ASV: reduce overall run time for GroupByMethods benchmarks #44604
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asv_bench/benchmarks/groupby.py
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| @@ -464,7 +465,12 @@ def setup(self, dtype, method, application, ncols): | |||
| # DataFrameGroupBy doesn't have these methods | |||
| raise NotImplementedError | |||
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| ngroups = 1000 | |||
| if method == "describe" and ncols == 5: | |||
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could do this without the ncols check?
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im imagining looking at results and seeing the ncols==5 case being faster than the ncols==1 case (or just much less than 5x slower) and being confused until i remember this special case
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i agree or add a comment here
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Yes, certainly (I initially used ngroups = 100 for each of the three methods for both ncols parameters, but then only describe for ncols=5 was still on the slow side.
Using the smaller ngroups for both cases of describe will make the ncols=1 still faster, but that can't hurt I suppose.
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See #44450 (comment)
This reduces 1) the data size for
describe,madandskew(all the slowest benchmarks that take more than a second is for one of those three), and 2) reduces the parametrization for ncols (this was added in #42841 to also benchmark the case of multiple columns (block-wise), but so I think by keeping two options (single col / multiple col) that aspect should still be captured adequately).This reduces the total runtime for this single class of benchmarks from 18min to 3min (and the
--quickruntime (as used on CI) from 4min to 10s. Note this is only the sum of the actual (repeated) timings, so not including the setup and other overhead of running the benchmarks).