

here object (x) alone increment the counter variable
from 0 to 1 by not object y. But result it as "static counter"

回答 13

是的,绝对可以在python中编写静态变量和方法。

静态变量: 在类级别声明的变量称为静态变量,可以使用类名称直接访问。

    >>> class A:
        ...my_var = "shagun"

    >>> print(A.my_var)
        shagun

实例变量:与某个类的实例相关并访问的变量是实例变量。

   >>> a = A()
   >>> a.my_var = "pruthi"
   >>> print(A.my_var,a.my_var)
       shagun pruthi

静态方法:与变量类似,可以使用Name类直接访问静态方法。无需创建实例。

但请记住,静态方法无法在python中调用非静态方法。

    >>> class A:
   ...     @staticmethod
   ...     def my_static_method():
   ...             print("Yippey!!")
   ... 
   >>> A.my_static_method()
   Yippey!!

Yes, definitely possible to write static variables and methods in python.

Static Variables : Variable declared at class level are called static variable which can be accessed directly using class name.

    >>> class A:
        ...my_var = "shagun"

    >>> print(A.my_var)
        shagun

Instance variables: Variables that are related and accessed by instance of a class are instance variables.

   >>> a = A()
   >>> a.my_var = "pruthi"
   >>> print(A.my_var,a.my_var)
       shagun pruthi

Static Methods: Similar to variables, static methods can be accessed directly using class Name. No need to create an instance.

But keep in mind, a static method cannot call a non-static method in python.

    >>> class A:
   ...     @staticmethod
   ...     def my_static_method():
   ...             print("Yippey!!")
   ... 
   >>> A.my_static_method()
   Yippey!!

回答 14

为了避免任何潜在的混乱,我想对比静态变量和不可变对象。

一些原始对象类型(例如整数,浮点数,字符串和touples)在Python中是不可变的。这意味着给定名称引用的对象如果属于上述对象类型之一,则无法更改。可以将名称重新分配给其他对象,但是对象本身不能更改。

使变量为静态使此步骤更进一步,它不允许变量名指向除当前指向的对象之外的任何对象。(注意:这是一个通用的软件概念,并不特定于Python;有关在Python中实现静态功能的信息,请参见其他人的帖子)。

To avoid any potential confusion, I would like to contrast static variables and immutable objects.

Some primitive object types like integers, floats, strings, and touples are immutable in Python. This means that the object that is referred to by a given name cannot change if it is of one of the aforementioned object types. The name can be reassigned to a different object, but the object itself may not be changed.

Making a variable static takes this a step further by disallowing the variable name to point to any object but that to which it currently points. (Note: this is a general software concept and not specific to Python; please see others’ posts for information about implementing statics in Python).


回答 15

我发现最好的方法是使用另一个类。您可以创建一个对象,然后在其他对象上使用它。

class staticFlag:
    def __init__(self):
        self.__success = False
    def isSuccess(self):
        return self.__success
    def succeed(self):
        self.__success = True

class tryIt:
    def __init__(self, staticFlag):
        self.isSuccess = staticFlag.isSuccess
        self.succeed = staticFlag.succeed

tryArr = []
flag = staticFlag()
for i in range(10):
    tryArr.append(tryIt(flag))
    if i == 5:
        tryArr[i].succeed()
    print tryArr[i].isSuccess()

在上面的示例中,我创建了一个名为的类staticFlag

此类应显示静态var __success(私有静态Var)。

tryIt 类代表我们需要使用的常规类。

现在,我为一个标志(staticFlag)创建了一个对象。该标志将作为对所有常规对象的引用发送。

所有这些对象都将添加到列表中tryArr


该脚本结果:

False
False
False
False
False
True
True
True
True
True

The best way I found is to use another class. You can create an object and then use it on other objects.

class staticFlag:
    def __init__(self):
        self.__success = False
    def isSuccess(self):
        return self.__success
    def succeed(self):
        self.__success = True

class tryIt:
    def __init__(self, staticFlag):
        self.isSuccess = staticFlag.isSuccess
        self.succeed = staticFlag.succeed

tryArr = []
flag = staticFlag()
for i in range(10):
    tryArr.append(tryIt(flag))
    if i == 5:
        tryArr[i].succeed()
    print tryArr[i].isSuccess()

With the example above, I made a class named staticFlag.

This class should present the static var __success (Private Static Var).

tryIt class represented the regular class we need to use.

Now I made an object for one flag (staticFlag). This flag will be sent as reference to all the regular objects.

All these objects are being added to the list tryArr.


This Script Results:

False
False
False
False
False
True
True
True
True
True

回答 16

类工厂python3.6中的静态变量

对于使用带有python3.6及更高版本的类工厂的任何人,请使用nonlocal关键字将其添加到正在创建的类的作用域/上下文中,如下所示:

>>> def SomeFactory(some_var=None):
...     class SomeClass(object):
...         nonlocal some_var
...         def print():
...             print(some_var)
...     return SomeClass
... 
>>> SomeFactory(some_var="hello world").print()
hello world

Static Variables in Class factory python3.6

For anyone using a class factory with python3.6 and up use the nonlocal keyword to add it to the scope / context of the class being created like so:

>>> def SomeFactory(some_var=None):
...     class SomeClass(object):
...         nonlocal some_var
...         def print():
...             print(some_var)
...     return SomeClass
... 
>>> SomeFactory(some_var="hello world").print()
hello world

回答 17

所以这可能是一个hack,但是我一直在使用 eval(str) python 3获取静态对象,这有点矛盾。

有一个Records.py文件,除了class用静态方法定义的对象和保存一些参数的构造函数外,什么都没有。然后从另一个.py文件中,import Records但我需要动态选择每个对象,然后根据要读取的数据类型按需实例化它。

因此object_name = 'RecordOne',我在哪里调用了类名,cur_type = eval(object_name)然后对其进行了实例化。cur_inst = cur_type(args) 但是,在实例化之前,您可以从cur_type.getName()例如静态类中调用静态方法,例如抽象基类的实现或目标是什么。但是在后端,它可能是在python中实例化的,并且不是真正的静态对象,因为eval返回的是一个对象……必须已被实例化……会产生类似静态的行为。

So this is probably a hack, but I’ve been using eval(str) to obtain an static object, kind of a contradiction, in python 3.

There is an Records.py file that has nothing but class objects defined with static methods and constructors that save some arguments. Then from another .py file I import Records but i need to dynamically select each object and then instantiate it on demand according to the type of data being read in.

So where object_name = 'RecordOne' or the class name, I call cur_type = eval(object_name) and then to instantiate it you do cur_inst = cur_type(args) However before you instantiate you can call static methods from cur_type.getName() for example, kind of like abstract base class implementation or whatever the goal is. However in the backend, it’s probably instantiated in python and is not truly static, because eval is returning an object….which must have been instantiated….that gives static like behavior.


回答 18

您可以使用列表或字典来获得实例之间的“静态行为”。

class Fud:

     class_vars = {'origin_open':False}

     def __init__(self, origin = True):
         self.origin = origin
         self.opened = True
         if origin:
             self.class_vars['origin_open'] = True


     def make_another_fud(self):
         ''' Generating another Fud() from the origin instance '''

         return Fud(False)


     def close(self):
         self.opened = False
         if self.origin:
             self.class_vars['origin_open'] = False


fud1 = Fud()
fud2 = fud1.make_another_fud()

print (f"is this the original fud: {fud2.origin}")
print (f"is the original fud open: {fud2.class_vars['origin_open']}")
# is this the original fud: False
# is the original fud open: True

fud1.close()

print (f"is the original fud open: {fud2.class_vars['origin_open']}")
# is the original fud open: False

You can use a list or a dictionary to get “static behavior” between instances.

class Fud:

     class_vars = {'origin_open':False}

     def __init__(self, origin = True):
         self.origin = origin
         self.opened = True
         if origin:
             self.class_vars['origin_open'] = True


     def make_another_fud(self):
         ''' Generating another Fud() from the origin instance '''

         return Fud(False)


     def close(self):
         self.opened = False
         if self.origin:
             self.class_vars['origin_open'] = False


fud1 = Fud()
fud2 = fud1.make_another_fud()

print (f"is this the original fud: {fud2.origin}")
print (f"is the original fud open: {fud2.class_vars['origin_open']}")
# is this the original fud: False
# is the original fud open: True

fud1.close()

print (f"is the original fud open: {fud2.class_vars['origin_open']}")
# is the original fud open: False

回答 19

例如,如果您尝试共享静态变量,以便在其他实例之间增加静态变量,则类似此脚本的代码可以正常工作:

# -*- coding: utf-8 -*-
class Worker:
    id = 1

    def __init__(self):
        self.name = ''
        self.document = ''
        self.id = Worker.id
        Worker.id += 1

    def __str__(self):
        return u"{}.- {} {}".format(self.id, self.name, self.document).encode('utf8')


class Workers:
    def __init__(self):
        self.list = []

    def add(self, name, doc):
        worker = Worker()
        worker.name = name
        worker.document = doc
        self.list.append(worker)


if __name__ == "__main__":
    workers = Workers()
    for item in (('Fiona', '0009898'), ('Maria', '66328191'), ("Sandra", '2342184'), ('Elvira', '425872')):
        workers.add(item[0], item[1])
    for worker in workers.list:
        print(worker)
    print("next id: %i" % Worker.id)

If you are attempting to share a static variable for, by example, increasing it across other instances, something like this script works fine:

# -*- coding: utf-8 -*-
class Worker:
    id = 1

    def __init__(self):
        self.name = ''
        self.document = ''
        self.id = Worker.id
        Worker.id += 1

    def __str__(self):
        return u"{}.- {} {}".format(self.id, self.name, self.document).encode('utf8')


class Workers:
    def __init__(self):
        self.list = []

    def add(self, name, doc):
        worker = Worker()
        worker.name = name
        worker.document = doc
        self.list.append(worker)


if __name__ == "__main__":
    workers = Workers()
    for item in (('Fiona', '0009898'), ('Maria', '66328191'), ("Sandra", '2342184'), ('Elvira', '425872')):
        workers.add(item[0], item[1])
    for worker in workers.list:
        print(worker)
    print("next id: %i" % Worker.id)

对象名称前的单下划线和双下划线是什么意思?

问题:对象名称前的单下划线和双下划线是什么意思?

有人可以解释一下在Python中对象名称前加下划线的确切含义,以及两者之间的区别吗?

此外,无论所讨论的对象是变量,函数,方法等,该含义是否都保持不变?

Can someone please explain the exact meaning of having leading underscores before an object’s name in Python, and the difference between both?

Also, does that meaning stay the same whether the object in question is a variable, a function, a method, etc.?


回答 0

单下划线

类中带有下划线的名称仅是为了向其他程序员表明该属性或方法旨在私有。但是,名称本身并没有做任何特别的事情。

引用PEP-8

_single_leading_underscore:“内部使用”指标较弱。例如from M import *,不导入名称以下划线开头的对象。

双下划线(名称改写)

Python文档

形式上的任何标识符__spam(至少两个前导下划线,至多一个尾随下划线)在文本上均替换为_classname__spam,其中classname是当前类名,其中已删除前导下划线。进行这种修饰时无需考虑标识符的语法位置,因此可用于定义类私有实例和类变量,方法,全局变量中存储的变量,甚至实例中存储的变量。在其他类的实例上对此类私有。

和来自同一页面的警告:

名称修饰旨在为类提供一种轻松的方法来定义“私有”实例变量和方法,而不必担心派生类定义的实例变量或类外部代码对实例变量的处理。请注意,修改规则主要是为了避免发生意外。坚定的灵魂仍然有可能访问或修改被视为私有的变量。

>>> class MyClass():
...     def __init__(self):
...             self.__superprivate = "Hello"
...             self._semiprivate = ", world!"
...
>>> mc = MyClass()
>>> print mc.__superprivate
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: myClass instance has no attribute '__superprivate'
>>> print mc._semiprivate
, world!
>>> print mc.__dict__
{'_MyClass__superprivate': 'Hello', '_semiprivate': ', world!'}

Single Underscore

Names, in a class, with a leading underscore are simply to indicate to other programmers that the attribute or method is intended to be private. However, nothing special is done with the name itself.

To quote PEP-8:

_single_leading_underscore: weak “internal use” indicator. E.g. from M import * does not import objects whose name starts with an underscore.

Double Underscore (Name Mangling)

From the Python docs:

Any identifier of the form __spam (at least two leading underscores, at most one trailing underscore) is textually replaced with _classname__spam, where classname is the current class name with leading underscore(s) stripped. This mangling is done without regard to the syntactic position of the identifier, so it can be used to define class-private instance and class variables, methods, variables stored in globals, and even variables stored in instances. private to this class on instances of other classes.

And a warning from the same page:

Name mangling is intended to give classes an easy way to define “private” instance variables and methods, without having to worry about instance variables defined by derived classes, or mucking with instance variables by code outside the class. Note that the mangling rules are designed mostly to avoid accidents; it still is possible for a determined soul to access or modify a variable that is considered private.

Example

>>> class MyClass():
...     def __init__(self):
...             self.__superprivate = "Hello"
...             self._semiprivate = ", world!"
...
>>> mc = MyClass()
>>> print mc.__superprivate
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: myClass instance has no attribute '__superprivate'
>>> print mc._semiprivate
, world!
>>> print mc.__dict__
{'_MyClass__superprivate': 'Hello', '_semiprivate': ', world!'}

回答 1

到目前为止,很好的答案,但缺少一些花絮。单个前导下划线并不仅仅是一个约定:如果使用from foobar import *,并且模块foobar未定义__all__列表,则从模块导入的名称将包括带有下划线的名称。可以说这主要是一个约定,因为这种情况是一个相当模糊的角落;-)。

领先的下划线惯例被广泛用于不只是私人的名字,而且对什么C ++称之为保护的-例如,都完全的方法名称由子类(甚至是那些覆盖被否决,因为在它们的基类raise NotImplementedError!-)通常是单引号下划线名称,用于指示使用该类(或子类)的实例进行编码时,这些方法并不旨在被直接调用。

例如,要创建一个与FIFO不同的排队规则的线程安全队列,可以导入Queue,将Queue.Queue子类化,并覆盖诸如_get_put;之类的方法。“客户端代码”从不调用那些(“挂钩”)方法,而是调用(和“组织”)公共方法,例如putget(称为模板方法设计模式),例如,有关基于视频的有趣演示,请参见此处。我关于这个话题的演讲,以及抄本的提要)。

编辑:演讲说明中的视频链接现在已断开。您可以在此处此处找到前两个视频。

Excellent answers so far but some tidbits are missing. A single leading underscore isn’t exactly just a convention: if you use from foobar import *, and module foobar does not define an __all__ list, the names imported from the module do not include those with a leading underscore. Let’s say it’s mostly a convention, since this case is a pretty obscure corner;-).

The leading-underscore convention is widely used not just for private names, but also for what C++ would call protected ones — for example, names of methods that are fully intended to be overridden by subclasses (even ones that have to be overridden since in the base class they raise NotImplementedError!-) are often single-leading-underscore names to indicate to code using instances of that class (or subclasses) that said methods are not meant to be called directly.

For example, to make a thread-safe queue with a different queueing discipline than FIFO, one imports Queue, subclasses Queue.Queue, and overrides such methods as _get and _put; “client code” never calls those (“hook”) methods, but rather the (“organizing”) public methods such as put and get (this is known as the Template Method design pattern — see e.g. here for an interesting presentation based on a video of a talk of mine on the subject, with the addition of synopses of the transcript).

Edit: The video links in the description of the talks are now broken. You can find the first two videos here and here.


回答 2

__foo__:这只是一个约定,这是Python系统使用不会与用户名冲突的名称的一种方式。

_foo:这只是一个约定,是程序员用来表明变量是私有的(无论在Python中是什么意思)的一种方式。

__foo:这具有实际含义:解释器将其替换为,_classname__foo以确保该名称不会与另一个类中的相似名称重叠。

在Python世界中,没有其他形式的下划线具有意义。

在这些约定中,类,变量,全局等之间没有区别。

__foo__: this is just a convention, a way for the Python system to use names that won’t conflict with user names.

_foo: this is just a convention, a way for the programmer to indicate that the variable is private (whatever that means in Python).

__foo: this has real meaning: the interpreter replaces this name with _classname__foo as a way to ensure that the name will not overlap with a similar name in another class.

No other form of underscores have meaning in the Python world.

There’s no difference between class, variable, global, etc in these conventions.


回答 3

._variable 是半私有的,仅用于约定

.__variable通常被错误地认为是超私有的,而其实际含义只是为了防止意外访问而使用 namemangle [1]

.__variable__ 通常为内置方法或变量保留

您仍然可以.__mangled根据需要访问变量。双下划线只是将变量命名或重命名为类似instance._className__mangled

例:

class Test(object):
    def __init__(self):
        self.__a = 'a'
        self._b = 'b'

>>> t = Test()
>>> t._b
'b'

t._b是可访问的,因为它仅被约定隐藏

>>> t.__a
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'Test' object has no attribute '__a'

找不到t .__ a,因为由于名称修改而不再存在

>>> t._Test__a
'a'

通过访问instance._className__variable而不只是双下划线名称,您可以访问隐藏值

._variable is semiprivate and meant just for convention

.__variable is often incorrectly considered superprivate, while it’s actual meaning is just to namemangle to prevent accidental access[1]

.__variable__ is typically reserved for builtin methods or variables

You can still access .__mangled variables if you desperately want to. The double underscores just namemangles, or renames, the variable to something like instance._className__mangled

Example:

class Test(object):
    def __init__(self):
        self.__a = 'a'
        self._b = 'b'

>>> t = Test()
>>> t._b
'b'

t._b is accessible because it is only hidden by convention

>>> t.__a
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'Test' object has no attribute '__a'

t.__a isn’t found because it no longer exists due to namemangling

>>> t._Test__a
'a'

By accessing instance._className__variable instead of just the double underscore name, you can access the hidden value


回答 4

开头下划线:

Python没有真正的私有方法。相反,在方法或属性名称的开头加下划线表示您不应访问此方法,因为它不是API的一部分。

class BaseForm(StrAndUnicode):

    def _get_errors(self):
        "Returns an ErrorDict for the data provided for the form"
        if self._errors is None:
            self.full_clean()
        return self._errors

    errors = property(_get_errors)

(此代码段摘自django源代码:django / forms / forms.py)。在这段代码中,它errors是一个公共属性,但是此属性调用的方法_get_errors是“私有”的,因此您不应访问它。

一开始有两个下划线:

这引起很多混乱。不应将其用于创建私有方法。应该使用它来避免您的方法被子类覆盖或意外访问。让我们来看一个例子:

class A(object):
    def __test(self):
        print "I'm a test method in class A"

    def test(self):
        self.__test()

a = A()
a.test()
# a.__test() # This fails with an AttributeError
a._A__test() # Works! We can access the mangled name directly!

输出:

$ python test.py
I'm test method in class A
I'm test method in class A

现在创建一个子类B并为__test方法进行自定义

class B(A):
    def __test(self):
        print "I'm test method in class B"

b = B()
b.test()

输出将是…。

$ python test.py
I'm test method in class A

如我们所见,A.test()没有像我们期望的那样调用B .__ test()方法。但实际上,这是__的正确行为。这两个称为__test()的方法会自动重命名(混合)为_A__test()和_B__test(),因此不会意外覆盖它们。当您创建以__开头的方法时,这意味着您不希望任何人都可以覆盖它,而只打算从其自己的类内部访问它。

开头和结尾有两个下划线:

当我们看到类似的方法时__this__,请勿调用它。这是python而不是您要调用的方法。让我们来看看:

>>> name = "test string"
>>> name.__len__()
11
>>> len(name)
11

>>> number = 10
>>> number.__add__(40)
50
>>> number + 50
60

总是有一个调用这些魔术方法的运算符或本机函数。有时,在特定情况下,这只是python的一个钩子调用。例如__init__()在创建对象之后创建对象时__new__()调用,以构建实例…

让我们举个例子…

class FalseCalculator(object):

    def __init__(self, number):
        self.number = number

    def __add__(self, number):
        return self.number - number

    def __sub__(self, number):
        return self.number + number

number = FalseCalculator(20)
print number + 10      # 10
print number - 20      # 40

有关更多详细信息,请参阅PEP-8指南。有关更多魔术方法,请参见此PDF

Single underscore at the beginning:

Python doesn’t have real private methods. Instead, one underscore at the start of a method or attribute name means you shouldn’t access this method, because it’s not part of the API.

class BaseForm(StrAndUnicode):

    def _get_errors(self):
        "Returns an ErrorDict for the data provided for the form"
        if self._errors is None:
            self.full_clean()
        return self._errors

    errors = property(_get_errors)

(This code snippet was taken from django source code: django/forms/forms.py). In this code, errors is a public property, but the method this property calls, _get_errors, is “private”, so you shouldn’t access it.

Two underscores at the beginning:

This causes a lot of confusion. It should not be used to create a private method. It should be used to avoid your method being overridden by a subclass or accessed accidentally. Let’s see an example:

class A(object):
    def __test(self):
        print "I'm a test method in class A"

    def test(self):
        self.__test()

a = A()
a.test()
# a.__test() # This fails with an AttributeError
a._A__test() # Works! We can access the mangled name directly!

Output:

$ python test.py
I'm test method in class A
I'm test method in class A

Now create a subclass B and do customization for __test method

class B(A):
    def __test(self):
        print "I'm test method in class B"

b = B()
b.test()

Output will be….

$ python test.py
I'm test method in class A

As we have seen, A.test() didn’t call B.__test() methods, as we might expect. But in fact, this is the correct behavior for __. The two methods called __test() are automatically renamed (mangled) to _A__test() and _B__test(), so they do not accidentally override. When you create a method starting with __ it means that you don’t want to anyone to be able to override it, and you only intend to access it from inside its own class.

Two underscores at the beginning and at the end:

When we see a method like __this__, don’t call it. This is a method which python is meant to call, not you. Let’s take a look:

>>> name = "test string"
>>> name.__len__()
11
>>> len(name)
11

>>> number = 10
>>> number.__add__(40)
50
>>> number + 50
60

There is always an operator or native function which calls these magic methods. Sometimes it’s just a hook python calls in specific situations. For example __init__() is called when the object is created after __new__() is called to build the instance…

Let’s take an example…

class FalseCalculator(object):

    def __init__(self, number):
        self.number = number

    def __add__(self, number):
        return self.number - number

    def __sub__(self, number):
        return self.number + number

number = FalseCalculator(20)
print number + 10      # 10
print number - 20      # 40

For more details, see the PEP-8 guide. For more magic methods, see this PDF.


回答 5

有时您看起来像是一个带下划线的元组,如下所示

def foo(bar):
    return _('my_' + bar)

在这种情况下,正在发生的情况是_()是本地化功能的别名,该功能基于文本在文本上进行操作以将其放入适当的语言等。例如,Sphinx执行此操作,您将在导入文件中找到

from sphinx.locale import l_, _

在sphinx.locale中,_()被分配为某些本地化功能的别名。

Sometimes you have what appears to be a tuple with a leading underscore as in

def foo(bar):
    return _('my_' + bar)

In this case, what’s going on is that _() is an alias for a localization function that operates on text to put it into the proper language, etc. based on the locale. For example, Sphinx does this, and you’ll find among the imports

from sphinx.locale import l_, _

and in sphinx.locale, _() is assigned as an alias of some localization function.


回答 6

既然有这么多人在谈论雷蒙德的演讲,我将写下他的话简化一下:

双下划线的目的不是关于隐私。目的是像这样完全使用它

class Circle(object):

    def __init__(self, radius):
        self.radius = radius

    def area(self):
        p = self.__perimeter()
        r = p / math.pi / 2.0
        return math.pi * r ** 2.0

    def perimeter(self):
        return 2.0 * math.pi * self.radius

    __perimeter = perimeter  # local reference


class Tire(Circle):

    def perimeter(self):
        return Circle.perimeter(self) * 1.25

实际上,这是隐私的对立面,它与自由有关。它使您的子类可以随意覆盖任何一种方法而不会破坏其他方法

假设您没有保留perimeterin 的本地引用Circle。现在,派生类将Tire覆盖实现perimeter,而无需触摸areaTire(5).area()从理论上讲,当您调用时,它仍Circle.perimeter应用于计算,但实际上,它正在使用Tire.perimeter,这不是预期的行为。这就是为什么我们在Circle中需要本地引用。

但是为什么要__perimeter代替_perimeter呢?因为_perimeter仍然给派生类重写的机会:

class Tire(Circle):

    def perimeter(self):
        return Circle.perimeter(self) * 1.25

    _perimeter = perimeter

双下划线具有名称修饰,因此父类中的本地引用在派生类中被覆盖的可能性很小。因此“ 使子类可以自由地覆盖任何一种方法而不会破坏其他方法 ”。

如果您的类不会被继承,或者方法重写不会破坏任何内容,那么您根本不需要__double_leading_underscore

Since so many people are referring to Raymond’s talk, I’ll just make it a little easier by writing down what he said:

The intention of the double underscores was not about privacy. The intention was to use it exactly like this

class Circle(object):

    def __init__(self, radius):
        self.radius = radius

    def area(self):
        p = self.__perimeter()
        r = p / math.pi / 2.0
        return math.pi * r ** 2.0

    def perimeter(self):
        return 2.0 * math.pi * self.radius

    __perimeter = perimeter  # local reference


class Tire(Circle):

    def perimeter(self):
        return Circle.perimeter(self) * 1.25

It’s actually the opposite of privacy, it’s all about freedom. It makes your subclasses free to override any one method without breaking the others.

Say you don’t keep a local reference of perimeter in Circle. Now, a derived class Tire overrides the implementation of perimeter, without touching area. When you call Tire(5).area(), in theory it should still be using Circle.perimeter for computation, but in reality it’s using Tire.perimeter, which is not the intended behavior. That’s why we need a local reference in Circle.

But why __perimeter instead of _perimeter? Because _perimeter still gives derived class the chance to override:

class Tire(Circle):

    def perimeter(self):
        return Circle.perimeter(self) * 1.25

    _perimeter = perimeter

Double underscores has name mangling, so there’s a very little chance that the local reference in parent class get override in derived class. thus “makes your subclasses free to override any one method without breaking the others“.

If your class won’t be inherited, or method overriding does not break anything, then you simply don’t need __double_leading_underscore.


回答 7

如果您真的想将变量设为只读,恕我直言,最好的方法是使用仅传递有getter的property()。使用property(),我们可以完全控制数据。

class PrivateVarC(object):

    def get_x(self):
        pass

    def set_x(self, val):
        pass

    rwvar = property(get_p, set_p)  

    ronly = property(get_p) 

我知道OP提出了一个稍有不同的问题,但是由于我发现了另一个问题,询问“如何设置私有变量”与此标记为重复,因此我想在此处添加此附加信息。

If one really wants to make a variable read-only, IMHO the best way would be to use property() with only getter passed to it. With property() we can have complete control over the data.

class PrivateVarC(object):

    def get_x(self):
        pass

    def set_x(self, val):
        pass

    rwvar = property(get_p, set_p)  

    ronly = property(get_p) 

I understand that OP asked a little different question but since I found another question asking for ‘how to set private variables’ marked duplicate with this one, I thought of adding this additional info here.


回答 8

进入https://dbader.org/blog/含义-underscores-in-python

  • 单个前导下划线(_var):表示名称的命名约定仅供内部使用。通常不由Python解释器强制执行(通配符导入除外),并且仅作为对程序员的提示。
  • 单个尾随下划线(var_):按惯例用于避免与Python关键字命名冲突。
  • 双引号下划线(__var):在类上下文中使用时触发名称修饰。由Python解释器实施。
  • 前后双下划线(__var__):表示Python语言定义的特殊方法。避免为您自己的属性使用此命名方案。
  • 单个下划线(_):有时用作临时或无关紧要的变量的名称(“无关紧要”)。另外:Python REPL中最后一个表达式的结果。

Accroding to https://dbader.org/blog/meaning-of-underscores-in-python

  • Single Leading Underscore(_var): Naming convention indicating a name is meant for internal use. Generally not enforced by the Python interpreter (except in wildcard imports) and meant as a hint to the programmer only.
  • Single Trailing Underscore(var_): Used by convention to avoid naming conflicts with Python keywords.
  • Double Leading Underscore(__var): Triggers name mangling when used in a class context. Enforced by the Python interpreter.
  • Double Leading and Trailing Underscore(__var__): Indicates special methods defined by the Python language. Avoid this naming scheme for your own attributes.
  • Single Underscore(_): Sometimes used as a name for temporary or insignificant variables (“don’t care”). Also: The result of the last expression in a Python REPL.

回答 9

单个下划线是惯例。名称是否以单个下划线开头,与解释器的观点没有区别。

双开头和结尾的下划线用于内置的方法,如__init____bool__

双下划线开头W / O尾随同行是惯例也是如此,但是,该类方法将被错位的解释。对于变量或基本函数名称,没有区别。

Single leading underscores is a convention. there is no difference from the interpreter’s point of view if whether names starts with a single underscore or not.

Double leading and trailing underscores are used for built-in methods, such as __init__, __bool__, etc.

Double leading underscores w/o trailing counterparts are a convention too, however, the class methods will be mangled by the interpreter. For variables or basic function names no difference exists.


回答 10

好的答案都正确。我提供了简单的示例以及简单的定义/含义。

含义:

some_variable–►任何人都可以看到这是公开的。

_some_variable–►任何人都可以看到这是公开的,但这是表示私有的惯例… 警告 Python不会执行任何强制执行。

__some_varaible–►Python将变量名替换为_classname__some_varaible(又名名称改写),它降低/隐藏了它的可见性,更像是私有变量。

坦白地说,根据Python文档

“只能从对象内部访问的“私有”实例变量在Python中不存在”

这个例子:

class A():
    here="abc"
    _here="_abc"
    __here="__abc"


aObject=A()
print(aObject.here) 
print(aObject._here)
# now if we try to print __here then it will fail because it's not public variable 
#print(aObject.__here)

Great answers and all are correct.I have provided simple example along with simple definition/meaning.

Meaning:

some_variable –► it’s public anyone can see this.

_some_variable –► it’s public anyone can see this but it’s a convention to indicate private…warning no enforcement is done by Python.

__some_varaible –► Python replaces the variable name with _classname__some_varaible (AKA name mangling) and it reduces/hides it’s visibility and be more like private variable.

Just to be honest here According to Python documentation

““Private” instance variables that cannot be accessed except from inside an object don’t exist in Python”

The example:

class A():
    here="abc"
    _here="_abc"
    __here="__abc"


aObject=A()
print(aObject.here) 
print(aObject._here)
# now if we try to print __here then it will fail because it's not public variable 
#print(aObject.__here)

回答 11

您的问题很好,不仅是方法。模块中的函数和对象通常也以一个下划线为前缀,并且可以以两个为前缀。

但是,例如,__double_underscore名称在模块中没有名称混杂。发生的情况是,如果您从模块(从module import *)全部导入,则不会导入以一个(或多个)下划线开头的名称,也不会在help(module)中显示名称。

Your question is good, it is not only about methods. Functions and objects in modules are commonly prefixed with one underscore as well, and can be prefixed by two.

But __double_underscore names are not name-mangled in modules, for example. What happens is that names beginning with one (or more) underscores are not imported if you import all from a module (from module import *), nor are the names shown in help(module).


