标签：笔记 代码 pytho file turn 文件 os.path jin data
1.tomorrow安装，用pip可以直接安装
pip install tomorrow
1.以下案例是单线程时候跑的情况，在下载图片的时候很耗时。
# coding:utf-8 from bs4 import BeautifulSoup import requests import os import time # 当前脚本所在的目录 cur_path = os.path.dirname(os.path.realpath(__file__)) def get_img_urls(): r = requests.get("http://699pic.com/sousuo-218808-13-1.html") fengjing = r.content soup = BeautifulSoup(fengjing, "html.parser") # 找出所有的标签 images = soup.find_all(class_="lazy") return images def save_img(imgUrl): try: jpg_rl = imgUrl["data-original"] title = imgUrl["title"] # print(title) # print(jpg_rl) # print("") # 判断是否有jpg文件夹，不存在创建一个 save_file = os.path.join(cur_path, "jpg") if not os.path.exists(save_file): os.makedirs(save_file) with open(os.path.join(save_file, title+‘.jpg‘), "wb") as f: f.write(requests.get(jpg_rl).content) except: pass if __name__ == "__main__": t1 = time.time() image_ulrs = get_img_urls() for i in image_ulrs: save_img(i) t2 = time.time() print("总耗时：%.2f 秒"%(t2-t1))
运行结果：
耗时：4.27 秒1.一行代码搞定多线程，在函数上加个@threads(5)，括号里面代码线程的数量，数字越大，运行的速度越快
# coding:utf-8 from bs4 import BeautifulSoup import requests import os import time from tomorrow import threads # 当前脚本所在的目录 cur_path = os.path.dirname(os.path.realpath(__file__)) def get_img_urls(): r = requests.get("http://699pic.com/sousuo-218808-13-1.html") fengjing = r.content soup = BeautifulSoup(fengjing, "html.parser") # 找出所有的标签 images = soup.find_all(class_="lazy") return images @threads(5) def save_img(imgUrl): try: jpg_rl = imgUrl["data-original"] title = imgUrl["title"] # print(title) # print(jpg_rl) # print("") # 判断是否有jpg文件夹，不存在创建一个 save_file = os.path.join(cur_path, "jpg") if not os.path.exists(save_file): os.makedirs(save_file) with open(os.path.join(save_file, title+‘.jpg‘), "wb") as f: f.write(requests.get(jpg_rl).content) except: pass if __name__ == "__main__": t1 = time.time() image_ulrs = get_img_urls() for i in image_ulrs: save_img(i) t2 = time.time() print("总耗时：%.2f 秒"%(t2-t1))
运行结果：
总耗时：0.24 秒参考github案例：Tomorrow
标签：笔记 代码 pytho file turn 文件 os.path jin data
原文地址：https://www.cnblogs.com/jason89/p/8998325.html
