numpy.max or max ? Which one is faster?

Question

In python, which one is faster ?

numpy.max(), numpy.min() 

or

max(), min() 

My list/array length varies from 2 to 600. Which one should I use to save some run time ?

Answer 1

Well from my timings it follows if you already have numpy array a you should use a.max (the source tells it's the same as np.max if a.max available). But if you have built-in list then most of the time takes converting it into np.ndarray => that's why max is better in your timings.

In essense: if np.ndarray then a.max, if list and no need for all the machinery of np.ndarray then standard max.

Answer 2

I was also interested in this and tested the three variants with perfplot (a little project of mine). Result: You're not going wrong with a.max().

Code to reproduce the plot:

import numpy as np import perfplot  b = perfplot.bench(     setup=np.random.rand,     kernels=[max, np.max, lambda a: a.max()],     labels=["max(a)", "np.max(a)", "a.max()"],     n_range=[2 ** k for k in range(25)],     xlabel="len(a)", ) b.show()