dsplit(ary, indices_or_sections)

Please refer to the split documentation. dsplit is equivalent to split with axis=2 , the array is always split along the third axis provided the array dimension is greater than or equal to 3.

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

See Also

split

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

Examples

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

>>> np.dsplit(x, 2)
[array([[[ 0.,  1.],
        [ 4.,  5.]],
       [[ 8.,  9.],
        [12., 13.]]]), array([[[ 2.,  3.],
        [ 6.,  7.]],
       [[10., 11.],
        [14., 15.]]])]

>>> np.dsplit(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=(2, 2, 0), dtype=float64)]

See :

Back References

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

numpy.ma.extras.dstack numpy.concatenate numpy.core._multiarray_umath.concatenate numpy.split numpy.dstack numpy.dsplit

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

---

GitHub : /numpy/lib/shape_base.py#994
type: <class 'function'>
Commit: