Python 15个高效代码写法：提升开发效率

# 传统写法
squares = {}
foriinrange(5):
    squares[i] = i*i

# 高效写法
squares = {i: i*iforiinrange(5)}
print(squares)          # {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}

name, age = "Alice", 25

# 过时写法
print("My name is {} and I'm {} years old".format(name, age))

# 推荐写法（f-string）
print(f"My name is {name} and I'm {age} years old")

# 一行赋值
a, b, c = 1, 2, 3

# 优雅交换
a, b = b, a

fruits = ['apple', 'banana', 'cherry']

# 不推荐
foriinrange(len(fruits)):
    print(i, fruits[i])

# 推荐
foridx, fruitinenumerate(fruits, start=1):   # 索引从 1 开始
    print(idx, fruit)

names = ['Alice', 'Bob', 'Charlie']
ages  = [25, 30, 35]

# 不推荐
foriinrange(len(names)):
    print(names[i], ages[i])

# 推荐
forname, ageinzip(names, ages):
    print(name, age)

fromcollectionsimportdefaultdict

words = ['apple', 'banana', 'apple', 'cherry', 'banana', 'apple']

# 传统写法
counter = {}
forwinwords:
    counter[w] = counter.get(w, 0) +1

# 高效写法
counter = defaultdict(int)
forwinwords:
    counter[w] += 1

numbers = [1, 2, 3, 4, 5]

has_even    = any(n%2 == 0forninnumbers)   # True
all_positive = all(n>0forninnumbers)       # True

deflog(*args, **kwargs):
    print("Positional:", args)
    print("Keywords :", kwargs)

log(1, 2, 3, name="Alice", age=25)

# 传统写法
data = {}
if'count'notindata:
    data['count'] = 0
data['count'] += 1

# 推荐写法
data = {}
data.setdefault('count', 0)
data['count'] += 1

fromdataclassesimportdataclass

@dataclass(slots=True)          # slots=True 节省内存（3.10+）
classPerson:
    name: str
    age: int
    email: str|None = None    # 3.10+ 联合类型写法

p = Person("Alice", 25)
print(p)                        # Person(name='Alice', age=25, email=None)

# 从日志里实时提取 ERROR 行并计数
importsys, re

total = 0
whilechunk := sys.stdin.read(8192):
    errors = re.findall(r'ERROR', chunk)
    total += len(errors)
print('ERROR 出现次数:', total)

# 快速统计 10 G 大文件行数
frompathlibimportPath

file = Path('big.log')
line_cnt = sum(1for_infile.open(encoding='utf-8', errors='ignore'))
print('总行数:', line_cnt)

fromfunctoolsimportlru_cache

@lru_cache(maxsize=None)   # 无界缓存
deffib(n: int) ->int:
    returnnifn<2elsefib(n-1) +fib(n-2)

print(fib(100))              # 瞬间返回
print(fib.cache_info())      # 查看命中情况

# 一行解析命令行指令
defparse(cmd: str):
    matchcmd.split():
        case ['quit'|'exit']:
            return'SHUTDOWN'
        case ['add', x, y] ifx.isdigit() andy.isdigit():
            returnint(x) +int(y)
        case_:
            return'UNKNOWN'

print(parse('add 3 5'))   # 8
print(parse('quit'))      # SHUTDOWN

# 一键并发下载 100 张图片
importasyncio, aiohttp, time
frompathlibimportPath

asyncdeffetch(url: str, session: aiohttp.ClientSession, dst: Path):
    asyncwithsession.get(url) asresp:
        dst.write_bytes(awaitresp.read())

asyncdefbulk_download(urls: list[str], folder='imgs'):
    Path(folder).mkdir(exist_ok=True)
    asyncwithaiohttp.ClientSession() assession:
        tasks = [
            fetch(u, session, Path(folder) /f'{i:03}.jpg')
            fori, uinenumerate(urls)
        ]
        awaitasyncio.gather(*tasks)

if__name__ == '__main__':
    urls = [f'https://picsum.photos/200/300?random={i}'foriinrange(100)]
    start = time.perf_counter()
    asyncio.run(bulk_download(urls))
    print('全部下载完成，耗时:', time.perf_counter() -start, 's')

fromcollectionsimportdefaultdict
fromdataclassesimportdataclass

@dataclass
classProduct:
    name: str
    price: float
    category: str

products = [
    Product("Laptop", 999.99, "Electronics"),
    Product("Phone",  699.99, "Electronics"),
    Product("Book",    19.99, "Books"),
    Product("Chair",  149.99, "Furniture"),
    Product("Table",  199.99, "Furniture"),
]

# 按类别聚合价格
group = defaultdict(list)
forpinproducts:
    group[p.category].append(p.price)

# 字典推导式计算平均价
avg_price = {c: sum(prices) /len(prices) forc, pricesingroup.items()}
print(avg_price)
# {'Electronics': 849.99, 'Books': 19.99, 'Furniture': 174.99}