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Fixed Series.apply performance regression #35166

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merged 3 commits into from
Jul 8, 2020

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TomAugspurger
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Set the option once, rather than in the loop.

Closes #35047

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(1000, 3), columns=list("ABC"))
%timeit df.apply(lambda x: x["A"] + x["B"], axis=1)
# 1.0.4
22.5 ms ± 4.73 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

# PR
10.4 ms ± 714 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Set the option once, rather than in the loop.

Closes pandas-dev#35047
@TomAugspurger TomAugspurger added this to the 1.1 milestone Jul 7, 2020
@TomAugspurger TomAugspurger added Apply Apply, Aggregate, Transform, Map Performance Memory or execution speed performance labels Jul 7, 2020
@jbrockmendel
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Nice!

@jreback jreback merged commit d9b56a1 into pandas-dev:master Jul 8, 2020
@jreback
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jreback commented Jul 8, 2020

thanks @TomAugspurger

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Performance regression in frame_methods.Apply.time_apply_ref_by_name
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