回答 12

这是一个关于双下划线属性如何影响继承的类的简单说明性示例。因此,使用以下设置:

class parent(object):
    __default = "parent"
    def __init__(self, name=None):
        self.default = name or self.__default

    @property
    def default(self):
        return self.__default

    @default.setter
    def default(self, value):
        self.__default = value


class child(parent):
    __default = "child"

如果您随后在python REPL中创建一个子实例,则会看到以下内容

child_a = child()
child_a.default            # 'parent'
child_a._child__default    # 'child'
child_a._parent__default   # 'parent'

child_b = child("orphan")
## this will show 
child_b.default            # 'orphan'
child_a._child__default    # 'child'
child_a._parent__default   # 'orphan'

这对于某些人可能是显而易见的,但是在更复杂的环境中使我措手不及

Here is a simple illustrative example on how double underscore properties can affect an inherited class. So with the following setup:

class parent(object):
    __default = "parent"
    def __init__(self, name=None):
        self.default = name or self.__default

    @property
    def default(self):
        return self.__default

    @default.setter
    def default(self, value):
        self.__default = value


class child(parent):
    __default = "child"

if you then create a child instance in the python REPL, you will see the below

child_a = child()
child_a.default            # 'parent'
child_a._child__default    # 'child'
child_a._parent__default   # 'parent'

child_b = child("orphan")
## this will show 
child_b.default            # 'orphan'
child_a._child__default    # 'child'
child_a._parent__default   # 'orphan'

This may be obvious to some, but it caught me off guard in a much more complex environment


回答 13

Python中不存在只能从对象内部访问的“私有”实例变量。但是,大多数Python代码遵循一个约定:以下划线开头的名称(例如_spam)应被视为API的非公开部分(无论是函数,方法还是数据成员) 。它应被视为实现细节,如有更改,恕不另行通知。

参考 https://docs.python.org/2/tutorial/classes.html#private-variables-and-class-local-references

“Private” instance variables that cannot be accessed except from inside an object don’t exist in Python. However, there is a convention that is followed by most Python code: a name prefixed with an underscore (e.g. _spam) should be treated as a non-public part of the API (whether it is a function, a method or a data member). It should be considered an implementation detail and subject to change without notice.

reference https://docs.python.org/2/tutorial/classes.html#private-variables-and-class-local-references


回答 14

得到_和__的事实非常容易。其他答案很好地表达了他们。使用情况很难确定。

这是我的看法:

_

应该用于表示某个功能不公开使用,例如API。这和导入限制使它的行为很像internal在c#中。

__

应该用来避免继承层次结构中的名称冲突以及避免后期绑定。就像C#中的private。

==>

如果您想指出某事不是公共用途,而是应该像protecteduse 一样_。如果您想指出某事不是公共用途,而是应该像privateuse 一样__

这也是我非常喜欢的报价:

问题在于,类的作者可能会合理地认为“此属性/方法名称应为私有的,只能从该类定义中访问”,并使用__private约定。但是稍后,该类的用户可能会创建合法需要访问该名称的子类。因此,要么必须修改超类(可能很难或不可能),要么子类代码必须使用手动修改的名称(充其量是丑陋和脆弱的)。

但是我的问题是,如果没有IDE在您重写方法时警告您,如果您意外地从基类中重写了一个方法,则发现错误可能会花费您一段时间。

Getting the facts of _ and __ is pretty easy; the other answers express them pretty well. The usage is much harder to determine.

This is how I see it:

_

Should be used to indicate that a function is not for public use as for example an API. This and the import restriction make it behave much like internal in c#.

__

Should be used to avoid name collision in the inheritace hirarchy and to avoid latebinding. Much like private in c#.

==>

If you want to indicate that something is not for public use, but it should act like protected use _. If you want to indicate that something is not for public use, but it should act like private use __.

This is also a quote that I like very much:

The problem is that the author of a class may legitimately think “this attribute/method name should be private, only accessible from within this class definition” and use the __private convention. But later on, a user of that class may make a subclass that legitimately needs access to that name. So either the superclass has to be modified (which may be difficult or impossible), or the subclass code has to use manually mangled names (which is ugly and fragile at best).

But the problem with that is in my opinion that if there’s no IDE that warns you when you override methods, finding the error might take you a while if you have accidentially overriden a method from a base-class.


type()和isinstance()有什么区别?

问题:type()和isinstance()有什么区别?

这两个代码片段之间有什么区别?

使用type()

import types

if type(a) is types.DictType:
    do_something()
if type(b) in types.StringTypes:
    do_something_else()

使用isinstance()

if isinstance(a, dict):
    do_something()
if isinstance(b, str) or isinstance(b, unicode):
    do_something_else()

What are the differences between these two code fragments?

Using type():

import types

if type(a) is types.DictType:
    do_something()
if type(b) in types.StringTypes:
    do_something_else()

Using isinstance():

if isinstance(a, dict):
    do_something()
if isinstance(b, str) or isinstance(b, unicode):
    do_something_else()

回答 0

总结其他(已经很好!)答案的内容,isinstance迎合继承(派生类实例也是基类实例),而检查的相等性type则不(要求类型的标识并拒绝实例)子类型,又称为AKA子类)。

通常,在Python中,当然,您希望您的代码支持继承(由于继承非常方便,因此停止使用您的代码来使用它会很糟糕!),这isinstance比检查types的身份要糟糕得多,因为它无缝地支持s遗产。

这并不是说isinstance不错的,你要知道,它只是不那么糟糕不是检查的类型平等。正常的,Python式的首选解决方案几乎总是“鸭式输入”:尝试使用参数,就好像它是某个所需的类型一样,在try/ except语句中进行处理,以捕获如果参数实际上不是该参数可能会出现的所有异常类型(或其他可以模仿它的其他类型;-),然后在except子句中尝试其他操作(使用参数“好像”是其他类型)。

basestring ,但是,相当多的特殊情况,一个内建存在类型让你使用isinstance(包括strunicode子类basestring)。字符串是序列(您可以对它们进行循环,对其进行索引,对其进行切片等),但是您通常希望将它们视为“标量”类型-处理各种类型的字符串有点不方便(但在相当频繁的情况下)字符串(可能还有其他标量类型,即您不能循环的类型),所有容器(列表,集合,字典,…)的另一种方法,basestring加上这些isinstance可以帮助您做到这一点–总体结构成语是这样的:

if isinstance(x, basestring)
  return treatasscalar(x)
try:
  return treatasiter(iter(x))
except TypeError:
  return treatasscalar(x)

您可以说这basestring是一个抽象基类(“ ABC”)-它没有为子类提供具体功能,而是作为“标记”存在,主要用于isinstance。自从引入PEP 3119(引入了它的概括)以来,该概念显然在Python中正在不断发展。自从PEP 3119以来,该概念已从Python 2.6和3.0开始实施。

PEP清楚地表明,尽管ABC通常可以代替鸭类打字,但这样做通常没有很大的压力(请参见此处)。但是,在最新的Python版本中实现的ABC确实提供了额外的好处:(isinstanceissubclass)现在的含义不仅仅是“派生类的一个实例”(特别是,任何类都可以在ABC中“注册”,以便它可以显示为子类,其实例显示为ABC实例);并且ABC还可以通过模板方法设计模式应用程序以一种非常自然的方式为实际子类提供额外的便利(有关TM DP的更多信息,请参见此处此处的 [[Part II]],有关Python的更多信息,特别是在Python中,与ABC无关) 。

有关Python 2.6中提供的ABC支持的基本机制,请参见此处;其3.1版本非常相似,请参见此处。在这两个版本中,标准库模块集合(即3.1版本,对于非常相似的2.6版本,请参见此处)都提供了一些有用的ABC。

出于这个答案的目的,保留ABC的关键是(与TM DP混合类​​的经典Python替代类(例如UserDict.DictMixin相比,TM DP功能可以说是更自然的放置))是它们使isinstance(和issubclass)具有更多优势(在Python 2.6及更高版本中)比以前(在2.5及更低版本中)更具吸引力和普遍性,因此,相比之下,使类型相等性检查在最近的Python版本中比以前更加糟糕。

To summarize the contents of other (already good!) answers, isinstance caters for inheritance (an instance of a derived class is an instance of a base class, too), while checking for equality of type does not (it demands identity of types and rejects instances of subtypes, AKA subclasses).

Normally, in Python, you want your code to support inheritance, of course (since inheritance is so handy, it would be bad to stop code using yours from using it!), so isinstance is less bad than checking identity of types because it seamlessly supports inheritance.

It’s not that isinstance is good, mind you—it’s just less bad than checking equality of types. The normal, Pythonic, preferred solution is almost invariably “duck typing”: try using the argument as if it was of a certain desired type, do it in a try/except statement catching all exceptions that could arise if the argument was not in fact of that type (or any other type nicely duck-mimicking it;-), and in the except clause, try something else (using the argument “as if” it was of some other type).

basestring is, however, quite a special case—a builtin type that exists only to let you use isinstance (both str and unicode subclass basestring). Strings are sequences (you could loop over them, index them, slice them, …), but you generally want to treat them as “scalar” types—it’s somewhat incovenient (but a reasonably frequent use case) to treat all kinds of strings (and maybe other scalar types, i.e., ones you can’t loop on) one way, all containers (lists, sets, dicts, …) in another way, and basestring plus isinstance helps you do that—the overall structure of this idiom is something like:

if isinstance(x, basestring)
  return treatasscalar(x)
try:
  return treatasiter(iter(x))
except TypeError:
  return treatasscalar(x)

You could say that basestring is an Abstract Base Class (“ABC”)—it offers no concrete functionality to subclasses, but rather exists as a “marker”, mainly for use with isinstance. The concept is obviously a growing one in Python, since PEP 3119, which introduces a generalization of it, was accepted and has been implemented starting with Python 2.6 and 3.0.

The PEP makes it clear that, while ABCs can often substitute for duck typing, there is generally no big pressure to do that (see here). ABCs as implemented in recent Python versions do however offer extra goodies: isinstance (and issubclass) can now mean more than just “[an instance of] a derived class” (in particular, any class can be “registered” with an ABC so that it will show as a subclass, and its instances as instances of the ABC); and ABCs can also offer extra convenience to actual subclasses in a very natural way via Template Method design pattern applications (see here and here [[part II]] for more on the TM DP, in general and specifically in Python, independent of ABCs).

For the underlying mechanics of ABC support as offered in Python 2.6, see here; for their 3.1 version, very similar, see here. In both versions, standard library module collections (that’s the 3.1 version—for the very similar 2.6 version, see here) offers several useful ABCs.

For the purpose of this answer, the key thing to retain about ABCs (beyond an arguably more natural placement for TM DP functionality, compared to the classic Python alternative of mixin classes such as UserDict.DictMixin) is that they make isinstance (and issubclass) much more attractive and pervasive (in Python 2.6 and going forward) than they used to be (in 2.5 and before), and therefore, by contrast, make checking type equality an even worse practice in recent Python versions than it already used to be.


回答 1

这是一个无法isinstance达到的目标的示例type

class Vehicle:
    pass

class Truck(Vehicle):
    pass

在这种情况下,卡车对象是车辆,但是您会得到以下信息:

isinstance(Vehicle(), Vehicle)  # returns True
type(Vehicle()) == Vehicle      # returns True
isinstance(Truck(), Vehicle)    # returns True
type(Truck()) == Vehicle        # returns False, and this probably won't be what you want.

换句话说,isinstance对于子类也是如此。

另请参阅:如何在Python中比较对象的类型?

Here’s an example where isinstance achieves something that type cannot:

class Vehicle:
    pass

class Truck(Vehicle):
    pass

in this case, a truck object is a Vehicle, but you’ll get this:

isinstance(Vehicle(), Vehicle)  # returns True
type(Vehicle()) == Vehicle      # returns True
isinstance(Truck(), Vehicle)    # returns True
type(Truck()) == Vehicle        # returns False, and this probably won't be what you want.

In other words, isinstance is true for subclasses, too.

Also see: How to compare type of an object in Python?


回答 2

isinstance()type()Python 之间的区别?

进行类型检查

isinstance(obj, Base)

允许子类实例和多个可能的基数:

isinstance(obj, (Base1, Base2))

而类型检查

type(obj) is Base

仅支持引用的类型。


附带说明,is可能比

type(obj) == Base

因为类是单例。

避免类型检查-使用多态(鸭式输入)

在Python中,通常您希望为您的参数允许任何类型,将其按预期方式对待,如果对象的行为不符合预期,则会引发适当的错误。这被称为多态,也被称为鸭式打字。

def function_of_duck(duck):
    duck.quack()
    duck.swim()

如果上面的代码有效,我们可以假设我们的论点是鸭子。因此,我们可以传入的其他东西是鸭子的实际子类型:

function_of_duck(mallard)

或者像鸭子一样工作:

function_of_duck(object_that_quacks_and_swims_like_a_duck)

并且我们的代码仍然有效。

但是,在某些情况下,需要显式地进行类型检查。也许您与不同的对象类型有关。例如,Pandas Dataframe对象可以由字典记录构造。在这种情况下,您的代码需要知道它获取的参数类型,以便它可以正确处理它。

所以,要回答这个问题:

isinstance()type()Python 之间的区别?

请允许我展示一下区别:

type

假设您的函数获得某种类型的参数(构造函数的常见用例),则需要确保某种行为。如果您检查像这样的类型:

def foo(data):
    '''accepts a dict to construct something, string support in future'''
    if type(data) is not dict:
        # we're only going to test for dicts for now
        raise ValueError('only dicts are supported for now')

如果我们尝试传递dict作为的子类的dict (我们应该能够,如果我们期望我们的代码遵循Liskov Substitution的原理,则可以用子类型代替类型),则代码将中断!:

from collections import OrderedDict

foo(OrderedDict([('foo', 'bar'), ('fizz', 'buzz')]))

引发错误!

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 3, in foo
ValueError: argument must be a dict

isinstance

但是,如果使用isinstance,我们可以支持Liskov替换!:

def foo(a_dict):
    if not isinstance(a_dict, dict):
        raise ValueError('argument must be a dict')
    return a_dict

foo(OrderedDict([('foo', 'bar'), ('fizz', 'buzz')]))

退货 OrderedDict([('foo', 'bar'), ('fizz', 'buzz')])

抽象基类

实际上,我们可以做得更好。collections提供抽象基本类,这些基本类对各种类型强制执行最少的协议。在我们的情况下,如果仅希望使用Mapping协议,则可以执行以下操作,并且代码变得更加灵活:

from collections import Mapping

def foo(a_dict):
    if not isinstance(a_dict, Mapping):
        raise ValueError('argument must be a dict')
    return a_dict

对评论的回应:

应该注意的是,类型可以用来检查使用 type(obj) in (A, B, C)

是的,您可以测试类型的相等性,但是除了上面的以外,请使用多个基础进行控制流,除非您专门允许这些类型:

isinstance(obj, (A, B, C))

同样,区别在于isinstance支持子类,这些子类可以替换父类而又不会破坏程序,该属性称为Liskov替换。

更好的是,倒置依赖项,根本不检查特定类型。

结论

因此,由于我们希望支持替换子类,因此在大多数情况下,我们希望避免使用-进行类型检查,type而更喜欢使用isinstance-进行类型检查-除非您确实需要知道实例的确切类。

Differences between isinstance() and type() in Python?

Type-checking with

isinstance(obj, Base)

allows for instances of subclasses and multiple possible bases:

isinstance(obj, (Base1, Base2))

whereas type-checking with

type(obj) is Base

only supports the type referenced.


As a sidenote, is is likely more appropriate than

type(obj) == Base

because classes are singletons.

Avoid type-checking – use Polymorphism (duck-typing)

In Python, usually you want to allow any type for your arguments, treat it as expected, and if the object doesn’t behave as expected, it will raise an appropriate error. This is known as polymorphism, also known as duck-typing.

def function_of_duck(duck):
    duck.quack()
    duck.swim()

If the code above works, we can presume our argument is a duck. Thus we can pass in other things are actual sub-types of duck:

function_of_duck(mallard)

or that work like a duck:

function_of_duck(object_that_quacks_and_swims_like_a_duck)

and our code still works.

However, there are some cases where it is desirable to explicitly type-check. Perhaps you have sensible things to do with different object types. For example, the Pandas Dataframe object can be constructed from dicts or records. In such a case, your code needs to know what type of argument it is getting so that it can properly handle it.

So, to answer the question:

Differences between isinstance() and type() in Python?

Allow me to demonstrate the difference:

type

Say you need to ensure a certain behavior if your function gets a certain kind of argument (a common use-case for constructors). If you check for type like this:

def foo(data):
    '''accepts a dict to construct something, string support in future'''
    if type(data) is not dict:
        # we're only going to test for dicts for now
        raise ValueError('only dicts are supported for now')

If we try to pass in a dict that is a subclass of dict (as we should be able to, if we’re expecting our code to follow the principle of Liskov Substitution, that subtypes can be substituted for types) our code breaks!:

from collections import OrderedDict

foo(OrderedDict([('foo', 'bar'), ('fizz', 'buzz')]))

raises an error!

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 3, in foo
ValueError: argument must be a dict

isinstance

But if we use isinstance, we can support Liskov Substitution!:

def foo(a_dict):
    if not isinstance(a_dict, dict):
        raise ValueError('argument must be a dict')
    return a_dict

foo(OrderedDict([('foo', 'bar'), ('fizz', 'buzz')]))

returns OrderedDict([('foo', 'bar'), ('fizz', 'buzz')])

Abstract Base Classes

In fact, we can do even better. collections provides Abstract Base Classes that enforce minimal protocols for various types. In our case, if we only expect the Mapping protocol, we can do the following, and our code becomes even more flexible:

from collections import Mapping

def foo(a_dict):
    if not isinstance(a_dict, Mapping):
        raise ValueError('argument must be a dict')
    return a_dict

Response to comment:

It should be noted that type can be used to check against multiple classes using type(obj) in (A, B, C)

Yes, you can test for equality of types, but instead of the above, use the multiple bases for control flow, unless you are specifically only allowing those types:

isinstance(obj, (A, B, C))

The difference, again, is that isinstance supports subclasses that can be substituted for the parent without otherwise breaking the program, a property known as Liskov substitution.

Even better, though, invert your dependencies and don’t check for specific types at all.

Conclusion

So since we want to support substituting subclasses, in most cases, we want to avoid type-checking with type and prefer type-checking with isinstance – unless you really need to know the precise class of an instance.


回答 3

首选后者,因为它将正确处理子类。实际上,由于isinstance()的第二个参数可能是元组,因此您的示例编写起来甚至更加容易:

if isinstance(b, (str, unicode)):
    do_something_else()

或者,使用basestring抽象类:

if isinstance(b, basestring):
    do_something_else()

The latter is preferred, because it will handle subclasses properly. In fact, your example can be written even more easily because isinstance()‘s second parameter may be a tuple:

if isinstance(b, (str, unicode)):
    do_something_else()

or, using the basestring abstract class:

if isinstance(b, basestring):
    do_something_else()

回答 4

根据python文档,这是一条语句:

8.15。类型-内置类型的名称

从Python 2.2开始,内置的工厂函数(例如int()和) str()也是相应类型的名称。

所以isinstance()应该优先于type()

According to python documentation here is a statement:

8.15. types — Names for built-in types

Starting in Python 2.2, built-in factory functions such as int() and str() are also names for the corresponding types.

So isinstance() should be preferred over type().


回答 5

实际用法的区别在于它们如何处理booleans

TrueFalse只是关键字,平均10Python编写的。从而,

isinstance(True, int)

isinstance(False, int)

都回来了True。两个布尔值都是整数的实例。type()但是,它更聪明:

type(True) == int

返回False

A practical usage difference is how they handle booleans:

True and False are just keywords that mean 1 and 0 in python. Thus,

isinstance(True, int)

and

isinstance(False, int)

both return True. Both booleans are an instance of an integer. type(), however, is more clever:

type(True) == int

returns False.


回答 6

对于真正的差异,我们可以在中找到它code,但我找不到的默认行为的实现isinstance()

但是,我们可以根据__instancecheck__获得类似的abc .__ instancecheck__

从上方abc.__instancecheck__,使用以下测试后:

# file tree
# /test/__init__.py
# /test/aaa/__init__.py
# /test/aaa/aa.py
class b():
pass

# /test/aaa/a.py
import sys
sys.path.append('/test')

from aaa.aa import b
from aa import b as c

d = b()

print(b, c, d.__class__)
for i in [b, c, object]:
    print(i, '__subclasses__',  i.__subclasses__())
    print(i, '__mro__', i.__mro__)
    print(i, '__subclasshook__', i.__subclasshook__(d.__class__))
    print(i, '__subclasshook__', i.__subclasshook__(type(d)))
print(isinstance(d, b))
print(isinstance(d, c))

<class 'aaa.aa.b'> <class 'aa.b'> <class 'aaa.aa.b'>
<class 'aaa.aa.b'> __subclasses__ []
<class 'aaa.aa.b'> __mro__ (<class 'aaa.aa.b'>, <class 'object'>)
<class 'aaa.aa.b'> __subclasshook__ NotImplemented
<class 'aaa.aa.b'> __subclasshook__ NotImplemented
<class 'aa.b'> __subclasses__ []
<class 'aa.b'> __mro__ (<class 'aa.b'>, <class 'object'>)
<class 'aa.b'> __subclasshook__ NotImplemented
<class 'aa.b'> __subclasshook__ NotImplemented
<class 'object'> __subclasses__ [..., <class 'aaa.aa.b'>, <class 'aa.b'>]
<class 'object'> __mro__ (<class 'object'>,)
<class 'object'> __subclasshook__ NotImplemented
<class 'object'> __subclasshook__ NotImplemented
True
False

我得到以下结论type

# according to `abc.__instancecheck__`, they are maybe different! I have not found negative one 
type(INSTANCE) ~= INSTANCE.__class__
type(CLASS) ~= CLASS.__class__

对于isinstance

# guess from `abc.__instancecheck__`
return any(c in cls.__mro__ or c in cls.__subclasses__ or cls.__subclasshook__(c) for c in {INSTANCE.__class__, type(INSTANCE)})

顺便说一句:最好不要混用use relative and absolutely import,而要使用absolutely importproject_dir(由添加sys.path

For the real differences, we can find it in code, but I can’t find the implement of the default behavior of the isinstance().

However we can get the similar one abc.__instancecheck__ according to __instancecheck__.

From above abc.__instancecheck__, after using test below:

# file tree
# /test/__init__.py
# /test/aaa/__init__.py
# /test/aaa/aa.py
class b():
pass

# /test/aaa/a.py
import sys
sys.path.append('/test')

from aaa.aa import b
from aa import b as c

d = b()

print(b, c, d.__class__)
for i in [b, c, object]:
    print(i, '__subclasses__',  i.__subclasses__())
    print(i, '__mro__', i.__mro__)
    print(i, '__subclasshook__', i.__subclasshook__(d.__class__))
    print(i, '__subclasshook__', i.__subclasshook__(type(d)))
print(isinstance(d, b))
print(isinstance(d, c))

<class 'aaa.aa.b'> <class 'aa.b'> <class 'aaa.aa.b'>
<class 'aaa.aa.b'> __subclasses__ []
<class 'aaa.aa.b'> __mro__ (<class 'aaa.aa.b'>, <class 'object'>)
<class 'aaa.aa.b'> __subclasshook__ NotImplemented
<class 'aaa.aa.b'> __subclasshook__ NotImplemented
<class 'aa.b'> __subclasses__ []
<class 'aa.b'> __mro__ (<class 'aa.b'>, <class 'object'>)
<class 'aa.b'> __subclasshook__ NotImplemented
<class 'aa.b'> __subclasshook__ NotImplemented
<class 'object'> __subclasses__ [..., <class 'aaa.aa.b'>, <class 'aa.b'>]
<class 'object'> __mro__ (<class 'object'>,)
<class 'object'> __subclasshook__ NotImplemented
<class 'object'> __subclasshook__ NotImplemented
True
False

I get this conclusion, For type:

# according to `abc.__instancecheck__`, they are maybe different! I have not found negative one 
type(INSTANCE) ~= INSTANCE.__class__
type(CLASS) ~= CLASS.__class__

For isinstance:

# guess from `abc.__instancecheck__`
return any(c in cls.__mro__ or c in cls.__subclasses__ or cls.__subclasshook__(c) for c in {INSTANCE.__class__, type(INSTANCE)})

BTW: better not to mix use relative and absolutely import, use absolutely import from project_dir( added by sys.path)


Python类继承对象

问题:Python类继承对象

是否有理由要声明类object

我刚刚找到了执行此操作的代码,但找不到很好的理由。

class MyClass(object):
    # class code follows...

Is there any reason for a class declaration to inherit from object?

I just found some code that does this and I can’t find a good reason why.

class MyClass(object):
    # class code follows...

回答 0

是否有理由要声明类object

在Python 3中,除了Python 2和3之间的兼容性之外,没有任何理由。在Python 2中,原因很多


Python 2.x故事:

在Python 2.x(从2.2开始)中,根据是否存在object基类,有两种样式的类:

  1. “经典”样式类:它们没有object作为基类:

    >>> class ClassicSpam:      # no base class
    ...     pass
    >>> ClassicSpam.__bases__
    ()
  2. “新”样式类:它们具有直接或间接(例如,从内置类型继承)object作为基类:

    >>> class NewSpam(object):           # directly inherit from object
    ...    pass
    >>> NewSpam.__bases__
    (<type 'object'>,)
    >>> class IntSpam(int):              # indirectly inherit from object...
    ...    pass
    >>> IntSpam.__bases__
    (<type 'int'>,) 
    >>> IntSpam.__bases__[0].__bases__   # ... because int inherits from object  
    (<type 'object'>,)

毫无疑问,在编写一个类时,您总是想参加新式的类。这样做的好处很多,列举其中一些:

  • 支持描述符。具体而言,使用描述符使以下构造成为可能:

    1. classmethod:一种将类作为隐式参数(而不是实例)接收的方法。
    2. staticmethod:一种不将隐式参数self作为第一个参数的方法。
    3. 具有property以下属性:创建用于管理属性的获取,设置和删除的功能。
    4. __slots__:节省了类的内存消耗,还可以更快地访问属性。当然,它确实有局限性
  • __new__静态方法:让您自定义如何将新创建类的实例。

  • 方法解析顺序(MRO):尝试解析要调用的方法时,将以什么顺序搜索类的基类。

  • 与MRO有关,请super调用。另见,super()算超级。

如果您不继承object,请忘记这些。可以在此处找到对以前的要点以及“新”样式类的其他特权的更为详尽的描述。

新型类的缺点之一是,类本身对内存的要求更高。但是,除非您要创建许多类对象,否则我怀疑这将是一个问题,并且它是一个消极的消极情绪。


Python 3.x故事:

在Python 3中,一切都得到了简化。仅存在新样式的类(统称为类),因此添加的唯一区别object是要求您再输入8个字符。这个:

class ClassicSpam:
    pass

完全等效(除了它们的名称:-)与此:

class NewSpam(object):
     pass

并为此:

class Spam():
    pass

所有房间都object在他们的__bases__

>>> [object in cls.__bases__ for cls in {Spam, NewSpam, ClassicSpam}]
[True, True, True]

那你该怎么办?

在Python 2中: 始终object显式继承。享受津贴。

在Python 3中:object如果您要编写尝试与Python无关的代码,则继承自它,也就是说,它需要在Python 2和Python 3中均能正常工作。否则,实际上并没有什么不同,因为Python会为您插入代码在幕后。

Is there any reason for a class declaration to inherit from object?

In Python 3, apart from compatibility between Python 2 and 3, no reason. In Python 2, many reasons.


Python 2.x story:

In Python 2.x (from 2.2 onwards) there’s two styles of classes depending on the presence or absence of object as a base-class:

  1. “classic” style classes: they don’t have object as a base class:

    >>> class ClassicSpam:      # no base class
    ...     pass
    >>> ClassicSpam.__bases__
    ()
    
  2. “new” style classes: they have, directly or indirectly (e.g inherit from a built-in type), object as a base class:

    >>> class NewSpam(object):           # directly inherit from object
    ...    pass
    >>> NewSpam.__bases__
    (<type 'object'>,)
    >>> class IntSpam(int):              # indirectly inherit from object...
    ...    pass
    >>> IntSpam.__bases__
    (<type 'int'>,) 
    >>> IntSpam.__bases__[0].__bases__   # ... because int inherits from object  
    (<type 'object'>,)
    

Without a doubt, when writing a class you’ll always want to go for new-style classes. The perks of doing so are numerous, to list some of them:

  • Support for descriptors. Specifically, the following constructs are made possible with descriptors:

    1. classmethod: A method that receives the class as an implicit argument instead of the instance.
    2. staticmethod: A method that does not receive the implicit argument self as a first argument.
    3. properties with property: Create functions for managing the getting, setting and deleting of an attribute.
    4. __slots__: Saves memory consumptions of a class and also results in faster attribute access. Of course, it does impose limitations.
  • The __new__ static method: lets you customize how new class instances are created.

  • Method resolution order (MRO): in what order the base classes of a class will be searched when trying to resolve which method to call.

  • Related to MRO, super calls. Also see, super() considered super.

If you don’t inherit from object, forget these. A more exhaustive description of the previous bullet points along with other perks of “new” style classes can be found here.

One of the downsides of new-style classes is that the class itself is more memory demanding. Unless you’re creating many class objects, though, I doubt this would be an issue and it’s a negative sinking in a sea of positives.


Python 3.x story:

In Python 3, things are simplified. Only new-style classes exist (referred to plainly as classes) so, the only difference in adding object is requiring you to type in 8 more characters. This:

class ClassicSpam:
    pass

is completely equivalent (apart from their name :-) to this:

class NewSpam(object):
     pass

and to this:

class Spam():
    pass

All have object in their __bases__.

>>> [object in cls.__bases__ for cls in {Spam, NewSpam, ClassicSpam}]
[True, True, True]

So, what should you do?

In Python 2: always inherit from object explicitly. Get the perks.

In Python 3: inherit from object if you are writing code that tries to be Python agnostic, that is, it needs to work both in Python 2 and in Python 3. Otherwise don’t, it really makes no difference since Python inserts it for you behind the scenes.


回答 1

Python 3

  • class MyClass(object): =新型班
  • class MyClass:=新型类(隐式继承自object

Python 2

  • class MyClass(object): =新型班
  • class MyClass:= 老式类

说明

在Python 3.x中定义基类时,可以object从定义中删除。但是,这可以为严重难以跟踪的问题打开大门。

Python早在Python 2.2中就引入了新样式的类,而现在旧样式的类确实非常老。旧式类的讨论包含在2.x文档中,而在3.x文档中则不存在。

问题在于,Python 2.x中旧类的语法与Python 3.x中新类的替代语法相同。Python 2.x仍被广泛使用(例如GAE,Web2Py),并且任何代码(或编码器)在不经意间将3.x样式的类定义引入2.x代码中都会导致一些严重过时的基础对象。而且由于老式的类不在任何人的注意范围内,因此他们很可能不知道是什么打击了他们。

因此,只要把它弄清楚就行了,并省去一些2.x开发人员的眼泪。

Python 3

  • class MyClass(object): = New-style class
  • class MyClass: = New-style class (implicitly inherits from object)

Python 2

  • class MyClass(object): = New-style class
  • class MyClass: = OLD-STYLE CLASS

Explanation:

When defining base classes in Python 3.x, you’re allowed to drop the object from the definition. However, this can open the door for a seriously hard to track problem…

Python introduced new-style classes back in Python 2.2, and by now old-style classes are really quite old. Discussion of old-style classes is buried in the 2.x docs, and non-existent in the 3.x docs.

The problem is, the syntax for old-style classes in Python 2.x is the same as the alternative syntax for new-style classes in Python 3.x. Python 2.x is still very widely used (e.g. GAE, Web2Py), and any code (or coder) unwittingly bringing 3.x-style class definitions into 2.x code is going to end up with some seriously outdated base objects. And because old-style classes aren’t on anyone’s radar, they likely won’t know what hit them.

So just spell it out the long way and save some 2.x developer the tears.


回答 2

是的,这是一个“新样式”对象。这是python2.2中引入的功能。

新样式对象与经典对象具有不同的对象模型,并且某些内容无法与旧样式对象一起正常工作,例如和super()@property以及描述符。有关什么是新样式类的详细说明,请参见本文

SO链接描述了这些差异:Python中旧样式类和新样式类之间有什么区别?

Yes, this is a ‘new style’ object. It was a feature introduced in python2.2.

New style objects have a different object model to classic objects, and some things won’t work properly with old style objects, for instance, super(), @property and descriptors. See this article for a good description of what a new style class is.

SO link for a description of the differences: What is the difference between old style and new style classes in Python?


回答 3

难学Python的历史:

Python最初对类的再现在很多方面都被破坏了。到发现此故障时,已经为时已晚,他们必须予以支持。为了解决该问题,他们需要某种“新类”样式,以便“旧类”继续工作,但是您可以使用更正确的新版本。

他们决定使用小写的“对象”一词作为继承自您的“类”以构成一个类。这很令人困惑,但是一个类继承自名为“ object”的类来构成一个类,但它实际上并不是一个对象,而是一个类,但不要忘记从object继承。

也只是为了让您知道新样式类和旧样式类之间的区别是,新样式类始终从object类继承 或从另一个继承自的类继承object

class NewStyle(object):
    pass

另一个示例是:

class AnotherExampleOfNewStyle(NewStyle):
    pass

虽然老式的基类如下所示:

class OldStyle():
    pass

一个老式的子类如下所示:

class OldStyleSubclass(OldStyle):
    pass

您可以看到,Old Style基类不会从任何其他类继承,但是,Old Style类当然可以彼此继承。从对象继承可确保某些功能在每个Python类中均可用。Python 2.2中引入了新样式类

History from Learn Python the Hard Way:

Python’s original rendition of a class was broken in many serious ways. By the time this fault was recognized it was already too late, and they had to support it. In order to fix the problem, they needed some “new class” style so that the “old classes” would keep working but you can use the new more correct version.

