Multiplying elements in a sparse array with rows in matrix

Unfortunatly the .multiply method of the CSR matrix seems to densify the matrix if the other one is dense. So this would be one way avoiding that:

# Assuming that Y is 1D, might need to do Y = Y.A.ravel() or such...

# just to make the point that this works only with CSR:
if not isinstance(X, scipy.sparse.csr_matrix):
    raise ValueError('Matrix must be CSR.')

Z = X.copy()
# simply repeat each value in Y by the number of nnz elements in each row: 
Z.data *= Y.repeat(np.diff(Z.indptr))

This does create some temporaries, but at least its fully vectorized, and it does not densify the sparse matrix.

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For a COO matrix the equivalent is:

Z.data *= Y[Z.row] # you can use np.take which is faster then indexing.

For a CSC matrix the equivalent would be:

Z.data *= Y[Z.indices]