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 = np.array([1, 2, 3, 4, 5])

# Obtain array of square of each element in x
squarer = lambda t: t ** 2
squares = np.array([squarer(xi) for xi in x])

However, this seems like it is probably very inefficient, since I am using a list comprehension to construct the new array as a Python list before converting it back to a numpy array.

Can we do better?