9. 类¶
类提供了一种组合数据和功能的方法。创建一个新类意味着创建一个新 类型 的对象,从而允许创建一个该类型的新 实例 。每个类的实例可以拥有保存自己状态的属性。一个类的实例也可以有改变自己状态的(定义在类中的)方法。
和其他编程语言相比,Python 用非常少的新语法和语义将类加入到语言中。它是 C++ 和 Modula-3 中类机制的结合。Python 的类提供了面向对象编程的所有标准特性:类继承机制允许多个基类,派生类可以覆盖它基类的任何方法,一个方法可以调用基类中相同名称的的方法。对象可以包含任意数量和类型的数据。和模块一样,类也拥有 Python 天然的动态特性:它们在运行时创建,可以在创建后修改。
在C++术语中,通常类成员(包括数据成员)是 public (除了见下文 私有变量 ),所有成员函数都是 virtual 。与在Modula-3中一样,没有用于从其方法引用对象成员的简写:方法函数使用表示对象的显式第一个参数声明,该参数由调用隐式提供。与Smalltalk一样,类本身也是对象。这为导入和重命名提供了语义。与C++和Modula-3不同,内置类型可以用作用户扩展的基类。此外,与C++一样,大多数具有特殊语法(算术运算符,下标等)的内置运算符都可以重新定义为类实例。
(Lacking universally accepted terminology to talk about classes, I will make occasional use of Smalltalk and C++ terms. I would use Modula-3 terms, since its object-oriented semantics are closer to those of Python than C++, but I expect that few readers have heard of it.)
9.1. 名称和对象¶
对象具有个性,多个名称(在多个作用域内)可以绑定到同一个对象。这在其他语言中称为别名。乍一看Python时通常不会理解这一点,在处理不可变的基本类型(数字,字符串,元组)时可以安全地忽略它。但是,别名对涉及可变对象,如列表,字典和大多数其他类型,的Python代码的语义可能会产生惊人的影响。这通常用于程序的好处,因为别名在某些方面表现得像指针。例如,传递一个对象很便宜,因为实现只传递一个指针;如果函数修改了作为参数传递的对象,调用者将看到更改 --- 这就不需要像 Pascal 中那样使用两个不同的参数传递机制。
9.2. Python 作用域和命名空间¶
在介绍类之前,我首先要告诉你一些Python的作用域规则。类定义对命名空间有一些巧妙的技巧,你需要知道作用域和命名空间如何工作才能完全理解正在发生的事情。顺便说一下,关于这个主题的知识对任何高级Python程序员都很有用。
让我们从一些定义开始。
namespace 是一个从名字到对象的映射。大部分命名空间当前都由 Python 字典实现,但一般情况下基本不会去关注它们(除了要面对性能问题时),而且也有可能在将来更改。下面是几个命名空间的例子:存放内置函数的集合(包含 abs()
这样的函数,和内建的异常等);模块中的全局名称;函数调用中的本地名称。从某种意义上说,对象的的属性集合也是一种命名空间的形式。关于命名空间的重要一点是,不同命名空间中的名称之间绝对没有关系;例如,两个不同的模块都可以定义函数“最大化”而不会产生混淆 - 模块的用户必须在其前面加上模块名称。
顺便说明一下,我把任何跟在一个点号之后的名称都称为 属性 --- 例如,在表达式 z.real
中,real
是对象 z
的一个属性。按严格的说法,对模块中名称的引用属于属性引用:在表达式 modname.funcname
中,modname
是一个模块对象而 funcname
是它的一个属性。在此情况下在模块的属性和模块中定义的全局名称之间正好存在一个直观的映射:它们共享相同的命名空间! [1]
属性可以是只读或者可写的。如果为后者,那么对属性的赋值是可行的。模块属性是可以写,你可以写出 modname.the_answer = 42
。可写的属性同样可以用 del
语句删除。例如,del modname.the_answer
将会从名为 modname
的对象中移除 the_answer
属性。
在不同时刻创建的命名空间拥有不同的生存期。包含内置名称的命名空间是在 Python 解释器启动时创建的,永远不会被删除。模块的全局命名空间在模块定义被读入时创建;通常,模块命名空间也会持续到解释器退出。被解释器的顶层调用执行的语句,从一个脚本文件读取或交互式地读取,被认为是 __main__
模块调用的一部分,因此它们拥有自己的全局命名空间。(内置名称实际上也存在于一个模块中;这个模块称作 builtins
。)
