python 爬取了租房数据

爬取链接：https://sh.lianjia.com/zufang/

代码如下:

import requests
# 用于解析html数据的框架
from bs4 import BeautifulSoup
# 用于操作excel的框架
from xlwt import *
import json# 创建一个工作
book = Workbook(encoding='utf-8');
# 向表格中增加一个sheet表，sheet1为表格名称 允许单元格覆盖
sheet = book.add_sheet('sheet1', cell_overwrite_ok=True)
# 设置样式
style = XFStyle();
pattern = Pattern();
pattern.pattern = Pattern.SOLID_PATTERN;
pattern.pattern_fore_colour="0x00";
style.pattern = pattern;
# 设置列标题
sheet.write(0, 0, "标题")
sheet.write(0, 1, "地址")
sheet.write(0, 2, "价格")
sheet.write(0, 3, "建筑年代")
sheet.write(0, 4, "满年限")
sheet.write(0, 5, "离地铁")# 设置列宽度
sheet.col(0).width = 0x0d00 + 200*50
sheet.col(1).width = 0x0d00 + 20*50
sheet.col(2).width = 0x0d00 + 10*50
sheet.col(3).width = 0x0d00 + 120*50
sheet.col(4).width = 0x0d00 + 1*50
sheet.col(5).width = 0x0d00 + 50*50# 指定爬虫所需的上海各个区域名称
citys = ['pudong', 'minhang', 'baoshan', 'xuhui', 'putuo', 'yangpu', 'changning', 'songjiang','jiading', 'huangpu', 'jinan', 'zhabei', 'hongkou', 'qingpu', 'fengxian', 'jinshan', 'chongming','shanghaizhoubian']def getHtml(city):url = 'http://sh.lianjia.com/ershoufang/%s/' % cityheaders = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36'}request = requests.get(url=url, headers=headers)# 获取源码内容比request.text好，对编码方式优化好respons = request.content# 使用bs4模块，对响应的链接源代码进行html解析，后面是python内嵌的解释器，也可以安装使用lxml解析器soup = BeautifulSoup(respons, 'html.parser')# 获取类名为c-pagination的div标签，是一个列表pageDiv = soup.select('div .page-box')[0]pageData =dict(pageDiv.contents[0].attrs)['page-data'];pageDataObj =json.loads(pageData);totalPage =pageDataObj['totalPage']curPage =pageDataObj['curPage'];print(pageData);# 如果标签a标签数大于1，说明多页，取出最后的一个页码，也就是总页数for i in range(totalPage):pageIndex=i+1;print(city+"=========================================第 " + str(pageIndex) + " 页")print("\n")saveData(city, url, pageIndex);# 调用方法解析每页数据，并且保存到表格中
def saveData(city, url, pageIndex):headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36'}urlStr ='%spg%s' % (url, pageIndex);print(urlStr);html = requests.get(urlStr, headers=headers).content;soup = BeautifulSoup(html, 'lxml')liList = soup.findAll("li", {"class": "clear LOGCLICKDATA"})print(len(liList));index=0;for info in liList:title =info.find("div",class_="title").find("a").text;address =info.find("div",class_="address").find("a").textflood = info.find("div", class_="flood").textsubway = info.find("div", class_="tag").findAll("span", {"class", "subway"});subway_col="";if len(subway) > 0:subway_col = subway[0].text;taxfree = info.find("div", class_="tag").findAll("span", {"class", "taxfree"});taxfree_col="";if len(taxfree) > 0:taxfree_col = taxfree[0].text;priceInfo =info.find("div",class_="priceInfo").find("div",class_="totalPrice").text;print(flood);global rowsheet.write(row, 0, title)sheet.write(row, 1, address)sheet.write(row, 2, priceInfo)sheet.write(row, 3, flood)sheet.write(row, 4,taxfree_col)sheet.write(row, 5,subway_col)row+=1;index=row;# 判断当前运行的脚本是否是该脚本，如果是则执行
# 如果有文件xxx继承该文件或导入该文件，那么运行xxx脚本的时候，这段代码将不会执行
if __name__ == '__main__':# getHtml('jinshan')row=1for i in citys:getHtml(i)# 最后执行完了保存表格，参数为要保存的路径和文件名，如果不写路径则默然当前路径book.save('lianjia-shanghai.xls')

