生成器理解到底如何工作？

您了解列表理解吗？如果是这样，生成器表达式就像一个列表推导，但是它没有找到您感兴趣的所有项目并将它们打包到列表中，而是等待并逐个生成表达式中的每个项目。

>>> my_list = [1, 3, 5, 9, 2, 6]
>>> filtered_list = [item for item in my_list if item > 3]
>>> print(filtered_list)
[5, 9, 6]
>>> len(filtered_list)
3
>>> # compare to generator expression
... 
>>> filtered_gen = (item for item in my_list if item > 3)
>>> print(filtered_gen)  # notice it's a generator object
<generator object <genexpr> at 0x7f2ad75f89e0>
>>> len(filtered_gen) # So technically, it has no length
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: object of type 'generator' has no len()
>>> # We extract each item out individually. We'll do it manually first.
... 
>>> next(filtered_gen)
5
>>> next(filtered_gen)
9
>>> next(filtered_gen)
6
>>> next(filtered_gen) # Should be all out of items and give an error
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
StopIteration
>>> # Yup, the generator is spent. No values for you!
... 
>>> # Let's prove it gives the same results as our list comprehension
... 
>>> filtered_gen = (item for item in my_list if item > 3)
>>> gen_to_list = list(filtered_gen)
>>> print(gen_to_list)
[5, 9, 6]
>>> filtered_list == gen_to_list
True
>>> 

由于生成器表达式一次只需要产生一项，因此可以节省大量内存。在需要一次获取一项，根据该项进行大量计算然后移至下一项的情况下，生成器表达式最有意义。如果需要多个值，则还可以使用生成器表达式，一次获取几个。如果在程序继续执行之前需要所有值，请改用列表理解。

>>> [x**2 for x in range(1,11)]
[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]

>>> (x**2 for x in xrange(1,11))
<generator object at 0x7f0a79273488>

def allEvens( L ):
    for number in L:
        if number % 2 is 0:
            yield number

evens = allEvens( yourList )

evens = ( number for number in your_list if number % 2 == 0 )

print 'Generator comprehensions'

def sq_num(n):
    for num in (x**2 for x in range(n)):    
        yield num

for x in sq_num(10):
    print x 

#to get all the even numbers in given range
 
def allevens(n):
    for x in range(2,n):
        if x%2==0:
            yield x

for x in allevens(10)
print(x)

#output
2
4
6
8