[Numpy-discussion] treating numpy arrays like lists is slow]
Jeff Whitaker
jswhit at fastmail.fm
Fri Feb 3 19:08:02 CST 2006
Travis Oliphant wrote:
> Jeff Whitaker wrote:
>
>>
>> Hi:
>>
>> I've noticed that code like this is really slow in numpy (0.9.4):
>>
>> import numpy as NP
>> a = NP.ones(10000,'d')
>> a = [2.*a1 for a1 in a]
>>
>>
>> the last line takes 0.17 seconds on my G5, while for Numeric and
>> numarray it takes only 0.01. Anyone know the reason for this?
>>
> We could actually change this right now, before the introduction of
> scalar math by using the standard float table for the corresponding
> array scalars. The only reason I didn't do this initially was that I
> wanted consistency in behavior for "division-by-zero" between arrays
> and scalars.
> Using the Python float math you will get divide-by-zero errors whereas
> you don't (unless you ask for them), with numpy arrays.
>
> Thus, current scalars are treated as 0-d arrays in the internals and
> go through the entire ufunc machinery for every operation.
> Now, the real question is why are you doing this? Using arrays in
> this way defeats their purpose :-)
>
> What is wrong with 2*a? Now, of course there will be situations that
> require this.
>
> -Travis
>
Travis: Of course I know this is a dumb thing to do - but sometimes it
does happen that a function that expects a list actually gets a rank-1
array. The workaround in that case is to just pass it a.tolist()
instead of a.
-Jeff
--
Jeffrey S. Whitaker Phone : (303)497-6313
Meteorologist FAX : (303)497-6449
NOAA/OAR/PSD R/PSD1 Email : Jeffrey.S.Whitaker at noaa.gov
325 Broadway Office : Skaggs Research Cntr 1D-124
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--
Jeffrey S. Whitaker Phone : (303)497-6313
Meteorologist FAX : (303)497-6449
NOAA/OAR/PSD R/PSD1 Email : Jeffrey.S.Whitaker at noaa.gov
325 Broadway Office : Skaggs Research Cntr 1D-124
Boulder, CO, USA 80303-3328 Web : http://tinyurl.com/5telg
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