import threading
import time
import requests
def crawl(url):
    print(f"線程{threading.current_thread().name}：請求{url}")
    resp = requests.get(url)   網(wǎng)絡(luò)IO等待（釋放GIL）
    print(f"{url}：狀態(tài)碼{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"總耗時：{time.time()-start:.2f}秒")   約2秒（并發(fā)），單線程約10秒

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}：字符數(shù){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)

import threading
import time
count = 0
lock = threading.Lock()
def add_count():
    global count
    for _ in range(10000):
        with lock:   加鎖避免數(shù)據(jù)競爭
            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"最終計數(shù)：{count}")   輸出20000（準確）

from concurrent.futures import ProcessPoolExecutor
import time
import numpy as np
def matrix_calc(matrix):
    return np.dot(matrix, matrix)   純CPU計算
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"總耗時：{time.time()-start:.2f}秒")   4核CPU約3秒，單進程約10秒

import multiprocessing
from PIL import Image
import os
def compress_img(filepath):
    img = Image.open(filepath)
    img.save(f"compressed_{filepath}", quality=50)   壓縮圖片（CPU密集）
    return f"{filepath}壓縮完成"
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)

from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
import requests
import numpy as np
 線程：爬取數(shù)據(jù)（IO）
def crawl_data(url):
    resp = requests.get(url)
    return resp.json()["data"]
 進程：處理數(shù)據(jù)（CPU）
def process_data(data_list):
    arr = np.array(data_list)
    return arr.mean()   數(shù)值計算
if __name__ == "__main__":
     1. 多線程爬取數(shù)據(jù)
    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. 多進程處理數(shù)據(jù)
    with ProcessPoolExecutor() as p_executor:
        result = p_executor.submit(process_data, data).result()
    print(f"數(shù)據(jù)均值：{result}")