Efficient boolean indexing of SciPy spare matrices

sparse_boolean_indexing.py

from scipy import sparse

# generate a random sparse matrix
M = sparse.random(100,100)
M

      # <100x100 sparse matrix of type '<class 'numpy.float64'>'
      #	with 100 stored elements in COOrdinate format>

# create boolean array of the data stord in the sparse matrix
mask = M.data < 0.7

# manipulate the values in the matrix that meet the condition
M.data[mask] = 0

# remove the newly assigned zero values to sparsify the matrix
M.eliminate_zeros()
M

      # <100x100 sparse matrix of type '<class 'numpy.float64'>'
      #	with 31 stored elements in COOrdinate format>