如下图：

思路是：

先爬取每个区域的 url 和名称，跟主 url 拼接成一个完整的 url，循环 url 列表，依次爬取每个区域的租房信息。
在爬每个区域的租房信息时，找到最大的页码，遍历页码，依次爬取每一页的二手房信息。

post 代码之前，先简单讲一下这里用到的几个爬虫 Python 包：

requests：是用来请求对链家网进行访问的包。
lxml：解析网页，用 Xpath 表达式与正则表达式一起来获取网页信息，相比 bs4 速度更快。

代码如下：

import requests
import time
import re
from lxml import etree # 获取某市区域的所有链接
def get_areas(url): print('start grabing areas') headers = { 'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.108 Safari/537.36'} resposne = requests.get(url, headers=headers) content = etree.HTML(resposne.text) areas = content.xpath("//dd[@data-index = '0']//div[@class='option-list']/a/text()") areas_link = content.xpath("//dd[@data-index = '0']//div[@class='option-list']/a/@href") for i in range(1,len(areas)): area = areas[i] area_link = areas_link[i] link = 'https://bj.lianjia.com' + area_link print("开始抓取页面") get_pages(area, link) #通过获取某一区域的页数，来拼接某一页的链接
def get_pages(area,area_link): headers = { 'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.108 Safari/537.36'} resposne = requests.get(area_link, headers=headers) pages =  int(re.findall("page-data=\'{\"totalPage\":(\d+),\"curPage\"", resposne.text)[0]) print("这个区域有" + str(pages) + "页") for page in range(1,pages+1): url = 'https://bj.lianjia.com/zufang/dongcheng/pg' + str(page) print("开始抓取" + str(page) +"的信息") get_house_info(area,url) #获取某一区域某一页的详细房租信息
def get_house_info(area, url): headers = { 'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.108 Safari/537.36'} time.sleep(2) try: resposne = requests.get(url, headers=headers) content = etree.HTML(resposne.text) info=[] for i in range(30): title = content.xpath("//div[@class='where']/a/span/text()")[i] room_type = content.xpath("//div[@class='where']/span[1]/span/text()")[i] square = re.findall("(\d+)",content.xpath("//div[@class='where']/span[2]/text()")[i])[0] position = content.xpath("//div[@class='where']/span[3]/text()")[i].replace(" ", "") try: detail_place = re.findall("([\u4E00-\u9FA5]+)租房", content.xpath("//div[@class='other']/div/a/text()")[i])[0] except Exception as e: detail_place = "" floor =re.findall("([\u4E00-\u9FA5]+)\(", content.xpath("//div[@class='other']/div/text()[1]")[i])[0] total_floor = re.findall("(\d+)",content.xpath("//div[@class='other']/div/text()[1]")[i])[0] try: house_year = re.findall("(\d+)",content.xpath("//div[@class='other']/div/text()[2]")[i])[0] except Exception as e: house_year = "" price = content.xpath("//div[@class='col-3']/div/span/text()")[i] with open('链家北京租房.txt','a',encoding='utf-8') as f: f.write(area + ',' + title + ',' + room_type + ',' + square + ',' +position+
','+ detail_place+','+floor+','+total_floor+','+price+','+house_year+'\n') print('writing work has done!continue the next page') except Exception as e: print( 'ooops! connecting error, retrying.....') time.sleep(20) return get_house_info(area, url) def main(): print('start!') url = 'https://bj.lianjia.com/zufang' get_areas(url) if __name__ == '__main__': main()

由于每个楼盘户型差别较大，区域位置比较分散，每个楼盘具体情况还需具体分析

代码：

    #北京路段_房屋均价分布图 detail_place = df.groupby(['detail_place']) house_com = detail_place['price'].agg(['mean','count']) house_com.reset_index(inplace=True) detail_place_main = house_com.sort_values('count',ascending=False)[0:20] attr = detail_place_main['detail_place'] v1 = detail_place_main['count'] v2 = detail_place_main['mean'] line = Line("北京主要路段房租均价") line.add("路段",attr,v2,is_stack=True,xaxis_rotate=30,yaxix_min=4.2, mark_point=['min','max'],xaxis_interval=0,line_color='lightblue', line_width=4,mark_point_textcolor='black',mark_point_color='lightblue', is_splitline_show=False) bar = Bar("北京主要路段房屋数量") bar.add("路段",attr,v1,is_stack=True,xaxis_rotate=30,yaxix_min=4.2, xaxis_interval=0,is_splitline_show=False) overlap = Overlap() overlap.add(bar) overlap.add(line,yaxis_index=1,is_add_yaxis=True) overlap.render('北京路段_房屋均价分布图.html')

面积&租金分布呈阶梯性

#房源价格区间分布图
price_info = df[['area', 'price']] #对价格分区
bins = [0,1000,1500,2000,2500,3000,4000,5000,6000,8000,10000]
level = ['0-1000','1000-1500', '1500-2000', '2000-3000', '3000-4000', '4000-5000', '5000-6000', '6000-8000', '8000-1000','10000以上']
price_stage = pd.cut(price_info['price'], bins = bins,labels = level).value_counts().sort_index() attr = price_stage.index
v1 = price_stage.values bar = Bar("价格区间&房源数量分布")
bar.add("",attr,v1,is_stack=True,xaxis_rotate=30,yaxix_min=4.2, xaxis_interval=0,is_splitline_show=False) overlap = Overlap()
overlap.add(bar)
overlap.render('价格区间&房源数量分布.html')

