爬虫+数据分析：重庆买房吗？爬取重庆房价

现在结婚，女方一般要求城里有套房。要了解近些年的房价，首先就要获取网上的房价信息，今天以重庆链家网上出售的房价信息为例，将数据爬取下来分析。

爬虫部分

一.网址分析
https://cq.fang.lianjia.com/loupan/

下面我们来分析我们所要提取的信息的位置，打开开发者模式查找元素，我们找到房子如下图.如图发现，一个房子信息被存储到一个li标签里。

单击一个li标签，再查找房子名，地址，房价信息。

网址分析，当我点击下一页时，网络地址pg参数会发生变化。
第一页pg1,第二页pg2…

二.单页网址爬取
采取requests-Beautiful Soup的方式来爬取

from bs4 import BeautifulSoup
import numpy as np
import requests
from requests.exceptions import  RequestException
import pandas as pd
#读取网页
def craw(url,page):
    try:

        headers = {
     
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3947.100 Safari/537.36"}
        html1 = requests.request("GET", url, headers=headers,timeout=10)
        html1.encoding ='utf-8' # 加编码，重要！转换为字符串编码，read()得到的是byte格式的
        html=html1.text
        return html
    except RequestException:#其他问题
        print('读取error')
        return None

for i  in range(1,2):#遍历网页1
    url="https://cq.fang.lianjia.com/loupan/pg"+str(i)+"/"
    html=craw(url,i)
    print(html)

print('结束')

三.网页信息提取

#解析网页并保存数据到表格
def pase_page(url,page):
    html=craw(url,page)
    html = str(html)
    if html is not None:
        soup = BeautifulSoup(html, 'lxml')
        "--先确定房子信息，即li标签列表--"
        houses=soup.select('.resblock-list-wrapper li')#房子列表
        "--再确定每个房子的信息--"
        for house in houses:#遍历每一个房子
            "名字"
            recommend_project=house.select('.resblock-name a.name')
            recommend_project=[i.get_text()for i in recommend_project]#名字 英华天元，斌鑫江南御府...
            #print(recommend_project)
            "类型"
            house_type=house.select('.resblock-name span.resblock-type')
            house_type=[i.get_text()for i in house_type]#写字楼,底商...
            #print(house_type)
            "销售状态"
            sale_status = house.select('.resblock-name span.sale-status')
            sale_status=[i.get_text()for i in sale_status]#在售,在售,售罄,在售...
            #print(sale_status)
            "大地址:如['南岸', '南坪']"
            big_address=house.select('.resblock-location span')
            big_address=[i.get_text()for i in big_address]#['南岸', '南坪']，['巴南', '李家沱']...
            #print(big_address)
            "具体地址:如：铜元局轻轨站菜园坝长江大桥南桥头堡上"
            small_address=house.select('.resblock-location a')
            small_address=[i.get_text()for i in small_address]#铜元局轻轨站菜园坝长江大桥南桥头堡上,龙洲大道1788号..
            #print(small_address)
            "优势。如：['环线房', '近主干道', '配套齐全', '购物方便']"
            advantage=house.select('.resblock-tag span')
            advantage=[i.get_text()for i in advantage]#['环线房', '近主干道', '配套齐全', '购物方便']，['地铁沿线', '公交直达', '配套齐全', '购物方便']
            #print(advantage)
            "均价：多少1平"
            average_price=house.select('.resblock-price .main-price .number')
            average_price=[i.get_text()for i in average_price]#16000,25000,价格待定..
            #print(average_price)
            "总价,单位万"
            total_price=house.select('.resblock-price .second')
            total_price=[i.get_text()for i in total_price]#总价400万/套，总价100万/套'...
            #print(total_price)

