首页 > 其他分享 >利用豆瓣爬虫构建推理小说关键字推荐器

利用豆瓣爬虫构建推理小说关键字推荐器

时间:2022-11-07 23:14:39浏览次数:66  
标签:url list 豆瓣 爬虫 book import 推理小说 txt

推理小说推荐器

原理

图书条目

利用爬虫爬取豆瓣图书信息,书名、作者、出版社、评分等

区分推理小说

利用相关推荐的栏目来爬取,大概率都是推理小说

要是爬出范围就手动调节一下,删除一些url

image

关键字功能

批量爬取豆瓣短评,利用jieba.analyse提取关键词

https://blog.csdn.net/qq_40082282/article/details/103433612

数据存储和提取

利用openpyxl库操作excel文档
image

image

注意事项

访问豆瓣太快会寄,不过访问太多次也会寄

ua伪装试过了fake_useragent,但是爬过一次后就会封ip

最好的办法是ip池

image

代码

方法

预备爬取的url(区分中文、日文、英文和其他,优先中文)
爬取下来的新url添加到列表末尾
image

旧url,防止重复爬取
image

关键词
image

爬取小说信息,保存到excel

点击查看代码
import json
import os
import time
import requests
from lxml import etree
import langid
import jieba
import jieba.analyse
from fake_useragent import UserAgent
from openpyxl import Workbook,load_workbook

ua = UserAgent(verify_ssl=False)
headers = {
    'User-Agent': ua.random,
    'cookie':'douban-fav-remind=1; gr_user_id=e832d640-c944-4401-b1a2-2efadf79ddaa; _ga=GA1.1.1276967334.1627091677; _ga_RXNMP372GL=GS1.1.1654269332.4.0.1654269333.59; ll="118172"; bid=wwjHsuJYL6k; __yadk_uid=cEprKpCD1gZ0jxfk1TCNFOPJGzzlAnin; push_doumail_num=0; __utmv=30149280.18994; __gads=ID=173333fa14a865d1-22fa508a93d700da:T=1667015292:RT=1667015292:S=ALNI_MaCmcMf3x03yQfiiGW6gmaIE0MW1g; dbcl2="189943544:g1Hv1KN49uE"; push_noty_num=0; __utmz=30149280.1667573191.139.28.utmcsr=baidu|utmccn=(organic)|utmcmd=organic; ct=y; ck=C2Dq; __utmc=30149280; __gpi=UID=000005a0402014ab:T=1653180716:RT=1667724069:S=ALNI_MbMuOQ-3iaHjVTQc-aYkptamx2ceQ; ap_v=0,6.0; _pk_ref.100001.8cb4=%5B%22%22%2C%22%22%2C1667736951%2C%22https%3A%2F%2Faccounts.douban.com%2F%22%5D; _pk_ses.100001.8cb4=*; __utma=30149280.1276967334.1627091677.1667724062.1667736951.145; gr_session_id_22c937bbd8ebd703f2d8e9445f7dfd03=35b03e36-a3a9-4877-9bac-e07df2271deb; gr_cs1_35b03e36-a3a9-4877-9bac-e07df2271deb=user_id%3A1; gr_session_id_22c937bbd8ebd703f2d8e9445f7dfd03_35b03e36-a3a9-4877-9bac-e07df2271deb=true; __utmt_douban=1; _pk_id.100001.8cb4=bb090c4f79dc080f.1627091669.145.1667737571.1667734816.; __utmt=1; __utmb=30149280.15.10.1667736951'
}
proxy={
    # "http":"223.96.90.216:8118",
    # "https":"223.96.90.216:8118"
}

session=requests.session()

def get_book(url):
    global headers
    headers['User-Agent']=ua.random
    html= session.get(url,headers=headers,proxies=proxy).text
    time.sleep(0.1)
    html=etree.HTML(html)
    
    new_names=''.join(html.xpath('//*[@id="db-rec-section"]/div//text()'))
    new_names=new_names.replace(' ','').split('\n')
    while '' in new_names:
        new_names.remove('')
    new_urls=html.xpath('//*[@id="db-rec-section"]/div/dl/dt/a/@href')
    sort_new(new_names,new_urls)
    
    name=html.xpath('//*[@id="wrapper"]/h1/span/text()')[0]
        
    txt=html.xpath('//*[@id="info"]')[0].xpath('string(.)').strip()
    txt=txt.replace(' ','').split('\n')
    while "" in txt:
        txt.remove("")

    book_infos=[name]
    info_list=['作者','出版社','出版年','页数','定价','装帧','ISBN']
    for info in info_list:
        for word in txt:
            if info in word:
                info_word=word.split(':')[1]
                if info_word in ['', ' ']:
                    info_word=txt[txt.index(word)+1]
                    if info_word[-1]==']':
                        info_word+=txt[txt.index(word)+2]
                break            
            else:
                info_word=''
        book_infos.append(info_word)
    
    try:
        score=html.xpath('//*[@id="interest_sectl"]/div[1]/div[2]/strong/text()')[0]
        score_num=html.xpath('//*[@id="interest_sectl"]/div[1]/div[2]/div/div[2]/span/a/span/text()')[0]
    except:
        score='无'
        score_num=''
    try:
        keywords=get_keywords(url)
    except:
        keywords='无评论'
    try:
        img=html.xpath('//*[@id="mainpic"]/a/img/@src')[0]
    except:
        img='无封面'
    book_infos+=[score,score_num,keywords,img]
    print(book_infos)
    
