numpy.argsort()：返回一个数组排好序后各元素对应的原来的位置序号。

arr = np.array([6, 1, 3])
arr_sorted = np.sort(arr)
index_sorted = np.argsort(arr)
print(f'排序之前：{arr}')
print(f'排序之后：{arr_sorted}')
print(f'对应索引：{index_sorted}')

排序之前：[6 1 3]
排序之后：[1 3 6]
对应索引：[1 2 0]

使用文档

Help on function argsort in module numpy:

argsort(a, axis=-1, kind=None, order=None)
    Returns the indices that would sort an array.
    
    Perform an indirect sort along the given axis using the algorithm specified
    by the `kind` keyword. It returns an array of indices of the same shape as
    `a` that index data along the given axis in sorted order.
    
    Parameters
    ----------
    a : array_like
        Array to sort.
    axis : int or None, optional
        Axis along which to sort.  The default is -1 (the last axis). If None,
        the flattened array is used.
    kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
        Sorting algorithm. The default is 'quicksort'. Note that both 'stable'
        and 'mergesort' use timsort under the covers and, in general, the
        actual implementation will vary with data type. The 'mergesort' option
        is retained for backwards compatibility.
    
        .. versionchanged:: 1.15.0.
           The 'stable' option was added.
    order : str or list of str, optional
        When `a` is an array with fields defined, this argument specifies
        which fields to compare first, second, etc.  A single field can
        be specified as a string, and not all fields need be specified,
        but unspecified fields will still be used, in the order in which
        they come up in the dtype, to break ties.
    
    Returns
    -------
    index_array : ndarray, int
        Array of indices that sort `a` along the specified `axis`.
        If `a` is one-dimensional, ``a[index_array]`` yields a sorted `a`.
        More generally, ``np.take_along_axis(a, index_array, axis=axis)``
        always yields the sorted `a`, irrespective of dimensionality.
    
    See Also
    --------
    sort : Describes sorting algorithms used.
    lexsort : Indirect stable sort with multiple keys.
    ndarray.sort : Inplace sort.
    argpartition : Indirect partial sort.
    
    Notes
    -----
    See `sort` for notes on the different sorting algorithms.
    
    As of NumPy 1.4.0 `argsort` works with real/complex arrays containing
    nan values. The enhanced sort order is documented in `sort`.
    
    Examples
    --------
    One dimensional array:
    
    >>> x = np.array([3, 1, 2])
    >>> np.argsort(x)
    array([1, 2, 0])
    
    Two-dimensional array:
    
    >>> x = np.array([[0, 3], [2, 2]])
    >>> x
    array([[0, 3],
           [2, 2]])
    
    >>> ind = np.argsort(x, axis=0)  # sorts along first axis (down)
    >>> ind
    array([[0, 1],
           [1, 0]])
    >>> np.take_along_axis(x, ind, axis=0)  # same as np.sort(x, axis=0)
    array([[0, 2],
           [2, 3]])
    
    >>> ind = np.argsort(x, axis=1)  # sorts along last axis (across)
    >>> ind
    array([[0, 1],
           [0, 1]])
    >>> np.take_along_axis(x, ind, axis=1)  # same as np.sort(x, axis=1)
    array([[0, 3],
           [2, 2]])
    
    Indices of the sorted elements of a N-dimensional array:
    
    >>> ind = np.unravel_index(np.argsort(x, axis=None), x.shape)
    >>> ind
    (array([0, 1, 1, 0]), array([0, 0, 1, 1]))
    >>> x[ind]  # same as np.sort(x, axis=None)
    array([0, 2, 2, 3])
    
    Sorting with keys:
    
    >>> x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])
    >>> x
    array([(1, 0), (0, 1)],
          dtype=[('x', '<i4'), ('y', '<i4')])
    
    >>> np.argsort(x, order=('x','y'))
    array([1, 0])
    
    >>> np.argsort(x, order=('y','x'))
    array([0, 1])