四.多页爬取，并将信息存储到表格

from bs4 import BeautifulSoup
import numpy as np
import requests
from requests.exceptions import  RequestException
import pandas as pd
#读取网页
def craw(url,page):
    try:

        headers = {
     
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3947.100 Safari/537.36"}
        html1 = requests.request("GET", url, headers=headers,timeout=10)
        html1.encoding ='utf-8' # 加编码，重要！转换为字符串编码，read()得到的是byte格式的
        html=html1.text

        return html
    except RequestException:#其他问题
        print('第{0}读取网页失败'.format(page))
        return None
#解析网页并保存数据到表格
def pase_page(url,page):
    html=craw(url,page)
    html = str(html)
    if html is not None:
        soup = BeautifulSoup(html, 'lxml')
        "--先确定房子信息，即li标签列表--"
        houses=soup.select('.resblock-list-wrapper li')#房子列表
        "--再确定每个房子的信息--"
        for j in range(len(houses)):#遍历每一个房子
            house=houses[j]
            "名字"
            recommend_project=house.select('.resblock-name a.name')
            recommend_project=[i.get_text()for i in recommend_project]#名字 英华天元，斌鑫江南御府...
            recommend_project=' '.join(recommend_project)
            #print(recommend_project)
            "类型"
            house_type=house.select('.resblock-name span.resblock-type')
            house_type=[i.get_text()for i in house_type]#写字楼,底商...
            house_type=' '.join(house_type)
            #print(house_type)
            "销售状态"
            sale_status = house.select('.resblock-name span.sale-status')
            sale_status=[i.get_text()for i in sale_status]#在售,在售,售罄,在售...
            sale_status=' '.join(sale_status)
            #print(sale_status)
            "大地址:如['南岸', '南坪']"
            big_address=house.select('.resblock-location span')
            big_address=[i.get_text()for i in big_address]#['南岸', '南坪']，['巴南', '李家沱']...
            big_address=''.join(big_address)
            #print(big_address)
            "具体地址:如：铜元局轻轨站菜园坝长江大桥南桥头堡上"
            small_address=house.select('.resblock-location a')
            small_address=[i.get_text()for i in small_address]#铜元局轻轨站菜园坝长江大桥南桥头堡上,龙洲大道1788号..
            small_address=' '.join(small_address)
            #print(small_address)
            "优势。如：['环线房', '近主干道', '配套齐全', '购物方便']"
            advantage=house.select('.resblock-tag span')
            advantage=[i.get_text()for i in advantage]#['环线房', '近主干道', '配套齐全', '购物方便']，['地铁沿线', '公交直达', '配套齐全', '购物方便']
            advantage=' '.join(advantage)
            #print(advantage)
            "均价：多少1平"
            average_price=house.select('.resblock-price .main-price .number')
            average_price=[i.get_text()for i in average_price]#16000,25000,价格待定..
            average_price=' '.join(average_price)
            #print(average_price)
            "总价,单位万"
            total_price=house.select('.resblock-price .second')
            total_price=[i.get_text()for i in total_price]#总价400万/套，总价100万/套'...
            total_price=' '.join(total_price)
            #print(total_price)

            "--------------写入表格-------------"
            information = [recommend_project, house_type, sale_status,big_address,small_address,advantage,average_price,total_price]
            information = np.array(information)
            information = information.reshape(-1, 8)
            information = pd.DataFrame(information, columns=['名称', '类型', '销售状态','大地址','具体地址','优势','均价','总价'])
            if page== 1 and j==0:
                information.to_csv('链家网重庆房子数据.csv', mode='a+', index=False)  # mode='a+'追加写入
            else:
                information.to_csv('链家网重庆房子数据.csv', mode='a+', index=False, header=False)  # mode='a+'追加写入
        print('第{0}页存储数据成功'.format(page))
    else:
        print('解析失败')


for i  in range(1,101):#遍历网页1
    url="https://cq.fang.lianjia.com/loupan/pg"+str(i)+"/"
    pase_page(url,i)


print('结束')

五.多线程爬取

from bs4 import BeautifulSoup
import numpy as np
import requests
from requests.exceptions import  RequestException
import pandas as pd


