Most efficient way to map function over numpy array

Question

What is the most efficient way to map a function over a numpy array? The way I\'ve been doing it in my current project is as follows:

import numpy as np 

x          


        

Answer 1

Edit: the original answer was misleading, np.sqrt was applied directly to the array, just with a small overhead.

In multidimensional cases where you want to apply a builtin function that operates on a 1d array, numpy.apply_along_axis is a good choice, also for more complex function compositions from numpy and scipy.

Previous misleading statement:

Adding the method:

def along_axis(x):
    return np.apply_along_axis(f, 0, x)

to the perfplot code gives performance results close to np.sqrt.