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

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 numpy.

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.

-CHB

-- 

Christopher Barker, Ph.D.
Oceanographer

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