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
331668890,MDU6SXNzdWUzMzE2Njg4OTA=,2227,Slow performance of isel,4180033,open,0,,,28,2018-06-12T16:46:14Z,2023-03-14T18:54:16Z,,NONE,,,,"Hi,

I get a very slow performance of Dataset.isel or DataArray.isel in comparison with the native numpy approach. Do you know where this comes from?

```python
ds = xr.Dataset(
    {
        ""a"": (""time"", np.arange(55_000_000))
    }, coords={
        ""time"": np.arange(55_000_000)
    }
)
time_filter = ds.time > 50_000
```

Select some values with DataArray.isel:
```python
%timeit ds.a.isel(time=time_filter)
```
`2.22 s ± 375 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)`

Use the native numpy approach:
```python
%timeit ds.a.values[time_filter]
```
`163 ms ± 12.1 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)`

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-4-amd64
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: en_US.utf8
LOCALE: en_US.UTF-8

xarray: 0.10.4
pandas: 0.23.0
numpy: 1.14.2
scipy: 1.1.0
netCDF4: 1.4.0
h5netcdf: 0.5.1
h5py: 2.8.0
Nio: None
zarr: None
bottleneck: 1.2.1
cyordereddict: None
dask: 0.17.5
distributed: 1.21.8
matplotlib: 2.2.2
cartopy: 0.16.0
seaborn: 0.8.1
setuptools: 39.1.0
pip: 9.0.3
conda: None
pytest: 3.5.1
IPython: 6.4.0
sphinx: 1.7.4
</details>
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