生成器、列表解析

1.生成器

生成器是一个对象，每次调用它的时候，都会调用next()方法返回一个值,直到抛出StopIteration异常;

一般生成器对象由两种:一种是对象本省就是生成器，另外一种即使包含yield语句的函数,可以简单理解为生成器;yield语句有两层含义:和return一样返回一个值，同时会记录解释器对栈的引用,在下次调用到来时，从上次yield执行的状态开始接着往下执行;

下面就是一个简单的生成器函数:

def mygenerator():
    yield 1
    yield 2
    yield 3
    yield 4

print mygenerator()
g=mygenerator()
print next(g)
print next(g)
print next(g)
print next(g)

检测函数是否为生成器函数,可以使用inspect模块中的方法实现

import inspect
inspect.isgeneratorfunction(mygenerator)
inspect.isgenerator(mygenerator())

inspect.isgeneratorfunction的源码如下:

def isgeneratorfunction(object):
    """Return true if the object is a user-defined generator function.

    Generator function objects provides same attributes as functions.

    See help(isfunction) for attributes listing."""
    return bool((isfunction(object) or ismethod(object)) and
                object.func_code.co_flags & CO_GENERATOR)

在python3中,有inspect.getgeneratorstate函数可以获取生成器的执行的状态,状态有:GEN_CREATED、GEN_SUSPENDED、GEN_CLOSED等;

>>> def mygenerator():
...     yield 1
>>> get = mygenerator() 
>>> get
<generator object mygenerator at 0x7ff49fdfe4c0>
>>> inspect.getgeneratorstate(get) 
'GEN_CREATED'
>>> next(get) 
1
>>> inspect.getgeneratorstate(get)
'GEN_SUSPENDED'
>>> next(get)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
StopIteration
>>> 
>>> inspect.getgeneratorstate(get)
'GEN_CLOSED'

生成器可以有效的处理即时生成的大量消耗内存的数据，因为处理这类数据的时候，就会在内存中加载全部的数据，非常消耗内存，而生成器可以让数据只有在被循环处理到的时候，才会在内存中创建数据;

这里我们将python的运行内存限制在128MB

[root@linux-node1 ~]# python
Python 2.7.5 (default, Nov  6 2016, 00:28:07) 
[GCC 4.8.5 20150623 (Red Hat 4.8.5-11)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> 
>>> a = list(range(10000000))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
MemoryError    #内存溢出


#使用生成器
>>> for value in xrange(10000000):
...     if value == 50000:
...         print ("fount it")
...         break
... 
fount it

yield有一个send()函数,通过生成器，可以向生成器函数传入参数,下面的例子，可以在单个线程中实现并发的效果

#!/usr/bin/env python
# _*_ coding:utf-8 _*_
__author__ = 'Charles Chang'


def mygenerator():
    yield 1
    yield 2
    yield 3
    yield 4

print mygenerator()
g=mygenerator()
print next(g)
print next(g)
print next(g)
print next(g)

import inspect
print inspect.isgeneratorfunction(mygenerator)
print inspect.isgenerator(mygenerator())

#生成者生产包子，两个消费者吃包子

import time
def consumer(name):
    print "33[32m;32%s ready to eat baozi33[0m" %name
    while True:
       baozi = yield

       print("33[31m baozi [%s] is coming,eaten by [%s]!33[0m" %(baozi,name))


li=[]
def producer(name):
    c = consumer('A')    #c和c2都是生成器
    c2 = consumer('B')
    c.next()
    c2.next()
    print("33[31m begin to eat baozi33[0m")
    while True:
        time.sleep(1)
        print("two baozi have been done")
        c.send("delicious")      "delicious"是向consumer传入的值,赋值给baozi
        c2.send("decilious") 

producer("haha")

结果:

;32A ready to eat baozi
;32B ready to eat baozi
 begin to eat baozi
two baozi have been done
 baozi [delicious] is coming,eaten by [A]!
 baozi [decilious] is coming,eaten by [B]!
two baozi have been done
 baozi [delicious] is coming,eaten by [A]!
 baozi [decilious] is coming,eaten by [B]!

生成器表达式

(x.upper for x in ['hello','world'])    #生成器
[x.upper for x in ['hello','world']]    #列表

2.列表解析

同时使用多条for和if实现过滤

x = [word.capitalize()
     for line in ("hello world?","world!","or not")
     for word in line.split()
     if not word.startswith("or")]
print x

结果:
['Hello', 'World?', 'World!', 'Not']

3、map、filter

python2上述方法返回的结果为列表,python3返回的是可以迭代的对象;

如果想要返回一个可以被迭代的对象,就需要使用itertools模块中的方法,itertools.ifilter、itertools.imap；