[Numpy-discussion] A roadmap for NumPy - longer term planning

Chris Barker chris.barker at noaa.gov
Fri Jun 1 12:46:57 EDT 2018

On Fri, Jun 1, 2018 at 4:43 AM, Marten van Kerkwijk <
m.h.vankerkwijk at gmail.com> wrote:

>  one thing that always slightly annoyed me is that numpy math is way
> slower for scalars than python math

numpy is also quite a bit slower than raw python for math with (very) small

In [31]: % timeit t2 = (t[0] * 10, t[1] * 10)
162 ns ± 0.79 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)

In [32]: a
Out[32]: array([ 3.4,  5.6])

In [33]: % timeit a2 = a * 10
941 ns ± 7.95 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

(I often want to so this sort of thing, not for performance, but for ease
of computation -- say you have 2 or three coordinates that represent a
point -- it's really nice to be able to scale or shift with array
operations, rather than all that indexing -- but it is pretty slo with

I've wondered if numpy could be optimized for small 1D arrays, and maybe
even 2d arrays with a small fixed second dimension (N x 2, N x 3), by
special-casing / short-cutting those cases.

It would require some careful profiling to see if it would help, but it
sure seems possible.

And maybe scalars could be fit into the same system.



Christopher Barker, Ph.D.

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Chris.Barker at noaa.gov
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