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import threading

import time

import requests

def crawl(url):

    print(f"线程{threading.current_thread().name}：请求{url}")

    resp = requests.get(url)   网络IO等待（释放GIL）

    print(f"{url}：状态码{resp.status_code}")

if __name__ == "__main__":

    start = time.time()

    urls = [f"https://httpbin.org/get?num={i}" for i in range(10)]

    threads = [threading.Thread(target=crawl, args=(url,)) for url in urls]

    [t.start() for t in threads]

    [t.join() for t in threads]

    print(f"总耗时：{time.time()-start:.2f}秒")   约2秒（并发），单线程约10秒

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from concurrent.futures import ThreadPoolExecutor

import os

def read_file(filepath):

    with open(filepath, "r", encoding="utf-8") as f:

        content = f.read()

    return f"{filepath}：字符数{len(content)}"

if __name__ == "__main__":

    files = [f for f in os.listdir() if f.endswith(".txt")][:10]

    with ThreadPoolExecutor(max_workers=5) as executor:

        results = executor.map(read_file, files)

    for res in results:

        print(res)

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import threading

import time

count = 0

lock = threading.Lock()

def add_count():

    global count

    for _ in range(10000):

        with lock:   加锁避免数据竞争

            count += 1

if __name__ == "__main__":

    t1 = threading.Thread(target=add_count)

    t2 = threading.Thread(target=add_count)

    t1.start()

    t2.start()

    t1.join()

    t2.join()

    print(f"最终计数：{count}")   输出20000（准确）

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from concurrent.futures import ProcessPoolExecutor

import time

import numpy as np

def matrix_calc(matrix):

    return np.dot(matrix, matrix)   纯CPU计算

if __name__ == "__main__":

    start = time.time()

    matrices = [np.random.rand(1000, 1000) for _ in range(10)]

    with ProcessPoolExecutor() as executor:

        executor.map(matrix_calc, matrices)

    print(f"总耗时：{time.time()-start:.2f}秒")   4核CPU约3秒，单进程约10秒

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import multiprocessing

from PIL import Image

import os

def compress_img(filepath):

    img = Image.open(filepath)

    img.save(f"compressed_{filepath}", quality=50)   压缩图片（CPU密集）

    return f"{filepath}压缩完成"

if __name__ == "__main__":

    imgs = [f for f in os.listdir() if f.endswith((".jpg", ".png"))][:5]

    pool = multiprocessing.Pool(processes=4)

    results = pool.map(compress_img, imgs)

    pool.close()

    pool.join()

    for res in results:

        print(res)

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from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor

import requests

import numpy as np

 线程：爬取数据（IO）

def crawl_data(url):

    resp = requests.get(url)

    return resp.json()["data"]

 进程：处理数据（CPU）

def process_data(data_list):

    arr = np.array(data_list)

    return arr.mean()   数值计算

if __name__ == "__main__":

     1. 多线程爬取数据

    urls = [f"https://httpbin.org/get?data={i}" for i in range(10)]

    with ThreadPoolExecutor(max_workers=5) as t_executor:

        data = list(t_executor.map(crawl_data, urls))

     2. 多进程处理数据

    with ProcessPoolExecutor() as p_executor:

        result = p_executor.submit(process_data, data).result()

    print(f"数据均值：{result}")