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# numpy.diagonal

`numpy.``diagonal`(a, offset=0, axis1=0, axis2=1)

  Return specified diagonals.

  If a is 2-D, returns the diagonal of a with the given offset, i.e., the collection of elements of the form `a[i, i+offset]`. If a has more than two dimensions, then the axes specified by axis1 and axis2 are used to determine the 2-D sub-array whose diagonal is returned. The shape of the resulting array can be determined by removing axis1 and axis2 and appending an index to the right equal to the size of the resulting diagonals.

  Parameters : | a : array_like Array from which the diagonals are taken. offset : int, optional Offset of the diagonal from the main diagonal. Can be positive or negative. Defaults to main diagonal (0). axis1 : int, optional Axis to be used as the first axis of the 2-D sub-arrays from which the diagonals should be taken. Defaults to first axis (0). axis2 : int, optional Axis to be used as the second axis of the 2-D sub-arrays from which the diagonals should be taken. Defaults to second axis (1).
  Returns : | array_of_diagonals : ndarray If a is 2-D, a 1-D array containing the diagonal is returned. If the dimension of a is larger, then an array of diagonals is returned, “packed” from left-most dimension to right-most (e.g., if a is 3-D, then the diagonals are “packed” along rows).
  Raises : | ValueError : If the dimension of a is less than 2.

  See also

  `diag`
    MATLAB work-a-like for 1-D and 2-D arrays.
  `diagflat`
    Create diagonal arrays.
  `trace`
    Sum along diagonals.

  Examples

      >>> a = np.arange(4).reshape(2,2)
      >>> a
      array([[0, 1],
             [2, 3]])
      >>> a.diagonal()
      array([0, 3])
      >>> a.diagonal(1)
      array([1])

  A 3-D example:

      >>> a = np.arange(8).reshape(2,2,2); a
      array([[[0, 1],
              [2, 3]],
             [[4, 5],
              [6, 7]]])
      >>> a.diagonal(0, # Main diagonals of two arrays created by skipping
      ...            0, # across the outer(left)-most axis last and
      ...            1) # the "middle" (row) axis first.
      array([[0, 6],
             [1, 7]])

  The sub-arrays whose main diagonals we just obtained; note that each corresponds to fixing the right-most (column) axis, and that the diagonals are “packed” in rows.

      >>> a[:,:,0] # main diagonal is [0 6]
      array([[0, 2],
             [4, 6]])
      >>> a[:,:,1] # main diagonal is [1 7]
      array([[1, 3],
             [5, 7]])

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