嵌套的for循环在时间上效率低下，正在寻找一个聪明的替代方案

# 假设我们要找出两个列表中相同的元素
list1 = [1, 2, 3, 4, 5]
list2 = [4, 5, 6, 7, 8]

# 使用哈希表
set1 = set(list1)
common_elements = [item for item in list2 if item in set1]

print(common_elements)  # 输出: [4, 5]

# 假设我们要计算两个列表中所有元素的乘积
list1 = [1, 2, 3]
list2 = [4, 5, 6]

result = [x * y for x in list1 for y in list2]
print(result)  # 输出: [4, 5, 6, 8, 10, 12, 12, 15, 18]

import numpy as np

# 假设我们要计算两个数组的点积
array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])

result = np.dot(array1, array2)
print(result)  # 输出: 32

# 假设我们要找出两个列表中相同的元素
list1 = [1, 2, 3, 4, 5]
list2 = [4, 5, 6, 7, 8]

common_elements = (item for item in list2 if item in set(list1))
print(list(common_elements))  # 输出: [4, 5]

import threading

def process_item(item):
    # 假设这里是耗时的操作
    print(f"Processing {item}")

items = [1, 2, 3, 4, 5]
threads = []

for item in items:
    thread = threading.Thread(target=process_item, args=(item,))
    threads.append(thread)
    thread.start()

for thread in threads:
    thread.join()