Should I use np.absolute or np.abs?

user_4640905 0
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Numpy provides both np.absolute and the alias np.abs defined via

from .numeric import absolute as abs

which seems to be in obvious violation of the zen of python:

There should be one-- and preferably only one --obvious way to do it.

So I'm guessing that there is a good reason for this.

I have personally been using np.abs in almost all of my code and looking at e.g. the number of search results for np.abs vs np.absolute on Stack Overflow it seems like an overwhelming majority does the same (2130 vs 244 hits).

Is there any reason i should preferentially use np.absolute over np.abs in my code, or should I simply go for the more "standard" np.abs?

absolute-value numpy python
2 Answers

There also is Python's built-in abs(), but really all those functions are doing the same thing. They're even exactly equally fast! (This is not the case for other functions, like max().)

enter image description here

Code to reproduce the plot:

import numpy as np
import perfplot


def np_absolute(x):
    return np.absolute(x)


def np_abs(x):
    return np.abs(x)


def builtin_abs(x):
    return abs(x)


b = perfplot.bench(
    setup=np.random.rand,
    kernels=[np_abs, np_absolute, builtin_abs],
    n_range=[2 ** k for k in range(25)],
    xlabel="len(data)",
)
b.save("out.png")
b.show()
user_3533370
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