当generator function被调用时,即会创建一个generator object并返回,这个generator object就叫做**generator iterator**。 它的部分特性可以理解为迭代器,同时isinstance Iterator和Iteratable,主要负责记忆内部执行状态,调用它的__next__()或者send()方法,可以让其开始执行generator function函数体,直到遇到第一个yiled表达式...
Example: Python Generator Here's an example of a generator function that produces a sequence of numbers, defmy_generator(n):# initialize countervalue =0# loop until counter is less than nwhilevalue < n:# produce the current value of the counteryieldvalue# increment the countervalue +=1# ...
1defgenerator_example():2yield13yield245if__name__=='__main__':6foreingenerator_example():7printe8#output 1 2 generator function产生的generator与普通的function有什么区别呢 (1)function每次都是从第一行开始运行,而generator从上一次yield开始的地方运行 (2)function调用一次返回一个(一组)值,而genera...
只要一个函数function中使用了 yield 这个关键字,就代表这个函数function每次调用时返回的是一个生成器对象 generator object。这个生成器对象的类型是<class ‘generator’>。 包含yield 语句的函数function本身并不是生成器generator,它仍然是一个函数function。生成器generator是一个类class,而不是函数function。 生成器g...
def gen_function(): yield "python" 1. 2. 当return语句完全终止一个函数时,yield只是暂停该函数,直到next()方法再次调用它为止。 例如,下面的程序同时使用yield和next()语句。 def myGenerator(l): total = 1 for n in l: yield total total += n ...
在python的函数(function)定义中,只要出现了yield表达式(Yield expression),那么事实上定义的是一个generator function, 调用这个generator function返回值是一个generator。这根普通的函数调用有所区别,For example: 复制 def gen_generator():yield 1def gen_value():return1if __name__ =='__main__':ret = ...
In this way, you can use the generator without calling a function: Python csv_gen = (row for row in open(file_name)) This is a more succinct way to create the list csv_gen. You’ll learn more about the Python yield statement soon. For now, just remember this key difference: ...
Example 17-13. The aritprog_gen generator function def aritprog_gen(begin, step, end=None): result = type(begin + step)(begin) forever = end is None index = 0 while forever or result < end: yield result index += 1 result = begin + step * index ...
“All of this makes generator functions quite similar to coroutines; they yield multiple times, they have more than one entry point and their execution can be suspended. The only difference is that a generator function cannot control where should the execution continue after it yields; the contro...
If you try to re-iterate this generator, you’ll find that it’s tapped out: >>> try_again = list(number_thing) >>> try_again [] You can create a generator from a generator comprehension, as we did here, or from a generator function. We’ll talk about functions in general first...