【京东】全站数据集合之Python详细教程★

网上购物已经成为大众生活的重要组成部分。人们在京东电商平台上浏览商品并购物，产生了海量的用户行为数据，用户对商品的评论数据对商家具有重要的意义。利用好这些碎片化、非结构化的数据，将有利于企业在电商平台上的持续发展，同时，对这部分数据进行分析，依据◁京东商品详情数据▷来优化现有产品也是大数据在企业经营中的实际应用......
1.定义数据采集的存储结构

【存储结构说明】
class CategoriesItem(Item)：存储京东类目信息class ProductsItem(Item)：存储京东商品信息
class ShopItem(Item)：存储京东店铺信息
class CommentSummaryItem(Item)：存储京东每个商品的评论概况信息
class CommentItem(Item)：存储京东每个商品的评论基本信息
class CommentImageItem(Item)：存储京东每个商品中每条评论的图像信息说明：类中所定义字段可依据具体采集要求或response内容进行调整。

【items.py程序】

# -*- coding: utf-8 -*- # Define here the models for your scraped items## See documentation in:# http://doc.scrapy.org/en/latest/topics/items.htmlfrom scrapy import Item, Field class CategoriesItem(Item): """ 存储京东类目信息 """ name = Field() # 商品三级类目名称 url = Field() # 商品三级类目对应url _id = Field() # 商品类目对应id[一级id,二级id,三级id] class ProductsItem(Item): """ 存储京东商品信息 """ name = Field() # 商品名称 url = Field() # 商品url[用于商品主图提取] _id = Field() # 商品sku category = Field() # 商品三级类目 description = Field() # 商品描述 shopId = Field() # 商品所在店铺id(名称) commentCount = Field() # 商品评价总数=CommentSummaryItem.commentCount # goodComment = Field() # 商品好评数 # generalComment = Field() # 商品中评数 # poolComment = Field() # 商品差评数 # favourableDesc1 = Field() # 商品优惠描述1 # favourableDesc2 = Field() # 商品优惠描述2 # venderId = Field() # 供应商id # reallyPrice = Field() # 商品现价 # originalPrice = Field() # 商品原价 class ShopItem(Item): _id = Field() # 店铺url shopName = Field() # 店铺名称 shopItemScore = Field() # 店铺[商品评价] shopLgcScore = Field() # 店铺[物流履约] shopAfterSale = Field() # 店铺[售后服务] class CommentItem(Item): _id = Field() # 评论id productId = Field() # 商品id=sku guid = Field() # 评论全局唯一标识符 firstCategory = Field() # 商品一级类目 secondCategory = Field() # 商品二级类目 thirdCategory = Field() # 商品三级类目 score = Field() # 用户评分 nickname = Field() # 用户昵称 plusAvailable = Field() # 用户账户等级(201：PLUS, 103:普通用户，0：无价值用户) content = Field() # 评论内容 creationTime = Field() # 评论时间 replyCount = Field() # 评论的评论数 usefulVoteCount = Field() # 用户评论的被点赞数 imageCount = Field() # 评论中图片的数量 class CommentImageItem(Item): _id = Field() # 晒图对应id(1张图对应1个id) commentGuid = Field() # 晒图所在评论的全局唯一标识符guid imgId = Field() # 晒图对应id imgUrl = Field() # 晒图url imgTitle = Field() # 晒图标题 imgStatus = Field() # 晒图状态 class CommentSummaryItem(Item): """商品评论总结""" _id = Field() # 商品sku productId = Field() # 商品pid commentCount = Field() # 商品累计评论数 score1Count = Field() # 用户评分为1的数量 score2Count = Field() # 用户评分为2的数量 score3Count = Field() # 用户评分为3的数量 score4Count = Field() # 用户评分为3的数量 score5Count = Field() # 用户评分为5的数量
2.定义管道文件

【管道文件说明】
数据库：MongoDB数据库名称
JD数据库集合：Categories、Products、Shop、CommentSummary、Comment和CommentImage
处理过程：先判断待插入数据库集合类型是否匹配，然后插入，并为重复数据插入抛出异常

