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

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):

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

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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)


perfplot.show(
    setup=lambda n: np.random.rand(n),
    n_range=[2 ** k for k in range(20)],
    kernels=[f, array_for, array_map, fromiter, vectorize, vectorize_without_init],
    xlabel="len(x)",
)