numpy.split¶

numpy.
split
(ary, indices_or_sections, axis=0)[source]¶ Split an array into multiple subarrays as views into ary.
 Parameters
 aryndarray
Array to be divided into subarrays.
 indices_or_sectionsint or 1D 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 1D array of sorted integers, the entries indicate where along axis the array is split. For example,
[2, 3]
would, foraxis=0
, result inary[:2]
ary[2:3]
ary[3:]
If an index exceeds the dimension of the array along axis, an empty subarray is returned correspondingly.
 axisint, optional
The axis along which to split, default is 0.
 Returns
 subarrayslist of ndarrays
A list of subarrays as views into ary.
 Raises
 ValueError
If indices_or_sections is given as an integer, but a split does not result in equal division.
See also
array_split
Split an array into multiple subarrays of equal or nearequal size. Does not raise an exception if an equal division cannot be made.
hsplit
Split array into multiple subarrays horizontally (columnwise).
vsplit
Split array into multiple subarrays vertically (row wise).
dsplit
Split array into multiple subarrays 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)]