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/5937#issuecomment-964310406,https://api.github.com/repos/pydata/xarray/issues/5937,964310406,IC_kwDOAMm_X845ejWG,2405019,2021-11-09T16:21:03Z,2021-11-09T16:21:03Z,CONTRIBUTOR,"> Your proposal sounds good to me.
> 
> Would you mind raising an issue on the pandas bug tracker asking if this is expected behaviour?

Great! Yes, I'm happy to raise it with the pandas developers.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1044693438
https://github.com/pydata/xarray/issues/4306#issuecomment-668501905,https://api.github.com/repos/pydata/xarray/issues/4306,668501905,MDEyOklzc3VlQ29tbWVudDY2ODUwMTkwNQ==,2405019,2020-08-04T09:54:46Z,2020-08-04T09:54:46Z,CONTRIBUTOR,"Ah, sorry. I tried to look for an existing issue. I'll close this in that case.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,672662079
https://github.com/pydata/xarray/issues/4010#issuecomment-620456062,https://api.github.com/repos/pydata/xarray/issues/4010,620456062,MDEyOklzc3VlQ29tbWVudDYyMDQ1NjA2Mg==,2405019,2020-04-28T08:20:16Z,2020-04-28T08:20:32Z,CONTRIBUTOR,"Ah! It's an issue with how I am using `np.timedelta64` then. I (stupidly) assumed that `np.timedelta64` has the same call signature as `datetime.timedelta`. It appears that `np.timedelta64` silently ignores any kwargs 😕

Anyway, I won't be making that mistake again. Thank you @keewis !","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,607678694
https://github.com/pydata/xarray/issues/3320#issuecomment-606715109,https://api.github.com/repos/pydata/xarray/issues/3320,606715109,MDEyOklzc3VlQ29tbWVudDYwNjcxNTEwOQ==,2405019,2020-03-31T15:52:36Z,2020-03-31T15:52:36Z,CONTRIBUTOR,"I just had this issue too @keewis, I think you're right. Once I remove timezone information from my datetime object it works just fine. Looking at https://github.com/pydata/xarray/issues/2512 it looks like the change which is required might be in `xarray/core/variable.py` (https://github.com/pydata/xarray/blob/master/xarray/core/variable.py#L165). I'll have a look and see if I can make it work","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,495382528
https://github.com/pydata/xarray/issues/1346#issuecomment-456149964,https://api.github.com/repos/pydata/xarray/issues/1346,456149964,MDEyOklzc3VlQ29tbWVudDQ1NjE0OTk2NA==,2405019,2019-01-21T17:33:31Z,2019-01-21T17:33:31Z,CONTRIBUTOR,"Sorry to unearth this issue again, but I just got bitten by this quite badly. I'm looking at absolute temperature perturbations and bottleneck's implementation together with my data being loaded as `float32` (correctly, as it's stored like that) causes an error on the size of the perturbations I'm looking for.

Example:

```
In [1]: import numpy as np
   ...: import bottleneck

In [2]: a = 300*np.ones((800**2,), dtype=np.float32)

In [3]: np.mean(a)
Out[3]: 300.0

In [4]: bottleneck.nanmean(a)
Out[4]: 302.6018981933594
```

Would it be worth adding a warning (until the right solution is found) if someone is doing `.mean()` on a `DataArray` which is `float32`?

Based a little experimentation (https://gist.github.com/leifdenby/8e874d3440a1ac96f96465a418f158ab) bottleneck's mean function builds up significant errors even with moderately sized arrays if they are `float32`, so I'm going to stop using `.mean()` as-is from now and always pass in `dtype=np.float64`.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,218459353