【help of Python】split

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本文详细介绍了 NumPy 库中的 split 函数，该函数用于将数组分割成多个子数组。文章解释了 split 函数的参数，包括 ary（待分割的数组）、indices_or_sections（指定如何分割数组）和 axis（沿哪个轴进行分割）。此外还提供了使用不同参数配置的示例。

>>> help(split)
Help on function split in module numpy.lib.shape_base:

split(ary, indices_or_sections, axis=0)
Split an array into multiple sub-arrays of equal size.

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)]