With numpy, what's the fastest way to generate an array from -n to n, excluding 0, being `n` an integer?

Question

With numpy, what's the fastest way to generate an array from -n to n, excluding 0, being n an integer?

Follows one solution, but I am not sure this is the fastest:

n = 100000
np.concatenate((np.arange(-n, 0), np.arange(1, n+1)))

Hi @gmagno, please check my post. If you like it, please give me an upvote — WY Hsu, Mar 01 '19 at 05:14

score 3 · Accepted Answer · edited Mar 01 '19 at 14:10

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An alternative approach is to create the range -n to n-1. Then add 1 to the elements from zero.

def non_zero_range(n):
    # The 2nd argument to np.arange is exclusive so it should be n and not n-1
    a=np.arange(-n,n)
    a[n:]+=1
    return a

n=1000000
%timeit np.concatenate((np.arange(-n,0), np.arange(1,n+1)))
# 4.28 ms ± 9.46 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

%timeit non_zero_range(n)
# 2.84 ms ± 13.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

I think the reduced response time is due to only creating one array, not three as in the concatenate approach.

edited Mar 01 '19 at 14:10

Stuxen

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answered Mar 01 '19 at 09:07

Tls Chris

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Agreed; for simple operations like this, minimizing temporaries is what it is all about, as far as numpy performance is concerned. – Eelco Hoogendoorn Mar 01 '19 at 09:44
Thanks for sharing – WY Hsu Mar 01 '19 at 14:14

WY Hsu · Answer 2 · 2019-03-01T14:10:49.497

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Edit

Thanks, everyone. I edited my post and updated new test time.

Interesting problem.

Experiment

I did it in my jupyter-notebook. All of them used numpy API. You can conduct the experiment of the following code by yourself.

About time measurement in jupyter-notebook, please see: Simple way to measure cell execution time in ipython notebook

Original `np.concatenate`

%%timeit 
n = 100000
t = np.concatenate((np.arange(-n, 0), np.arange(1, n+1)))

#175 µs ± 2.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

Sol 1. `np.delete`

%%timeit 
n = 100000
a = np.arange(-n, n+1)
b = np.delete(a, n)

# 179 µs ± 5.66 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

Sol 2. List comprehension + `np.arrary`

%%timeit
c = np.array([x for x in range(-n, n+1) if x != 0])

# 16.6 ms ± 693 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

Conclusion

There's no big difference between original and solution 1, but solution 2 is the worst among the three. I'm looking for faster solutions, too.

Reference

For those who are:

interested in initialize and fill an numpy array

Best way to initialize and fill an numpy array?

get confused of is vs ==

The Difference Between “is” and “==” in Python

edited Mar 01 '19 at 14:10

answered Mar 01 '19 at 04:47

WY Hsu

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There isn't a need for downvotes though. If you feel that his proof may be lacking then please point out what's lacking. I don't see anything that's inherently wrong. – kerwei Mar 01 '19 at 04:50
@kerwei: I think you've misunderstood. This answer is only timing **the statement** `n = 100000`. The array creation is not timed. – user2357112 Mar 01 '19 at 04:52
@Tomothy32 got it, I'll check it. – WY Hsu Mar 01 '19 at 04:53
@user2357112 Oh yes, I get your point. I did miss that too lol – kerwei Mar 01 '19 at 04:53
use `%%timeit` to use it on entire block – Sociopath Mar 01 '19 at 04:54
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@WeiYuangHsu Thanks for editing, but I don't believe the timings have been updated. – iz_ Mar 01 '19 at 05:00
@Hi user2357112, is there anything I missed? – WY Hsu Mar 01 '19 at 05:01
@Tomothy32 I got you. Updated. – WY Hsu Mar 01 '19 at 05:03
@kerwei If there's anything I haven't noticed, please leave a comment. Btw, thank you, guys. – WY Hsu Mar 01 '19 at 05:08
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Looks like the timing problems have been fixed. – user2357112 Mar 01 '19 at 05:18
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Side note, `if x is not 0` is not the correct way to compare integers. `is` is not a replacement for `==`. Use `if x != 0` or simply `if x`. – iz_ Mar 01 '19 at 05:46

With numpy, what's the fastest way to generate an array from -n to n, excluding 0, being `n` an integer?

2 Answers2