# xarray.IndexVariable.cumsum

#### IndexVariable.cumsum(dim=None, , skipna=None, \*\*kwargs)[[source]](https://github.com/pydata/xarray/blob/main/xarray/namedarray/_aggregations.py#L790-L863)

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

* **Parameters:**
  * **dim** ([`str`](https://docs.python.org/3/builtins/stdtypes.html#str), `Iterable` of `Hashable`, `"..."` or [`None`](https://docs.python.org/3/builtins/constants.html#None), *default*: [`None`](https://docs.python.org/3/builtins/constants.html#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`](https://docs.python.org/3/builtins/functions.html#bool) or [`None`](https://docs.python.org/3/builtins/constants.html#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`](https://numpy.org/doc/stable/reference/generated/numpy.cumsum.html#numpy.cumsum), [`dask.array.cumsum`](https://docs.dask.org/en/latest/generated/dask.array.cumsum.html#dask.array.cumsum), [`Dataset.cumsum`](xarray.Dataset.cumsum.html.md#xarray.Dataset.cumsum), [`DataArray.cumsum`](xarray.DataArray.cumsum.html.md#xarray.DataArray.cumsum), `NamedArray.cumulative`

[Aggregation](../user-guide/computation.html.md#agg)
: 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

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```pycon
>>> 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])
```

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

Use `skipna` to control whether NaNs are ignored.

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

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