xarray.DataArray.rolling

DataArray.rolling(min_periods=None, center=False, **windows)

Rolling window object.

Rolling window aggregations are much faster when bottleneck is installed.

Parameters:

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.

**windows : dim=window

dim : str

Name of the dimension to create the rolling iterator along (e.g., time).

window : int

Size of the moving window.

Returns:

rolling : type of input argument

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

Remove the NaNs using dropna():

>>> da.rolling(time=3).mean().dropna('time')
<xarray.DataArray (time: 10)>
array([  1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.])
Coordinates:
  * time     (time) datetime64[ns] 2000-02-15 2000-03-15 2000-04-15 ...