On Jul 9, 12:26 am, Benjamin Goudey <[EMAIL PROTECTED]> wrote: > I have a very large list of integers representing data needed for a > histogram that I'm going to plot using pylab. However, most of these > values (85%-95%) are zero and I would like to remove them to reduce > the amount of memory I'm using and save time when it comes to plotting > the data. To do this, I'm trying to find the best way to remove all of > the zero values and produce a list of indices of where the non-zero > values used to be. > > For example, if my original list is [0,0,1,2,1,0,0] I would like to > produce the lists [1,2,1] (the non zero values) and [2,3,4] (indices > of where the non-zero values used to be). Removing non-zero values is > very easy but determining the indicies is where I'm having difficulty. >
>>> sparse_data = [0, 0, 1, 2, 1, 0, 0] >>> values,locns = zip(*[ (x,i) for i,x in enumerate(sparse_data) if x ]) >>> print values (1, 2, 1) >>> print locns (2, 3, 4) >>> -- Paul -- http://mail.python.org/mailman/listinfo/python-list