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 4print 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 inspectinspect.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
     
      >>> inspect.getgeneratorstate(get) 'GEN_CREATED'>>> next(get) 1>>> inspect.getgeneratorstate(get)'GEN_SUSPENDED'>>> next(get)Traceback (most recent call last):  File "
      
       ", line 1, in 
       
        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 ~]# pythonPython 2.7.5 (default, Nov  6 2016, 00:28:07) [GCC 4.8.5 20150623 (Red Hat 4.8.5-11)] on linux2Type "help", "copyright", "credits" or "license" for more information.>>> >>> a = list(range(10000000))Traceback (most recent call last):  File "
     
      ", line 1, in 
      
       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 4print mygenerator()g=mygenerator()print next(g)print next(g)print next(g)print next(g)import inspectprint inspect.isgeneratorfunction(mygenerator)print inspect.isgenerator(mygenerator())#生成者生产包子，两个消费者吃包子import timedef consumer(name):    print "\033[32m;32%s ready to eat baozi\033[0m" %name    while True:       baozi = yield       print("\033[31m baozi [%s] is coming,eaten by [%s]!\033[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("\033[31m begin to eat baozi\033[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 baozitwo 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；