# 仅需修改这个地方https://jn.lianjia.com/ershoufang/pg{}rs/ 将jn换成你所在城市的拼写首字母小写
import requests
from lxml import etree
import time
import random
import csv
class LianjiaSpider(object):
def __init__(self):
self.url = "http://www.66ip.cn/areaindex_15/{}.html"
self.headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.1 (KHTML, like Gecko) Chrome/14.0.835.163 Safari/535.1"}
def get_page(self, url):
res = requests.get(url=url, headers=self.headers)
res.encoding = "utf-8"
html = res.text
# 调用解析函数
self.parse_page(html)
print(html)
def parse_page(self, html):
parse_html = etree.HTML(html)
house_dict = {}
house_list = parse_html.xpath("//div//ul[@class='sellListContent']//li")
for house in house_list:
house_dict["name"] = house.xpath(".//div[@class='title']/a//text()")[0]
house_dict["totalprice"] = house.xpath(".//div[@class='totalPrice']/span//text()")[0]
house_dict["uniteprice"] = house.xpath(".//div[@class='unitPrice']/span//text()")[0]
house_dict["houseInfo"] = house.xpath(".//div[@class='houseInfo']/text()")[0]
house_dict["positionInfoShequ"] = house.xpath(".//div[@class='positionInfo']/a[1]//text()")[0]
house_dict["positionInfoJiedao"] = house.xpath(".//div[@class='positionInfo']/a[2]//text()")[0]
house_dict["followInfo"] = house.xpath(".//div[@class='followInfo']/text()")[0]
print(house_dict)
with open('F:/top250/lianjia.csv', 'a', newline='', encoding='utf-8')as f:
write = csv.writer(f)
# write.writerow([house_dict["name"],house_dict["totalprice"],house_dict["uniteprice"],house_dict["info"]])
write.writerow(
[house_dict["name"], house_dict["totalprice"], house_dict["uniteprice"], house_dict["houseInfo"],
house_dict["positionInfoShequ"], house_dict["positionInfoJiedao"], house_dict["followInfo"]])
f.close()
def main(self):
try:
for i in range(1, 2):
time.sleep(random.randint(3, 5))
url = self.url.format(i)
self.get_page(url)
except:
self.main()
if __name__ == '__main__':
start = time.time()
spider = LianjiaSpider()
spider.main()
end = time.time()
print("执行时间:%.2f" % (end - start))
23python免费代理测试第一步成功输出66代理框架内容
最新推荐文章于 2025-11-01 09:47:18 发布
本文介绍了一款链家二手房信息爬虫的实现过程,通过Python的requests和lxml库获取并解析二手房详细信息,包括房屋名称、总价、单价、房屋信息、社区位置、街道位置及关注信息,并将数据保存为CSV格式。
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