在线商城爬虫 带爬取记录 以11TREET 为例

整体思路

第一步  抓取全部的列表页链接

第二步 抓取每个列表页的商品总数，页数 

第三步 单个列表页 进行分业 抓取商品价格

第四步 单个列表页抓取完成后 输出商品数据并在本地文件记录本次抓取

最后一步 合并各个列页表抓取的商品数据

 

第一步

爬取的网站，获得分类信息

https://global.11st.co.kr/glb/

 

 

 这里分类是动态加载的，需要用selenium + chromedriver

代码如下

import  requests,random,os,math,time,re,pandas as pd,numpy as np
from bs4 import BeautifulSoup
from selenium import webdriver

#chomedriver 地址
CHROME_DRIVER_PATH = '/Users/xxxx/Downloads/chromedriver'



#爬取动态界面
def get_dynamic_html(site_url):
    print('开始加载',site_url,'动态页面')
    chrome_options = webdriver.ChromeOptions()
    #ban sandbox
    chrome_options.add_argument('--no-sandbox')
    chrome_options.add_argument('--disable-dev-shm-usage')
    #use headless
    #chrome_options.add_argument('--headless')
    chrome_options.add_argument('--disable-gpu')
    chrome_options.add_argument('--ignore-ssl-errors')
    driver = webdriver.Chrome(executable_path=CHROME_DRIVER_PATH,chrome_options=chrome_options)
    #print('dynamic laod web is', site_url)
    driver.set_page_load_timeout(100)
    #driver.set_script_timeout(100)
    try:
        driver.get(site_url)
    except Exception as e:
        driver.execute_script('window.stop()')  # 超出时间则不加载
        print(e, 'dynamic web load timeout')
    data = driver.page_source
    soup = BeautifulSoup(data, 'html.parser')
    try:
        driver.quit()
    except:
        pass
    return soup



#获得列表页链接
def get_page_url_list(cate_path):
    cate_url_list = []

    print('开始爬取')
    page_url = 'https://global.11st.co.kr/glb/en/browsing/Category.tmall?method=getCategory2Depth&dispCtgrNo=1001819#'
    soup = get_dynamic_html(page_url)
    print(soup.prettify())
    one_cate_ul_list = soup.select('#lnbMenu > ul > li')

    for i in range(0, len(one_cate_ul_list)):
        one_cate_ul = one_cate_ul_list[i]
        one_cate_name = one_cate_ul.select('a')[0].text
        one_cate_url = one_cate_ul.select('a')[0].attrs['href']

        two_cate_ul_list = one_cate_ul.select('ul.list_category > li')
        for two_cate_ul in two_cate_ul_list:
            two_cate_name = two_cate_ul.select('a')[0].text
            two_cate_url = two_cate_ul.select('a')[0].attrs['href']

            three_cate_ul_list = two_cate_ul.select('li .list_sub_cate > li')
            for three_cate_ul in three_cate_ul_list:
                three_cate_name = three_cate_ul.select('a')[0].text
                three_cate_url = three_cate_ul.select('a')[0].attrs['href']

                cate_obj = {
                    'brand': 'global.11st.co',
                    'site': 'kr',
                    'one_cate_name': one_cate_name,
                    'one_cate_url': one_cate_url,
                    'two_cate_name': two_cate_name,
                    'two_cate_url': two_cate_url,
                    'three_cate_name': three_cate_name,
                    'three_cate_url': three_cate_url,
                }
                cate_url_list.append(cate_obj)

    cate_url_df = pd.DataFrame(cate_url_list)
    cate_url_df.to_excel(cate_path, index=False)



if __name__ == '__main__':
    #列表页链接存放位置
    cate_excel_path = '/Users/xxxx/Downloads/11st_kr_page_list.xlsx'
    get_page_url_list(cate_excel_path)

　　

 

 

 

 第二步

如图每个列表页可以看到总商品数量，每页展示40件商品，可以计算总页数

 

 基于步骤一获得文件，去计算每个列表页的页数

#需要引入的包 都在步骤一

#获得总数量 和 总页数
def account_page_num(cate_path, reocrd_path):
    out_page_list = []
    page_list_df = pd.read_excel(cate_path)
    for index, row in page_list_df.iterrows():
        print(index, row)

        page_item = {
            'brand': row['brand'],
            'site': row['site'],
            'one_cate_name': row['one_cate_name'],
            'two_cate_name': row['two_cate_name'],
            'two_cate_url': row['two_cate_url'],
            'three_cate_name': row['three_cate_name'],
            'three_cate_url': row['three_cate_url']
        }
        page_item['total_item_num'] = 'not found tag'
        page_item['total_page_num'] = 0
        page_item['per_page_num'] = 40
        page_item['start_page_num'] = 0
        soup = get_static_html(page_item['three_cate_url'])
        total_num_tag_list = soup.select('ul.categ > li.active')
        if len(total_num_tag_list) > 0:

            total_num_tag = total_num_tag_list[0]
            tag_text = total_num_tag.text
            num_pattern = re.compile('\(([0-9 ,]+)\)')
            num_arr = num_pattern.findall(tag_text)
            if len(num_arr) > 0:
                page_item['total_item_num'] = int(num_arr[0].replace(',', ''))
                page_item['total_page_num'] = math.ceil(page_item['total_item_num'] / page_item['per_page_num'])
            else:
                page_item['total_item_num'] = f'text error:{tag_text}'
        print(page_item)
        out_page_list.append(page_item)

    record_url_df = pd.DataFrame(out_page_list)
    record_url_df.to_excel(reocrd_path, index=False)



if __name__ == '__main__':
    date_str = '2023-04-06'
    #爬虫记录 记录已经爬取的页数，以防中途爬取失败，不用从头开始爬，可接着爬
    crawl_record_path = f'/Users/xxxx/Downloads/11st_kr_page_reocrd_{date_str}.xlsx'
    account_page_num(cate_excel_path, crawl_record_path)

