scrapy爬取京东前后一星期图书价格

布满荆棘的人生 2023-02-22 14:58 24阅读 0赞

## scrapy框架 ##

Scrapy是适用于Python的一个快速、高层次的屏幕抓取和web抓取框架，用于抓取web站点并从页面中提取结构化的数据。Scrapy用途广泛，可以用于数据挖掘、监测和自动化测试。；框架的力量，用户只需要定制开发几个模块就可以轻松的实现一个爬虫，用来抓取网页内容以及各种图片，非常之方便

## 思路分析 ##

大数据可视化需要对不同行业不同种类的数据分析，那么python爬虫与大数据就更加形影不离，本次是对京东图书前后一星期价格进行爬取分析。  
1、想要爬取某个网站第一步必须进行该网页数据分析，首先看图书页面信息：  
https://search.jd.com/Search?keyword=%E5%9B%BE%E4%B9%A6&wq=%E5%9B%BE%E4%B9%A6&page=1  
https://search.jd.com/Search?keyword=%E5%9B%BE%E4%B9%A6&wq=%E5%9B%BE%E4%B9%A6&page=2  
对上面连接可知图书列表每次显示30条数据，页面用page变量复制  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3d1aHVpMjEwMA_size_16_color_FFFFFF_t_70]  
2、对图书列表进行检查：  
1）、获取列表xpath路径：//*\[@id=“J\_goodsList”\]/ul  
2）、获取每个图书信息连接（因为图书详情页链接都是以京东主页面连接加上商品编号）：  
![在这里插入图片描述][20200704150136464.png]  
3）、那么想要详细获取商品详情页面就必须进入商品详情页：  
xpath：//*\[@id=“J\_goodsList”\]/ul/li\[1\]/div/div\[3\]/a  
![在这里插入图片描述][20200704150410579.png]  
4）、进入详情，找到指定商品信息并进行获取：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3d1aHVpMjEwMA_size_16_color_FFFFFF_t_70 1]  
5）、此时你会发现商品信息价格有点获取不了，因为价格和商品名称是动态加载的；一般的爬虫获取不了动态信息，那么我们就开始想办法了，我在检查元素是发现一个有趣的信息：“https://p.3.cn/prices/mgets?skuIds=J\_” + 商品id就可以显示商品的价格信息  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3d1aHVpMjEwMA_size_16_color_FFFFFF_t_70 2]  
![在这里插入图片描述][20200704151438509.png]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3d1aHVpMjEwMA_size_16_color_FFFFFF_t_70 3]

3、那么总的思路：因为要爬取同一件商品不同时间段的价格信息，那么就不只是一次爬取那么简单了；首先第一次爬取商品的详细信息（价格除外）放入一个json文件中，然后根据JSON中的商品ID（利用：“https://p.3.cn/prices/mgets?skuIds=J\_” + 商品id）获取价格信息，加上当天爬取的时间段

## 代码部分 ##

1、创建good.py获取商品详细信息（出价格外）

# -*- coding: utf-8 -*-
    # -*- coding: utf-8 -*-
    import scrapy
    from urllib.parse import quote
    from goods.items import GoodsItem
    
    class GoodSpider(scrapy.Spider):
        name = 'good'
        allowed_domains = ['search.jd.com/Search?keyword=图书']
        start_urls = ['https://search.jd.com/Search?keyword=图书&wq=图书&page=1']
    
        def parse(self, response):
            print("开始爬虫")
            #输入要爬取的页数
            keyword = int(input("请输入要爬取的页数："))
            #遍历访问的页数
            a_href = []
            a = 1
            # 关键词中有中文的话，百度就会将其转码为%开始的编码
            keyword1 = quote("图书", encoding="utf-8")
            print(keyword1)
            for a in range(0,keyword):
                b = "https://search.jd.com/Search?keyword=%E5%9B%BE%E4%B9%A6&wq=%E5%9B%BE%E4%B9%A6&page="+str(a+1)
                a_href.append(b)
                a= a+1
            print(a_href)
            for href in a_href:
                print("==" * 40)
                print(href)
                # 因为请求被去重过滤了，所以才调试不了啊！要加入dont_filter=True才可正常访问
                yield scrapy.Request(url=href,callback=self.parse_book_list,dont_filter=True)
    
