# encoding: utf-8 # File_name: import requests from bs4 import BeautifulSoup import xlrd #导入xlrd库 import pandas as pd import openpyxl # 定义函数来获取南京最新的二手房房子成交价 def get_nanjing_latest_second_hand_prices(): cookies = { 'select_city': '320100', 'lianjia_ssid': '', '02eaefcc-d3ac-468d-a2d5-b1b816bc830f': '', 'Qs_lvt_200116': '', 'sajssdk_2015_cross_new_user': '', 'sensorsdata2015jssdkcross': '', 'Qs_pv_200116': '', # ... 其他cookie } # 设置请求头，模拟浏览器访问 headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36', 'Cookie': '; '.join(f'{name}={value}' for name, value in cookies.items()), } price_0_list = list() price_100_list = list() price_200_list = list() price_300_list = list() price_400_list = list() # 假设这是提供南京最新二手房成交价的网页URL for i in range(1,4): print(f'运行次数:{i}') url = f'https://nj.ke.com/chengjiao/pukouqita11/pg{i}ie2y4ba80ea130l2l3p3p4p5p6/' print('url:'+url) # 发送HTTP请求 response = requests.get(url, headers=headers) # 检查请求是否成功 if response.status_code == 200: # 使用BeautifulSoup解析HTML内容 soup = BeautifulSoup(response.text, 'html.parser') # 根据实际的网页结构，找到包含二手房成交价的容器 # 假设成交价的容器是一个带有特定class的元素 price_container = soup.find('ul', class_='listContent') li_tags = price_container.find_all('li') print(''+str(i)+'该页多少房源:'+str(len(li_tags))) # 遍历li标签并输出内容 for li in li_tags: # 二手房交易初始化 house_dict = dict() houseInfo = li.findAll('div', class_='info') for infoDetail in houseInfo: # 小区名称+户型+面积 title = infoDetail.find('div', class_='title') a_tag = title.find('a', class_='CLICKDATA maidian-detail') # 提取并输出<a>标签内的文本 if a_tag: text_value = a_tag.string tlist=text_value.split(" ") house_dict['小区名称名称'] = tlist[0] house_dict['户型'] = tlist[1] house_dict['面积'] = tlist[2] print('小区名称:'+tlist[0]) print('户型:'+tlist[1]) print('面积:'+tlist[2]) # address # address = infoDetail.findAll('div', class_='address') # for addressDetail in address: # pass # 朝向，装修风格 fangxiang = infoDetail.find('div', class_='houseInfo') house_dict['朝向，装修风格'] = fangxiang.text.strip() print(fangxiang.text.strip()) deal_date = infoDetail.find('div', class_='dealDate') house_dict['成交时间'] = deal_date.text.strip() print(deal_date.text.strip()) total_price = infoDetail.find('div', class_='totalPrice') if '暂无价格' not in total_price.text: total_number = infoDetail.find('span', class_='number').text print(f'{total_number}万') house_dict['成交价格'] = total_number else: total_number = '0' house_dict['成交价格'] = total_number print(total_number) # 楼层 louceng = infoDetail.find('div', class_='positionInfo').text.strip() house_dict['楼层'] = louceng print(louceng) # 单价 unit_price = infoDetail.find('div', class_='unitPrice').text.strip() if '暂无单价' not in unit_price: unit_price = infoDetail.findAll('span', class_='number')[1].text.strip() else: unit_price = '0' house_dict['单价'] = unit_price print(unit_price) # 房屋满几年 deal_house_year = infoDetail.find('span', class_='dealHouseTxt') if deal_house_year is None: deal_house_year = '' else: deal_house_year = deal_house_year.text.strip() house_dict['房屋满几年'] = deal_house_year print(deal_house_year) # 挂牌时长 deal_cycle_txts = infoDetail.find('span', class_='dealCycleTxt') cycle_txts_find_all = deal_cycle_txts.findAll('span') if(len(cycle_txts_find_all)==2): house_dict['挂牌价'] = cycle_txts_find_all[0].text.strip() print(cycle_txts_find_all[0].text.strip()) house_dict['成交周期'] = cycle_txts_find_all[1].text.strip() print(cycle_txts_find_all[1].text.strip()) else: house_dict['挂牌价'] = '' for cycle_txts_find_all_span in cycle_txts_find_all: house_dict['成交周期'] = cycle_txts_find_all_span.text.strip() print(cycle_txts_find_all_span.text.strip()) try: unit_price_int = float(house_dict['成交价格']) if (unit_price_int == 0): price_0_list.append(house_dict) if (0<unit_price_int <=100 ): price_100_list.append(house_dict) if (100<unit_price_int <=200 ): price_200_list.append(house_dict) if (200<unit_price_int <=300 ): price_300_list.append(house_dict) if (300<unit_price_int <=400 ): price_400_list.append(house_dict) except ValueError: print("转换错误：字符串无法转换为整数") file = 'D:/house/pukou_pukouqita11.xlsx' # 文件路径 # 将列表字典转换为DataFrame df = pd.DataFrame(price_0_list) # 将数据写入不同的工作表中 # 将每个DataFrame写入到对应名字的工作表 with pd.ExcelWriter(file, mode='a', engine='openpyxl') as writer: # 将DataFrame写入新的工作表 df.to_excel(writer, sheet_name='无报价') # 将列表字典转换为DataFrame df = pd.DataFrame(price_100_list) # 将数据写入不同的工作表中 # 将每个DataFrame写入到对应名字的工作表 with pd.ExcelWriter(file, mode='a', engine='openpyxl') as writer: # 将DataFrame写入新的工作表 df.to_excel(writer, sheet_name='100w以内') # 将列表字典转换为DataFrame df = pd.DataFrame(price_200_list) # 将数据写入不同的工作表中 # 将每个DataFrame写入到对应名字的工作表 with pd.ExcelWriter(file, mode='a', engine='openpyxl') as writer: # 将DataFrame写入新的工作表 df.to_excel(writer, sheet_name='200w以内') # 将列表字典转换为DataFrame df = pd.DataFrame(price_300_list) # 将数据写入不同的工作表中 # 将每个DataFrame写入到对应名字的工作表 with pd.ExcelWriter(file, mode='a', engine='openpyxl') as writer: # 将DataFrame写入新的工作表 df.to_excel(writer, sheet_name='300w以内') # 将列表字典转换为DataFrame df = pd.DataFrame(price_400_list) # 将数据写入不同的工作表中 # 将每个DataFrame写入到对应名字的工作表 # 使用ExcelWriter追加模式打开文件 with pd.ExcelWriter(file, mode='a', engine='openpyxl') as writer: # 将DataFrame写入新的工作表 df.to_excel(writer, sheet_name='400w以内') # 调用函数并打印结果 latest_price = get_nanjing_latest_second_hand_prices()