issue_comments: 464016154

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https://github.com/pydata/xarray/issues/1346#issuecomment-464016154 https://api.github.com/repos/pydata/xarray/issues/1346 464016154 MDEyOklzc3VlQ29tbWVudDQ2NDAxNjE1NA== 691772 2019-02-15T11:41:36Z 2019-02-15T11:41:36Z CONTRIBUTOR

Oh hm, I think I didn't really understand what happens in bottleneck.nanmean(). I understand that integers can overflow and that float32 have varying absolute precision. The max float32 3.4E+38 is not hit here. So how can the mean of a list of ones be 0.5?

Isn't this what bottleneck is doing? Summing up a bunch of float32 values and then dividing by the length?

```

d = np.ones(2**25, dtype=np.float32) d.sum()/np.float32(len(d)) 1.0 ```

 
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