链家全国各省城市房屋数据批量爬取，别再为房屋发愁！_如何拉取各省二手房价均值

1、前言

本文爬取的是链家的二手房信息，相信个位小伙伴看完后一定能自己动手爬取链家的其他模块，比如：租房、新房等等模块房屋数据。

话不多说，来到链家首页，点击北京

来到如下页面，这里有全国各个各个省份城市，而且点击某个城市会跳转到以该城市的为定位的页面

点击二手房，来到二手房页面，可以发现链接地址只是在原先的URL上拼接了 /ershoufang/,所以我们之后也可以直接拼接

 

但注意，以下这种我们不需要的需要排除

多页爬取，规律如下，多的也不用我说了，大家都能看出来

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2、基本环境搭建

建立数据库

建表语句

CREATE TABLE `lianjia` (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `city` varchar(100) DEFAULT NULL,
  `money` varchar(100) DEFAULT NULL,
  `address` varchar(100) DEFAULT NULL,
  `house_pattern` varchar(100) DEFAULT NULL,
  `house_size` varchar(100) DEFAULT NULL,
  `house_degree` varchar(100) DEFAULT NULL,
  `house_floor` varchar(100) DEFAULT NULL,
  `price` varchar(50) DEFAULT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=212 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci;
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创建scrapy项目

start.py

from scrapy import cmdline

cmdline.execute("scrapy crawl lianjia".split())
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3、代码注释分析

lianjia.py

# -*- coding: utf-8 -*-
import scrapy
import time
from Lianjia.items import LianjiaItem


class LianjiaSpider(scrapy.Spider):
    name = 'lianjia'
    allowed_domains = ['lianjia.com']
    #拥有各个省份城市的URL
    start_urls = ['https://www.lianjia.com/city/']

    def parse(self, response):
    	#参考图1，找到class值为city_list_ul的ul标签，在获取其下的所有li标签
        ul = response.xpath("//ul[@class='city_list_ul']/li")
        
        #遍历ul，每个省份代表一个li标签
        for li in ul:
        	#参考图2，获取每个省份下的所有城市的li标签
            data_ul = li.xpath(".//ul/li")
			
			#遍历得到每个城市
            for li_data in data_ul:
            	#参考图3，获取每个城市的URL和名称
                city = li_data.xpath(".//a/text()").get()
                #拼接成为二手房链接
                page_url = li_data.xpath(".//a/@href").get() + "/ershoufang/"
				
				#多页爬取
                for i in range(3):
                    url = page_url + "pg" + str(i+1)
                    print(url)
                    yield scrapy.Request(url=url,callback=self.pageData,meta={"info":city})

    def pageData(self,response):
        print("="*50)
        #获取传过来的城市名称
        city = response.meta.get("info")
        
        #参考图4，找到class值为sellListContent的ul标签，在获取其下的所有li标签
        detail_li = response.xpath("//ul[@class='sellListContent']/li")
        
        #遍历
        for page_li in detail_li:
        	#参考图5，获取class值判断排除多余的广告
            if page_li.xpath("@class").get() == "list_app_daoliu":
                continue
                
            #参考图6，获取房屋总价
            money = page_li.xpath(".//div[@class='totalPrice']/span/text()").get()
            money = str(money) + "万"
			
			#参考图7
            address = page_li.xpath(".//div[@class='positionInfo']/a/text()").get()
            
           	#参考图8，获取到房屋的全部数据，进行分割
            house_data = page_li.xpath(".//div[@class='houseInfo']/text()").get().split("|")

            #房屋格局
            house_pattern = house_data[0]
            
            #面积大小
            house_size = house_data[1].strip()
            #装修程度
            house_degree = house_data[3].strip()
            #楼层
            house_floor = house_data[4].strip()
            #单价，参考图9
            price = page_li.xpath(".//div[@class='unitPrice']/span/text()").get().replace("单价","")
            
            time.sleep(0.5)
            item = LianjiaItem(city=city,money=money,address=address,house_pattern=house_pattern,house_size=house_size,house_degree=house_degree,house_floor=house_floor,price=price)
            yield item

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3、图片辅助分析

图1

图2

图3

图4

图5

图6

图7

图8

图9

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4、完整代码

lianjia.py

# -*- coding: utf-8 -*-
import scrapy
import time
from Lianjia.items import LianjiaItem


class LianjiaSpider(scrapy.Spider):
    name = 'lianjia'
    allowed_domains = ['lianjia.com']
    start_urls = ['https://www.lianjia.com/city/']

    def parse(self, response):
        ul = response.xpath("//ul[@class='city_list_ul']/li")
        for li in ul:
            data_ul = li.xpath(".//ul/li")

            for li_data in data_ul:
                city = li_data.xpath(".//a/text()").get()
                page_url = li_data.xpath(".//a/@href").get() + "/ershoufang/"
                for i in range(3):
                    url = page_url + "pg" + str(i+1)
                    print(url)
                    yield scrapy.Request(url=url,callback=self.pageData,meta={"info":city})

    def pageData(self,response):
        print("="*50)
        city = response.meta.get("info")
        detail_li = response.xpath("//ul[@class='sellListContent']/li")
        for page_li in detail_li:
            if page_li.xpath("@class").get() == "list_app_daoliu":
                continue
            money = page_li.xpath(".//div[@class='totalPrice']/span/text()").get()
            money = str(money) + "万"
            address = page_li.xpath(".//div[@class='positionInfo']/a/text()").get()

