Implement rolling groupby #460
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fjetter
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Dec 5, 2023
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| if self.groupby_kwargs is not None: | ||
| return type(parent)( | ||
| type(self)(self.frame[columns], *self.operands[1:]), | ||
| *parent.operands[1:], | ||
| ) |
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I'm honestly not even sure what this is doing and it does feel like we're duplicating work but this is what I found for the groupby implementations and this seems to work.
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This drops all columns except the parent columns and the by columns from the df before the groupby and then drops the by column after the groupby if not in parent.columns
Yes this is needed for groupby to avoid dropping too many columns
phofl
reviewed
Dec 6, 2023
phofl
reviewed
Dec 6, 2023
dask_expr/tests/test_groupby.py
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| actual = ddf.groupby("group1").rolling("1D").sum()["column1"] | ||
| expected = df.groupby("group1").rolling("1D").sum()["column1"] | ||
| actual.optimize() |
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Ideally, we would test this against
df[["column1", "group1"]].groupby("group1").rolling("1D").sum()["column1"].optimize()._name
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phofl
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Dec 6, 2023
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So, I'm just expanding the existing
Rollingbecause the groupby rolling is actually doing the same thing as the ordinary rolling just with a local groupby. The implementation feels very unclean. I'll review the class hierarchy again if there is a more elegant way to modify the logic here. Any pointers appreciated