# -*- coding: utf-8 -*- # Define your item pipelines here## Don't forget to add your pipeline to the ITEM_PIPELINES setting# See: http://doc.scrapy.org/en/latest/topics/item-pipeline.html import pymongofrom JDSpider.items import * class MongoDBPipeline(object): def __init__(self): clinet = pymongo.MongoClient("localhost", 27017) db = clinet["JD"] self.Categories = db["Categories"] self.Products = db["Products"] self.Shop = db["Shop"] self.Comment = db["Comment"] self.CommentImage = db["CommentImage"] self.CommentSummary = db["CommentSummary"] def process_item(self, item, spider): """ 判断item的类型，并作相应的处理，再入数据库 """ if isinstance(item, CategoriesItem): try: self.Categories.insert(dict(item)) except Exception as e: print('get failed:', e) elif isinstance(item, ProductsItem): try: self.Products.insert(dict(item)) except Exception as e: print('get failed:', e) elif isinstance(item, ShopItem): try: self.Shop.insert(dict(item)) except Exception as e: print('get failed:', e) elif isinstance(item, CommentItem): try: self.Comment.insert(dict(item)) except Exception as e: print('get failed:', e) elif isinstance(item, CommentImageItem): try: self.CommentImage.insert(dict(item)) except Exception as e: print('get failed:', e) elif isinstance(item, CommentSummaryItem): try: self.CommentSummary.insert(dict(item)) except Exception as e: print('get failed:', e) elif isinstance(item, ShopItem): try: self.Shop.insert(dict(item)) except Exception as e: print('get failed:', e) return item
3.定义中间件文件

【中间件文件说明】
包括“爬虫代理中间件”和“缓存中间件”
爬虫代理中间件：防止连续请求被京东后台发现并拉黑
缓存中间件：判断京东后台服务器响应情况，并作出针对性处理

【middlewares.py】

# -*- coding: utf-8 -*- # Define here the models for your spider middleware## See documentation in:# http://doc.scrapy.org/en/latest/topics/spider-middleware.html import osimport loggingfrom scrapy.exceptions import IgnoreRequestfrom scrapy.utils.response import response_status_messagefrom scrapy.downloadermiddlewares.retry import RetryMiddlewareimport random logger = logging.getLogger(__name__) class UserAgentMiddleware(object): """ 换User-Agent """ def process_request(self, request, spider): """设置爬虫代理""" with open("E://proxy.txt", "r") as f: PROXIES = f.readlines() agent = random.choice(PROXIES) agent = agent.strip() request.headers["User-Agent"] = agent class CookiesMiddleware(RetryMiddleware): """ 维护Cookie """ def process_request(self, request, spider): pass def process_response(self, request, response, spider): if response.status in [300, 301, 302, 303]: try: reason = response_status_message(response.status) return self._retry(request, reason, spider) or response # 重试 except Exception as e: raise IgnoreRequest elif response.status in [403, 414]: logger.error("%s! Stopping..." % response.status) os.system("pause") else: return response

 

4.scrapy爬虫设置文件修改

【修改说明】
robot协议：置位False，防止京东网站不允许爬虫抓取数据爬虫
最大并发请求：可依据电脑实际性能进行设置下载
中间件优先级：值越小，优先级越高
管道文件优先级：值越小，优先级越高说明：代码文件过长，故不再展示

5.商品类目抓取

【商品类目抓取说明】
有些类别里面包含有很多子类别，所以对于这样的url，需要再次yield并进行抓取

texts = selector.xpath('//div[@class="category-item m"]/div[@class="mc"]/div[@class="items"]/dl/dd/a').extract() for text in texts: # 获取全部三级类目链接+三级类目名称 items = re.findall(r'<a href="(.*?)" target="_blank">(.*?)</a>', text) for item in items: # 判断“商品链接”是否需要继续请求 if item[0].split('.')[0][2:] in key_word: if item[0].split('.')[0][2:] != 'list': yield Request(url='https:' + item[0], callback=self.parse_category) else: # 记录一级类目：名称/可提数URL/id编码 categoriesItem = CategoriesItem() categoriesItem['name'] = item[1] categoriesItem['url'] = 'https:' + item[0] categoriesItem['_id'] = item[0].split('=')[1].split('&')[0] yield categoriesItem meta = dict() meta["category"] = item[0].split("=")[1] yield Request(url='https:' + item[0], callback=self.parse_list, meta=meta)
6.商品信息抓取

【店铺信息抓取说明】
流程：访问每个类别的url，在产品列表中获取每个商品对应的url,进入详情页面抓取产品的详情
注意：此处要通过分析得出翻页请求对应的response地址，并解析规律进行翻页

获取商品链接】

selector = Selector(response) texts = selector.xpath('//*[@id="J_goodsList"]/ul/li/div/div[@class="p-img"]/a').extract() for text in texts: items = text.split("=")[3].split('"')[1] yield Request(url='https:' + items, callback=self.parse_product, meta=meta) # 翻页[仅翻前50页] maxPage = int(response.xpath('//div[@id="J_filter"]/div/div/span/i/text()').extract()[0]) if maxPage > 1: if maxPage > 50: maxPage = 50 for i in range(2, maxPage): num = 2*i - 1 caterory = meta["category"].split(",")[0]+'%2C' + meta["category"].split(",")[1] + '%2C' + meta["category"].split(",")[2] url = list_url % (caterory, num, 30*num) print('products next page:', url) yield Request(url=url, callback=self.parse_list2, meta=meta)
7.店铺信息抓取

