使用scrapy框架爬取某商城部分数据并存入MongoDB

爬取电商网站的商品信息:

    URL为: https://www.zhe800.com/ju_type/baoyou
    抓取不同分类下的商品数据
    抓取内容为商品的名称, 价格数字, 商品图片
    将商品图片二进制流, 商品名称和价格数字一同存储于MongoDB数据库

存储数据结构为:

{

          ‘name’: ‘懒人神奇, 看电影必备’,

          ‘price’: ‘5.5’,  

       ‘img’: ….,

           “category”: ‘家纺’

}

这里抓包就不说了,很简单,利用xpath进行解析

  • by.py
    • # -*- coding: utf-8 -*-
      import scrapy
      from ..items import BywItem
      class BySpider(scrapy.Spider):
          name = by
          # allowed_domains = [‘baidu.com‘]
          start_urls = [https://www.zhe800.com/ju_type/baoyou]
        
      def img_parse(self,response): item = BywItem() item[name] = response.meta[name] # print(name) item[cate] = response.meta[cate] # print(cate) item[price] = response.meta[price] item[img] = response.body yield item #详情 def xq_parse(self,response): cate = response.meta[cate] print(cate) xq_list = response.xpath(//div[@class="con "]) print(xq_list) for xq in xq_list: name = xq.xpath(./h3/a/@title).extract_first() print(name) price = xq.xpath(./h4/em/text()).extract_first() print(price) img_link = https: +xq.xpath(.//a/img/@data-original).extract_first() print(img_link) meta = { name:name, price:price, cate:cate } yield scrapy.Request(url=img_link,callback=self.img_parse,meta=meta) def parse(self, response): a_list = response.xpath(//div[@class="area"]/a[position()>1]) for a in a_list: cate = a.xpath(./em/text()).extract_first() # print(cate) cate_link = https: +a.xpath(./@href).extract_first() # print(cate_link) yield scrapy.Request(url=cate_link,callback=self.xq_parse,meta={cate:cate})
  • items.py
    • import scrapy
      
      
      class BywItem(scrapy.Item):
          # define the fields for your item here like:
          name = scrapy.Field()
          cate = scrapy.Field()
          price = scrapy.Field()
          img = scrapy.Field()
  • pipelines.py
    • import pymongo
      conn = pymongo.MongoClient()  #连接
      db = conn.byw  #创建数据库
      table = db.by  #创建表
      
      class BywPipeline:
          def process_item(self, item, spider):
              table.insert_one(dict(item))  #插入数据
              return item
  • settings.py
    • #ua
      
      USER_AGENT = Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.92 Safari/537.36
      
      #robots协议
      ROBOTSTXT_OBEY = False
      
      
      
      #管道
      ITEM_PIPELINES = {
         byw.pipelines.BywPipeline: 300,
      }
  • 效果

    使用scrapy框架爬取某商城部分数据并存入MongoDB

     

      

     

 

使用scrapy框架爬取某商城部分数据并存入MongoDB

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