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# numpy.dsplit

`numpy.``dsplit`(ary, indices_or_sections)

  Split array into multiple sub-arrays along the 3rd axis (depth).

  Parameters: | ary : ndarray An array, with at least 3 dimensions, to be divided into sub-arrays depth-wise, or along the third axis. indices_or_sections: integer or 1D array : If indices_or_sections is an integer, N, the array will be divided into N equal arrays along axis. If an equal split is not possible, a ValueError is raised. if indices_or_sections is a 1D array of sorted integers representing indices along axis, the array will be divided such that each index marks the start of each sub-array. If an index exceeds the dimension of the array along axis, and empty sub-array is returned for that index. axis : integer, optional the axis along which to split. Default is 0.
  Returns: | sub-arrays : list A list of sub-arrays.

  See also

  `array_split`
    Split an array into a list of multiple sub-arrays of near-equal size.
  `split`
    Split array into a list of multiple sub-arrays of equal size.
  `hsplit`
    Split array into a list of multiple sub-arrays horizontally
  `vsplit`
    Split array into a list of multiple sub-arrays vertically
  `concatenate`
    Join arrays together.
  `hstack`
    Stack arrays in sequence horizontally (column wise)
  `vstack`
    Stack arrays in sequence vertically (row wise)
  `dstack`
    Stack arrays in sequence depth wise (along third dimension)

  Notes

  dsplit requires that sub-arrays are of equal shape, whereas array_split allows for sub-arrays to have nearly-equal shape. Equivalent to split with axis = 2.

  Examples

      >>> x = np.arange(16.0).reshape(2, 2, 4)
      >>> np.dsplit(x, 2)
      <BLANKLINE>
      [array([[[  0.,   1.],
              [  4.,   5.]],
      <BLANKLINE>
             [[  8.,   9.],
              [ 12.,  13.]]]),
       array([[[  2.,   3.],
              [  6.,   7.]],
      <BLANKLINE>
             [[ 10.,  11.],
              [ 14.,  15.]]])]
      <BLANKLINE>
      >>> x = np.arange(16.0).reshape(2, 2, 4)
      >>> np.dsplit(x, array([3, 6]))
      <BLANKLINE>
      [array([[[  0.,   1.,   2.],
              [  4.,   5.,   6.]],
      <BLANKLINE>
             [[  8.,   9.,  10.],
              [ 12.,  13.,  14.]]]),
       array([[[  3.],
              [  7.]],
      <BLANKLINE>
             [[ 11.],
              [ 15.]]]),
       array([], dtype=float64)]

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