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# pandas.DatetimeIndex.strides

`DatetimeIndex.``strides`

  Tuple of bytes to step in each dimension when traversing an array.

  The byte offset of element `(i[0], i[1], ..., i[n])` in an array a is:

      offset = sum(np.array(i) * a.strides)

  A more detailed explanation of strides can be found in the “ndarray.rst” file in the NumPy reference guide.

  See also

  `numpy.lib.stride_tricks.as_strided`

  Notes

  Imagine an array of 32-bit integers (each 4 bytes):

      x = np.array([[0, 1, 2, 3, 4],
                    [5, 6, 7, 8, 9]], dtype=np.int32)

  This array is stored in memory as 40 bytes, one after the other (known as a contiguous block of memory). The strides of an array tell us how many bytes we have to skip in memory to move to the next position along a certain axis. For example, we have to skip 4 bytes (1 value) to move to the next column, but 20 bytes (5 values) to get to the same position in the next row. As such, the strides for the array x will be `(20, 4)`.

  Examples

      >>> y = np.reshape(np.arange(2*3*4), (2,3,4))
      >>> y
      array([[[ 0,  1,  2,  3],
              [ 4,  5,  6,  7],
              [ 8,  9, 10, 11]],
             [[12, 13, 14, 15],
              [16, 17, 18, 19],
              [20, 21, 22, 23]]])
      >>> y.strides
      (48, 16, 4)
      >>> y[1,1,1]
      17
      >>> offset=sum(y.strides * np.array((1,1,1)))
      >>> offset/y.itemsize
      17

      >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)
      >>> x.strides
      (32, 4, 224, 1344)
      >>> i = np.array([3,5,2,2])
      >>> offset = sum(i * x.strides)
      >>> x[3,5,2,2]
      813
      >>> offset / x.itemsize
      813

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