        def parse_book_list(self, response):
            # 进入京东图书详情页
            print("进入京东图书详情页")
            li_list = response.xpath("//*[@id='J_goodsList']/ul")
            print(li_list)
            for li in li_list:
                item = { }
                a_href = li.xpath(".//li/div/div[3]/a/@href").extract()
                print(a_href)
                if a_href is not None:
                    a_href = ['https:' + x for x in a_href]
                    print(len(a_href))
                    for c_href in a_href:
                        print("==" * 40)
                        print(c_href)
                        yield scrapy.Request(
                            url=c_href,
                            callback=self.parse_book_li,
                            dont_filter=True
                        )
    
        def parse_book_li(self,response):
            item = { }
            print("进入详情页")
            # 获取书名
            item["Title"] = response.xpath("//*[@id='name']/div[1]/text()").extract_first().strip()
            print("书名===：",item["Title"])
            # 获取作者
            item["Author"] = response.xpath("//*[@id='p-author']/a[1]/@data-name").extract_first()
            print("作者===：",item["Author"])
    
            li = response.xpath("//*[@id='parameter2']")
            b = '：'
            # print(li)
            for li_book in li:
                books=li_book.xpath(".//li/text()").extract()
                # print(books)
                # 正则表达式替换:之前的字符串
                for a in books:
                    # print(a)
                    c = a[:a.find(b)]
                    # print('c=',c)
                    if c == '出版社':
                        # 出版社
                        item["PublishingHouse"] = li.xpath(".//li/a[1]/@title").extract_first()
                        # print("出版社===:",books["publishing_house"])
                    elif c == 'ISBN':
                        # ISBN
                        item["ISBN"] = a[a.find(b):].replace("：","")
                        # print("ISBN===:",books["ISBN"])
                    elif c == '版次':
                        # 版次
                        item["Edition"] = a[a.find(b):].replace("：","")
                        # print("版次===:",books["Edition"])
                    elif c == '商品编码':
                        # 商品编号
                        item["ProductID"] = a[a.find(b):].replace("：","")
                        # print("商品编号===:",books["ProductID"])
                    elif c == '包装':
                        # 包装
                        item["Packaging"] = a[a.find(b):].replace("：","")
                        # print("包装===:",books["Packaging"])
                    elif c == '开本':
                        # 开本
                        item["Format"] = a[a.find(b):].replace("：","")
                        # print("开本===:",books["Format"])
                    elif c == '出版时间':
                        # 出版时间
                        item["PublicationTime"] = a[a.find(b):].replace("：","")
                        # print("出版时间===:",books["PublicationTime"])
                    elif c == '用纸类型':
                        # 用纸类型
                        item["PaperType"] = a[a.find(b):].replace("：","")
                        # print("用纸类型===:",books["PaperType"])
                    elif c == '页数':
                        # 页数
                        item["BookNum"] = a[a.find(b):].replace("：","")
                        # print("页数===:",books["BookNum"])
                    # else:
                    # print("格式不正确！")
                yield item
    
            # # 出版社
            # publishing_house = li.xpath(".//li[1]/[@title='']").extract_first()
            # print("出版社:",publishing_house)
            # # ISBN
            # ISBN = li.xpath(".//li[2]/@title").extract_first()
            # print("ISBN:",ISBN)
            # # 版次
            # edition = li.xpath(".//li[3]/@title").extract_first()
            # print("版次:",edition)
            # # 商品编号
            # product_iD = li.xpath(".//li[4]/@title").extract_first()
            # print("商品编号:",product_iD)
            # # 品牌
            # brand = li.xpath(".//li[5]/@title").extract_first()
            # print("品牌:",brand)
            # # 包装
            # packaging = li.xpath(".//li[6]/@title").extract_first()
            # print("包装:",packaging)
            # # 开本
            # format = li.xpath(".//li[7]/@title").extract_first()
            # print("开本:",format)
            # # 出版时间
            # publication_time = li.xpath(".//li[8]/@title").extract_first()
            # print("出版时间:",publication_time)
            # # 用纸类型
            # paper_type = li.xpath(".//li[9]/@title").extract_first()
            # print("用纸类型:",paper_type)
            # # 页数
            # book_num = li.xpath(".//li[10]/@title").extract_first()
            # print("页数:",book_num)

