python爬虫：爬取链家深圳全部二手房的详细信息

1、问题描述：

爬取链家深圳全部二手房的详细信息，并将爬取的数据存储到CSV文件中

2、思路分析:

(1)目标网址：https://sz.lianjia.com/ershoufang/

(2)代码结构：

class LianjiaSpider(object):

    def __init__(self):

    def getMaxPage(self, url): # 获取maxPage

    def parsePage(self, url): # 解析每个page，获取每个huose的Link

    def parseDetail(self, url): # 根据Link，获取每个house的详细信息

(3) init(self)初始化函数

· hearders用到了fake_useragent库，用来随机生成请求头。

· datas空列表，用于保存爬取的数据。

def __init__(self):
    self.headers = {"User-Agent": UserAgent().random}
    self.datas = list()

(4) getMaxPage()函数

主要用来获取二手房页面的最大页数.

def getMaxPage(self, url):
    response = requests.get(url, headers = self.headers)
    if response.status_code == 200:
        source = response.text
        soup = BeautifulSoup(source, "html.parser")
        pageData = soup.find("div", class_ = "page-box house-lst-page-box")["page-data"]
        # pageData = ‘{"totalPage":100,"curPage":1}‘，通过eval()函数把字符串转换为字典
        maxPage = eval(pageData)["totalPage"]
        return  maxPage
    else:
        print("Fail status: {}".format(response.status_code))
        return None

(5)parsePage()函数

主要是用来进行翻页的操作，得到每一页的所有二手房的Links链接。它通过利用一个for循环来重构 url实现翻页操作，而循环最大页数就是通过上面的 getMaxPage() 来获取到。

def parsePage(self, url):
    maxPage = self.getMaxPage(url)
    #  解析每个page，获取每个二手房的链接
    for pageNum in range(1, maxPage+1 ):
        url = "https://sz.lianjia.com/ershoufang/pg{}/".format(pageNum)
        print("当前正在爬取: {}".format(url))
        response = requests.get(url, headers = self.headers)
        soup = BeautifulSoup(response.text, "html.parser")
        links = soup.find_all("div", class_ = "info clear")
        for i in links:
            link = i.find("a")["href"]    #每个<info clear>标签有很多<a>,而我们只需要第一个，所以用find
            detail = self.parseDetail(link)
            self.datas.append(detail)

(6)parseDetail()函数

根据parsePage()函数获取的二手房Link链接，向该链接发送请求，获取出详细页面信息。

def parseDetail(self, url):
    response = requests.get(url, headers = self.headers)
    detail = {}
    if response.status_code == 200:
        soup = BeautifulSoup(response.text, "html.parser")
        detail["价格"] = soup.find("span", class_ = "total").text
        detail["单价"] = soup.find("span", class_ = "unitPriceValue").text
        detail["小区"] = soup.find("div", class_ = "communityName").find("a", class_ = "info").text
        detail["位置"] = soup.find("div", class_="areaName").find("span", class_="info").text
        detail["地铁"] = soup.find("div", class_="areaName").find("a", class_="supplement").text
        base = soup.find("div", class_ = "base").find_all("li") # 基本信息
        detail["户型"] = base[0].text[4:]
        detail["面积"] = base[2].text[4:]
        detail["朝向"] = base[6].text[4:]
        detail["电梯"] = base[10].text[4:]
        return detail
    else:
        return None

(7)将数据存储到CSV文件中

这里用到了 pandas 库的 DataFrame() 方法，它默认的是按照列名的字典顺序排序的。想要自定义列的顺序，可以加columns字段。

    #  将所有爬取的二手房数据存储到csv文件中
    data = pd.DataFrame(self.datas)
    # columns字段：自定义列的顺序（DataFrame默认按列名的字典序排序）
    columns = ["小区", "户型", "面积", "价格", "单价", "朝向", "电梯", "位置", "地铁"]
    data.to_csv(".\Lianjia_II.csv", encoding=‘utf_8_sig‘, index=False, columns=columns)

3、效果展示

4、完整代码：

# -* coding: utf-8 *-
#author: wangshx6
#data: 2018-11-07
#descriptinon: 爬取链家深圳全部二手房的详细信息，并将爬取的数据存储到CSV文

import requests
from bs4 import BeautifulSoup
import pandas as pd
from fake_useragent import UserAgent

class LianjiaSpider(object):

    def __init__(self):
        self.headers = {"User-Agent": UserAgent().random}
        self.datas = list()

    def getMaxPage(self, url):
        response = requests.get(url, headers = self.headers)
        if response.status_code == 200:
            source = response.text
            soup = BeautifulSoup(source, "html.parser")
            pageData = soup.find("div", class_ = "page-box house-lst-page-box")["page-data"]
            # pageData = ‘{"totalPage":100,"curPage":1}‘，通过eval()函数把字符串转换为字典
            maxPage = eval(pageData)["totalPage"]
            return  maxPage
        else:
            print("Fail status: {}".format(response.status_code))
            return None

    def parsePage(self, url):
        maxPage = self.getMaxPage(url)
        #  解析每个page，获取每个二手房的链接
        for pageNum in range(1, maxPage+1 ):
            url = "https://sz.lianjia.com/ershoufang/pg{}/".format(pageNum)
            print("当前正在爬取: {}".format(url))
            response = requests.get(url, headers = self.headers)
            soup = BeautifulSoup(response.text, "html.parser")
            links = soup.find_all("div", class_ = "info clear")
            for i in links:
                link = i.find("a")["href"]    #每个<info clear>标签有很多<a>,而我们只需要第一个，所以用find
                detail = self.parseDetail(link)
                self.datas.append(detail)

        #  将所有爬取的二手房数据存储到csv文件中
        data = pd.DataFrame(self.datas)
        # columns字段：自定义列的顺序（DataFrame默认按列名的字典序排序）
        columns = ["小区", "户型", "面积", "价格", "单价", "朝向", "电梯", "位置", "地铁"]
        data.to_csv(".\Lianjia_II.csv", encoding=‘utf_8_sig‘, index=False, columns=columns)

    def parseDetail(self, url):
        response = requests.get(url, headers = self.headers)
        detail = {}
        if response.status_code == 200:
            soup = BeautifulSoup(response.text, "html.parser")
            detail["价格"] = soup.find("span", class_ = "total").text
            detail["单价"] = soup.find("span", class_ = "unitPriceValue").text
            detail["小区"] = soup.find("div", class_ = "communityName").find("a", class_ = "info").text
            detail["位置"] = soup.find("div", class_="areaName").find("span", class_="info").text
            detail["地铁"] = soup.find("div", class_="areaName").find("a", class_="supplement").text
            base = soup.find("div", class_ = "base").find_all("li") # 基本信息
            detail["户型"] = base[0].text[4:]
            detail["面积"] = base[2].text[4:]
            detail["朝向"] = base[6].text[4:]
            detail["电梯"] = base[10].text[4:]
            return detail
        else:
            return None

if __name__ == "__main__":
    Lianjia = LianjiaSpider()
    Lianjia.parsePage("https://sz.lianjia.com/ershoufang/")

原文地址：https://www.cnblogs.com/wangshx6/p/9923553.html