Element-wise operations on scipy.sparse.coo_matrix (e.g. modulo)?

Question

How can I perform element-wise operations (e.g. modulo) for a scipy.sparse.coo_matrix?

Do I have to create a NumPy dense array from it? If that's the case won't I lose all the benefits gained from creating a sparse matrix?

Answer 1

As long as the element-wise operation does not change the sparsity of the data, you can use the .data attribute, e.g.:

import scipy as sp
import scipy.sparse


m = (sp.sparse.rand(5, 10, 0.2) * 100).astype(int)
print(m)

  (1, 0)        34
  (1, 2)        2
  (2, 2)        39
  (1, 4)        54
  (3, 4)        22
  (4, 4)        46
  (1, 6)        40
  (1, 7)        97
  (4, 8)        60
  (4, 9)        97

m.data %= 10
print(m)

  (1, 0)        4
  (1, 2)        2
  (2, 2)        9
  (1, 4)        4
  (3, 4)        2
  (4, 4)        6
  (1, 6)        0
  (1, 7)        7
  (4, 8)        0
  (4, 9)        7

This should be efficient and should not have any conversion overhead.

Note that this can be done for any operation for which zero 0 is the absorbing element, at least on one side (left or right) for non-symmetric (commutative) operations.

For example, in the modulo % operation, 0 is the left-absorbing element, since 0 % a = 0 for any a. The same is true for integer division //, division / (as long as 0.0 and -0.0 distinction is not needed) and exponentiation ** (for positive numbers). For multiplication *, 0 is the absorbing element.

For the case of element-wise multiplication * and / (by a scalar at least) are supported with the regular syntax by scipy.sparse.coo_matrix objects.