python實戰之Scrapy框架爬蟲爬取微博熱搜
前言：大概一年前寫的，前段時間跑瞭下，發現還能用，就分享出來瞭供大傢學習，代碼的很多細節不太記得瞭，也盡力做瞭優化。
因為畢竟是微博，反爬技術手段還是很周全的，怎麼繞過反爬的話要在這說都可以單獨寫幾篇文章瞭（包括網頁動態加載，ajax動態請求，token密鑰等等，特別是二級評論，藏得很深，記得當時想瞭很久才成功拿到），直接上代碼。
主要實現的功能：
0.理所應當的,繞過瞭各種反爬。
1.爬取全部的熱搜主要內容。
2.爬取每條熱搜的相關微博。
3.爬取每條相關微博的評論，評論用戶的各種詳細信息。
4.實現瞭自動翻譯，理論上來說，是可以拿下與熱搜相關的任何細節，但數據量比較大，推薦使用數據庫對這個爬蟲程序進行優化（因為當時還沒學數據庫，不會用，就按照一定格式在本地進行瞭存儲）
（未實現功能）：
利用爬取數據構建社交網。可構建python的數據分析，將爬取的用戶構成一個社交網絡。
項目結構：
weibo.py
用於爬取需要數據，調用回調分析數據後移交給item，再由item移交給管道進行處理，包括持久化數據等等。
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/"
#攜帶cookie發起請求
def start_requests(self):
cookies = "" #獲取一個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
)
#分析熱搜和鏈接
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,}
#分析每種熱搜下的各種首頁消息
def parse_item(self, response, **kwargs):
# print("開始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)
#創建名字為標題的文本存儲熱搜
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為:",author)
item['author'] = author
item['news_id'] = news_id
item['news_time'] = news_time
item['brief_con'] = brief_con
item['details_url'] = link
#json鏈接模板: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('.//')
#分析每條消息的詳情和評論
#https://weibo.com/1649173367/JwjbPDW00?refer_flag=1001030103__&type=comment
#json數據包
#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開始")
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)
# #因為評論前有個中文的引號,正則格外的好用
# #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
items.py
在這裡定義分析的數據,移交給管道處理
import scrapy
class WeiboproItem(scrapy.Item):
# define the fields for your item here like:
# name = scrapy.Field()
#熱搜標題
title = scrapy.Field()
#熱搜的鏈接
href = scrapy.Field()
#發佈每條相關熱搜消息的作者
author = scrapy.Field()
#發佈每條相關熱搜消息的時間
news_time = scrapy.Field()
#發佈每條相關熱搜消息的內容
brief_con = scrapy.Field()
#發佈每條相關熱搜消息的詳情鏈接
details_url = scrapy.Field()
#詳情頁ID,拿json必備
news_id = scrapy.Field()
#傳入每條熱搜消息微博詳情頁下的作者
username = scrapy.Field()
#傳入每條熱搜消息微博詳情頁下的時間
usertime = scrapy.Field()
#傳入每條熱搜消息微博詳情頁下的評論
usercontent = scrapy.Field()
#所有評論和人
user = scrapy.Field()
middlewares.py
中間件，用於處理spider和服務器中間的通訊。
import random
# 自定義微博請求的中間件
class WeiboproDownloaderMiddleware(object):
def process_request(self, request, spider):
# "設置cookie"
cookies = ""
cookies = {i.split("=")[0]: i.split("=")[1] for i in cookies.split("; ")}
request.cookies = cookies
# 設置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
設置spider的屬性，包括在這裡已經加入瞭各種瀏覽器請求頭，設置線程數，爬取頻率等等，能夠讓spider擁有更強大的反爬
# 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",
# 淘寶瀏覽器
"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",
# 獵豹瀏覽器
"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瀏覽器
"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瀏覽器
"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瀏覽器
"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瀏覽器
"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,沒啥好寫的
[settings] default = weiboPro.settings [deploy] #url = http://localhost:6800/ project = weiboPro
剩下的兩個__init__文件空著就行，用不上。
到此這篇關於python實戰之Scrapy框架爬蟲爬取微博熱搜的文章就介紹到這瞭,更多相關python Scrapy 爬取微博熱搜內容請搜索WalkonNet以前的文章或繼續瀏覽下面的相關文章希望大傢以後多多支持WalkonNet！
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