一个函数的本地命名空间在这个函数被调用时创建,并在函数返回或抛出一个不在函数内部处理的错误时被删除。(事实上,比起描述到底发生了什么,忘掉它更好。)当然,每次递归调用都会有它自己的本地命名空间。
一个 作用域 是一个命名空间可直接访问的 Python 程序的文本区域。 这里的 “可直接访问” 意味着对名称的非限定引用会尝试在命名空间中查找名称。
Although scopes are determined statically, they are used dynamically. At any time during execution, there are at least three nested scopes whose namespaces are directly accessible:
- 最先搜索的最内部作用域包含局部名称
- 从最近的封闭作用域开始搜索的任何封闭函数的范围包含非局部名称,也包括非全局名称
- 倒数第二个作用域包含当前模块的全局名称
- 最外面的范围(最后搜索)是包含内置名称的命名空间
如果一个名称被声明为全局变量,则所有引用和赋值将直接指向包含该模块的全局名称的中间作用域。 要重新绑定在最内层作用域以外找到的变量,可以使用 nonlocal
语句声明为非本地变量。 如果没有被声明为非本地变量,这些变量将是只读的(尝试写入这样的变量只会在最内层作用域中创建一个 新的 局部变量,而同名的外部变量保持不变)。
通常,当前局部作为域将(按字面文本)引用当前函数的局部名称。 在函数以外,局部作用域将引用与全局作用域相一致的命名空间:模块的命名空间。 类定义将在局部命名空间内再放置另一个命名空间。
重要的是应该意识到作用域是按字面文本来确定的:在一个模块内定义的函数的全局作用域就是该模块的命名空间,无论该函数从什么地方或以什么别名被调用。 另一方面,实际的名称搜索是在运行时动态完成的 --- 但是,语言定义在 编译时 是朝着静态名称解析的方向演化的,因此不要过于依赖动态名称解析! (事实上,局部变量已经是被静态确定了。)
Python 的一个特殊之处在于 -- 如果不存在生效的 global
语句 -- 对名称的赋值总是进入最内层作用域。 赋值不会复制数据 --- 它们只是将名称绑定到对象。 删除也是如此:语句 del x
会从局部命名空间的引用中移除对 x
的绑定。 事实上,所有引入新名称的操作都使用局部作用域:特别地,import
语句和函数定义会在局部作用域中绑定模块或函数名称。
global
语句可被用来表明特定变量生存于全局作用域并且应当在其中被重新绑定;nonlocal
语句表明特定变量生存于外层作用域中并且应当在其中被重新绑定。
9.2.1. 作用域和命名空间示例¶
This is an example demonstrating how to reference the different scopes and
namespaces, and how global
and nonlocal
affect variable
binding:
def scope_test():
def do_local():
spam = "local spam"
def do_nonlocal():
nonlocal spam
spam = "nonlocal spam"
def do_global():
global spam
spam = "global spam"
spam = "test spam"
do_local()
print("After local assignment:", spam)
do_nonlocal()
print("After nonlocal assignment:", spam)
do_global()
print("After global assignment:", spam)
scope_test()
print("In global scope:", spam)
示例代码的输出是:
After local assignment: test spam
After nonlocal assignment: nonlocal spam
After global assignment: nonlocal spam
In global scope: global spam
请注意 局部 赋值(这是默认状态)不会改变 scope_test 对 spam 的绑定。 nonlocal
赋值会改变 scope_test 对 spam 的绑定,而 global
赋值会改变模块层级的绑定。
您还可以在 global
赋值之前看到之前没有 spam 的绑定。
9.3. 初探类¶
类引入了一些新语法,三种新对象类型和一些新语义。
9.3.1. 类定义语法¶
最简单的类定义看起来像这样:
class ClassName:
<statement-1>
.
.
.
