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.

See also

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)]