# numpy.split

numpy.split(ary, indices_or_sections, axis=0)

  Split an array into multiple sub-arrays as views into ary.

  Parameters:
    ary ndarray

      Array to be divided into sub-arrays.

    indices_or_sections int or 1-D array

      If indices_or_sections is an integer, N, the array will be divided into N equal arrays along axis. If such a split is not possible, an error is raised.

      If indices_or_sections is a 1-D array of sorted integers, the entries indicate where along axis the array is split. For example, `[2, 3]` would, for `axis=0`, result in

      > - ary[:2]

      > - ary[2:3]

      > - ary[3:]

      If an index exceeds the dimension of the array along axis, an empty sub-array is returned correspondingly.

    axis int, optional

      The axis along which to split, default is 0.

  Returns:
    sub-arrays list of ndarrays

      A list of sub-arrays as views into ary.

  Raises:
    ValueError

      If indices_or_sections is given as an integer, but a split does not result in equal division.

  See also

  `array_split`

    Split an array into multiple sub-arrays of equal or near-equal size. Does not raise an exception if an equal division cannot be made.

  `hsplit`

    Split array into multiple sub-arrays horizontally (column-wise).

  `vsplit`

    Split array into multiple sub-arrays vertically (row wise).

  `dsplit`

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

  `concatenate`

    Join a sequence of arrays along an existing axis.

  `stack`

    Join a sequence of arrays along a new axis.

  `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).

  Examples

      >>> x = np.arange(9.0)
      >>> np.split(x, 3)
      [array([0.,  1.,  2.]), array([3.,  4.,  5.]), array([6.,  7.,  8.])]

      >>> x = np.arange(8.0)
      >>> np.split(x, [3, 5, 6, 10])
      [array([0.,  1.,  2.]),
       array([3.,  4.]),
       array([5.]),
       array([6.,  7.]),
       array([], dtype=float64)]