numpy.ma.hsplit

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

hsplit

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

>>> 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])]