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PerformanceMemory or execution speed performanceMemory or execution speed performanceRegressionFunctionality that used to work in a prior pandas versionFunctionality that used to work in a prior pandas version
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ASV shows a gigantic regression (14629.25x) in a certain replace
benchmark: https://pandas.pydata.org/speed/pandas/#replace.ReplaceList.time_replace_list?python=3.8&Cython=0.29.21&p-inplace=True&commits=07559156-dbee8fae
The simplified case is:
In [5]: df = pd.DataFrame({"A": 0, "B": 0}, index=range(4 * 10 ** 7))
In [6]: %timeit df.replace([np.inf, -np.inf], np.nan)
100 ms ± 6.76 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # 1.1
1.2 s ± 31.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # master
Compared to 1.1, I don't see such a huge difference, but still a decent slowdown (x10).
Now, in this case, we have integer columns, but trying to replace infinity, which of course can never be present. So maybe before we had some shortcut for that.
This also seems quite a cornercase, though. So not sure how critical the regression is.
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PerformanceMemory or execution speed performanceMemory or execution speed performanceRegressionFunctionality that used to work in a prior pandas versionFunctionality that used to work in a prior pandas version