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hsplit(ary, indices_or_sections)

Please refer to the split documentation. hsplit is equivalent to split with axis=1 , the array is always split along the second axis regardless of the array dimension.

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

See Also

split

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

Examples

>>> 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.]]])]
See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.ma.extras.hsplit numpy.hsplit numpy.ma.extras.hstack numpy.concatenate numpy.core._multiarray_umath.concatenate numpy.split numpy.hstack

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GitHub : /numpy/lib/shape_base.py#881
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