在 Python 中，可以通过 `threading` 模块实现多线程编程，并使用多种方式等待多个线程完成执行并获取它们的结果。以下是几种常见的方法： ### 使用 `join()` 方法 可以利用 `Thread.join()` 阻塞主线程，直到所有子线程完成执行。 ```python import threading def worker(result, index): # 模拟任务处理 result[index] = f"Result from thread {index}" # 存储结果的容器 results = [None] * 3 threads = [] for i in range(3): t = threading.Thread(target=worker, args=(results, i)) threads.append(t) t.start() # 等待所有线程完成 for t in threads: t.join() print("All threads completed:", results) ``` ### 使用 `concurrent.futures.ThreadPoolExecutor` `ThreadPoolExecutor` 提供了更高级的接口来管理线程池，并通过 `Future` 对象获取任务结果。 ```python from concurrent.futures import ThreadPoolExecutor def worker(index): return f"Result from thread {index}" with ThreadPoolExecutor(max_workers=3) as executor: futures = [executor.submit(worker, i) for i in range(3)] results = [future.result() for future in futures] print("All threads completed:", results) ``` ### 使用 `queue.Queue` 实现线程间通信 如果需要在线程之间传递数据或收集结果，可以使用 `Queue` 来存储每个线程的输出。 ```python import threading import queue def worker(q, index): q.put(f"Result from thread {index}") q = queue.Queue() threads = [] for i in range(3): t = threading.Thread(target=worker, args=(q, i)) threads.append(t) t.start() # 等待所有线程完成 for t in threads: t.join() # 收集结果 results = [] while not q.empty(): results.append(q.get()) print("All threads completed:", results) ``` ### 使用 `multiprocessing.dummy` 模块 该模块是 `multiprocessing` 的线程版本，适用于 I/O 密集型任务，提供与进程池一致的 API。 ```python from multiprocessing.dummy import Pool as ThreadPool def worker(index): return f"Result from thread {index}" pool = ThreadPool(3) results = pool.map(worker, range(3)) pool.close() pool.join() print("All threads completed:", results) ``` ###