rolling
- UnitsAwareDataArray.rolling(dim: Optional[Mapping[Any, int]] = None, min_periods: Optional[int] = None, center: Union[bool, Mapping[Any, bool]] = False, **window_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 (bool or mapping, 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
A rolling object (
DataArrayRolling
forDataArray
,DatasetRolling
forDataset
)- Return type
core.rolling.DataArrayRolling or core.rolling.DatasetRolling
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 ... 2000-11-15 >>> 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 ... 2000-11-15
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 ... 2000-10-15
See also
core.rolling.DataArrayRolling
,core.rolling.DatasetRolling