<statement-N>
类定义与函数定义 (def
语句) 一样必须被执行才会起作用。 (你可以尝试将类定义放在 if
语句的一个分支或是函数的内部。)
在实践中,类定义内的语句通常都是函数定义,但也允许有其他语句,有时还很有用 --- 我们会稍后再回来说明这个问题。 在类内部的函数定义通常具有一种特别形式的参数列表,这是方法调用的约定规范所指明的 --- 这个问题也将在稍后再说明。
当进入类定义时,将创建一个新的命名空间,并将其用作局部作用域 --- 因此,所有对局部变量的赋值都是在这个新命名空间之内。 特别的,函数定义会绑定到这里的新函数名称。
当(从结尾处)正常离开类定义时,将创建一个 类对象。 这基本上是一个包围在类定义所创建命名空间内容周围的包装器;我们将在下一节了解有关类对象的更多信息。 原始的(在进入类定义之前起作用的)局部作用域将重新生效,类对象将在这里被绑定到类定义头所给出的类名称 (在这个示例中为 ClassName
)。
9.3.2. 类对象¶
类对象支持两种操作:属性引用和实例化。
属性引用 使用 Python 中所有属性引用所使用的标准语法: obj.name
。 有效的属性名称是类对象被创建时存在于类命名空间中的所有名称。 因此,如果类定义是这样的:
class MyClass:
"""A simple example class"""
i = 12345
def f(self):
return 'hello world'
那么 MyClass.i
和 MyClass.f
就是有效的属性引用,将分别返回一个整数和一个函数对象。 类属性也可以被赋值,因此可以通过赋值来更改 MyClass.i
的值。 __doc__
也是一个有效的属性,将返回所属类的文档字符串: "A simple example class"
。
类的 实例化 是使用函数表示法。 可以相像类对象就是会返回一个新的类实例的不带参数的函数。 举例来说(假设使用上述的类):
x = MyClass()
创建类的新 实例 并将此对象分配给局部变量 x
。
实例化操作(“调用”类对象)会创建一个空对象。 许多类喜欢创建带有特定初始状态的自定义实例。 为此类定义可能包含一个名为 __init__()
的特殊方法,就像这样:
def __init__(self):
self.data = []
当一个类定义了 __init__()
方法时,类的实例化操作会自动为新创建的类实例发起调用 __init__()
。 因此在这个示例中,可以通过以下语句获得一个经初始化的新实例:
x = MyClass()
当然,__init__()
方法还可以有额外参数以实现更高灵活性。 在这种情况下,提供给类实例化运算符的参数将被传递给 __init__()
。 例如,:
>>> class Complex:
... def __init__(self, realpart, imagpart):
... self.r = realpart
... self.i = imagpart
...
>>> x = Complex(3.0, -4.5)
>>> x.r, x.i
(3.0, -4.5)
9.3.3. 实例对象¶
现在我们可以用实例对象做什么?实例对象理解的唯一操作是属性引用。有两种有效的属性名称,数据属性和方法。
数据属性 对应于 Smalltalk 中的“实例变量”,以及 C++ 中的“数据成员”。 数据属性不需要声明;像局部变量一样,它们将在第一次被赋值时产生。 例如,如果 x
是上面创建的 MyClass
的实例,则以下代码段将打印数值 16
,且不保留任何追踪信息:
x.counter = 1
while x.counter < 10:
x.counter = x.counter * 2
print(x.counter)
del x.counter
另一类实例属性引用称为 方法。 方法是“从属于”对象的函数。 (在 Python 中,方法这个术语并不是类实例所特有的:其他对方也可以有方法。 例如,列表对象具有 append, insert, remove, sort 等方法。 然而,在以下讨论中,我们使用方法一词将专指类实例对象的方法,除非另外显式地说明。)
实例对象的有效方法名称依赖于其所属的类。 根据定义,一个类中所有是函数对象的属性都是定义了其实例的相应方法。 因此在我们的示例中,x.f
是有效的方法引用,因为 MyClass.f
是一个函数,而 x.i
不是方法,因为 MyClass.i
不是一个函数。 但是 x.f
与 MyClass.f
并不是一回事 --- 它是一个 方法对象,不是函数对象。
9.3.4. 方法对象¶
通常,方法在绑定后立即被调用:
x.f()
In the MyClass
example, this will return the string 'hello world'
.
However, it is not necessary to call a method right away: x.f
is a method
object, and can be stored away and called at a later time. For example:
xf = x.f
while True:
print(xf())
将继续打印 hello world
,直到结束。
What exactly happens when a method is called? You may have noticed that
x.f()
was called without an argument above, even though the function
definition for f()
specified an argument. What happened to the argument?
Surely Python raises an exception when a function that requires an argument is
called without any --- even if the argument isn't actually used...
Actually, you may have guessed the answer: 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.
If you still don't understand how methods work, a look at the implementation can perhaps clarify matters. When a non-data attribute of an instance is referenced, the instance's class is searched. If the name denotes a valid class attribute that is a function object, a method object is created by packing (pointers to) the instance object and the function object just found together in an abstract object: this is the method object. When the method object is called with an argument list, a new argument list is constructed from the instance object and the argument list, and the function object is called with this new argument list.
