13.7. Comprehension Performance
13.7.1. Microbenchmark
Date: 2025-10-30
Python: 3.14.0
IPython: 9.6.0
System: macOS 26.0.1
Computer: MacBook M3 Max
CPU: 16 cores (12 performance and 4 efficiency) / 3nm
RAM: 128 GB RAM LPDDR5
Case Study A - range(0,5):
>>> data = list(range(0,5))
>>> # doctest: +SKIP
... %%timeit -r 1000 -n 1000
... result = []
... for x in data:
... result.append(x)
...
133 ns ± 27.4 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
134 ns ± 33.1 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
137 ns ± 35.7 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
138 ns ± 35.3 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
139 ns ± 33.8 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
>>> # doctest: +SKIP
... %%timeit -r 1000 -n 1000
... result = [x for x in data]
...
119 ns ± 34.6 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
120 ns ± 31.1 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
121 ns ± 28.4 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
121 ns ± 35.4 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
123 ns ± 32.2 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
Case Study B - range(0,50):
>>> data = list(range(0,50))
>>> # doctest: +SKIP
... %%timeit -r 1000 -n 1000
... result = []
... for x in data:
... result.append(x)
...
945 ns ± 92.4 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
952 ns ± 93.1 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
954 ns ± 96.7 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
954 ns ± 92.1 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
962 ns ± 42.2 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
>>> # doctest: +SKIP
... %%timeit -r 1000 -n 1000
... result = [x for x in data]
...
610 ns ± 79.0 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
612 ns ± 88.2 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
613 ns ± 73.1 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
615 ns ± 71.5 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
616 ns ± 58.9 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
Case Study C - range(0,500):
>>> data = list(range(0,500))
>>> # doctest: +SKIP
... %%timeit -r 1000 -n 1000
... result = []
... for x in data:
... result.append(x)
...
8.85 μs ± 231 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
8.85 μs ± 234 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
8.86 μs ± 212 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
8.86 μs ± 254 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
8.86 μs ± 274 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
>>> # doctest: +SKIP
... %%timeit -r 1000 -n 1000
... result = [x for x in data]
...
5.48 μs ± 224 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
5.49 μs ± 197 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
5.49 μs ± 200 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
5.49 μs ± 219 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
5.49 μs ± 239 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
Conclusion:
In this case comprehensions are faster then regular loops (PEP 20).
13.7.2. Performance
Date: 2025-10-30
Python: 3.14.0
IPython: 9.6.0
System: macOS 26.0.1
Computer: MacBook M3 Max
CPU: 16 cores (12 performance and 4 efficiency) / 3nm
RAM: 128 GB RAM LPDDR5
Setup:
>>> DATA = [
... ('sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species'),
... (5.8, 2.7, 5.1, 1.9, 'virginica'),
... (5.1, 3.5, 1.4, 0.2, 'setosa'),
... (5.7, 2.8, 4.1, 1.3, 'versicolor'),
... (6.3, 2.9, 5.6, 1.8, 'virginica'),
... (6.4, 3.2, 4.5, 1.5, 'versicolor'),
... (4.7, 3.2, 1.3, 0.2, 'setosa'),
... (7.0, 3.2, 4.7, 1.4, 'versicolor'),
... ]
>>> # doctest: +SKIP
... %%timeit -n 1000 -r 1000
... result = []
... for row in DATA[1:]:
... for value in row:
... if isinstance(value, float):
... result.append(value >= 1.0)
... result = all(result)
...
1.55 μs ± 111 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
1.55 μs ± 111 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
1.55 μs ± 122 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
1.56 μs ± 104 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
1.56 μs ± 110 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
>>> # doctest: +SKIP
... %%timeit -n 1000 -r 1000
... result = True
... for row in DATA[1:]:
... for value in row:
... if isinstance(value, float):
... if not value >= 1.0:
... result = False
... break
... if not result:
... break
...
395 ns ± 56.7 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
395 ns ± 57.2 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
396 ns ± 56.3 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
396 ns ± 59.1 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
397 ns ± 66.1 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
>>> # doctest: +SKIP
... %%timeit -n 1000 -r 1000
... result = all(value >= 1.0
... for row in DATA[1:]
... for value in row
... if isinstance(value, float))
...
543 ns ± 63.3 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
543 ns ± 71.0 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
544 ns ± 67.2 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
544 ns ± 69.6 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
547 ns ± 71.5 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
>>> # doctest: +SKIP
... %%timeit -n 1000 -r 1000
... result = all(value >= 1.0 for row in DATA[1:] for value in row if isinstance(value, float))
...
541 ns ± 74.4 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
542 ns ± 61.2 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
542 ns ± 71.8 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
542 ns ± 74.4 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
543 ns ± 66.5 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
>>> # doctest: +SKIP
... %%timeit -n 1000 -r 1000
... result = all(y >= 1.0 for x in DATA[1:] for y in x if isinstance(y, float))
...
542 ns ± 67.8 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
542 ns ± 71.7 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
542 ns ± 74.6 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
543 ns ± 65.8 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
543 ns ± 75.5 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
>>> # doctest: +SKIP
... %%timeit -n 1000 -r 1000
... result = all(x >= 1.0 for X in DATA[1:] for x in X if isinstance(x, float))
...
542 ns ± 53.7 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
542 ns ± 54.2 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
542 ns ± 56.6 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
544 ns ± 55.1 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
544 ns ± 62.5 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)