# 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?

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.

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(
setup=np.random.rand,
n_range=[2 ** k for k in range(20)],
kernels=[
f,
array_for,
array_map,
fromiter,
vectorize,
vectorize_without_init,
],
xlabel="len(x)",
)
b.save("out1.svg")
b.show()
``````