# xarray.Dataset.rolling

`Dataset.``rolling`(dim: Mapping[Hashable, int] = None, min_periods: int = None, center: bool = False, **window_kwargs)

  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.
  - **window_kwargs (optional) – The keyword arguments form of `dim`. One of dim or window_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()`