perf.py

import timeit
import warnings
import numpy as np

setup = """
import pandas as pd
import polars as pl
import minimal_plugin  # noqa: F401
import numpy as np
rng = np.random.default_rng(12345)
N = 10_000_000

df = pl.DataFrame({'a': rng.integers(low=-100, high=100, size=N)})
df = df.with_row_index().with_columns(
    pl.when(pl.col('index')%2==1).then(pl.lit(None)).otherwise(pl.col('a')).alias('a')
)
"""

results = (
    np.array(
        timeit.Timer(
            stmt="df.select(pl.col('a').mp.abs_i64_fast())",
            setup=setup,
        ).repeat(7, 3)
    )
    / 3
)
print(f"min: {min(results)}")
print(f"max: {max(results)}")
print(f"{np.mean(results)} +/- {np.std(results)/np.sqrt(len(results))}")

results = (
    np.array(
        timeit.Timer(
            stmt="df.select(pl.col('a').mp.abs_i64())",
            setup=setup,
        ).repeat(7, 3)
    )
    / 3
)
print(f"min: {min(results)}")
print(f"max: {max(results)}")
print(f"{np.mean(results)} +/- {np.std(results)/np.sqrt(len(results))}")

with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    results = (
        np.array(
            timeit.Timer(
                stmt="df.select(pl.col('a').map_elements(lambda x: abs(x)))",
                setup=setup,
            ).repeat(7, 3)
        )
        / 3
    )
print(f"min: {min(results)}")
print(f"max: {max(results)}")
print(f"{np.mean(results)} +/- {np.std(results)/np.sqrt(len(results))}")