9.3.5. 类和实例变量¶
一般来说,实例变量用于每个实例的唯一数据,而类变量用于类的所有实例共享的属性和方法:
class Dog:
kind = 'canine' # class variable shared by all instances
def __init__(self, name):
self.name = name # instance variable unique to each instance
>>> d = Dog('Fido')
>>> e = Dog('Buddy')
>>> d.kind # shared by all dogs
'canine'
>>> e.kind # shared by all dogs
'canine'
>>> d.name # unique to d
'Fido'
>>> e.name # unique to e
'Buddy'
As discussed in 名称和对象, shared data can have possibly surprising effects with involving mutable objects such as lists and dictionaries. For example, the tricks list in the following code should not be used as a class variable because just a single list would be shared by all Dog instances:
class Dog:
tricks = [] # mistaken use of a class variable
def __init__(self, name):
self.name = name
def add_trick(self, trick):
self.tricks.append(trick)
>>> d = Dog('Fido')
>>> e = Dog('Buddy')
>>> d.add_trick('roll over')
>>> e.add_trick('play dead')
>>> d.tricks # unexpectedly shared by all dogs
['roll over', 'play dead']
正确的类设计应该使用实例变量:
class Dog:
def __init__(self, name):
self.name = name
self.tricks = [] # creates a new empty list for each dog
def add_trick(self, trick):
self.tricks.append(trick)
>>> d = Dog('Fido')
>>> e = Dog('Buddy')
>>> d.add_trick('roll over')
>>> e.add_trick('play dead')
>>> d.tricks
['roll over']
>>> e.tricks
['play dead']
9.4. 补充说明¶
Data attributes override method attributes with the same name; to avoid accidental name conflicts, which may cause hard-to-find bugs in large programs, it is wise to use some kind of convention that minimizes the chance of conflicts. Possible conventions include capitalizing method names, prefixing data attribute names with a small unique string (perhaps just an underscore), or using verbs for methods and nouns for data attributes.
Data attributes may be referenced by methods as well as by ordinary users ("clients") of an object. In other words, classes are not usable to implement pure abstract data types. In fact, nothing in Python makes it possible to enforce data hiding --- it is all based upon convention. (On the other hand, the Python implementation, written in C, can completely hide implementation details and control access to an object if necessary; this can be used by extensions to Python written in C.)
Clients should use data attributes with care --- clients may mess up invariants maintained by the methods by stamping on their data attributes. Note that clients may add data attributes of their own to an instance object without affecting the validity of the methods, as long as name conflicts are avoided --- again, a naming convention can save a lot of headaches here.
There is no shorthand for referencing data attributes (or other methods!) from within methods. I find that this actually increases the readability of methods: there is no chance of confusing local variables and instance variables when glancing through a method.
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.
Any function object that is a class attribute defines a method for instances of that class. It is not necessary that the function definition is textually enclosed in the class definition: assigning a function object to a local variable in the class is also ok. For example:
# Function defined outside the class
def f1(self, x, y):
return min(x, x+y)
class C:
f = f1
def g(self):
return 'hello world'
h = g
Now f
, g
and h
are all attributes of class C
that refer to
function objects, and consequently they are all methods of instances of
C
--- h
being exactly equivalent to g
. Note that this practice
usually only serves to confuse the reader of a program.
方法可以通过使用 self
参数的方法属性调用其他方法:
class Bag:
def __init__(self):
self.data = []
def add(self, x):
self.data.append(x)
def addtwice(self, x):
self.add(x)
self.add(x)
Methods may reference global names in the same way as ordinary functions. The global scope associated with a method is the module containing its definition. (A class is never used as a global scope.) While one rarely encounters a good reason for using global data in a method, there are many legitimate uses of the global scope: for one thing, functions and modules imported into the global scope can be used by methods, as well as functions and classes defined in it. Usually, the class containing the method is itself defined in this global scope, and in the next section we'll find some good reasons why a method would want to reference its own class.
每个值都是一个对象,因此具有 class (也称为 type)。它存储在 object .__ class__
。
9.5. 继承¶
当然,如果不支持继承,语言特性就不值得称为“类”。派生类定义的语法如下所示:
class DerivedClassName(BaseClassName):
<statement-1>
.
.
.
