使用python爬虫爬取链家潍坊市二手房项目

需求分析

需要将潍坊市各县市区页面所展示的二手房信息按要求爬取下来，同时保存到本地。

流程设计

明确目标网站URL（ https://wf.lianjia.com/ ）
确定爬取二手房哪些具体信息（字段名）
python爬虫关键实现：requests库和lxml库
将爬取的数据存储到CSV或数据库中

实现过程

项目目录

1、在数据库中创建数据表

我电脑上使用的是MySQL8.0，图形化工具用的是Navicat.

数据库字段对应

id-编号、title-标题、total_price-房屋总价、unit_price-房屋单价、

square-面积、size-户型、floor-楼层、direction-朝向、type-楼型、

district-地区、nearby-附近区域、community-小区、elevator-电梯有无、

elevatorNum-梯户比例、ownership-房屋性质

该图显示的是字段名、数据类型、长度等信息。

2、自定义数据存储函数

这部分代码放到Spider_wf.py文件中

通过write_csv函数将数据存入CSV文件，通过write_db函数将数据存入数据库

点击查看代码



import csv

import pymysql

#写入CSV

def write_csv(example_1):

    csvfile = open('二手房数据.csv', mode='a', encoding='utf-8', newline='')

    fieldnames = ['title', 'total_price', 'unit_price', 'square', 'size', 'floor','direction','type',

                  'BuildTime','district','nearby', 'community', 'decoration', 'elevator','elevatorNum','ownership']

    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)

    writer.writerow(example_1)

#写入数据库

def write_db(example_2):

    conn = pymysql.connect(host='127.0.0.1',port= 3306,user='changziru',

                           password='ru123321',database='secondhouse_wf',charset='utf8mb4'

                           )

    cursor =conn.cursor()

    title = example_2.get('title', '')

    total_price = example_2.get('total_price', '0')

    unit_price = example_2.get('unit_price', '')

    square = example_2.get('square', '')

    size = example_2.get('size', '')

    floor = example_2.get('floor', '')

    direction = example_2.get('direction', '')

    type = example_2.get('type', '')

    BuildTime = example_2.get('BuildTime','')

    district = example_2.get('district', '')

    nearby = example_2.get('nearby', '')

    community = example_2.get('community', '')

    decoration = example_2.get('decoration', '')

    elevator = example_2.get('elevator', '')

    elevatorNum = example_2.get('elevatorNum', '')

    ownership = example_2.get('ownership', '')

    cursor.execute('insert into wf (title, total_price, unit_price, square, size, floor,direction,type,BuildTime,district,nearby, community, decoration, elevator,elevatorNum,ownership)'

                   'values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)',

                   [title, total_price, unit_price, square, size, floor,direction,type,

                  BuildTime,district,nearby, community, decoration, elevator,elevatorNum,ownership])

    conn.commit()#传入数据库

    conn.close()#关闭数据库

3、爬虫程序实现

这部分代码放到lianjia_house.py文件，调用项目Spider_wf.py文件中的write_csv和write_db函数

点击查看代码

#爬取链家二手房详情页信息

import time

from random import randint

import requests

from lxml import etree

from secondhouse_spider.Spider_wf import write_csv,write_db

#模拟浏览器操作

USER_AGENTS = [

    "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; AcooBrowser; .NET CLR 1.1.4322; .NET CLR 2.0.50727)",

    "Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.0; Acoo Browser; SLCC1; .NET CLR 2.0.50727; Media Center PC 5.0; .NET CLR 3.0.04506)",

    "Mozilla/4.0 (compatible; MSIE 7.0; AOL 9.5; AOLBuild 4337.35; Windows NT 5.1; .NET CLR 1.1.4322; .NET CLR 2.0.50727)",

    "Mozilla/5.0 (Windows; U; MSIE 9.0; Windows NT 9.0; en-US)",

    "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Win64; x64; Trident/5.0; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 2.0.50727; Media Center PC 6.0)",

