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# numpy.ma.hsplit

ma.hsplit = <numpy.ma.extras._fromnxfunction_single object>

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

  Please refer to the `split` documentation. `hsplit` is equivalent to `split` with `axis=1`, the array is always split along the second axis except for 1-D arrays, where it is split at `axis=0`.

  See also

  `split`

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

  Notes

  The function is applied to both the _data and the _mask, if any.

  Examples

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

  With a higher dimensional array the split is still along the second axis.

      >>> x = np.arange(8.0).reshape(2, 2, 2)
      >>> x
      array([[[0.,  1.],
              [2.,  3.]],
             [[4.,  5.],
              [6.,  7.]]])
      >>> np.hsplit(x, 2)
      [array([[[0.,  1.]],
             [[4.,  5.]]]),
       array([[[2.,  3.]],
             [[6.,  7.]]])]

  With a 1-D array, the split is along axis 0.

      >>> x = np.array([0, 1, 2, 3, 4, 5])
      >>> np.hsplit(x, 2)
      [array([0, 1, 2]), array([3, 4, 5])]