cumsum drops index coordinates

[dcherian]

What happened?

cumsum drops index coordinates. Seen in #6525, #3417

What did you expect to happen?

Preserve index coordinates

Minimal Complete Verifiable Example

import xarray as xr

ds = xr.Dataset(
        {"foo": (("x",), [7, 3, 1, 1, 1, 1, 1])},
        coords={"x": [0, 1, 2, 3, 4, 5, 6]},
    )
ds.cumsum("x")

<xarray.Dataset>
Dimensions:  (x: 7)
Dimensions without coordinates: x
Data variables:
    foo      (x) int64 7 10 11 12 13 14 15

Relevant log output

No response

Anything else we need to know?

No response

Environment

xarray main

[Illviljan]

xarray/xarray/core/dataset.py

Lines 5239 to 5240 in 7173bd3

	if not reduce_dims:
	variables[name] = var

If you comment out this if-check so it always add the variable at least the example seems to work.

Not sure how to handle this:

• Add a new keyword argument to reduce? keepcoords or can keepdims be expanded to handle this as well?
• Since cumsum is not really a reduction, should we create a new method instead of reduce? I guess it would look very similar to reduce though.

[dcherian]

Since cumsum is not really a reduction, should we create a new method instead of reduce? I guess it would look very similar to reduce though.

👍 I think we should add cumsum to generate_reductions.py. which maybe should become generate_aggregations.py

[max-sixty]

There was some discussion a while ago about how to handle reduce on things that aren't reductions. There's some overlap with #1618, and that has some links to other issues.

[max-sixty]

Was this closed by #7152?

Edit: No, I think there was a suggestion to change the approach from .reduce to .apply_ufunc here, but it didn't make it in

---

The general reduce issue overlaps with #6528.

[max-sixty]

I had a look at moving this to .apply_ufunc — if I'm thinking about it correctly — it's actually not trivial, since it's not just a gufunc like the .rolling_exp ones from #8114.

Instead, it's a function that we've written which can execute over multiple dimensions

xarray/xarray/core/duck_array_ops.py

Lines 621 to 631 in c9d89e2

	def _nd_cum_func(cum_func, array, axis, **kwargs):
	array = asarray(array)
	if axis is None:
	axis = tuple(range(array.ndim))
	if isinstance(axis, int):
	axis = (axis,)
	out = array
	for ax in axis:
	out = cum_func(out, axis=ax, **kwargs)
	return out

. (not super difficult! But IIUC, less of a lift-and-shift...)

[max-sixty]

Another alternative is to focus on Cumulative — ds.cumulative('lat').sum() works great!

[dcherian]

Can we just redirect cumsum to that then?

[max-sixty]

Can we just redirect cumsum to that then?

I looked at this but it has slightly more features such as skipna...

[jhollowed]

Can we just redirect cumsum to that then?

fwiw, I just tested this with a 33 MB Dataset. I observed that

data.cumsum('time')

is anywhere from 18 to 30x faster than

data.cumulative('time').sum()