大众点评抓取
网址链接/shoplist/shopRank/pcChannelRankingV2?rankId=fce2e3a36450422b7fad3f2b90370efd71862f838d1255ea693b953b1d49c7c0
通过观察每个城市的链接主要区别于ranKld，每个城市有特定的ID，因此先获取到相应城市的ID，便可进行后续抓取。
获取到的城市ID为：
["上海","fce2e3a36450422b7fad3f2b90370efd71862f838d1255ea693b953b1d49c7c0"],
["北京","d5036cf54fcb57e9dceb9fefe3917fff71862f838d1255ea693b953b1d49c7c0"],
["广州","e749e3e04032ee6b165fbea6fe2dafab71862f838d1255ea693b953b1d49c7c0"],
["深圳","e049aa251858f43d095fc4c61d62a9ec71862f838d1255ea693b953b1d49c7c0"],
["天津","2e5d0080237ff3c8f5b5d3f315c7c4a508e25c702ab1b810071e8e2c39502be1"],
["杭州","91621282e559e9fc9c5b3e816cb1619c71862f838d1255ea693b953b1d49c7c0"]
,["南京","d6339a01dbd98141f8e684e1ad8af5c871862f838d1255ea693b953b1d49c7c0"],
["苏州","536e0e568df850d1e6ba74b0cf72e19771862f838d1255ea693b953b1d49c7c0"],
["成都","c950bc35ad04316c76e89bf2dc86bfe071862f838d1255ea693b953b1d49c7c0"],
["武汉","d96a24c312ed7b96fcc0cedd6c08f68c08e25c702ab1b810071e8e2c39502be1"],
["重庆","6229984ceb373efb8fd1beec7eb4dcfd71862f838d1255ea693b953b1d49c7c0"],
["西安","ad66274c7f5f8d27ffd7f6b39ec447b608e25c702ab1b810071e8e2c39502be1"]
抓取页面抓取分析
通过浏览器分析可发现该网站通过Ajax请求，所有数据来源于：
/mylist/ajax/shoprank?rankId=fce2e3a36450422b7fad3f2b90370efd71862f838d1255ea693b953b1d49c7c0
该链接同之前请求一样，只需要替换rankId便可进行多城市数据获取。最终抓取的数据只需要解析json边可获得所需字段，由于大众没有特别反爬限制，只需要不断轮换userAgent便可绕过反爬。
我们对上海，北京，广州，深圳，天津，杭州，南京，苏州，成都，武汉，重庆，西安等城市的前100家商铺进行数据获取，并分析最终所获取数据集，见《大众点评数据分析》
# 请求头
USER_AGENT_LIST = [
"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1",
"Mozilla/5.0 (X11; CrOS i686 2268.111.0) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.57 Safari/536.11",
"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1092.0 Safari/536.6",
"Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1090.0 Safari/536.6",
"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/19.77.34.5 Safari/537.1",
"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.9 Safari/536.5"]
head = {
'User-Agent': '{0}'.format(random.sample(USER_AGENT_LIST, 1)[0]) # 随机获取
}
代码展示：
#!/usr/bin/env python # encoding: utf-8 """ @version: v1.0 @author: W_H_J @license: Apache Licence @contact: 415900617@ @site: @software: PyCharm @file: dazhongFood.py @time: /7/18 15:46 @describe: 大众点评美食抓取list_city :城市的ID号码，依次是：上海，北京，广州，深圳，天津，杭州，南京，苏州，成都，武汉，重庆，西安"""import jsonimport randomimport requestsfrom base.dbhelper import DBHelper# 城市列表list_city = [["上海","fce2e3a36450422b7fad3f2b90370efd71862f838d1255ea693b953b1d49c7c0"],["北京","d5036cf54fcb57e9dceb9fefe3917fff71862f838d1255ea693b953b1d49c7c0"],["广州","e749e3e04032ee6b165fbea6fe2dafab71862f838d1255ea693b953b1d49c7c0"],["深圳","e049aa251858f43d095fc4c61d62a9ec71862f838d1255ea693b953b1d49c7c0"],["天津","2e5d0080237ff3c8f5b5d3f315c7c4a508e25c702ab1b810071e8e2c39502be1"],["杭州","91621282e559e9fc9c5b3e816cb1619c71862f838d1255ea693b953b1d49c7c0"],["南京","d6339a01dbd98141f8e684e1ad8af5c871862f838d1255ea693b953b1d49c7c0"],["苏州","536e0e568df850d1e6ba74b0cf72e19771862f838d1255ea693b953b1d49c7c0"],["成都","c950bc35ad04316c76e89bf2dc86bfe071862f838d1255ea693b953b1d49c7c0"],["武汉","d96a24c312ed7b96fcc0cedd6c08f68c08e25c702ab1b810071e8e2c39502be1"],["重庆","6229984ceb373efb8fd1beec7eb4dcfd71862f838d1255ea693b953b1d49c7c0"],["西安","ad66274c7f5f8d27ffd7f6b39ec447b608e25c702ab1b810071e8e2c39502be1"]]# 请求头USER_AGENT_LIST = ["Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1","Mozilla/5.0 (X11; CrOS i686 2268.111.0) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.57 Safari/536.11","Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1092.0 Safari/536.6","Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1090.0 Safari/536.6","Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/19.77.34.5 Safari/537.1","Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.9 Safari/536.5"]head = {'User-Agent': '{0}'.format(random.sample(USER_AGENT_LIST, 1)[0]) # 随机获取}flag = 0code = 0# 解析def findFood(city,data):global flag,codemysql_db = DBHelper()for data in json.loads(data)["shopBeans"]:flag +=1# 详细地址shopAddress = data["address"]# 人均消费avgPrice = data["avgPrice"]# 商铺图片defaultPic = data["defaultPic"]# 分类名称mainCategoryName = data["mainCategoryName"]# 所在区域名称mainRegionName = data["mainRegionName"]# 口味评分tasteScore = data["score1"]# 环境评分environmentScore = data["score2"]# 服务评分serviceScore = data["score3"]# 商品编号shopId = data["shopId"]# 商铺网址shopUrl = "/shop/"+shopId# 商铺名称shopName = data["shopName"]# 商铺星级shopPower = data["shopPower"]sql = '''insert into dazhongfood(shopUrl,shopName, shopId, shopPower, mainRegionName, mainCategoryName, tasteScore, environmentScore, serviceScore, avgPrice, shopAddress, defaultPic, city) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)'''params = (shopUrl,shopName, shopId, shopPower, mainRegionName, mainCategoryName, tasteScore, environmentScore, serviceScore, avgPrice, shopAddress, defaultPic, city)try:mysql_db.insert(sql,*params)code +=1print("----- 插入:", code, "条------")except:print("已存在不再重复插入！！")print("总条数：", flag)# 抓取def foodSpider(city_list):city = city_list[0]url = city_list[1]base_url = "/mylist/ajax/shoprank?rankId="+urlhtml = requests.get(base_url, headers=head)findFood(city=city, data=str(html.text))if __name__ == '__main__':for city_data in list_city:foodSpider(city_data)
最终获取结果存储至MySQL。(完整数据集见daZhongFood/data)
最终结果
完整代码见github：/Liangchengdeye/DaZhongdianping.git
后续后发布对抓取结果的《大众点评热门餐厅抓取与数据分析》，数据分析结果同上见github。
