Python爬取链家北京二手房数据

今天分享一下前段时间抓取链家上北京二手房数据的项目。本次分享分为两部分，第一部分介绍如何使用scrapy抓取二手房数据，第二部分我将抓下来的数据进行了一些简单的分析和可视化。最后我会贴上数据，感兴趣的朋友可以深入分析

Github地址：https://github.com/HunterChao/Crawler

1、使用scrapy抓取二手房数据

文章目录结构

D:.
│ run.py
│ scrapy.cfg
│ 
└─LianJia
  │ items.py
  │ pipelines.py
  │ settings.py
  │ __init__.py
  │    
  ├─spiders
    │ lianjia.py
    │ __init__.py

lianjia.py是程序的主要运行文件，run.py为程序启动文件。在pycharm下执行run.py即可启动程序。

项目分析：

链接的构造：我们通过抓取首页可以获得北京市各城区的名称（如：东城、西城、朝阳）及对应的拼音，进一步通过遍历每个城区对应的页码数（Pn）即可构造出各城区的二手房链接。

信息的抓取：在进入各个城区的二手房页面时，可匹配出每个房源的详细信息。这里需要注意的是，由于我想将各房源的经纬度信息获取以便可视化到地图上，需要找到每个房源的详情页链接，进入该链接，匹配出经纬度相关的字段。（resblockPosition）

数据字段：item.py

# -*- coding: utf-8 -*-

import scrapy

class LianjiaItem(scrapy.Item):
  # 标签 小区 户型  面积  关注人数 观看人数 发布时间 价格  均价 详情链接 经纬度 城区
  title = scrapy.Field()
  community = scrapy.Field()
  model = scrapy.Field()
  area = scrapy.Field()
  focus_num = scrapy.Field()
  watch_num = scrapy.Field()
  time = scrapy.Field()
  price = scrapy.Field()
  average_price = scrapy.Field()
  link = scrapy.Field()
  Latitude = scrapy.Field()
  city = scrapy.Field()

主要运行函数：lianjia.py

# -*- coding: utf-8 -*-
import scrapy
import requests
import re
import time
from lxml import etree
from ..items import LianjiaItem
from scrapy_redis.spiders import RedisSpider

class LianjiaSpider(RedisSpider):
  name = 'lianjiaspider'
  redis_key = 'lianjiaspider:urls'
  start_urls = 'http://bj.lianjia.com/ershoufang/'

  def start_requests(self):
    user_agent = 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/49.0.2623.22 \
             Safari/537.36 SE 2.X MetaSr 1.0'
    headers = {'User-Agent': user_agent}
    yield scrapy.Request(url=self.start_urls, headers=headers, method='GET', callback=self.parse)

  def parse(self, response):
    user_agent = 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/49.0.2623.22 \
                 Safari/537.36 SE 2.X MetaSr 1.0'
    headers = {'User-Agent': user_agent}
    lists = response.body.decode('utf-8')
    selector = etree.HTML(lists)
    area_list = selector.xpath('/html/body/div[3]/div[2]/dl[2]/dd/div[1]/div/a')
    for area in area_list:
      try:
        area_han = area.xpath('text()').pop()  # 地点

        area_pin = area.xpath('@href').pop().split('/')[2]  # 拼音
        area_url = 'http://bj.lianjia.com/ershoufang/{}/'.format(area_pin)
        print(area_url)
        yield scrapy.Request(url=area_url, headers=headers, callback=self.detail_url, meta={"id1":area_han,"id2":area_pin} )
      except Exception:
        pass

  def get_latitude(self,url): # 进入每个房源链接抓经纬度
    p = requests.get(url)
    contents = etree.HTML(p.content.decode('utf-8'))
    latitude = contents.xpath('/ html / body / script[19]/text()').pop()
    time.sleep(3)
    regex = '''resblockPosition(. )'''
    items = re.search(regex, latitude)
    content = items.group()[:-1] # 经纬度
    longitude_latitude = content.split(':')[1]
    return longitude_latitude[1:-1]

  def detail_url(self,response):
    'http://bj.lianjia.com/ershoufang/dongcheng/pg2/'
    for i in range(1,101):
      url = 'http://bj.lianjia.com/ershoufang/{}/pg{}/'.format(response.meta["id2"],str(1))
      time.sleep(2)
      try:
        contents = requests.get(url)
        contents = etree.HTML(contents.content.decode('utf-8'))
        houselist = contents.xpath('/html/body/div[4]/div[1]/ul/li')
        for house in houselist:
          try:
            item = LianjiaItem()
            item['title'] = house.xpath('div[1]/div[1]/a/text()').pop()
            item['community'] = house.xpath('div[1]/div[2]/div/a/text()').pop()
            item['model'] = house.xpath('div[1]/div[2]/div/text()').pop().split('|')[1]
            item['area'] = house.xpath('div[1]/div[2]/div/text()').pop().split('|')[2]
            item['focus_num'] = house.xpath('div[1]/div[4]/text()').pop().split('/')[0]
            item['watch_num'] = house.xpath('div[1]/div[4]/text()').pop().split('/')[1]
            item['time'] = house.xpath('div[1]/div[4]/text()').pop().split('/')[2]
            item['price'] = house.xpath('div[1]/div[6]/div[1]/span/text()').pop()
            item['average_price'] = house.xpath('div[1]/div[6]/div[2]/span/text()').pop()
            item['link'] = house.xpath('div[1]/div[1]/a/@href').pop()
            item['city'] = response.meta["id1"]
            self.url_detail = house.xpath('div[1]/div[1]/a/@href').pop()
            item['Latitude'] = self.get_latitude(self.url_detail)
          except Exception:
            pass
          yield item
      except Exception:
        pass

抓取效果：

2、北京二手房数据的简单分析

北京二手房数据：https://pan.baidu.com/share/init?shareid=237386059