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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_sectionsis 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_sectionsis a 1-D array of sorted integers, the entries indicate where alongaxisthe 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_sectionsis given as an integer, but a split does not result in equal division.See also
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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).
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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
123>>> x=np.arange(9.0)>>> np.split(x,3)[array([0.,1.,2.]), array([3.,4.,5.]), array([6.,7.,8.])]1234567>>> 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()
2025-01-10 15:47:30
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