Implement groupby.head and groupby.tail
#12939
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- Closes rapidsai#2592 - Closes rapidsai#12245
wence-
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| # into the grouping, but that probably requires a new | ||
| # aggregation scheme in libcudf. This is probably "fast | ||
| # enough" for most reasonable input sizes. | ||
| _, offsets, _, group_values = self._grouped() |
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Note that this OOMs for setups where I really think it shouldn't. I tried (on an A6000, 48GiB device memory) creating a dataframe with two int-columns with 10^9 rows (and 10^6 groups) [8GiB data]. df.groupby("a")._grouped() consistently OOMs for me. AIUI this is a sort-based implementation of reordering which I would have thought runs in-place on a copy of the table, so I was expecting to peak at around 16GiB of data (even if it is out-of-place I would expect not much more than 24GiB of peak footprint). That said, I don't know enough about the libcudf implementation of groupbys to understand if my expectation is reasonable.
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|
/merge |
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Description
These methods can be implemented by grouping the dataframe and
then selecting appropriate slices from each group. This is less
memory-efficient than it could be (since the entire grouping must
be constructed before discarding most of it).
Checklist