eval_performance.py

from timeit import repeat as timeit

import numpy as np
import seaborn as sns

from pandas import DataFrame

setup_common = """from pandas import DataFrame
import numpy as np
df = DataFrame(np.random.randn(%d, 3), columns=list('abc'))
%s"""

setup_with = "s = 'a + b * (c ** 2 + b ** 2 - a) / (a * c) ** 3'"


def bench_with(n, times=10, repeat=3, engine="numexpr"):
    return (
        np.array(
            timeit(
                f"df.eval(s, engine={repr(engine)})",
                setup=setup_common % (n, setup_with),
                repeat=repeat,
                number=times,
            )
        )
        / times
    )


setup_subset = "s = 'a <= b <= c ** 2 + b ** 2 - a and b > c'"


def bench_subset(n, times=20, repeat=3, engine="numexpr"):
    return (
        np.array(
            timeit(
                f"df.query(s, engine={repr(engine)})",
                setup=setup_common % (n, setup_subset),
                repeat=repeat,
                number=times,
            )
        )
        / times
    )


def bench(mn=3, mx=7, num=100, engines=("python", "numexpr"), verbose=False):
    r = np.logspace(mn, mx, num=num).round().astype(int)

    ev = DataFrame(np.empty((num, len(engines))), columns=engines)
    qu = ev.copy(deep=True)

    ev["size"] = qu["size"] = r

    for engine in engines:
        for i, n in enumerate(r):
            if verbose & (i % 10 == 0):
                print(f"engine: {repr(engine)}, i == {i:d}")
            ev_times = bench_with(n, times=1, repeat=1, engine=engine)
            ev.loc[i, engine] = np.mean(ev_times)
            qu_times = bench_subset(n, times=1, repeat=1, engine=engine)
            qu.loc[i, engine] = np.mean(qu_times)

    return ev, qu


def plot_perf(df, engines, title, filename=None) -> None:
    from matplotlib.pyplot import figure

    sns.set()
    sns.set_palette("Set2")

    fig = figure(figsize=(4, 3), dpi=120)
    ax = fig.add_subplot(111)

    for engine in engines:
        ax.loglog(df["size"], df[engine], label=engine, lw=2)

    ax.set_xlabel("Number of Rows")
    ax.set_ylabel("Time (s)")
    ax.set_title(title)
    ax.legend(loc="best")
    ax.tick_params(top=False, right=False)

    fig.tight_layout()

    if filename is not None:
        fig.savefig(filename)


if __name__ == "__main__":
    import os

    pandas_dir = os.path.dirname(
        os.path.dirname(os.path.abspath(os.path.dirname(__file__)))
    )
    static_path = os.path.join(pandas_dir, "doc", "source", "_static")

    join = lambda p: os.path.join(static_path, p)

    fn = join("eval-query-perf-data.h5")

    engines = "python", "numexpr"

    ev, qu = bench(verbose=True)  # only this one

    plot_perf(ev, engines, "DataFrame.eval()", filename=join("eval-perf.png"))
    plot_perf(qu, engines, "DataFrame.query()", filename=join("query-perf.png"))