def sortby(
        self,
        variables: (
            Hashable
            | DataArray
            | Sequence[Hashable | DataArray]
            | Callable[[Self], Hashable | DataArray | Sequence[Hashable | DataArray]]
        ),
        ascending: bool = True,
    ) -> Self:
        """Sort object by labels or values (along an axis).

        Sorts the dataarray, either along specified dimensions,
        or according to values of 1-D dataarrays that share dimension
        with calling object.

        If the input variables are dataarrays, then the dataarrays are aligned
        (via left-join) to the calling object prior to sorting by cell values.
        NaNs are sorted to the end, following Numpy convention.

        If multiple sorts along the same dimension is
        given, numpy's lexsort is performed along that dimension:
        https://numpy.org/doc/stable/reference/generated/numpy.lexsort.html
        and the FIRST key in the sequence is used as the primary sort key,
        followed by the 2nd key, etc.

        Parameters
        ----------
        variables : Hashable, DataArray, sequence of Hashable or DataArray, or Callable
            1D DataArray objects or name(s) of 1D variable(s) in coords whose values are
            used to sort this array. If a callable, the callable is passed this object,
            and the result is used as the value for cond.
        ascending : bool, default: True
            Whether to sort by ascending or descending order.

        Returns
        -------
        sorted : DataArray
            A new dataarray where all the specified dims are sorted by dim
            labels.

        See Also
        --------
        Dataset.sortby
        numpy.sort
        pandas.sort_values
        pandas.sort_index

        Examples
        --------
        >>> da = xr.DataArray(
        ...     np.arange(5, 0, -1),
        ...     coords=[pd.date_range("1/1/2000", periods=5)],
        ...     dims="time",
        ... )
        >>> da
        <xarray.DataArray (time: 5)> Size: 40B
        array([5, 4, 3, 2, 1])
        Coordinates:
          * time     (time) datetime64[ns] 40B 2000-01-01 2000-01-02 ... 2000-01-05

        >>> da.sortby(da)
        <xarray.DataArray (time: 5)> Size: 40B
        array([1, 2, 3, 4, 5])
        Coordinates:
          * time     (time) datetime64[ns] 40B 2000-01-05 2000-01-04 ... 2000-01-01

        >>> da.sortby(lambda x: x)
        <xarray.DataArray (time: 5)> Size: 40B
        array([1, 2, 3, 4, 5])
        Coordinates:
          * time     (time) datetime64[ns] 40B 2000-01-05 2000-01-04 ... 2000-01-01
        """
        # We need to convert the callable here rather than pass it through to the
        # dataset method, since otherwise the dataset method would try to call the
        # callable with the dataset as the object
        if callable(variables):
            variables = variables(self)
        ds = self._to_temp_dataset().sortby(variables, ascending=ascending)
        return self._from_temp_dataset(ds)
Sort object by labels or values (along an axis).

        Sorts the dataarray, either along specified dimensions,
        or according to values of 1-D dataarrays that share dimension
        with calling object.

        If the input variables are dataarrays, then the dataarrays are aligned
        (via left-join) to the calling object prior to sorting by cell values.
        NaNs are sorted to the end, following Numpy convention.

        If multiple sorts along the same dimension is
        given, numpy's lexsort is performed along that dimension:
        https://numpy.org/doc/stable/reference/generated/numpy.lexsort.html
        and the FIRST key in the sequence is used as the primary sort key,
        followed by the 2nd key, etc.

        Parameters
        ----------
        variables : Hashable, DataArray, sequence of Hashable or DataArray, or Callable
            1D DataArray objects or name(s) of 1D variable(s) in coords whose values are
            used to sort this array. If a callable, the callable is passed this object,
            and the result is used as the value for cond.
        ascending : bool, default: True
            Whether to sort by ascending or descending order.

        Returns
        -------
        sorted : DataArray
            A new dataarray where all the specified dims are sorted by dim
            labels.

        See Also
        --------
        Dataset.sortby
        numpy.sort
        pandas.sort_values
        pandas.sort_index

        Examples
        --------
        >>> da = xr.DataArray(
        ...     np.arange(5, 0, -1),
        ...     coords=[pd.date_range("1/1/2000", periods=5)],
        ...     dims="time",
        ... )
        >>> da
        <xarray.DataArray (time: 5)> Size: 40B
        array([5, 4, 3, 2, 1])
        Coordinates:
          * time     (time) datetime64[ns] 40B 2000-01-01 2000-01-02 ... 2000-01-05

        >>> da.sortby(da)
        <xarray.DataArray (time: 5)> Size: 40B
        array([1, 2, 3, 4, 5])
        Coordinates:
          * time     (time) datetime64[ns] 40B 2000-01-05 2000-01-04 ... 2000-01-01

        >>> da.sortby(lambda x: x)
        <xarray.DataArray (time: 5)> Size: 40B
        array([1, 2, 3, 4, 5])
        Coordinates:
          * time     (time) datetime64[ns] 40B 2000-01-05 2000-01-04 ... 2000-01-01
        
xarray/core/dataarray.py