为什么numpy.array这么慢？

Question

我对此感到困惑

def main():
    for i in xrange(2560000):
        a = [0.0, 0.0, 0.0]

main()

$ time python test.py

real     0m0.793s

现在让我们来看看numpy：

import numpy

def main():
    for i in xrange(2560000):
        a = numpy.array([0.0, 0.0, 0.0])

main()

$ time python test.py

real    0m39.338s

神圣的CPU周期蝙蝠侠！

使用numpy.zeros(3)改进，但恕我直言仍然不够

$ time python test.py

real    0m5.610s
user    0m5.449s
sys 0m0.070s

numpy.version.version =‘1.5.1’

如果您想知道在第一个示例中是否跳过了列表创建以进行优化，则不是：

  5          19 LOAD_CONST               2 (0.0)
             22 LOAD_CONST               2 (0.0)
             25 LOAD_CONST               2 (0.0)
             28 BUILD_LIST               3
             31 STORE_FAST               1 (a)

Answer 1

Numpy已针对大量数据进行了优化。给它一个很小的3长度数组，毫不奇怪，它的性能很差。

考虑单独的测试

import timeit

reps = 100

pythonTest = timeit.Timer('a = [0.] * 1000000')
numpyTest = timeit.Timer('a = numpy.zeros(1000000)', setup='import numpy')
uninitialised = timeit.Timer('a = numpy.empty(1000000)', setup='import numpy')
# empty simply allocates the memory. Thus the initial contents of the array 
# is random noise

print 'python list:', pythonTest.timeit(reps), 'seconds'
print 'numpy array:', numpyTest.timeit(reps), 'seconds'
print 'uninitialised array:', uninitialised.timeit(reps), 'seconds'

输出是

python list: 1.22042918205 seconds
numpy array: 1.05412316322 seconds
uninitialised array: 0.0016028881073 seconds

似乎是数组的归零一直花费在numpy上。因此，除非您需要初始化数组，否则请尝试使用empty。