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ASV: benchmark for DataFrame.Update #58228

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Merged
merged 5 commits into from
Apr 15, 2024
Merged

ASV: benchmark for DataFrame.Update #58228

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a13ed9f
ASV: Add benchmark for DataFrame.Update
aureliobarbosa Apr 12, 2024
a854531
Merge remote-tracking branch 'upstream/main' into asv_frame_method_up…
aureliobarbosa Apr 12, 2024
877e6fe
ASV: increase runtime of frame_methods.Update
aureliobarbosa Apr 13, 2024
7766488
ASV: increase runtime of frame_methods.Update (again)
aureliobarbosa Apr 13, 2024
e80cc59
ASV: increase runtime of frame_methods.Update (again)
aureliobarbosa Apr 14, 2024
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24 changes: 24 additions & 0 deletions asv_bench/benchmarks/frame_methods.py
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Original file line number Diff line number Diff line change
Expand Up @@ -862,4 +862,28 @@ def time_last_valid_index(self, dtype):
self.df.last_valid_index()


class Update:
def setup(self):
rng = np.random.default_rng()
self.df = DataFrame(rng.uniform(size=(100_000, 10)))

idx = rng.choice(range(100_000), size=100_000, replace=False)
self.df_random = DataFrame(self.df, index=idx)

idx = rng.choice(range(100_000), size=10_000, replace=False)
cols = rng.choice(range(10), size=2, replace=False)
self.df_sample = DataFrame(
rng.uniform(size=(10_000, 2)), index=idx, columns=cols
)
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@rhshadrach rhshadrach Apr 14, 2024 •
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Suggested change
rng = np.random.default_rng()
self.df = DataFrame(rng.uniform(size=(100_000, 10)))
idx = rng.choice(range(100_000), size=100_000, replace=False)
self.df_random = DataFrame(self.df, index=idx)
idx = rng.choice(range(100_000), size=10_000, replace=False)
cols = rng.choice(range(10), size=2, replace=False)
self.df_sample = DataFrame(
rng.uniform(size=(10_000, 2)), index=idx, columns=cols
)
rng = np.random.default_rng()
self.df = DataFrame(rng.uniform(size=(1_000_000, 10)))
idx = rng.choice(range(1_000_000), size=1_000_000, replace=False)
self.df_random = DataFrame(self.df, index=idx)
idx = rng.choice(range(1_000_000), size=100_000, replace=False)
cols = rng.choice(range(10), size=2, replace=False)
self.df_sample = DataFrame(
rng.uniform(size=(100_000, 2)), index=idx, columns=cols
)

Can you go one more order of magnitude larger. You increased by two orders of magnitude and saw very little change in runtime - meaning that you were mostly measuring overhead. With the above changes, I'm seeing:

%timeit df.update(df_sample)
%timeit df_random.update(df_sample)
%timeit df_sample.update(df)
33.9 ms ± 220 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
46.3 ms ± 744 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
19.4 ms ± 268 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

which is more in the realm of what we want for ASVs.

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Done.

Just for consistency, the benchmarks comparing the last version of DataFrame.Update() and the current one. At some point there is some performance crossover, for a smaller dataframe, it seems to be a win-win. But for larger, there is performance decrease in some cases.

Change Before [4fe49b1] After [93ea131] <fix_gh55509_v2~14> Ratio Benchmark (Parameter)
+ 40.4±1ms 50.7±0.9ms 1.25 frame_methods.Update.time_to_update_big_frame_small_arg
- 68.1±0.8ms 35.9±0.9ms 0.53 frame_methods.Update.time_to_update_random_indices
- 26.4±0.2ms 5.98±0.07ms 0.23 frame_methods.Update.time_to_update_small_frame_big_arg


def time_to_update_big_frame_small_arg(self):
self.df.update(self.df_sample)

def time_to_update_random_indices(self):
self.df_random.update(self.df_sample)

def time_to_update_small_frame_big_arg(self):
self.df_sample.update(self.df)


from .pandas_vb_common import setup # noqa: F401 isort:skip