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ASV: reduce overall run time for GroupByMethods benchmarks #44604

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Nov 25, 2021
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ASV: reduce overall run time for GroupByMethods benchmarks #44604

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ASV: reduce overall run time for GroupByMethods benchmarks
jorisvandenbossche Nov 24, 2021
41c810d
ngroups=20 for all describe
jorisvandenbossche Nov 25, 2021
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13 changes: 8 additions & 5 deletions asv_bench/benchmarks/groupby.py
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Original file line number Diff line number Diff line change
Expand Up @@ -443,7 +443,7 @@ class GroupByMethods:
"var",
],
["direct", "transformation"],
[1, 2, 5, 10],
[1, 5],
]

def setup(self, dtype, method, application, ncols):
Expand All @@ -455,6 +455,7 @@ def setup(self, dtype, method, application, ncols):
raise NotImplementedError

if application == "transformation" and method in [
"describe",
"head",
"tail",
"unique",
Expand All @@ -464,7 +465,12 @@ def setup(self, dtype, method, application, ncols):
# DataFrameGroupBy doesn't have these methods
raise NotImplementedError

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.

ngroups = 20
elif method in ["describe", "mad", "skew"]:
ngroups = 100
else:
ngroups = 1000
size = ngroups * 2
rng = np.arange(ngroups).reshape(-1, 1)
rng = np.broadcast_to(rng, (len(rng), ncols))
Expand All @@ -491,9 +497,6 @@ def setup(self, dtype, method, application, ncols):
cols = cols[0]

if application == "transformation":
if method == "describe":
raise NotImplementedError

self.as_group_method = lambda: df.groupby("key")[cols].transform(method)
self.as_field_method = lambda: df.groupby(cols)["key"].transform(method)
else:
Expand Down