    "Mozilla/5.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0; WOW64; Trident/4.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 1.0.3705; .NET CLR 1.1.4322)",

    "Mozilla/4.0 (compatible; MSIE 7.0b; Windows NT 5.2; .NET CLR 1.1.4322; .NET CLR 2.0.50727; InfoPath.2; .NET CLR 3.0.04506.30)",

    "Mozilla/5.0 (Windows; U; Windows NT 5.1; zh-CN) AppleWebKit/523.15 (KHTML, like Gecko, Safari/419.3) Arora/0.3 (Change: 287 c9dfb30)",

    "Mozilla/5.0 (X11; U; Linux; en-US) AppleWebKit/527+ (KHTML, like Gecko, Safari/419.3) Arora/0.6",

    "Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.8.1.2pre) Gecko/20070215 K-Ninja/2.1.1",

    "Mozilla/5.0 (Windows; U; Windows NT 5.1; zh-CN; rv:1.9) Gecko/20080705 Firefox/3.0 Kapiko/3.0",

    "Mozilla/5.0 (X11; Linux i686; U;) Gecko/20070322 Kazehakase/0.4.5",

    "Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.9.0.8) Gecko Fedora/1.9.0.8-1.fc10 Kazehakase/0.5.6",

    "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.56 Safari/535.11",

    "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_3) AppleWebKit/535.20 (KHTML, like Gecko) Chrome/19.0.1036.7 Safari/535.20",

    "Opera/9.80 (Macintosh; Intel Mac OS X 10.6.8; U; fr) Presto/2.9.168 Version/11.52",

]

#随机USER_AGENTS

random_agent = USER_AGENTS[randint(0, len(USER_AGENTS) - 1)]

headers = {'User-Agent': random_agent,}

class SpiderFunc:

    def __init__(self):

        self.count = 0

    def spider(self ,list):

        for sh in list:

            response = requests.get(url=sh, params={'param':'1'},headers={'Connection':'close'}).text

            tree = etree.HTML(response)

            li_list = tree.xpath('//ul[@class="sellListContent"]/li[@class="clear LOGVIEWDATA LOGCLICKDATA"]')

            for li in li_list:

                # 获取每套房子详情页的URL

                detail_url = li.xpath('.//div[@class="title"]/a/@href')[0]

                try:

                    # 向每个详情页发送请求

                    detail_response = requests.get(url=detail_url, headers={'Connection': 'close'}).text

                except Exception as e:

                    sleeptime = randint(15,30)

                    time.sleep(sleeptime)#随机时间延迟

                    print(repr(e))#打印异常信息

                    continue

                else:

                    detail_tree = etree.HTML(detail_response)

                    item = {}

                    title_list = detail_tree.xpath('//div[@class="title"]/h1/text()')

                    item['title'] = title_list[0] if title_list else None  # 1简介

                    total_price_list = detail_tree.xpath('//span[@class="total"]/text()')

                    item['total_price'] = total_price_list[0] if total_price_list else None  # 2总价

                    unit_price_list = detail_tree.xpath('//span[@class="unitPriceValue"]/text()')

                    item['unit_price'] = unit_price_list[0] if unit_price_list else None  # 3单价

                    square_list = detail_tree.xpath('//div[@class="area"]/div[@class="mainInfo"]/text()')

                    item['square'] = square_list[0] if square_list else None  # 4面积

                    size_list = detail_tree.xpath('//div[@class="base"]/div[@class="content"]/ul/li[1]/text()')

                    item['size'] = size_list[0] if size_list else None  # 5户型

                    floor_list = detail_tree.xpath('//div[@class="base"]/div[@class="content"]/ul/li[2]/text()')

                    item['floor'] = floor_list[0] if floor_list else None#6楼层

                    direction_list = detail_tree.xpath('//div[@class="type"]/div[@class="mainInfo"]/text()')

