On May 22, 8:03 pm, Yash Ganthe <yas...@gmail.com> wrote: > Hi, > > I would like to shrink a large list in the following way: > If the List has 1000 integers, we need only 100 averages such that the > 1000 points are average for every 10 consecutive values. So p0 to p9 > will be averaged to obtain t0. p10 to p19 will be averaged to obtain > t1 and so on. This is a 10-point mean. > > We are doing this as we collect a lot of data and plot it on a graph. > Too many samples makes the graph cluttered. So we need to reduce the > number of values in the way described above. > > Does SciPy or NumPy
What do their docs say? > or any other module have functions for achieving > this? > > Which function can be used for doing this? Perhaps one like this: | >>> def n_point_means(alist, n): | ... blist = alist[:] | ... blist.sort() | ... size = len(blist) | ... assert 1 <= n <= size | ... assert size % n == 0 | ... clist = [] | ... fn = float(n) | ... for i in xrange(0, size, n): | ... clist.append(sum(blist[i:i+n]) / fn) | ... return clist | ... | >>> aaa = [9,8,7,6,5,4,3,2,0] | >>> n_point_means(aaa,2) | Traceback (most recent call last): | File "<stdin>", line 1, in <module> | File "<stdin>", line 6, in n_point_means | AssertionError | >>> aaa = [9,8,7,6,5,4,3,2,1,0] | >>> n_point_means(aaa,2) | [0.5, 2.5, 4.5, 6.5, 8.5] | >>> n_point_means(aaa,5) | [2.0, 7.0] | >>> Does that do what you want? If your requirement is so simple, why not write it yourself? -- http://mail.python.org/mailman/listinfo/python-list
