html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/5727#issuecomment-904642129,https://api.github.com/repos/pydata/xarray/issues/5727,904642129,IC_kwDOAMm_X841675R,4160723,2021-08-24T13:27:20Z,2021-08-24T13:27:20Z,MEMBER,"I agree that this kind of ""boolean indexing"" could be clarified a bit in the docs.

I also had to double check here since I use `isel()` / `sel()` much more often than `[]` and `.loc[]`.

> Even now, it's not really clear to me why ""boolean""-masking should only work with integer-indexing and not label-indexing. Given that the highlighted maybe_cast_to_coords_dtype is the only reason it does not work for label-based indexing.

Hmm you're right. The primary reason of `maybe_cast_to_coords_dtype` is because pandas indexes don't support floating point values with lower precision than `float64` (e.g., #5700). But here https://github.com/pydata/xarray/blob/a6b44d72010c5311fad4f60194b527194e26094b/xarray/core/indexes.py#L236-L237 we actually bypass the underlying pandas index when the labels are a boolean array-like so that boolean indexing should normally work with both integer and label based indexing (#182).

So we should probably fix `maybe_cast_to_coords_dtype` with something like

```python
def maybe_cast_to_coords_dtype(label, coords_dtype):
    label_dtype = np.dtype(getattr(label, ""dtype"", None))
    if coords_dtype.kind == ""f"" and not isinstance(label, slice) and label_dtype.kind != ""b"":
        label = np.asarray(label, dtype=coords_dtype)
    return label
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,976207971
https://github.com/pydata/xarray/issues/5727#issuecomment-904578829,https://api.github.com/repos/pydata/xarray/issues/5727,904578829,IC_kwDOAMm_X8416scN,4160723,2021-08-24T12:05:44Z,2021-08-24T12:05:44Z,MEMBER,"With `da.loc[{'x':~mask}]`, you're actually doing label-based indexing, so `~mask` will select repeatedly the items for which x=1.0 (True) and x=0.0 (False):

```python
>>> da.loc[{'x':~mask}]
<xarray.DataArray (x: 10)>
array([1., 0., 1., 0., 1., 0., 1., 0., 1., 0.])
Coordinates:
  * x        (x) float64 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0
``` 

What you want is integer-based indexing (i.e., not using `.loc`):

```python
>>> da[{'x':~mask}]
<xarray.DataArray (x: 5)>
array([0., 2., 4., 6., 8.])
Coordinates:
  * x        (x) float64 0.0 2.0 4.0 6.0 8.0
```

[doc link](http://xarray.pydata.org/en/stable/user-guide/indexing.html#indexing-and-selecting-data).","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,976207971