cumulative

UnitsAwareDataArray.cumulative(dim: str | Iterable[Hashable], min_periods: int = 1) → DataArrayRolling

Accumulating object for DataArrays.

Parameters:

dims (iterable of hashable) – The name(s) of the dimensions to create the cumulative window along
min_periods (int, default: 1) – Minimum number of observations in window required to have a value (otherwise result is NA). The default is 1 (note this is different from Rolling, whose default is the size of the window).

Return type:

core.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.cumulative("time").sum()
<xarray.DataArray (time: 12)> Size: 96B
array([ 0.,  1.,  3.,  6., 10., 15., 21., 28., 36., 45., 55., 66.])
Coordinates:
  * time     (time) datetime64[ns] 96B 1999-12-15 2000-01-15 ... 2000-11-15