ENH: Implement IntegerArray reductions #36761
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dsaxton
added
Enhancement
NA - MaskedArrays
jbrockmendel
reviewed
Oct 1, 2020
pandas/core/arrays/integer.py
Outdated
| return super()._reduce("sum", skipna=skipna, min_count=min_count) | ||
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| def prod(self, skipna=True, min_count=0, **kwargs): | ||
| nv.validate_sum((), kwargs) |
jorisvandenbossche
approved these changes
Oct 1, 2020
jreback
requested changes
Oct 2, 2020
| return super()._reduce("sum", skipna=skipna, min_count=min_count) | ||
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| def prod(self, skipna=True, min_count=0, **kwargs): | ||
| nv.validate_prod((), kwargs) |
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why can't we do this like sum?
I would rather fix this generally
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I thought this was cleaner because it removed code duplication between here and BaseMaskedArray (which is already using masked_reductions)
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oh ic, ok then. yeah let's merge this and FloatingArray, then pls de-duplicate what you can.
jreback
approved these changes
Oct 2, 2020
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black pandasgit diff upstream/master -u -- "*.py" | flake8 --diffI'm not sure if this is the "best" way to implement these reductions (see linked issue), but in any case I figure this is better than nothing since we can get these reductions pretty much for free. As far as I can tell FloatingArray can use the same machinery.
(I'm not able to pass on kwargs here because the masked reductions don't take them all, e.g.
axis, and various tests break as a result.)