How to transform numpy.matrix or array to scipy sparse matrix

Question

For SciPy sparse matrix, one can use todense() or toarray() to transform to NumPy matrix or array. What are the functions to do the inverse?

I searched, but got no idea what keywords should be the right hit.

Answer 1

You can pass a numpy array or matrix as an argument when initializing a sparse matrix. For a CSR matrix, for example, you can do the following.

>>> import numpy as np
>>> from scipy import sparse
>>> A = np.array([[1,2,0],[0,0,3],[1,0,4]])
>>> B = np.matrix([[1,2,0],[0,0,3],[1,0,4]])

>>> A
array([[1, 2, 0],
       [0, 0, 3],
       [1, 0, 4]])

>>> sA = sparse.csr_matrix(A)   # Here's the initialization of the sparse matrix.
>>> sB = sparse.csr_matrix(B)

>>> sA
<3x3 sparse matrix of type '<type 'numpy.int32'>'
        with 5 stored elements in Compressed Sparse Row format>

>>> print sA
  (0, 0)        1
  (0, 1)        2
  (1, 2)        3
  (2, 0)        1
  (2, 2)        4