# xarray.DataTree.cumsum

#### DataTree.cumsum(dim=None, , skipna=None, keep_attrs=None, \*\*kwargs)[[source]](https://github.com/pydata/xarray/blob/main/xarray/core/_aggregations.py#L1131-L1239)

Reduce this DataTree’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).
  * **keep_attrs** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool) or [`None`](https://docs.python.org/3/builtins/constants.html#None), *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 calculating `cumsum` on this object’s data.
    These could include dask-specific kwargs like `split_every`.
* **Returns:**
  **reduced** ([`DataTree`](xarray.DataTree.html.md#xarray.DataTree)) – New DataTree 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), `DataTree.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

<div class="try_examples_button_container"><button class="try_examples_button" onclick="window.tryExamplesShowIframe('96b89207-17dd-494c-9505-4f95494173b7','d3da575d-be3c-4e98-90f1-b62dbc09e0b2','1b2dc0f4-5292-4ce9-9498-94b969b317b9','../lite/tree/../notebooks/index.html?path=b5665eba_4335_4f71_95ba_f4e6feff70f5.ipynb','None')">Try it in your browser!</button></div>
```pycon
>>> 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[us] 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
```

```pycon
>>> dt.cumsum()
<xarray.DataTree>
Group: /
    Dimensions:  (time: 6)
    Coordinates:
      * time     (time) datetime64[us] 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 3.0 6.0 6.0 8.0 8.0
```

Use `skipna` to control whether NaNs are ignored.

```pycon
>>> dt.cumsum(skipna=False)
<xarray.DataTree>
Group: /
    Dimensions:  (time: 6)
    Coordinates:
      * time     (time) datetime64[us] 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 3.0 6.0 6.0 8.0 nan
```

<div id="1b2dc0f4-5292-4ce9-9498-94b969b317b9" class="try_examples_outer_iframe  hidden"><div class="try_examples_button_container"><button class="try_examples_button" onclick="window.tryExamplesHideIframe('96b89207-17dd-494c-9505-4f95494173b7','1b2dc0f4-5292-4ce9-9498-94b969b317b9')">Go Back</button><button class="try_examples_button" onclick="window.openInNewTab('96b89207-17dd-494c-9505-4f95494173b7','1b2dc0f4-5292-4ce9-9498-94b969b317b9')">Open In Tab</button></div><div id="d3da575d-be3c-4e98-90f1-b62dbc09e0b2" class="jupyterlite_sphinx_iframe_container"></div></div><script>document.addEventListener("DOMContentLoaded", function() {window.loadTryExamplesConfig("../try_examples.json");});</script>