I can't tell you if this is faster or slower than Matlab, since I have no idea what numbers you're seeing there (you provided no quantitative data at all). However, as far as alternatives go:
import numpy as np
a = np.random.randn(75, 150)
aSign = np.sign(a)
Testing using %timeit
in IPython
:
In [15]: %timeit np.sign(a)
10000 loops, best of 3: 180 µs per loop
Because the loop over the array (and what happens inside it) is implemented in optimized C code rather than generic Python code, it tends to be about an order of magnitude faster—in the same ballpark as Matlab.
Comparing the exact same code as a numpy vectorized operation vs. a Python loop:
In [276]: %timeit [np.sign(x) for x in a]
1000 loops, best of 3: 276 us per loop
In [277]: %timeit np.sign(a)
10000 loops, best of 3: 63.1 us per loop
So, only 4x as fast here. (But then a
is pretty small here.)