<statement-N>
The name BaseClassName
must be defined in a scope containing the
derived class definition. In place of a base class name, other arbitrary
expressions are also allowed. This can be useful, for example, when the base
class is defined in another module:
class DerivedClassName(modname.BaseClassName):
Execution of a derived class definition proceeds the same as for a base class. When the class object is constructed, the base class is remembered. This is used for resolving attribute references: if a requested attribute is not found in the class, the search proceeds to look in the base class. This rule is applied recursively if the base class itself is derived from some other class.
There's nothing special about instantiation of derived classes:
DerivedClassName()
creates a new instance of the class. Method references
are resolved as follows: the corresponding class attribute is searched,
descending down the chain of base classes if necessary, and the method reference
is valid if this yields a function object.
Derived classes may override methods of their base classes. Because methods
have no special privileges when calling other methods of the same object, a
method of a base class that calls another method defined in the same base class
may end up calling a method of a derived class that overrides it. (For C++
programmers: all methods in Python are effectively virtual
.)
An overriding method in a derived class may in fact want to extend rather than
simply replace the base class method of the same name. There is a simple way to
call the base class method directly: just call BaseClassName.methodname(self,
arguments)
. This is occasionally useful to clients as well. (Note that this
only works if the base class is accessible as BaseClassName
in the global
scope.)
Python有两个内置函数同继承一起工作:
- Use
isinstance()
to check an instance's type:isinstance(obj, int)
will beTrue
only ifobj.__class__
isint
or some class derived fromint
. - Use
issubclass()
to check class inheritance:issubclass(bool, int)
isTrue
sincebool
is a subclass ofint
. However,issubclass(float, int)
isFalse
sincefloat
is not a subclass ofint
.
9.5.1. 多重继承¶
Python supports a form of multiple inheritance as well. A class definition with multiple base classes looks like this:
class DerivedClassName(Base1, Base2, Base3):
<statement-1>
.
.
.
<statement-N>
For most purposes, in the simplest cases, you can think of the search for
attributes inherited from a parent class as depth-first, left-to-right, not
searching twice in the same class where there is an overlap in the hierarchy.
Thus, if an attribute is not found in DerivedClassName
, it is searched
for in Base1
, then (recursively) in the base classes of Base1
,
and if it was not found there, it was searched for in Base2
, and so on.
In fact, it is slightly more complex than that; the method resolution order
changes dynamically to support cooperative calls to super()
. This
approach is known in some other multiple-inheritance languages as
call-next-method and is more powerful than the super call found in
single-inheritance languages.
Dynamic ordering is necessary because all cases of multiple inheritance exhibit
one or more diamond relationships (where at least one of the parent classes
can be accessed through multiple paths from the bottommost class). For example,
all classes inherit from object
, so any case of multiple inheritance
provides more than one path to reach object
. To keep the base classes
from being accessed more than once, the dynamic algorithm linearizes the search
order in a way that preserves the left-to-right ordering specified in each
class, that calls each parent only once, and that is monotonic (meaning that a
class can be subclassed without affecting the precedence order of its parents).
Taken together, these properties make it possible to design reliable and
extensible classes with multiple inheritance. For more detail, see
https://www.python.org/download/releases/2.3/mro/.
9.6. 私有变量¶
"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.
Since there is a valid use-case for class-private members (namely to avoid name
clashes of names with names defined by subclasses), there is limited support for
such a mechanism, called name mangling. 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, as long as it
occurs within the definition of a class.
名称修改有助于让子类重载方法而不破坏类内方法调用。例如:
class Mapping:
def __init__(self, iterable):
self.items_list = []
self.__update(iterable)
def update(self, iterable):
for item in iterable:
self.items_list.append(item)
__update = update # private copy of original update() method
class MappingSubclass(Mapping):
def update(self, keys, values):
# provides new signature for update()
# but does not break __init__()
for item in zip(keys, values):
self.items_list.append(item)
The above example would work even if MappingSubclass
were to introduce a
__update
identifier since it is replaced with _Mapping__update
in the
Mapping
class and _MappingSubclass__update
in the MappingSubclass
class respectively.
请注意,修剪规则主要是为了避免事故;它仍然可以访问或修改被视为私有的变量。这在特殊情况下甚至可能很有用,例如在调试器中。
Notice that code passed to exec()
or eval()
does not consider the
classname of the invoking class to be the current class; this is similar to the
effect of the global
statement, the effect of which is likewise restricted
to code that is byte-compiled together. The same restriction applies to
getattr()
, setattr()
and delattr()
, as well as when referencing
__dict__
directly.
