# 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。

Namespaces are created at different moments and have different lifetimes. The namespace containing the built-in names is created when the Python interpreter starts up, and is never deleted. The global namespace for a module is created when the module definition is read in; normally, module namespaces also last until the interpreter quits. The statements executed by the top-level invocation of the interpreter, either read from a script file or interactively, are considered part of a module called `__main__`, so they have their own global namespace. (The built-in names actually also live in a module; this is called `builtins`.)

The local namespace for a function is created when the function is called, and deleted when the function returns or raises an exception that is not handled within the function. (Actually, forgetting would be a better way to describe what actually happens.) Of course, recursive invocations each have their own local namespace.

A scope is a textual region of a Python program where a namespace is directly accessible. “Directly accessible” here means that an unqualified reference to a name attempts to find the name in the namespace.

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

If a name is declared global, then all references and assignments go directly to the middle scope containing the module’s global names. To rebind variables found outside of the innermost scope, the `nonlocal` statement can be used; if not declared nonlocal, those variables are read-only (an attempt to write to such a variable will simply create a new local variable in the innermost scope, leaving the identically named outer variable unchanged).

Usually, the local scope references the local names of the (textually) current function. Outside functions, the local scope references the same namespace as the global scope: the module’s namespace. Class definitions place yet another namespace in the local scope.

It is important to realize that scopes are determined textually: the global scope of a function defined in a module is that module’s namespace, no matter from where or by what alias the function is called. On the other hand, the actual search for names is done dynamically, at run time — however, the language definition is evolving towards static name resolution, at “compile” time, so don’t rely on dynamic name resolution! (In fact, local variables are already determined statically.)

A special quirk of Python is that – if no `global` statement is in effect – assignments to names always go into the innermost scope. Assignments do not copy data — they just bind names to objects. The same is true for deletions: the statement `del x` removes the binding of `x` from the namespace referenced by the local scope. In fact, all operations that introduce new names use the local scope: in particular, `import` statements and function definitions bind the module or function name in the local scope.

The `global` statement can be used to indicate that particular variables live in the global scope and should be rebound there; the `nonlocal` statement indicates that particular variables live in an enclosing scope and should be rebound there.

### 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

Note how the local assignment (which is default) didn’t change scope_test’s binding of spam. The `nonlocal` assignment changed scope_test’s binding of spam, and the `global` assignment changed the module-level binding.

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>

Class definitions, like function definitions (`def` statements) must be executed before they have any effect. (You could conceivably place a class definition in a branch of an `if` statement, or inside a function.)

In practice, the statements inside a class definition will usually be function definitions, but other statements are allowed, and sometimes useful — we’ll come back to this later. The function definitions inside a class normally have a peculiar form of argument list, dictated by the calling conventions for methods — again, this is explained later.

When a class definition is entered, a new namespace is created, and used as the local scope — thus, all assignments to local variables go into this new namespace. In particular, function definitions bind the name of the new function here.

When a class definition is left normally (via the end), a class object is created. This is basically a wrapper around the contents of the namespace created by the class definition; we’ll learn more about class objects in the next section. The original local scope (the one in effect just before the class definition was entered) is reinstated, and the class object is bound here to the class name given in the class definition header (`ClassName` in the example).

### 9.3.2. 类对象

类对象支持两种操作：属性引用和实例化。

Attribute references use the standard syntax used for all attribute references in Python: `obj.name`. Valid attribute names are all the names that were in the class’s namespace when the class object was created. So, if the class definition looked like this:

    class MyClass:
        """A simple example class"""
        i = 12345

        def f(self):
            return 'hello world'

then `MyClass.i` and `MyClass.f` are valid attribute references, returning an integer and a function object, respectively. Class attributes can also be assigned to, so you can change the value of `MyClass.i` by assignment. `__doc__` is also a valid attribute, returning the docstring belonging to the class: `"A simple example class"`.

Class instantiation uses function notation. Just pretend that the class object is a parameterless function that returns a new instance of the class. For example (assuming the above class):

    x = MyClass()

创建类的新 实例 并将此对象分配给局部变量 `x`。

The instantiation operation (“calling” a class object) creates an empty object. Many classes like to create objects with instances customized to a specific initial state. Therefore a class may define a special method named `__init__()`, like this:

    def __init__(self):
        self.data = []

When a class defines an `__init__()` method, class instantiation automatically invokes `__init__()` for the newly-created class instance. So in this example, a new, initialized instance can be obtained by:

    x = MyClass()

Of course, the `__init__()` method may have arguments for greater flexibility. In that case, arguments given to the class instantiation operator are passed on to `__init__()`. For example,

    >>> 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。

data attributes correspond to “instance variables” in Smalltalk, and to “data members” in C++. Data attributes need not be declared; like local variables, they spring into existence when they are first assigned to. For example, if `x` is the instance of `MyClass` created above, the following piece of code will print the value `16`, without leaving a trace:

    x.counter = 1
    while x.counter < 10:
        x.counter = x.counter * 2
    print(x.counter)
    del x.counter

The other kind of instance attribute reference is a method. A method is a function that “belongs to” an object. (In Python, the term method is not unique to class instances: other object types can have methods as well. For example, list objects have methods called append, insert, remove, sort, and so on. However, in the following discussion, we’ll use the term method exclusively to mean methods of class instance objects, unless explicitly stated otherwise.)

Valid method names of an instance object depend on its class. By definition, all attributes of a class that are function objects define corresponding methods of its instances. So in our example, `x.f` is a valid method reference, since `MyClass.f` is a function, but `x.i` is not, since `MyClass.i` is not. But `x.f` is not the same thing as `MyClass.f` — it is a method object, not a function object.

### 9.3.4. 方法对象

通常，方法在绑定后立即调用:

    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 be `True` only if `obj.__class__` is `int` or some class derived from `int`.
- Use `issubclass()` to check class inheritance: `issubclass(bool, int)` is `True` since `bool` is a subclass of `int`. However, `issubclass(float, int)` is `False` since `float` is not a subclass of `int`.

### 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

    >>> from math import pi, sin
    >>> sine_table = {x: sin(x*pi/180) for x in range(0, 91)}

    >>> 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.