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while working #3762, I found that the way we handle nan in columns when doing a groupby aggregation doesn't match pandas
with skipna=True (the default right now) we ignore NaN values and leave whatever the identity for that aggregation (i.e. MAX_INT when doing min, 0 when doing sum, etc). when we set this to False, we take NaN values into account but they override every other value (they end up being min and max in their segment.
This is not the best example, but it's the one that was popping up in my test
while working #3762, I found that the way we handle
nan
in columns when doing a groupby aggregation doesn't match pandaswith
skipna=True
(the default right now) we ignoreNaN
values and leave whatever the identity for that aggregation (i.e. MAX_INT when doing min, 0 when doing sum, etc). when we set this toFalse
, we takeNaN
values into account but they override every other value (they end up being min and max in their segment.This is not the best example, but it's the one that was popping up in my test
min aggregation with
skipna
:min aggregation without
skipna
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