Hello, We are working to convert Circuitscape (a landscape ecology tool) from MATLAB to Python. http://www.nceas.ucsb.edu/~mcrae/circuitscape.html It exercises a lot of sparse array functionality (indexing, assignment, concatenation, and MATLAB sparse()/find(), and sparse linear algebra). I am new to Numpy/Scipy, and I'd be delighted to figure out if there were a better way to do sparse matrix indexing. The general array indexing didn't seem to work for arbitrary index sets, at least in the latest release. I did come across this on scipy-dev, but it seems like it works for only CSR ? http://article.gmane.org/gmane.comp.python.scientific.devel/6702/match=spars... I currently use a wrapper that can be used to implement general sparse matrix indexing and row/col deletion using sparse matrix multiplication. It implements sparse indexing. Deletion, is then implemented by simply taking arange(0,len) and removing the indices you want to delete, and then calling the indexing code. This may be a useful way to complete the functionality for data structures that may not have full indexing yet. I'm not sure if this can be made to handle slices correctly. This is the code snippet I use. If its useful, I can help make it robust for inclusion. # Implement B = A[I, J] def subsref(self,A,I,J): nr = A.shape[0] nc = A.shape[1] nI = len(I) nJ = len(J) IM = sparse.csc_matrix((ones(nI), c_[arange(0,nI), I].T), dims=[nI,nr]) JM = sparse.csc_matrix((ones(nJ), c_[J, arange(0,nJ)].T), dims=[nc,nJ]) B = IM*A*JM return B -viral