Question

I can’t find more info about scipy.sparse indexing except SciPy v0.11 Reference Guide, which says that

The lil_matrix class supports basic slicing and fancy indexing with a similar syntax to NumPy arrays.

. I have read numpy document about index, but I didn’t understand it clearly, for example,

Asp = sparse.lil_matrix((3,3))
Asp.setdiag(zeros(3))
Asp[0, 1:3] = 10
print Asp.todense()

1.
why the output is

[[  0.  10.  10.]
 [  0.   0.   0.]
 [  0.   0.   0.]]

what does [0,1:3] meaning? if I use

Asp[0, 1:2,3] = 10

there’s a error:

IndexError: invalid index

, I don’t know the reason.

2.what’s the fastest way to get all non-zero values for each row?

Answer 1

For your second question, use the nonzero() method. I had to dig through the source to find it, since I couldn’t find it in any of the reference documentation.

def nonzero(self):
    """nonzero indices

    Returns a tuple of arrays (row,col) containing the indices
    of the non-zero elements of the matrix.

    Examples
    --------
    >>> from scipy.sparse import csr_matrix
    >>> A = csr_matrix([[1,2,0],[0,0,3],[4,0,5]])
    >>> A.nonzero()
    (array([0, 0, 1, 2, 2]), array([0, 1, 2, 0, 2]))

    """