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 = 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?

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)   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()