numpy vs list comprehension, which is faster? [closed]

Question

I ran a simple speed test comparing numpy and python list comprehension, and apparently list comprehension was faster. Is that correct?

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

I think you might want to consider varying your testing parameter:

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

Ratio of Numpy vs List comprehension timings:

10: 0.6x

100: 3.1x

1000: 9x

10000: 46x

Thus, Numpy is much faster for large N.

Answer 2

Your size is too small. I tried again with size=1000000 and numpy outperformed the list comprehension by 9x.

I'm guessing numpy has a higher setup overhead, but in general for non-trivial input sizes (10 is definitely trivial) you can expect it to be at least as fast as a list comprehension, and in most cases much faster.