They decided that they would use a word “object”, lowercased, to be the “class” that you inherit from to make a class. It is confusing, but a class inherits from the class named “object” to make a class but it’s not an object really its a class, but don’t forget to inherit from object.

Also just to let you know what the difference between new-style classes and old-style classes is, it’s that new-style classes always inherit from object class or from another class that inherited from object:

class NewStyle(object):
    pass

Another example is:

class AnotherExampleOfNewStyle(NewStyle):
    pass

While an old-style base class looks like this:

class OldStyle():
    pass

And an old-style child class looks like this:

class OldStyleSubclass(OldStyle):
    pass

You can see that an Old Style base class doesn’t inherit from any other class, however, Old Style classes can, of course, inherit from one another. Inheriting from object guarantees that certain functionality is available in every Python class. New style classes were introduced in Python 2.2


回答 4

是的,这是历史性的。没有它,它将创建一个老式的类。

如果type()在旧式对象上使用,则只会得到“实例”。在新型对象上,您可以得到其类。

Yes, it’s historical. Without it, it creates an old-style class.

If you use type() on an old-style object, you just get “instance”. On a new-style object you get its class.


回答 5

类创建语句的语法:

class <ClassName>(superclass):
    #code follows

如果没有您要特别继承的其他超类,则superclass始终应为object,这是Python中所有类的根。

object从技术上讲,它是Python中“新型”类的根。但是,如今的新型类与唯一的类一样好。

但是,如果您object在创建类时未明确使用该词,那么正如其他人提到的那样,Python 3.x隐式继承自object超类。但是我想显式总是比隐式好(地狱)

参考

The syntax of the class creation statement:

class <ClassName>(superclass):
    #code follows

In the absence of any other superclasses that you specifically want to inherit from, the superclass should always be object, which is the root of all classes in Python.

object is technically the root of “new-style” classes in Python. But the new-style classes today are as good as being the only style of classes.

But, if you don’t explicitly use the word object when creating classes, then as others mentioned, Python 3.x implicitly inherits from the object superclass. But I guess explicit is always better than implicit (hell)

Reference


“自我”一词的目的是什么?

问题:“自我”一词的目的是什么?

selfPython 中的单词的目的是什么?我知道它是指从该类创建的特定对象,但是我看不到为什么要将它显式地作为参数添加到每个函数中。为了说明这一点,在Ruby中,我可以这样做:

class myClass
    def myFunc(name)
        @name = name
    end
end

我很容易理解。但是在Python中,我需要包括self

class myClass:
    def myFunc(self, name):
        self.name = name

有人可以通过这个告诉我吗?我的经历(公认有限)并不是我遇到的。

What is the purpose of the self word in Python? I understand it refers to the specific object created from that class, but I can’t see why it explicitly needs to be added to every function as a parameter. To illustrate, in Ruby I can do this:

class myClass
    def myFunc(name)
        @name = name
    end
end

Which I understand, quite easily. However in Python I need to include self:

class myClass:
    def myFunc(self, name):
        self.name = name

Can anyone talk me through this? It is not something I’ve come across in my (admittedly limited) experience.


回答 0

需要使用的原因self.是因为Python不使用@语法来引用实例属性。Python决定以一种使该方法所属的实例自动传递但不会自动接收的方式进行方法:方法的第一个参数是调用该方法的实例。这使方法与函数完全相同,并保留实际名称供您使用(尽管self是约定俗成的,当您使用其他方法时,人们通常会皱着眉头。)self对于代码而言并不特殊,它只是另一个对象。

Python可以做一些其他事情来区分普通名称和属性-像Ruby这样的特殊语法,或者像C ++和Java这样的声明都需要,或者也许还有其他不同-但事实并非如此。Python的全部目的是使事情变得明确,使事情变得显而易见,尽管它并非在所有地方都做到这一点,但它确实为实例属性做到了。因此,分配给实例属性需要知道要分配给哪个实例,这就是为什么需要的原因self.

The reason you need to use self. is because Python does not use the @ syntax to refer to instance attributes. Python decided to do methods in a way that makes the instance to which the method belongs be passed automatically, but not received automatically: the first parameter of methods is the instance the method is called on. That makes methods entirely the same as functions, and leaves the actual name to use up to you (although self is the convention, and people will generally frown at you when you use something else.) self is not special to the code, it’s just another object.

Python could have done something else to distinguish normal names from attributes — special syntax like Ruby has, or requiring declarations like C++ and Java do, or perhaps something yet more different — but it didn’t. Python’s all for making things explicit, making it obvious what’s what, and although it doesn’t do it entirely everywhere, it does do it for instance attributes. That’s why assigning to an instance attribute needs to know what instance to assign to, and that’s why it needs self..


回答 1

让我们看一个简单的向量类:

class Vector:
    def __init__(self, x, y):
        self.x = x
        self.y = y

我们希望有一种计算长度的方法。如果我们想在类中定义它,它将是什么样?

    def length(self):
        return math.sqrt(self.x ** 2 + self.y ** 2)

当我们将其定义为全局方法/函数时,它应该是什么样?

def length_global(vector):
    return math.sqrt(vector.x ** 2 + vector.y ** 2)

因此,整个结构保持不变。我该如何利用呢?如果我们暂时假设没有lengthVector类编写方法,则可以执行以下操作:

Vector.length_new = length_global
v = Vector(3, 4)
print(v.length_new()) # 5.0

之所以有效,是因为的第一个参数length_global可以用作中的self参数length_new。没有明确的说法,这是不可能的self


理解显式需求的另一种方法self是查看Python在何处添加了一些语法糖。当您牢记时,基本上,

v_instance.length()

在内部转换为

Vector.length(v_instance)

很容易看到self适合的位置。您实际上并没有用Python编写实例方法;您编写的是必须将实例作为第一个参数的类方法。因此,您必须将实例参数显式放置在某处。

Let’s take a simple vector class:

class Vector:
    def __init__(self, x, y):
        self.x = x
        self.y = y

We want to have a method which calculates the length. What would it look like if we wanted to define it inside the class?

    def length(self):
        return math.sqrt(self.x ** 2 + self.y ** 2)

What should it look like when we were to define it as a global method/function?

def length_global(vector):
    return math.sqrt(vector.x ** 2 + vector.y ** 2)

So the whole structure stays the same. How can me make use of this? If we assume for a moment that we hadn’t written a length method for our Vector class, we could do this:

Vector.length_new = length_global
v = Vector(3, 4)
print(v.length_new()) # 5.0

This works because the first parameter of length_global, can be re-used as the self parameter in length_new. This would not be possible without an explicit self.


Another way of understanding the need for the explicit self is to see where Python adds some syntactical sugar. When you keep in mind, that basically, a call like

v_instance.length()

is internally transformed to

Vector.length(v_instance)

it is easy to see where the self fits in. You don’t actually write instance methods in Python; what you write is class methods which must take an instance as a first parameter. And therefore, you’ll have to place the instance parameter somewhere explicitly.


回答 2

假设您有一个ClassA包含methodA定义为以下方法的类:

def methodA(self, arg1, arg2):
    # do something

并且ObjectA是此类的一个实例。

现在,当ObjectA.methodA(arg1, arg2)被调用时,python在内部将其转换为:

ClassA.methodA(ObjectA, arg1, arg2)

self变量是指对象本身。

Let’s say you have a class ClassA which contains a method methodA defined as:

def methodA(self, arg1, arg2):
    # do something

and ObjectA is an instance of this class.

Now when ObjectA.methodA(arg1, arg2) is called, python internally converts it for you as:

ClassA.methodA(ObjectA, arg1, arg2)

The self variable refers to the object itself.


回答 3

实例化对象时,对象本身将传递到self参数中。

在此处输入图片说明

因此,对象的数据绑定到该对象。下面是一个示例,您可以如何可视化每个对象的数据外观。注意如何用对象名称替换“自我”。我并不是说下面的示例图是完全准确的,但希望它可以用于可视化自我的使用。

在此处输入图片说明

将对象传递到self参数中,以便对象可以保留其自己的数据。

尽管这可能并不完全准确,但是请考虑如下实例化对象的过程:制作对象时,它将类用作其自己的数据和方法的模板。如果不将其自身的名称传递给self参数,则该类中的属性和方法将保留为常规模板,并且不会引用该对象(属于该对象)。因此,通过将对象的名称传递给self参数,这意味着,如果从一个类实例化100个对象,则它们都可以跟踪自己的数据和方法。

请参见下图:

在此处输入图片说明

When objects are instantiated, the object itself is passed into the self parameter.

enter image description here

Because of this, the object’s data is bound to the object. Below is an example of how you might like to visualize what each object’s data might look. Notice how ‘self’ is replaced with the objects name. I’m not saying this example diagram below is wholly accurate but it hopefully with serve a purpose in visualizing the use of self.

enter image description here

The Object is passed into the self parameter so that the object can keep hold of its own data.

Although this may not be wholly accurate, think of the process of instantiating an object like this: When an object is made it uses the class as a template for its own data and methods. Without passing it’s own name into the self parameter, the attributes and methods in the class would remain as a general template and would not be referenced to (belong to) the object. So by passing the object’s name into the self parameter it means that if 100 objects are instantiated from the one class, they can all keep track of their own data and methods.

See the illustration below:

enter image description here


回答 4

我喜欢这个例子:

class A: 
    foo = []
a, b = A(), A()
a.foo.append(5)
b.foo
ans: [5]

class A: 
    def __init__(self): 
        self.foo = []
a, b = A(), A()
a.foo.append(5)
b.foo
ans: []

I like this example:

class A: 
    foo = []
a, b = A(), A()
a.foo.append(5)
b.foo
ans: [5]

class A: 
    def __init__(self): 
        self.foo = []
a, b = A(), A()
a.foo.append(5)
b.foo
ans: []

回答 5

我将用不使用类的代码进行演示:

def state_init(state):
    state['field'] = 'init'

def state_add(state, x):
    state['field'] += x

def state_mult(state, x):
    state['field'] *= x

def state_getField(state):
    return state['field']

myself = {}
state_init(myself)
state_add(myself, 'added')
state_mult(myself, 2)

print( state_getField(myself) )
#--> 'initaddedinitadded'

类只是避免始终传递此“状态”事物的一种方法(以及其他诸如初始化,类组合,很少需要的元类以及支持自定义方法以覆盖运算符之类的美好事物)的方法。

现在,让我们使用内置的python类机制来演示上面的代码,以显示其基本相同之处。

class State(object):
    def __init__(self):
        self.field = 'init'
    def add(self, x):
        self.field += x
    def mult(self, x):
        self.field *= x

s = State()
s.add('added')    # self is implicitly passed in
s.mult(2)         # self is implicitly passed in
print( s.field )

[从重复的封闭式问题中迁移了我的答案]

I will demonstrate with code that does not use classes:

def state_init(state):
    state['field'] = 'init'

def state_add(state, x):
    state['field'] += x

def state_mult(state, x):
    state['field'] *= x

def state_getField(state):
    return state['field']

myself = {}
state_init(myself)
state_add(myself, 'added')
state_mult(myself, 2)

print( state_getField(myself) )
#--> 'initaddedinitadded'

Classes are just a way to avoid passing in this “state” thing all the time (and other nice things like initializing, class composition, the rarely-needed metaclasses, and supporting custom methods to override operators).

Now let’s demonstrate the above code using the built-in python class machinery, to show how it’s basically the same thing.

class State(object):
    def __init__(self):
        self.field = 'init'
    def add(self, x):
        self.field += x
    def mult(self, x):
        self.field *= x

s = State()
s.add('added')    # self is implicitly passed in
s.mult(2)         # self is implicitly passed in
print( s.field )

[migrated my answer from duplicate closed question]


回答 6

以下摘录来自Python文档中关于self的内容

与Modula-3中一样,[Python]中没有用于从其方法引用该对象的成员的简写:方法函数以表示该对象的显式第一个参数声明,该参数由调用隐式提供。

通常,方法的第一个参数称为self。这无非是一种约定:self对Python绝对没有特殊的含义。但是请注意,如果不遵循该约定,则其他Python程序员可能对代码的可读性较低,并且还可以想到可能会依赖此类约定编写类浏览器程序。

有关更多信息,请参见关于类Python文档教程

The following excerpts are from the Python documentation about self:

As in Modula-3, there are no shorthands [in Python] for referencing the object’s members from its methods: the method function is declared with an explicit first argument representing the object, which is provided implicitly by the call.

Often, the first argument of a method is called self. This is nothing more than a convention: the name self has absolutely no special meaning to Python. Note, however, that by not following the convention your code may be less readable to other Python programmers, and it is also conceivable that a class browser program might be written that relies upon such a convention.

For more information, see the Python documentation tutorial on classes.


回答 7

除已说明的所有其他原因外,它还允许更轻松地访问重写的方法;你可以打电话Class.some_method(inst)

一个有用的例子:

class C1(object):
    def __init__(self):
         print "C1 init"

class C2(C1):
    def __init__(self): #overrides C1.__init__
        print "C2 init"
        C1.__init__(self) #but we still want C1 to init the class too
>>> C2()
"C2 init"
"C1 init"

As well as all the other reasons already stated, it allows for easier access to overridden methods; you can call Class.some_method(inst).

An example of where it’s useful:

class C1(object):
    def __init__(self):
         print "C1 init"

class C2(C1):
    def __init__(self): #overrides C1.__init__
        print "C2 init"
        C1.__init__(self) #but we still want C1 to init the class too
>>> C2()
"C2 init"
"C1 init"

回答 8

它的使用类似于thisJava 中关键字的使用,即提供对当前对象的引用。

Its use is similar to the use of this keyword in Java, i.e. to give a reference to the current object.


回答 9

与Java或C ++不同,Python不是为面向对象编程而构建的语言。

在Python中调用静态方法时,只需编写一个内部带有常规参数的方法。

class Animal():
    def staticMethod():
        print "This is a static method"

但是,对象方法需要您创建一个变量,在这种情况下,该方法是动物,需要使用self参数

class Animal():
    def objectMethod(self):
        print "This is an object method which needs an instance of a class"

self方法还用于引用类中的变量字段。

class Animal():
    #animalName made in constructor
    def Animal(self):
        self.animalName = "";


    def getAnimalName(self):
        return self.animalName

在这种情况下,self指的是整个类的animalName变量。记住:如果方法中有变量,则self将不起作用。该变量仅在该方法运行时才存在。为了定义字段(整个类的变量),您必须在类方法之外定义它们。

如果您听不懂我在说什么,请使用Google“面向对象编程”。一旦理解了这一点,您甚至不需要问这个问题:)。

Python is not a language built for Object Oriented Programming unlike Java or C++.

When calling a static method in Python, one simply writes a method with regular arguments inside it.

class Animal():
    def staticMethod():
        print "This is a static method"

However, an object method, which requires you to make a variable, which is an Animal, in this case, needs the self argument

class Animal():
    def objectMethod(self):
        print "This is an object method which needs an instance of a class"

The self method is also used to refer to a variable field within the class.

class Animal():
    #animalName made in constructor
    def Animal(self):
        self.animalName = "";


    def getAnimalName(self):
        return self.animalName

In this case, self is referring to the animalName variable of the entire class. REMEMBER: If you have a variable within a method, self will not work. That variable is simply existent only while that method is running. For defining fields (the variables of the entire class), you have to define them OUTSIDE the class methods.

If you don’t understand a single word of what I am saying, then Google “Object Oriented Programming.” Once you understand this, you won’t even need to ask that question :).


回答 10

可以遵循Python禅宗的“显式优于隐式”的说法。它确实是对您的类对象的引用。例如,在Java和PHP中,它称为this

如果user_type_name模型上的字段为,则可以通过进行访问self.user_type_name

It’s there to follow the Python zen “explicit is better than implicit”. It’s indeed a reference to your class object. In Java and PHP, for example, it’s called this.

If user_type_name is a field on your model you access it by self.user_type_name.


回答 11

首先,自我是一个常规名称,您可以代之以其他任何东西(连贯一致)。

它指的是对象本身,因此,在使用它时,您声明.name和.age是要创建的Student对象的属性(注意,不是Student类的属性)。

class Student:
    #called each time you create a new Student instance
    def __init__(self,name,age): #special method to initialize
        self.name=name
        self.age=age

    def __str__(self): #special method called for example when you use print
        return "Student %s is %s years old" %(self.name,self.age)

    def call(self, msg): #silly example for custom method
        return ("Hey, %s! "+msg) %self.name

#initializing two instances of the student class
bob=Student("Bob",20)
alice=Student("Alice",19)

#using them
print bob.name
print bob.age
print alice #this one only works if you define the __str__ method
print alice.call("Come here!") #notice you don't put a value for self

#you can modify attributes, like when alice ages
alice.age=20
print alice

代码在这里

First of all, self is a conventional name, you could put anything else (being coherent) in its stead.

It refers to the object itself, so when you are using it, you are declaring that .name and .age are properties of the Student objects (note, not of the Student class) you are going to create.

class Student:
    #called each time you create a new Student instance
    def __init__(self,name,age): #special method to initialize
        self.name=name
        self.age=age

    def __str__(self): #special method called for example when you use print
        return "Student %s is %s years old" %(self.name,self.age)

    def call(self, msg): #silly example for custom method
        return ("Hey, %s! "+msg) %self.name

#initializing two instances of the student class
bob=Student("Bob",20)
alice=Student("Alice",19)

#using them
print bob.name
print bob.age
print alice #this one only works if you define the __str__ method
print alice.call("Come here!") #notice you don't put a value for self

#you can modify attributes, like when alice ages
alice.age=20
print alice

Code is here


回答 12

self是对对象本身的对象引用,因此它们是相同的。在对象本身的上下文中未调用Python方法。 self在Python中,可能用于处理自定义对象模型之类的东西。

self is an object reference to the object itself, therefore, they are same. Python methods are not called in the context of the object itself. self in Python may be used to deal with custom object models or something.


回答 13

使用通常称为参数的参数self并不难理解,为什么要这样做呢?还是关于为什么要明确提及?我想,对于大多数查询此问题的用户来说,这是一个更大的问题,或者如果不是,则在继续学习python时,他们肯定会遇到相同的问题。我建议他们阅读以下两个博客:

1:自我解释

请注意,它不是关键字。

每个类方法(包括init)的第一个参数始终是对当前类实例的引用。按照惯例,此参数始终命名为self。在init方法中,self指的是新创建的对象;在其他类方法中,它引用其方法被调用的实例。例如,下面的代码与上面的代码相同。

2:为什么要用这种方式,为什么不能像Java那样将其作为参数消除,而要用关键字代替

我想补充的另一件事是,可选self参数允许我通过不编写而在类内声明静态方法self

代码示例:

class MyClass():
    def staticMethod():
        print "This is a static method"

    def objectMethod(self):
        print "This is an object method which needs an instance of a class, and that is what self refers to"

聚苯乙烯:仅在Python 3.x中有效。

在以前的版本中,必须显式添加@staticmethod装饰器,否则self必须使用参数。

The use of the argument, conventionally called self isn’t as hard to understand, as is why is it necessary? Or as to why explicitly mention it? That, I suppose, is a bigger question for most users who look up this question, or if it is not, they will certainly have the same question as they move forward learning python. I recommend them to read these couple of blogs:

1: Use of self explained

Note that it is not a keyword.

The first argument of every class method, including init, is always a reference to the current instance of the class. By convention, this argument is always named self. In the init method, self refers to the newly created object; in other class methods, it refers to the instance whose method was called. For example the below code is the same as the above code.

2: Why do we have it this way and why can we not eliminate it as an argument, like Java, and have a keyword instead

Another thing I would like to add is, an optional self argument allows me to declare static methods inside a class, by not writing self.

Code examples:

class MyClass():
    def staticMethod():
        print "This is a static method"

    def objectMethod(self):
        print "This is an object method which needs an instance of a class, and that is what self refers to"

PS:This works only in Python 3.x.

In previous versions, you have to explicitly add @staticmethod decorator, otherwise self argument is obligatory.


回答 14

我很惊讶没有人提出Lua。Lua也使用’self’变量,但是可以省略但仍然使用。C ++对“ this”的作用相同。我没有看到任何理由必须在每个函数中声明“ self”,但是您仍然应该能够像在lua和C ++中一样使用它。对于一种以简短为荣的语言,奇怪的是它要求您声明自变量。

I’m surprised nobody has brought up Lua. Lua also uses the ‘self’ variable however it can be omitted but still used. C++ does the same with ‘this’. I don’t see any reason to have to declare ‘self’ in each function but you should still be able to use it just like you can with lua and C++. For a language that prides itself on being brief it’s odd that it requires you to declare the self variable.


回答 15

请看以下示例,该示例清楚地说明了 self

class Restaurant(object):  
    bankrupt = False

    def open_branch(self):
        if not self.bankrupt:
           print("branch opened")

#create instance1
>>> x = Restaurant()
>>> x.bankrupt
False

#create instance2
>>> y = Restaurant()
>>> y.bankrupt = True   
>>> y.bankrupt
True

>>> x.bankrupt
False  

self 用于/需要区分实例。

资料来源:python中的self变量解释-Pythontips

Take a look at the following example, which clearly explains the purpose of self

class Restaurant(object):  
    bankrupt = False

    def open_branch(self):
        if not self.bankrupt:
           print("branch opened")

#create instance1
>>> x = Restaurant()
>>> x.bankrupt
False

#create instance2
>>> y = Restaurant()
>>> y.bankrupt = True   
>>> y.bankrupt
True

>>> x.bankrupt
False  

self is used/needed to distinguish between instances.

Source: self variable in python explained – Pythontips


回答 16

是因为按照python的设计方式,替代方法几乎行不通。Python旨在允许在无法使用隐式this(a-la Java / C ++)或显式@(a-la ruby​​)的上下文中定义方法或函数。我们来看一个使用python约定的显式方法的示例:

def fubar(x):
    self.x = x

class C:
    frob = fubar

现在,该fubar功能将无法使用,因为它将假定它self是一个全局变量(以及in frob)。另一种方法是执行具有替换后的全局范围的方法(其中self对象)。

隐式方法是

def fubar(x)
    myX = x

class C:
    frob = fubar

这意味着myX它将被解释为fubarfrob以及)中的局部变量。这里的替代方法是执行具有替换的局部作用域的方法,该局部作用域在调用之间保留,但是这将消除方法局部变量的可能性。

但是,目前的情况很好:

 def fubar(self, x)
     self.x = x

 class C:
     frob = fubar

这里的时候,被称为一方法frob将接收上它通过调用对象self的参数,并且fubar仍然可以用一个对象作为参数调用和工作一样(这一样的C.frob,我认为)。

Is because by the way python is designed the alternatives would hardly work. Python is designed to allow methods or functions to be defined in a context where both implicit this (a-la Java/C++) or explicit @ (a-la ruby) wouldn’t work. Let’s have an example with the explicit approach with python conventions:

def fubar(x):
    self.x = x

class C:
    frob = fubar

Now the fubar function wouldn’t work since it would assume that self is a global variable (and in frob as well). The alternative would be to execute method’s with a replaced global scope (where self is the object).

The implicit approach would be

def fubar(x)
    myX = x

class C:
    frob = fubar

This would mean that myX would be interpreted as a local variable in fubar (and in frob as well). The alternative here would be to execute methods with a replaced local scope which is retained between calls, but that would remove the posibility of method local variables.

However the current situation works out well:

 def fubar(self, x)
     self.x = x

 class C:
     frob = fubar

here when called as a method frob will receive the object on which it’s called via the self parameter, and fubar can still be called with an object as parameter and work the same (it is the same as C.frob I think).


回答 17

在该__init__方法中,self指的是新创建的对象;在其他类方法中,它引用其方法被调用的实例。

自我,正如名字一样,只是一个约定,可以随意称呼它!但是在使用它(例如删除对象)时,必须使用相同的名称:__del__(var),其中var在使用__init__(var,[...])

您也应该看一下cls,以了解更大的情况。这篇文章可能会有所帮助。

In the __init__ method, self refers to the newly created object; in other class methods, it refers to the instance whose method was called.

self, as a name, is just a convention, call it as you want ! but when using it, for example to delete the object, you have to use the same name: __del__(var), where var was used in the __init__(var,[...])

You should take a look at cls too, to have the bigger picture. This post could be helpful.


回答 18

self的作用类似于当前的对象名称或class的实例。

# Self explanation.


 class classname(object):

    def __init__(self,name):

        self.name=name
        # Self is acting as a replacement of object name.
        #self.name=object1.name

   def display(self):
      print("Name of the person is :",self.name)
      print("object name:",object1.name)


 object1=classname("Bucky")
 object2=classname("ford")

 object1.display()
 object2.display()

###### Output 
Name of the person is : Bucky
object name: Bucky
Name of the person is : ford
object name: Bucky

self is acting as like current object name or instance of class .

# Self explanation.


 class classname(object):

    def __init__(self,name):

        self.name=name
        # Self is acting as a replacement of object name.
        #self.name=object1.name

   def display(self):
      print("Name of the person is :",self.name)
      print("object name:",object1.name)


 object1=classname("Bucky")
 object2=classname("ford")

 object1.display()
 object2.display()

###### Output 
Name of the person is : Bucky
object name: Bucky
Name of the person is : ford
object name: Bucky

回答 19

self 是不可避免的。

只是有一个问题应该self是隐性或显性的。 Guido van Rossum解决了这个问题,说self必须留下

那么self住在哪里?

如果我们只是坚持使用函数式编程,那就不需要了self。进入Python OOP之后,我们发现self其中。

这是class C该方法的典型用例m1

class C:
    def m1(self, arg):
        print(self, ' inside')
        pass

ci =C()
print(ci, ' outside')
ci.m1(None)
print(hex(id(ci))) # hex memory address

该程序将输出:

<__main__.C object at 0x000002B9D79C6CC0>  outside
<__main__.C object at 0x000002B9D79C6CC0>  inside
0x2b9d79c6cc0

因此self保留了类实例的内存地址。 的目的self实例方法保留引用,并让我们可以显式访问该引用。


请注意,有三种不同类型的类方法:

  • 静态方法(阅读:函数),
  • 类方法
  • 实例方法(提到)。

self is inevitable.

There was just a question should self be implicit or explicit. Guido van Rossum resolved this question saying self has to stay.

So where the self live?

If we would just stick to functional programming we would not need self. Once we enter the Python OOP we find self there.

Here is the typical use case class C with the method m1

class C:
    def m1(self, arg):
        print(self, ' inside')
        pass

ci =C()
print(ci, ' outside')
ci.m1(None)
print(hex(id(ci))) # hex memory address

This program will output:

<__main__.C object at 0x000002B9D79C6CC0>  outside
<__main__.C object at 0x000002B9D79C6CC0>  inside
0x2b9d79c6cc0

So self holds the memory address of the class instance. The purpose of self would be to hold the reference for instance methods and for us to have explicit access to that reference.


Note there are three different types of class methods:

  • static methods (read: functions),
  • class methods,
  • instance methods (mentioned).

回答 20

文档

方法的特殊之处在于,实例对象作为函数的第一个参数传递。在我们的示例中,该调用x.f()与完全等效MyClass.f(x)。通常,调用带有n个参数列表的方法等同于调用带有参数列表的函数,该参数列表是通过在第一个参数之前插入方法的实例对象而创建的。

在相关片段之前,

class MyClass:
    """A simple example class"""
    i = 12345

    def f(self):
        return 'hello world'

x = MyClass()

from the docs,

the special thing about methods is that the instance object is passed as the first argument of the function. In our example, the call x.f() is exactly equivalent to MyClass.f(x). In general, calling a method with a list of n arguments is equivalent to calling the corresponding function with an argument list that is created by inserting the method’s instance object before the first argument.

preceding this the related snippet,

class MyClass:
    """A simple example class"""
    i = 12345

    def f(self):
        return 'hello world'

x = MyClass()


回答 21

它是对类实例对象的显式引用。

it’s an explicit reference to the class instance object.


Python中旧样式类与新样式类有什么区别?

问题:Python中旧样式类与新样式类有什么区别?

Python中旧样式类与新样式类有什么区别?什么时候应该使用其中一个?

What is the difference between old style and new style classes in Python? When should I use one or the other?


回答 0

新式和经典类

直到Python 2.1,旧式类才是供用户使用的唯一样式。

(旧式)类的概念与类型的概念无关:如果x是旧式类的实例,则x.__class__ 指定的类x,但type(x)始终为<type 'instance'>

这反映了这样一个事实,即所有旧式实例(独立于其类)均使用称为实例的单个内置类型实现。

在Python 2.2中引入了新型类,以统一类和类型的概念。新型类只是用户定义的类型,不多也不少。

如果x是新样式类的实例,则type(x)通常与x 相同x.__class__(尽管不能保证–允许新样式类实例覆盖所返回的值x.__class__)。

引入新型类的主要动机是提供具有完整元模型的统一对象模型

它还具有许多直接的好处,例如能够对大多数内置类型进行子类化,或者引入了“描述符”,以启用计算属性。

出于兼容性原因,默认情况下,类仍为旧样式

通过将另一个新样式类(即一种类型)指定为父类或“顶级类型”对象(如果不需要其他父类)来创建新样式类。

新样式类的行为与旧样式类的行为不同,除了返回什么类型外,还有许多重要的细节。

其中一些更改是新对象模型的基础,例如调用特殊方法的方式。其他是出于兼容性考虑而无法实现的“修补程序”,例如在多重继承的情况下的方法解析顺序。

Python 3仅具有新型类

无论是否从中继承子类object,类都是Python 3中的新型样式。

From New-style and classic classes:

Up to Python 2.1, old-style classes were the only flavour available to the user.

The concept of (old-style) class is unrelated to the concept of type: if x is an instance of an old-style class, then x.__class__ designates the class of x, but type(x) is always <type 'instance'>.

This reflects the fact that all old-style instances, independently of their class, are implemented with a single built-in type, called instance.

New-style classes were introduced in Python 2.2 to unify the concepts of class and type. A new-style class is simply a user-defined type, no more, no less.

If x is an instance of a new-style class, then type(x) is typically the same as x.__class__ (although this is not guaranteed – a new-style class instance is permitted to override the value returned for x.__class__).

The major motivation for introducing new-style classes is to provide a unified object model with a full meta-model.

It also has a number of immediate benefits, like the ability to subclass most built-in types, or the introduction of “descriptors”, which enable computed properties.

For compatibility reasons, classes are still old-style by default.

New-style classes are created by specifying another new-style class (i.e. a type) as a parent class, or the “top-level type” object if no other parent is needed.

The behaviour of new-style classes differs from that of old-style classes in a number of important details in addition to what type returns.

Some of these changes are fundamental to the new object model, like the way special methods are invoked. Others are “fixes” that could not be implemented before for compatibility concerns, like the method resolution order in case of multiple inheritance.

Python 3 only has new-style classes.

No matter if you subclass from object or not, classes are new-style in Python 3.


回答 1

声明方式:

新样式类继承自object或另一个新类。

class NewStyleClass(object):
    pass

class AnotherNewStyleClass(NewStyleClass):
    pass

老式的类没有。

class OldStyleClass():
    pass

Python 3注意:

Python 3不支持旧样式类,因此上述任何一种形式都会生成新样式类。

Declaration-wise:

New-style classes inherit from object, or from another new-style class.

class NewStyleClass(object):
    pass

class AnotherNewStyleClass(NewStyleClass):
    pass

Old-style classes don’t.

class OldStyleClass():
    pass

Python 3 Note:

Python 3 doesn’t support old style classes, so either form noted above results in a new-style class.


