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/7662#issuecomment-1480973335,https://api.github.com/repos/pydata/xarray/issues/7662,1480973335,IC_kwDOAMm_X85YRdwX,43635101,2023-03-23T10:51:27Z,2023-03-23T10:51:27Z,NONE,"Values outside of the month are filled with NaN when aggregating after your month filter.
It seems to be a side effect of grouping with a frequency.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1636435706
https://github.com/pydata/xarray/issues/7662#issuecomment-1480964097,https://api.github.com/repos/pydata/xarray/issues/7662,1480964097,IC_kwDOAMm_X85YRbgB,43635101,2023-03-23T10:44:26Z,2023-03-23T10:46:22Z,NONE,"Idk if this comes from xarray, pandas seems to have the same behaviour:

```python
import xarray as xr
import pandas as pd
import numpy as np

time = pd.date_range(""2000-01-01"", ""2022-01-01"", freq=""1h"")
ds = xr.Dataset({""time"": time, ""v"": (""time"",np.random.rand(len(time)))})
ds

df = ds.to_dataframe()
df = df[df.index.month.isin([6,7,8])]
df.groupby(pd.Grouper(freq=""D"")).mean()

                   v
time                
2000-06-01  0.451577
2000-06-02  0.524039
2000-06-03  0.540553
2000-06-04  0.537686
2000-06-05  0.521928
...              ...
2021-08-27  0.441088
2021-08-28  0.473274
2021-08-29  0.455666
2021-08-30  0.559104
2021-08-31  0.424167

[7762 rows x 1 columns]
```

You can select your months after performing aggregation and you will have the correct number of rows","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1636435706