9.7. 其余琐碎¶
有时,使用类似于Pascal “record”或C “struct”的数据类型是有用的,将一些命名数据项捆绑在一起。定义一个空类就很适合:
class Employee:
pass
john = Employee() # Create an empty employee record
# Fill the fields of the record
john.name = 'John Doe'
john.dept = 'computer lab'
john.salary = 1000
A piece of Python code that expects a particular abstract data type can often be
passed a class that emulates the methods of that data type instead. For
instance, if you have a function that formats some data from a file object, you
can define a class with methods read()
and readline()
that get the
data from a string buffer instead, and pass it as an argument.
Instance method objects have attributes, too: m.__self__
is the instance
object with the method m()
, and m.__func__
is the function object
corresponding to the method.
9.8. 迭代器¶
到目前为止,您可能已经注意到大多数容器对象都可以使用 for
语句:
for element in [1, 2, 3]:
print(element)
for element in (1, 2, 3):
print(element)
for key in {'one':1, 'two':2}:
print(key)
for char in "123":
print(char)
for line in open("myfile.txt"):
print(line, end='')
This style of access is clear, concise, and convenient. The use of iterators
pervades and unifies Python. Behind the scenes, the for
statement
calls iter()
on the container object. The function returns an iterator
object that defines the method __next__()
which accesses
elements in the container one at a time. When there are no more elements,
__next__()
raises a StopIteration
exception which tells the
for
loop to terminate. You can call the __next__()
method
using the next()
built-in function; this example shows how it all works:
>>> s = 'abc'
>>> it = iter(s)
>>> it
<iterator object at 0x00A1DB50>
>>> next(it)
'a'
>>> next(it)
'b'
>>> next(it)
'c'
>>> next(it)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
next(it)
StopIteration
Having seen the mechanics behind the iterator protocol, it is easy to add
iterator behavior to your classes. Define an __iter__()
method which
returns an object with a __next__()
method. If the class
defines __next__()
, then __iter__()
can just return self
:
class Reverse:
"""Iterator for looping over a sequence backwards."""
def __init__(self, data):
self.data = data
self.index = len(data)
def __iter__(self):
return self
def __next__(self):
if self.index == 0:
raise StopIteration
self.index = self.index - 1
return self.data[self.index]
>>> rev = Reverse('spam')
>>> iter(rev)
<__main__.Reverse object at 0x00A1DB50>
>>> for char in rev:
... print(char)
...
m
a
p
s
9.9. 生成器¶
Generators are a simple and powerful tool for creating iterators. They
are written like regular functions but use the yield
statement
whenever they want to return data. Each time next()
is called on it, the
generator resumes where it left off (it remembers all the data values and which
statement was last executed). An example shows that generators can be trivially
easy to create:
def reverse(data):
for index in range(len(data)-1, -1, -1):
yield data[index]
>>> for char in reverse('golf'):
... print(char)
...
f
l
o
g
Anything that can be done with generators can also be done with class-based
iterators as described in the previous section. What makes generators so
compact is that the __iter__()
and __next__()
methods
are created automatically.
Another key feature is that the local variables and execution state are
automatically saved between calls. This made the function easier to write and
much more clear than an approach using instance variables like self.index
and self.data
.
In addition to automatic method creation and saving program state, when
generators terminate, they automatically raise StopIteration
. In
combination, these features make it easy to create iterators with no more effort
than writing a regular function.
9.10. 生成器表达式¶
Some simple generators can be coded succinctly as expressions using a syntax similar to list comprehensions but with parentheses instead of square brackets. These expressions are designed for situations where the generator is used right away by an enclosing function. Generator expressions are more compact but less versatile than full generator definitions and tend to be more memory friendly than equivalent list comprehensions.
例如:
>>> sum(i*i for i in range(10)) # sum of squares
285
>>> xvec = [10, 20, 30]
>>> yvec = [7, 5, 3]
>>> sum(x*y for x,y in zip(xvec, yvec)) # dot product
260
>>> unique_words = set(word for line in page for word in line.split())
>>> valedictorian = max((student.gpa, student.name) for student in graduates)
>>> data = 'golf'
>>> list(data[i] for i in range(len(data)-1, -1, -1))
['f', 'l', 'o', 'g']
脚注
[1] | Except for one thing. Module objects have a secret read-only attribute called
__dict__ which returns the dictionary used to implement the module's
namespace; the name __dict__ is an attribute but not a global name.
Obviously, using this violates the abstraction of namespace implementation, and
should be restricted to things like post-mortem debuggers. |