Descriptors (descriptors) are a profound but important black magic in the Python language. They are widely used in the core of the Python language. Mastering descriptors will help Python Adds an extra trick to the programmer's toolbox. In this article, I will describe the definition of descriptors and some common scenarios, and at the end of the article I will add __getattr, __getattribute__, __getitem__, which also involve attributes. Access the Magic Method.
descr__get__(self, obj, objtype=None) --> value descr.__set__(self, obj, value) --> None descr.__delete__(self, obj) --> None
object attribute (object attribute) defines any one of the above three methods, then this class can be called Descriptor class.
RevealAcess class and implement the __get__ method. Now this class can be called is a descriptor class.
class RevealAccess(object):
def __get__(self, obj, objtype):
print('self in RevealAccess: {}'.format(self))
print('self: {}\nobj: {}\nobjtype: {}'.format(self, obj, objtype))
class MyClass(object):
x = RevealAccess()
def test(self):
print('self in MyClass: {}'.format(self))EX1 instance attributes
Next let’s take a look at the meaning of each parameter of the__get__ method. In the following example, self is the instance x of the RevealAccess class, obj is the instance m of the MyClass class, objtype as the name suggests is the MyClass class itself. As can be seen from the output statement, m.xaccess descriptor x will call the __get__ method.
>>> m = MyClass() >>> m.test() self in MyClass: <__main__.MyClass object at 0x7f19d4e42160> >>> m.x self in RevealAccess: <__main__.RevealAccess object at 0x7f19d4e420f0> self: <__main__.RevealAccess object at 0x7f19d4e420f0> obj: <__main__.MyClass object at 0x7f19d4e42160> objtype: <class '__main__.MyClass'>
EX2 class attribute
If the attributex is directly accessed through the class, then obj is directly None, which is still It's easier to understand, because there is no instance of MyClass.
>>> MyClass.x self in RevealAccess: <__main__.RevealAccess object at 0x7f53651070f0> self: <__main__.RevealAccess object at 0x7f53651070f0> obj: None objtype: <class '__main__.MyClass'>
instance attribute, the __getattribute__ method of the base class object is actually called. In this method Obj.d is translated into type(obj).__dict__['d'].__get__(obj, type(obj)).
class attribute , it is equivalent to calling the __getattribute__ method of the metaclass type, which translates cls.d into cls.__dict__['d'].__get__(None, cls), here the obj of __get__() is None because there is no instance.
__getattribute__ magic method. This method will be called unconditionally when we access the attributes of an object. The detailed details are such as __getattr I will make an additional supplement at the end of the article about the difference between , __getitem__, but we will not delve into it for now.
data descriptor.
non-data descriptor.
data descriptor > instance dict > non-data descriptor > __getattr__()
data descriptor->d and instance attribute->d with the same name appear in the instance object obj, obj.d will pair with the attribute dWhen accessing, since the data descriptor has a higher priority, Python will call type(obj).__dict__['d'].__get__(obj, type(obj)) instead of calling obj.__dict__['d']. But if the descriptor is a non-data descriptor, Python will call obj.__dict__['d'].
property(fget=None, fset=None, fdel=None, doc=None) -> property attribute
class Account(object):
def __init__(self):
self._acct_num = None
def get_acct_num(self):
return self._acct_num
def set_acct_num(self, value):
self._acct_num = value
def del_acct_num(self):
del self._acct_num
acct_num = property(get_acct_num, set_acct_num, del_acct_num, '_acct_num property.')>>> acct = Account() >>> acct.acct_num = 1000 >>> acct.acct_num 1000
@property decorator, which can be used to create properties for simple application scenarios. A property object has getter, setter and delete decorator methods, which can be used to create a copy of the property through the accessor function of the corresponding decorated function.
class Account(object):
def __init__(self):
self._acct_num = None
@property
# the _acct_num property. the decorator creates a read-only property
def acct_num(self):
return self._acct_num
@acct_num.setter
# the _acct_num property setter makes the property writeable
def set_acct_num(self, value):
self._acct_num = value
@acct_num.deleter
def del_acct_num(self):
del self._acct_numclass Person(object):
def addProperty(self, attribute):
# create local setter and getter with a particular attribute name
getter = lambda self: self._getProperty(attribute)
setter = lambda self, value: self._setProperty(attribute, value)
# construct property attribute and add it to the class
setattr(self.__class__, attribute, property(fget=getter, \
fset=setter, \
doc="Auto-generated method"))
