xarray.DataArray.rolling (original) (raw)

DataArray.rolling(dim=None, min_periods=None, center=False, **window_kwargs)[source]#

Rolling window object for DataArrays.

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 or None, 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 to int, default: False) – Set the labels at the center of the window. The default, False, sets the labels at the right edge of the window.
• **window_kwargs (optional) – The keyword arguments form of dim. One of dim or window_kwargs must be provided.

Returns:

computation.rolling.DataArrayRolling

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( ... "1999-12-15", ... periods=12, ... freq=pd.DateOffset(months=1), ... ) ... ], ... dims="time", ... ) da <xarray.DataArray (time: 12)> Size: 96B array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11.]) Coordinates:

• time (time) datetime64[ns] 96B 1999-12-15 2000-01-15 ... 2000-11-15

  da.rolling(time=3, center=True).mean() <xarray.DataArray (time: 12)> Size: 96B array([nan, 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., nan]) Coordinates:

• time (time) datetime64[ns] 96B 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)> Size: 80B array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]) Coordinates:

• time (time) datetime64[ns] 80B 2000-01-15 2000-02-15 ... 2000-10-15