Skip to content

Instantly share code, notes, and snippets.

@michaeldorner

michaeldorner/benchmark_a_range.py

Last active November 19, 2024 06:24
Show Gist options
  • Save michaeldorner/c0fba1bc108fa230afd40d1d63d5df97 to your computer and use it in GitHub Desktop.
Save michaeldorner/c0fba1bc108fa230afd40d1d63d5df97 to your computer and use it in GitHub Desktop.
Download ZIP

Revisions

  1. michaeldorner revised this gist Nov 19, 2024. 1 changed file with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion benchmark_a_range.py
    View file
    Original file line number Diff line number Diff line change
    @@ -11,7 +11,7 @@
    sizes = [2**exp for exp in range(12)] # or np.arange(12) ;-)
    for size in sizes:
    numpy_results[size] = min(timeit.repeat(f'np.arange({size})', setup='import numpy as np', **config))
    python_results[size] = min(timeit.repeat(f'list(range({size}))', **config))
    python_results[size] = min(timeit.repeat(f'range({size})', **config))

    df = pd.concat((pd.Series(numpy_results, name='np.arange'), pd.Series(python_results, name='range')), axis=1)

  2. michaeldorner revised this gist Feb 22, 2023. 1 changed file with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion benchmark_a_range.py
    View file
    Original file line number Diff line number Diff line change
    @@ -13,7 +13,7 @@
    numpy_results[size] = min(timeit.repeat(f'np.arange({size})', setup='import numpy as np', **config))
    python_results[size] = min(timeit.repeat(f'list(range({size}))', **config))

    df = pd.concat((pd.Series(numpy_results, name='numpy'), pd.Series(python_results, name='Python')), axis=1)
    df = pd.concat((pd.Series(numpy_results, name='np.arange'), pd.Series(python_results, name='range')), axis=1)

    # plotting

  3. michaeldorner created this gist Feb 22, 2023.
    26 changes: 26 additions & 0 deletions benchmark_a_range.py
    View file
    Original file line number Diff line number Diff line change
    @@ -0,0 +1,26 @@
    import timeit
    import pandas as pd
    import matplotlib.pyplot as plt

    # measuring

    numpy_results = {}
    python_results = {}

    config = {'number': 100, 'repeat': 100}
    sizes = [2**exp for exp in range(12)] # or np.arange(12) ;-)
    for size in sizes:
    numpy_results[size] = min(timeit.repeat(f'np.arange({size})', setup='import numpy as np', **config))
    python_results[size] = min(timeit.repeat(f'list(range({size}))', **config))

    df = pd.concat((pd.Series(numpy_results, name='numpy'), pd.Series(python_results, name='Python')), axis=1)

    # plotting

    fig, ax = plt.subplots()
    df.plot(ax=ax)
    ax.set_xscale('log', base=2)
    ax.set_xticks(sizes)
    ax.set_xticklabels(sizes)
    ax.set_xlabel('Size')
    ax.set_ylabel('Best runtime for 10 runs out of 10 repetition')