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  • mathause · 2 ✖

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  • Rolling() gives values different from pd.rolling() · 2 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
948691519 https://github.com/pydata/xarray/issues/5877#issuecomment-948691519 https://api.github.com/repos/pydata/xarray/issues/5877 IC_kwDOAMm_X844i-I_ mathause 10194086 2021-10-21T14:45:47Z 2021-10-21T14:45:47Z MEMBER

AFAIK bottleneck uses a less precise algorithm for sums than numpy (pydata/bottleneck#379). However, I don't know why this yields 0 at the beginning but not at the end.

A slightly more minimal example:

```python import bottleneck as bn import numpy as np import pandas as pd

data = np.array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.31, 0.91999996, 8.3, 1.42, 0.03, 1.22, 0.09999999, 0.14, 0.13, 0.0, 0.12, 0.03, 2.53, 0.0, 0.19999999, 0.19999999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ], dtype="float32", )

bn.move_sum(data, window=3) pd.Series(data).rolling(3).mean() np.convolve(data, np.ones(3), 'valid') / 3 ```

 
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   Rolling() gives values different from pd.rolling() 1030768250
947893467 https://github.com/pydata/xarray/issues/5877#issuecomment-947893467 https://api.github.com/repos/pydata/xarray/issues/5877 IC_kwDOAMm_X844f7Tb mathause 10194086 2021-10-20T17:41:04Z 2021-10-20T17:41:04Z MEMBER

Thanks for the report. Without testing anything I suspect that this is due to the use of float32 data and/ or bottleneck - see also #1346. You can test this by uninstalling bottleneck (there is an option to disable bottleneck but it's not yet released (#5560).

 
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   Rolling() gives values different from pd.rolling() 1030768250

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