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 Ifindices_or_sections is an integer, N, the array will be dividedinto N equal arrays alongaxis. If such a split is not possible,an error is raised. Ifindices_or_sections is a 1-D array of sorted integers, the entriesindicate where alongaxis the array is split. For example,[2,3] would, foraxis=0, result in ary[:2] ary[2:3] ary[3:] If an index exceeds the dimension of the array alongaxis,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 Ifindices_or_sections is given as an integer, buta 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)]