Matlab has a (clunky) interface to python. Passing data can be awkward. There is no built in support (AFAIK) for passing matlab sparse matrices to scipy sparse matrices.
For starters, don't do this:
A = sprandn(10000,10000,0.0001);
pyA = py.scipy.sparse.csc_matrix(full(A));
casting to full defeats the purpose of sparse storage.
Instead you can build a scipy sparse matrix directly. In scipy, you must explicitly specify the storage, for this example I'll use CSC:
[AI,AJ,AV] = find(A);
pyA = py.scipy.sparse.csc_matrix({AV,{uint64(AI-1) uint64(AJ-1)}},{uint64(size(A,1)),uint64(size(A,2))});
Notice the -1 on the indices. Python uses 0-based indexing.
To convert back from a python sparse matrix to a matlab sparse matrix, a general approach is:
coo = pyA.tocoo;
A = sparse(double(coo.row+1),double(coo.col+1),double(coo.data),double(coo.shape{1}),double(coo.shape{2}));
Rebuilding the sparse matrix directly from the CSC indices and indptr is also possible (i.e., avoiding the copy), but this version will happily work regardless of the original storage type of pyA.