SciPy

numpy.split

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

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)]

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