回答 2

新旧样式类之间的重要行为更改

  • 超级添加
  • MRO已更改(说明如下)
  • 添加了描述符
  • 除非派生自Exception(以下示例),否则不能引发新样式类对象
  • __slots__ 添加

MRO(方法解析顺序)已更改

它在其他答案中也提到过,但是这里有一个具体示例,说明了经典MRO和C3 MRO(用于新样式类)之间的区别。

问题是在多重继承中搜索属性(包括方法和成员变量)的顺序。

经典类从左到右进行深度优先搜索。停在第一场比赛。他们没有__mro__属性。

class C: i = 0
class C1(C): pass
class C2(C): i = 2
class C12(C1, C2): pass
class C21(C2, C1): pass

assert C12().i == 0
assert C21().i == 2

try:
    C12.__mro__
except AttributeError:
    pass
else:
    assert False

新式类 MRO在单个英语句子中合成起来更加复杂。在这里详细解释。它的特性之一是,只有在所有基类的派生类都被查找之后才搜索基类。它们具有__mro__显示搜索顺序的属性。

class C(object): i = 0
class C1(C): pass
class C2(C): i = 2
class C12(C1, C2): pass
class C21(C2, C1): pass

assert C12().i == 2
assert C21().i == 2

assert C12.__mro__ == (C12, C1, C2, C, object)
assert C21.__mro__ == (C21, C2, C1, C, object)

除非衍生自新样式类对象,否则无法引发 Exception

在Python 2.5左右,可能会引发许多类,而在Python 2.6左右,这已被删除。在Python 2.7.3上:

# OK, old:
class Old: pass
try:
    raise Old()
except Old:
    pass
else:
    assert False

# TypeError, new not derived from `Exception`.
class New(object): pass
try:
    raise New()
except TypeError:
    pass
else:
    assert False

# OK, derived from `Exception`.
class New(Exception): pass
try:
    raise New()
except New:
    pass
else:
    assert False

# `'str'` is a new style object, so you can't raise it:
try:
    raise 'str'
except TypeError:
    pass
else:
    assert False

Important behavior changes between old and new style classes

  • super added
  • MRO changed (explained below)
  • descriptors added
  • new style class objects cannot be raised unless derived from Exception (example below)
  • __slots__ added

MRO (Method Resolution Order) changed

It was mentioned in other answers, but here goes a concrete example of the difference between classic MRO and C3 MRO (used in new style classes).

The question is the order in which attributes (which include methods and member variables) are searched for in multiple inheritance.

Classic classes do a depth-first search from left to right. Stop on the first match. They do not have the __mro__ attribute.

class C: i = 0
class C1(C): pass
class C2(C): i = 2
class C12(C1, C2): pass
class C21(C2, C1): pass

assert C12().i == 0
assert C21().i == 2

try:
    C12.__mro__
except AttributeError:
    pass
else:
    assert False

New-style classes MRO is more complicated to synthesize in a single English sentence. It is explained in detail here. One of its properties is that a base class is only searched for once all its derived classes have been. They have the __mro__ attribute which shows the search order.

class C(object): i = 0
class C1(C): pass
class C2(C): i = 2
class C12(C1, C2): pass
class C21(C2, C1): pass

assert C12().i == 2
assert C21().i == 2

assert C12.__mro__ == (C12, C1, C2, C, object)
assert C21.__mro__ == (C21, C2, C1, C, object)

New style class objects cannot be raised unless derived from Exception

Around Python 2.5 many classes could be raised, and around Python 2.6 this was removed. On Python 2.7.3:

# OK, old:
class Old: pass
try:
    raise Old()
except Old:
    pass
else:
    assert False

# TypeError, new not derived from `Exception`.
class New(object): pass
try:
    raise New()
except TypeError:
    pass
else:
    assert False

# OK, derived from `Exception`.
class New(Exception): pass
try:
    raise New()
except New:
    pass
else:
    assert False

# `'str'` is a new style object, so you can't raise it:
try:
    raise 'str'
except TypeError:
    pass
else:
    assert False

回答 3

旧样式的类仍然比属性查找要快一些。这通常并不重要,但是在对性能敏感的Python 2.x代码中可能有用:

在[3]中:A类:
   ...:def __init __(self):
   ...:self.a ='hi there'
   ...:

在[4]中:B类(对象):
   ...:def __init __(self):
   ...:self.a ='hi there'
   ...:

在[6]中:aobj = A()
在[7]中:bobj = B()

在[8]中:%timeit aobj.a
10000000次循环,每循环3:78.7 ns最佳

在[10]中:%timeit bobj.a
10000000次循环,每循环3:86.9 ns最佳

Old style classes are still marginally faster for attribute lookup. This is not usually important, but it may be useful in performance-sensitive Python 2.x code:

In [3]: class A:
   ...:     def __init__(self):
   ...:         self.a = 'hi there'
   ...:

In [4]: class B(object):
   ...:     def __init__(self):
   ...:         self.a = 'hi there'
   ...:

In [6]: aobj = A()
In [7]: bobj = B()

In [8]: %timeit aobj.a
10000000 loops, best of 3: 78.7 ns per loop

In [10]: %timeit bobj.a
10000000 loops, best of 3: 86.9 ns per loop

回答 4

Guido撰写了有关New-Style Classes的The Inside Story,这是一篇有关Python中的新风格和旧风格类的非常不错的文章。

Python 3只有新样式的类。即使您编写了一个“旧类”,它也是从隐式派生的object

新式类具有一些旧式类所缺少的高级功能,例如super,新的C3 mro,一些神奇的方法等。

Guido has written The Inside Story on New-Style Classes, a really great article about new-style and old-style class in Python.

Python 3 has only new-style class. Even if you write an ‘old-style class’, it is implicitly derived from object.

New-style classes have some advanced features lacking in old-style classes, such as super, the new C3 mro, some magical methods, etc.


回答 5

这是一个非常实际的,正确/错误的区别。以下代码的两个版本之间的唯一区别是,在第二个版本中,Personobject继承。除此之外,两个版本相同,但结果不同:

  1. 老式班

    class Person():
        _names_cache = {}
        def __init__(self,name):
            self.name = name
        def __new__(cls,name):
            return cls._names_cache.setdefault(name,object.__new__(cls,name))
    
    ahmed1 = Person("Ahmed")
    ahmed2 = Person("Ahmed")
    print ahmed1 is ahmed2
    print ahmed1
    print ahmed2
    
    
    >>> False
    <__main__.Person instance at 0xb74acf8c>
    <__main__.Person instance at 0xb74ac6cc>
    >>>
    
  2. 新型班

    class Person(object):
        _names_cache = {}
        def __init__(self,name):
            self.name = name
        def __new__(cls,name):
            return cls._names_cache.setdefault(name,object.__new__(cls,name))
    
    ahmed1 = Person("Ahmed")
    ahmed2 = Person("Ahmed")
    print ahmed2 is ahmed1
    print ahmed1
    print ahmed2
    
    >>> True
    <__main__.Person object at 0xb74ac66c>
    <__main__.Person object at 0xb74ac66c>
    >>>

Here’s a very practical, true/false difference. The only difference between the two versions of the following code is that in the second version Person inherits from object. Other than that, the two versions are identical, but with different results:

  1. Old-style classes

    class Person():
        _names_cache = {}
        def __init__(self,name):
            self.name = name
        def __new__(cls,name):
            return cls._names_cache.setdefault(name,object.__new__(cls,name))
    
    ahmed1 = Person("Ahmed")
    ahmed2 = Person("Ahmed")
    print ahmed1 is ahmed2
    print ahmed1
    print ahmed2
    
    
    >>> False
    <__main__.Person instance at 0xb74acf8c>
    <__main__.Person instance at 0xb74ac6cc>
    >>>
    
    
  2. New-style classes

    class Person(object):
        _names_cache = {}
        def __init__(self,name):
            self.name = name
        def __new__(cls,name):
            return cls._names_cache.setdefault(name,object.__new__(cls,name))
    
    ahmed1 = Person("Ahmed")
    ahmed2 = Person("Ahmed")
    print ahmed2 is ahmed1
    print ahmed1
    print ahmed2
    
    >>> True
    <__main__.Person object at 0xb74ac66c>
    <__main__.Person object at 0xb74ac66c>
    >>>
    

回答 6

新样式的类继承自objectPython ,并且必须从Python 2.2开始编写(即class Classname(object):而不是class Classname:)。核心更改是统一类型和类,这样做的好处是它允许您从内置类型继承。

阅读descrintro以获得更多详细信息。

New-style classes inherit from object and must be written as such in Python 2.2 onwards (i.e. class Classname(object): instead of class Classname:). The core change is to unify types and classes, and the nice side-effect of this is that it allows you to inherit from built-in types.

Read descrintro for more details.


回答 7

新样式类可以使用super(Foo, self)where Foo是一个类,并且self是一个实例。

super(type[, object-or-type])

返回将方法调用委托给类型的父级或同级类的代理对象。这对于访问已在类中重写的继承方法很有用。搜索顺序与getattr()使用的顺序相同,只是类型本身被跳过。

在Python 3.x中,您可以super()在没有任何参数的类内部简单地使用。

New style classes may use super(Foo, self) where Foo is a class and self is the instance.

super(type[, object-or-type])

Return a proxy object that delegates method calls to a parent or sibling class of type. This is useful for accessing inherited methods that have been overridden in a class. The search order is same as that used by getattr() except that the type itself is skipped.

And in Python 3.x you can simply use super() inside a class without any parameters.


Python中的元类是什么?

问题:Python中的元类是什么?

在Python中,什么是元类?我们将它们用于什么?

In Python, what are metaclasses and what do we use them for?


回答 0

元类是类的类。类定义类的实例(即对象)的行为,而元类定义类的行为。类是元类的实例。

虽然在Python中,您可以对元类使用任意可调用对象(例如Jerub演示),但是更好的方法是使其成为实际的类。type是Python中常见的元类。type它本身是一个类,并且是它自己的类型。您将无法type纯粹使用Python 重新创建类似的东西,但是Python有点作弊。要在Python中创建自己的元类,您实际上只想将其子类化type

元类最常用作类工厂。当通过调用类创建对象时,Python通过调用元类来创建一个新类(执行“ class”语句时)。因此,将元类与普通方法__init____new__方法结合使用,可以使您在创建类时做“额外的事情”,例如使用某些注册表注册新类或将其完全替换为其他类。

class执行该语句时,Python首先将class语句的主体作为普通代码块执行。生成的命名空间(一个dict)保存了将来类的属性。通过查看要成为类的基类(继承了元类),要成为__metaclass__的类(如果有)的属性或__metaclass__全局变量来确定元类。然后使用该类的名称,基数和属性调用该元类以实例化它。

但是,元类实际上定义了类的类型,而不仅仅是它的工厂,因此您可以使用它们做更多的事情。例如,您可以在元类上定义常规方法。这些元类方法就像类方法,因为它们可以在没有实例的情况下在类上调用,但是它们也不像类方法,因为它们不能在类的实例上被调用。type.__subclasses__()type元类上方法的示例。您还可以定义正常的“魔力”的方法,如__add____iter____getattr__,执行或如何变化的类的行为。

这是一些汇总示例:

def make_hook(f):
    """Decorator to turn 'foo' method into '__foo__'"""
    f.is_hook = 1
    return f

class MyType(type):
    def __new__(mcls, name, bases, attrs):

        if name.startswith('None'):
            return None

        # Go over attributes and see if they should be renamed.
        newattrs = {}
        for attrname, attrvalue in attrs.iteritems():
            if getattr(attrvalue, 'is_hook', 0):
                newattrs['__%s__' % attrname] = attrvalue
            else:
                newattrs[attrname] = attrvalue

        return super(MyType, mcls).__new__(mcls, name, bases, newattrs)

    def __init__(self, name, bases, attrs):
        super(MyType, self).__init__(name, bases, attrs)

        # classregistry.register(self, self.interfaces)
        print "Would register class %s now." % self

    def __add__(self, other):
        class AutoClass(self, other):
            pass
        return AutoClass
        # Alternatively, to autogenerate the classname as well as the class:
        # return type(self.__name__ + other.__name__, (self, other), {})

    def unregister(self):
        # classregistry.unregister(self)
        print "Would unregister class %s now." % self

class MyObject:
    __metaclass__ = MyType


class NoneSample(MyObject):
    pass

# Will print "NoneType None"
print type(NoneSample), repr(NoneSample)

class Example(MyObject):
    def __init__(self, value):
        self.value = value
    @make_hook
    def add(self, other):
        return self.__class__(self.value + other.value)

# Will unregister the class
Example.unregister()

inst = Example(10)
# Will fail with an AttributeError
#inst.unregister()

print inst + inst
class Sibling(MyObject):
    pass

ExampleSibling = Example + Sibling
# ExampleSibling is now a subclass of both Example and Sibling (with no
# content of its own) although it will believe it's called 'AutoClass'
print ExampleSibling
print ExampleSibling.__mro__

A metaclass is the class of a class. A class defines how an instance of the class (i.e. an object) behaves while a metaclass defines how a class behaves. A class is an instance of a metaclass.

While in Python you can use arbitrary callables for metaclasses (like Jerub shows), the better approach is to make it an actual class itself. type is the usual metaclass in Python. type is itself a class, and it is its own type. You won’t be able to recreate something like type purely in Python, but Python cheats a little. To create your own metaclass in Python you really just want to subclass type.

A metaclass is most commonly used as a class-factory. When you create an object by calling the class, Python creates a new class (when it executes the ‘class’ statement) by calling the metaclass. Combined with the normal __init__ and __new__ methods, metaclasses therefore allow you to do ‘extra things’ when creating a class, like registering the new class with some registry or replace the class with something else entirely.

When the class statement is executed, Python first executes the body of the class statement as a normal block of code. The resulting namespace (a dict) holds the attributes of the class-to-be. The metaclass is determined by looking at the baseclasses of the class-to-be (metaclasses are inherited), at the __metaclass__ attribute of the class-to-be (if any) or the __metaclass__ global variable. The metaclass is then called with the name, bases and attributes of the class to instantiate it.

However, metaclasses actually define the type of a class, not just a factory for it, so you can do much more with them. You can, for instance, define normal methods on the metaclass. These metaclass-methods are like classmethods in that they can be called on the class without an instance, but they are also not like classmethods in that they cannot be called on an instance of the class. type.__subclasses__() is an example of a method on the type metaclass. You can also define the normal ‘magic’ methods, like __add__, __iter__ and __getattr__, to implement or change how the class behaves.

Here’s an aggregated example of the bits and pieces:

def make_hook(f):
    """Decorator to turn 'foo' method into '__foo__'"""
    f.is_hook = 1
    return f

class MyType(type):
    def __new__(mcls, name, bases, attrs):

        if name.startswith('None'):
            return None

        # Go over attributes and see if they should be renamed.
        newattrs = {}
        for attrname, attrvalue in attrs.iteritems():
            if getattr(attrvalue, 'is_hook', 0):
                newattrs['__%s__' % attrname] = attrvalue
            else:
                newattrs[attrname] = attrvalue

        return super(MyType, mcls).__new__(mcls, name, bases, newattrs)

    def __init__(self, name, bases, attrs):
        super(MyType, self).__init__(name, bases, attrs)

        # classregistry.register(self, self.interfaces)
        print "Would register class %s now." % self

    def __add__(self, other):
        class AutoClass(self, other):
            pass
        return AutoClass
        # Alternatively, to autogenerate the classname as well as the class:
        # return type(self.__name__ + other.__name__, (self, other), {})

    def unregister(self):
        # classregistry.unregister(self)
        print "Would unregister class %s now." % self

class MyObject:
    __metaclass__ = MyType


class NoneSample(MyObject):
    pass

# Will print "NoneType None"
print type(NoneSample), repr(NoneSample)

class Example(MyObject):
    def __init__(self, value):
        self.value = value
    @make_hook
    def add(self, other):
        return self.__class__(self.value + other.value)

# Will unregister the class
Example.unregister()

inst = Example(10)
# Will fail with an AttributeError
#inst.unregister()

print inst + inst
class Sibling(MyObject):
    pass

ExampleSibling = Example + Sibling
# ExampleSibling is now a subclass of both Example and Sibling (with no
# content of its own) although it will believe it's called 'AutoClass'
print ExampleSibling
print ExampleSibling.__mro__

回答 1

类作为对象

在理解元类之前,您需要掌握Python的类。Python从Smalltalk语言中借用了一个非常特殊的类概念。

在大多数语言中,类只是描述如何产生对象的代码段。在Python中也是如此:

>>> class ObjectCreator(object):
...       pass
...

>>> my_object = ObjectCreator()
>>> print(my_object)
<__main__.ObjectCreator object at 0x8974f2c>

但是类比Python中更多。类也是对象。

是的,对象。

一旦使用关键字class,Python就会执行它并创建一个对象。指令

>>> class ObjectCreator(object):
...       pass
...

在内存中创建一个名称为“ ObjectCreator”的对象。

这个对象(类)本身具有创建对象(实例)的能力,这就是为什么它是一个类

但是,它仍然是一个对象,因此:

  • 您可以将其分配给变量
  • 你可以复制它
  • 您可以为其添加属性
  • 您可以将其作为函数参数传递

例如:

>>> print(ObjectCreator) # you can print a class because it's an object
<class '__main__.ObjectCreator'>
>>> def echo(o):
...       print(o)
...
>>> echo(ObjectCreator) # you can pass a class as a parameter
<class '__main__.ObjectCreator'>
>>> print(hasattr(ObjectCreator, 'new_attribute'))
False
>>> ObjectCreator.new_attribute = 'foo' # you can add attributes to a class
>>> print(hasattr(ObjectCreator, 'new_attribute'))
True
>>> print(ObjectCreator.new_attribute)
foo
>>> ObjectCreatorMirror = ObjectCreator # you can assign a class to a variable
>>> print(ObjectCreatorMirror.new_attribute)
foo
>>> print(ObjectCreatorMirror())
<__main__.ObjectCreator object at 0x8997b4c>

动态创建类

由于类是对象,因此您可以像创建任何对象一样即时创建它们。

首先,您可以使用class以下方法在函数中创建一个类:

>>> def choose_class(name):
...     if name == 'foo':
...         class Foo(object):
...             pass
...         return Foo # return the class, not an instance
...     else:
...         class Bar(object):
...             pass
...         return Bar
...
>>> MyClass = choose_class('foo')
>>> print(MyClass) # the function returns a class, not an instance
<class '__main__.Foo'>
>>> print(MyClass()) # you can create an object from this class
<__main__.Foo object at 0x89c6d4c>

但这并不是那么动态,因为您仍然必须自己编写整个类。

由于类是对象,因此它们必须由某种东西生成。

使用class关键字时,Python会自动创建此对象。但是,与Python中的大多数事情一样,它为您提供了一种手动进行操作的方法。

还记得功能type吗?好的旧函数可以让您知道对象的类型:

>>> print(type(1))
<type 'int'>
>>> print(type("1"))
<type 'str'>
>>> print(type(ObjectCreator))
<type 'type'>
>>> print(type(ObjectCreator()))
<class '__main__.ObjectCreator'>

嗯,type具有完全不同的功能,它也可以动态创建类。type可以将类的描述作为参数,并返回一个类。

(我知道,根据传递给它的参数,同一个函数可以有两种完全不同的用法是很愚蠢的。由于在Python中向后兼容,这是一个问题)

type 这样工作:

type(name, bases, attrs)

哪里:

  • name:类的名称
  • bases:父类的元组(对于继承,可以为空)
  • attrs:包含属性名称和值的字典

例如:

>>> class MyShinyClass(object):
...       pass

可以通过以下方式手动创建:

>>> MyShinyClass = type('MyShinyClass', (), {}) # returns a class object
>>> print(MyShinyClass)
<class '__main__.MyShinyClass'>
>>> print(MyShinyClass()) # create an instance with the class
<__main__.MyShinyClass object at 0x8997cec>

您会注意到,我们使用“ MyShinyClass”作为类的名称和变量来保存类引用。它们可以不同,但​​是没有理由使事情复杂化。

type接受字典来定义类的属性。所以:

>>> class Foo(object):
...       bar = True

可以翻译为:

>>> Foo = type('Foo', (), {'bar':True})

并用作普通类:

>>> print(Foo)
<class '__main__.Foo'>
>>> print(Foo.bar)
True
>>> f = Foo()
>>> print(f)
<__main__.Foo object at 0x8a9b84c>
>>> print(f.bar)
True

当然,您可以从中继承,因此:

>>>   class FooChild(Foo):
...         pass

将会:

>>> FooChild = type('FooChild', (Foo,), {})
>>> print(FooChild)
<class '__main__.FooChild'>
>>> print(FooChild.bar) # bar is inherited from Foo
True

最终,您需要向类中添加方法。只需定义具有适当签名的函数并将其分配为属性即可。

>>> def echo_bar(self):
...       print(self.bar)
...
>>> FooChild = type('FooChild', (Foo,), {'echo_bar': echo_bar})
>>> hasattr(Foo, 'echo_bar')
False
>>> hasattr(FooChild, 'echo_bar')
True
>>> my_foo = FooChild()
>>> my_foo.echo_bar()
True

在动态创建类之后,您可以添加更多方法,就像将方法添加到正常创建的类对象中一样。

>>> def echo_bar_more(self):
...       print('yet another method')
...
>>> FooChild.echo_bar_more = echo_bar_more
>>> hasattr(FooChild, 'echo_bar_more')
True

您会看到我们要去的方向:在Python中,类是对象,您可以动态动态地创建一个类。

这是Python在使用关键字class时所做的,并且是通过使用元类来完成的。

什么是元类(最终)

元类是创建类的“东西”。

您定义类是为了创建对象,对吗?

但是我们了解到Python类是对象。

好吧,元类是创建这些对象的原因。它们是类的类,您可以通过以下方式描绘它们:

MyClass = MetaClass()
my_object = MyClass()

您已经看到,type您可以执行以下操作:

MyClass = type('MyClass', (), {})

这是因为该函数type实际上是一个元类。type是Python用于在幕后创建所有类的元类。

现在您想知道为什么用小写而不是小写Type

好吧,我想这与str,创建字符串对象int的类和创建整数对象的类的一致性有关。type只是创建类对象的类。

您可以通过检查__class__属性来看到。

一切,我的意思是,一切都是Python中的对象。其中包括整数,字符串,函数和类。它们都是对象。所有这些都是从一个类创建的:

>>> age = 35
>>> age.__class__
<type 'int'>
>>> name = 'bob'
>>> name.__class__
<type 'str'>
>>> def foo(): pass
>>> foo.__class__
<type 'function'>
>>> class Bar(object): pass
>>> b = Bar()
>>> b.__class__
<class '__main__.Bar'>

现在,什么是__class__任何__class__

>>> age.__class__.__class__
<type 'type'>
>>> name.__class__.__class__
<type 'type'>
>>> foo.__class__.__class__
<type 'type'>
>>> b.__class__.__class__
<type 'type'>

因此,元类只是创建类对象的东西。

如果愿意,可以将其称为“Class工厂”。

type 是Python使用的内置元类,但是您当然可以创建自己的元类。

__metaclass__属性

在Python 2中,您可以__metaclass__在编写类时添加属性(有关Python 3语法,请参见下一部分):

class Foo(object):
    __metaclass__ = something...
    [...]

如果这样做,Python将使用元类来创建class Foo

小心点,这很棘手。

class Foo(object)先编写,但Foo尚未在内存中创建类对象。

Python将__metaclass__在类定义中查找。如果找到它,它将使用它来创建对象类Foo。如果没有,它将 type用于创建类。

读几次。

当您这样做时:

class Foo(Bar):
    pass

Python执行以下操作:

中有__metaclass__属性Foo吗?

如果是的话,在内存中创建一个类对象(我说的是类对象,陪在我身边在这里),名称Foo使用是什么__metaclass__

如果Python找不到__metaclass__,它将__metaclass__在MODULE级别查找a ,然后尝试执行相同的操作(但仅适用于不继承任何内容的类,基本上是老式的类)。

然后,如果根本找不到任何对象__metaclass__,它将使用Bar的(第一个父对象)自己的元类(可能是默认值type)创建类对象。

请注意,该__metaclass__属性将不会被继承,父(Bar.__class__)的元类将被继承。如果Bar使用的__metaclass__是创建的属性Bartype()(不是type.__new__()),子类不会继承该行为。

现在最大的问题是,您可以输入__metaclass__什么?

答案是:可以创建类的东西。

什么可以创建一个类?type,或任何继承或使用它的内容。

Python 3中的元类

设置元类的语法在Python 3中已更改:

class Foo(object, metaclass=something):
    ...

__metaclass__不再使用该属性,而在基类列表中使用关键字参数。

但是,元类的行为基本保持不变

在python 3中添加到元类的一件事是,您还可以将属性作为关键字参数传递给元类,如下所示:

class Foo(object, metaclass=something, kwarg1=value1, kwarg2=value2):
    ...

阅读以下部分,了解python如何处理此问题。

自定义元类

元类的主要目的是在创建类时自动更改它。

通常,您要针对要在其中创建与当前上下文匹配的类的API进行此操作。

想象一个愚蠢的示例,在该示例中,您决定模块中的所有类的属性都应以大写形式编写。有多种方法可以执行此操作,但是一种方法是__metaclass__在模块级别进行设置。

这样,将使用此元类创建该模块的所有类,而我们只需要告诉元类将所有属性都转换为大写即可。

幸运的是,__metaclass__实际上可以是任何可调用的,它不需要是正式的类(我知道,名称中带有“ class”的东西不必是类,请弄清楚……但这很有用)。

因此,我们将从使用函数的简单示例开始。

# the metaclass will automatically get passed the same argument
# that you usually pass to `type`
def upper_attr(future_class_name, future_class_parents, future_class_attrs):
    """
      Return a class object, with the list of its attribute turned
      into uppercase.
    """
    # pick up any attribute that doesn't start with '__' and uppercase it
    uppercase_attrs = {
        attr if attr.startswith("__") else attr.upper(): v
        for attr, v in future_class_attrs.items()
    }

    # let `type` do the class creation
    return type(future_class_name, future_class_parents, uppercase_attrs)

__metaclass__ = upper_attr # this will affect all classes in the module

class Foo(): # global __metaclass__ won't work with "object" though
    # but we can define __metaclass__ here instead to affect only this class
    # and this will work with "object" children
    bar = 'bip'

让我们检查:

>>> hasattr(Foo, 'bar')
False
>>> hasattr(Foo, 'BAR')
True
>>> Foo.BAR
'bip'

现在,让我们做完全一样的操作,但是对元类使用真实的类:

# remember that `type` is actually a class like `str` and `int`
# so you can inherit from it
class UpperAttrMetaclass(type):
    # __new__ is the method called before __init__
    # it's the method that creates the object and returns it
    # while __init__ just initializes the object passed as parameter
    # you rarely use __new__, except when you want to control how the object
    # is created.
    # here the created object is the class, and we want to customize it
    # so we override __new__
    # you can do some stuff in __init__ too if you wish
    # some advanced use involves overriding __call__ as well, but we won't
    # see this
    def __new__(upperattr_metaclass, future_class_name,
                future_class_parents, future_class_attrs):
        uppercase_attrs = {
            attr if attr.startswith("__") else attr.upper(): v
            for attr, v in future_class_attrs.items()
        }
        return type(future_class_name, future_class_parents, uppercase_attrs)

让我们重写上面的内容,但是现在有了更短,更实际的变量名,我们知道它们的含义了:

class UpperAttrMetaclass(type):
    def __new__(cls, clsname, bases, attrs):
        uppercase_attrs = {
            attr if attr.startswith("__") else attr.upper(): v
            for attr, v in attrs.items()
        }
        return type(clsname, bases, uppercase_attrs)

您可能已经注意到了额外的参数cls。它没有什么特别的:__new__始终将其定义的类作为第一个参数。就像您有self将实例作为第一个参数接收的普通方法一样,还是为类方法定义了类。

但这不是适当的OOP。我们正在type直接调用,而不是覆盖或调用父母的__new__。让我们改为:

class UpperAttrMetaclass(type):
    def __new__(cls, clsname, bases, attrs):
        uppercase_attrs = {
            attr if attr.startswith("__") else attr.upper(): v
            for attr, v in attrs.items()
        }
        return type.__new__(cls, clsname, bases, uppercase_attrs)

通过使用super,我们可以使其更加整洁,这将简化继承(因为是的,您可以具有元类,从元类继承,从类型继承):

class UpperAttrMetaclass(type):
    def __new__(cls, clsname, bases, attrs):
        uppercase_attrs = {
            attr if attr.startswith("__") else attr.upper(): v
            for attr, v in attrs.items()
        }
        return super(UpperAttrMetaclass, cls).__new__(
            cls, clsname, bases, uppercase_attrs)

哦,在python 3中,如果您使用关键字参数进行此调用,例如:

class Foo(object, metaclass=MyMetaclass, kwarg1=value1):
    ...

它将在元类中转换为使用它:

class MyMetaclass(type):
    def __new__(cls, clsname, bases, dct, kwargs1=default):
        ...

而已。实际上,关于元类的更多信息了。

使用元类编写代码的复杂性背后的原因不是因为元类,而是因为您通常使用元类依靠自省,操纵继承以及诸如var之类的变量来做扭曲的事情__dict__

实际上,元类对于进行黑魔法特别有用,因此也很复杂。但就其本身而言,它们很简单:

  • 拦截类创建
  • 修改Class
  • 返回修改后的类

为什么要使用元类类而不是函数?

既然__metaclass__可以接受任何可调用对象,那么为什么要使用一个类,因为它显然更复杂?

这样做有几个原因:

  • 意图很明确。阅读时UpperAttrMetaclass(type),您会知道接下来会发生什么
  • 您可以使用OOP。元类可以继承元类,重写父方法。元类甚至可以使用元类。
  • 如果您指定了元类类,但未指定元类函数,则该类的子类将是其元类的实例。
  • 您可以更好地构建代码。绝对不要像上面的示例那样将元类用于琐碎的事情。通常用于复杂的事情。能够制作几种方法并将它们分组在一个类中的能力对于使代码易于阅读非常有用。
  • 您可以勾上__new____init____call__。这将使您可以做不同的事情。即使通常可以全部使用__new__,有些人也更习惯使用__init__
  • 这些被称为元类,该死!它一定意味着什么!

为什么要使用元类?

现在是个大问题。为什么要使用一些晦涩的易错功能?

好吧,通常您不会:

元类是更深层的魔术,99%的用户永远不必担心。如果您想知道是否需要它们,则不需要(实际上需要它们的人肯定会知道他们需要它们,并且不需要解释原因)。

Python大师Tim Peters

元类的主要用例是创建一个API。一个典型的例子是Django ORM。它允许您定义如下内容:

class Person(models.Model):
    name = models.CharField(max_length=30)
    age = models.IntegerField()

但是,如果您这样做:

person = Person(name='bob', age='35')
print(person.age)

它不会返回IntegerField对象。它将返回一个int,甚至可以直接从数据库中获取它。

这是可能的,因为models.Modeldefine __metaclass__并使用了一些魔术,这些魔术将使Person您使用简单语句定义的对象变成与数据库字段的复杂挂钩。

Django通过公开一个简单的API并使用元类,从该API重新创建代码来完成幕后的实际工作,使看起来复杂的事情变得简单。

最后

首先,您知道类是可以创建实例的对象。

实际上,类本身就是实例。元类的。

>>> class Foo(object): pass
>>> id(Foo)
142630324

一切都是Python中的对象,它们都是类的实例或元类的实例。

除了type

type实际上是它自己的元类。这不是您可以在纯Python中复制的东西,而是通过在实现级别上作弊来完成的。

其次,元类很复杂。您可能不希望将它们用于非常简单的类更改。您可以使用两种不同的技术来更改类:

99%的时间您需要Class变更,最好使用这些。

但是在98%的时间中,您根本不需要更改Class。

Classes as objects

Before understanding metaclasses, you need to master classes in Python. And Python has a very peculiar idea of what classes are, borrowed from the Smalltalk language.

In most languages, classes are just pieces of code that describe how to produce an object. That’s kinda true in Python too:

>>> class ObjectCreator(object):
...       pass
...

>>> my_object = ObjectCreator()
>>> print(my_object)
<__main__.ObjectCreator object at 0x8974f2c>

But classes are more than that in Python. Classes are objects too.

Yes, objects.

As soon as you use the keyword class, Python executes it and creates an OBJECT. The instruction

>>> class ObjectCreator(object):
...       pass
...

creates in memory an object with the name “ObjectCreator”.

This object (the class) is itself capable of creating objects (the instances), and this is why it’s a class.

But still, it’s an object, and therefore:

  • you can assign it to a variable
  • you can copy it
  • you can add attributes to it
  • you can pass it as a function parameter

e.g.:

>>> print(ObjectCreator) # you can print a class because it's an object
<class '__main__.ObjectCreator'>
>>> def echo(o):
...       print(o)
...
>>> echo(ObjectCreator) # you can pass a class as a parameter
<class '__main__.ObjectCreator'>
>>> print(hasattr(ObjectCreator, 'new_attribute'))
False
>>> ObjectCreator.new_attribute = 'foo' # you can add attributes to a class
>>> print(hasattr(ObjectCreator, 'new_attribute'))
True
>>> print(ObjectCreator.new_attribute)
foo
>>> ObjectCreatorMirror = ObjectCreator # you can assign a class to a variable
>>> print(ObjectCreatorMirror.new_attribute)
foo
>>> print(ObjectCreatorMirror())
<__main__.ObjectCreator object at 0x8997b4c>

Creating classes dynamically

Since classes are objects, you can create them on the fly, like any object.

