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爬取链家网上海二手房数据,并进行分析建模

 

一.分析网页结构并编写程序

import requests
import csv
import time
import math
import random
from lxml import etree
from multiprocessing.dummy import Pool
def getPage(url):
    time.sleep(random.choice([2, 2.5, 3, 3.5]))
    page = requests.get(url, headers={
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 OPR/57.0.3098.110"})
    return etree.HTML(page.text)
def csvWrite(item):
    with open("lianjia_sh_ershoufang_data.csv", "a", encoding="utf-8", newline="") as f:
        csv.writer(f).writerow(item)
def get_areas_regions_urls():
    areas = [
        "pudong",
        "minhang",
        "baoshan",
        "xuhui",
        "putuo",
        "yangpu",
        "changning",
        "songjiang",
        "jiading",
        "huangpu",
        "jingan",
        "zhabei",
        "hongkou",
        "qingpu",
        "fengxian",
        "jinshan",
        "chongming",
        "shanghaizhoubian"]
    areas_regions_urls = []#这是我们要返回的元组列表,其内每一个元组将包含地区、地点、和url
    for area in areas:
        page = getPage("https://sh.lianjia.com/ershoufang/" + area)
        region_names = page.xpath("/html/body/div[3]/div/div[1]/dl[2]/dd/div[1]/div[2]/a/text()")#获取地点名
        region_urls = page.xpath("/html/body/div[3]/div/div[1]/dl[2]/dd/div[1]/div[2]/a/@href")#获取地点对应的url
        for url in region_urls:
            #创建元组并将其写入目标列表
            areas_regions_urls.append((area,region_names[region_urls.index(url)], "https://gz.lianjia.com"+url))
            # print(area,region_names[region_urls.index(url)],"https://gz.lianjia.com"+url)
        # print("Region urls in Area {} have been added!".format(area))
    print("All regions urls have been added")
    return areas_regions_urls
def region_spider(x):
    #获取信息条数
    info_num = int(getPage(x[2]).xpath("/html/body/div[4]/div[1]/div[2]/h2/span/text()")[0])
    #计算信息页数(已知每页最多30条数据)
    page_num = math.ceil(info_num/30)
    # print("{}有{}条数据,共{}页".format(x[1],info_num,page_num))
    for url in [x[2]+"pg" + str(num+1) for num in range(page_num)]:
        page = getPage(url)
        for house in page.xpath("/html/body/div[4]/div[1]/ul/li"):
            try:
                # print(house.xpath("div[1]/div[1]/a/text()")[0])
                #x代表get_areas_regions_urls()返回的列表中的每一个元组,则x[0]代表地区,x[1]代表地点,x[2]代表url
                Area = x[0]
                Region = x[1]
                info = house.xpath("div[1]/div[2]/div/text()")[0].split("|")
                #由于别墅房源和普通房源的网页结构稍有不同,所以这里我们需要做一个判断
                if info[1].strip()[-2:]=="别墅":
                    Garden = house.xpath("div[1]/div[2]/div/a/text()")[0]
                    Layout = info[2]
                    Acreage = info[3].strip()
                    Direction = info[4].strip()
                    Renovation = info[5].strip()
                    Elevator = info[6].strip()
                    Price = int(house.xpath("div[1]/div[6]/div[1]/span/text()")[0])
                    BuiltYear = re.search("\d{4}",house.xpath("div[1]/div[3]/div/text()")[0]).group()
                    Height = re.search("\d层",house.xpath("div[1]/div[3]/div/text()")[0]).group()
                    Building = info[1].strip()
                
                else:
                    Garden = house.xpath("div[1]/div[2]/div/a/text()")[0]
                    Layout = info[1]
                    Acreage = info[2].strip()
                    Direction = info[3].strip()
                    Renovation = info[4].strip()
                    try:
                        Elevator = info[5].strip()
                        #并不是所有房源都有电梯信息,若无则设为“无数据”
                    except:
                        Elevator = "无数据"
                    Price = house.xpath("div[1]/div[6]/div[1]/span/text()")[0]
                    try:    
                        BuiltYear = re.search("\d{4}",house.xpath("div[1]/div[3]/div/text()")[0]).group()
                        #并不是所有房源都有年代信息,若无则设为0
                    except:
                        BuiltYear = 0
                    Height = house.xpath("div[1]/div[3]/div/text()")[0][0:3]
                    try:
                        #并不是所有房源都有建筑类型信息,若无则设为“无数据”
                        Building = re.search("..楼",house.xpath("div[1]/div[3]/div/text()")[0]).group()[-2:]
                    except:
                        Building = "无数据"
            except:
                print("Error")
            else:
                #写入并打印爬到的数据
                csvWrite([Area,Region,Garden,Acreage,Direction,Layout,Renovation,Height,Elevator,BuiltYear,Building,Price])
                # print([Area,Region,Garden,Acreage,Direction,Layout,Renovation,Height,Elevator,BuiltYear,Building,Price])
                
    print("All data of District{} in Area {} have sbeen downloaded!".format(x[1],x[0]))
if __name__ == "__main__":
    url_list = get_areas_regions_urls()
    pool = Pool()#创建线程池
    pool.map(region_spider,url_list)#使用多线程运行爬虫
    pool.close()#关闭线程池
    pool.join()#等待所有线程结束

二.数据分析

import pandas as pd
import pandas_profiling as pp
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline
/opt/conda/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
  return f(*args, **kwds)
/opt/conda/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
  return f(*args, **kwds)
df = pd.read_csv("/home/kesci/work/lianjia_sh_ershoufang_data.csv", header = None)
df.columns = ["Area", "Region", "Garden", "Acreage", "Direction", "Layout", "Renovation", \
              "Height", "Elevator", "BuiltYear", "Building", "Price"]
df.head()

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原文链接:爬取链家网上海二手房数据,并进行分析建模,转载请注明来源!

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