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# numpy.split

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

  Split an array into multiple sub-arrays of equal size.

  Parameters: | ary : ndarray Array to be divided into sub-arrays. 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 such a split is not possible, an error is raised. If indices_or_sections is a 1D 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 : integer, optional The axis along which to split. Default is 0.
  Returns: | sub-arrays : list A list of sub-arrays.
  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 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).

  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])
      <BLANKLINE>
      [array([ 0.,  1.,  2.]),
       array([ 3.,  4.]),
       array([ 5.]),
       array([ 6.,  7.]),
       array([], dtype=float64)]

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