消除循环

高效编写 Python 代码

Logan Thomas

Scientific Software Technical Trainer, Enthought

Python 中的循环

  • 循环模式：
    • for 循环：逐个遍历序列
    • while 循环：条件满足则重复
    • 嵌套循环：在循环内再用循环
    • 成本高！
高效编写 Python 代码

消除循环的好处

  • 代码更少
  • 可读性更好
    • "扁平优于嵌套"
  • 更高效率
高效编写 Python 代码

用内置函数消除循环

# List of HP, Attack, Defense, Speed
poke_stats = [
    [90,  92, 75, 60],
    [25,  20, 15, 90],
    [65, 130, 60, 75],
    ...
]

alt="宝可梦 Abomasnow、Abra、Absol 及其生命值、攻击、防御、速度等元数据被高亮显示"

高效编写 Python 代码
# List of HP, Attack, Defense, Speed
poke_stats = [
    [90,  92, 75, 60],
    [25,  20, 15, 90],
    [65, 130, 60, 75],
    ...
]

# For loop approach
totals = []
for row in poke_stats:
    totals.append(sum(row))

# List comprehension
totals_comp = [sum(row) for row in poke_stats]

# Built-in map() function
totals_map = [*map(sum, poke_stats)]
高效编写 Python 代码
%%timeit
totals = []
for row in poke_stats:
    totals.append(sum(row))

140 µs ± 1.94 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

%timeit totals_comp = [sum(row) for row in poke_stats]

114 µs ± 3.55 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

%timeit totals_map = [*map(sum, poke_stats)]

95 µs ± 2.94 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
高效编写 Python 代码

用内置模块消除循环

poke_types = ['Bug', 'Fire', 'Ghost', 'Grass', 'Water']

# Nested for loop approach
combos = []
for x in poke_types:
    for y in poke_types:
        if x == y:
            continue
        if ((x,y) not in combos) & ((y,x) not in combos):
            combos.append((x,y))

# Built-in module approach
from itertools import combinations
combos2 = [*combinations(poke_types, 2)]
高效编写 Python 代码

用 NumPy 消除循环

# Array of HP, Attack, Defense, Speed
import numpy as np

poke_stats = np.array([
    [90,  92, 75, 60],
    [25,  20, 15, 90],
    [65, 130, 60, 75],
    ...
])
高效编写 Python 代码

用 NumPy 消除循环

avgs = []
for row in poke_stats:
    avg = np.mean(row)
    avgs.append(avg)

print(avgs)

[79.25, 37.5, 82.5, ...]

avgs_np = poke_stats.mean(axis=1)

print(avgs_np)

[ 79.25  37.5   82.5  ...]
高效编写 Python 代码

用 NumPy 消除循环

%timeit avgs = poke_stats.mean(axis=1)

23.1 µs ± 235 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

%%timeit
avgs = []
for row in poke_stats:
    avg = np.mean(row)
    avgs.append(avg)

5.54 ms ± 224 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
高效编写 Python 代码

Passons à la pratique !

高效编写 Python 代码

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