python中如何使用Scrapy框架爬虫爬取微博热搜

这篇文章给大家分享的是有关python中如何使用Scrapy框架爬虫爬取微博热搜的内容。小编觉得挺实用的，因此分享给大家做个参考，一起跟随小编过来看看吧。

主要实现的功能：
0.理所应当的,绕过了各种反爬。
1.爬取全部的热搜主要内容。
2.爬取每条热搜的相关微博。
3.爬取每条相关微博的评论，评论用户的各种详细信息。
4.实现了自动翻译，理论上来说，是可以拿下与热搜相关的任何细节，但数据量比较大，推荐使用数据库对这个爬虫程序进行优化（因为当时还没学数据库，不会用，就按照一定格式在本地进行了存储）

（未实现功能）：
利用爬取数据构建社交网。可构建python的数据分析，将爬取的用户构成一个社交网络。

项目结构：

weibo.py

用于爬取需要数据，调用回调分析数据后移交给item，再由item移交给管道进行处理，包括持久化数据等等。

import scrapyfrom copy import deepcopyfrom time import sleepimport jsonfrom lxml import etreeimport reclass WeiboSpider(scrapy.Spider):    name = 'weibo'    start_urls = ['https://s.weibo.com/top/summary?Refer=top_hot&topnav=1&wvr=6']    home_page = "https://s.weibo.com/"    #携带cookie发起请求    def start_requests(self):        cookies = "" #获取一个cookie        cookies = {i.split("=")[0]: i.split("=")[1] for i in cookies.split("; ")}        yield scrapy.Request(            self.start_urls[0],            callback=self.parse,            cookies=cookies        )    #分析热搜和链接    def parse(self, response, **kwargs):        page_text = response.text        with open('first.html','w',encoding='utf-8') as fp:            fp.write(page_text)        item = {}        tr = response.xpath('//*[@id="pl_top_realtimehot"]/table//tr')[1:]        #print(tr)        for t in tr:            item['title'] = t.xpath('./td[2]//text()').extract()[1]            print('title : ',item['title'])        #item['domain_id'] = response.xpath('//input[@id="sid"]/@value').get()        #item['description'] = response.xpath('//div[@id="description"]').get()            detail_url = self.home_page + t.xpath('./td[2]//@href').extract_first()            item['href'] = detail_url            print("href:",item['href'])            #print(item)            #yield item            yield scrapy.Request(detail_url,callback=self.parse_item, meta={'item':deepcopy(item)})            # print("parse完成")            sleep(3)            #print(item)#       item{'title':href,}    #分析每种热搜下的各种首页消息    def parse_item(self, response, **kwargs):        # print("开始parse_item")        item = response.meta['item']        #print(item)        div_list = response.xpath('//div[@id="pl_feedlist_index"]//div[@class="card-wrap"]')[1:]        #print('--------------')        #print(div_list)        #details_url_list = []        #print("div_list : ",div_list)        #创建名字为标题的文本存储热搜        name = item['title']        file_path = './' + name        for div in div_list:            author = div.xpath('.//div[@class="info"]/div[2]/a/@nick-name').extract_first()            brief_con = div.xpath('.//p[@node-type="feed_list_content_full"]//text()').extract()            if brief_con is None:                brief_con = div.xpath('.//p[@class="txt"]//text()').extract()            brief_con = ''.join(brief_con)            print("brief_con : ",brief_con)            link = div.xpath('.//p[@class="from"]/a/@href').extract_first()            if author is None or link is None:                continue            link = "https:" + link + '_&type=comment'            news_id = div.xpath('./@mid').extract_first()            print("news_id : ",news_id)            # print(link)            news_time = div.xpath(".//p[@class='from']/a/text()").extract()            news_time = ''.join(news_time)            print("news_time:", news_time)            print("author为:",author)            item['author'] = author            item['news_id'] = news_id            item['news_time'] = news_time            item['brief_con'] = brief_con            item['details_url'] = link            #json链接模板:https://weibo.com/aj/v6/comment/big?ajwvr=6&id=4577307216321742&from=singleWeiBo            link = "https://weibo.com/aj/v6/comment/big?ajwvr=6&id="+ news_id + "&from=singleWeiBo"            # print(link)            yield scrapy.Request(link,callback=self.parse_detail,meta={'item':deepcopy(item)})        #if response.xpath('.//')    #分析每条消息的详情和评论    #https://weibo.com/1649173367/JwjbPDW00?refer_flag=1001030103__&type=comment    #json数据包    #https://weibo.com/aj/v6/comment/big?ajwvr=6&id=4577307216321742&from=singleWeiBo&__rnd=1606879908312    def parse_detail(self, response, **kwargs):        # print("status:",response.status)        # print("ur;:",response.url)        # print("request:",response.request)        # print("headers:",response.headers)        # #print(response.text)        # print("parse_detail开始")        item = response.meta['item']        all= json.loads(response.text)['data']['html']        # #print(all)        with open('3.html','w',encoding='utf-8') as fp:            fp.write(all)        tree = etree.HTML(all)        # print(type(tree))        # username = tree.xpath('//div[@class="list_con"]/div[@class="WB_text"]/a[1]/text()')        # usertime = re.findall('
(.*?)
', all)        # comment = tree.xpath('//div[@class="list_con"]/div[@class="WB_text"]//text()')        # print(usertime)        # #因为评论前有个中文的引号,正则格外的好用        # #comment = re.findall(r'：(.*?)<',all)        # for i in comment:        #     for w in i:        #         if i == "\\n":        #             comment.pop(i)        #             break        # with open("12.txt","w",encoding='utf-8') as fp:        #     for i in comment:        #         fp.write(i)        # print(comment)        #95-122        div_lists = tree.xpath('.//div[@class="list_con"]')        final_lists = []        #print(div_lists)        with open('13.txt', 'a', encoding='utf-8') as fp:            for div in div_lists:                list = []                username = div.xpath('./div[@class="WB_text"]/a[1]/text()')[0]                usertime = div.xpath('.//div[@class="WB_from S_txt2"]/text()')[0]                usercontent = div.xpath('./div[@class="WB_text"]/text()')                str = usertime + '\n' + username                #print(username,usertime,usercontent)                # fp.write(usertime + '\n' + username)                for con in usercontent[1:]:                    str += '\n' + username + '\n' + usertime + '\n' + con + '\n'                #                usercontent = ''.join(usercontent)                #print('usercontent:',usercontent)                item['username'] = username                item['usertime'] = usertime                item['usercontent'] = usercontent                list.append(username)                list.append(usertime)                list.append(usercontent)                final_lists.append(list)                #item['user'] = [username,usertime,usercontent]            item['user'] = final_lists            yield item