　　

第三步，第四步

 

 代码如下

#需要引入的包都在步骤1


#获得静态的界面
def get_static_html(site_url):
    print('开始加载', site_url, '页面')
    headers_list = [
        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.79 Safari/537.36',
        'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:34.0) Gecko/20100101 Firefox/34.0 ',
        'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/534.57.2 (KHTML, like Gecko) Version/5.1.7 Safari/534.57.2',
        'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36'
    ]
    headers = {
        'user-agent': headers_list[random.randint(0,len(headers_list))-1],
        'Connection': 'keep - alive'
    }
    try:
        resp = requests.get(site_url, headers=headers)
    except Exception as inst:
        print(inst)
        requests.packages.urllib3.disable_warnings()
        resp = requests.get(site_url, headers=headers,verify=False)
    soup = BeautifulSoup(resp.text, 'html.parser')
    return soup



#dateframe格式 输出为无url格式的excel
def obj_list_to_df_wihout_url(obj_df, out_path):
    conten_writer = pd.ExcelWriter(out_path, engine='xlsxwriter', options={'strings_to_urls': False})
    obj_df.to_excel(conten_writer , index=False)
    conten_writer.close()



#获取列表页的商品信息
def info_from_page_list(index, page_item):
    #爬取最大列表数限制
    max_limit = 250

    #存放位置
    three_cate_name = page_item['three_cate_name'].strip().replace(' ', '&').replace('/', '&')
    now_out_path = f"{crawl_tmp_dir}/{index}_{three_cate_name}.xlsx"

    total_page_num = page_item['total_page_num'] if page_item['total_page_num'] <= max_limit else max_limit
    finsh_page_num = page_item['finsh_page_num']
    print(finsh_page_num, total_page_num)

    #如果从头开始
    if finsh_page_num == 0 and not os.path.exists(now_out_path):
        out_goods_list = []
    #接着上次爬取
    else:
        already_obj_df = pd.read_excel(now_out_path)
        out_goods_list = np.array(already_obj_df).to_list()


    if finsh_page_num == total_page_num:
        print(f"{index} {page_item['three_cate_name']}  抓取结束")
    for i in range(finsh_page_num, total_page_num):
        page_url = f"{page_item['three_cate_url']}#pageNum%%{i + 1}"

        soup = get_static_html(page_url)
        info_tag_list = soup.select('ul.tt_listbox > li')

        for goods_tag in info_tag_list:
            info_item = page_item.copy()
            pattern_tag_3 = re.compile('products\/([0-9]+)')
            href_tag = goods_tag.select('.photo_wrap >  a')[0]
            desc_tag = goods_tag.select('.list_info > .info_tit')[0]
            #feedback_tag = goods_tag.select('.list_info  .sfc')
            #collect_tag = goods_tag.select('.list_info  .def_likethis')
            price_tag = goods_tag.select('.list_price  .dlr')[0]

            info_item['href'] = href_tag.attrs['href']
            info_item['product_id'] = ''
            info_item['desc'] = desc_tag.text
            #info_item['feedback'] = feedback_tag.text
            #info_item['collect'] = collect_tag.text


            info_item['price_kr'] = int(price_tag.attrs['data-finalprice'])
            info_item['price_us'] = round(info_item['price_kr'] * 0.0007959, 2)


            if info_item['href'] != '':
                id_arr = pattern_tag_3.findall(info_item['href'])
                if len(id_arr) > 0:
                    info_item['product_id'] = id_arr[0]
            out_goods_list.append(info_item)

        #每50页保存一次
        if i == total_page_num - 1 or i % 50 == 0:
            print('开始保存')
            #临时保存
            out_goods_df = pd.DataFrame(out_goods_list)
            obj_list_to_df_wihout_url(out_goods_df, now_out_path)
            print('更新记录')
            #更新记录
            crawl_record_df = pd.read_excel(crawl_record_path)
            crawl_record_df.loc[index, 'finsh_page_num'] = i + 1
            print(crawl_record_df.loc[index, 'finsh_page_num'])
            obj_list_to_df_wihout_url(crawl_record_df, crawl_record_path)



if __name__ == '__main__':
    date_str = '2023-04-06'
    #本次爬虫记录
    crawl_record_path = f'/Users/xxx/Downloads/11st_kr_page_reocrd_{date_str}.xlsx'
    #临时存放爬取的商品数据目录
    crawl_tmp_dir = f'/Users/xxx/Downloads/11st_kr_page_reocrd_{date_str}'
    if not os.path.exists(crawl_tmp_dir):
        os.mkdir(crawl_tmp_dir)

    crawl_record_df = pd.read_excel(crawl_record_path)
    new_recrod_list = []
    for index, row in crawl_record_df.iterrows():
        info_from_page_list(index, row)

　

最后一步

合并临时存放商品数据的excel

crawl_tmp_dir