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
1376109308,I_kwDOAMm_X85SBcL8,7045,Should Xarray stop doing automatic index-based alignment?,1217238,open,0,,,13,2022-09-16T15:31:03Z,2023-08-23T07:42:34Z,,MEMBER,,,,"### What is your issue?

I am increasingly thinking that automatic index-based alignment in Xarray (copied from pandas) may have been a design mistake. Almost every time I work with datasets with different indexes, I find myself writing code to explicitly align them:

1. Automatic alignment is **hard to predict**. The implementation is complicated, and the exact mode of automatic alignment (outer vs inner vs left join) depends on the specific operation. It's also no longer possible to predict the shape (or even the dtype) resulting from most Xarray operations purely from input shape/dtype.
2. Automatic alignment brings unexpected **performance penalty**. In some domains (analytics) this is OK, but in others (e.g,. numerical modeling or deep learning) this is a complete deal-breaker.
3. Automatic alignment is **not useful for float indexes**, because exact matches are rare. In practice, this makes it less useful in Xarray's usual domains than it for pandas.

Would it be insane to consider changing Xarray's behavior to stop doing automatic alignment? I imagine we could roll this out slowly, first with warnings and then with an option for disabling it.

If you think this is a good or bad idea, consider responding to this issue with a 👍  or 👎  reaction.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7045/reactions"", ""total_count"": 13, ""+1"": 9, ""-1"": 2, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 2}",,,13221727,issue
294241734,MDU6SXNzdWUyOTQyNDE3MzQ=,1887,Boolean indexing with multi-dimensional key arrays,1217238,open,0,,,13,2018-02-04T23:28:45Z,2021-04-22T21:06:47Z,,MEMBER,,,,"Originally from https://github.com/pydata/xarray/issues/974

For _boolean indexing_:
- `da[key]` where `key` is a boolean labelled array (with _any_ number of dimensions) is made equivalent to `da.where(key.reindex_like(ds), drop=True)`. This matches the existing behavior if `key` is a 1D boolean array. For multi-dimensional arrays, even though the result is now multi-dimensional, this coupled with automatic skipping of NaNs means that `da[key].mean()` gives the same result as in NumPy.
- `da[key] = value` where `key` is a boolean labelled array can be made equivalent to `da = da.where(*align(key.reindex_like(da), value.reindex_like(da)))` (that is, the three argument form of `where`).
- `da[key_0, ..., key_n]` where all of `key_i` are boolean arrays gets handled in the usual way. It is an `IndexingError` to supply multiple labelled keys if any of them are not already aligned with as the corresponding index coordinates (and share the same dimension name). If they want alignment, we suggest users simply write `da[key_0 & ... & key_n]`.
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