9.
类
¶
Classes provide a means of bundling data and functionality together. Creating a new class creates a new
type
of object, allowing new
instances
of that type to be made. Each class instance can have attributes attached to it for maintaining its state. Class instances can also have methods (defined by its class) for modifying its state.
Compared with other programming languages, Python’s class mechanism adds classes with a minimum of new syntax and semantics. It is a mixture of the class mechanisms found in C++ and Modula-3. Python classes provide all the standard features of Object Oriented Programming: the class inheritance mechanism allows multiple base classes, a derived class can override any methods of its base class or classes, and a method can call the method of a base class with the same name. Objects can contain arbitrary amounts and kinds of data. As is true for modules, classes partake of the dynamic nature of Python: they are created at runtime, and can be modified further after creation.
In C++ terminology, normally class members (including the data members) are
public
(except see below
私有变量
), and all member functions are
virtual
. As in Modula-3, there are no shorthands 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. As in Smalltalk, classes themselves are objects. This provides semantics for importing and renaming. Unlike C++ and Modula-3, built-in types can be used as base classes for extension by the user. Also, like in C++, most built-in operators with special syntax (arithmetic operators, subscripting etc.) can be redefined for class instances.
(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.
关于名称和对象的单词
¶
Objects have individuality, and multiple names (in multiple scopes) can be bound to the same object. This is known as aliasing in other languages. This is usually not appreciated on a first glance at Python, and can be safely ignored when dealing with immutable basic types (numbers, strings, tuples). However, aliasing has a possibly surprising effect on the semantics of Python code involving mutable objects such as lists, dictionaries, and most other types. This is usually used to the benefit of the program, since aliases behave like pointers in some respects. For example, passing an object is cheap since only a pointer is passed by the implementation; and if a function modifies an object passed as an argument, the caller will see the change — this eliminates the need for two different argument passing mechanisms as in Pascal.
9.2.
Python 作用域和名称空间
¶
Before introducing classes, I first have to tell you something about Python’s scope rules. Class definitions play some neat tricks with namespaces, and you need to know how scopes and namespaces work to fully understand what’s going on. Incidentally, knowledge about this subject is useful for any advanced Python programmer.
Let’s begin with some definitions.
A
namespace
is a mapping from names to objects. Most namespaces are currently implemented as Python dictionaries, but that’s normally not noticeable in any way (except for performance), and it may change in the future. Examples of namespaces are: the set of built-in names (containing functions such as
abs()
, and built-in exception names); the global names in a module; and the local names in a function invocation. In a sense the set of attributes of an object also form a namespace. The important thing to know about namespaces is that there is absolutely no relation between names in different namespaces; for instance, two different modules may both define a function
maximize
without confusion — users of the modules must prefix it with the module name.
By the way, I use the word
属性
for any name following a dot — for example, in the expression
z.real
,
real
is an attribute of the object
z
. Strictly speaking, references to names in modules are attribute references: in the expression
modname.funcname
,
modname
is a module object and
funcname
is an attribute of it. In this case there happens to be a straightforward mapping between the module’s attributes and the global names defined in the module: they share the same namespace!
Attributes may be read-only or writable. In the latter case, assignment to attributes is possible. Module attributes are writable: you can write
modname.the_answer = 42
. Writable attributes may also be deleted with the
del
statement. For example,
del modname.the_answer
will remove the attribute
the_answer
from the object named by
modname
.
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 3 or 4 nested scopes whose namespaces are directly accessible:
-
the innermost scope, which is searched first, contains the local names
-
the scopes of any enclosing functions, which are searched starting with the nearest enclosing scope, contain non-local, but also non-global names
-
the next-to-last scope contains the current module’s global names
-
the outermost scope (searched last) is the namespace containing built-in names
If a name is declared global, then all references and assignments go directly to the next-to-last 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
or
nonlocal
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.
作用域和名称空间范例
¶
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)
The output of the example code is:
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
。
nonlocal
assignment changed
scope_test
's binding of
spam
，和
global
assignment changed the module-level binding.
You can also see that there was no previous binding for
spam
先于
global
赋值。
9.3.
A First Look at Classes
¶
Classes introduce a little bit of new syntax, three new object types, and some new semantics.
9.3.1.
Class Definition Syntax
¶
The simplest form of class definition looks like this:
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
¶
Class objects support two kinds of operations: attribute references and instantiation.
属性引用
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"
.
类
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()
creates a new
instance
of the class and assigns this object to the local variable
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__()
，像这样：
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__()
。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.
Instance Objects
¶
Now what can we do with instance objects? The only operations understood by instance objects are attribute references. There are two kinds of valid attribute names: data attributes and methods.
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
方法
. A method is a function that “belongs to” an object.
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
¶
Usually, a method is called right after it is bound:
x.f()
在
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())
will continue to print
hello world
until the end of time.
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.
In general, methods work as follows. 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, references to both the instance object and the function object are packed into a 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.
Class and Instance Variables
¶
Generally speaking, instance variables are for data unique to each instance and class variables are for attributes and methods shared by all instances of the class:
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
关于名称和对象的单词
, shared data can have possibly surprising effects with involving
可变
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
实例：
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']
Correct design of the class should use an instance variable instead:
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.
随机备注
¶
If the same attribute name occurs in both an instance and in a class, then attribute lookup prioritizes the instance:
>>> class Warehouse:
... purpose = 'storage'
... region = 'west'
...
>>> w1 = Warehouse()
>>> print(w1.purpose, w1.region)
storage west
>>> w2 = Warehouse()
>>> w2.region = 'east'
>>> print(w2.purpose, w2.region)
storage east
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
现在
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.
Methods may call other methods by using method attributes of the
self
自变量：
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.
Each value is an object, and therefore has a
class
(also called its
type
). It is stored as
object.__class__
.
9.5.
继承
¶
Of course, a language feature would not be worthy of the name “class” without supporting inheritance. The syntax for a derived class definition looks like this:
class DerivedClassName(BaseClassName):
<statement-1>
.
.
.
<statement-N>
名称
BaseClassName
must be defined in a namespace accessible from the 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 has two built-in functions that work with inheritance:
-
使用
isinstance()
to check an instance’s type:
isinstance(obj, int)
将是
True
only if
obj.__class__
is
int
or some class derived from
int
.
-
使用
issubclass()
to check class inheritance:
issubclass(bool, int)
is
True
since
bool
是子类化的
int
. However,
issubclass(float, int)
is
False
since
float
is not a subclass of
int
.
9.5.1.
Multiple Inheritance
¶
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
Python 2.3 MRO (方法分辨次序)
.
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
，其中
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.
另请参阅
The
private name mangling specifications
for details and special cases.
Name mangling is helpful for letting subclasses override methods without breaking intraclass method calls. For example:
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
在
Mapping
class and
_MappingSubclass__update
在
MappingSubclass
class respectively.
Note that the mangling rules are designed mostly to avoid accidents; it still is possible to access or modify a variable that is considered private. This can even be useful in special circumstances, such as in the debugger.
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__
直接。
9.7.
零零碎碎
¶
Sometimes it is useful to have a data type similar to the Pascal “record” or C “struct”, bundling together a few named data items. The idiomatic approach is to use
dataclasses
for this purpose:
from dataclasses import dataclass
@dataclass
class Employee:
name: str
dept: str
salary: int
>>> john = Employee('john', 'computer lab', 1000)
>>> john.dept
'computer lab'
>>> john.salary
1000
经常可以将期望特定抽象数据类型的一段 Python 代码传递给模拟数据类型的类，而不是方法。例如，若拥有格式化文件对象中一些数据的函数，就可以定义类带有方法
read()
and
readline()
从字符串缓冲获取数据，而不是将其传递作为自变量。
Instance method objects
have attributes, too:
m.__self__
是实例对象具有方法
m()
，和
m.__func__
是
function object
corresponding to the method.
9.8.
迭代器
¶
By now you have probably noticed that most container objects can be looped over using a
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__()
引发
StopIteration
exception which tells the
for
循环要终止。可以调用
__next__()
方法使用
next()
内置函数；此范例展示它是如何工作的：
>>> s = 'abc'
>>> it = iter(s)
>>> it
<str_iterator object at 0x10c90e650>
>>> 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__()
，那么
__iter__()
可以仅仅返回
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
¶
生成器
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.
范例：
>>> 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
