首先我们导入 requests 和 Beautifulsoup 两个库,requests 用于发送HTTP请求Beautifulsoup 用于解析HTML. def get_google_rank(keyword, website): try: url = f"https://www.google.com/search?q={keyword}" headers = {'User-Agent': 'Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit...
爬虫代码如下 defget_rank(date_id):url="https://www.fifa.com/api/ranking-overview?locale=en&dateId={}".format(date_id)r=requests.get(url,headers=headers,verify=False)data=r.json()result=[]foritemindata["rankings"]:ranking_item=item["rankingItem"]result.append({"name":ranking_item["na...
defget_google_rank(keyword,website):try:url=f"https://www.google.com/search?q={keyword}"headers={'User-Agent':'Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Mobile Safari/537.36'}response=requests.get(url,headers=headers)...
rank=comm.Get_rank()size=comm.Get_size()# 数据生成data=np.zeros(1000000)# 测量运行时间start=time.time()comm.Bcast([data,MPI.DOUBLE],root=0)end=time.time()# 输出运行时间ifrank==0:print(f"Broadcast time:{end-start}seconds") 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13....
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rank = int(result['id'])#排名 if host not in items.keys(): items[host] = [] items[host].append(score[rank]) else: items[host].append(score[rank]) return items#返回单个词前十数据 def get_keywords(filename):#读取关键词返回列表 kwfile = open(filename,'r') keywords = kwfile.readli...
head_image = i.get('avatar') user_nickname = i.get('user_nickname') # 用户名 username = i.get('username') # 用户id fans_num = i.get('fans_num') # 粉丝 fav_num = i.get('fav_num') # 获赞 last_rank = i.get('last_ranking') # 上周排名 ...
rank = data[0].getText() company = data[1].getText() location = data[2].getText() yearend = data[3].getText() salesrise = data[4].getText() sales = data[5].getText() staff = data[6].getText() comments = data[7].getText() ...
Card(rank='Q', suit='hearts')3.列表推导 列表推导可以在列表填写里代替讨厌的循环，其基本语法为 [ expression for item in list if conditional ]来看一个非常基本的示例，用数字序列填充列表：mylist = [i for i inrange(10)]print(mylist)# [0, 1, 2, 3,4, 5, 6, 7, 8, 9]因为可以使用...
(n)] return user_set k_fold = 5 trn_df = trn_user_item_feats_df_rank_model user_set = get_kfold_users(trn_df, n=k_fold) score_list = [] score_df = trn_df[['user_id', 'click_article_id','label']] sub_preds = np.zeros(tst_user_item_feats_df_rank_model.shape[0]) ...