mul python_python – int .__ mul__,比operator.mul慢2倍
如果你看下面的时间：
C:\Users\Henry>python -m timeit -s "mul = int.__mul__" "reduce(mul,range(10000))"
1000 loops, best of 3: 908 usec per loop
C:\Users\Henry>python -m timeit -s "from operator import mul" "reduce(mul,range(10000))"
1000 loops, best of 3: 410 usec per loop
执行速度之间有显着差异
reduce(int .__ mul __,range(10000))和reduce(mul,range(10000)),后者更快.
使用dis模块来查看发生了什么：
使用int .__ mul__方法：
C:\Users\Henry>python
Python 2.7.4 (default, Apr 6 2013, 19:55:15) [MSC v.1500 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> mul = int.__mul__
>>> def test():
... mul(1,2)
...
>>> import dis
>>> dis.dis(test)
2 0 LOAD_GLOBAL 0 (mul)
3 LOAD_CONST 1 (1)
6 LOAD_CONST 2 (2)
9 CALL_FUNCTION 2
12 POP_TOP
13 LOAD_CONST 0 (None)
16 RETURN_VALUE
>>>
和运算符mul方法
C:\Users\Henry>python
Python 2.7.4 (default, Apr 6 2013, 19:55:15) [MSC v.1500 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from operator import mul
>>> def test():
... mul(1,2)
...
>>> import dis
>>> dis.dis(test)
2 0 LOAD_GLOBAL 0 (mul)
3 LOAD_CONST 1 (1)
6 LOAD_CONST 2 (2)
9 CALL_FUNCTION 2
12 POP_TOP
13 LOAD_CONST 0 (None)
16 RETURN_VALUE
>>>
它们看起来是一样的,为什么执行速度有差异？我指的是Python的CPython实现
在python3上也是如此：
$python3 -m timeit -s 'mul=int.__mul__;from functools import reduce' 'reduce(mul, range(10000))'
1000 loops, best of 3: 1.18 msec per loop
$python3 -m timeit -s 'from operator import mul;from functools import reduce' 'reduce(mul, range(10000))'
1000 loops, best of 3: 643 usec per loop
$python3 -m timeit -s 'mul=lambda x,y:x*y;from functools import reduce' 'reduce(mul, range(10000))'
1000 loops, best of 3: 1.26 msec per loop