2、创建book\_message.py根据上面获取的good.py商品ID获取该商品的当天价格：

# -*- coding: utf-8 -*-
    import scrapy
    import json
    import datetime
    import copy
    from urllib.parse import quote
    from goods.items import GoodsItem
    
    class GoodSpider(scrapy.Spider):
        name = 'book_message'
        allowed_domains = ['search.jd.com/Search?keyword=图书']
        start_urls = ['https://search.jd.com/Search?keyword=图书&wq=图书&page=1']
    
        def parse(self, response):
            with open('.//books.json', 'r', encoding='utf-8') as load_f:
                strF = load_f.read()
                if len(strF) > 0:
                    datas = json.loads(strF)
                    a = datas["books"]
                    print("这是文件中的json数据：")
                    print(a)
                    item = { }
                    for b in a:
                        #遍历获取的商品信息
                        #书名
                        if 'Title' in b:
                            item["Title"] = b["Title"]
                        else:
                            item["Title"] = None
                        # 获取作者
                        if 'Author' in b:
                            item["Author"] = b["Author"]
                        # 出版社
                        if 'PublishingHouse' in b:
                            item["PublishingHouse"] = b["PublishingHouse"]
                        # ISBN
                        if 'ISBN' in b:
                            item["ISBN"] = b["ISBN"]
                        # 版次
                        if 'Edition' in b:
                            item["Edition"] = b["Edition"]
                        # 商品编号
                        if 'ProductID' in b:
                            item["ProductID"] = b["ProductID"]
                        # 包装
                        if 'Packaging' in b:
                            item["Packaging"] = b["Packaging"]
                        # 开本
                        if 'Format' in b:
                            item["Format"] = b["Format"]
                        # 出版时间
                        if 'PublicationTime' in b:
                            item["PublicationTime"] = b["PublicationTime"]
                        # # 用纸类型
                        # item["PaperType"] = b["PaperType"]
                        # 页数
                        if 'BookNum' in b:
                            item["BookNum"] = b["BookNum"]
                        #爬取时间
                        item["crawlTime"] = datetime.datetime.now().strftime('%Y-%m-%d')
                        print("time:",item["crawlTime"])
    
                        #遍历商品编码
                        c = b["ProductID"]
                        print(c)
                        url = "https://p.3.cn/prices/mgets?skuIds=J_"+c
                        # print(url)
                        yield scrapy.Request(
                            url=url,
                            callback=self.parse_books,
                            meta={ "item": copy.deepcopy(item)},
                            dont_filter=True
                        )
                else:
                    print("这是文件中的json数据为空")
                    datas = { }
    
        def parse_books(self,response):
            item = response.meta["item"]
            # 调用body_as_unicode()是为了能处理unicode编码的数据
            price_list = json.loads(response.body_as_unicode())
            # print sites['k'].split(',')
            print("price_list:",price_list)
            for li in price_list:
                item["ProductPrice"] = li["p"]
                #商品价格
                print("ProductPrice",item["ProductPrice"])
            yield item

3、items部分接收item字典

import scrapy
    
    
    class GoodsItem(scrapy.Item):
        # define the fields for your item here like:
        # name = scrapy.Field()
        # 书名
        Title = scrapy.Field()
        # 作者
        Author = scrapy.Field()
        # 出版社
        PublishingHouse = scrapy.Field()
        # 书ISBN
        ISBN = scrapy.Field()
        # 版次
        Edition = scrapy.Field()
        # 商品编码
        ProductID = scrapy.Field()
        # 包装
        Packaging = scrapy.Field()
        # 开本
        Format = scrapy.Field()
        # 出版时间
        PublicationTime = scrapy.Field()
        # 用纸类型
        PaperType = scrapy.Field()
        #页数
        BookNum = scrapy.Field()
        #爬取时间
        crawlTime = scrapy.Field()
        #爬取当天价格
        ProductPrice = scrapy.Field()

