A useful and underappreciated MATLAB function is accumarray(), which aggregates a vector of data elements into a multi-dimensional array based on a separate set of index vectors. It's essentially the same as the R function xtabs(), except that the indices in accumarray() have to be numeric. I found myself needing something like this function in python when I was writing an algorithm for calculating reassigned spectrograms (more on this later; I'm working on a little monograph on time-frequency representations of birdsong). I couldn't find anything, and wound up writing the algorithm in weave/blitz -- which I probably would have done anyway to tweak the performance.

Later I discovered that there is an equivalent to accumarray() in scipy: it's the scipy.sparse.coo_matrix() constructor. Here's the example from the docstring:

row  = array([0,0,1,3,1,0,0])

col  = array([0,2,1,3,1,0,0])

data = array([1,1,1,1,1,1,1])

coo_matrix( (data,(row,col)), shape=(4,4)).todense()



matrix([[3, 0, 1, 0],

            [0, 2, 0, 0],

            [0, 0, 0, 0],

            [0, 0, 0, 1]])

The main downside to this method is that it only works with 2D arrays. I also don't know anything about the performance. Probably if your matrix is fairly sparse (i.e. lots of zeros) this is the way to go. If you've got more or fewer dimensions, or the output is dense, you're probably better off just writing the loop in C++.