Why is numpy slower in this code?
for i in range(10000):
array = [[0.0,] * 1024 for x in range(1024)]
0,021539204 seconds time elapsed (39.616.810 instructions)
import numpy as np
for i in range(10000):
array = np.zeros((1024,1024))
0.209111860 seconds time elapsed (1.067.923.180 instructions)
Are you running in the exact same machine? I'm getting faster result in numpy.
In [7]: %%time
...: import numpy as np
...: for i in range(10000):
...: array = np.zeros((1024,1024))
...:
CPU times: user 3.33 s, sys: 0 ns, total: 3.33 s
Wall time: 3.32 s
In [8]: %%time
...: for i in range(10000):
...: array = [[0.0,] * 1024 for x in range(1024)]
...:
CPU times: user 1min 14s, sys: 0 ns, total: 1min 14s
Wall time: 1min 14s
This answer in the numpy vs list thread also agrees.
array = [[0.0,] * 1024 for x in range(1024)]doesn't give you an array, it gives you a listtimeitrather than shoving it in a loop, you'd get7.4 ms ± 287 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)for the Python list and11.2 µs ± 137 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)for the numpy approach. The timings in your approach are completely flawed because the pythonforloop dominates