#房屋面积分布
bins =[0,30,60,90,120,150,200,300,400,700]
level = ['0-30', '30-60', '60-90', '90-120', '120-150', '150-200', '200-300','300-400','400+']
df['square_level'] = pd.cut(df['square'],bins = bins,labels = level) df_digit= df[['area', 'room_type', 'square', 'position', 'total_floor', 'floor', 'house_year', 'price', 'square_level']]
s = df_digit['square_level'].value_counts() attr = s.index
v1 = s.values pie = Pie("房屋面积分布",title_pos='center') pie.add( "", attr, v1, radius=[40, 75], label_text_color=None, is_label_show=True, legend_orient="vertical", legend_pos="left",
) overlap = Overlap()
overlap.add(pie)
overlap.render('房屋面积分布.html') #房屋面积&价位分布
bins =[0,30,60,90,120,150,200,300,400,700]
level = ['0-30', '30-60', '60-90', '90-120', '120-150', '150-200', '200-300','300-400','400+']
df['square_level'] = pd.cut(df['square'],bins = bins,labels = level) df_digit= df[['area', 'room_type', 'square', 'position', 'total_floor', 'floor', 'house_year', 'price', 'square_level']] square = df_digit[['square_level','price']]
prices = square.groupby('square_level').mean().reset_index()
amount = square.groupby('square_level').count().reset_index() attr = prices['square_level']
v1 = prices['price'] pie = Bar("房屋面积&价位分布布")
pie.add("", attr, v1, is_label_show=True)
pie.render()
bar = Bar("房屋面积&价位分布")
bar.add("",attr,v1,is_stack=True,xaxis_rotate=30,yaxix_min=4.2, xaxis_interval=0,is_splitline_show=False) overlap = Overlap()
overlap.add(bar)
overlap.render('房屋面积&价位分布.html')

摘录:爬取了上万条租房数据，你还要不要北漂

python 爬取了租房数据相关推荐

python 爬取自如租房的租房数据，使用图像识别获取价格信息
python 爬取自如租房的租房数据完整代码下载:https://github.com/tanjunchen/SpiderProject/tree/master/ziru #!/usr/bin/py ...
python爬虫爬取58网站数据_Python爬虫，爬取58租房数据字体反爬
Python爬虫,爬取58租房数据这俩天项目主管给了个爬虫任务,要爬取58同城上福州区域的租房房源信息.因为58的前端页面做了base64字体加密所以爬取比较费力,前前后后花了俩天才搞完. 项目演示 ...
Python应用实战-Python爬取4000+股票数据，并用plotly绘制了树状热力图(treemap)
目录: 1. 准备工作 2. 开始绘图 2.1. 简单的例子 2.2. px.treemap常用参数介绍 2.3. color_continuous_scale参数介绍 2.4. 大A股市树状热力图来 ...
python爬去朋友圈_利用Python爬取朋友圈数据，爬到你开始怀疑人生
人生最难的事是自我认知,用Python爬取朋友圈数据,让我们重新审视自己,审视我们周围的圈子. 文:朱元禄(@数据分析-jacky) 哲学的两大问题:1.我是谁?2.我们从哪里来? 本文 jacky试 ...
python 爬取拉钩数据
Python通过Request库爬取拉钩数据爬取方法数据页面建表存储职位信息解析页面核心代码完整代码结果展示爬取方法采用python爬取拉钩数据,有很多方法可以爬取,我采用的是通过Re ...
python 爬取拉钩网数据
python 爬取拉钩网数据完整代码下载:https://github.com/tanjunchen/SpiderProject/blob/master/lagou/LaGouSpider.py # ...
利用Python爬取国家水稻数据中心的品种数据
利用Python爬取国家水稻数据中心的品种数据一.页面获取 python可以进行对网页的访问,主要用到requests,beautifulsoup4包. 首先新建一个page的py文件,用来获取页面 ...
利用Python爬取朋友圈数据，爬到你开始怀疑人生
人生最难的事是自我认知,用Python爬取朋友圈数据,让我们重新审视自己,审视我们周围的圈子. 文:朱元禄(@数据分析-jacky) 哲学的两大问题:1.我是谁?2.我们从哪里来? 本文 jacky试 ...
利用python爬取2019-nCoV确诊数据并制作pyecharts可视化地图
1.本章利用python爬取2019-nCoV确诊数据并制作pyecharts可视化地图: 2.主要内容为绘制出中国各省疫情数据,疫情数据从四个维度进行可视化展示:累积确诊人数.现存确诊人数.治愈人数 ...

python 爬取了租房数据

python 爬取了租房数据相关推荐

最新文章

热门文章