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/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