[RyanCahoon]

Here's a bit of a quick hack:

import scipy.sparse
import numpy

def read_sparse(filename):
  (r, c, d) = numpy.transpose(numpy.loadtxt(filename, 'int'))
  return scipy.sparse.coo_matrix((d, (r-1, c-1)))

Note that the matrix's dimensions may be truncated if there are trailing rows and columns that are all 0, but this is not the case with your example matrix. It would need a little more work to read the dimensions from the file.

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A better approach would be to have Octave save the data into MATLAB format (using something like
save -6 A_sparse_octave.mat A), then read it in Python using scipy.io.loadmat