REGR: groupby.idxmin/idxmax wrong result on extreme values #57046
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rhshadrach added Bug Groupby Regression 
mroeschke reviewed Jan 24, 2024
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| @pytest.mark.parametrize("how", ["idxmin", "idxmax"]) | ||
| @pytest.mark.parametrize("dtype", ["float32", "float64"]) |
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float_numpy_dtype fixture please
mroeschke reviewed Jan 24, 2024
Comment on lines 317 to 318
| print(df) | ||
| print(result) |
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| print(df) | |
| print(result) |
mroeschke reviewed Jan 24, 2024
| mask=mask, | ||
| result_mask=result_mask, | ||
| is_datetimelike=is_datetimelike, | ||
| **kwargs, |
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To pass skipna to group_idxmin_idxmax
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Ah OK and I guess not all these accept skipna (IIRC there was that related issue related skipna, but forgot if it was for groupby ops or not)
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Yea - I'm planning on turning #56939 into a tracking issue on adding skipna consistently throughout groupby. Can include removing these kwargs as part of that.
…ndas into reg_groupby_idxmin
# Conflicts: # doc/source/whatsnew/v2.2.1.rst
WillAyd approved these changes Jan 25, 2024
mroeschke approved these changes Jan 26, 2024
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| Thanks @rhshadrach |
meeseeksmachine pushed a commit to meeseeksmachine/pandas that referenced this pull request Jan 26, 2024
…t on extreme values
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pmhatre1 pushed a commit to pmhatre1/pandas-pmhatre1 that referenced this pull request May 7, 2024
…v#57046) * WIP * REGR: groupby.idxmin/idxmax wrong result on extreme values * fixes * NumPy 2.0 can't come soon enough * fixup * fixup
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idxmaxgroupby aggregation on uint column #57040 (Replace xxxx with the GitHub issue number)doc/source/whatsnew/vX.X.X.rstfile if fixing a bug or adding a new feature.While working on this, I noticed that the
skipnaargument was also being ignored.The only way I see solving this is adding a
seenBoolean array instead of the sentinel values. cc @WillAyd