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/pull/4871#issuecomment-774684442,https://api.github.com/repos/pydata/xarray/issues/4871,774684442,MDEyOklzc3VlQ29tbWVudDc3NDY4NDQ0Mg==,6628425,2021-02-07T14:36:50Z,2021-02-07T15:08:17Z,MEMBER,"Thanks @mathause -- indeed the timings are pretty similar. 

Before:
```
In [1]: import xarray as xr

In [2]: times = xr.cftime_range(""2000"", periods=1000).values

In [3]: units = ""days since 2000-01-01""

In [4]: calendar = ""gregorian""

In [5]: %%timeit
   ...: xr.coding.times._encode_datetime_with_cftime(times, units, calendar)

15.5 ms ± 170 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```

After:
```
In [1]: import xarray as xr

In [2]: times = xr.cftime_range(""2000"", periods=1000).values

In [3]: units = ""days since 2000-01-01""

In [4]: calendar = ""gregorian""

In [5]: %%timeit
   ...: xr.coding.times._encode_datetime_with_cftime(times, units, calendar)

14.9 ms ± 160 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,802737682