Python爬虫之链家二手房数据爬取

本是古典何须时尚 2022-09-11 07:23 365阅读 0赞

# Python 依赖模块： #

*  requests
 *  parsel
 *  csv

# 功能要求： #

![watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5Zyo5aWL5paX55qE5aSn6YGT_size_20_color_FFFFFF_t_70_g_se_x_16][]

# 请求网页 #

打开开发者工具（ **`F12或者鼠标右键点击检查`** ）选择 **`notework`** 查看数据返回的内容。

![watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5Zyo5aWL5paX55qE5aSn6YGT_size_20_color_FFFFFF_t_70_g_se_x_16 1][]

通过开发者工具可以看到，网站是静态网页数据，请求url地址是可以直接获取数据内容的。

url = 'https://cs.lianjia.com/ershoufang/'
    headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) '
                          'Chrome/81.0.4044.138 '
                          'Safari/537.36 '
        }
        response = requests.get(url=url, headers=headers)
        print(response.text)

# 解析数据 #

网站是静态网页数据，那么就可以直接在开发者工具中 **`Elements`** 查看数据在哪

![watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5Zyo5aWL5paX55qE5aSn6YGT_size_20_color_FFFFFF_t_70_g_se_x_16 2][]

如上图所示，相关的数据内容都包含在 **`li`** 标签里面。通过 **`parsel`** 解析库，进行解析提取数据就可以了。

selector = parsel.Selector(response.text)
        lis = selector.css('.sellListContent li')
        for li in lis:
            # 标题
            title = li.css('.title a::text').get()
            # 地址
            positionInfo = li.css('.positionInfo a::text').getall()
    
            community = ''
            address = ''
            if len(positionInfo):
                # 小区
                community = positionInfo[0]
                # 地名
                address = positionInfo[1]
            # 房子基本信息
            houseInfo = li.css('.houseInfo::text').get()
            # 房价
            print('数据类型:', type(li.css('.totalPrice span::text').get()))
            txt = li.css('.totalPrice span::text').get()
            Price = ''
            if isinstance(txt, str):
                Price = li.css('.totalPrice span::text').get() + '万'
            # 单价
            print('单价数据类型:', type(li.css('.unitPrice span::text').get()))
            txt = li.css('.unitPrice span::text').get()
            unitPrice = ''
            if isinstance(txt, str):
                unitPrice = li.css('.unitPrice span::text').get().replace('单价', '')
            # 发布信息
            followInfo = li.css('.followInfo::text').get()
            dit = {
                '标题': title,
                '小区': community,
                '地名': address,
                '房子基本信息': houseInfo,
                '房价': Price,
                '单价': unitPrice,
                '发布信息': followInfo,
            }
            print(dit)

# 保存数据（数据持久化） #

使用csv模块，把数据保存到Excel里面

# 创建文件
            f = open('长沙二手房数据.csv', mode='a', encoding='utf-8', newline='')
            csv_writer = csv.DictWriter(f, fieldnames=['标题', '小区', '地名', '房子基本信息',
                                                       '房价', '单价', '发布信息'])
            # 写入表头
            csv_writer.writeheader()
            '''
            '''
            csv_writer.writerow(dit)

# 多页爬取 #

for page in range(1, 101):
        url = 'https://cs.lianjia.com/ershoufang/'
        downloadLianjia(url)
    
    def downloadLianjia(url):
        headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) '
                          'Chrome/81.0.4044.138 '
                          'Safari/537.36 '
        }
        response = requests.get(url=url, headers=headers)
        print(response.text)
    
        selector = parsel.Selector(response.text)
        lis = selector.css('.sellListContent li')
        for li in lis:
            # 标题
            title = li.css('.title a::text').get()
            # 地址
            positionInfo = li.css('.positionInfo a::text').getall()
    
            community = ''
            address = ''
            if len(positionInfo):
                # 小区
                community = positionInfo[0]
                # 地名
                address = positionInfo[1]
            # 房子基本信息
            houseInfo = li.css('.houseInfo::text').get()
            # 房价
            print('数据类型:', type(li.css('.totalPrice span::text').get()))
            txt = li.css('.totalPrice span::text').get()
            Price = ''
            if isinstance(txt, str):
                Price = li.css('.totalPrice span::text').get() + '万'
            # 单价
            print('单价数据类型:', type(li.css('.unitPrice span::text').get()))
            txt = li.css('.unitPrice span::text').get()
            unitPrice = ''
            if isinstance(txt, str):
                unitPrice = li.css('.unitPrice span::text').get().replace('单价', '')
            # 发布信息
            followInfo = li.css('.followInfo::text').get()
            dit = {
                '标题': title,
                '小区': community,
                '地名': address,
                '房子基本信息': houseInfo,
                '房价': Price,
                '单价': unitPrice,
                '发布信息': followInfo,
            }
            print(dit)
    
            # 创建文件
            f = open('长沙二手房数据.csv', mode='a', encoding='utf-8', newline='')
            csv_writer = csv.DictWriter(f, fieldnames=['标题', '小区', '地名', '房子基本信息',
                                                       '房价', '单价', '发布信息'])
            # 写入表头
            csv_writer.writeheader()
            '''
            '''
            csv_writer.writerow(dit)

效果展示：

![watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5Zyo5aWL5paX55qE5aSn6YGT_size_20_color_FFFFFF_t_70_g_se_x_16 3][]

[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5Zyo5aWL5paX55qE5aSn6YGT_size_20_color_FFFFFF_t_70_g_se_x_16]: /images/20220828/5adc8571ac364182bd9b7a87d2b038e7.png
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5Zyo5aWL5paX55qE5aSn6YGT_size_20_color_FFFFFF_t_70_g_se_x_16 1]: /images/20220828/03a8289518624fa6a028b762919b995b.png
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5Zyo5aWL5paX55qE5aSn6YGT_size_20_color_FFFFFF_t_70_g_se_x_16 2]: /images/20220828/765206d98a9b4eba99892f006b354e15.png
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5Zyo5aWL5paX55qE5aSn6YGT_size_20_color_FFFFFF_t_70_g_se_x_16 3]: /images/20220828/4fadd0683296422ab8e8d67086b8449f.png