xarray.IndexVariable.cumsum#
- IndexVariable.cumsum(dim=None, *, skipna=None, **kwargs)[source]#
Reduce this NamedArray’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).**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 (
NamedArray
) – New NamedArray withcumsum
applied to its data and the indicated dimension(s) removed
See also
numpy.cumsum
,dask.array.cumsum
,Dataset.cumsum
,DataArray.cumsum
- Aggregation
User guide on reduction or aggregation operations.
Notes
Non-numeric variables will be removed prior to reducing.
Examples
>>> from xarray.namedarray.core import NamedArray >>> na = NamedArray( ... "x", ... np.array([1, 2, 3, 0, 2, np.nan]), ... ) >>> na <xarray.NamedArray (x: 6)> array([ 1., 2., 3., 0., 2., nan])
>>> na.cumsum() <xarray.NamedArray (x: 6)> array([1., 3., 6., 6., 8., 8.])
Use
skipna
to control whether NaNs are ignored.>>> na.cumsum(skipna=False) <xarray.NamedArray (x: 6)> array([ 1., 3., 6., 6., 8., nan])