Python爬取數萬條北京租房資料，從6個維度告訴你房租瘋漲的秘密！

import requests
import time
import re
from lxml import etree

# 獲取某市區域的所有連結
def get_areas(url):
    print('start grabing areas')
    essay-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, essay-headers=essay-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("開始抓取頁面")
        get_pages(area, link)

#透過獲取某一區域的頁數，來拼接某一頁的連結
def get_pages(area,area_link):
    essay-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, essay-headers=essay-headers)
    pages =  int(re.findall("page-data=\'{\"totalPage\":(\d+),\"curPage\"", resposne.text)[0])
    print("這個區域有" + str(pages) + "頁")
    for page in range(1,pages+1):
        url = 'https://bj.lianjia.com/zufang/dongcheng/pg' + str(page)
        print("開始抓取" + str(page) +"的資訊")
        get_house_info(area,url)

#獲取某一區域某一頁的詳細房租資訊
def get_house_info(area, url):
    essay-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, essay-headers=essay-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('鏈家北京租房.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()

#北京路段_房屋均價分佈圖
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價")
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("北京主要路段房屋數量")
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_房屋均價分佈圖.html')

#房源價格區間分佈圖
price_info = df[['area', 'price']]

#對價格分割槽
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("價格區間&房源數量分佈")
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('價格區間&房源數量分佈.html')

#房屋面積分佈
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("房屋面積分佈",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('房屋面積分佈.html')

#房屋面積&價位分佈
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("房屋面積&價位分佈布")
pie.add("", attr, v1, is_label_show=True)
pie.render()
bar = Bar("房屋面積&價位分佈")
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('房屋面積&價位分佈.html')

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