Rolling window object.

        Parameters
        ----------
        dim: dict, optional
            Mapping from the dimension name to create the rolling iterator
            along (e.g. `time`) to its moving window size.
        min_periods : int, default None
            Minimum number of observations in window required to have a value
            (otherwise result is NA). The default, None, is equivalent to
            setting min_periods equal to the size of the window.
        center : boolean, default False
            Set the labels at the center of the window.
        **dim_kwargs : optional
            The keyword arguments form of ``dim``.
            One of dim or dim_kwargs must be provided.

        Returns
        -------
        Rolling object (core.rolling.DataArrayRolling for DataArray,
        core.rolling.DatasetRolling for Dataset.)

        Examples
        --------
        Create rolling seasonal average of monthly data e.g. DJF, JFM, ..., SON:

        >>> 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.rolling(time=3, center=True).mean()
        <xarray.DataArray (time: 12)>
        array([nan,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., nan])
        Coordinates:
          * time     (time) datetime64[ns] 1999-12-15 2000-01-15 2000-02-15 ...

        Remove the NaNs using ``dropna()``:

        >>> da.rolling(time=3, center=True).mean().dropna('time')
        <xarray.DataArray (time: 10)>
        array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])
        Coordinates:
          * time     (time) datetime64[ns] 2000-01-15 2000-02-15 2000-03-15 ...

        See Also
        --------
        core.rolling.DataArrayRolling
        core.rolling.DatasetRolling

def rolling(self, dim: Optional[Mapping[Hashable, int]] = None,
                min_periods: Optional[int] = None, center: bool = False,
                **dim_kwargs: int):
        """
        Rolling window object.

        Parameters
        ----------
        dim: dict, optional
            Mapping from the dimension name to create the rolling iterator
            along (e.g. `time`) to its moving window size.
        min_periods : int, default None
            Minimum number of observations in window required to have a value
            (otherwise result is NA). The default, None, is equivalent to
            setting min_periods equal to the size of the window.
        center : boolean, default False
            Set the labels at the center of the window.
        **dim_kwargs : optional
            The keyword arguments form of ``dim``.
            One of dim or dim_kwargs must be provided.

        Returns
        -------
        Rolling object (core.rolling.DataArrayRolling for DataArray,
        core.rolling.DatasetRolling for Dataset.)

        Examples
        --------
        Create rolling seasonal average of monthly data e.g. DJF, JFM, ..., SON:

        >>> 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.rolling(time=3, center=True).mean()
        <xarray.DataArray (time: 12)>
        array([nan,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., nan])
        Coordinates:
          * time     (time) datetime64[ns] 1999-12-15 2000-01-15 2000-02-15 ...

        Remove the NaNs using ``dropna()``:

        >>> da.rolling(time=3, center=True).mean().dropna('time')
        <xarray.DataArray (time: 10)>
        array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])
        Coordinates:
          * time     (time) datetime64[ns] 2000-01-15 2000-02-15 2000-03-15 ...

        See Also
        --------
        core.rolling.DataArrayRolling
        core.rolling.DatasetRolling
        """  # noqa
        dim = either_dict_or_kwargs(dim, dim_kwargs, 'rolling')
        return self._rolling_cls(self, dim, min_periods=min_periods,
                                 center=center)