                    item['direction'] = direction_list[0] if direction_list else None  # 7朝向

                    type_list = detail_tree.xpath('//div[@class="area"]/div[@class="subInfo"]/text()')

                    item['type'] = type_list[0] if type_list else None  # 8楼型

                    BuildTime_list = detail_tree.xpath('//div[@class="transaction"]/div[@class="content"]/ul/li[5]/span[2]/text()')

                    item['BuildTime'] = BuildTime_list[0] if BuildTime_list else None  # 9房屋年限

                    district_list = detail_tree.xpath('//div[@class="areaName"]/span[@class="info"]/a[1]/text()')

                    item['district'] = district_list[0] if district_list else None  # 10地区

                    nearby_list = detail_tree.xpath('//div[@class="areaName"]/span[@class="info"]/a[2]/text()')

                    item['nearby'] = nearby_list[0] if nearby_list else None  # 11区域

                    community_list = detail_tree.xpath('//div[@class="communityName"]/a[1]/text()')

                    item['community'] = community_list[0] if community_list else None  # 12小区

                    decoration_list = detail_tree.xpath('//div[@class="base"]/div[@class="content"]/ul/li[9]/text()')

                    item['decoration'] = decoration_list[0] if decoration_list else None  # 13装修

                    elevator_list = detail_tree.xpath('//div[@class="base"]/div[@class="content"]/ul/li[11]/text()')

                    item['elevator'] = elevator_list[0] if elevator_list else None  # 14电梯

                    elevatorNum_list = detail_tree.xpath('//div[@class="base"]/div[@class="content"]/ul/li[10]/text()')

                    item['elevatorNum'] = elevatorNum_list[0] if elevatorNum_list else None  # 15梯户比例

                    ownership_list = detail_tree.xpath('//div[@class="transaction"]/div[@class="content"]/ul/li[2]/span[2]/text()')

                    item['ownership'] = ownership_list[0] if ownership_list else None  # 16交易权属

                    self.count += 1

                    print(self.count,title_list)

                    # 将爬取到的数据存入CSV文件

                    write_csv(item)

                    # 将爬取到的数据存取到MySQL数据库中

                    write_db(item)

#循环目标网站

count =0

for page in range(1,101):

    if page <=40:

        url_qingzhoushi = 'https://wf.lianjia.com/ershoufang/qingzhoushi/pg' + str(page)  # 青州市40

        url_hantingqu = 'https://wf.lianjia.com/ershoufang/hantingqu/pg' + str(page)  # 寒亭区 76

        url_fangzi = 'https://wf.lianjia.com/ershoufang/fangziqu/pg' + str(page)  # 坊子区

        url_kuiwenqu = 'https://wf.lianjia.com/ershoufang/kuiwenqu/pg' + str(page)  # 奎文区

        url_gaoxin = 'https://wf.lianjia.com/ershoufang/gaoxinjishuchanyekaifaqu/pg' + str(page)  # 高新区

        url_jingji = 'https://wf.lianjia.com/ershoufang/jingjijishukaifaqu2/pg' + str(page)  # 经济技术85

        url_shouguangshi = 'https://wf.lianjia.com/ershoufang/shouguangshi/pg' + str(page)  # 寿光市 95

        url_weichengqu = 'https://wf.lianjia.com/ershoufang/weichengqu/pg' + str(page)  # 潍城区

        list_wf = [url_qingzhoushi, url_hantingqu,url_jingji, url_shouguangshi, url_weichengqu, url_fangzi, url_kuiwenqu, url_gaoxin]

