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
1597701713,PR_kwDOAMm_X85Kpptu,7555,DOC: cross ref the groupby tutorial,1562854,closed,0,,,10,2023-02-24T00:19:15Z,2023-02-24T17:30:36Z,2023-02-24T17:29:51Z,CONTRIBUTOR,,0,pydata/xarray/pulls/7555,"There are probably many more of these that could be done, but xarray has great explainers that are not linked in the API reference.  Not sure if that is on purpose (obviously they are kind of useless if you aren't looking at the http version), but if not, this at least does them for `groupby`, which is something I always need the explainer for...","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7555/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
1382831051,PR_kwDOAMm_X84_cuv4,7070,DOC: improve name and intro to groupby,1562854,closed,0,,,0,2022-09-22T18:09:04Z,2022-09-22T20:12:49Z,2022-09-22T20:12:49Z,CONTRIBUTOR,,0,pydata/xarray/pulls/7070,"This is a very small change to the group-by title and an intro sentence.   I think sometimes the user docs assume knowledge of pandas GroupBy, whereas I think a decent chunk of xarray users don't have background with pandas.  The rest of the tutorial is a really nice overview, but if you are scanning docs, its nice to have the end goal explained.   ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7070/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
1087126812,I_kwDOAMm_X85AzD0c,6102,Regression in datetime handling in plots,1562854,closed,0,,,10,2021-12-22T19:31:53Z,2022-01-09T20:33:29Z,2022-01-09T20:33:29Z,CONTRIBUTOR,,,,"#5794 (ea2886136dec7047186d) introduced a regression in whether or not pandas datetime converters are loaded or Matplotlib's.  This leads to basic Matplotlib-native plotting failing https://github.com/matplotlib/matplotlib/issues/22023  Previously matplotlib's converters were loaded, now pandas are being loaded, despite the downstream user not ever using xarray's plotting utilities.  

## test code

```python
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr

import matplotlib.units as munits
print(munits.registry)
ds = xr.Dataset({""time"": [np.datetime64('2000-01-01'), np.datetime64('2000-01-02')],
                     ""sir"": [0, 1]})

fig, ax = plt.subplots()
# crashes:
ax.scatter(ds['time'], ds['sir'])
plt.show()
```

#### Previously:
```
{... 
<class 'numpy.datetime64'>: <matplotlib.dates._SwitchableDateConverter object at 0x106434ac0>, 
<class 'datetime.date'>: <matplotlib.dates._SwitchableDateConverter object at 0x106434ac0>, 
<class 'datetime.datetime'>: <matplotlib.dates._SwitchableDateConverter object at 0x106434ac0>}
```

#### Now:

```
{... <class 'numpy.datetime64'>: <pandas.plotting._matplotlib.converter.DatetimeConverter object at 0x17f288160>, 
<class 'datetime.date'>: <pandas.plotting._matplotlib.converter.DatetimeConverter object at 0x17f182250>, 
<class 'datetime.datetime'>: <pandas.plotting._matplotlib.converter.DatetimeConverter object at 0x17f1821c0>, 
<class 'pandas._libs.tslibs.timestamps.Timestamp'>: <pandas.plotting._matplotlib.converter.DatetimeConverter object at 0x17f16ff40>, 
<class 'pandas._libs.tslibs.period.Period'>: <pandas.plotting._matplotlib.converter.PeriodConverter object at 0x17f16ffa0>, 
<class 'datetime.time'>: <pandas.plotting._matplotlib.converter.TimeConverter object at 0x17f288130>}
```

As you can see, the pandas converters have been loaded without any use of pandas nor xarray plotting utilities.  

### Suggestion

Of course if xarray plotting is loaded, you should use and register what date converters you would like (I'd suggest `matplotlib.dates.ConciseConverter`, but your mileage may vary).  But I think if the user is just trying to use xarray to load a data set, they should not have decisions made for them about the converter (or any other plotting functions), and to prevent confusion they should get the default matplotlib converter since it handles `datetime64` just fine.  

I think it could also be argued that this is a pandas issue, in that just importing pandas should not automatically register their converters unless their plotting is used.  ping @TomAugspurger because I thought that was the plan, but apparently things changed.  And it indeed appears their converter has a bug in it for matplotlib scatter.  

Thanks!   
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6102/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
1091822549,PR_kwDOAMm_X84wbv5R,6128,TST: check datetime converter is Matplotlibs,1562854,closed,0,,,4,2022-01-01T14:02:11Z,2022-01-03T18:14:53Z,2022-01-03T18:14:53Z,CONTRIBUTOR,,0,pydata/xarray/pulls/6128,"<!-- Feel free to remove check-list items aren't relevant to your change -->

- [x] Tests added

Adds a test that says what the locator should be if the xaxis is datetime.  ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6128/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
809876123,MDExOlB1bGxSZXF1ZXN0NTc0NjYxNTg4,4919,Update matplotlib's canonical,1562854,closed,0,,,1,2021-02-17T05:47:38Z,2021-02-17T15:21:15Z,2021-02-17T08:34:02Z,CONTRIBUTOR,,0,pydata/xarray/pulls/4919,"Please see: https://discourse.matplotlib.org/t/canonical-documentation-have-moved/21863
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