id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
124154674,MDU6SXNzdWUxMjQxNTQ2NzQ=,688,Keep attrs & Add a 'keep_coords' argument to Dataset.apply,5635139,closed,0,,,14,2015-12-29T02:42:48Z,2023-09-30T18:47:07Z,2023-09-30T18:47:07Z,MEMBER,,,,"Generally this isn't a problem, since the coords are carried over by the resulting `DataArray`s:

``` python
In [11]:

ds = xray.Dataset({
        'a':pd.DataFrame(pd.np.random.rand(10,3)),
        'b':pd.Series(pd.np.random.rand(10))
    })
ds.coords['c'] = pd.Series(pd.np.random.rand(10))
ds
Out[11]:
<xray.Dataset>
Dimensions:  (dim_0: 10, dim_1: 3)
Coordinates:
  * dim_0    (dim_0) int64 0 1 2 3 4 5 6 7 8 9
  * dim_1    (dim_1) int64 0 1 2
    c        (dim_0) float64 0.9318 0.2899 0.3853 0.6235 0.9436 0.7928 ...
Data variables:
    a        (dim_0, dim_1) float64 0.5707 0.9485 0.3541 0.5987 0.406 0.7992 ...
    b        (dim_0) float64 0.4106 0.2316 0.5804 0.6393 0.5715 0.6463 ...
In [12]:

ds.apply(lambda x: x*2)
Out[12]:
<xray.Dataset>
Dimensions:  (dim_0: 10, dim_1: 3)
Coordinates:
    c        (dim_0) float64 0.9318 0.2899 0.3853 0.6235 0.9436 0.7928 ...
  * dim_0    (dim_0) int64 0 1 2 3 4 5 6 7 8 9
  * dim_1    (dim_1) int64 0 1 2
Data variables:
    a        (dim_0, dim_1) float64 1.141 1.897 0.7081 1.197 0.812 1.598 ...
    b        (dim_0) float64 0.8212 0.4631 1.161 1.279 1.143 1.293 0.3507 ...
```

But if there's an operation that removes the coords from the `DataArray`s, the coords are not there on the result (notice `c` below). 
Should the `Dataset` retain them? Either _always_ or with a `keep_coords` argument, similar to `keep_attrs`.

``` python
In [13]:

ds = xray.Dataset({
        'a':pd.DataFrame(pd.np.random.rand(10,3)),
        'b':pd.Series(pd.np.random.rand(10))
    })
ds.coords['c'] = pd.Series(pd.np.random.rand(10))
ds
Out[13]:
<xray.Dataset>
Dimensions:  (dim_0: 10, dim_1: 3)
Coordinates:
  * dim_0    (dim_0) int64 0 1 2 3 4 5 6 7 8 9
  * dim_1    (dim_1) int64 0 1 2
    c        (dim_0) float64 0.4121 0.2507 0.6326 0.4031 0.6169 0.441 0.1146 ...
Data variables:
    a        (dim_0, dim_1) float64 0.4813 0.2479 0.5158 0.2787 0.06672 ...
    b        (dim_0) float64 0.2638 0.5788 0.6591 0.7174 0.3645 0.5655 ...
In [14]:

ds.apply(lambda x: x.to_pandas()*2)
Out[14]:
<xray.Dataset>
Dimensions:  (dim_0: 10, dim_1: 3)
Coordinates:
  * dim_0    (dim_0) int64 0 1 2 3 4 5 6 7 8 9
  * dim_1    (dim_1) int64 0 1 2
Data variables:
    a        (dim_0, dim_1) float64 0.9627 0.4957 1.032 0.5574 0.1334 0.8289 ...
    b        (dim_0) float64 0.5275 1.158 1.318 1.435 0.7291 1.131 0.1903 ...
```
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/688/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
587895591,MDU6SXNzdWU1ODc4OTU1OTE=,3891,Keep attrs by default? (keep_attrs),5635139,open,0,,,14,2020-03-25T18:17:35Z,2023-09-22T02:27:50Z,,MEMBER,,,,"<!-- A short summary of the issue, if appropriate -->

I've held this view in low confidence for a while and wanted to socialize it to see whether there's something to it: Should we keep attrs in operations by default? 

Advantages:
- I think most of the time people want to keep attrs after operations
    - Is that right? Are there cases where it wouldn't be a reasonable default? e.g. good points [here](https://github.com/pydata/xarray/issues/688#issuecomment-167832185) for not always keeping _coords_ around
- It's easy to remove them with a (currently unimplemented) `drop_attrs` method when people do want to remove them

Disadvantages:
- Backward incompatible change with an expensive deprecate cycle (would be impractical to have a deprecation warning every time someone ran a function on an object with attrs I think? At least without adding a `once` filter warning)
- ?

Here are some existing relevant discussions:
- https://github.com/pydata/xarray/issues/3815#issuecomment-603974527
- https://github.com/pydata/xarray/issues/688
- https://github.com/pydata/xarray/pull/2482
- https://github.com/pydata/xarray/issues/3304

I think this is an easy situation to get into:
- We make an incorrect-but-insignificant design decision; e.g. some methods don't keep attrs 
- We want to change that, but avoid breaking backward-compatibility
- So we add kwargs and eventually a global config
- But now we have a global config that requires global context and lots of kwargs! :(

I'm up for leaning towards breaking changes if it makes the library better: I think xarray will grow immensely, and so the narrow immediate pain is worth the broader future positive impact. Clearly if the immediate pain stops xarray growing, then it's not a good tradeoff.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3891/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue