# numpy.split#

numpy.split(ary, indices_or_sections, axis=0)[source]#

Split an array into multiple sub-arrays as views into ary.

Parameters:
aryndarray

Array to be divided into sub-arrays.

indices_or_sectionsint or 1-D 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 1-D 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.

axisint, optional

The axis along which to split, default is 0.

Returns:
sub-arrayslist of ndarrays

A list of sub-arrays as views into ary.

Raises:
ValueError

If indices_or_sections is given as an integer, but a split does not result in equal division.

`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 a sequence of arrays along an existing axis.

`stack`

Join a sequence of arrays along a new axis.

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