xarray.DataArray.rolling¶
-
DataArray.
rolling
(dim=None, min_periods=None, center=False, keep_attrs=None, **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 (
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
core.rolling.DataArrayRolling
orcore.rolling.DatasetRolling
– A rolling object (DataArrayRolling
forDataArray
,DatasetRolling
forDataset
)
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