On Tue, May 31, 2011 at 9:26 PM, Charles R Harris <charlesr.har...@gmail.com> wrote: > > > On Tue, May 31, 2011 at 8:00 PM, Skipper Seabold <jsseab...@gmail.com> > wrote: >> >> On Tue, May 31, 2011 at 9:53 PM, Warren Weckesser >> <warren.weckes...@enthought.com> wrote: >> > >> > >> > On Tue, May 31, 2011 at 8:36 PM, Skipper Seabold <jsseab...@gmail.com> >> > wrote: >> >> I don't know if it's one pass off the top of my head, but I've used >> >> percentile for interpercentile ranges. >> >> >> >> [docs] >> >> [1]: X = np.random.random(1000) >> >> >> >> [docs] >> >> [2]: np.percentile(X,[0,100]) >> >> [2]: [0.00016535235312509222, 0.99961513543316571] >> >> >> >> [docs] >> >> [3]: X.min(),X.max() >> >> [3]: (0.00016535235312509222, 0.99961513543316571) >> >> >> > >> > >> > percentile() isn't one pass; using percentile like that is much slower: >> > >> > In [25]: %timeit np.percentile(X,[0,100]) >> > 10000 loops, best of 3: 103 us per loop >> > >> > In [26]: %timeit X.min(),X.max() >> > 100000 loops, best of 3: 11.8 us per loop >> > >> >> Probably should've checked that before opening my mouth. Never >> actually used it for a minmax, but it is faster than two calls to >> scipy.stats.scoreatpercentile. Guess I'm +1 to fast order statistics. >> > > So far the biggest interest seems to be in order statistics of various > sorts, so to speak. > > Order Statistics > > minmax > median > k'th element > largest/smallest k elements > > Other Statistics > > mean/std > > Nan functions > > nanadd > > Chuck > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
How about including all or some of Keith's Bottleneck package? He has tried to include some of the discussed functions and tried to make them very fast. Also, this Wikipedia "Algorithms for calculating variance" (http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance) has some basic info on calculating the variance as well as higher order moments.However, there are probably more efficient algorithms available. Bruce _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