4、setting部分：

# -*- coding: utf-8 -*-
    
    # Scrapy settings for goods project
    #
    # For simplicity, this file contains only settings considered important or
    # commonly used. You can find more settings consulting the documentation:
    #
    # https://docs.scrapy.org/en/latest/topics/settings.html
    # https://docs.scrapy.org/en/latest/topics/downloader-middleware.html
    # https://docs.scrapy.org/en/latest/topics/spider-middleware.html
    
    BOT_NAME = 'goods'
    
    SPIDER_MODULES = ['goods.spiders']
    NEWSPIDER_MODULE = 'goods.spiders'
    
    
    # Crawl responsibly by identifying yourself (and your website) on the user-agent
    #USER_AGENT = 'goods (+http://www.yourdomain.com)'
    
    # Obey robots.txt rules
    ROBOTSTXT_OBEY = False
    
    # Configure maximum concurrent requests performed by Scrapy (default: 16)
    #CONCURRENT_REQUESTS = 32
    
    # Configure a delay for requests for the same website (default: 0)
    # See https://docs.scrapy.org/en/latest/topics/settings.html#download-delay
    # See also autothrottle settings and docs
    DOWNLOAD_DELAY = 3
    # The download delay setting will honor only one of:
    #CONCURRENT_REQUESTS_PER_DOMAIN = 16
    #CONCURRENT_REQUESTS_PER_IP = 16
    
    # Disable cookies (enabled by default)
    #COOKIES_ENABLED = False
    
    # Disable Telnet Console (enabled by default)
    #TELNETCONSOLE_ENABLED = False
    
    # Override the default request headers:
    DEFAULT_REQUEST_HEADERS = { 
      'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
      'Accept-Language': 'en',
      }
    
    # Enable or disable spider middlewares
    # See https://docs.scrapy.org/en/latest/topics/spider-middleware.html
    #SPIDER_MIDDLEWARES = { 
    # 'goods.middlewares.GoodsSpiderMiddleware': 543,
    #}
    
    # Enable or disable downloader middlewares
    # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html
    DOWNLOADER_MIDDLEWARES = { 
       # 'goods.middlewares.GoodsDownloaderMiddleware': 543,
        'goods.middlewares.UserAgentDownloadMiddleware': 10,
    }
    
    # Enable or disable extensions
    # See https://docs.scrapy.org/en/latest/topics/extensions.html
    #EXTENSIONS = { 
    # 'scrapy.extensions.telnet.TelnetConsole': None,
    #}
    
    # Configure item pipelines
    # See https://docs.scrapy.org/en/latest/topics/item-pipeline.html
    ITEM_PIPELINES = { 
       'goods.pipelines.GoodsPipeline': 300,
    }
    
    # Enable and configure the AutoThrottle extension (disabled by default)
    # See https://docs.scrapy.org/en/latest/topics/autothrottle.html
    #AUTOTHROTTLE_ENABLED = True
    # The initial download delay
    #AUTOTHROTTLE_START_DELAY = 5
    # The maximum download delay to be set in case of high latencies
    #AUTOTHROTTLE_MAX_DELAY = 60
    # The average number of requests Scrapy should be sending in parallel to
    # each remote server
    #AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0
    # Enable showing throttling stats for every response received:
    #AUTOTHROTTLE_DEBUG = False
    
    # Enable and configure HTTP caching (disabled by default)
    # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings
    #HTTPCACHE_ENABLED = True
    #HTTPCACHE_EXPIRATION_SECS = 0
    #HTTPCACHE_DIR = 'httpcache'
    #HTTPCACHE_IGNORE_HTTP_CODES = []
    #HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'

