房价分析（0）反爬虫机制

往期精彩内容：

Python房价分析（一）pyton爬虫-CSDN博客

Python房价分析（二）随机森林分类模型-CSDN博客

Python房价分析（三）支持向量机SVM分类模型-CSDN博客

1.常见的房价数据网站如58同城、安居客、房天下都有一定反爬措施，注意构建用户代理池和ip池来避免反爬机制

2.以武汉市为例，爬取武汉市 区域 位置 户型 建筑面积 均价等房价相关数据

3.采用经典的 BeautifulSoup 方法借助from bs4 import BeautifulSoup，然后通过soup = BeautifulSoup(html, "lxml")将文本转换为特定规范的结构，利用find系列方法进行解析，代码如下

4.代码：

（1）首先构建User-Agent池

def get_user_agent):
    import random
    user_agents = [
        'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.1 (KHTML, like Gecko) Chrome/14.0.835.163 Safari/535.1',
        'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:6.0) Gecko/20100101 Firefox/6.0',
        'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/534.50 (KHTML, like Gecko) Version/5.1 Safari/534.50',
        'Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Win64; x64; Trident/5.0; .NET CLR 2.0.50727; SLCC2; .NET CLR 3.5.30729',
    ]
    user_agent = random.choice(user_agents)  # 随机抽取对象
    return user_agent

(2) 获取网页信息

def get_page_data(data_url):
    headers = {'User-Agent': get_ua()}
    req = urllib.request.Request(data_url, headers=headers)
    content = urllib.request.urlopen(req).read().decode('utf-8')  # python3
    page = BeautifulSoup(content, 'html.parser')
    return page

(3)数据解析

def analyse_data(page):
    list_content = page.find('div', attrs={'class': 'key-list imglazyload'})
    if list_content == None:
        print('error:反爬失败')
        return None
    else:
        item_content = list_content.find_all('div', class_='item-mod')
        df_data = pd.DataFrame(columns=['city','quyu','address','house_type','area','price'])
        count = 0
        for item in item_content:
            if item.find('div', class_='infos').find('a',class_='address') is None:
                continue
            index_data = item.find('div', class_='infos')
            # 位置区域
            all_address = index_data.find('a',class_='address').find('span').text.split()
            city = all_address[1]
            quyu = all_address[2]
            address = all_address[4]
            # 户型面积Item
            house_item = index_data.find('a', class_='huxing')
            if house_item is not None:
                # 有户型  有建筑面积  没户型 有建筑面积
                house_type = index_data.find('a', class_='huxing').find('span').text
                area = index_data.find('a', class_='huxing').find('span', class_='building-area').text
            else:
                # 没有户型且尚未开盘
                house_type = '尚未开盘'
                area = 0
            # 房价Item
            item_price = item.find('a', class_='favor-pos')
            if item_price.find('p', class_='price') is not None:
                # 均价
                price = item_price.find('p', class_='price').find('span').text
        
            else:
                if item_price.find('p', attrs={'class': 'favor-tag around-price'}) is not None:
                    # 周边均价
                    price = item_price.find('p', attrs={'class': 'favor-tag around-price'}).find('span').text
                else:
                    # 售价待定
                    price = 0
            df_data.loc[count] = [address, house_type, area, price]
            count += 1
    
        return df_data

(4)把数据写入文件中

if __name__ == '__main__':
    page_list = []
    # 页面总页数
    ALL_page = 37
    for i in range(ALL_page):
        page_list.append(str(i+1))
    # 读取空文件
    wuhan = pd.read_csv('wuhan.csv')
    for page in page_list:
        url = 'https://wh.fang.anjuke.com/loupan/all/p' + page + '/'
        # url = "https://wh.fang.anjuke.com/loupan/all/"
        page = get_page_data(url)
        df_data = analyse_data(page)
        # 写入CSV文件
        wuhan = pd.concat([wuhan, df_data])
        print(len(wuhan))
    wuhan.set_index('address', inplace=True)
    wuhan.to_csv('wuhan.csv')
    print(wuhan)

5.数据样例