xarray.DataTree.cumsum#
- DataTree.cumsum(dim=None, *, skipna=None, keep_attrs=None, **kwargs)[source]#
Reduce this DataTree’s data by applying
cumsum
along some dimension(s).- Parameters
dim (
str
,Iterable
ofHashable
,"..."
orNone
, default:None
) – Name of dimension[s] along which to applycumsum
. For e.g.dim="x"
ordim=["x", "y"]
. If “…” or None, will reduce over all dimensions.skipna (
bool
orNone
, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) orskipna=True
has not been implemented (object, datetime64 or timedelta64).keep_attrs (
bool
orNone
, optional) – If True,attrs
will be copied from the original object to the new one. If False, the new object will be returned without attributes.**kwargs (
Any
) – Additional keyword arguments passed on to the appropriate array function for calculatingcumsum
on this object’s data. These could include dask-specific kwargs likesplit_every
.
- Returns
reduced (
DataTree
) – New DataTree withcumsum
applied to its data and the indicated dimension(s) removed
See also
numpy.cumsum
,dask.array.cumsum
,Dataset.cumsum
,DataArray.cumsum
,DataTree.cumulative
- Aggregation
User guide on reduction or aggregation operations.
Notes
Non-numeric variables will be removed prior to reducing.
Note that the methods on the
cumulative
method are more performant (with numbagg installed) and better supported.cumsum
andcumprod
may be deprecated in the future.Examples
>>> dt = xr.DataTree( ... xr.Dataset( ... data_vars=dict(foo=("time", np.array([1, 2, 3, 0, 2, np.nan]))), ... coords=dict( ... time=( ... "time", ... pd.date_range("2001-01-01", freq="ME", periods=6), ... ), ... labels=("time", np.array(["a", "b", "c", "c", "b", "a"])), ... ), ... ), ... ) >>> dt <xarray.DataTree> Group: / Dimensions: (time: 6) Coordinates: * time (time) datetime64[ns] 48B 2001-01-31 2001-02-28 ... 2001-06-30 labels (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a' Data variables: foo (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan
>>> dt.cumsum() <xarray.DataTree> Group: / Dimensions: (time: 6) Dimensions without coordinates: time Data variables: foo (time) float64 48B 1.0 3.0 6.0 6.0 8.0 8.0
Use
skipna
to control whether NaNs are ignored.>>> dt.cumsum(skipna=False) <xarray.DataTree> Group: / Dimensions: (time: 6) Dimensions without coordinates: time Data variables: foo (time) float64 48B 1.0 3.0 6.0 6.0 8.0 nan