def to_xarray(self):
        """
        Return an xarray object from the pandas object.

        Returns
        -------
        xarray.DataArray or xarray.Dataset
            Data in the pandas structure converted to Dataset if the object is
            a DataFrame, or a DataArray if the object is a Series.

        See Also
        --------
        DataFrame.to_hdf : Write DataFrame to an HDF5 file.
        DataFrame.to_parquet : Write a DataFrame to the binary parquet format.

        Notes
        -----
        See the `xarray docs <http://xarray.pydata.org/en/stable/>`__

        Examples
        --------
        >>> df = pd.DataFrame([('falcon', 'bird',  389.0, 2),
        ...                    ('parrot', 'bird', 24.0, 2),
        ...                    ('lion',   'mammal', 80.5, 4),
        ...                    ('monkey', 'mammal', np.nan, 4)],
        ...                    columns=['name', 'class', 'max_speed',
        ...                             'num_legs'])
        >>> df
             name   class  max_speed  num_legs
        0  falcon    bird      389.0         2
        1  parrot    bird       24.0         2
        2    lion  mammal       80.5         4
        3  monkey  mammal        NaN         4

        >>> df.to_xarray()
        <xarray.Dataset>
        Dimensions:    (index: 4)
        Coordinates:
          * index      (index) int64 0 1 2 3
        Data variables:
            name       (index) object 'falcon' 'parrot' 'lion' 'monkey'
            class      (index) object 'bird' 'bird' 'mammal' 'mammal'
            max_speed  (index) float64 389.0 24.0 80.5 nan
            num_legs   (index) int64 2 2 4 4

        >>> df['max_speed'].to_xarray()
        <xarray.DataArray 'max_speed' (index: 4)>
        array([389. ,  24. ,  80.5,   nan])
        Coordinates:
          * index    (index) int64 0 1 2 3

        >>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',
        ...                         '2018-01-02', '2018-01-02'])
        >>> df_multiindex = pd.DataFrame({'date': dates,
        ...                    'animal': ['falcon', 'parrot', 'falcon',
        ...                               'parrot'],
        ...                    'speed': [350, 18, 361, 15]}).set_index(['date',
        ...                                                    'animal'])
        >>> df_multiindex
                           speed
        date       animal
        2018-01-01 falcon    350
                   parrot     18
        2018-01-02 falcon    361
                   parrot     15

        >>> df_multiindex.to_xarray()
        <xarray.Dataset>
        Dimensions:  (animal: 2, date: 2)
        Coordinates:
          * date     (date) datetime64[ns] 2018-01-01 2018-01-02
          * animal   (animal) object 'falcon' 'parrot'
        Data variables:
            speed    (date, animal) int64 350 18 361 15
        """

        try:
            import xarray
        except ImportError:
            # Give a nice error message
            raise ImportError("the xarray library is not installed\n"
                              "you can install via conda\n"
                              "conda install xarray\n"
                              "or via pip\n"
                              "pip install xarray\n")

        if self.ndim == 1:
            return xarray.DataArray.from_series(self)
        elif self.ndim == 2:
            return xarray.Dataset.from_dataframe(self)

        # > 2 dims
        coords = [(a, self._get_axis(a)) for a in self._AXIS_ORDERS]
        return xarray.DataArray(self,
                                coords=coords,
                                )
Return an xarray object from the pandas object.

        Returns
        -------
        xarray.DataArray or xarray.Dataset
            Data in the pandas structure converted to Dataset if the object is
            a DataFrame, or a DataArray if the object is a Series.

        See Also
        --------
        DataFrame.to_hdf : Write DataFrame to an HDF5 file.
        DataFrame.to_parquet : Write a DataFrame to the binary parquet format.

        Notes
        -----
        See the `xarray docs <http://xarray.pydata.org/en/stable/>`__

        Examples
        --------
        >>> df = pd.DataFrame([('falcon', 'bird',  389.0, 2),
        ...                    ('parrot', 'bird', 24.0, 2),
        ...                    ('lion',   'mammal', 80.5, 4),
        ...                    ('monkey', 'mammal', np.nan, 4)],
        ...                    columns=['name', 'class', 'max_speed',
        ...                             'num_legs'])
        >>> df
             name   class  max_speed  num_legs
        0  falcon    bird      389.0         2
        1  parrot    bird       24.0         2
        2    lion  mammal       80.5         4
        3  monkey  mammal        NaN         4

        >>> df.to_xarray()
        <xarray.Dataset>
        Dimensions:    (index: 4)
        Coordinates:
          * index      (index) int64 0 1 2 3
        Data variables:
            name       (index) object 'falcon' 'parrot' 'lion' 'monkey'
            class      (index) object 'bird' 'bird' 'mammal' 'mammal'
            max_speed  (index) float64 389.0 24.0 80.5 nan
            num_legs   (index) int64 2 2 4 4

        >>> df['max_speed'].to_xarray()
        <xarray.DataArray 'max_speed' (index: 4)>
        array([389. ,  24. ,  80.5,   nan])
        Coordinates:
          * index    (index) int64 0 1 2 3

        >>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',
        ...                         '2018-01-02', '2018-01-02'])
        >>> df_multiindex = pd.DataFrame({'date': dates,
        ...                    'animal': ['falcon', 'parrot', 'falcon',
        ...                               'parrot'],
        ...                    'speed': [350, 18, 361, 15]}).set_index(['date',
        ...                                                    'animal'])
        >>> df_multiindex
                           speed
        date       animal
        2018-01-01 falcon    350
                   parrot     18
        2018-01-02 falcon    361
                   parrot     15

        >>> df_multiindex.to_xarray()
        <xarray.Dataset>
        Dimensions:  (animal: 2, date: 2)
        Coordinates:
          * date     (date) datetime64[ns] 2018-01-01 2018-01-02
          * animal   (animal) object 'falcon' 'parrot'
        Data variables:
            speed    (date, animal) int64 350 18 361 15