First, you can create a class in a function using class:

>>> def choose_class(name):
...     if name == 'foo':
...         class Foo(object):
...             pass
...         return Foo # return the class, not an instance
...     else:
...         class Bar(object):
...             pass
...         return Bar
...
>>> MyClass = choose_class('foo')
>>> print(MyClass) # the function returns a class, not an instance
<class '__main__.Foo'>
>>> print(MyClass()) # you can create an object from this class
<__main__.Foo object at 0x89c6d4c>

But it’s not so dynamic, since you still have to write the whole class yourself.

Since classes are objects, they must be generated by something.

When you use the class keyword, Python creates this object automatically. But as with most things in Python, it gives you a way to do it manually.

Remember the function type? The good old function that lets you know what type an object is:

>>> print(type(1))
<type 'int'>
>>> print(type("1"))
<type 'str'>
>>> print(type(ObjectCreator))
<type 'type'>
>>> print(type(ObjectCreator()))
<class '__main__.ObjectCreator'>

Well, type has a completely different ability, it can also create classes on the fly. type can take the description of a class as parameters, and return a class.

(I know, it’s silly that the same function can have two completely different uses according to the parameters you pass to it. It’s an issue due to backwards compatibility in Python)

type works this way:

type(name, bases, attrs)

Where:

  • name: name of the class
  • bases: tuple of the parent class (for inheritance, can be empty)
  • attrs: dictionary containing attributes names and values

e.g.:

>>> class MyShinyClass(object):
...       pass

can be created manually this way:

>>> MyShinyClass = type('MyShinyClass', (), {}) # returns a class object
>>> print(MyShinyClass)
<class '__main__.MyShinyClass'>
>>> print(MyShinyClass()) # create an instance with the class
<__main__.MyShinyClass object at 0x8997cec>

You’ll notice that we use “MyShinyClass” as the name of the class and as the variable to hold the class reference. They can be different, but there is no reason to complicate things.

type accepts a dictionary to define the attributes of the class. So:

>>> class Foo(object):
...       bar = True

Can be translated to:

>>> Foo = type('Foo', (), {'bar':True})

And used as a normal class:

>>> print(Foo)
<class '__main__.Foo'>
>>> print(Foo.bar)
True
>>> f = Foo()
>>> print(f)
<__main__.Foo object at 0x8a9b84c>
>>> print(f.bar)
True

And of course, you can inherit from it, so:

>>>   class FooChild(Foo):
...         pass

would be:

>>> FooChild = type('FooChild', (Foo,), {})
>>> print(FooChild)
<class '__main__.FooChild'>
>>> print(FooChild.bar) # bar is inherited from Foo
True

Eventually you’ll want to add methods to your class. Just define a function with the proper signature and assign it as an attribute.

>>> def echo_bar(self):
...       print(self.bar)
...
>>> FooChild = type('FooChild', (Foo,), {'echo_bar': echo_bar})
>>> hasattr(Foo, 'echo_bar')
False
>>> hasattr(FooChild, 'echo_bar')
True
>>> my_foo = FooChild()
>>> my_foo.echo_bar()
True

And you can add even more methods after you dynamically create the class, just like adding methods to a normally created class object.

>>> def echo_bar_more(self):
...       print('yet another method')
...
>>> FooChild.echo_bar_more = echo_bar_more
>>> hasattr(FooChild, 'echo_bar_more')
True

You see where we are going: in Python, classes are objects, and you can create a class on the fly, dynamically.

This is what Python does when you use the keyword class, and it does so by using a metaclass.

What are metaclasses (finally)

Metaclasses are the ‘stuff’ that creates classes.

You define classes in order to create objects, right?

But we learned that Python classes are objects.

Well, metaclasses are what create these objects. They are the classes’ classes, you can picture them this way:

MyClass = MetaClass()
my_object = MyClass()

You’ve seen that type lets you do something like this:

MyClass = type('MyClass', (), {})

It’s because the function type is in fact a metaclass. type is the metaclass Python uses to create all classes behind the scenes.

Now you wonder why the heck is it written in lowercase, and not Type?

Well, I guess it’s a matter of consistency with str, the class that creates strings objects, and int the class that creates integer objects. type is just the class that creates class objects.

You see that by checking the __class__ attribute.

Everything, and I mean everything, is an object in Python. That includes ints, strings, functions and classes. All of them are objects. And all of them have been created from a class:

>>> age = 35
>>> age.__class__
<type 'int'>
>>> name = 'bob'
>>> name.__class__
<type 'str'>
>>> def foo(): pass
>>> foo.__class__
<type 'function'>
>>> class Bar(object): pass
>>> b = Bar()
>>> b.__class__
<class '__main__.Bar'>

Now, what is the __class__ of any __class__ ?

>>> age.__class__.__class__
<type 'type'>
>>> name.__class__.__class__
<type 'type'>
>>> foo.__class__.__class__
<type 'type'>
>>> b.__class__.__class__
<type 'type'>

So, a metaclass is just the stuff that creates class objects.

You can call it a ‘class factory’ if you wish.

type is the built-in metaclass Python uses, but of course, you can create your own metaclass.

The __metaclass__ attribute

In Python 2, you can add a __metaclass__ attribute when you write a class (see next section for the Python 3 syntax):

class Foo(object):
    __metaclass__ = something...
    [...]

If you do so, Python will use the metaclass to create the class Foo.

Careful, it’s tricky.

You write class Foo(object) first, but the class object Foo is not created in memory yet.

Python will look for __metaclass__ in the class definition. If it finds it, it will use it to create the object class Foo. If it doesn’t, it will use type to create the class.

Read that several times.

When you do:

class Foo(Bar):
    pass

Python does the following:

Is there a __metaclass__ attribute in Foo?

If yes, create in memory a class object (I said a class object, stay with me here), with the name Foo by using what is in __metaclass__.

If Python can’t find __metaclass__, it will look for a __metaclass__ at the MODULE level, and try to do the same (but only for classes that don’t inherit anything, basically old-style classes).

Then if it can’t find any __metaclass__ at all, it will use the Bar‘s (the first parent) own metaclass (which might be the default type) to create the class object.

Be careful here that the __metaclass__ attribute will not be inherited, the metaclass of the parent (Bar.__class__) will be. If Bar used a __metaclass__ attribute that created Bar with type() (and not type.__new__()), the subclasses will not inherit that behavior.

Now the big question is, what can you put in __metaclass__ ?

The answer is: something that can create a class.

And what can create a class? type, or anything that subclasses or uses it.

Metaclasses in Python 3

The syntax to set the metaclass has been changed in Python 3:

class Foo(object, metaclass=something):
    ...

i.e. the __metaclass__ attribute is no longer used, in favor of a keyword argument in the list of base classes.

The behaviour of metaclasses however stays largely the same.

One thing added to metaclasses in python 3 is that you can also pass attributes as keyword-arguments into a metaclass, like so:

class Foo(object, metaclass=something, kwarg1=value1, kwarg2=value2):
    ...

Read the section below for how python handles this.

Custom metaclasses

The main purpose of a metaclass is to change the class automatically, when it’s created.

You usually do this for APIs, where you want to create classes matching the current context.

Imagine a stupid example, where you decide that all classes in your module should have their attributes written in uppercase. There are several ways to do this, but one way is to set __metaclass__ at the module level.

This way, all classes of this module will be created using this metaclass, and we just have to tell the metaclass to turn all attributes to uppercase.

Luckily, __metaclass__ can actually be any callable, it doesn’t need to be a formal class (I know, something with ‘class’ in its name doesn’t need to be a class, go figure… but it’s helpful).

So we will start with a simple example, by using a function.

# the metaclass will automatically get passed the same argument
# that you usually pass to `type`
def upper_attr(future_class_name, future_class_parents, future_class_attrs):
    """
      Return a class object, with the list of its attribute turned
      into uppercase.
    """
    # pick up any attribute that doesn't start with '__' and uppercase it
    uppercase_attrs = {
        attr if attr.startswith("__") else attr.upper(): v
        for attr, v in future_class_attrs.items()
    }

    # let `type` do the class creation
    return type(future_class_name, future_class_parents, uppercase_attrs)

__metaclass__ = upper_attr # this will affect all classes in the module

class Foo(): # global __metaclass__ won't work with "object" though
    # but we can define __metaclass__ here instead to affect only this class
    # and this will work with "object" children
    bar = 'bip'

Let’s check:

>>> hasattr(Foo, 'bar')
False
>>> hasattr(Foo, 'BAR')
True
>>> Foo.BAR
'bip'

Now, let’s do exactly the same, but using a real class for a metaclass:

# remember that `type` is actually a class like `str` and `int`
# so you can inherit from it
class UpperAttrMetaclass(type):
    # __new__ is the method called before __init__
    # it's the method that creates the object and returns it
    # while __init__ just initializes the object passed as parameter
    # you rarely use __new__, except when you want to control how the object
    # is created.
    # here the created object is the class, and we want to customize it
    # so we override __new__
    # you can do some stuff in __init__ too if you wish
    # some advanced use involves overriding __call__ as well, but we won't
    # see this
    def __new__(upperattr_metaclass, future_class_name,
                future_class_parents, future_class_attrs):
        uppercase_attrs = {
            attr if attr.startswith("__") else attr.upper(): v
            for attr, v in future_class_attrs.items()
        }
        return type(future_class_name, future_class_parents, uppercase_attrs)

Let’s rewrite the above, but with shorter and more realistic variable names now that we know what they mean:

class UpperAttrMetaclass(type):
    def __new__(cls, clsname, bases, attrs):
        uppercase_attrs = {
            attr if attr.startswith("__") else attr.upper(): v
            for attr, v in attrs.items()
        }
        return type(clsname, bases, uppercase_attrs)

You may have noticed the extra argument cls. There is nothing special about it: __new__ always receives the class it’s defined in, as first parameter. Just like you have self for ordinary methods which receive the instance as first parameter, or the defining class for class methods.

But this is not proper OOP. We are calling type directly and we aren’t overriding or calling the parent’s __new__. Let’s do that instead:

class UpperAttrMetaclass(type):
    def __new__(cls, clsname, bases, attrs):
        uppercase_attrs = {
            attr if attr.startswith("__") else attr.upper(): v
            for attr, v in attrs.items()
        }
        return type.__new__(cls, clsname, bases, uppercase_attrs)

We can make it even cleaner by using super, which will ease inheritance (because yes, you can have metaclasses, inheriting from metaclasses, inheriting from type):

class UpperAttrMetaclass(type):
    def __new__(cls, clsname, bases, attrs):
        uppercase_attrs = {
            attr if attr.startswith("__") else attr.upper(): v
            for attr, v in attrs.items()
        }
        return super(UpperAttrMetaclass, cls).__new__(
            cls, clsname, bases, uppercase_attrs)

Oh, and in python 3 if you do this call with keyword arguments, like this:

class Foo(object, metaclass=MyMetaclass, kwarg1=value1):
    ...

It translates to this in the metaclass to use it:

class MyMetaclass(type):
    def __new__(cls, clsname, bases, dct, kwargs1=default):
        ...

That’s it. There is really nothing more about metaclasses.

The reason behind the complexity of the code using metaclasses is not because of metaclasses, it’s because you usually use metaclasses to do twisted stuff relying on introspection, manipulating inheritance, vars such as __dict__, etc.

Indeed, metaclasses are especially useful to do black magic, and therefore complicated stuff. But by themselves, they are simple:

  • intercept a class creation
  • modify the class
  • return the modified class

Why would you use metaclasses classes instead of functions?

Since __metaclass__ can accept any callable, why would you use a class since it’s obviously more complicated?

There are several reasons to do so:

  • The intention is clear. When you read UpperAttrMetaclass(type), you know what’s going to follow
  • You can use OOP. Metaclass can inherit from metaclass, override parent methods. Metaclasses can even use metaclasses.
  • Subclasses of a class will be instances of its metaclass if you specified a metaclass-class, but not with a metaclass-function.
  • You can structure your code better. You never use metaclasses for something as trivial as the above example. It’s usually for something complicated. Having the ability to make several methods and group them in one class is very useful to make the code easier to read.
  • You can hook on __new__, __init__ and __call__. Which will allow you to do different stuff. Even if usually you can do it all in __new__, some people are just more comfortable using __init__.
  • These are called metaclasses, damn it! It must mean something!

Why would you use metaclasses?

Now the big question. Why would you use some obscure error prone feature?

Well, usually you don’t:

Metaclasses are deeper magic that 99% of users should never worry about. If you wonder whether you need them, you don’t (the people who actually need them know with certainty that they need them, and don’t need an explanation about why).

Python Guru Tim Peters

The main use case for a metaclass is creating an API. A typical example of this is the Django ORM. It allows you to define something like this:

class Person(models.Model):
    name = models.CharField(max_length=30)
    age = models.IntegerField()

But if you do this:

person = Person(name='bob', age='35')
print(person.age)

It won’t return an IntegerField object. It will return an int, and can even take it directly from the database.

This is possible because models.Model defines __metaclass__ and it uses some magic that will turn the Person you just defined with simple statements into a complex hook to a database field.

Django makes something complex look simple by exposing a simple API and using metaclasses, recreating code from this API to do the real job behind the scenes.

The last word

First, you know that classes are objects that can create instances.

Well in fact, classes are themselves instances. Of metaclasses.

>>> class Foo(object): pass
>>> id(Foo)
142630324

Everything is an object in Python, and they are all either instances of classes or instances of metaclasses.

Except for type.

type is actually its own metaclass. This is not something you could reproduce in pure Python, and is done by cheating a little bit at the implementation level.

Secondly, metaclasses are complicated. You may not want to use them for very simple class alterations. You can change classes by using two different techniques:

99% of the time you need class alteration, you are better off using these.

But 98% of the time, you don’t need class alteration at all.


回答 2

请注意,此答案适用于2008年编写的Python 2.x,元类在3.x中略有不同。

元类是使“类”工作的秘诀。新样式对象的默认元类称为“类型”。

class type(object)
  |  type(object) -> the object's type
  |  type(name, bases, dict) -> a new type

元类带有3个参数。’ 名称 ‘,’ 基数 ‘和’ 字典

这是秘密的开始。在此示例类定义中查找名称,基数和字典来自何处。

class ThisIsTheName(Bases, Are, Here):
    All_the_code_here
    def doesIs(create, a):
        dict

让我们定义一个元类,该元类将演示“ class: ” 如何调用它。

def test_metaclass(name, bases, dict):
    print 'The Class Name is', name
    print 'The Class Bases are', bases
    print 'The dict has', len(dict), 'elems, the keys are', dict.keys()

    return "yellow"

class TestName(object, None, int, 1):
    __metaclass__ = test_metaclass
    foo = 1
    def baz(self, arr):
        pass

print 'TestName = ', repr(TestName)

# output => 
The Class Name is TestName
The Class Bases are (<type 'object'>, None, <type 'int'>, 1)
The dict has 4 elems, the keys are ['baz', '__module__', 'foo', '__metaclass__']
TestName =  'yellow'

现在,一个实际上意味着含义的示例将自动使列表中的变量在类上设置为“属性”,并设置为“无”。

def init_attributes(name, bases, dict):
    if 'attributes' in dict:
        for attr in dict['attributes']:
            dict[attr] = None

    return type(name, bases, dict)

class Initialised(object):
    __metaclass__ = init_attributes
    attributes = ['foo', 'bar', 'baz']

print 'foo =>', Initialised.foo
# output=>
foo => None

请注意,Initialised通过拥有元类而获得的不可思议的行为init_attributes不会传递到的子类上Initialised

这是一个更具体的示例,显示了如何子类化“类型”以创建一个在创建类时执行操作的元类。这很棘手:

class MetaSingleton(type):
    instance = None
    def __call__(cls, *args, **kw):
        if cls.instance is None:
            cls.instance = super(MetaSingleton, cls).__call__(*args, **kw)
        return cls.instance

class Foo(object):
    __metaclass__ = MetaSingleton

a = Foo()
b = Foo()
assert a is b

Note, this answer is for Python 2.x as it was written in 2008, metaclasses are slightly different in 3.x.

Metaclasses are the secret sauce that make ‘class’ work. The default metaclass for a new style object is called ‘type’.

class type(object)
  |  type(object) -> the object's type
  |  type(name, bases, dict) -> a new type

Metaclasses take 3 args. ‘name‘, ‘bases‘ and ‘dict

Here is where the secret starts. Look for where name, bases and the dict come from in this example class definition.

class ThisIsTheName(Bases, Are, Here):
    All_the_code_here
    def doesIs(create, a):
        dict

Lets define a metaclass that will demonstrate how ‘class:‘ calls it.

def test_metaclass(name, bases, dict):
    print 'The Class Name is', name
    print 'The Class Bases are', bases
    print 'The dict has', len(dict), 'elems, the keys are', dict.keys()

    return "yellow"

class TestName(object, None, int, 1):
    __metaclass__ = test_metaclass
    foo = 1
    def baz(self, arr):
        pass

print 'TestName = ', repr(TestName)

# output => 
The Class Name is TestName
The Class Bases are (<type 'object'>, None, <type 'int'>, 1)
The dict has 4 elems, the keys are ['baz', '__module__', 'foo', '__metaclass__']
TestName =  'yellow'

And now, an example that actually means something, this will automatically make the variables in the list “attributes” set on the class, and set to None.

def init_attributes(name, bases, dict):
    if 'attributes' in dict:
        for attr in dict['attributes']:
            dict[attr] = None

    return type(name, bases, dict)

class Initialised(object):
    __metaclass__ = init_attributes
    attributes = ['foo', 'bar', 'baz']

print 'foo =>', Initialised.foo
# output=>
foo => None

Note that the magic behaviour that Initialised gains by having the metaclass init_attributes is not passed onto a subclass of Initialised.

Here is an even more concrete example, showing how you can subclass ‘type’ to make a metaclass that performs an action when the class is created. This is quite tricky:

class MetaSingleton(type):
    instance = None
    def __call__(cls, *args, **kw):
        if cls.instance is None:
            cls.instance = super(MetaSingleton, cls).__call__(*args, **kw)
        return cls.instance

class Foo(object):
    __metaclass__ = MetaSingleton

a = Foo()
b = Foo()
assert a is b

回答 3

其他人则解释了元类如何工作以及它们如何适合Python类型系统。这是它们可以用来做什么的一个例子。在我编写的测试框架中,我想跟踪定义类的顺序,以便以后可以按此顺序实例化它们。我发现使用元类执行此操作最简单。

class MyMeta(type):

    counter = 0

    def __init__(cls, name, bases, dic):
        type.__init__(cls, name, bases, dic)
        cls._order = MyMeta.counter
        MyMeta.counter += 1

class MyType(object):              # Python 2
    __metaclass__ = MyMeta

class MyType(metaclass=MyMeta):    # Python 3
    pass

子类的任何内容都MyType将获得一个class属性_order,该属性记录定义类的顺序。

Others have explained how metaclasses work and how they fit into the Python type system. Here’s an example of what they can be used for. In a testing framework I wrote, I wanted to keep track of the order in which classes were defined, so that I could later instantiate them in this order. I found it easiest to do this using a metaclass.

class MyMeta(type):

    counter = 0

    def __init__(cls, name, bases, dic):
        type.__init__(cls, name, bases, dic)
        cls._order = MyMeta.counter
        MyMeta.counter += 1

class MyType(object):              # Python 2
    __metaclass__ = MyMeta

class MyType(metaclass=MyMeta):    # Python 3
    pass

Anything that’s a subclass of MyType then gets a class attribute _order that records the order in which the classes were defined.


回答 4

元类的一种用途是自动向实例添加新的属性和方法。

例如,如果您查看Django模型,则其定义看起来有些混乱。似乎您只是在定义类属性:

class Person(models.Model):
    first_name = models.CharField(max_length=30)
    last_name = models.CharField(max_length=30)

但是,在运行时,Person对象充满了各种有用的方法。请参阅源代码中一些惊人的元类。

One use for metaclasses is adding new properties and methods to an instance automatically.

For example, if you look at Django models, their definition looks a bit confusing. It looks as if you are only defining class properties:

class Person(models.Model):
    first_name = models.CharField(max_length=30)
    last_name = models.CharField(max_length=30)

However, at runtime the Person objects are filled with all sorts of useful methods. See the source for some amazing metaclassery.


回答 5

我认为ONLamp对元类编程的介绍写得很好,尽管已经有好几年历史了,但它对该主题却提供了非常好的介绍。

http://www.onlamp.com/pub/a/python/2003/04/17/metaclasses.html(存档于https://web.archive.org/web/20080206005253/http://www.onlamp。 com / pub / a / python / 2003/04/17 / metaclasses.html

简而言之:类是创建实例的蓝图,元类是创建类的蓝图。很容易看出,在Python中,类也必须是一流的对象才能启用此行为。

我从来没有自己写过书,但是我认为可以在Django框架中看到元数据类的最佳用法之一。模型类使用元类方法来启用声明性样式,以编写新模型或表单类。当元类创建类时,所有成员都可以自定义类本身。

剩下要说的是:如果您不知道什么是元类,则不需要它们的可能性为99%。

I think the ONLamp introduction to metaclass programming is well written and gives a really good introduction to the topic despite being several years old already.

http://www.onlamp.com/pub/a/python/2003/04/17/metaclasses.html (archived at https://web.archive.org/web/20080206005253/http://www.onlamp.com/pub/a/python/2003/04/17/metaclasses.html)

In short: A class is a blueprint for the creation of an instance, a metaclass is a blueprint for the creation of a class. It can be easily seen that in Python classes need to be first-class objects too to enable this behavior.

I’ve never written one myself, but I think one of the nicest uses of metaclasses can be seen in the Django framework. The model classes use a metaclass approach to enable a declarative style of writing new models or form classes. While the metaclass is creating the class, all members get the possibility to customize the class itself.

The thing that’s left to say is: If you don’t know what metaclasses are, the probability that you will not need them is 99%.


回答 6

什么是元类?你用它们做什么?

TLDR:元类实例化并定义类的行为,就像类实例化并定义实例的行为一样。

伪代码:

>>> Class(...)
instance

上面看起来应该很熟悉。好吧,它Class来自哪里?它是一个元类的实例(也是伪代码):

>>> Metaclass(...)
Class

在实际代码中,我们可以传递默认的元类,type实例化一个类并获得一个类所需的一切:

>>> type('Foo', (object,), {}) # requires a name, bases, and a namespace
<class '__main__.Foo'>

换个说法

  • 类是实例,而元类是实例。

    当我们实例化一个对象时,我们得到一个实例:

    >>> object()                          # instantiation of class
    <object object at 0x7f9069b4e0b0>     # instance

    同样,当我们使用默认的元类显式定义一个类时type,我们将其实例化:

    >>> type('Object', (object,), {})     # instantiation of metaclass
    <class '__main__.Object'>             # instance
  • 换句话说,类是元类的实例:

    >>> isinstance(object, type)
    True
  • 换句话说,元类是类的类。

    >>> type(object) == type
    True
    >>> object.__class__
    <class 'type'>

当您编写一个类定义并由Python执行时,它使用一个元类来实例化该类对象(而该对象又将被用于实例化该类的实例)。

正如我们可以使用类定义来更改自定义对象实例的行为一样,我们可以使用元类类定义来更改类对象的行为。

它们可以用来做什么?从文档

元类的潜在用途是无限的。已探索的一些想法包括日志记录,接口检查,自动委派,自动属性创建,代理,框架和自动资源锁定/同步。

然而,除非绝对必要,否则通常鼓励用户避免使用元类。

每次创建类时都使用一个元类:

例如,当您编写类定义时,

class Foo(object): 
    'demo'

您实例化一个类对象。

>>> Foo
<class '__main__.Foo'>
>>> isinstance(Foo, type), isinstance(Foo, object)
(True, True)

这与在功能上调用type适当的参数并将结果分配给该名称的变量相同:

name = 'Foo'
bases = (object,)
namespace = {'__doc__': 'demo'}
Foo = type(name, bases, namespace)

请注意,一些东西会自动添加到__dict__,即命名空间:

>>> Foo.__dict__
dict_proxy({'__dict__': <attribute '__dict__' of 'Foo' objects>, 
'__module__': '__main__', '__weakref__': <attribute '__weakref__' 
of 'Foo' objects>, '__doc__': 'demo'})

在这两种情况下,我们创建的对象的元类都是type

(关于类内容的注释__dict____module__是因为类必须知道它们的定义位置,而 因为我们没有定义__dict____weakref__所以存在__slots__–如果定义,__slots__我们将在实例中节省一些空间,例如我们可以禁止__dict____weakref__排除它们,例如:

>>> Baz = type('Bar', (object,), {'__doc__': 'demo', '__slots__': ()})
>>> Baz.__dict__
mappingproxy({'__doc__': 'demo', '__slots__': (), '__module__': '__main__'})

…但是我离题了。)

我们可以type像其他任何类定义一样扩展:

这是默认__repr__的类:

>>> Foo
<class '__main__.Foo'>

默认情况下,我们在编写Python对象时可以做的最有价值的事情之一就是为其提供良好的支持__repr__。当我们打电话时,help(repr)我们知道对a有一个好的测试__repr__,也需要对相等性进行测试- obj == eval(repr(obj))。以下是我们的类型类的类实例的简单实现,__repr____eq__为我们提供了一个示例,该示例可能会改进__repr__类的默认设置:

class Type(type):
    def __repr__(cls):
        """
        >>> Baz
        Type('Baz', (Foo, Bar,), {'__module__': '__main__', '__doc__': None})
        >>> eval(repr(Baz))
        Type('Baz', (Foo, Bar,), {'__module__': '__main__', '__doc__': None})
        """
        metaname = type(cls).__name__
        name = cls.__name__
        parents = ', '.join(b.__name__ for b in cls.__bases__)
        if parents:
            parents += ','
        namespace = ', '.join(': '.join(
          (repr(k), repr(v) if not isinstance(v, type) else v.__name__))
               for k, v in cls.__dict__.items())
        return '{0}(\'{1}\', ({2}), {{{3}}})'.format(metaname, name, parents, namespace)
    def __eq__(cls, other):
        """
        >>> Baz == eval(repr(Baz))
        True            
        """
        return (cls.__name__, cls.__bases__, cls.__dict__) == (
                other.__name__, other.__bases__, other.__dict__)

因此,现在当我们使用该元类创建对象时,__repr__命令行上的回显所提供的视觉效果要比默认情况少得多:

>>> class Bar(object): pass
>>> Baz = Type('Baz', (Foo, Bar,), {'__module__': '__main__', '__doc__': None})
>>> Baz
Type('Baz', (Foo, Bar,), {'__module__': '__main__', '__doc__': None})

通过__repr__为类实例定义良好的代码,我们可以更强大地调试代码。但是,进行进一步检查eval(repr(Class))的可能性不大(因为将函数从默认值转换为函数是相当不可能__repr__的)。

预期的用法:__prepare__命名空间

例如,如果我们想知道类的方法以什么顺序创建,则可以提供一个有序的dict作为类的命名空间。如果这样做是在Python 3中实现的,我们将使用__prepare__该方法返回该类的命名空间dict

from collections import OrderedDict

class OrderedType(Type):
    @classmethod
    def __prepare__(metacls, name, bases, **kwargs):
        return OrderedDict()
    def __new__(cls, name, bases, namespace, **kwargs):
        result = Type.__new__(cls, name, bases, dict(namespace))
        result.members = tuple(namespace)
        return result

和用法:

class OrderedMethodsObject(object, metaclass=OrderedType):
    def method1(self): pass
    def method2(self): pass
    def method3(self): pass
    def method4(self): pass

现在,我们记录了这些方法(和其他类属性)的创建顺序:

>>> OrderedMethodsObject.members
('__module__', '__qualname__', 'method1', 'method2', 'method3', 'method4')

请注意,此示例改编自文档标准库中的新枚举可实现此目的。

因此,我们要做的是通过创建一个类实例化一个元类。我们也可以像对待其他任何类一样对待元类。它具有方法解析顺序:

>>> inspect.getmro(OrderedType)
(<class '__main__.OrderedType'>, <class '__main__.Type'>, <class 'type'>, <class 'object'>)

而且它大致正确repr(除非找到能够表示函数的方法,否则我们将无法再评估它):

>>> OrderedMethodsObject
OrderedType('OrderedMethodsObject', (object,), {'method1': <function OrderedMethodsObject.method1 at 0x0000000002DB01E0>, 'members': ('__module__', '__qualname__', 'method1', 'method2', 'method3', 'method4'), 'method3': <function OrderedMet
hodsObject.method3 at 0x0000000002DB02F0>, 'method2': <function OrderedMethodsObject.method2 at 0x0000000002DB0268>, '__module__': '__main__', '__weakref__': <attribute '__weakref__' of 'OrderedMethodsObject' objects>, '__doc__': None, '__d
ict__': <attribute '__dict__' of 'OrderedMethodsObject' objects>, 'method4': <function OrderedMethodsObject.method4 at 0x0000000002DB0378>})

What are metaclasses? What do you use them for?

TLDR: A metaclass instantiates and defines behavior for a class just like a class instantiates and defines behavior for an instance.

Pseudocode:

>>> Class(...)
instance

The above should look familiar. Well, where does Class come from? It’s an instance of a metaclass (also pseudocode):

>>> Metaclass(...)
Class

In real code, we can pass the default metaclass, type, everything we need to instantiate a class and we get a class:

>>> type('Foo', (object,), {}) # requires a name, bases, and a namespace
<class '__main__.Foo'>

Putting it differently

  • A class is to an instance as a metaclass is to a class.

    When we instantiate an object, we get an instance:

    >>> object()                          # instantiation of class
    <object object at 0x7f9069b4e0b0>     # instance
    

    Likewise, when we define a class explicitly with the default metaclass, type, we instantiate it:

    >>> type('Object', (object,), {})     # instantiation of metaclass
    <class '__main__.Object'>             # instance
    
  • Put another way, a class is an instance of a metaclass:

    >>> isinstance(object, type)
    True
    
  • Put a third way, a metaclass is a class’s class.

    >>> type(object) == type
    True
    >>> object.__class__
    <class 'type'>
    

When you write a class definition and Python executes it, it uses a metaclass to instantiate the class object (which will, in turn, be used to instantiate instances of that class).

Just as we can use class definitions to change how custom object instances behave, we can use a metaclass class definition to change the way a class object behaves.

What can they be used for? From the docs:

The potential uses for metaclasses are boundless. Some ideas that have been explored include logging, interface checking, automatic delegation, automatic property creation, proxies, frameworks, and automatic resource locking/synchronization.

Nevertheless, it is usually encouraged for users to avoid using metaclasses unless absolutely necessary.

You use a metaclass every time you create a class:

When you write a class definition, for example, like this,

class Foo(object): 
    'demo'

You instantiate a class object.

>>> Foo
<class '__main__.Foo'>
>>> isinstance(Foo, type), isinstance(Foo, object)
(True, True)

It is the same as functionally calling type with the appropriate arguments and assigning the result to a variable of that name:

name = 'Foo'
bases = (object,)
namespace = {'__doc__': 'demo'}
Foo = type(name, bases, namespace)

Note, some things automatically get added to the __dict__, i.e., the namespace:

>>> Foo.__dict__
dict_proxy({'__dict__': <attribute '__dict__' of 'Foo' objects>, 
'__module__': '__main__', '__weakref__': <attribute '__weakref__' 
of 'Foo' objects>, '__doc__': 'demo'})

The metaclass of the object we created, in both cases, is type.

(A side-note on the contents of the class __dict__: __module__ is there because classes must know where they are defined, and __dict__ and __weakref__ are there because we don’t define __slots__ – if we define __slots__ we’ll save a bit of space in the instances, as we can disallow __dict__ and __weakref__ by excluding them. For example:

>>> Baz = type('Bar', (object,), {'__doc__': 'demo', '__slots__': ()})
>>> Baz.__dict__
mappingproxy({'__doc__': 'demo', '__slots__': (), '__module__': '__main__'})

… but I digress.)

