numpy vs list comprehension,哪个更快？

Question

我运行了一个比较numpy和python列表理解的简单速度测试,显然列表理解更快.那是对的吗？

import sys, numpy
from datetime import datetime

def numpysum(n):
    a = numpy.arange(n) ** 2
    b = numpy.arange(n) ** 3
    return a + b

def pythonsum(n):
    a = [i ** 2 for i in range(n)]
    b = [i ** 3 for i in range(n)]
    return [a[i] + b[i] for i in range(n)]

size = 10
start = datetime.now()
c1 = pythonsum(size)
delta1 = datetime.now() - start

start = datetime.now()
c2 = numpysum(size)
delta2 = datetime.now() - start

print c1
print c2

print delta1
print delta2

Answer 1

我想你可能想考虑改变你的测试参数:

In [39]: %timeit pythonsum(10)
100000 loops, best of 3: 8.41 us per loop

In [40]: %timeit pythonsum(100)
10000 loops, best of 3: 51.9 us per loop

In [41]: %timeit pythonsum(1000)
1000 loops, best of 3: 451 us per loop

In [42]: %timeit pythonsum(10000)
100 loops, best of 3: 17.9 ms per loop

In [43]: %timeit numpysum(10)
100000 loops, best of 3: 13.4 us per loop

In [44]: %timeit numpysum(100)
100000 loops, best of 3: 17 us per loop

In [45]: %timeit numpysum(1000)
10000 loops, best of 3: 50.3 us per loop

In [46]: %timeit numpysum(10000)
1000 loops, best of 3: 385 us per loop

Numpy与列表理解时间的比率:

10:0.6x

100:3.1x

1000:9x

10000:46x

因此,Numpy的速度要快得多N.

Answer 2

你size太小了.我再次尝试size=1000000和numpy表现优于9x的列表理解.

我猜numpy有一个更高的设置开销,但一般来说,非平凡的输入大小(10绝对是微不足道的)你可以期望它至少与列表理解一样快,并且在大多数情况下要快得多.