xarray.IndexVariable.cumsum#

IndexVariable.cumsum(dim=None, *, skipna=None, **kwargs)[source]#

Reduce this NamedArray’s data by applying cumsum (i.e., cumulative sum) along some dimension(s).

Parameters:

  • dim (str, Iterable of Hashable, "..." or None, default: None) – Name of dimension[s] along which to apply cumsum. For e.g. dim="x" or dim=["x", "y"]. If “…” or None, will reduce over all dimensions.

  • skipna (bool or None, 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) or skipna=True has not been implemented (object, datetime64 or timedelta64).

  • **kwargs (Any) – Additional keyword arguments passed on to the appropriate array function for calculating cumsum on this object’s data. These could include dask-specific kwargs like split_every.

Returns:

reduced (NamedArray) – New NamedArray with cumsum applied to its data and the indicated dimension(s) removed

See also

numpy.cumsum, dask.array.cumsum, Dataset.cumsum, DataArray.cumsum, NamedArray.cumulative

Aggregation

User guide on reduction or aggregation operations.

Notes

Non-numeric variables will be removed prior to reducing. datetime64 and timedelta64 dtypes are treated as numeric for aggregation operations.

Note that the methods on the cumulative method are more performant (with numbagg installed) and better supported. cumsum and cumprod may be deprecated in the future.

Examples

>>> from xarray.namedarray.core import NamedArray
>>> na = NamedArray("x", np.array([1, 2, 3, 0, 2, np.nan]))
>>> na
<xarray.NamedArray (x: 6)> Size: 48B
array([ 1.,  2.,  3.,  0.,  2., nan])

>>> na.cumsum()
<xarray.NamedArray (x: 6)> Size: 48B
array([1., 3., 6., 6., 8., 8.])

Use skipna to control whether NaNs are ignored.

>>> na.cumsum(skipna=False)
<xarray.NamedArray (x: 6)> Size: 48B
array([ 1.,  3.,  6.,  6.,  8., nan])
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