用python爬虫获取房子多少钱一套（python爬虫）

大数据下的房地产+时代”，互联网平台上每天有高达上百万条房地产资讯，产生网友们数亿次的阅读量。一方面国民对房地产的关注热度只增不减，另一方面房地产行业只为更加以用户为本，不断优化产业链上的各个环节以“更了解用户”为宗旨，为消费者营提供更加透明的更加多维的房地产资讯。

数据获取思路

这里对安居客网站上二手房价格的获取方法做一讲解。

step 1,获取安居客网站中某市二手房楼盘的每个区域的网址；

step 2,进入到某个区域后，得到该区域的二手房楼盘的总页数(即总共的小区数)；

step 3,接下来，就可以一页一页地爬取每一页上二手房信息。

注意：若没有代理IP，个人获取的数据量有限，想要获取整个的数据则需要再寻求下其他方法（以后会安利给大家哒）。

下面给大家聊聊，我个人是怎样写python脚本获取部分数据的。

import requests

from lxml import etree

import pandas as pd

import json

import math

import random

import time

import urllib.request

# 获取安居客网站中某市二手房楼盘的每个区域的网址

def get_different_area_wang_zhi(url):

headers = {"Cache-Control": "max-age=0",

"User-Agent": "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/65.0.3325.181 Safari/537.36",

"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8",

"Accept-Language": "zh-CN,zh;q=0.9"}

response = requests.get(url, headers=headers).text

re = etree.HTML(response)

# 返回列表格式

wang_zhi = re.xpath('//div[@class="w1180"]//div[@class="items"]/span[@class="item-title longtag"][contains(text(),"区域：")]/../span[@class="elems-l pp-mod"]/a/@href')

return wang_zhi[1:] #第一个网址为全部的小区的网址，应该去掉

# 得到一个区域的二手房楼盘的总页数(n)

def get_one_area_number(url):

headers = {"Cache-Control": "max-age=0",

"User-Agent": "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/65.0.3325.181 Safari/537.36",

"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8",

"Accept-Language": "zh-CN,zh;q=0.9"}

response = requests.get(url, headers=headers).text

re = etree.HTML(response)

number = re.xpath('//div[@class="w1180"]//span[@class="tit"]/em[2]/text()')

nu = int(number[0])

n = int((nu / 30) + 2) # 这里的+3是一个弹性数值

return n

#爬取安居客的二手房小区信息

def anjuke_new(item,n,city_name):

mozilla = [

"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.82 Safari/537.36",

"Mozilla/5.0 (Windows NT 6.1; WOW64; rv:34.0) Gecko/20100101 Firefox/34.0",

"Mozilla/5.0 (Windows; U; Windows NT 6.1; en-us) AppleWebKit/534.50 (KHTML, like Gecko) Version/5.1 Safari/534.50",

"Mozilla/5.0 (Windows NT 10.0; WOW64; rv:38.0) Gecko/20100101 Firefox/38.0",

"Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7.0; .NET4.0C; .NET4.0E; .NET CLR 2.0.50727; .NET CLR 3.0.30729; .NET CLR 3.5.30729; InfoPath.3; rv:11.0) like Gecko",

"Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0)",

"Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0)",

"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.0)",

"Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1)",

"Mozilla/5.0 (Macintosh; Intel Mac OS X 10.6; rv:2.0.1) Gecko/20100101 Firefox/4.0.1",

"Mozilla/5.0 (Windows NT 6.1; rv:2.0.1) Gecko/20100101 Firefox/4.0.1",

"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_0) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.56 Safari/535.11",

"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Maxthon 2.0)",

"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; TencentTraveler 4.0)",

"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1)",

"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; The World)"

]

headers = {"Cache-Control": "max-age=0",

"User-Agent": "{}".format(random.choice(mozilla)),

"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8",

"Accept-Language": "zh-CN,zh;q=0.9"}

for i in range(1,n):

url = item + "p" +str(i)+'/'

l = []

try:

print("正在爬取{}的第{}页".format(url,i))

res = requests.get(url,headers = headers).text

f = etree.HTML(res)

# 获取当前页所有小区的网址

name = f.xpath('//div[@class="list-content"]/div[@_soj="xqlb"]/div[@class="li-info"]/h3/a/text()')

price = f.xpath('//div[@class="list-content"]/div[@_soj="xqlb"]/div[@class="li-side"]/p/strong/text()')

dan_wei = f.xpath('//div[@class="list-content"]/div[@_soj="xqlb"]/div[@class="li-side"]/p[1]/text()')

address = f.xpath('//div[@class="list-content"]/div[@_soj="xqlb"]/div[@class="li-info"]/address/text()')

year = f.xpath('//div[@class="list-content"]/div[@_soj="xqlb"]/div[@class="li-info"]/p[@class="date"]/text()')

for j in range(len(price)):

d = {}

d["小区名称"] = name[j]

d["价格"] = price[j]

k = 2 * j

d["价格单位"] = dan_wei[k + 1]

d["小区地址"] = address[j]

d["建造年代"] = year[k]

l.append(d)

except:

print("爬取{}的第{}页错误".format(url, i))

with open(r"./anjuke_second_house_error.txt", "a") as f:

f.write("{}第{}页出错\n".format(url, i))

continue

print("爬取{}的第{}页完成".format(url,i))

data = pd.DataFrame(l)

data.to_csv(r"./{0}_anjuke_second_house_zero.csv".format(city_name), mode="a", header=None, encoding="utf-8_sig")

time.sleep(random.randint(5,10))

#安居客二手房主函数调用

def anjuke_second_main(url,city_name):

wang_zhi = get_different_area_wang_zhi(url)

for item in wang_zhi:

n = get_one_area_number(item)

anjuke_new(item,n,city_name)

time.sleep(random.randint(10,15))

#爬取其他城市修改url,只需要修改城市名称的简称即可，例如西安是xa,厦门是xm,具体看安居客的网址。

if __name__ == "__main__":

url = "https://xa.anjuke.com/community/"

#用于储存文件

city_name = "xian"

anjuke_second_main(url,city_name)

通过运行以上脚本，获取的数据样本如下。

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