Paul McGuire wrote: > <[EMAIL PROTECTED]> wrote in message > news:[EMAIL PROTECTED] >> >> n = len(a) >> >> mean = sum(a) / n >> >> sd = sqrt(sum((x-mean)**2 for x in a) / n) >> ... >> >> If there is a faster way... like transferring the array to a >> >> different container class... but what? >> >> Perhaps: >> >> >>> import scipy >> >>> print scipy.mean([1,2,3,4,5,6]) >> 3.5 >> >>> print scipy.std([1,2,3,4,5,6]) >> 1.87082869339 >> >> Skip
Note that those are also in numpy, now. > Can scipy work with an iterator/generator? There is a fromiter() constructor for 1D arrays. The basic array() constructor (which is used by the other functions like mean() and std() to create an array from a non-array sequence) doesn't quite have enough information to consume generic iterators. The mean() and std() algorithms operate on arrays, not generic iterators. > If you can only make one pass > through the data, you can try this: > > lst = (random.gauss(0,10) for i in range(1000)) > # compute n, sum(x), and sum(x**2) with single pass through list > n,sumx,sumx2 = reduce(lambda a,b:(a[0]+b[0],a[1]+b[1],a[2]+b[2]), ((1,x,x*x) > for x in lst) ) > sd = sqrt( (sumx2 - (sumx*sumx/n))/(n-1) ) Don't do that. If you *must* restrict yourself to one pass, there are better algorithms. http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco -- http://mail.python.org/mailman/listinfo/python-list