We can extend type just like any other class definition:

Here’s the default __repr__ of classes:

>>> Foo
<class '__main__.Foo'>

One of the most valuable things we can do by default in writing a Python object is to provide it with a good __repr__. When we call help(repr) we learn that there’s a good test for a __repr__ that also requires a test for equality – obj == eval(repr(obj)). The following simple implementation of __repr__ and __eq__ for class instances of our type class provides us with a demonstration that may improve on the default __repr__ of classes:

class Type(type):
    def __repr__(cls):
        """
        >>> Baz
        Type('Baz', (Foo, Bar,), {'__module__': '__main__', '__doc__': None})
        >>> eval(repr(Baz))
        Type('Baz', (Foo, Bar,), {'__module__': '__main__', '__doc__': None})
        """
        metaname = type(cls).__name__
        name = cls.__name__
        parents = ', '.join(b.__name__ for b in cls.__bases__)
        if parents:
            parents += ','
        namespace = ', '.join(': '.join(
          (repr(k), repr(v) if not isinstance(v, type) else v.__name__))
               for k, v in cls.__dict__.items())
        return '{0}(\'{1}\', ({2}), {{{3}}})'.format(metaname, name, parents, namespace)
    def __eq__(cls, other):
        """
        >>> Baz == eval(repr(Baz))
        True            
        """
        return (cls.__name__, cls.__bases__, cls.__dict__) == (
                other.__name__, other.__bases__, other.__dict__)

So now when we create an object with this metaclass, the __repr__ echoed on the command line provides a much less ugly sight than the default:

>>> class Bar(object): pass
>>> Baz = Type('Baz', (Foo, Bar,), {'__module__': '__main__', '__doc__': None})
>>> Baz
Type('Baz', (Foo, Bar,), {'__module__': '__main__', '__doc__': None})

With a nice __repr__ defined for the class instance, we have a stronger ability to debug our code. However, much further checking with eval(repr(Class)) is unlikely (as functions would be rather impossible to eval from their default __repr__‘s).

An expected usage: __prepare__ a namespace

If, for example, we want to know in what order a class’s methods are created in, we could provide an ordered dict as the namespace of the class. We would do this with __prepare__ which returns the namespace dict for the class if it is implemented in Python 3:

from collections import OrderedDict

class OrderedType(Type):
    @classmethod
    def __prepare__(metacls, name, bases, **kwargs):
        return OrderedDict()
    def __new__(cls, name, bases, namespace, **kwargs):
        result = Type.__new__(cls, name, bases, dict(namespace))
        result.members = tuple(namespace)
        return result

And usage:

class OrderedMethodsObject(object, metaclass=OrderedType):
    def method1(self): pass
    def method2(self): pass
    def method3(self): pass
    def method4(self): pass

And now we have a record of the order in which these methods (and other class attributes) were created:

>>> OrderedMethodsObject.members
('__module__', '__qualname__', 'method1', 'method2', 'method3', 'method4')

Note, this example was adapted from the documentation – the new enum in the standard library does this.

So what we did was instantiate a metaclass by creating a class. We can also treat the metaclass as we would any other class. It has a method resolution order:

>>> inspect.getmro(OrderedType)
(<class '__main__.OrderedType'>, <class '__main__.Type'>, <class 'type'>, <class 'object'>)

And it has approximately the correct repr (which we can no longer eval unless we can find a way to represent our functions.):

>>> OrderedMethodsObject
OrderedType('OrderedMethodsObject', (object,), {'method1': <function OrderedMethodsObject.method1 at 0x0000000002DB01E0>, 'members': ('__module__', '__qualname__', 'method1', 'method2', 'method3', 'method4'), 'method3': <function OrderedMet
hodsObject.method3 at 0x0000000002DB02F0>, 'method2': <function OrderedMethodsObject.method2 at 0x0000000002DB0268>, '__module__': '__main__', '__weakref__': <attribute '__weakref__' of 'OrderedMethodsObject' objects>, '__doc__': None, '__d
ict__': <attribute '__dict__' of 'OrderedMethodsObject' objects>, 'method4': <function OrderedMethodsObject.method4 at 0x0000000002DB0378>})

回答 7

Python 3更新

(在这一点上)元类中有两个关键方法:

  • __prepare__
  • __new__

__prepare__使您可以提供自定义映射(例如OrderedDict),以在创建类时用作命名空间。您必须返回选择的任何命名空间的实例。如果您没有实现__prepare__一个正常dict使用。

__new__ 负责最终类的实际创建/修改。

一个简单的,不做任何事情的超类将是:

class Meta(type):

    def __prepare__(metaclass, cls, bases):
        return dict()

    def __new__(metacls, cls, bases, clsdict):
        return super().__new__(metacls, cls, bases, clsdict)

一个简单的例子:

假设您要在属性上运行一些简单的验证代码-就像它必须始终为intstr。没有元类,您的类将类似于:

class Person:
    weight = ValidateType('weight', int)
    age = ValidateType('age', int)
    name = ValidateType('name', str)

如您所见,您必须重复两次属性名称。这使得输入错误以及令人烦恼的错误成为可能。

一个简单的元类可以解决该问题:

class Person(metaclass=Validator):
    weight = ValidateType(int)
    age = ValidateType(int)
    name = ValidateType(str)

这是元类的外观(不使用,__prepare__因为不需要它):

class Validator(type):
    def __new__(metacls, cls, bases, clsdict):
        # search clsdict looking for ValidateType descriptors
        for name, attr in clsdict.items():
            if isinstance(attr, ValidateType):
                attr.name = name
                attr.attr = '_' + name
        # create final class and return it
        return super().__new__(metacls, cls, bases, clsdict)

示例运行:

p = Person()
p.weight = 9
print(p.weight)
p.weight = '9'

生成:

9
Traceback (most recent call last):
  File "simple_meta.py", line 36, in <module>
    p.weight = '9'
  File "simple_meta.py", line 24, in __set__
    (self.name, self.type, value))
TypeError: weight must be of type(s) <class 'int'> (got '9')

注意:该示例非常简单,它也可以使用类装饰器来完成,但是大概一个实际的元类会做更多的事情。

“ ValidateType”类供参考:

class ValidateType:
    def __init__(self, type):
        self.name = None  # will be set by metaclass
        self.attr = None  # will be set by metaclass
        self.type = type
    def __get__(self, inst, cls):
        if inst is None:
            return self
        else:
            return inst.__dict__[self.attr]
    def __set__(self, inst, value):
        if not isinstance(value, self.type):
            raise TypeError('%s must be of type(s) %s (got %r)' %
                    (self.name, self.type, value))
        else:
            inst.__dict__[self.attr] = value

Python 3 update

There are (at this point) two key methods in a metaclass:

  • __prepare__, and
  • __new__

__prepare__ lets you supply a custom mapping (such as an OrderedDict) to be used as the namespace while the class is being created. You must return an instance of whatever namespace you choose. If you don’t implement __prepare__ a normal dict is used.

__new__ is responsible for the actual creation/modification of the final class.

A bare-bones, do-nothing-extra metaclass would like:

class Meta(type):

    def __prepare__(metaclass, cls, bases):
        return dict()

    def __new__(metacls, cls, bases, clsdict):
        return super().__new__(metacls, cls, bases, clsdict)

A simple example:

Say you want some simple validation code to run on your attributes — like it must always be an int or a str. Without a metaclass, your class would look something like:

class Person:
    weight = ValidateType('weight', int)
    age = ValidateType('age', int)
    name = ValidateType('name', str)

As you can see, you have to repeat the name of the attribute twice. This makes typos possible along with irritating bugs.

A simple metaclass can address that problem:

class Person(metaclass=Validator):
    weight = ValidateType(int)
    age = ValidateType(int)
    name = ValidateType(str)

This is what the metaclass would look like (not using __prepare__ since it is not needed):

class Validator(type):
    def __new__(metacls, cls, bases, clsdict):
        # search clsdict looking for ValidateType descriptors
        for name, attr in clsdict.items():
            if isinstance(attr, ValidateType):
                attr.name = name
                attr.attr = '_' + name
        # create final class and return it
        return super().__new__(metacls, cls, bases, clsdict)

A sample run of:

p = Person()
p.weight = 9
print(p.weight)
p.weight = '9'

produces:

9
Traceback (most recent call last):
  File "simple_meta.py", line 36, in <module>
    p.weight = '9'
  File "simple_meta.py", line 24, in __set__
    (self.name, self.type, value))
TypeError: weight must be of type(s) <class 'int'> (got '9')

Note: This example is simple enough it could have also been accomplished with a class decorator, but presumably an actual metaclass would be doing much more.

The ‘ValidateType’ class for reference:

class ValidateType:
    def __init__(self, type):
        self.name = None  # will be set by metaclass
        self.attr = None  # will be set by metaclass
        self.type = type
    def __get__(self, inst, cls):
        if inst is None:
            return self
        else:
            return inst.__dict__[self.attr]
    def __set__(self, inst, value):
        if not isinstance(value, self.type):
            raise TypeError('%s must be of type(s) %s (got %r)' %
                    (self.name, self.type, value))
        else:
            inst.__dict__[self.attr] = value

回答 8

__call__()创建类实例时元类方法的作用

如果您已经完成Python编程超过几个月,那么您最终会发现以下代码:

# define a class
class SomeClass(object):
    # ...
    # some definition here ...
    # ...

# create an instance of it
instance = SomeClass()

# then call the object as if it's a function
result = instance('foo', 'bar')

当您__call__()在类上实现magic方法时,后者是可能的。

class SomeClass(object):
    # ...
    # some definition here ...
    # ...

    def __call__(self, foo, bar):
        return bar + foo

__call__()当类的实例用作可调用对象时,将调用该方法。但是,正如我们从前面的答案中看到的那样,类本身是元类的实例,因此,当我们使用该类作为可调用对象时(即,当我们创建它的实例时),实际上是在调用其元类的__call__()方法。在这一点上,大多数Python程序员有些困惑,因为有人告诉他们在创建这样的实例时instance = SomeClass()要调用其__init__()方法。有些人已经挖一个深一点知道,之前__init__()__new__()。好吧,今天,在__new__()元类出现之前,另一层真相被揭示出来了__call__()

让我们从创建类实例的角度专门研究方法调用链。

这是一个元类,它准确记录实例创建之前和实例返回之前的时间。

class Meta_1(type):
    def __call__(cls):
        print "Meta_1.__call__() before creating an instance of ", cls
        instance = super(Meta_1, cls).__call__()
        print "Meta_1.__call__() about to return instance."
        return instance

这是使用该元类的类

class Class_1(object):

    __metaclass__ = Meta_1

    def __new__(cls):
        print "Class_1.__new__() before creating an instance."
        instance = super(Class_1, cls).__new__(cls)
        print "Class_1.__new__() about to return instance."
        return instance

    def __init__(self):
        print "entering Class_1.__init__() for instance initialization."
        super(Class_1,self).__init__()
        print "exiting Class_1.__init__()."

现在让我们创建一个实例 Class_1

instance = Class_1()
# Meta_1.__call__() before creating an instance of <class '__main__.Class_1'>.
# Class_1.__new__() before creating an instance.
# Class_1.__new__() about to return instance.
# entering Class_1.__init__() for instance initialization.
# exiting Class_1.__init__().
# Meta_1.__call__() about to return instance.

请注意,上面的代码除了记录任务之外实际上没有做任何其他事情。每个方法将实际工作委托给其父级的实现,从而保留默认行为。由于typeMeta_1的父类(type是默认的父元类),并考虑了上面输出的排序顺序,因此我们现在可以知道什么是伪实现type.__call__()

class type:
    def __call__(cls, *args, **kwarg):

        # ... maybe a few things done to cls here

        # then we call __new__() on the class to create an instance
        instance = cls.__new__(cls, *args, **kwargs)

        # ... maybe a few things done to the instance here

        # then we initialize the instance with its __init__() method
        instance.__init__(*args, **kwargs)

        # ... maybe a few more things done to instance here

        # then we return it
        return instance

我们可以看到metaclass’ __call__()方法是第一个被调用的方法。然后,它将实例的创建委托给类的__new__()方法,并将实例的初始化委托给实例的__init__()。它也是最终返回该实例的对象。

从上面可以得出结论,元类__call__()也有机会决定是否调用Class_1.__new__()Class_1.__init__()最终将进行调用。在执行过程中,它实际上可能返回这两个方法都未触及的对象。以这种单例模式的方法为例:

class Meta_2(type):
    singletons = {}

    def __call__(cls, *args, **kwargs):
        if cls in Meta_2.singletons:
            # we return the only instance and skip a call to __new__()
            # and __init__()
            print ("{} singleton returning from Meta_2.__call__(), "
                   "skipping creation of new instance.".format(cls))
            return Meta_2.singletons[cls]

        # else if the singleton isn't present we proceed as usual
        print "Meta_2.__call__() before creating an instance."
        instance = super(Meta_2, cls).__call__(*args, **kwargs)
        Meta_2.singletons[cls] = instance
        print "Meta_2.__call__() returning new instance."
        return instance

class Class_2(object):

    __metaclass__ = Meta_2

    def __new__(cls, *args, **kwargs):
        print "Class_2.__new__() before creating instance."
        instance = super(Class_2, cls).__new__(cls)
        print "Class_2.__new__() returning instance."
        return instance

    def __init__(self, *args, **kwargs):
        print "entering Class_2.__init__() for initialization."
        super(Class_2, self).__init__()
        print "exiting Class_2.__init__()."

让我们观察一下反复尝试创建类型的对象时会发生什么 Class_2

a = Class_2()
# Meta_2.__call__() before creating an instance.
# Class_2.__new__() before creating instance.
# Class_2.__new__() returning instance.
# entering Class_2.__init__() for initialization.
# exiting Class_2.__init__().
# Meta_2.__call__() returning new instance.

b = Class_2()
# <class '__main__.Class_2'> singleton returning from Meta_2.__call__(), skipping creation of new instance.

c = Class_2()
# <class '__main__.Class_2'> singleton returning from Meta_2.__call__(), skipping creation of new instance.

a is b is c # True

Role of a metaclass’ __call__() method when creating a class instance

If you’ve done Python programming for more than a few months you’ll eventually stumble upon code that looks like this:

# define a class
class SomeClass(object):
    # ...
    # some definition here ...
    # ...

# create an instance of it
instance = SomeClass()

# then call the object as if it's a function
result = instance('foo', 'bar')

The latter is possible when you implement the __call__() magic method on the class.

class SomeClass(object):
    # ...
    # some definition here ...
    # ...

    def __call__(self, foo, bar):
        return bar + foo

The __call__() method is invoked when an instance of a class is used as a callable. But as we’ve seen from previous answers a class itself is an instance of a metaclass, so when we use the class as a callable (i.e. when we create an instance of it) we’re actually calling its metaclass’ __call__() method. At this point most Python programmers are a bit confused because they’ve been told that when creating an instance like this instance = SomeClass() you’re calling its __init__() method. Some who’ve dug a bit deeper know that before __init__() there’s __new__(). Well, today another layer of truth is being revealed, before __new__() there’s the metaclass’ __call__().

Let’s study the method call chain from specifically the perspective of creating an instance of a class.

This is a metaclass that logs exactly the moment before an instance is created and the moment it’s about to return it.

class Meta_1(type):
    def __call__(cls):
        print "Meta_1.__call__() before creating an instance of ", cls
        instance = super(Meta_1, cls).__call__()
        print "Meta_1.__call__() about to return instance."
        return instance

This is a class that uses that metaclass

class Class_1(object):

    __metaclass__ = Meta_1

    def __new__(cls):
        print "Class_1.__new__() before creating an instance."
        instance = super(Class_1, cls).__new__(cls)
        print "Class_1.__new__() about to return instance."
        return instance

    def __init__(self):
        print "entering Class_1.__init__() for instance initialization."
        super(Class_1,self).__init__()
        print "exiting Class_1.__init__()."

And now let’s create an instance of Class_1

instance = Class_1()
# Meta_1.__call__() before creating an instance of <class '__main__.Class_1'>.
# Class_1.__new__() before creating an instance.
# Class_1.__new__() about to return instance.
# entering Class_1.__init__() for instance initialization.
# exiting Class_1.__init__().
# Meta_1.__call__() about to return instance.

Observe that the code above doesn’t actually do anything more than logging the tasks. Each method delegates the actual work to its parent’s implementation, thus keeping the default behavior. Since type is Meta_1‘s parent class (type being the default parent metaclass) and considering the ordering sequence of the output above, we now have a clue as to what would be the pseudo implementation of type.__call__():

class type:
    def __call__(cls, *args, **kwarg):

        # ... maybe a few things done to cls here

        # then we call __new__() on the class to create an instance
        instance = cls.__new__(cls, *args, **kwargs)

        # ... maybe a few things done to the instance here

        # then we initialize the instance with its __init__() method
        instance.__init__(*args, **kwargs)

        # ... maybe a few more things done to instance here

        # then we return it
        return instance

We can see that the metaclass’ __call__() method is the one that’s called first. It then delegates creation of the instance to the class’s __new__() method and initialization to the instance’s __init__(). It’s also the one that ultimately returns the instance.

From the above it stems that the metaclass’ __call__() is also given the opportunity to decide whether or not a call to Class_1.__new__() or Class_1.__init__() will eventually be made. Over the course of its execution it could actually return an object that hasn’t been touched by either of these methods. Take for example this approach to the singleton pattern:

class Meta_2(type):
    singletons = {}

    def __call__(cls, *args, **kwargs):
        if cls in Meta_2.singletons:
            # we return the only instance and skip a call to __new__()
            # and __init__()
            print ("{} singleton returning from Meta_2.__call__(), "
                   "skipping creation of new instance.".format(cls))
            return Meta_2.singletons[cls]

        # else if the singleton isn't present we proceed as usual
        print "Meta_2.__call__() before creating an instance."
        instance = super(Meta_2, cls).__call__(*args, **kwargs)
        Meta_2.singletons[cls] = instance
        print "Meta_2.__call__() returning new instance."
        return instance

class Class_2(object):

    __metaclass__ = Meta_2

    def __new__(cls, *args, **kwargs):
        print "Class_2.__new__() before creating instance."
        instance = super(Class_2, cls).__new__(cls)
        print "Class_2.__new__() returning instance."
        return instance

    def __init__(self, *args, **kwargs):
        print "entering Class_2.__init__() for initialization."
        super(Class_2, self).__init__()
        print "exiting Class_2.__init__()."

Let’s observe what happens when repeatedly trying to create an object of type Class_2

a = Class_2()
# Meta_2.__call__() before creating an instance.
# Class_2.__new__() before creating instance.
# Class_2.__new__() returning instance.
# entering Class_2.__init__() for initialization.
# exiting Class_2.__init__().
# Meta_2.__call__() returning new instance.

b = Class_2()
# <class '__main__.Class_2'> singleton returning from Meta_2.__call__(), skipping creation of new instance.

c = Class_2()
# <class '__main__.Class_2'> singleton returning from Meta_2.__call__(), skipping creation of new instance.

a is b is c # True

回答 9

元类是一个告诉应该如何创建(某些)其他类的类。

在这种情况下,我将元类视为解决问题的方法:我遇到了一个非常复杂的问题,可能可以用其他方法解决,但我选择使用元类来解决。由于其复杂性,它是我编写的为数不多的模块之一,其中模块中的注释超过了已编写的代码量。这里是…

#!/usr/bin/env python

# Copyright (C) 2013-2014 Craig Phillips.  All rights reserved.

# This requires some explaining.  The point of this metaclass excercise is to
# create a static abstract class that is in one way or another, dormant until
# queried.  I experimented with creating a singlton on import, but that did
# not quite behave how I wanted it to.  See now here, we are creating a class
# called GsyncOptions, that on import, will do nothing except state that its
# class creator is GsyncOptionsType.  This means, docopt doesn't parse any
# of the help document, nor does it start processing command line options.
# So importing this module becomes really efficient.  The complicated bit
# comes from requiring the GsyncOptions class to be static.  By that, I mean
# any property on it, may or may not exist, since they are not statically
# defined; so I can't simply just define the class with a whole bunch of
# properties that are @property @staticmethods.
#
# So here's how it works:
#
# Executing 'from libgsync.options import GsyncOptions' does nothing more
# than load up this module, define the Type and the Class and import them
# into the callers namespace.  Simple.
#
# Invoking 'GsyncOptions.debug' for the first time, or any other property
# causes the __metaclass__ __getattr__ method to be called, since the class
# is not instantiated as a class instance yet.  The __getattr__ method on
# the type then initialises the class (GsyncOptions) via the __initialiseClass
# method.  This is the first and only time the class will actually have its
# dictionary statically populated.  The docopt module is invoked to parse the
# usage document and generate command line options from it.  These are then
# paired with their defaults and what's in sys.argv.  After all that, we
# setup some dynamic properties that could not be defined by their name in
# the usage, before everything is then transplanted onto the actual class
# object (or static class GsyncOptions).
#
# Another piece of magic, is to allow command line options to be set in
# in their native form and be translated into argparse style properties.
#
# Finally, the GsyncListOptions class is actually where the options are
# stored.  This only acts as a mechanism for storing options as lists, to
# allow aggregation of duplicate options or options that can be specified
# multiple times.  The __getattr__ call hides this by default, returning the
# last item in a property's list.  However, if the entire list is required,
# calling the 'list()' method on the GsyncOptions class, returns a reference
# to the GsyncListOptions class, which contains all of the same properties
# but as lists and without the duplication of having them as both lists and
# static singlton values.
#
# So this actually means that GsyncOptions is actually a static proxy class...
#
# ...And all this is neatly hidden within a closure for safe keeping.
def GetGsyncOptionsType():
    class GsyncListOptions(object):
        __initialised = False

    class GsyncOptionsType(type):
        def __initialiseClass(cls):
            if GsyncListOptions._GsyncListOptions__initialised: return

            from docopt import docopt
            from libgsync.options import doc
            from libgsync import __version__

            options = docopt(
                doc.__doc__ % __version__,
                version = __version__,
                options_first = True
            )

            paths = options.pop('<path>', None)
            setattr(cls, "destination_path", paths.pop() if paths else None)
            setattr(cls, "source_paths", paths)
            setattr(cls, "options", options)

            for k, v in options.iteritems():
                setattr(cls, k, v)

            GsyncListOptions._GsyncListOptions__initialised = True

        def list(cls):
            return GsyncListOptions

        def __getattr__(cls, name):
            cls.__initialiseClass()
            return getattr(GsyncListOptions, name)[-1]

        def __setattr__(cls, name, value):
            # Substitut option names: --an-option-name for an_option_name
            import re
            name = re.sub(r'^__', "", re.sub(r'-', "_", name))
            listvalue = []

            # Ensure value is converted to a list type for GsyncListOptions
            if isinstance(value, list):
                if value:
                    listvalue = [] + value
                else:
                    listvalue = [ None ]
            else:
                listvalue = [ value ]

            type.__setattr__(GsyncListOptions, name, listvalue)

    # Cleanup this module to prevent tinkering.
    import sys
    module = sys.modules[__name__]
    del module.__dict__['GetGsyncOptionsType']

    return GsyncOptionsType

# Our singlton abstract proxy class.
class GsyncOptions(object):
    __metaclass__ = GetGsyncOptionsType()

A metaclass is a class that tells how (some) other class should be created.

This is a case where I saw metaclass as a solution to my problem: I had a really complicated problem, that probably could have been solved differently, but I chose to solve it using a metaclass. Because of the complexity, it is one of the few modules I have written where the comments in the module surpass the amount of code that has been written. Here it is…

#!/usr/bin/env python

# Copyright (C) 2013-2014 Craig Phillips.  All rights reserved.

# This requires some explaining.  The point of this metaclass excercise is to
# create a static abstract class that is in one way or another, dormant until
# queried.  I experimented with creating a singlton on import, but that did
# not quite behave how I wanted it to.  See now here, we are creating a class
# called GsyncOptions, that on import, will do nothing except state that its
# class creator is GsyncOptionsType.  This means, docopt doesn't parse any
# of the help document, nor does it start processing command line options.
# So importing this module becomes really efficient.  The complicated bit
# comes from requiring the GsyncOptions class to be static.  By that, I mean
# any property on it, may or may not exist, since they are not statically
# defined; so I can't simply just define the class with a whole bunch of
# properties that are @property @staticmethods.
#
# So here's how it works:
#
# Executing 'from libgsync.options import GsyncOptions' does nothing more
# than load up this module, define the Type and the Class and import them
# into the callers namespace.  Simple.
#
# Invoking 'GsyncOptions.debug' for the first time, or any other property
# causes the __metaclass__ __getattr__ method to be called, since the class
# is not instantiated as a class instance yet.  The __getattr__ method on
# the type then initialises the class (GsyncOptions) via the __initialiseClass
# method.  This is the first and only time the class will actually have its
# dictionary statically populated.  The docopt module is invoked to parse the
# usage document and generate command line options from it.  These are then
# paired with their defaults and what's in sys.argv.  After all that, we
# setup some dynamic properties that could not be defined by their name in
# the usage, before everything is then transplanted onto the actual class
# object (or static class GsyncOptions).
#
# Another piece of magic, is to allow command line options to be set in
# in their native form and be translated into argparse style properties.
#
# Finally, the GsyncListOptions class is actually where the options are
# stored.  This only acts as a mechanism for storing options as lists, to
# allow aggregation of duplicate options or options that can be specified
# multiple times.  The __getattr__ call hides this by default, returning the
# last item in a property's list.  However, if the entire list is required,
# calling the 'list()' method on the GsyncOptions class, returns a reference
# to the GsyncListOptions class, which contains all of the same properties
# but as lists and without the duplication of having them as both lists and
# static singlton values.
#
# So this actually means that GsyncOptions is actually a static proxy class...
#
# ...And all this is neatly hidden within a closure for safe keeping.
def GetGsyncOptionsType():
    class GsyncListOptions(object):
        __initialised = False

    class GsyncOptionsType(type):
        def __initialiseClass(cls):
            if GsyncListOptions._GsyncListOptions__initialised: return

            from docopt import docopt
            from libgsync.options import doc
            from libgsync import __version__

            options = docopt(
                doc.__doc__ % __version__,
                version = __version__,
                options_first = True
            )

            paths = options.pop('<path>', None)
            setattr(cls, "destination_path", paths.pop() if paths else None)
            setattr(cls, "source_paths", paths)
            setattr(cls, "options", options)

            for k, v in options.iteritems():
                setattr(cls, k, v)

            GsyncListOptions._GsyncListOptions__initialised = True

        def list(cls):
            return GsyncListOptions

        def __getattr__(cls, name):
            cls.__initialiseClass()
            return getattr(GsyncListOptions, name)[-1]

        def __setattr__(cls, name, value):
            # Substitut option names: --an-option-name for an_option_name
            import re
            name = re.sub(r'^__', "", re.sub(r'-', "_", name))
            listvalue = []

            # Ensure value is converted to a list type for GsyncListOptions
            if isinstance(value, list):
                if value:
                    listvalue = [] + value
                else:
                    listvalue = [ None ]
            else:
                listvalue = [ value ]

            type.__setattr__(GsyncListOptions, name, listvalue)

    # Cleanup this module to prevent tinkering.
    import sys
    module = sys.modules[__name__]
    del module.__dict__['GetGsyncOptionsType']

    return GsyncOptionsType

# Our singlton abstract proxy class.
class GsyncOptions(object):
    __metaclass__ = GetGsyncOptionsType()

回答 10

tl; dr版本

type(obj)函数获取对象的类型。

type()一类是它的元类

要使用元类:

class Foo(object):
    __metaclass__ = MyMetaClass

type是它自己的元类。类的类是元类-类的主体是传递给用于构造类的元类的参数。

在这里,您可以了解有关如何使用元类自定义类构造的信息。

The tl;dr version

The type(obj) function gets you the type of an object.

The type() of a class is its metaclass.

To use a metaclass:

class Foo(object):
    __metaclass__ = MyMetaClass

type is its own metaclass. The class of a class is a metaclass– the body of a class is the arguments passed to the metaclass that is used to construct the class.

Here you can read about how to use metaclasses to customize class construction.


回答 11

type实际上是一个metaclass创建另一个类的类。大多数metaclass是的子类type。所述metaclass接收new类作为其第一个参数,如下面所提到提供访问与细节类对象:

>>> class MetaClass(type):
...     def __init__(cls, name, bases, attrs):
...         print ('class name: %s' %name )
...         print ('Defining class %s' %cls)
...         print('Bases %s: ' %bases)
...         print('Attributes')
...         for (name, value) in attrs.items():
...             print ('%s :%r' %(name, value))
... 

>>> class NewClass(object, metaclass=MetaClass):
...    get_choch='dairy'
... 
class name: NewClass
Bases <class 'object'>: 
Defining class <class 'NewClass'>
get_choch :'dairy'
__module__ :'builtins'
__qualname__ :'NewClass'

Note:

注意,该类在任何时候都没有实例化。创建类的简单动作触发了metaclass

type is actually a metaclass — a class that creates another classes. Most metaclass are the subclasses of type. The metaclass receives the new class as its first argument and provide access to class object with details as mentioned below:

>>> class MetaClass(type):
...     def __init__(cls, name, bases, attrs):
...         print ('class name: %s' %name )
...         print ('Defining class %s' %cls)
...         print('Bases %s: ' %bases)
...         print('Attributes')
...         for (name, value) in attrs.items():
...             print ('%s :%r' %(name, value))
... 

>>> class NewClass(object, metaclass=MetaClass):
...    get_choch='dairy'
... 
class name: NewClass
Bases <class 'object'>: 
Defining class <class 'NewClass'>
get_choch :'dairy'
__module__ :'builtins'
__qualname__ :'NewClass'

Note:

Notice that the class was not instantiated at any time; the simple act of creating the class triggered execution of the metaclass.


回答 12

Python类本身就是其元类的对象(例如,实例)。

默认元类,当您将类确定为:

class foo:
    ...

元类用于将规则应用于整个类集。例如,假设您正在构建一个ORM来访问数据库,并且希望每个表中的记录属于映射到该表的类(基于字段,业务规则等),并可能使用元类例如,连接池逻辑由所有表中的所有记录类别共享。另一个用途是支持外键的逻辑,该外键涉及多个记录类别。

当您定义元类时,您将子类化,并且可以覆盖以下魔术方法来插入您的逻辑。

class somemeta(type):
    __new__(mcs, name, bases, clsdict):
      """
  mcs: is the base metaclass, in this case type.
  name: name of the new class, as provided by the user.
  bases: tuple of base classes 
  clsdict: a dictionary containing all methods and attributes defined on class

  you must return a class object by invoking the __new__ constructor on the base metaclass. 
 ie: 
    return type.__call__(mcs, name, bases, clsdict).

  in the following case:

  class foo(baseclass):
        __metaclass__ = somemeta

  an_attr = 12

  def bar(self):
      ...

  @classmethod
  def foo(cls):
      ...

      arguments would be : ( somemeta, "foo", (baseclass, baseofbase,..., object), {"an_attr":12, "bar": <function>, "foo": <bound class method>}

      you can modify any of these values before passing on to type
      """
      return type.__call__(mcs, name, bases, clsdict)


    def __init__(self, name, bases, clsdict):
      """ 
      called after type has been created. unlike in standard classes, __init__ method cannot modify the instance (cls) - and should be used for class validaton.
      """
      pass


    def __prepare__():
        """
        returns a dict or something that can be used as a namespace.
        the type will then attach methods and attributes from class definition to it.

        call order :

        somemeta.__new__ ->  type.__new__ -> type.__init__ -> somemeta.__init__ 
        """
        return dict()

    def mymethod(cls):
        """ works like a classmethod, but for class objects. Also, my method will not be visible to instances of cls.
        """
        pass

无论如何,这两个是最常用的钩子。元分类功能强大,而元数据分类的用途清单也不是详尽无遗。

Python classes are themselves objects – as in instance – of their meta-class.

The default metaclass, which is applied when when you determine classes as:

class foo:
    ...

meta class are used to apply some rule to an entire set of classes. For example, suppose you’re building an ORM to access a database, and you want records from each table to be of a class mapped to that table (based on fields, business rules, etc..,), a possible use of metaclass is for instance, connection pool logic, which is share by all classes of record from all tables. Another use is logic to to support foreign keys, which involves multiple classes of records.

when you define metaclass, you subclass type, and can overrided the following magic methods to insert your logic.

class somemeta(type):
    __new__(mcs, name, bases, clsdict):
      """
  mcs: is the base metaclass, in this case type.
  name: name of the new class, as provided by the user.
  bases: tuple of base classes 
  clsdict: a dictionary containing all methods and attributes defined on class

  you must return a class object by invoking the __new__ constructor on the base metaclass. 
 ie: 
    return type.__call__(mcs, name, bases, clsdict).

  in the following case:

  class foo(baseclass):
        __metaclass__ = somemeta

  an_attr = 12

  def bar(self):
      ...

