Code profiling for runtime

Scrivere codice Python efficiente

Logan Thomas

Scientific Software Technical Trainer, Enthought

Code profiling

Detailed stats on frequency and duration of function calls
Line-by-line analyses
Package used: line_profiler

pip install line_profiler

Code profiling: runtime

heroes = ['Batman', 'Superman', 'Wonder Woman']

hts = np.array([188.0, 191.0, 183.0])

wts = np.array([ 95.0, 101.0,  74.0])

def convert_units(heroes, heights, weights):

    new_hts = [ht * 0.39370  for ht in heights]
    new_wts = [wt * 2.20462  for wt in weights]

    hero_data = {}

    for i,hero in enumerate(heroes):
        hero_data[hero] = (new_hts[i], new_wts[i])

    return hero_data

convert_units(heroes, hts, wts)

{'Batman': (74.0156, 209.4389),
 'Superman': (75.1967, 222.6666),
 'Wonder Woman': (72.0471, 163.1419)}

Code profiling: runtime

%timeit convert_units(heroes, hts, wts)

3 µs ± 32 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

%timeit new_hts = [ht * 0.39370  for ht in hts]

1.09 µs ± 11 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

%timeit new_wts = [wt * 2.20462  for wt in wts]

1.08 µs ± 6.42 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

%%timeit
hero_data = {}
for i,hero in enumerate(heroes):
    hero_data[hero] = (new_hts[i], new_wts[i])

634 ns ± 9.29 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

Code profiling: line_profiler

Using line_profiler package

%load_ext line_profiler

Magic command for line-by-line times

%lprun -f

Code profiling: line_profiler

Using line_profiler package

%load_ext line_profiler

Magic command for line-by-line times

%lprun -f convert_units

Code profiling: line_profiler

Using line_profiler package

%load_ext line_profiler

Magic command for line-by-line times

%lprun -f convert_units convert_units(heroes, hts, wts)

%lprun output

%lprun -f convert_units convert_units(heroes, hts, wts)

alt=”Magic command lprun tabular output summarizing runtime profiling statistics”

%lprun output

%lprun -f convert_units convert_units(heroes, hts, wts)

alt=”Magic command lprun tabular output summarizing runtime profiling statistics with Line # and Hits columns highlighted”

%lprun output

%lprun -f convert_units convert_units(heroes, hts, wts)

alt=”Magic command lprun tabular output summarizing runtime profiling statistics with Time column highlighted”

%lprun output

%lprun -f convert_units convert_units(heroes, hts, wts)

alt=”Magic command lprun tabular output summarizing runtime profiling statistics with first line Time unit highlighted”

%lprun output

%lprun -f convert_units convert_units(heroes, hts, wts)

alt=”Magic command lprun tabular output summarizing runtime profiling statistics with line three highlighted”

%lprun output

%lprun -f convert_units convert_units(heroes, hts, wts)

alt=”Magic command lprun tabular output summarizing runtime profiling statistics with Per Hit column highlighted”

%lprun output

%lprun -f convert_units convert_units(heroes, hts, wts)

Magic command lprun tabular output summarizing runtime profiling statistics with line nine highlighted

%lprun output

%lprun -f convert_units convert_units(heroes, hts, wts)

alt=”Magic command lprun tabular output summarizing runtime profiling statistics with % Time column highlighted”

%lprun output

%lprun -f convert_units convert_units(heroes, hts, wts)

alt=”Magic command lprun tabular output summarizing runtime profiling statistics with Line Contents column highlighted”

%timeit convert_units convert_units(heroes, hts, wts)

3 µs ± 32 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

%lprun -f convert_units convert_units(heroes, hts, wts)

alt="Magic command lprun tabular output summarizing runtime profiling statistics with second line Total time highlighted"

Let's practice your new profiling powers!

Scrivere codice Python efficiente