python抓取链家房源信息(三)
之前写过一个链家网北京二手房的数据抓取,然后本来今天想着要把所有的东西弄完,但是临时有事出去了一趟,耽搁了一下,然后现在是想着把北京的二手房的信息都进行抓取,并且存储在mongodb中,
首先是通过\’https://bj.lianjia.com\’的url将按照区域划分和地铁路线图进行划分的所有的url抓取出来进行存储,然后在进行下一步的分析,然后会每一套房源信息都会有一个data-housecode,标识是那一套房间,为了避免有重复的房源信息,在每套房的数据中将data-housecode,数据作为每一套房的标识,进行存储,然后在抓取到房源信息存储到mongodb中时,通过data-housecode进行判断,看当前房源是否已经存储完全,如果已经存储了,则不必插入,否则将该房源信息插入到mongodb中。
用的还是scrapy框架,然后只是在spider.py中添加了按照区和地铁路线图的所有的房源信息,当然根据区域和地铁还可以分的更细。。。
大致的爬虫的框架是:
在scrapy框架中,使用过程是,在spider.py中,将要获取的url请求给scheduler,然后通过download模块进行Request下载数据,如果下载失败,会将结果告诉scrapy engine,然后scrapy engine会稍后进行重新请求,然后download将下载的数据给spider,spider进行数据处理,抓取需要保存的按照地铁路线或者是区域的url,然后跟进url,将个个不同的url进行告诉scrapy engine,然后又通过相同的远离然后进行抓取,然后存储每个房源的标识和条件情况,然后将处理结果返回给item,通过item进行mongodb的存储。
scrapy.py中的代码如下:
#-*-coding:utf-8-*- import scrapy import re from bs4 import BeautifulSoup import time import json from scrapy.http import Request from House.items import HouseItem import lxml.html from lxml import etree class spider(scrapy.Spider): name = \'House\' url = \'https://bj.lianjia.com\' base_url = \'https://bj.lianjia.com/ershoufang\' def start_requests(self): print(self.base_url) yield Request(self.base_url,self.get_area_url,dont_filter=True) def get_area_url(self,response): selector = etree.HTML(response.text) results = selector.xpath(\'//dd/div/div/a/@href\') for each in results: if \'lianjia\' not in each: url = self.url + each else: url = each print(url) yield Request(url, self.get_total_page, dont_filter=True) def get_total_page(self,response): soup = BeautifulSoup(response.text, \'lxml\') Total_page = soup.find_all(\'div\', class_=\'page-box house-lst-page-box\') res = r\'<div .*? page-data=\\'{\"totalPage\":(.*?),"curPage":.*?}\\' page-url=".*?\' total_num = re.findall(res, str(Total_page), re.S | re.M) for i in range(1, int(total_num[0])): print(i) url = response.url + \'pg\' + str(i) print(url) yield Request(url, self.parse, dont_filter=True) def parse(self, response): soup = BeautifulSoup(response.text,\'lxml\') message1 = soup.find_all(\'div\',class_ = \'houseInfo\') message2 = soup.find_all(\'div\',class_ = \'followInfo\') message3 = soup.find_all(\'div\',class_ = \'positionInfo\') message4 = soup.find_all(\'div\',class_ = \'title\') message5 = soup.find_all(\'div\',class_ = \'totalPrice\') message6 = soup.find_all(\'div\',class_ = \'unitPrice\') message7 = soup.find_all(name=\'a\', attrs={\'class\': \'img\'}) Flags = [] for each in message7: Flags.append(each.get(\'data-housecode\')) num = 0 for flag,each,each1,each2,each3,each4,each5 in zip(Flags,message1,message2,message3,message4,message5,message6): List = each.get_text().split(\'|\') item = HouseItem() item[\'flag\'] = flag item[\'address\'] = List[0].strip() item[\'house_type\'] = List[1].strip() item[\'area\'] = List[2].strip() item[\'toward\'] = List[3].strip() item[\'decorate\'] = List[4].strip() if len(List) == 5: item[\'elevate\'] = \'None\' else: item[\'elevate\'] = List[5].strip() List = each1.get_text().split(\'/\') item[\'interest\'] = List[0].strip() item[\'watch\'] = List[1].strip() item[\'publish\'] = List[2].strip() List = each2.get_text().split(\'-\') item[\'build\'] = List[0].strip() item[\'local\'] = List[1].strip() item[\'advantage\'] = each3.get_text().strip() item[\'price\'] = each4.get_text().strip() item[\'unit\'] = each5.get_text().strip() print("%s %s %s %s %s %s %s %s %s %s %s %s %s %s %s "%(item[\'flag\'],item[\'address\'],item[\'house_type\'],item[\'area\'],item[\'toward\'], item[\'decorate\'],item[\'elevate\'],item[\'interest\'], item[\'watch\'],item[\'publish\'],item[\'build\'],item[\'local\'],item[\'advantage\'],item[\'price\'],item[\'unit\'])) num += 1 yield item