python实战之Scrapy框架爬虫爬取微博热搜

前言:大概一年前写的,前段时间跑了下,发现还能用,就分享出来了供大家学习,代码的很多细节不太记得了,也尽力做了优化。
因为毕竟是微博,反爬技术手段还是很周全的,怎么绕过反爬的话要在这说都可以单独写几篇文章了(包括网页动态加载,ajax动态请求,token密钥等等,特别是二级评论,藏得很深,记得当时想了很久才成功拿到),直接上代码。

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:

python实战之Scrapy框架爬虫爬取微博热搜_第1张图片

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 scrapy
from copy import deepcopy
from time import sleep
import json
from lxml import etree
import re


class 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 scrapy


class 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 ItemAdapter
class 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.html

BOT_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 = True
USER_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 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 = {
#    'weiboPro.middlewares.WeiboproSpiderMiddleware': 543,
# }

# Enable or disable downloader middlewares
# See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html
DOWNLOADER_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.html
ITEM_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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