  @classmethod
  def foo(cls):
      ...

      arguments would be : ( somemeta, "foo", (baseclass, baseofbase,..., object), {"an_attr":12, "bar": <function>, "foo": <bound class method>}

      you can modify any of these values before passing on to type
      """
      return type.__call__(mcs, name, bases, clsdict)


    def __init__(self, name, bases, clsdict):
      """ 
      called after type has been created. unlike in standard classes, __init__ method cannot modify the instance (cls) - and should be used for class validaton.
      """
      pass


    def __prepare__():
        """
        returns a dict or something that can be used as a namespace.
        the type will then attach methods and attributes from class definition to it.

        call order :

        somemeta.__new__ ->  type.__new__ -> type.__init__ -> somemeta.__init__ 
        """
        return dict()

    def mymethod(cls):
        """ works like a classmethod, but for class objects. Also, my method will not be visible to instances of cls.
        """
        pass

anyhow, those two are the most commonly used hooks. metaclassing is powerful, and above is nowhere near and exhaustive list of uses for metaclassing.


回答 13

type()函数可以返回对象的类型或创建新的类型,

例如,我们可以使用type()函数创建一个Hi类,而无需在Hi(object)类中使用这种方式:

def func(self, name='mike'):
    print('Hi, %s.' % name)

Hi = type('Hi', (object,), dict(hi=func))
h = Hi()
h.hi()
Hi, mike.

type(Hi)
type

type(h)
__main__.Hi

除了使用type()动态创建类之外,您还可以控制类的创建行为并使用元类。

根据Python对象模型,类是对象,因此该类必须是另一个特定类的实例。默认情况下,Python类是类型类的实例。也就是说,类型是大多数内置类的元类和用户定义类的元类。

class ListMetaclass(type):
    def __new__(cls, name, bases, attrs):
        attrs['add'] = lambda self, value: self.append(value)
        return type.__new__(cls, name, bases, attrs)

class CustomList(list, metaclass=ListMetaclass):
    pass

lst = CustomList()
lst.add('custom_list_1')
lst.add('custom_list_2')

lst
['custom_list_1', 'custom_list_2']

当我们在元类中传递关键字参数时,Magic将会生效,它指示Python解释器通过ListMetaclass创建CustomList。new(),此时,我们可以例如修改类定义,并添加新方法,然后返回修改后的定义。

The type() function can return the type of an object or create a new type,

for example, we can create a Hi class with the type() function and do not need to use this way with class Hi(object):

def func(self, name='mike'):
    print('Hi, %s.' % name)

Hi = type('Hi', (object,), dict(hi=func))
h = Hi()
h.hi()
Hi, mike.

type(Hi)
type

type(h)
__main__.Hi

In addition to using type() to create classes dynamically, you can control creation behavior of class and use metaclass.

According to the Python object model, the class is the object, so the class must be an instance of another certain class. By default, a Python class is instance of the type class. That is, type is metaclass of most of the built-in classes and metaclass of user-defined classes.

class ListMetaclass(type):
    def __new__(cls, name, bases, attrs):
        attrs['add'] = lambda self, value: self.append(value)
        return type.__new__(cls, name, bases, attrs)

class CustomList(list, metaclass=ListMetaclass):
    pass

lst = CustomList()
lst.add('custom_list_1')
lst.add('custom_list_2')

lst
['custom_list_1', 'custom_list_2']

Magic will take effect when we passed keyword arguments in metaclass, it indicates the Python interpreter to create the CustomList through ListMetaclass. new (), at this point, we can modify the class definition, for example, and add a new method and then return the revised definition.


回答 14

除了已发布的答案,我可以说a metaclass定义了一个类的行为。因此,您可以显式设置您的元类。每当Python获得关键字时,class它就会开始搜索metaclass。如果未找到,则使用默认的元类类型创建类的对象。使用该__metaclass__属性,可以设置metaclass您的类:

class MyClass:
   __metaclass__ = type
   # write here other method
   # write here one more method

print(MyClass.__metaclass__)

它将产生如下输出:

class 'type'

当然,您可以创建自己的类metaclass来定义使用您的类创建的任何类的行为。

为此,metaclass必须继承默认类型类,因为这是主要的metaclass

class MyMetaClass(type):
   __metaclass__ = type
   # you can write here any behaviour you want

class MyTestClass:
   __metaclass__ = MyMetaClass

Obj = MyTestClass()
print(Obj.__metaclass__)
print(MyMetaClass.__metaclass__)

输出将是:

class '__main__.MyMetaClass'
class 'type'

In addition to the published answers I can say that a metaclass defines the behaviour for a class. So, you can explicitly set your metaclass. Whenever Python gets a keyword class then it starts searching for the metaclass. If it’s not found – the default metaclass type is used to create the class’s object. Using the __metaclass__ attribute, you can set metaclass of your class:

class MyClass:
   __metaclass__ = type
   # write here other method
   # write here one more method

print(MyClass.__metaclass__)

It’ll produce the output like this:

class 'type'

And, of course, you can create your own metaclass to define the behaviour of any class that are created using your class.

For doing that, your default metaclass type class must be inherited as this is the main metaclass:

class MyMetaClass(type):
   __metaclass__ = type
   # you can write here any behaviour you want

class MyTestClass:
   __metaclass__ = MyMetaClass

Obj = MyTestClass()
print(Obj.__metaclass__)
print(MyMetaClass.__metaclass__)

The output will be:

class '__main__.MyMetaClass'
class 'type'

回答 15

在面向对象的编程中,元类是一个类,其实例是类。就像普通类定义某些对象的行为一样,元类定义某些类及其实例的行为。术语“元类”仅表示用于创建类的内容。换句话说,它是一个类的类。元类用于创建类,因此就像对象是类的实例一样,类是元类的实例。在python中,类也被视为对象。

In object-oriented programming, a metaclass is a class whose instances are classes. Just as an ordinary class defines the behavior of certain objects, a metaclass defines the behavior of certain class and their instances The term metaclass simply means something used to create classes. In other words, it is the class of a class. The metaclass is used to create the class so like the object being an instance of a class, a class is an instance of a metaclass. In python classes are also considered objects.


回答 16

这是其用途的另一个示例:

  • 您可以使用metaclass更改其实例(类)的功能。
class MetaMemberControl(type):
    __slots__ = ()

    @classmethod
    def __prepare__(mcs, f_cls_name, f_cls_parents,  # f_cls means: future class
                    meta_args=None, meta_options=None):  # meta_args and meta_options is not necessarily needed, just so you know.
        f_cls_attr = dict()
        if not "do something or if you want to define your cool stuff of dict...":
            return dict(make_your_special_dict=None)
        else:
            return f_cls_attr

    def __new__(mcs, f_cls_name, f_cls_parents, f_cls_attr,
                meta_args=None, meta_options=None):

        original_getattr = f_cls_attr.get('__getattribute__')
        original_setattr = f_cls_attr.get('__setattr__')

        def init_getattr(self, item):
            if not item.startswith('_'):  # you can set break points at here
                alias_name = '_' + item
                if alias_name in f_cls_attr['__slots__']:
                    item = alias_name
            if original_getattr is not None:
                return original_getattr(self, item)
            else:
                return super(eval(f_cls_name), self).__getattribute__(item)

        def init_setattr(self, key, value):
            if not key.startswith('_') and ('_' + key) in f_cls_attr['__slots__']:
                raise AttributeError(f"you can't modify private members:_{key}")
            if original_setattr is not None:
                original_setattr(self, key, value)
            else:
                super(eval(f_cls_name), self).__setattr__(key, value)

        f_cls_attr['__getattribute__'] = init_getattr
        f_cls_attr['__setattr__'] = init_setattr

        cls = super().__new__(mcs, f_cls_name, f_cls_parents, f_cls_attr)
        return cls


class Human(metaclass=MetaMemberControl):
    __slots__ = ('_age', '_name')

    def __init__(self, name, age):
        self._name = name
        self._age = age

    def __getattribute__(self, item):
        """
        is just for IDE recognize.
        """
        return super().__getattribute__(item)

    """ with MetaMemberControl then you don't have to write as following
    @property
    def name(self):
        return self._name

    @property
    def age(self):
        return self._age
    """


def test_demo():
    human = Human('Carson', 27)
    # human.age = 18  # you can't modify private members:_age  <-- this is defined by yourself.
    # human.k = 18  # 'Human' object has no attribute 'k'  <-- system error.
    age1 = human._age  # It's OK, although the IDE will show some warnings. (Access to a protected member _age of a class)

    age2 = human.age  # It's OK! see below:
    """
    if you do not define `__getattribute__` at the class of Human,
    the IDE will show you: Unresolved attribute reference 'age' for class 'Human'
    but it's ok on running since the MetaMemberControl will help you.
    """


if __name__ == '__main__':
    test_demo()

metaclass是强大的,有很多东西(如Monkey魔术),你可以用它做,但要小心,这可能只知道给你。

Here’s another example of what it can be used for:

  • You can use the metaclass to change the function of its instance (the class).
class MetaMemberControl(type):
    __slots__ = ()

    @classmethod
    def __prepare__(mcs, f_cls_name, f_cls_parents,  # f_cls means: future class
                    meta_args=None, meta_options=None):  # meta_args and meta_options is not necessarily needed, just so you know.
        f_cls_attr = dict()
        if not "do something or if you want to define your cool stuff of dict...":
            return dict(make_your_special_dict=None)
        else:
            return f_cls_attr

    def __new__(mcs, f_cls_name, f_cls_parents, f_cls_attr,
                meta_args=None, meta_options=None):

        original_getattr = f_cls_attr.get('__getattribute__')
        original_setattr = f_cls_attr.get('__setattr__')

        def init_getattr(self, item):
            if not item.startswith('_'):  # you can set break points at here
                alias_name = '_' + item
                if alias_name in f_cls_attr['__slots__']:
                    item = alias_name
            if original_getattr is not None:
                return original_getattr(self, item)
            else:
                return super(eval(f_cls_name), self).__getattribute__(item)

        def init_setattr(self, key, value):
            if not key.startswith('_') and ('_' + key) in f_cls_attr['__slots__']:
                raise AttributeError(f"you can't modify private members:_{key}")
            if original_setattr is not None:
                original_setattr(self, key, value)
            else:
                super(eval(f_cls_name), self).__setattr__(key, value)

        f_cls_attr['__getattribute__'] = init_getattr
        f_cls_attr['__setattr__'] = init_setattr

        cls = super().__new__(mcs, f_cls_name, f_cls_parents, f_cls_attr)
        return cls


class Human(metaclass=MetaMemberControl):
    __slots__ = ('_age', '_name')

    def __init__(self, name, age):
        self._name = name
        self._age = age

    def __getattribute__(self, item):
        """
        is just for IDE recognize.
        """
        return super().__getattribute__(item)

    """ with MetaMemberControl then you don't have to write as following
    @property
    def name(self):
        return self._name

    @property
    def age(self):
        return self._age
    """


def test_demo():
    human = Human('Carson', 27)
    # human.age = 18  # you can't modify private members:_age  <-- this is defined by yourself.
    # human.k = 18  # 'Human' object has no attribute 'k'  <-- system error.
    age1 = human._age  # It's OK, although the IDE will show some warnings. (Access to a protected member _age of a class)

    age2 = human.age  # It's OK! see below:
    """
    if you do not define `__getattribute__` at the class of Human,
    the IDE will show you: Unresolved attribute reference 'age' for class 'Human'
    but it's ok on running since the MetaMemberControl will help you.
    """


if __name__ == '__main__':
    test_demo()

The metaclass is powerful, there are many things (such as monkey magic) you can do with it, but be careful this may only be known to you.


回答 17

在Python中,一个类是一个对象,就像其他任何对象一样,它是“某物”的实例。这种“东西”就是所谓的元类。这个元类是一种特殊的类,它创建其他类的对象。因此,元类负责创建新类。这使程序员可以自定义类的生成方式。

要创建一个元类,通常要重写new()和init()方法。可以重写new()来更改对象的创建方式,而可以重写init()来更改对象的初始化方式。元类可以通过多种方式创建。一种方法是使用type()函数。当使用3个参数调用type()函数时,它将创建一个元类。参数是:

  1. 类的名称
  2. 具有由类继承的基类的元组
  3. 具有所有类方法和类变量的字典

创建元类的另一种方法包括“元类”关键字。将元类定义为简单类。在继承的类的参数中,传递metaclass = metaclass_name

元类可以在以下情况下专门使用:

  1. 当必须将特殊效果应用于所有子类时
  2. 需要自动更改Class(创建时)
  3. 由API开发人员

A class, in Python, is an object, and just like any other object, it is an instance of “something”. This “something” is what is termed as a Metaclass. This metaclass is a special type of class that creates other class’s objects. Hence, metaclass is responsible for making new classes. This allows the programmer to customize the way classes are generated.

To create a metaclass, overriding of new() and init() methods is usually done. new() can be overridden to change the way objects are created, while init() can be overridden to change the way of initializing the object. Metaclass can be created by a number of ways. One of the ways is to use type() function. type() function, when called with 3 parameters, creates a metaclass. The parameters are :-

  1. Class Name
  2. Tuple having base classes inherited by class
  3. A dictionary having all class methods and class variables

Another way of creating a metaclass comprises of ‘metaclass’ keyword. Define the metaclass as a simple class. In the parameters of inherited class, pass metaclass=metaclass_name

Metaclass can be specifically used in the following situations :-

  1. when a particular effect has to be applied to all the subclasses
  2. Automatic change of class (on creation) is required
  3. By API developers

回答 18

请注意,在python 3.6中__init_subclass__(cls, **kwargs),引入了新的dunder方法来替换元类的许多常见用例。创建定义类的子类时调用is。参见python docs

Note that in python 3.6 a new dunder method __init_subclass__(cls, **kwargs) was introduced to replace a lot of common use cases for metaclasses. Is is called when a subclass of the defining class is created. See python docs.


回答 19

元类是一种类,它定义类的行为方式,或者我们可以说类本身是元类的实例。

Metaclass is a kind of class which defines how the class will behave like or we can say that A class is itself an instance of a metaclass.


静态方法和类方法之间的区别

问题:静态方法和类方法之间的区别

@staticmethod修饰的功能和用修饰的功能有什么区别@classmethod

What is the difference between a function decorated with @staticmethod and one decorated with @classmethod?


回答 0

也许有点示例代码将有助于:发现其中的差别在调用签名fooclass_foo并且static_foo

class A(object):
    def foo(self, x):
        print "executing foo(%s, %s)" % (self, x)

    @classmethod
    def class_foo(cls, x):
        print "executing class_foo(%s, %s)" % (cls, x)

    @staticmethod
    def static_foo(x):
        print "executing static_foo(%s)" % x    

a = A()

以下是对象实例调用方法的常用方法。对象实例,a作为第一个参数隐式传递。

a.foo(1)
# executing foo(<__main__.A object at 0xb7dbef0c>,1)

使用classmethods时,对象实例的类作为第一个参数而不是隐式传递self

a.class_foo(1)
# executing class_foo(<class '__main__.A'>,1)

您也可以class_foo使用该类进行呼叫。实际上,如果您将某些东西定义为类方法,则可能是因为您打算从类而不是从类实例调用它。A.foo(1)本来会引发TypeError,但A.class_foo(1)效果很好:

A.class_foo(1)
# executing class_foo(<class '__main__.A'>,1)

人们发现类方法的一种用途是创建可继承的替代构造函数


使用staticmethods时self(对象实例)和 cls(类)都不会隐式传递为第一个参数。它们的行为类似于普通函数,不同之处在于您可以从实例或类中调用它们:

a.static_foo(1)
# executing static_foo(1)

A.static_foo('hi')
# executing static_foo(hi)

静态方法用于对与类之间具有某种逻辑联系的函数进行分组。


foo只是一个函数,但是当您调用a.foo它时,不仅得到函数,还会得到函数的“部分应用”版本,其中对象实例a绑定为函数的第一个参数。foo期望有2个参数,而a.foo只期望有1个参数。

a势必到foo。这就是下面的术语“绑定”的含义:

print(a.foo)
# <bound method A.foo of <__main__.A object at 0xb7d52f0c>>

a.class_fooa不绑定class_foo,而是与类A绑定class_foo

print(a.class_foo)
# <bound method type.class_foo of <class '__main__.A'>>

在这里,使用静态方法,即使它是一种方法,也a.static_foo只是返回一个没有绑定参数的良好的’ole函数。static_foo期望有1个参数,也 a.static_foo期望有1个参数。

print(a.static_foo)
# <function static_foo at 0xb7d479cc>

当然,当您static_foo使用类进行调用时,也会发生同样的事情A

print(A.static_foo)
# <function static_foo at 0xb7d479cc>

Maybe a bit of example code will help: Notice the difference in the call signatures of foo, class_foo and static_foo:

class A(object):
    def foo(self, x):
        print "executing foo(%s, %s)" % (self, x)

    @classmethod
    def class_foo(cls, x):
        print "executing class_foo(%s, %s)" % (cls, x)

    @staticmethod
    def static_foo(x):
        print "executing static_foo(%s)" % x    

a = A()

Below is the usual way an object instance calls a method. The object instance, a, is implicitly passed as the first argument.

a.foo(1)
# executing foo(<__main__.A object at 0xb7dbef0c>,1)

With classmethods, the class of the object instance is implicitly passed as the first argument instead of self.

a.class_foo(1)
# executing class_foo(<class '__main__.A'>,1)

You can also call class_foo using the class. In fact, if you define something to be a classmethod, it is probably because you intend to call it from the class rather than from a class instance. A.foo(1) would have raised a TypeError, but A.class_foo(1) works just fine:

A.class_foo(1)
# executing class_foo(<class '__main__.A'>,1)

One use people have found for class methods is to create inheritable alternative constructors.


With staticmethods, neither self (the object instance) nor cls (the class) is implicitly passed as the first argument. They behave like plain functions except that you can call them from an instance or the class:

a.static_foo(1)
# executing static_foo(1)

A.static_foo('hi')
# executing static_foo(hi)

Staticmethods are used to group functions which have some logical connection with a class to the class.


foo is just a function, but when you call a.foo you don’t just get the function, you get a “partially applied” version of the function with the object instance a bound as the first argument to the function. foo expects 2 arguments, while a.foo only expects 1 argument.

a is bound to foo. That is what is meant by the term “bound” below:

print(a.foo)
# <bound method A.foo of <__main__.A object at 0xb7d52f0c>>

With a.class_foo, a is not bound to class_foo, rather the class A is bound to class_foo.

print(a.class_foo)
# <bound method type.class_foo of <class '__main__.A'>>

Here, with a staticmethod, even though it is a method, a.static_foo just returns a good ‘ole function with no arguments bound. static_foo expects 1 argument, and a.static_foo expects 1 argument too.

print(a.static_foo)
# <function static_foo at 0xb7d479cc>

And of course the same thing happens when you call static_foo with the class A instead.

print(A.static_foo)
# <function static_foo at 0xb7d479cc>

回答 1

一个静态方法是一无所知,它被称为上类或实例的方法。它只是获取传递的参数,没有隐式的第一个参数。在Python中基本上没有用-您可以使用模块函数代替静态方法。

类方法,在另一方面,是获取传递的类,它被称为上,或该类的实例,它被称为上的,作为第一个参数的方法。当您希望该方法成为类的工厂时,这很有用:由于它获得了作为第一个参数调用的实际类,因此即使涉及子类,也始终可以实例化正确的类。例如dict.fromkeys(),观察在子类上调用时,类方法如何返回子类的实例:

>>> class DictSubclass(dict):
...     def __repr__(self):
...         return "DictSubclass"
... 
>>> dict.fromkeys("abc")
{'a': None, 'c': None, 'b': None}
>>> DictSubclass.fromkeys("abc")
DictSubclass
>>> 

A staticmethod is a method that knows nothing about the class or instance it was called on. It just gets the arguments that were passed, no implicit first argument. It is basically useless in Python — you can just use a module function instead of a staticmethod.

A classmethod, on the other hand, is a method that gets passed the class it was called on, or the class of the instance it was called on, as first argument. This is useful when you want the method to be a factory for the class: since it gets the actual class it was called on as first argument, you can always instantiate the right class, even when subclasses are involved. Observe for instance how dict.fromkeys(), a classmethod, returns an instance of the subclass when called on a subclass:

>>> class DictSubclass(dict):
...     def __repr__(self):
...         return "DictSubclass"
... 
>>> dict.fromkeys("abc")
{'a': None, 'c': None, 'b': None}
>>> DictSubclass.fromkeys("abc")
DictSubclass
>>> 

回答 2

基本上@classmethod使方法的第一个参数是从其调用的类(而不是类实例),@staticmethod它没有任何隐式参数。

Basically @classmethod makes a method whose first argument is the class it’s called from (rather than the class instance), @staticmethod does not have any implicit arguments.


回答 3

官方python文档:

@classmethod

类方法将类作为隐式第一个参数接收,就像实例方法接收实例一样。要声明类方法,请使用以下惯用法:

class C:
    @classmethod
    def f(cls, arg1, arg2, ...): ... 

@classmethod表单是一个函数 装饰器 –有关详细信息,请参见函数定义中的函数定义描述。

可以在类(如C.f())或实例(如C().f())上调用它。该实例除其类外均被忽略。如果为派生类调用类方法,则派生类对象作为隐式第一个参数传递。

类方法不同于C ++或Java静态方法。如果需要这些,请参阅staticmethod()本节。

@staticmethod

静态方法不会收到隐式的第一个参数。要声明静态方法,请使用以下惯用法:

class C:
    @staticmethod
    def f(arg1, arg2, ...): ... 

@staticmethod表单是一个函数 装饰器 –有关详细信息,请参见函数定义中的函数定义描述。

可以在类(如C.f())或实例(如C().f())上调用它。该实例除其类外均被忽略。

Python中的静态方法类似于Java或C ++中的静态方法。有关更高级的概念,请参阅 classmethod()本节。

Official python docs:

@classmethod

A class method receives the class as implicit first argument, just like an instance method receives the instance. To declare a class method, use this idiom:

class C:
    @classmethod
    def f(cls, arg1, arg2, ...): ... 

The @classmethod form is a function decorator – see the description of function definitions in Function definitions for details.

It can be called either on the class (such as C.f()) or on an instance (such as C().f()). The instance is ignored except for its class. If a class method is called for a derived class, the derived class object is passed as the implied first argument.

Class methods are different than C++ or Java static methods. If you want those, see staticmethod() in this section.

@staticmethod

A static method does not receive an implicit first argument. To declare a static method, use this idiom:

class C:
    @staticmethod
    def f(arg1, arg2, ...): ... 

The @staticmethod form is a function decorator – see the description of function definitions in Function definitions for details.

It can be called either on the class (such as C.f()) or on an instance (such as C().f()). The instance is ignored except for its class.

Static methods in Python are similar to those found in Java or C++. For a more advanced concept, see classmethod() in this section.


回答 4

是关于这个问题的简短文章

@staticmethod函数不过是在类内部定义的函数。可调用而无需先实例化该类。它的定义通过继承是不可变的。

@classmethod函数也可以在不实例化类的情况下调用,但是其定义是通过继承遵循Sub类,而不是Parent类。这是因为@classmethod函数的第一个参数必须始终为cls(类)。

Here is a short article on this question

@staticmethod function is nothing more than a function defined inside a class. It is callable without instantiating the class first. It’s definition is immutable via inheritance.

@classmethod function also callable without instantiating the class, but its definition follows Sub class, not Parent class, via inheritance. That’s because the first argument for @classmethod function must always be cls (class).


回答 5

要决定使用@staticmethod还是@classmethod,您必须查看方法内部。如果您的方法访问类中的其他变量/方法,请使用@classmethod。另一方面,如果您的方法未触及类的其他任何部分,请使用@staticmethod。

class Apple:

    _counter = 0

    @staticmethod
    def about_apple():
        print('Apple is good for you.')

        # note you can still access other member of the class
        # but you have to use the class instance 
        # which is not very nice, because you have repeat yourself
        # 
        # For example:
        # @staticmethod
        #    print('Number of apples have been juiced: %s' % Apple._counter)
        #
        # @classmethod
        #    print('Number of apples have been juiced: %s' % cls._counter)
        #
        #    @classmethod is especially useful when you move your function to other class,
        #       you don't have to rename the class reference 

    @classmethod
    def make_apple_juice(cls, number_of_apples):
        print('Make juice:')
        for i in range(number_of_apples):
            cls._juice_this(i)

    @classmethod
    def _juice_this(cls, apple):
        print('Juicing %d...' % apple)
        cls._counter += 1

To decide whether to use @staticmethod or @classmethod you have to look inside your method. If your method accesses other variables/methods in your class then use @classmethod. On the other hand, if your method does not touches any other parts of the class then use @staticmethod.

class Apple:

    _counter = 0

    @staticmethod
    def about_apple():
        print('Apple is good for you.')

        # note you can still access other member of the class
        # but you have to use the class instance 
        # which is not very nice, because you have repeat yourself
        # 
        # For example:
        # @staticmethod
        #    print('Number of apples have been juiced: %s' % Apple._counter)
        #
        # @classmethod
        #    print('Number of apples have been juiced: %s' % cls._counter)
        #
        #    @classmethod is especially useful when you move your function to other class,
        #       you don't have to rename the class reference 

    @classmethod
    def make_apple_juice(cls, number_of_apples):
        print('Make juice:')
        for i in range(number_of_apples):
            cls._juice_this(i)

    @classmethod
    def _juice_this(cls, apple):
        print('Juicing %d...' % apple)
        cls._counter += 1

回答 6

Python中的@staticmethod和@classmethod有什么区别?

您可能已经看到了类似此伪代码的Python代码,该代码演示了各种方法类型的签名,并提供了一个文档字符串来说明每种方法:

class Foo(object):

    def a_normal_instance_method(self, arg_1, kwarg_2=None):
        '''
        Return a value that is a function of the instance with its
        attributes, and other arguments such as arg_1 and kwarg2
        '''

    @staticmethod
    def a_static_method(arg_0):
        '''
        Return a value that is a function of arg_0. It does not know the 
        instance or class it is called from.
        '''

    @classmethod
    def a_class_method(cls, arg1):
        '''
        Return a value that is a function of the class and other arguments.
        respects subclassing, it is called with the class it is called from.
        '''

普通实例方法

首先,我会解释a_normal_instance_method。这就是所谓的“ 实例方法 ”。使用实例方法时,它用作部分函数(与总函数相反,在源代码中查看时为所有值定义的总函数),即在使用时,将第一个参数预定义为具有所有给定属性的对象。它具有绑定到其对象的实例,并且必须从该对象的实例调用它。通常,它将访问实例的各种属性。

例如,这是一个字符串的实例:

', '

如果我们join在该字符串上使用实例方法来连接另一个可迭代对象,则很明显,它是实例的功能,除了是可迭代列表的功能之外,还['a', 'b', 'c']

>>> ', '.join(['a', 'b', 'c'])
'a, b, c'

绑定方法

可以通过点分查找来绑定实例方法,以备后用。

例如,这将str.join方法绑定到':'实例:

>>> join_with_colons = ':'.join 

之后,我们可以将其用作已绑定第一个参数的函数。这样,它就像实例上的部分函数一样工作:

>>> join_with_colons('abcde')
'a:b:c:d:e'
>>> join_with_colons(['FF', 'FF', 'FF', 'FF', 'FF', 'FF'])
'FF:FF:FF:FF:FF:FF'

静态方法

静态方法并没有把实例作为参数。

它与模块级功能非常相似。

但是,模块级功能必须存在于模块中,并且必须专门导入到其他使用该功能的地方。

但是,如果将其附加到对象上,它也将通过导入和继承方便地跟随对象。

静态方法的一个示例是str.maketransstringPython 3 的模块中移出的。它使转换表适合由占用str.translate。从字符串的实例使用时,看起来确实很愚蠢,如下所示,但是从string模块导入函数相当笨拙,并且能够从类中调用它很好,例如str.maketrans

# demonstrate same function whether called from instance or not:
>>> ', '.maketrans('ABC', 'abc')
{65: 97, 66: 98, 67: 99}
>>> str.maketrans('ABC', 'abc')
{65: 97, 66: 98, 67: 99}

在python 2中,您必须从越来越少用的字符串模块中导入此函数:

>>> import string
>>> 'ABCDEFG'.translate(string.maketrans('ABC', 'abc'))
'abcDEFG'

类方法

类方法与实例方法类似,因为它采用了隐式的第一个参数,但是它采用了类,而不是采用实例。通常,它们被用作替代构造函数以更好地使用语义,并且它将支持继承。

内建类方法的最典型示例是dict.fromkeys。它用作dict的替代构造函数(非常适合当您知道键是什么并且想要它们的默认值时)。

>>> dict.fromkeys(['a', 'b', 'c'])
{'c': None, 'b': None, 'a': None}

当我们对dict进行子类化时,可以使用相同的构造函数,该构造函数创建子类的实例。

>>> class MyDict(dict): 'A dict subclass, use to demo classmethods'
>>> md = MyDict.fromkeys(['a', 'b', 'c'])
>>> md
{'a': None, 'c': None, 'b': None}
>>> type(md)
<class '__main__.MyDict'>

看到熊猫的源代码的替代构造其它类似的例子,同时也看到了官方的Python文档classmethodstaticmethod

What is the difference between @staticmethod and @classmethod in Python?

You may have seen Python code like this pseudocode, which demonstrates the signatures of the various method types and provides a docstring to explain each:

class Foo(object):

    def a_normal_instance_method(self, arg_1, kwarg_2=None):
        '''
        Return a value that is a function of the instance with its
        attributes, and other arguments such as arg_1 and kwarg2
        '''

    @staticmethod
    def a_static_method(arg_0):
        '''
        Return a value that is a function of arg_0. It does not know the 
        instance or class it is called from.
        '''

    @classmethod
    def a_class_method(cls, arg1):
        '''
        Return a value that is a function of the class and other arguments.
        respects subclassing, it is called with the class it is called from.
        '''

The Normal Instance Method

First I’ll explain a_normal_instance_method. This is precisely called an “instance method“. When an instance method is used, it is used as a partial function (as opposed to a total function, defined for all values when viewed in source code) that is, when used, the first of the arguments is predefined as the instance of the object, with all of its given attributes. It has the instance of the object bound to it, and it must be called from an instance of the object. Typically, it will access various attributes of the instance.

For example, this is an instance of a string:

', '

if we use the instance method, join on this string, to join another iterable, it quite obviously is a function of the instance, in addition to being a function of the iterable list, ['a', 'b', 'c']:

>>> ', '.join(['a', 'b', 'c'])
'a, b, c'

Bound methods

Instance methods can be bound via a dotted lookup for use later.

For example, this binds the str.join method to the ':' instance:

>>> join_with_colons = ':'.join 

And later we can use this as a function that already has the first argument bound to it. In this way, it works like a partial function on the instance:

>>> join_with_colons('abcde')
'a:b:c:d:e'
>>> join_with_colons(['FF', 'FF', 'FF', 'FF', 'FF', 'FF'])
'FF:FF:FF:FF:FF:FF'

Static Method

The static method does not take the instance as an argument.

It is very similar to a module level function.

However, a module level function must live in the module and be specially imported to other places where it is used.

If it is attached to the object, however, it will follow the object conveniently through importing and inheritance as well.

An example of a static method is str.maketrans, moved from the string module in Python 3. It makes a translation table suitable for consumption by str.translate. It does seem rather silly when used from an instance of a string, as demonstrated below, but importing the function from the string module is rather clumsy, and it’s nice to be able to call it from the class, as in str.maketrans

# demonstrate same function whether called from instance or not:
>>> ', '.maketrans('ABC', 'abc')
{65: 97, 66: 98, 67: 99}
>>> str.maketrans('ABC', 'abc')
{65: 97, 66: 98, 67: 99}

In python 2, you have to import this function from the increasingly less useful string module:

>>> import string
>>> 'ABCDEFG'.translate(string.maketrans('ABC', 'abc'))
'abcDEFG'

Class Method

A class method is a similar to an instance method in that it takes an implicit first argument, but instead of taking the instance, it takes the class. Frequently these are used as alternative constructors for better semantic usage and it will support inheritance.

The most canonical example of a builtin classmethod is dict.fromkeys. It is used as an alternative constructor of dict, (well suited for when you know what your keys are and want a default value for them.)

>>> dict.fromkeys(['a', 'b', 'c'])
{'c': None, 'b': None, 'a': None}

When we subclass dict, we can use the same constructor, which creates an instance of the subclass.

>>> class MyDict(dict): 'A dict subclass, use to demo classmethods'
>>> md = MyDict.fromkeys(['a', 'b', 'c'])
>>> md
{'a': None, 'c': None, 'b': None}
>>> type(md)
<class '__main__.MyDict'>

See the pandas source code for other similar examples of alternative constructors, and see also the official Python documentation on classmethod and staticmethod.


