1. 建立項目 scrapy startproject yingke cd yingkehtml
2. 建立爬蟲 scrapy genspider livedom
3. 分析http://www.inke.cn/hotlive_list.html網頁的response,找到響應裏面數據的規律,並找到的位置,經過response.xpath()獲取到scrapy
4. 經過在pipline裏面進行數據的清洗,過濾,保存ide
5. 實現翻頁,進行下一頁的請求處理url
6. 運行爬蟲 scrapy crawl livespa
說明:這個程序直接在parse方法裏面進行圖片保存,保存在本地,正常使用yield關鍵字進行在pipline中保存。code
# -*- coding: utf-8 -*- import scrapy import re class LiveSpider(scrapy.Spider): name = 'live' allowed_domains = ['inke.cn'] start_urls = ['http://www.inke.cn/hotlive_list.html?page=1'] def parse(self, response): div_list = response.xpath("//div[@class='list_box']") for div in div_list: item = {} img_src = div.xpath("./div[@class='list_pic']/a/img/@src").extract_first() item["user_name"] = div.xpath( "./div[@class='list_user_info']/span[@class='list_user_name']/text()").extract_first() print(item["user_name"]) yield scrapy.Request( # 發送詳情頁的請求 img_src, callback=self.parse_img, meta={"item": item} ) # 下一頁 now_page = re.findall("page=(.*)", response.request.url)[0] now_page= int(now_page) next_url = "http://www.inke.cn/hotlive_list.html?page={}".format(str(now_page+ 1)) yield scrapy.Request( next_url, callback=self.parse ) def parse_img(self, response): user_name = response.meta["item"]["user_name"] with open("images/{}.png".format(user_name), "wb") as f: f.write(response.body)
運行效果:orm