[Numpy-discussion] mysql -> record array
Travis Oliphant
oliphant at ee.byu.edu
Thu Nov 16 18:44:56 CST 2006
John Hunter wrote:
>>>>>> "Erin" == Erin Sheldon <erin.sheldon at gmail.com> writes:
>>>>>>
>>>>>
>
> Erin> The question I have been asking myself is "what is the
> Erin> advantage of such an approach?". It would be faster, but by
>
> In the use case that prompted this message, the pull from mysql took
> almost 3 seconds, and the conversion from lists to numpy arrays took
> more that 4 seconds. We have a list of about 500000 2 tuples of
> floats.
>
> Digging in a little bit, we found that numpy is about 3x slower than
> Numeric here
>
> peds-pc311:~> python test.py
> with dtype: 4.25 elapsed seconds
> w/o dtype 5.79 elapsed seconds
> Numeric 1.58 elapsed seconds
> 24.0b2
> 1.0.1.dev3432
>
> Hmm... So maybe the question is -- is there some low hanging fruit
> here to get numpy speeds up?
>
> import time
> import numpy
> import numpy.random
> rand = numpy.random.rand
>
> x = [(rand(), rand()) for i in xrange(500000)]
> tnow = time.time()
> y = numpy.array(x, dtype=numpy.float_)
> tdone = time.time()
> print 'with dtype: %1.2f elapsed seconds'%(tdone - tnow)
>
> tnow = time.time()
> y = numpy.array(x)
> tdone = time.time()
> print 'w/o dtype %1.2f elapsed seconds'%(tdone - tnow)
>
> import Numeric
> tnow = time.time()
> y = Numeric.array(x, Numeric.Float)
> tdone = time.time()
> print 'Numeric %1.2f elapsed seconds'%(tdone - tnow)
>
> print Numeric.__version__
> print numpy.__version__
>
>
I just adapted Numarray's version of array (using the fromlist method)
to NumPy. This new change needs some testing as it is called in many,
many ways. But, I think it should be right (all tests of numpy and
scipy pass with it).
With the change I get:
with dtype: 0.22 elapsed seconds
w/o dtype 5.02 elapsed seconds
Numeric 7.38 elapsed seconds
numarray 0.55 elapsed seconds
24.2
1.0.1.dev3437
1.5.1
-Travis
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