使用scrapy框架爬取某商城部分数据并存入MongoDB

标签：data-   size   code   价格   you   baidu   xpath   item   cal   

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

标签：data-   size   code   价格   you   baidu   xpath   item   cal   

原文地址：https://www.cnblogs.com/u-damowang1/p/12896523.html