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