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# pandas.DatetimeIndex.transpose

`DatetimeIndex.``transpose`(*axes)

  Returns a view of the array with axes transposed.

  For a 1-D array, this has no effect. (To change between column and row vectors, first cast the 1-D array into a matrix object.) For a 2-D array, this is the usual matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted (see Examples). If axes are not provided and `a.shape = (i[0], i[1], ... i[n-2], i[n-1])`, then `a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])`.

  Parameters : | axes : None, tuple of ints, or n ints None or no argument: reverses the order of the axes. tuple of ints: i in the j-th place in the tuple means a‘s i-th axis becomes a.transpose()‘s j-th axis. n ints: same as an n-tuple of the same ints (this form is intended simply as a “convenience” alternative to the tuple form)
  Returns : | out : ndarray View of a, with axes suitably permuted.

  See also

  `ndarray.T`
    Array property returning the array transposed.

  Examples

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

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