#读取网页
def craw(url,page):
    try:

        headers = {
     
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3947.100 Safari/537.36"}
        html1 = requests.request("GET", url, headers=headers,timeout=10)
        html1.encoding ='utf-8' # 加编码，重要！转换为字符串编码，read()得到的是byte格式的
        html=html1.text

        return html
    except RequestException:#其他问题
        print('第{0}读取网页失败'.format(page))
        return None
#解析网页并保存数据到表格
def pase_page(url,page):
    html=craw(url,page)
    html = str(html)
    if html is not None:
        soup = BeautifulSoup(html, 'lxml')
        "--先确定房子信息，即li标签列表--"
        houses=soup.select('.resblock-list-wrapper li')#房子列表
        "--再确定每个房子的信息--"
        for j in range(len(houses)):#遍历每一个房子
            house=houses[j]
            "名字"
            recommend_project=house.select('.resblock-name a.name')
            recommend_project=[i.get_text()for i in recommend_project]#名字 英华天元，斌鑫江南御府...
            recommend_project=' '.join(recommend_project)
            #print(recommend_project)
            "类型"
            house_type=house.select('.resblock-name span.resblock-type')
            house_type=[i.get_text()for i in house_type]#写字楼,底商...
            house_type=' '.join(house_type)
            #print(house_type)
            "销售状态"
            sale_status = house.select('.resblock-name span.sale-status')
            sale_status=[i.get_text()for i in sale_status]#在售,在售,售罄,在售...
            sale_status=' '.join(sale_status)
            #print(sale_status)
            "大地址:如['南岸', '南坪']"
            big_address=house.select('.resblock-location span')
            big_address=[i.get_text()for i in big_address]#['南岸', '南坪']，['巴南', '李家沱']...
            big_address=''.join(big_address)
            #print(big_address)
            "具体地址:如：铜元局轻轨站菜园坝长江大桥南桥头堡上"
            small_address=house.select('.resblock-location a')
            small_address=[i.get_text()for i in small_address]#铜元局轻轨站菜园坝长江大桥南桥头堡上,龙洲大道1788号..
            small_address=' '.join(small_address)
            #print(small_address)
            "优势。如：['环线房', '近主干道', '配套齐全', '购物方便']"
            advantage=house.select('.resblock-tag span')
            advantage=[i.get_text()for i in advantage]#['环线房', '近主干道', '配套齐全', '购物方便']，['地铁沿线', '公交直达', '配套齐全', '购物方便']
            advantage=' '.join(advantage)
            #print(advantage)
            "均价：多少1平"
            average_price=house.select('.resblock-price .main-price .number')
            average_price=[i.get_text()for i in average_price]#16000,25000,价格待定..
            average_price=' '.join(average_price)
            #print(average_price)
            "总价,单位万"
            total_price=house.select('.resblock-price .second')
            total_price=[i.get_text()for i in total_price]#总价400万/套，总价100万/套'...
            total_price=' '.join(total_price)
            #print(total_price)

            "--------------写入表格-------------"
            information = [recommend_project, house_type, sale_status,big_address,small_address,advantage,average_price,total_price]
            information = np.array(information)
            information = information.reshape(-1, 8)
            information = pd.DataFrame(information, columns=['名称', '类型', '销售状态','大地址','具体地址','优势','均价','总价'])

            information.to_csv('链家网重庆房子数据.csv', mode='a+', index=False, header=False)  # mode='a+'追加写入
        print('第{0}页存储数据成功'.format(page))
    else:
        print('解析失败')


#双线程
import threading
for i  in range(1,99,2):#遍历网页1-101
    url1="https://cq.fang.lianjia.com/loupan/pg"+str(i)+"/"
    url2 = "https://cq.fang.lianjia.com/loupan/pg" + str(i+1) + "/"

    t1 = threading.Thread(target=pase_page, args=(url1,i))#线程1
    t2 = threading.Thread(target=pase_page, args=(url2,i+1))#线程2
    t1.start()
    t2.start()

可能是网的问题，很多页的数据没有读取下来。

存储到的信息有近438条。原始数据有1838条。
可以自己把失败的页数存储下来，再重新请求一次。我这里就不搞啦。将就用。