            #获取到房屋的全部数据，进行分割
            house_data = page_li.xpath(".//div[@class='houseInfo']/text()").get().split("|")

            #房屋格局
            house_pattern = house_data[0]
            #面积大小
            house_size = house_data[1].strip()
            #装修程度
            house_degree = house_data[3].strip()
            #楼层
            house_floor = house_data[4].strip()
            #单价
            price = page_li.xpath(".//div[@class='unitPrice']/span/text()").get().replace("单价","")
            time.sleep(0.5)
            item = LianjiaItem(city=city,money=money,address=address,house_pattern=house_pattern,house_size=house_size,house_degree=house_degree,house_floor=house_floor,price=price)
            yield item

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items.py

# -*- coding: utf-8 -*-
import scrapy


class LianjiaItem(scrapy.Item):
    #城市
    city = scrapy.Field()
    #总价
    money = scrapy.Field()
    #地址
    address = scrapy.Field()
    # 房屋格局
    house_pattern = scrapy.Field()
    # 面积大小
    house_size = scrapy.Field()
    # 装修程度
    house_degree = scrapy.Field()
    # 楼层
    house_floor = scrapy.Field()
    # 单价
    price = scrapy.Field()
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pipelines.py

import pymysql


class LianjiaPipeline:
    def __init__(self):
        dbparams = {
            'host': '127.0.0.1',
            'port': 3306,
            'user': 'root',  #数据库账号
            'password': 'root',	#数据库密码
            'database': 'lianjia', #数据库名称
            'charset': 'utf8'
        }
        #初始化数据库连接
        self.conn = pymysql.connect(**dbparams)
        self.cursor = self.conn.cursor()
        self._sql = None


    def process_item(self, item, spider):
    	#执行sql
        self.cursor.execute(self.sql,(item['city'],item['money'],item['address'],item['house_pattern'],item['house_size'],item['house_degree']
                                      ,item['house_floor'],item['price']))
        self.conn.commit()  #提交
        return item

    @property
    def sql(self):
        if not self._sql:
        	#数据库插入语句
            self._sql = """
                    insert into lianjia(id,city,money,address,house_pattern,house_size,house_degree,house_floor,price)
                    values(null,%s,%s,%s,%s,%s,%s,%s,%s)
                """
            return self._sql
        return self._sql
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settings.py

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

BOT_NAME = 'Lianjia'

SPIDER_MODULES = ['Lianjia.spiders']
NEWSPIDER_MODULE = 'Lianjia.spiders'

LOG_LEVEL="ERROR"

# Crawl responsibly by identifying yourself (and your website) on the user-agent
#USER_AGENT = 'Lianjia (+http://www.yourdomain.com)'

# Obey robots.txt rules
ROBOTSTXT_OBEY = False

# Configure maximum concurrent requests performed by Scrapy (default: 16)
#CONCURRENT_REQUESTS = 32

# Configure a delay for requests for the same website (default: 0)
# See https://docs.scrapy.org/en/latest/topics/settings.html#download-delay
# See also autothrottle settings and docs
#DOWNLOAD_DELAY = 3
# The download delay setting will honor only one of:
#CONCURRENT_REQUESTS_PER_DOMAIN = 16
#CONCURRENT_REQUESTS_PER_IP = 16

# Disable cookies (enabled by default)
#COOKIES_ENABLED = False

# Disable Telnet Console (enabled by default)
#TELNETCONSOLE_ENABLED = False

# Override the default request headers:
DEFAULT_REQUEST_HEADERS = {
  'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
  'Accept-Language': 'en',
  "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.135 Safari/537.36 Edg/84.0.522.63"
}

# Enable or disable spider middlewares
# See https://docs.scrapy.org/en/latest/topics/spider-middleware.html
#SPIDER_MIDDLEWARES = {
#    'Lianjia.middlewares.LianjiaSpiderMiddleware': 543,
#}

# Enable or disable downloader middlewares
# See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html
#DOWNLOADER_MIDDLEWARES = {
#    'Lianjia.middlewares.LianjiaDownloaderMiddleware': 543,
#}

# Enable or disable extensions
# See https://docs.scrapy.org/en/latest/topics/extensions.html
#EXTENSIONS = {
#    'scrapy.extensions.telnet.TelnetConsole': None,
#}

# Configure item pipelines
# See https://docs.scrapy.org/en/latest/topics/item-pipeline.html
ITEM_PIPELINES = {
   'Lianjia.pipelines.LianjiaPipeline': 300,
}

# Enable and configure the AutoThrottle extension (disabled by default)
# See https://docs.scrapy.org/en/latest/topics/autothrottle.html
#AUTOTHROTTLE_ENABLED = True
# The initial download delay
#AUTOTHROTTLE_START_DELAY = 5
# The maximum download delay to be set in case of high latencies
#AUTOTHROTTLE_MAX_DELAY = 60
# The average number of requests Scrapy should be sending in parallel to
# each remote server
#AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0
# Enable showing throttling stats for every response received:
#AUTOTHROTTLE_DEBUG = False

# Enable and configure HTTP caching (disabled by default)
# See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings
#HTTPCACHE_ENABLED = True
#HTTPCACHE_EXPIRATION_SECS = 0
#HTTPCACHE_DIR = 'httpcache'
#HTTPCACHE_IGNORE_HTTP_CODES = []
#HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'
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5、运行结果

全部数据远远大于518条，博主爬取一会就停下来了，这里只是个演示

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