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/659#issuecomment-1126083413,https://api.github.com/repos/pydata/xarray/issues/659,1126083413,IC_kwDOAMm_X85DHqtV,13301940,2022-05-13T13:55:20Z,2022-05-13T13:55:20Z,MEMBER,"#5734 has greatly improved the performance. Fantastic work @dcherian 👏🏽

```python
In [13]: import xarray as xr, pandas as pd, numpy as np

In [14]: ds = xr.Dataset({""a"": xr.DataArray(np.r_[np.arange(500.), np.arange(500.)]),
    ...:               ""b"": xr.DataArray(np.arange(1000.))})

In [15]: ds
Out[15]: 
<xarray.Dataset>
Dimensions:  (dim_0: 1000)
Dimensions without coordinates: dim_0
Data variables:
    a        (dim_0) float64 0.0 1.0 2.0 3.0 4.0 ... 496.0 497.0 498.0 499.0
    b        (dim_0) float64 0.0 1.0 2.0 3.0 4.0 ... 996.0 997.0 998.0 999.0
```

```python
In [16]: xr.set_options(use_flox=True)
Out[16]: <xarray.core.options.set_options at 0x104de21a0>

In [17]: %%timeit
    ...: ds.groupby(""a"").mean()
    ...: 
    ...: 
1.5 ms ± 3.3 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)

In [18]: xr.set_options(use_flox=False)
Out[18]: <xarray.core.options.set_options at 0x144382350>

In [19]: %%timeit
    ...: ds.groupby(""a"").mean()
    ...: 
    ...: 
94 ms ± 715 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
```","{""total_count"": 4, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 4, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,117039129