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

How about using numpy.vectorize.

import numpy as np
x = np.array([1, 2, 3, 4, 5])
squarer = lambda t: t ** 2
vfunc = np.vectorize(squarer)
vfunc(x)
# Output : array([ 1,  4,  9, 16, 25])