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# scipy.sparse.diags

`scipy.sparse.``diags`(diagonals, offsets, shape=None, format=None, dtype=None)

  Construct a sparse matrix from diagonals.

  Parameters: | diagonals : sequence of array_like Sequence of arrays containing the matrix diagonals, corresponding to offsets. offsets : sequence of int Diagonals to set: k = 0 the main diagonal k > 0 the k-th upper diagonal k < 0 the k-th lower diagonal shape : tuple of int, optional Shape of the result. If omitted, a square matrix large enough to contain the diagonals is returned. format : {“dia”, “csr”, “csc”, “lil”, ...}, optional Matrix format of the result. By default (format=None) an appropriate sparse matrix format is returned. This choice is subject to change. dtype : dtype, optional Data type of the matrix.

  See also

  `spdiags`
    construct matrix from diagonals

  Notes

  This function differs from `spdiags` in the way it handles off-diagonals.

  The result from `diags` is the sparse equivalent of:

      np.diag(diagonals[0], offsets[0])
      + ...
      + np.diag(diagonals[k], offsets[k])

  Repeated diagonal offsets are disallowed.

  New in version 0.11.

  Examples

      >>> diagonals = [[1, 2, 3, 4], [1, 2, 3], [1, 2]]
      >>> diags(diagonals, [0, -1, 2]).toarray()
      array([[1, 0, 1, 0],
             [1, 2, 0, 2],
             [0, 2, 3, 0],
             [0, 0, 3, 4]])

  Broadcasting of scalars is supported (but shape needs to be specified):

      >>> diags([1, -2, 1], [-1, 0, 1], shape=(4, 4)).toarray()
      array([[-2.,  1.,  0.,  0.],
             [ 1., -2.,  1.,  0.],
             [ 0.,  1., -2.,  1.],
             [ 0.,  0.,  1., -2.]])

  If only one diagonal is wanted (as in `numpy.diag`), the following works as well:

      >>> diags([1, 2, 3], 1).toarray()
      array([[ 0.,  1.,  0.,  0.],
             [ 0.,  0.,  2.,  0.],
             [ 0.,  0.,  0.,  3.],
             [ 0.,  0.,  0.,  0.]])