[Numpy-discussion] Relative speed

Anubhab Baksi anubhab91@gmail....
Thu Aug 29 23:20:10 CDT 2013


Thanks all, my client actually wants the output at a minimum time.

On Thu, Aug 29, 2013 at 11:30 PM, Ralf Gommers <ralf.gommers@gmail.com>wrote:

>
> if you have a reasonably large amount of data (say O(100)),
>
> I need to deal with nearly 2**19 or 2**20 arrays of length about 250 each.


On Thu, Aug 29, 2013 at 11:30 PM, Ralf Gommers <ralf.gommers@gmail.com>wrote:

>
>
>
> On Thu, Aug 29, 2013 at 3:41 PM, Jonathan T. Niehof <jniehof@lanl.gov>wrote:
>
>> On 08/29/2013 09:33 AM, Anubhab Baksi wrote:
>> > Hi,
>> > I need to know about the relative speed (i.e., which one is faster) of
>> > the followings:
>> > 1. list and numpy array, tuples and numpy array
>> > 2. list of tuples and numpy matrix (first one is rectangular)
>> > 3. random.randint() and numpy.random.random_integers()
>>
>
> Hi Anubhab, if you have a reasonably large amount of data (say O(100)),
> always try to use numpy arrays and not lists or tuples - it'll be faster.
> I'd recommend not to use numpy.matrix, it's speed will be similar to numpy
> arrays but it has some peculiarities that you'd rather not deal with. For
> the random numbers I'm not sure without checking, just timing it in ipython
> with %timeit is indeed the way to go.
>
> Cheers,
> Ralf
>
>
>> African or European?
>>
>
> Why on earth would you ask that?
>
>
>
>> It really depends on what you're doing with it. The ipython %timeit
>> magic is pretty useful for answering that question. Note that the answer
>> may change dramatically based on the size of the data set.
>>
>> --
>> Jonathan Niehof
>> ISR-3 Space Data Systems
>> Los Alamos National Laboratory
>> MS-D466
>> Los Alamos, NM 87545
>>
>> Phone: 505-667-9595
>> email: jniehof@lanl.gov
>>
>> Correspondence /
>> Technical data or Software Publicly Available
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>
>
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