Returns a Resample object for performing resampling operations.

        Handles both downsampling and upsampling. If any intervals contain no
        values from the original object, they will be given the value ``NaN``.

        Parameters
        ----------
        indexer : {dim: freq}, optional
            Mapping from the dimension name to resample frequency.
        skipna : bool, optional
            Whether to skip missing values when aggregating in downsampling.
        closed : 'left' or 'right', optional
            Side of each interval to treat as closed.
        label : 'left or 'right', optional
            Side of each interval to use for labeling.
        base : int, optional
            For frequencies that evenly subdivide 1 day, the "origin" of the
            aggregated intervals. For example, for '24H' frequency, base could
            range from 0 through 23.
        loffset : timedelta or str, optional
            Offset used to adjust the resampled time labels. Some pandas date
            offset strings are supported.
        keep_attrs : bool, optional
            If True, the object's attributes (`attrs`) will be copied from
            the original object to the new one.  If False (default), the new
            object will be returned without attributes.
        **indexer_kwargs : {dim: freq}
            The keyword arguments form of ``indexer``.
            One of indexer or indexer_kwargs must be provided.

        Returns
        -------
        resampled : same type as caller
            This object resampled.

        Examples
        --------
        Downsample monthly time-series data to seasonal data:

        >>> da = xr.DataArray(np.linspace(0, 11, num=12),
        ...                   coords=[pd.date_range('15/12/1999',
        ...                           periods=12, freq=pd.DateOffset(months=1))],
        ...                   dims='time')
        >>> da
        <xarray.DataArray (time: 12)>
        array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7., 8.,   9.,  10.,  11.])
        Coordinates:
          * time     (time) datetime64[ns] 1999-12-15 2000-01-15 2000-02-15 ...
        >>> da.resample(time="QS-DEC").mean()
        <xarray.DataArray (time: 4)>
        array([ 1.,  4.,  7., 10.])
        Coordinates:
          * time     (time) datetime64[ns] 1999-12-01 2000-03-01 2000-06-01 2000-09-01

        Upsample monthly time-series data to daily data:

        >>> da.resample(time='1D').interpolate('linear')
        <xarray.DataArray (time: 337)>
        array([ 0.      ,  0.032258,  0.064516, ..., 10.935484, 10.967742, 11.      ])
        Coordinates:
          * time     (time) datetime64[ns] 1999-12-15 1999-12-16 1999-12-17 ...

        Limit scope of upsampling method
        >>> da.resample(time='1D').nearest(tolerance='1D')
        <xarray.DataArray (time: 337)>
        array([ 0.,  0., nan, ..., nan, 11., 11.])
        Coordinates:
          * time     (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-11-15

        References
        ----------

        .. [1] http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases

def resample(self, indexer: Optional[Mapping[Hashable, str]] = None,
                 skipna=None, closed: Optional[str] = None,
                 label: Optional[str] = None,
                 base: int = 0, keep_attrs: Optional[bool] = None,
                 loffset=None,
                 **indexer_kwargs: str):
        """Returns a Resample object for performing resampling operations.

        Handles both downsampling and upsampling. If any intervals contain no
        values from the original object, they will be given the value ``NaN``.

        Parameters
        ----------
        indexer : {dim: freq}, optional
            Mapping from the dimension name to resample frequency.
        skipna : bool, optional
            Whether to skip missing values when aggregating in downsampling.
        closed : 'left' or 'right', optional
            Side of each interval to treat as closed.
        label : 'left or 'right', optional
            Side of each interval to use for labeling.
        base : int, optional
            For frequencies that evenly subdivide 1 day, the "origin" of the
            aggregated intervals. For example, for '24H' frequency, base could
            range from 0 through 23.
        loffset : timedelta or str, optional
            Offset used to adjust the resampled time labels. Some pandas date
            offset strings are supported.
        keep_attrs : bool, optional
            If True, the object's attributes (`attrs`) will be copied from
            the original object to the new one.  If False (default), the new
            object will be returned without attributes.
        **indexer_kwargs : {dim: freq}
            The keyword arguments form of ``indexer``.
            One of indexer or indexer_kwargs must be provided.

        Returns
        -------
        resampled : same type as caller
            This object resampled.

        Examples
        --------
        Downsample monthly time-series data to seasonal data:

        >>> da = xr.DataArray(np.linspace(0, 11, num=12),
        ...                   coords=[pd.date_range('15/12/1999',
        ...                           periods=12, freq=pd.DateOffset(months=1))],
        ...                   dims='time')
        >>> da
        <xarray.DataArray (time: 12)>
        array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7., 8.,   9.,  10.,  11.])
        Coordinates:
          * time     (time) datetime64[ns] 1999-12-15 2000-01-15 2000-02-15 ...
        >>> da.resample(time="QS-DEC").mean()
        <xarray.DataArray (time: 4)>
        array([ 1.,  4.,  7., 10.])
        Coordinates:
          * time     (time) datetime64[ns] 1999-12-01 2000-03-01 2000-06-01 2000-09-01

        Upsample monthly time-series data to daily data:

        >>> da.resample(time='1D').interpolate('linear')
        <xarray.DataArray (time: 337)>
        array([ 0.      ,  0.032258,  0.064516, ..., 10.935484, 10.967742, 11.      ])
        Coordinates:
          * time     (time) datetime64[ns] 1999-12-15 1999-12-16 1999-12-17 ...

        Limit scope of upsampling method
        >>> da.resample(time='1D').nearest(tolerance='1D')
        <xarray.DataArray (time: 337)>
        array([ 0.,  0., nan, ..., nan, 11., 11.])
        Coordinates:
          * time     (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-11-15

        References
        ----------

        .. [1] http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
        """  # noqa
        # TODO support non-string indexer after removing the old API.

        from .dataarray import DataArray
        from .resample import RESAMPLE_DIM
        from ..coding.cftimeindex import CFTimeIndex

        if keep_attrs is None:
            keep_attrs = _get_keep_attrs(default=False)

        # note: the second argument (now 'skipna') use to be 'dim'
        if ((skipna is not None and not isinstance(skipna, bool))
                or ('how' in indexer_kwargs and 'how' not in self.dims)
                or ('dim' in indexer_kwargs and 'dim' not in self.dims)):
            raise TypeError(
                'resample() no longer supports the `how` or '
                '`dim` arguments. Instead call methods on resample '
                "objects, e.g., data.resample(time='1D').mean()")

        indexer = either_dict_or_kwargs(indexer, indexer_kwargs, 'resample')
        if len(indexer) != 1:
            raise ValueError(
                "Resampling only supported along single dimensions."
            )
        dim, freq = next(iter(indexer.items()))

        dim_name = dim
        dim_coord = self[dim]

        if isinstance(self.indexes[dim_name], CFTimeIndex):
            from .resample_cftime import CFTimeGrouper
            grouper = CFTimeGrouper(freq, closed, label, base, loffset)
        else:
            # TODO: to_offset() call required for pandas==0.19.2
            grouper = pd.Grouper(freq=freq, closed=closed, label=label,
                                 base=base,
                                 loffset=pd.tseries.frequencies.to_offset(
                                     loffset))
        group = DataArray(dim_coord, coords=dim_coord.coords,
                          dims=dim_coord.dims, name=RESAMPLE_DIM)
        resampler = self._resample_cls(self, group=group, dim=dim_name,
                                       grouper=grouper,
                                       resample_dim=RESAMPLE_DIM)

        return resampler