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  • chiaral · 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
947906426 https://github.com/pydata/xarray/issues/5877#issuecomment-947906426 https://api.github.com/repos/pydata/xarray/issues/5877 IC_kwDOAMm_X844f-d6 chiaral 8453445 2021-10-20T17:59:13Z 2021-10-20T17:59:13Z CONTRIBUTOR

Yup - just followed your suggestion and:

1) conda removed bottleneck and it removed xarray and pandas as well 2) conda installed xarray which installed xarray, pandas, and pytz

and now the xr.rolling(time=3).sum() yields:

array([ nan, nan, 0. , 0. , 0. , 0. , 0. , 0.31 , 1.23 , 9.530001 , 10.64 , 9.75 , 2.67 , 1.35 , 1.46 , 0.36999997, 0.26999998, 0.25 , 0.14999999, 2.68 , 2.56 , 2.73 , 0.39999998, 0.39999998, 0.19999999, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], dtype=float32)

could you elaborate more on the issue? is this because of some bouncing between precisions across packages? But why do I have zeros at the beginning of the rolling sum and non zeros after having calculated a sum? it is not consistent in the behaviour.

Thanks tho! 

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   Rolling() gives values different from pd.rolling() 1030768250
947195221 https://github.com/pydata/xarray/issues/5877#issuecomment-947195221 https://api.github.com/repos/pydata/xarray/issues/5877 IC_kwDOAMm_X844dQ1V chiaral 8453445 2021-10-20T00:02:58Z 2021-10-20T00:02:58Z CONTRIBUTOR

Adding a few extra observations:

python ds_ex.rolling(time=3).mean().pr.values df_ex.rolling(window=3).mean().values.T have a similar behaviour, in that once again xr.rolling() doesn't have zero where it should, but pd.rolling does.

But when I switch to other operations, like var or std the behaviour is the opposite, i.e.: ds_ex.rolling(time=3).std().pr.values array([ nan, nan, 0. , 0. , 0. , 0. , 0. , 0.1461354 , 0.38218665, 3.631293 , 3.367307 , 3.6156974 , 0.61356837, 0.54522127, 0.5188016 , 0.01698606, 0.06376763, 0.05906381, 0.05098677, 1.157881 , 1.1856455 , 1.148419 , 0.09427918, 0.09427918, 0.09427926, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], dtype=float32)

whereas

df_ex.rolling(window=3).std().values.T gives

array([[ nan, nan, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.78978585e-01, 4.68081166e-01, 4.44740760e+00, 4.12409195e+00, 4.42830679e+00, 7.51465227e-01, 6.67757461e-01, 6.35400157e-01, 2.08166670e-02, 7.81024957e-02, 7.23417792e-02, 6.24499786e-02, 1.41810905e+00, 1.45211339e+00, 1.40652052e+00, 1.15470047e-01, 1.15470047e-01, 1.15470047e-01, 9.60572442e-08, 9.60572442e-08, 9.60572442e-08, 9.60572442e-08, 9.60572442e-08, 9.60572442e-08, 9.60572442e-08, 9.60572442e-08, 9.60572442e-08, 9.60572442e-08]])

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

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