Scipy: Do sparse matrices support advanced indexing?

Question

No problem:

>>> t = np.array([[1,1,1,1,1],[2,2,2,2,2],[3,3,3,3,3],[4,4,4,4,4],[5,5,5,5,5]])
>>> x = np.arange(5).reshape((-1,1)); y = np.ar         


        

Answer 1

sparse matrices have a very limited indexing support, and what is available depends on the format of the matrix.

For example:

>>> a = scipy.sparse.rand(100,100,format='coo')
>>> a[2:5, 6:8]
Traceback (most recent call last):
  File "", line 1, in 
TypeError: 'coo_matrix' object has no attribute '__getitem__'

but

>>> a = scipy.sparse.rand(100,100,format='csc')
>>> a[2:5, 6:8]
<3x2 sparse matrix of type ''
    with 0 stored elements in Compressed Sparse Column format>

although

>>> a[2:5:2, 6:8:3]
Traceback (most recent call last):
...
ValueError: slicing with step != 1 not supported

There is also

>>> a = scipy.sparse.rand(100,100,format='dok')
>>> a[2:5:2, 6:8:3]
Traceback (most recent call last):
...
NotImplementedError: fancy indexing supported over one axis only
>>> a[2:5:2,1]
<3x1 sparse matrix of type ''
    with 0 stored elements in Dictionary Of Keys format>

And even

>>> a = scipy.sparse.rand(100,100,format='lil')
>>> a[2:5:2,1]
<2x1 sparse matrix of type ''
    with 0 stored elements in LInked List format>
C:\Python27\lib\site-packages\scipy\sparse\lil.py:230: SparseEfficiencyWarning: Indexing into a lil_matrix with multiple indices is slow. Pre-converting to CSC or CSR beforehand is more efficient.
  SparseEfficiencyWarning)
>>> a[2:5:2, 6:8:3]
<2x1 sparse matrix of type ''
    with 0 stored elements in LInked List format>