Python性能优化：让你的代码更快更流畅

```python
# 使用内置函数 sum 计算列表的和
numbers = [1, 2, 3, 4, 5]
total = sum(numbers)
print(total)  # 输出 15
```

```python
# 使用列表推导式替代显式循环
numbers = [1, 2, 3, 4, 5]
squares = [n**2 for n in numbers]
print(squares)  # 输出 [1, 4, 9, 16, 25]
```

```python
def calculate_sum(numbers):
    total = 0  # 局部变量
    for num in numbers:
        total += num
    return total
numbers = [1, 2, 3, 4, 5]
result = calculate_sum(numbers)
print(result)  # 输出 15
```

```python
def infinite_sequence():
    num = 0
    while True:
        yield num
        num += 1
# 创建一个生成器对象
generator = infinite_sequence()
# 使用 next 函数获取生成器的下一个值
print(next(generator))  # 输出 0
print(next(generator))  # 输出 1
```

```python
# 使用集合进行元素查找
numbers = {1, 2, 3, 4, 5}
if 3 in numbers:
    print("3 is in the set")  # 输出 "3 is in the set"
# 使用字典存储键值对
person = {"name": "Alice", "age": 30}
print(person["name"])  # 输出 "Alice"
```

```python
import threading
def worker():
    # 模拟耗时操作
    import time
    time.sleep(1)
    print("Worker thread finished")
# 创建并启动线程
thread = threading.Thread(target=worker)
thread.start()
# 主线程继续执行其他任务
print("Main thread continues execution")
# 等待线程完成
thread.join()
```

```python
import numpy as np
# 使用 NumPy 创建一个大数组
large_array = np.random.rand(1000000)
# 使用 NumPy 的内置函数进行计算，通常比纯 Python 快得多
squared_array = np.square(large_array)
sum_of_squares = np.sum(squared_array)
print(sum_of_squares)
```

```python
from functools import lru_cache
@lru_cache(maxsize=None)
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n-1) + fibonacci(n-2)
# 计算斐波那契数列的第 10 项
print(fibonacci(10))  # 输出 55
```

```python
import cProfile
def slow_function():
    # 这里是一些慢的代码
    pass
# 使用 cProfile 剖析函数
cProfile.run('slow_function()')
```

```python
with open('output.txt', 'w') as f:
    for item in large_data_set:
        # 避免频繁写入，而是先将数据存储在内存中
        f.write(str(item) + '\n')
```