Pros and cons to using sparse matrices in python/R?
There are several ways to represent sparse matrices (documentation for the R SparseM package reports 20 different ways to store sparse matrix data), so complete compatibility with all solutions is probably out of question. The number options also suggests that there is no best-in-all-situations solution.
Pick either the numpy sparse matrices or R's SparseM (through rpy2) according to where your heavy number crunching routines on those matrices are found (numpy or R).
I'm working with large, sparse matrices (document-feature matrices generated from text) in python. It's taking quite a bit of processing time and memory to chew through these, and I imagine that sparse matrices could offer some improvements.  But I'm worried that using a sparse matrix library is going to make it harder to plug into other python (and R, through rpy2) modules.
Can people who've crossed this bridge already offer some advice?  What are the pros and cons of using sparse matrices in python/R, in terms of performance, scalability, and compatibility?