items.py

在这里定义分析的数据,移交给管道处理

import scrapyclass WeiboproItem(scrapy.Item):    # define the fields for your item here like:    # name = scrapy.Field()    #热搜标题    title = scrapy.Field()    #热搜的链接    href = scrapy.Field()    #发布每条相关热搜消息的作者    author = scrapy.Field()    #发布每条相关热搜消息的时间    news_time = scrapy.Field()    #发布每条相关热搜消息的内容    brief_con = scrapy.Field()    #发布每条相关热搜消息的详情链接    details_url = scrapy.Field()    #详情页ID,拿json必备    news_id = scrapy.Field()    #传入每条热搜消息微博详情页下的作者    username = scrapy.Field()    #传入每条热搜消息微博详情页下的时间    usertime = scrapy.Field()    #传入每条热搜消息微博详情页下的评论    usercontent = scrapy.Field()    #所有评论和人    user = scrapy.Field()

middlewares.py

中间件，用于处理spider和服务器中间的通讯。

import random# 自定义微博请求的中间件class WeiboproDownloaderMiddleware(object):    def process_request(self, request, spider):        # "设置cookie"        cookies = ""        cookies = {i.split("=")[0]: i.split("=")[1] for i in cookies.split("; ")}        request.cookies = cookies        #  设置ua        ua = random.choice(spider.settings.get("USER_AGENT_LIST"))        request.headers["User-Agent"] = ua        return None

