我正在尝试对 2.6 设置理解，并遇到以下两种方式。我认为第一种方法会比第二种方法快，timeit否则建议。为什么第二种方法更快，即使第二种方法有一个额外的列表实例化，然后是一个集合实例化？
方法一：
In [16]: %timeit set(node[0] for node in pwnodes if node[1].get('pm'))
1000000 loops, best of 3: 568 ns per loop
方法二：
In [17]: %timeit set([node[0] for node in pwnodes if node[1].get('pm')])
1000000 loops, best of 3: 469 ns per loop
哪里pwnodes = [('e1', dict(pm=1, wired=1)), ('e2', dict(pm=1, wired=1))]。
Iteration is simply faster when using a list comprehension:
In [23]: from collections import deque
In [24]: %timeit deque((node[0] for node in pwnodes if node[1].get('pm')), maxlen=0)
1000 loops, best of 3: 305 µs per loop
In [25]: %timeit deque([node[0] for node in pwnodes if node[1].get('pm')], maxlen=0)
1000 loops, best of 3: 246 µs per loop
The deque is used to illustrate iteration speed; a deque with maxlen set to 0 discards all elements taken from the iterable so there are no memory allocation differences to skew the results.
That's because in Python 2, list comprehensions don't use a separate namespace, while a generator expression does (it has to, by necessity). That extra namespace requires a new frame on the stack, and this is expensive. The major advantage of generator expressions is their low memory footprint, not their speed.
In Python 3, list comprehensions have a separate namespace as well, and list comprehension and generator iteration speed is comparable. You also have set comprehensions, which are fastest still, even on Python 2.
My guess is because the second one involves a generator and the first one doesn't. Generators are generally slower than the equivalent list if the equivalent list fits in memory.
In [4]: timeit for i in [i for i in range(1000)]: pass
10000 loops, best of 3: 47.2 µs per loop
In [5]: timeit for i in (i for i in range(1000)): pass
10000 loops, best of 3: 57.8 µs per loop