基于Python实现最新房价信息的获取

这篇文章主要为大家介绍了如何利用Python获取房价信息（以北京为例），整个数据获取的信息是通过房源平台获取的，通过下载网页元素并进行数据提取分析完成整个过程，需要的可以参考一下

整个数据获取的信息是通过房源平台获取的，通过下载网页元素并进行数据提取分析完成整个过程

导入相关的网页下载、数据解析、数据处理库

from fake_useragent import UserAgent  # 身份信息生成库 from bs4 import BeautifulSoup  # 网页元素解析库 import numpy as np  # 科学计算库 import requests  # 网页下载库 from requests.exceptions import RequestException  # 网络请求异常库 import pandas as pd  # 数据处理库

然后，在开始之前初始化一个身份信息生成的对象，用于后面随机生成网页下载时的身份信息。

user_agent = UserAgent()

编写一个网页下载函数get_html_txt，从相应的url地址下载网页的html文本。

def get_html_txt(url, page_index): ''' 获取网页html文本信息 :param url: 爬取地址 :param page_index:当前页数 :return: ''' try: headers = { 'user-agent': user_agent.random } response = requests.request("GET", url, headers=headers, timeout=10) html_txt = response.text return html_txt except RequestException as e: print('获取第{0}页网页元素失败！'.format(page_index)) return ''

编写网页元素处理函数catch_html_data，用于解析网页元素，并将解析后的数据元素保存到csv文件中。

def catch_html_data(url, page_index): ''' 处理网页元素数据 :param url: 爬虫地址 :param page_index: :return: ''' # 下载网页元素 html_txt = str(get_html_txt(url, page_index)) if html_txt.strip() != '': # 初始化网页元素对象 beautifulSoup = BeautifulSoup(html_txt, 'lxml') # 解析房源列表 h_list = beautifulSoup.select('.resblock-list-wrapper li') # 遍历当前房源的详细信息 for n in range(len(h_list)): h_detail = h_list[n] # 提取房源名称 h_detail_name = h_detail.select('.resblock-name a.name') h_detail_name = [m.get_text() for m in h_detail_name] h_detail_name = ' '.join(map(str, h_detail_name)) # 提取房源类型 h_detail_type = h_detail.select('.resblock-name span.resblock-type') h_detail_type = [m.get_text() for m in h_detail_type] h_detail_type = ' '.join(map(str, h_detail_type)) # 提取房源销售状态 h_detail_status = h_detail.select('.resblock-name span.sale-status') h_detail_status = [m.get_text() for m in h_detail_status] h_detail_status = ' '.join(map(str, h_detail_status)) # 提取房源单价信息 h_detail_price = h_detail.select('.resblock-price .main-price .number') h_detail_price = [m.get_text() for m in h_detail_price] h_detail_price = ' '.join(map(str, h_detail_price)) # 提取房源总价信息 h_detail_total_price = h_detail.select('.resblock-price .second') h_detail_total_price = [m.get_text() for m in h_detail_total_price] h_detail_total_price = ' '.join(map(str, h_detail_total_price)) h_info = [h_detail_name, h_detail_type, h_detail_status, h_detail_price, h_detail_total_price] h_info = np.array(h_info) h_info = h_info.reshape(-1, 5) h_info = pd.DataFrame(h_info, columns=['房源名称', '房源类型', '房源状态', '房源均价', '房源总价']) h_info.to_csv('北京房源信息.csv', mode='a+', index=False, header=False) print('第{0}页房源信息数据存储成功！'.format(page_index)) else: print('网页元素解析失败！')

编写多线程处理函数，初始化网络网页下载地址，并使用多线程启动调用业务处理函数catch_html_data，启动线程完成整个业务流程。

import threading  # 导入线程处理模块 def thread_catch(): ''' 线程处理函数 :return: ''' for num in range(1, 50, 3): url_pre = "https://bj.fang.lianjia.com/loupan/pg{0}/".format(str(num)) url_cur = "https://bj.fang.lianjia.com/loupan/pg{0}/".format(str(num + 1)) url_aft = "https://bj.fang.lianjia.com/loupan/pg{0}/".format(str(num + 2)) thread_pre = threading.Thread(target=catch_html_data, args=(url_pre, num)) thread_cur = threading.Thread(target=catch_html_data, args=(url_cur, num + 1)) thread_aft = threading.Thread(target=catch_html_data, args=(url_aft, num + 2)) thread_pre.start() thread_cur.start() thread_aft.start() thread_catch()

数据存储结果展示效果