5、middlewares.py

# -*- coding: utf-8 -*-
    
    # Define here the models for your spider middleware
    #
    # See documentation in:
    # https://docs.scrapy.org/en/latest/topics/spider-middleware.html
    
    from scrapy import signals
    import random
    
    class UserAgentDownloadMiddleware(object):
        USER_AGENTS=[
            "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.100 Safari/537.36",
            "Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36",
            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.1 Safari/537.36",
            "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.0 Safari/537.36",
            "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.0 Safari/537.36",
            "Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2226.0 Safari/537.36",
            "Mozilla/5.0 (Windows NT 6.4; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2225.0 Safari/537.36",
            "Opera/9.80 (X11; Linux i686; Ubuntu/14.10) Presto/2.12.388 Version/12.16",
            "Opera/9.80 (Windows NT 6.0) Presto/2.12.388 Version/12.14",
            "Mozilla/5.0 (Windows NT 6.0; rv:2.0) Gecko/20100101 Firefox/4.0 Opera 12.14",
            "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.0) Opera 12.14",
            "Opera/12.80 (Windows NT 5.1; U; en) Presto/2.10.289 Version/12.02",
            "Mozilla/5.0 (Windows NT 6.1; WOW64; rv:40.0) Gecko/20100101 Firefox/40.1",
            "Mozilla/5.0 (Windows NT 6.3; rv:36.0) Gecko/20100101 Firefox/36.0",
            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10; rv:33.0) Gecko/20100101 Firefox/33.0",
            "Mozilla/5.0 (X11; Linux i586; rv:31.0) Gecko/20100101 Firefox/31.0",
        ]
    
        def process_request(self,request,spider):
            user_agent = random.choice(self.USER_AGENTS)
            request.headers['User-Agent']=user_agent

6、pipelines.py

# -*- coding: utf-8 -*-
    
    # Define your item pipelines here
    #
    # Don't forget to add your pipeline to the ITEM_PIPELINES setting
    # See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html
    from scrapy.exporters import JsonLinesItemExporter
    from openpyxl import Workbook
    
    class GoodsPipeline(object):
        def __init__(self):
            # 转为json格式
            self.book_fp = open('book.json', 'wb')
            self.book_exporter = JsonLinesItemExporter(self.book_fp, ensure_ascii=False)
    
            #转为excel表
            self.wb = Workbook()  # 类实例化
            self.ws = self.wb.active  # 激活工作表
            self.ws.append(['Title', 'Author', 'PublishingHouse', 'ISBN', 'Edition', 'ProductID', 'Packaging',
                            'Format','PublicationTime','BookNum','crawlTime','ProductPrice'])  # 添加表头
    
        def process_item(self, item, spider):
            #接收item（json）
            self.book_exporter.export_item(item)
    
            # excel
            data = [item["Title"], item["Author"], item["PublishingHouse"], item["ISBN"], item["Edition"],
                    item["ProductID"], item["Packaging"], item["Format"],item["PublicationTime"],
                    item["BookNum"], item["crawlTime"], item["ProductPrice"]]
            self.ws.append(data)  # 将数据以行的形式添加到工作表中
            self.wb.save('books_1.xlsx')  # 保存
    
            return item
        def close_spider(self,spider):
             self.book_fp.close()

7、首先运行main启动类，获取商品ID及详细信息  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3d1aHVpMjEwMA_size_16_color_FFFFFF_t_70 4]  
最后运行main1获取同一件商品当前爬取的时间段价格  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3d1aHVpMjEwMA_size_16_color_FFFFFF_t_70 5]

## 运行结果： ##

1、运行main结果如下  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3d1aHVpMjEwMA_size_16_color_FFFFFF_t_70 6]  
注意：这里使用需要把获取的json前面加上\{“books”:\[ ，尾部加上 \] \} 复制全部内容放入自己创建的books.json中  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3d1aHVpMjEwMA_size_16_color_FFFFFF_t_70 7]  
2、再运行main1即可获取自己想要的当前爬取的商品价格信息了  
![在这里插入图片描述][20200704154211104.png]  
打开结果：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3d1aHVpMjEwMA_size_16_color_FFFFFF_t_70 8]

最累的时候，家人是你最好的归宿！

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[20200704150136464.png]: https://img-blog.csdnimg.cn/20200704150136464.png
[20200704150410579.png]: https://img-blog.csdnimg.cn/20200704150410579.png
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[20200704151438509.png]: https://img-blog.csdnimg.cn/20200704151438509.png
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[20200704154211104.png]: https://img-blog.csdnimg.cn/20200704154211104.png
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