回答 7

我开始使用C ++,Java和Python学习编程语言,所以这个问题也困扰着我,直到我理解了每种语言的简单用法。

类方法:与Java和C ++不同,Python没有构造函数重载。因此,可以使用实现此目的classmethod。以下示例将对此进行解释

让我们考虑,我们有一个Person类,它有两个参数first_name,并last_name与创建的实例Person

class Person(object):

    def __init__(self, first_name, last_name):
        self.first_name = first_name
        self.last_name = last_name

现在,如果您只需要使用一个名称创建一个类(仅使用一个名称)first_name,那么您将无法在Python中执行类似的操作。

当您尝试创建对象(实例)时,这将给您一个错误。

class Person(object):

    def __init__(self, first_name, last_name):
        self.first_name = first_name
        self.last_name = last_name

    def __init__(self, first_name):
        self.first_name = first_name

但是,您可以使用@classmethod以下方法实现相同的目的

class Person(object):

    def __init__(self, first_name, last_name):
        self.first_name = first_name
        self.last_name = last_name

    @classmethod
    def get_person(cls, first_name):
        return cls(first_name, "")

静态方法:这很简单,它不受实例或类的约束,您可以使用类名简单地调用它。

因此,假设在上面的示例中,您需要一个first_name不超过20个字符的验证,您只需执行此操作即可。

@staticmethod  
def validate_name(name):
    return len(name) <= 20

你可以简单地使用 class name

Person.validate_name("Gaurang Shah")

I started learning programming language with C++ and then Java and then Python and so this question bothered me a lot as well, until I understood the simple usage of each.

Class Method: Python unlike Java and C++ doesn’t have constructor overloading. And so to achieve this you could use classmethod. Following example will explain this

Let’s consider we have a Person class which takes two arguments first_name and last_name and creates the instance of Person.

class Person(object):

    def __init__(self, first_name, last_name):
        self.first_name = first_name
        self.last_name = last_name

Now, if the requirement comes where you need to create a class using a single name only, just a first_name, you can’t do something like this in Python.

This will give you an error when you will try to create an object (instance).

class Person(object):

    def __init__(self, first_name, last_name):
        self.first_name = first_name
        self.last_name = last_name

    def __init__(self, first_name):
        self.first_name = first_name

However, you could achieve the same thing using @classmethod as mentioned below

class Person(object):

    def __init__(self, first_name, last_name):
        self.first_name = first_name
        self.last_name = last_name

    @classmethod
    def get_person(cls, first_name):
        return cls(first_name, "")

Static Method: This is rather simple, it’s not bound to instance or class and you can simply call that using class name.

So let’s say in above example you need a validation that first_name should not exceed 20 characters, you can simply do this.

@staticmethod  
def validate_name(name):
    return len(name) <= 20

and you could simply call using class name

Person.validate_name("Gaurang Shah")

回答 8

我认为一个更好的问题是“何时使用@classmethod与@staticmethod?”

@classmethod允许您轻松访问与类定义关联的私有成员。这是完成单例或工厂类的一种好方法,该类控制已创建对象的实例数量。

@staticmethod可以提供少量的性能提升,但是我还没有看到在类中有效地使用静态方法,而该方法不能作为类外的独立函数来实现。

I think a better question is “When would you use @classmethod vs @staticmethod?”

@classmethod allows you easy access to private members that are associated to the class definition. this is a great way to do singletons, or factory classes that control the number of instances of the created objects exist.

@staticmethod provides marginal performance gains, but I have yet to see a productive use of a static method within a class that couldn’t be achieved as a standalone function outside the class.


回答 9

@decorators是在python 2.4中添加的。如果您使用的是python <2.4,则可以使用classmethod()和staticmethod()函数。

例如,如果您想创建一个工厂方法(一个函数根据得到的参数返回一个类的不同实现的实例),您可以执行以下操作:

class Cluster(object):

    def _is_cluster_for(cls, name):
        """
        see if this class is the cluster with this name
        this is a classmethod
        """ 
        return cls.__name__ == name
    _is_cluster_for = classmethod(_is_cluster_for)

    #static method
    def getCluster(name):
        """
        static factory method, should be in Cluster class
        returns a cluster object for the given name
        """
        for cls in Cluster.__subclasses__():
            if cls._is_cluster_for(name):
                return cls()
    getCluster = staticmethod(getCluster)

还要注意,这是使用类方法和静态方法的一个很好的例子。静态方法显然属于该类,因为它在内部使用类Cluster。类方法仅需要有关类的信息,而无需对象的实例。

_is_cluster_for方法设为类方法的另一个好处是,子类可以决定更改其实现,这可能是因为它非常通用并且可以处理多种类型的集群,因此仅检查类的名称是不够的。

@decorators were added in python 2.4 If you’re using python < 2.4 you can use the classmethod() and staticmethod() function.

For example, if you want to create a factory method (A function returning an instance of a different implementation of a class depending on what argument it gets) you can do something like:

class Cluster(object):

    def _is_cluster_for(cls, name):
        """
        see if this class is the cluster with this name
        this is a classmethod
        """ 
        return cls.__name__ == name
    _is_cluster_for = classmethod(_is_cluster_for)

    #static method
    def getCluster(name):
        """
        static factory method, should be in Cluster class
        returns a cluster object for the given name
        """
        for cls in Cluster.__subclasses__():
            if cls._is_cluster_for(name):
                return cls()
    getCluster = staticmethod(getCluster)

Also observe that this is a good example for using a classmethod and a static method, The static method clearly belongs to the class, since it uses the class Cluster internally. The classmethod only needs information about the class, and no instance of the object.

Another benefit of making the _is_cluster_for method a classmethod is so a subclass can decide to change it’s implementation, maybe because it is pretty generic and can handle more than one type of cluster, so just checking the name of the class would not be enough.


回答 10

静态方法:

  • 没有自变量的简单函数。
  • 处理类属性;不在实例属性上。
  • 可以通过类和实例调用。
  • 内置函数staticmethod()用于创建它们。

静态方法的好处:

  • 它在类范围内本地化函数名称
  • 它将功能代码移近使用位置
  • 与模块级函数相比,导入更方便,因为不必专门导入每种方法

    @staticmethod
    def some_static_method(*args, **kwds):
        pass

类方法:

  • 具有第一个参数作为类名的函数。
  • 可以通过类和实例调用。
  • 这些是使用classmethod内置函数创建的。

     @classmethod
     def some_class_method(cls, *args, **kwds):
         pass

Static Methods:

  • Simple functions with no self argument.
  • Work on class attributes; not on instance attributes.
  • Can be called through both class and instance.
  • The built-in function staticmethod()is used to create them.

Benefits of Static Methods:

  • It localizes the function name in the classscope
  • It moves the function code closer to where it is used
  • More convenient to import versus module-level functions since each method does not have to be specially imported

    @staticmethod
    def some_static_method(*args, **kwds):
        pass
    

Class Methods:

  • Functions that have first argument as classname.
  • Can be called through both class and instance.
  • These are created with classmethod in-built function.

     @classmethod
     def some_class_method(cls, *args, **kwds):
         pass
    

回答 11

@staticmethod只是禁用默认函数作为方法描述符。classmethod将函数包装在可调用的容器中,该容器将对拥有类的引用作为第一个参数传递:

>>> class C(object):
...  pass
... 
>>> def f():
...  pass
... 
>>> staticmethod(f).__get__(None, C)
<function f at 0x5c1cf0>
>>> classmethod(f).__get__(None, C)
<bound method type.f of <class '__main__.C'>>

实际上,classmethod它具有运行时开销,但可以访问拥有的类。另外,我建议使用元类并将类方法放在该元类上:

>>> class CMeta(type):
...  def foo(cls):
...   print cls
... 
>>> class C(object):
...  __metaclass__ = CMeta
... 
>>> C.foo()
<class '__main__.C'>

@staticmethod just disables the default function as method descriptor. classmethod wraps your function in a container callable that passes a reference to the owning class as first argument:

>>> class C(object):
...  pass
... 
>>> def f():
...  pass
... 
>>> staticmethod(f).__get__(None, C)
<function f at 0x5c1cf0>
>>> classmethod(f).__get__(None, C)
<bound method type.f of <class '__main__.C'>>

As a matter of fact, classmethod has a runtime overhead but makes it possible to access the owning class. Alternatively I recommend using a metaclass and putting the class methods on that metaclass:

>>> class CMeta(type):
...  def foo(cls):
...   print cls
... 
>>> class C(object):
...  __metaclass__ = CMeta
... 
>>> C.foo()
<class '__main__.C'>

回答 12

关于如何在Python中使用静态,类或抽象方法的权威指南是该主题的一个很好的链接,并总结如下。

@staticmethod函数不过是在类内部定义的函数。可调用而无需先实例化该类。它的定义通过继承是不可变的。

  • Python不必实例化对象的绑定方法。
  • 它简化了代码的可读性,并且不依赖于对象本身的状态。

@classmethod函数也可以在不实例化该类的情况下调用,但是其定义遵循子类,而不是父类,通过继承可以被子类覆盖。这是因为@classmethodfunction 的第一个参数必须始终为cls(类)。

  • 工厂方法,用于使用例如某种预处理为类创建实例。
  • 静态方法调用静态方法:如果将静态方法拆分为多个静态方法,则不应硬编码类名,而应使用类方法

The definitive guide on how to use static, class or abstract methods in Python is one good link for this topic, and summary it as following.

@staticmethod function is nothing more than a function defined inside a class. It is callable without instantiating the class first. It’s definition is immutable via inheritance.

  • Python does not have to instantiate a bound-method for object.
  • It eases the readability of the code, and it does not depend on the state of object itself;

@classmethod function also callable without instantiating the class, but its definition follows Sub class, not Parent class, via inheritance, can be overridden by subclass. That’s because the first argument for @classmethod function must always be cls (class).

  • Factory methods, that are used to create an instance for a class using for example some sort of pre-processing.
  • Static methods calling static methods: if you split a static methods in several static methods, you shouldn’t hard-code the class name but use class methods

回答 13

只有第一个参数不同

  • 普通方法:当前对象(如果自动作为(附加)第一个参数传递)
  • classmethod:当前对象的类作为(附加)fist参数自动传递
  • 静态方法:不会自动传递其他参数。您传递给该函数的就是所得到的。

更详细地…

普通方法

调用对象的方法时,它会自动获得一个额外的参数self作为其第一个参数。即方法

def f(self, x, y)

必须使用2个参数调用。self是自动传递的,它是对象本身

类方法

装饰方法时

@classmethod
def f(cls, x, y)

自动提供的参数不是 self,而是的类 self

静态方法

装饰方法时

@staticmethod
def f(x, y)

该方法根本没有任何自动参数。仅提供调用它的参数。

用法

  • classmethod 主要用于替代构造函数。
  • staticmethod不使用对象的状态。它可能是类外部的函数。它仅放在类中以将具有相似功能的功能分组(例如,类似于Java的Math类静态方法)
class Point
    def __init__(self, x, y):
        self.x = x
        self.y = y

    @classmethod
    def frompolar(cls, radius, angle):
        """The `cls` argument is the `Point` class itself"""
        return cls(radius * cos(angle), radius * sin(angle))

    @staticmethod
    def angle(x, y):
        """this could be outside the class, but we put it here 
just because we think it is logically related to the class."""
        return atan(y, x)


p1 = Point(3, 2)
p2 = Point.frompolar(3, pi/4)

angle = Point.angle(3, 2)

Only the first argument differs:

  • normal method: the current object if automatically passed as an (additional) first argument
  • classmethod: the class of the current object is automatically passed as an (additional) fist argument
  • staticmethod: no extra arguments are automatically passed. What you passed to the function is what you get.

In more detail…

normal method

When an object’s method is called, it is automatically given an extra argument self as its first argument. That is, method

def f(self, x, y)

must be called with 2 arguments. self is automatically passed, and it is the object itself.

class method

When the method is decorated

@classmethod
def f(cls, x, y)

the automatically provided argument is not self, but the class of self.

static method

When the method is decorated

@staticmethod
def f(x, y)

the method is not given any automatic argument at all. It is only given the parameters that it is called with.

usages

  • classmethod is mostly used for alternative constructors.
  • staticmethod does not use the state of the object. It could be a function external to a class. It only put inside the class for grouping functions with similar functionality (for example, like Java’s Math class static methods)
class Point
    def __init__(self, x, y):
        self.x = x
        self.y = y

    @classmethod
    def frompolar(cls, radius, angle):
        """The `cls` argument is the `Point` class itself"""
        return cls(radius * cos(angle), radius * sin(angle))

    @staticmethod
    def angle(x, y):
        """this could be outside the class, but we put it here 
just because we think it is logically related to the class."""
        return atan(y, x)


p1 = Point(3, 2)
p2 = Point.frompolar(3, pi/4)

angle = Point.angle(3, 2)


回答 14

让我先说一下用@classmethod装饰的方法与@staticmethod装饰的方法之间的相似性。

相似:两者都可以在本身上调用,而不仅仅是类的实例。因此,从某种意义上来说,它们都是Class的方法

区别:类方法将接收类本身作为第一个参数,而静态方法则不接收。

因此,从某种意义上说,静态方法并不绑定于Class本身,而只是因为它可能具有相关的功能而挂在这里。

>>> class Klaus:
        @classmethod
        def classmthd(*args):
            return args

        @staticmethod
        def staticmthd(*args):
            return args

# 1. Call classmethod without any arg
>>> Klaus.classmthd()  
(__main__.Klaus,)  # the class gets passed as the first argument

# 2. Call classmethod with 1 arg
>>> Klaus.classmthd('chumma')
(__main__.Klaus, 'chumma')

# 3. Call staticmethod without any arg
>>> Klaus.staticmthd()  
()

# 4. Call staticmethod with 1 arg
>>> Klaus.staticmthd('chumma')
('chumma',)

Let me tell the similarity between a method decorated with @classmethod vs @staticmethod first.

Similarity: Both of them can be called on the Class itself, rather than just the instance of the class. So, both of them in a sense are Class’s methods.

Difference: A classmethod will receive the class itself as the first argument, while a staticmethod does not.

So a static method is, in a sense, not bound to the Class itself and is just hanging in there just because it may have a related functionality.

>>> class Klaus:
        @classmethod
        def classmthd(*args):
            return args

        @staticmethod
        def staticmthd(*args):
            return args

# 1. Call classmethod without any arg
>>> Klaus.classmthd()  
(__main__.Klaus,)  # the class gets passed as the first argument

# 2. Call classmethod with 1 arg
>>> Klaus.classmthd('chumma')
(__main__.Klaus, 'chumma')

# 3. Call staticmethod without any arg
>>> Klaus.staticmthd()  
()

# 4. Call staticmethod with 1 arg
>>> Klaus.staticmthd('chumma')
('chumma',)

回答 15

关于静态方法与类方法的另一个考虑是继承。假设您有以下类:

class Foo(object):
    @staticmethod
    def bar():
        return "In Foo"

然后,您想覆盖bar()一个子类:

class Foo2(Foo):
    @staticmethod
    def bar():
        return "In Foo2"

这是可行的,但是请注意,现在bar()子类(Foo2)中的实现不再可以利用该类的任何特定优势。例如,假设Foo2有一个magic()要在Foo2实现中使用的名为的方法bar()

class Foo2(Foo):
    @staticmethod
    def bar():
        return "In Foo2"
    @staticmethod
    def magic():
        return "Something useful you'd like to use in bar, but now can't" 

这里的解决办法是打电话Foo2.magic()bar(),但此时你重复自己(如果名称Foo2的改变,你必须记住要更新bar()方法)。

对我来说,这有点违反开放式/封闭式原则,因为做出的决定Foo会影响您在派生类中重构通用代码的能力(即扩展性较小)。如果bar()是a,classmethod我们会没事的:

class Foo(object):
    @classmethod
    def bar(cls):
        return "In Foo"

class Foo2(Foo):
    @classmethod
    def bar(cls):
        return "In Foo2 " + cls.magic()
    @classmethod
    def magic(cls):
        return "MAGIC"

print Foo2().bar()

给出: In Foo2 MAGIC

Another consideration with respect to staticmethod vs classmethod comes up with inheritance. Say you have the following class:

class Foo(object):
    @staticmethod
    def bar():
        return "In Foo"

And you then want to override bar() in a child class:

class Foo2(Foo):
    @staticmethod
    def bar():
        return "In Foo2"

This works, but note that now the bar() implementation in the child class (Foo2) can no longer take advantage of anything specific to that class. For example, say Foo2 had a method called magic() that you want to use in the Foo2 implementation of bar():

class Foo2(Foo):
    @staticmethod
    def bar():
        return "In Foo2"
    @staticmethod
    def magic():
        return "Something useful you'd like to use in bar, but now can't" 

The workaround here would be to call Foo2.magic() in bar(), but then you’re repeating yourself (if the name of Foo2 changes, you’ll have to remember to update that bar() method).

To me, this is a slight violation of the open/closed principle, since a decision made in Foo is impacting your ability to refactor common code in a derived class (ie it’s less open to extension). If bar() were a classmethod we’d be fine:

class Foo(object):
    @classmethod
    def bar(cls):
        return "In Foo"

class Foo2(Foo):
    @classmethod
    def bar(cls):
        return "In Foo2 " + cls.magic()
    @classmethod
    def magic(cls):
        return "MAGIC"

print Foo2().bar()

Gives: In Foo2 MAGIC


回答 16

我将尝试通过一个示例来说明基本区别。

class A(object):
    x = 0

    def say_hi(self):
        pass

    @staticmethod
    def say_hi_static():
        pass

    @classmethod
    def say_hi_class(cls):
        pass

    def run_self(self):
        self.x += 1
        print self.x # outputs 1
        self.say_hi()
        self.say_hi_static()
        self.say_hi_class()

    @staticmethod
    def run_static():
        print A.x  # outputs 0
        # A.say_hi() #  wrong
        A.say_hi_static()
        A.say_hi_class()

    @classmethod
    def run_class(cls):
        print cls.x # outputs 0
        # cls.say_hi() #  wrong
        cls.say_hi_static()
        cls.say_hi_class()

1-我们可以直接调用静态方法和类方法而无需初始化

# A.run_self() #  wrong
A.run_static()
A.run_class()

2-静态方法不能调用self方法,但可以调用其他static和classmethod

3-静态方法属于类,根本不会使用对象。

4-类方法不绑定到对象而是绑定到类。

I will try to explain the basic difference using an example.

class A(object):
    x = 0

    def say_hi(self):
        pass

    @staticmethod
    def say_hi_static():
        pass

    @classmethod
    def say_hi_class(cls):
        pass

    def run_self(self):
        self.x += 1
        print self.x # outputs 1
        self.say_hi()
        self.say_hi_static()
        self.say_hi_class()

    @staticmethod
    def run_static():
        print A.x  # outputs 0
        # A.say_hi() #  wrong
        A.say_hi_static()
        A.say_hi_class()

    @classmethod
    def run_class(cls):
        print cls.x # outputs 0
        # cls.say_hi() #  wrong
        cls.say_hi_static()
        cls.say_hi_class()

1 – we can directly call static and classmethods without initializing

# A.run_self() #  wrong
A.run_static()
A.run_class()

2- Static method cannot call self method but can call other static and classmethod

3- Static method belong to class and will not use object at all.

4- Class method are not bound to an object but to a class.


回答 17

@classmethod:可用于创建对该类创建的所有实例的共享全局访问……例如由多个用户更新记录….我特别发现创建单例时它也很有效..: )

@static方法:与与…相关联的类或实例无关,但出于可读性考虑,可以使用static方法

@classmethod : can be used to create a shared global access to all the instances created of that class….. like updating a record by multiple users…. I particulary found it use ful when creating singletons as well..:)

@static method: has nothing to do with the class or instance being associated with …but for readability can use static method


回答 18

您可能需要考虑以下两者之间的区别:

Class A:
    def foo():  # no self parameter, no decorator
        pass

Class B:
    @staticmethod
    def foo():  # no self parameter
        pass

这在python2和python3之间发生了变化:

python2:

>>> A.foo()
TypeError
>>> A().foo()
TypeError
>>> B.foo()
>>> B().foo()

python3:

>>> A.foo()
>>> A().foo()
TypeError
>>> B.foo()
>>> B().foo()

因此@staticmethod,仅在类中直接使用 for方法已成为python3中的可选方法。如果要从类和实例中调用它们,则仍需要使用@staticmethod装饰器。

unutbus的答案很好地涵盖了其他情况。

You might want to consider the difference between:

Class A:
    def foo():  # no self parameter, no decorator
        pass

and

Class B:
    @staticmethod
    def foo():  # no self parameter
        pass

This has changed between python2 and python3:

python2:

>>> A.foo()
TypeError
>>> A().foo()
TypeError
>>> B.foo()
>>> B().foo()

python3:

>>> A.foo()
>>> A().foo()
TypeError
>>> B.foo()
>>> B().foo()

So using @staticmethod for methods only called directly from the class has become optional in python3. If you want to call them from both class and instance, you still need to use the @staticmethod decorator.

The other cases have been well covered by unutbus answer.


回答 19

我的贡献演示之间的差异@classmethod@staticmethod以及实例方法,包括如何实例可以间接调用@staticmethod。但是@staticmethod与其从实例中间接调用a ,不如将其设为私有可能更像是“ pythonic”。这里没有演示从私有方法获取某些东西,但是基本上是相同的概念。

#!python3

from os import system
system('cls')
# %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %

class DemoClass(object):
    # instance methods need a class instance and
    # can access the instance through 'self'
    def instance_method_1(self):
        return 'called from inside the instance_method_1()'

    def instance_method_2(self):
        # an instance outside the class indirectly calls the static_method
        return self.static_method() + ' via instance_method_2()'

    # class methods don't need a class instance, they can't access the
    # instance (self) but they have access to the class itself via 'cls'
    @classmethod
    def class_method(cls):
        return 'called from inside the class_method()'

    # static methods don't have access to 'cls' or 'self', they work like
    # regular functions but belong to the class' namespace
    @staticmethod
    def static_method():
        return 'called from inside the static_method()'
# %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %

# works even if the class hasn't been instantiated
print(DemoClass.class_method() + '\n')
''' called from inside the class_method() '''

# works even if the class hasn't been instantiated
print(DemoClass.static_method() + '\n')
''' called from inside the static_method() '''
# %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %

# >>>>> all methods types can be called on a class instance <<<<<
# instantiate the class
democlassObj = DemoClass()

# call instance_method_1()
print(democlassObj.instance_method_1() + '\n')
''' called from inside the instance_method_1() '''

# # indirectly call static_method through instance_method_2(), there's really no use
# for this since a @staticmethod can be called whether the class has been
# instantiated or not
print(democlassObj.instance_method_2() + '\n')
''' called from inside the static_method() via instance_method_2() '''

# call class_method()
print(democlassObj.class_method() + '\n')
'''  called from inside the class_method() '''

# call static_method()
print(democlassObj.static_method())
''' called from inside the static_method() '''

"""
# whether the class is instantiated or not, this doesn't work
print(DemoClass.instance_method_1() + '\n')
'''
TypeError: TypeError: unbound method instancemethod() must be called with
DemoClass instance as first argument (got nothing instead)
'''
"""

My contribution demonstrates the difference amongst @classmethod, @staticmethod, and instance methods, including how an instance can indirectly call a @staticmethod. But instead of indirectly calling a @staticmethod from an instance, making it private may be more “pythonic.” Getting something from a private method isn’t demonstrated here but it’s basically the same concept.

#!python3

from os import system
system('cls')
# %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %

class DemoClass(object):
    # instance methods need a class instance and
    # can access the instance through 'self'
    def instance_method_1(self):
        return 'called from inside the instance_method_1()'

    def instance_method_2(self):
        # an instance outside the class indirectly calls the static_method
        return self.static_method() + ' via instance_method_2()'

    # class methods don't need a class instance, they can't access the
    # instance (self) but they have access to the class itself via 'cls'
    @classmethod
    def class_method(cls):
        return 'called from inside the class_method()'

    # static methods don't have access to 'cls' or 'self', they work like
    # regular functions but belong to the class' namespace
    @staticmethod
    def static_method():
        return 'called from inside the static_method()'
# %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %

# works even if the class hasn't been instantiated
print(DemoClass.class_method() + '\n')
''' called from inside the class_method() '''

# works even if the class hasn't been instantiated
print(DemoClass.static_method() + '\n')
''' called from inside the static_method() '''
# %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %   %

# >>>>> all methods types can be called on a class instance <<<<<
# instantiate the class
democlassObj = DemoClass()

# call instance_method_1()
print(democlassObj.instance_method_1() + '\n')
''' called from inside the instance_method_1() '''

# # indirectly call static_method through instance_method_2(), there's really no use
# for this since a @staticmethod can be called whether the class has been
# instantiated or not
print(democlassObj.instance_method_2() + '\n')
''' called from inside the static_method() via instance_method_2() '''

# call class_method()
print(democlassObj.class_method() + '\n')
'''  called from inside the class_method() '''

# call static_method()
print(democlassObj.static_method())
''' called from inside the static_method() '''

"""
# whether the class is instantiated or not, this doesn't work
print(DemoClass.instance_method_1() + '\n')
'''
TypeError: TypeError: unbound method instancemethod() must be called with
DemoClass instance as first argument (got nothing instead)
'''
"""

回答 20

类方法将类作为隐式第一个参数接收,就像实例方法接收实例一样。它是绑定到类而不是类对象的方法,因为它使用指向类而不是对象实例的类参数,所以可以访问类的状态。它可以修改适用于该类所有实例的类状态。例如,它可以修改将适用于所有实例的类变量。

另一方面,与类方法或实例方法相比,静态方法不接收隐式的第一个参数。并且无法访问或修改类状态。它仅属于该类,因为从设计的角度来看这是正确的方法。但是就功能而言,在运行时未绑定到该类。

作为准则,请将静态方法用作实用程序,将类方法用作例如factory。或定义一个单例。并使用实例方法对实例的状态和行为进行建模。

希望我很清楚!

A class method receives the class as implicit first argument, just like an instance method receives the instance. It is a method which is bound to the class and not the object of the class.It has access to the state of the class as it takes a class parameter that points to the class and not the object instance. It can modify a class state that would apply across all the instances of the class. For example it can modify a class variable that will be applicable to all the instances.

On the other hand, a static method does not receive an implicit first argument, compared to class methods or instance methods. And can’t access or modify class state. It only belongs to the class because from design point of view that is the correct way. But in terms of functionality is not bound, at runtime, to the class.

as a guideline, use static methods as utilities, use class methods for example as factory . Or maybe to define a singleton. And use instance methods to model the state and behavior of instances.

Hope I was clear !


回答 21

顾名思义,类方法用于更改类而不是对象。为了更改类,他们将修改类属性(而不是对象属性),因为这是更新类的方式。这就是类方法将类(通常用“ cls”表示)作为第一个参数的原因。

class A(object):
    m=54

    @classmethod
    def class_method(cls):
        print "m is %d" % cls.m

另一方面,静态方法用于执行未绑定到类的功能,即它们不会读取或写入类变量。因此,静态方法不将类作为参数。使用它们是为了使类执行与该类目的不直接相关的功能。

class X(object):
    m=54 #will not be referenced

    @staticmethod
    def static_method():
        print "Referencing/calling a variable or function outside this class. E.g. Some global variable/function."

Class methods, as the name suggests, are used to make changes to classes and not the objects. To make changes to classes, they will modify the class attributes(not object attributes), since that is how you update classes. This is the reason that class methods take the class(conventionally denoted by ‘cls’) as the first argument.

class A(object):
    m=54

    @classmethod
    def class_method(cls):
        print "m is %d" % cls.m

Static methods on the other hand, are used to perform functionalities that are not bound to the class i.e. they will not read or write class variables. Hence, static methods do not take classes as arguments. They are used so that classes can perform functionalities that are not directly related to the purpose of the class.

class X(object):
    m=54 #will not be referenced

    @staticmethod
    def static_method():
        print "Referencing/calling a variable or function outside this class. E.g. Some global variable/function."

回答 22

从字面上分析@staticmethod可以提供不同的见解。

类的常规方法是隐式动态方法,该方法将实例作为第一个参数。
相反,静态方法不将实例作为第一个参数,因此称为“静态”

静态方法确实是一种正常的功能,与类定义之外的功能相同。
幸运的是,将它分组在类中只是为了靠近它的应用位置,或者您可以滚动查找它。

Analyze @staticmethod literally providing different insights.

A normal method of a class is an implicit dynamic method which takes the instance as first argument.
In contrast, a staticmethod does not take the instance as first argument, so is called ‘static’.

A staticmethod is indeed such a normal function the same as those outside a class definition.
It is luckily grouped into the class just in order to stand closer where it is applied, or you might scroll around to find it.


回答 23

我认为给出一个纯Python版本的staticmethodclassmethod将有助于在语言级别上理解它们之间的区别。

它们都是非数据描述符(如果您先熟悉描述符,会更容易理解它们)。

class StaticMethod(object):
    "Emulate PyStaticMethod_Type() in Objects/funcobject.c"

    def __init__(self, f):
        self.f = f

    def __get__(self, obj, objtype=None):
        return self.f


class ClassMethod(object):
    "Emulate PyClassMethod_Type() in Objects/funcobject.c"
    def __init__(self, f):
        self.f = f

    def __get__(self, obj, cls=None):
        def inner(*args, **kwargs):
            if cls is None:
                cls = type(obj)
            return self.f(cls, *args, **kwargs)
        return inner

I think giving a purely Python version of staticmethod and classmethod would help to understand the difference between them at language level.

Both of them are non-data descriptors (It would be easier to understand them if you are familiar with descriptors first).

class StaticMethod(object):
    "Emulate PyStaticMethod_Type() in Objects/funcobject.c"

    def __init__(self, f):
        self.f = f

    def __get__(self, obj, objtype=None):
        return self.f


class ClassMethod(object):
    "Emulate PyClassMethod_Type() in Objects/funcobject.c"
    def __init__(self, f):
        self.f = f

    def __get__(self, obj, cls=None):
        def inner(*args, **kwargs):
            if cls is None:
                cls = type(obj)
            return self.f(cls, *args, **kwargs)
        return inner

回答 24

静态方法无法访问继承层次结构中的对象,类或父类的服装。可以直接在类上调用它(无需创建对象)。

classmethod无法访问该对象的属性。但是,它可以访问继承层次结构中的类和父类的属性。可以直接在类上调用它(无需创建对象)。如果在该对象上调用,则它与普通方法相同,后者不会访问self.<attribute(s)>并且self.__class__.<attribute(s)>只能访问。

认为我们有一个带有的类b=2,我们将创建一个对象并将其重新设置为b=4其中。静态方法无法访问以前的任何内容。Classmethod .b==2只能通过进行访问cls.b。:普通方法可以同时访问.b==4通过self.b.b==2通过self.__class__.b

我们可以遵循KISS风格(保持简单,愚蠢):不要使用静态方法和类方法,不要在未实例化它们的情况下使用类,仅访问对象的属性self.attribute(s)。在某些语言中,以这种方式实现了OOP,我认为这不是一个坏主意。:)

staticmethod has no access to attibutes of the object, of the class, or of parent classes in the inheritance hierarchy. It can be called at the class directly (without creating an object).

classmethod has no access to attributes of the object. It however can access attributes of the class and of parent classes in the inheritance hierarchy. It can be called at the class directly (without creating an object). If called at the object then it is the same as normal method which doesn’t access self.<attribute(s)> and accesses self.__class__.<attribute(s)> only.

Think we have a class with b=2, we will create an object and re-set this to b=4 in it. Staticmethod cannot access nothing from previous. Classmethod can access .b==2 only, via cls.b. Normal method can access both: .b==4 via self.b and .b==2 via self.__class__.b.

We could follow the KISS style (keep it simple, stupid): Don’t use staticmethods and classmethods, don’t use classes without instantiating them, access only the object’s attributes self.attribute(s). There are languages where the OOP is implemented that way and I think it is not bad idea. :)


回答 25

在iPython中对其他相同方法的快速分析表明,该方法会@staticmethod产生少量的性能提升(以纳秒为单位),但否则似乎无济于事。另外,staticmethod()在编译过程中(通过运行脚本执行任何代码之前),通过处理该方法的其他工作可能会消除所有性能提升。

出于代码可读性的考虑,@staticmethod除非您的方法用于纳秒级的工作负载,否则我将避免使用。

A quick hack-up ofotherwise identical methods in iPython reveals that @staticmethod yields marginal performance gains (in the nanoseconds), but otherwise it seems to serve no function. Also, any performance gains will probably be wiped out by the additional work of processing the method through staticmethod() during compilation (which happens prior to any code execution when you run a script).

For the sake of code readability I’d avoid @staticmethod unless your method will be used for loads of work, where the nanoseconds count.