        SpiderFunc().spider(list_wf)

    elif page <=76:

        url_hantingqu = 'https://wf.lianjia.com/ershoufang/hantingqu/pg' + str(page)  # 寒亭区 76

        url_fangzi = 'https://wf.lianjia.com/ershoufang/fangziqu/pg' + str(page)  # 坊子区

        url_kuiwenqu = 'https://wf.lianjia.com/ershoufang/kuiwenqu/pg' + str(page)  # 奎文区

        url_gaoxin = 'https://wf.lianjia.com/ershoufang/gaoxinjishuchanyekaifaqu/pg' + str(page)  # 高新区

        url_jingji = 'https://wf.lianjia.com/ershoufang/jingjijishukaifaqu2/pg' + str(page)  # 经济技术85

        url_shouguangshi = 'https://wf.lianjia.com/ershoufang/shouguangshi/pg' + str(page)  # 寿光市 95

        url_weichengqu = 'https://wf.lianjia.com/ershoufang/weichengqu/pg' + str(page)  # 潍城区

        list_wf = [url_hantingqu,url_jingji, url_shouguangshi, url_weichengqu, url_fangzi, url_kuiwenqu, url_gaoxin]

        SpiderFunc().spider(list_wf)

    elif page<=85:

        url_fangzi = 'https://wf.lianjia.com/ershoufang/fangziqu/pg' + str(page)  # 坊子区

        url_kuiwenqu = 'https://wf.lianjia.com/ershoufang/kuiwenqu/pg' + str(page)  # 奎文区

        url_gaoxin = 'https://wf.lianjia.com/ershoufang/gaoxinjishuchanyekaifaqu/pg' + str(page)  # 高新区

        url_jingji = 'https://wf.lianjia.com/ershoufang/jingjijishukaifaqu2/pg' + str(page)  # 经济技术85

        url_shouguangshi = 'https://wf.lianjia.com/ershoufang/shouguangshi/pg' + str(page)  # 寿光市 95

        url_weichengqu = 'https://wf.lianjia.com/ershoufang/weichengqu/pg' + str(page)  # 潍城区

        list_wf = [url_jingji, url_shouguangshi, url_weichengqu, url_fangzi, url_kuiwenqu, url_gaoxin]

        SpiderFunc().spider(list_wf)

    elif page <=95:

        url_shouguangshi = 'https://wf.lianjia.com/ershoufang/shouguangshi/pg' + str(page)  # 寿光市 95

        url_weichengqu = 'https://wf.lianjia.com/ershoufang/weichengqu/pg' + str(page)  # 潍城区

        url_fangzi = 'https://wf.lianjia.com/ershoufang/fangziqu/pg' + str(page)  # 坊子区

        url_kuiwenqu = 'https://wf.lianjia.com/ershoufang/kuiwenqu/pg' + str(page)  # 奎文区

        url_gaoxin = 'https://wf.lianjia.com/ershoufang/gaoxinjishuchanyekaifaqu/pg' + str(page)  # 高新区

        list_wf = [url_shouguangshi, url_weichengqu, url_fangzi, url_kuiwenqu, url_gaoxin]

        SpiderFunc().spider(list_wf)

    else:

        url_weichengqu = 'https://wf.lianjia.com/ershoufang/weichengqu/pg' + str(page)  # 潍城区

        url_fangzi = 'https://wf.lianjia.com/ershoufang/fangziqu/pg' + str(page)  # 坊子区

        url_kuiwenqu = 'https://wf.lianjia.com/ershoufang/kuiwenqu/pg' + str(page)  # 奎文区

        url_gaoxin = 'https://wf.lianjia.com/ershoufang/gaoxinjishuchanyekaifaqu/pg' + str(page)  # 高新区

        list_wf = [url_weichengqu, url_fangzi,url_kuiwenqu, url_gaoxin]

        SpiderFunc().spider(list_wf)

4、效果展示

总共获取到20826条数据，

我数据库因为要做数据分析，因而作了预处理，获得18031条

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使用python爬虫爬取链家潍坊市二手房项目

使用python爬虫爬取链家潍坊市二手房项目

需求分析

流程设计

实现过程

1、在数据库中创建数据表

2、自定义数据存储函数

3、爬虫程序实现

4、效果展示

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