issue_comments

Oh yes, of course! I've underestimated the low precision of float32 values above 2**24. Thanks for the hint.

I think (!) xarray is not effected any longer, but pandas is. Bisecting the GIT history leads to commit 0b9ab2d1, which means that xarray >= v0.10.9 should not be affected. Uninstalling bottleneck is also a valid workaround.

<s>Bottleneck's documentation explicitly mentions that no error is raised in case of an overflow. But it seams to be very evil behavior, so it might be worth reporting upstream.</s> What do you think? (I think kwgoodman/bottleneck#164 is something different, isn't it?) Edit: this is not an overflow. It's a numerical error by not applying pairwise summation.

A couple of minimal examples:

```python

import numpy as np import pandas as pd import xarray as xr import bottleneck as bn bn.nanmean(np.ones(225, dtype=np.float32))
0.5 pd.Series(np.ones(225, dtype=np.float32)).mean()
0.5 xr.DataArray(np.ones(2**25, dtype=np.float32)).mean() # not affected for this version <xarray.DataArray ()> array(1., dtype=float32) ```

Done with the following versions: bash $ pip3 freeze Bottleneck==1.2.1 numpy==1.16.1 pandas==0.24.1 xarray==0.11.3 ...

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 ```