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/3942#issuecomment-965595392,https://api.github.com/repos/pydata/xarray/issues/3942,965595392,IC_kwDOAMm_X845jdEA,460756,2021-11-10T17:56:41Z,2021-11-10T17:57:17Z,NONE,"Cool, I agree that an error and a documentation change is likely to be sufficient :slightly_smiling_face:  (and I'd be keen to write a PR to help out!)

But, before we commit to that path:  Please may I ask:  Why not default to xarray encoding time as `'units': 'nanoseconds since 1970-01-01'` to be consistent with `np.datetime64[ns]`?   Sorry if I've missed something obvious!","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,595492608
https://github.com/pydata/xarray/issues/3942#issuecomment-965562434,https://api.github.com/repos/pydata/xarray/issues/3942,965562434,IC_kwDOAMm_X845jVBC,460756,2021-11-10T17:17:29Z,2021-11-10T17:49:22Z,NONE,"I think I've bumped into a symptom of this issue (my issue is described in #5969).  And I think #3379 may be another symptom of this issue.

Perhaps I'm biased (because I work with timeseries which only span a few years) but I wonder if xarray should default to encoding time as `'units': 'nanoseconds since 1970-01-01'` (to be consistent with `np.datetime64[ns]`) unless the timeseries includes dates before the year 1677, or after the year 2262 :slightly_smiling_face:?  Would that work?

If that's no good, then let's definitely add a note to the documentation to say that it might be a good idea for users to manually specify the encoding for datetimes if they wish to append to Zarrs.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,595492608
https://github.com/pydata/xarray/issues/3379#issuecomment-965543913,https://api.github.com/repos/pydata/xarray/issues/3379,965543913,IC_kwDOAMm_X845jQfp,460756,2021-11-10T16:56:58Z,2021-11-10T16:56:58Z,NONE,I think the underlying issue might be #3942.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,503583044
https://github.com/pydata/xarray/issues/1900#issuecomment-938691112,https://api.github.com/repos/pydata/xarray/issues/1900,938691112,IC_kwDOAMm_X84380oo,460756,2021-10-08T14:32:44Z,2021-10-08T14:35:46Z,NONE,"OK, I think `pandera` isn't the way forwards because it appears very tighly coupled to Pandas (so, for example, I don't think it's possible to use `pandera` with n-dimensional arrays).

But Pydantic looks promising.  [Here's a very quick coding experiment](https://github.com/openclimatefix/nowcasting_dataset/blob/jack/pydantic/notebooks/pydantic_xarray.ipynb) showing one way to use pydantic with xarray... it validates a few things; but it's not super-useful as a human-readable specification for what's going on inside a DataArray or Dataset.","{""total_count"": 2, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 1}",,295959111
https://github.com/pydata/xarray/issues/1900#issuecomment-938397801,https://api.github.com/repos/pydata/xarray/issues/1900,938397801,IC_kwDOAMm_X8437tBp,460756,2021-10-08T07:04:51Z,2021-10-08T07:04:51Z,NONE,"I'm really interested in a machine-readable schema for xarray!

[Pandera](https://pandera.readthedocs.io) provides machine-readable schemas for Pandas and, as of version 0.7, [panderas has decoupled pandera and pandas types](https://github.com/pandera-dev/pandera/pull/559) to make pandera more useful for things like xarray.  I haven't tried `pandera` yet but I plan to do some experiments soon.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,295959111
https://github.com/pydata/xarray/issues/1075#issuecomment-636641496,https://api.github.com/repos/pydata/xarray/issues/1075,636641496,MDEyOklzc3VlQ29tbWVudDYzNjY0MTQ5Ng==,460756,2020-06-01T06:37:08Z,2020-06-01T06:37:08Z,NONE,"FWIW, I've also tested @delgadom's technique, using `netCDF4` and it also works well (and is useful in situations where we don't want to install `h5netcdf`).  Thanks!","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,186895655
https://github.com/pydata/xarray/issues/1075#issuecomment-635415386,https://api.github.com/repos/pydata/xarray/issues/1075,635415386,MDEyOklzc3VlQ29tbWVudDYzNTQxNTM4Ng==,460756,2020-05-28T15:18:34Z,2020-05-28T15:19:06Z,NONE,"Is this now implemented (and hence can this issue be closed?)  It appears that this works well:

```python
    boto_s3 = boto3.client('s3')
    s3_object = boto_s3.get_object(Bucket=bucket, Key=key)
    netcdf_bytes = s3_object['Body'].read()
    netcdf_bytes_io = io.BytesIO(netcdf_bytes)
    ds = xr.open_dataset(netcdf_bytes_io)
```

Is that the right approach to opening a NetCDF file on S3, using the latest xarray code?","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,186895655