REF: Use numpy set methods in interpolate #57997
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mroeschke
added
Refactor
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Neat! Is there a perf bump or just the principle of the thing? |
Looks like there's a perf bump when the refactored In [1]: import pandas as pd
+ /opt/miniconda3/envs/pandas-dev/bin/ninja
[4/4] Linking target pandas/_libs/tslibs/parsing.cpython-310-darwin.so
In [2]: ser = pd.Series([None, 1, None] * 250_000, dtype="Int64")
In [3]: %timeit ser.interpolate(limit_direction="forward", limit_area="outside")
24.7 ms ± 1.54 ms per loop (mean ± std. dev. of 7 runs, 10 loops each). # PR
In [3]: %timeit ser.interpolate(limit_direction="forward", limit_area="outside")
63.3 ms ± 563 µs per loop (mean ± std. dev. of 7 runs, 10 loops each). # main |
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Going to merge as all green here. Can follow up if needed |
pmhatre1
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May 7, 2024
* Use numpy arrays instead of sets in interp * Enable assume_unique in intersect1d * Typing
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_interpolate_1dgoes back an forth between Python objects and Numpy objects in order to use Pythonsetmethods. Refactoring to just use Numpy set routines instead