Most efficient way to map function over numpy array

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?

1Best Answer

I’ve tested all suggested methods plus np.array(map(f, x)) with perfplot (a small project of mine).

Message #1: If you can use numpy’s native functions, do that.

If the function you’re trying to vectorize already is vectorized (like the x**2 example in the original post), using that is much faster than anything else (note the log scale):

enter image description here

If you actually need vectorization, it doesn’t really matter much which variant you use.

enter image description here

Code to reproduce the plots:

import numpy as np
import perfplot
import math

def f(x):
    # return math.sqrt(x)
    return np.sqrt(x)

vf = np.vectorize(f)

def array_for(x):
    return np.array([f(xi) for xi in x])

def array_map(x):
    return np.array(list(map(f, x)))

def fromiter(x):
    return np.fromiter((f(xi) for xi in x), x.dtype)

def vectorize(x):
    return np.vectorize(f)(x)

def vectorize_without_init(x):
    return vf(x)

b = perfplot.bench(
    n_range=[2 ** k for k in range(20)],

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