numpy.split

numpy.split(ary, indices_or_sections, axis=0)

Split an array into multiple sub-arrays.

Parameters:

ary : ndarray

Array to be divided into sub-arrays.

indices_or_sections : int 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.

axis : int, optional

The axis along which to split, default is 0.

Returns:

sub-arrays : list of ndarrays

A list of sub-arrays.

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 arrays together.

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)]