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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。
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('<div class="WB_from S_txt2">(.*?)</div>', 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'</a>：(.*?)<',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()
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 Nonefrom 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'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。
感谢各位的阅读！关于“python中如何使用Scrapy框架爬虫爬取微博热搜”这篇文章就分享到这里了，希望以上内容可以对大家有一定的帮助，让大家可以学到更多知识，如果觉得文章不错，可以把它分享出去让更多的人看到吧！
原文地址：https://blog.csdn.net/qq_29003363/article/details/120394580
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