def _setProperty(self, attribute, value):
print("Setting: {} = {}".format(attribute, value))
setattr(self, '_' + attribute, value.title())
def _getProperty(self, attribute):
print("Getting: {}".format(attribute))
return getattr(self, '_' + attribute)>>> user = Person()
>>> user.addProperty('name')
>>> user.addProperty('phone')
>>> user.name = 'john smith'
Setting: name = john smith
>>> user.phone = '12345'
Setting: phone = 12345
>>> user.name
Getting: name
'John Smith'
>>> user.__dict__
{'_phone': '12345', '_name': 'John Smith'}@staticmethod and @classmethod in Python. Let’s first browse the following table:
| Transformation | Called from an Object | Called from a Class |
|---|---|---|
| function | f(obj, *args) | f(*args) |
| staticmethod | f(*args) | f(*args) |
| classmethod | f(type(obj), *args) | f(klass, *args) |
对于静态方法f。c.f和C.f是等价的，都是直接查询object.__getattribute__(c, ‘f’)或者object.__getattribute__(C, ’f‘)。静态方法一个明显的特征就是没有self变量。
静态方法有什么用呢？假设有一个处理专门数据的容器类，它提供了一些方法来求平均数，中位数等统计数据方式，这些方法都是要依赖于相应的数据的。但是类中可能还有一些方法，并不依赖这些数据，这个时候我们可以将这些方法声明为静态方法，同时这也可以提高代码的可读性。
使用非数据描述符来模拟一下静态方法的实现：
class StaticMethod(object):
def __init__(self, f):
self.f = f
def __get__(self, obj, objtype=None):
return self.f我们来应用一下：
class MyClass(object):
@StaticMethod
def get_x(x):
return x
print(MyClass.get_x(100)) # output: 100Python的@classmethod和@staticmethod的用法有些类似，但是还是有些不同，当某些方法只需要得到类的引用而不关心类中的相应的数据的时候就需要使用classmethod了。
使用非数据描述符来模拟一下类方法的实现：
class ClassMethod(object):
def __init__(self, f):
self.f = f
def __get__(self, obj, klass=None):
if klass is None:
klass = type(obj)
def newfunc(*args):
return self.f(klass, *args)
return newfunc首次接触Python魔术方法的时候，我也被__get__, __getattribute__, __getattr__, __getitem__之间的区别困扰到了，它们都是和属性访问相关的魔术方法，其中重写__getattr__，__getitem__来构造一个自己的集合类非常的常用，下面我们就通过一些例子来看一下它们的应用。
Python默认访问类/实例的某个属性都是通过__getattribute__来调用的，__getattribute__会被无条件调用，没有找到的话就会调用__getattr__。如果我们要定制某个类，通常情况下我们不应该重写__getattribute__，而是应该重写__getattr__，很少看见重写__getattribute__的情况。
从下面的输出可以看出，当一个属性通过__getattribute__无法找到的时候会调用__getattr__。
In [1]: class Test(object):
...: def __getattribute__(self, item):
...: print('call __getattribute__')
...: return super(Test, self).__getattribute__(item)
...: def __getattr__(self, item):
...: return 'call __getattr__'
...:
In [2]: Test().a
call __getattribute__
Out[2]: 'call __getattr__'对于默认的字典，Python只支持以obj['foo']形式来访问，不支持obj.foo的形式，我们可以通过重写__getattr__让字典也支持obj['foo']的访问形式，这是一个非常经典常用的用法：
class Storage(dict):
"""
A Storage object is like a dictionary except `obj.foo` can be used
in addition to `obj['foo']`.
"""
def __getattr__(self, key):
try:
return self[key]
except KeyError as k:
raise AttributeError(k)
def __setattr__(self, key, value):
self[key] = value
def __delattr__(self, key):
try:
del self[key]
except KeyError as k:
raise AttributeError(k)
def __repr__(self):
return '<Storage ' + dict.__repr__(self) + '>'我们来使用一下我们自定义的加强版字典：
>>> s = Storage(a=1) >>> s['a'] 1 >>> s.a 1 >>> s.a = 2 >>> s['a'] 2 >>> del s.a >>> s.a ... AttributeError: 'a'
getitem用于通过下标[]的形式来获取对象中的元素，下面我们通过重写__getitem__来实现一个自己的list。
class MyList(object):
def __init__(self, *args):
self.numbers = args
def __getitem__(self, item):
return self.numbers[item]
my_list = MyList(1, 2, 3, 4, 6, 5, 3)
print my_list[2]这个实现非常的简陋，不支持slice和step等功能，请读者自行改进，这里我就不重复了。
下面是参考requests库中对于__getitem__的一个使用，我们定制了一个忽略属性大小写的字典类。
程序有些复杂，我稍微解释一下：由于这里比较简单，没有使用描述符的需求，所以使用了@property装饰器来代替，lower_keys的功能是将实例字典中的键全部转换成小写并且存储在字典self._lower_keys中。重写了__getitem__方法，以后我们访问某个属性首先会将键转换为小写的方式，然后并不会直接访问实例字典，而是会访问字典self._lower_keys去查找。赋值/删除操作的时候由于实例字典会进行变更，为了保持self._lower_keys和实例字典同步，首先清除self._lower_keys的内容，以后我们重新查找键的时候再调用__getitem__的时候会重新新建一个self._lower_keys。
class CaseInsensitiveDict(dict):
@property
def lower_keys(self):
if not hasattr(self, '_lower_keys') or not self._lower_keys:
self._lower_keys = dict((k.lower(), k) for k in self.keys())
return self._lower_keys
def _clear_lower_keys(self):
if hasattr(self, '_lower_keys'):
self._lower_keys.clear()
def __contains__(self, key):
return key.lower() in self.lower_keys
def __getitem__(self, key):
if key in self:
return dict.__getitem__(self, self.lower_keys[key.lower()])
def __setitem__(self, key, value):
dict.__setitem__(self, key, value)
self._clear_lower_keys()
def __delitem__(self, key):
dict.__delitem__(self, key)
self._lower_keys.clear()
def get(self, key, default=None):
if key in self:
return self[key]
else:
return default我们来调用一下这个类：
>>> d = CaseInsensitiveDict()
>>> d['ziwenxie'] = 'ziwenxie'
>>> d['ZiWenXie'] = 'ZiWenXie'
>>> print(d)
{'ZiWenXie': 'ziwenxie', 'ziwenxie': 'ziwenxie'}
>>> print(d['ziwenxie'])
ziwenxie
# d['ZiWenXie'] => d['ziwenxie']
>>> print(d['ZiWenXie'])
ziwenxieThe above is the detailed content of Magic descriptors in Python. For more information, please follow other related articles on the PHP Chinese website!
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