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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 alongaxis
. 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 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 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.
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hsplit
- Split array into multiple sub-arrays horizontally (column-wise).
-
vsplit
- Split array into multiple sub-arrays vertically (row wise).
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dsplit
- Split array into multiple sub-arrays along the 3rd axis (depth).
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concatenate
- Join a sequence of arrays along an existing axis.
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stack
- Join a sequence of arrays along a new axis.
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hstack
- Stack arrays in sequence horizontally (column wise).
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vstack
- Stack arrays in sequence vertically (row wise).
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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)]
numpy.split()
2017-01-10 18:18:46
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