pipelines.py

from itemadapter import ItemAdapterclass WeiboproPipeline:    fp = None    def open_spider(self,spider):        print("starting...")    def process_item(self, item, spider):        title = item['title']        href = item['href']        author = item['author']        news_time = item['news_time']        brief_con = item['brief_con']        details_url = item['details_url']        news_id = item['news_id']        #username = item['username']        #usertime = item['usertime']        #usercontent = item['usercontent']        user = item['user']        filepath = './' + title + '.txt'        with open(filepath,'a',encoding='utf-8') as fp:            fp.write('title:\n' + title + '\n' + 'href:\n'+href + '\n' +'author:\n' + author + '\n' + 'news_time:\n' +news_time + '\n' + 'brief_con\n' + brief_con + '\n' +'details_url:\n' + details_url + '\n' +'news_id'+news_id + '\n')            for u in user:                fp.write('username:'+u[0] + '\n' + u[1] + '\n' +'usercontent:\n'+u[2] + '\n\n\n')            fp.write('---------------------------------------------------------\n')        fp.close()        return item

setting.py

设置spider的属性，包括在这里已经加入了各种浏览器请求头，设置线程数，爬取频率等等，能够让spider拥有更强大的反爬

# Scrapy settings for weiboPro 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.htmlBOT_NAME = 'weiboPro'SPIDER_MODULES = ['weiboPro.spiders']NEWSPIDER_MODULE = 'weiboPro.spiders'# Crawl responsibly by identifying yourself (and your website) on the user-agent#USER_AGENT = 'weiboPro (+http://www.yourdomain.com)'MEDIA_ALLOW_REDIRECTS = TrueUSER_AGENT_LIST = ["Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36 OPR/26.0.1656.60",        "Opera/8.0 (Windows NT 5.1; U; en)",        "Mozilla/5.0 (Windows NT 5.1; U; en; rv:1.8.1) Gecko/20061208 Firefox/2.0.0 Opera 9.50",        "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; en) Opera 9.50",        # Firefox        "Mozilla/5.0 (Windows NT 6.1; WOW64; rv:34.0) Gecko/20100101 Firefox/34.0",        "Mozilla/5.0 (X11; U; Linux x86_64; zh-CN; rv:1.9.2.10) Gecko/20100922 Ubuntu/10.10 (maverick) Firefox/3.6.10",        # Safari        "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/534.57.2 (KHTML, like Gecko) Version/5.1.7 Safari/534.57.2",        # chrome        "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.71 Safari/537.36",        "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11",        "Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US) AppleWebKit/534.16 (KHTML, like Gecko) Chrome/10.0.648.133 Safari/534.16",        # 360        "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/30.0.1599.101 Safari/537.36",        "Mozilla/5.0 (Windows NT 6.1; WOW64; Trident/7.0; rv:11.0) like Gecko",        # 淘宝浏览器        "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.11 TaoBrowser/2.0 Safari/536.11",        # 猎豹浏览器        "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/21.0.1180.71 Safari/537.1 LBBROWSER",        "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; WOW64; Trident/5.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; .NET4.0E; LBBROWSER)",        "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; QQDownload 732; .NET4.0C; .NET4.0E; LBBROWSER)",        # QQ浏览器        "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; WOW64; Trident/5.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; .NET4.0E; QQBrowser/7.0.3698.400)",        "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; QQDownload 732; .NET4.0C; .NET4.0E)",        # sogou浏览器        "Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.84 Safari/535.11 SE 2.X MetaSr 1.0",        "Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Trident/4.0; SV1; QQDownload 732; .NET4.0C; .NET4.0E; SE 2.X MetaSr 1.0)",        # maxthon浏览器        "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Maxthon/4.4.3.4000 Chrome/30.0.1599.101 Safari/537.36",        # UC浏览器        "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/38.0.2125.122 UBrowser/4.0.3214.0 Safari/537.36"              ]LOG_LEVEL = 'ERROR'# Obey robots.txt rulesROBOTSTXT_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 = {#    'weiboPro.middlewares.WeiboproSpiderMiddleware': 543,# }# Enable or disable downloader middlewares# See https://docs.scrapy.org/en/latest/topics/downloader-middleware.htmlDOWNLOADER_MIDDLEWARES = {   'weiboPro.middlewares.WeiboproDownloaderMiddleware': 543,}# 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.htmlITEM_PIPELINES = {   'weiboPro.pipelines.WeiboproPipeline': 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'

scrapy.cfg

配置文件,没啥好写的

[settings]default = weiboPro.settings[deploy]#url = http://localhost:6800/project = weiboPro

剩下的两个__init__文件空着就行，用不上。

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