【店铺信息抓取说明】

店铺信息在抓取商品信息的页面便可以获取但是，要区分自营和非自营，因为自营缺少一些内容

# 商品在售店铺id+店铺信息获取 shopItem["shopName"] = response.xpath('//div[@class="m m-aside popbox"]/div/div/h3/a/text()').extract()[0] shopItem["_id"] = "https:" + response.xpath('//div[@class="m m-aside popbox"]/div/div/h3/a/@href').extract()[0] productsItem['shopId'] = shopItem["_id"] # 区分是否自营 res = response.xpath('//div[@class="score-parts"]/div/span/em/@title').extract() if len(res) == 0: shopItem["shopItemScore"] = "京东自营" shopItem["shopLgcScore"] = "京东自营" shopItem["shopAfterSale"] = "京东自营" else: shopItem["shopItemScore"] = res[0] shopItem["shopLgcScore"] = res[1] shopItem["shopAfterSale"] = res[2] # shopItem["_id"] = response.xpath('//div[@class="m m-aside popbox"]/div/div/h3/a/@href').extract()[0].split("-")[1].split(".")[0] yield shopItem
8.评论信息抓取

【评论信息抓取说明】
评论的信息也是动态加载，返回的格式也是json，且会不定期进行更新，访问格式如下：comment_url = 'https://club.jd.com/comment/productPageComments.action?productId=%s&score=0&sortType=5&page=%s&pageSize=10'

def parse_comments(self, response): """ 获取商品评论 :param response: 评论相应的json脚本 :return: """ try: data = json.loads(response.text) except Exception as e: print('get comment failed:', e) return None product_id = response.meta['product_id'] # 商品评论概况获取[仅导入一次] commentSummaryItem = CommentSummaryItem() commentSummary = data.get('productCommentSummary') commentSummaryItem['_id'] = commentSummary.get('skuId') commentSummaryItem['productId'] = commentSummary.get('productId') commentSummaryItem['commentCount'] = commentSummary.get('commentCount') commentSummaryItem['score1Count'] = commentSummary.get('score1Count') commentSummaryItem['score2Count'] = commentSummary.get('score2Count') commentSummaryItem['score3Count'] = commentSummary.get('score3Count') commentSummaryItem['score4Count'] = commentSummary.get('score4Count') commentSummaryItem['score5Count'] = commentSummary.get('score5Count') # 判断commentSummaryItem类型 yield commentSummaryItem # 商品评论[第一页，剩余页面评论由，parse_comments2] for comment_item in data['comments']: comment = CommentItem() comment['_id'] = str(product_id)+","+str(comment_item.get("id")) comment['productId'] = product_id comment["guid"] = comment_item.get('guid') comment['firstCategory'] = comment_item.get('firstCategory') comment['secondCategory'] = comment_item.get('secondCategory') comment['thirdCategory'] = comment_item.get('thirdCategory') comment['score'] = comment_item.get('score') comment['nickname'] = comment_item.get('nickname') comment['plusAvailable'] = comment_item.get('plusAvailable') comment['content'] = comment_item.get('content') comment['creationTime'] = comment_item.get('creationTime') comment['replyCount'] = comment_item.get('replyCount') comment['usefulVoteCount'] = comment_item.get('usefulVoteCount') comment['imageCount'] = comment_item.get('imageCount') yield comment # 存储当前用户评论中的图片 if 'images' in comment_item: for image in comment_item['images']: commentImageItem = CommentImageItem() commentImageItem['commentGuid'] = comment_item.get('guid') commentImageItem['imgId'] = image.get('id') commentImageItem['_id'] = str(product_id)+","+str(comment_item.get('id'))+","+str(image.get('id')) commentImageItem['imgUrl'] = 'http:' + image.get('imgUrl') commentImageItem['imgTitle'] = image.get('imgTitle') commentImageItem['imgStatus'] = image.get('status') yield commentImageItem # 评论翻页[尽量保证评分充足] max_page = int(data.get('maxPage', '1')) # if max_page > 60: # # 设置评论的最大翻页数 # max_page = 60 for i in range(1, max_page): url = comment_url % (product_id, str(i)) meta = dict() meta['product_id'] = product_id yield Request(url=url, callback=self.parse_comments2, meta=meta)