    make_excel(book_infos)
    

def sort_new(new_names,new_urls):
    global book_list
    for name_url in zip(new_names,new_urls):
        lineTuple = langid.classify(name_url[0])  
        if lineTuple[0] == "zh":
            book_list_zh.append(name_url[-1])
        elif lineTuple[0] == "ja":
            book_list_ja.append(name_url[-1])
        elif lineTuple[0] == "en":
            book_list_en.append(name_url[-1])
        else:
            book_list_ot.append(name_url[-1])
        
        
def get_keywords(url):
    comments=''
    for i in range(10):
        comment_url = f'{url}comments/?start={i*20}&limit=20&status=P&sort=score'
        global headers
        headers['User-Agent']=ua.random
        html= session.get(comment_url,headers=headers,proxies=proxy).text
        time.sleep(0.05)
        html=etree.HTML(html)
        comments+=','.join(html.xpath('//*[@id="comments"]/div[1]/ul/li/div[2]/p/span/text()'))
        
    jieba.load_userdict("Python/实战项目/爬虫推理小说推荐器/word_dict.txt")
    return ','.join(jieba.analyse.extract_tags(comments))
    
    
def make_excel(book_infos):
    path='Python/实战项目/爬虫推理小说推荐器/推理小说信息收集.xlsx'
    if not os.path.exists(path):
        wb=Workbook()
        sheet=wb.active
        sheet.append(['书名','作者','出版社','出版年','页数','定价','装帧','ISBN','评分','评分人数','评论关键词','封面'])
        wb.save(path)
    wb = load_workbook(path)
    sheet=wb.active
    sheet.append(book_infos)
    wb.save(path)

if __name__=='__main__':
    
    with open('Python/实战项目/爬虫推理小说推荐器/old_urls.txt','r') as f:
        old_urls=json.load(f)
    with open('Python/实战项目/爬虫推理小说推荐器/book_list_zh.txt','r') as f:
        book_list_zh=json.load(f)
    with open('Python/实战项目/爬虫推理小说推荐器/book_list_ja.txt','r') as f:
        book_list_ja=json.load(f)
    with open('Python/实战项目/爬虫推理小说推荐器/book_list_en.txt','r') as f:
        book_list_en=json.load(f)
    with open('Python/实战项目/爬虫推理小说推荐器/book_list_ot.txt','r') as f:
        book_list_ot=json.load(f)

    book_list=book_list_zh+book_list_ja+book_list_en+book_list_ot

    count=0
    while book_list:
        url=book_list_ja[0]
        
        if url in old_urls:
            del book_list_ja[0]
            count+=1
            continue
        print(count)
        
        get_book(url)
        old_urls.append(url)
        with open('Python/实战项目/爬虫推理小说推荐器/old_urls.txt','w') as f:
            json.dump(old_urls,f)
        del book_list_zh[0]
        

        if book_list_zh:
            with open('Python/实战项目/爬虫推理小说推荐器/book_list_zh.txt','w') as f:
                json.dump(book_list_zh,f)
        if book_list_ja:
            with open('Python/实战项目/爬虫推理小说推荐器/book_list_ja.txt','w') as f:
                json.dump(book_list_ja,f)
        if book_list_en:
            with open('Python/实战项目/爬虫推理小说推荐器/book_list_en.txt','w') as f:
                json.dump(book_list_en,f)
        if book_list_ot:
            with open('Python/实战项目/爬虫推理小说推荐器/book_list_ot.txt','w') as f:
                json.dump(book_list_ot,f)

制作ui界面

点击查看代码
import random
import jieba
import zhconv
import tkinter as tk
from tkinter import messagebox
from openpyxl import load_workbook


wb = load_workbook('Python/实战项目/爬虫推理小说推荐器/推理小说信息收集.xlsx')
ws = wb.active
results={}


def find_by_name(key,count):
    global results
    count+=1
    if count>10:
        return 
    for index,cell in enumerate(ws["A"]):
        try:
            if key in zhconv.convert(cell.value,'zh-hans'):
                results[index]=[each[index].value for each in ws.iter_cols()]
        except:
            pass
    if len(results)<=10:
        keys=jieba.cut(key)
        for key in keys:
            find_by_name(key,count)
    
    
def find_by_author(key,count):
    global results
    count+=1
    if count>10:
        return 
    for index,cell in enumerate(ws["B"]):
        try:
            if key in zhconv.convert(cell.value,'zh-hans'):
                results[index]=[each[index].value for each in ws.iter_cols()]
        except:
            pass
    if len(results)<=10:
        keys=jieba.cut(key)
        for key in keys:
            find_by_author(key,count)


def find_by_keyword(key,count): 
    global results
    count+=1
    if count>10:
        return 
    for index,cell in enumerate(ws["K"]):
        try:
            if key in zhconv.convert(cell.value,'zh-hans'):
                results[index]=[each[index].value for each in ws.iter_cols()]
        except:
            pass
    if len(results)<=10:
        keys=jieba.cut(key)
        for key in keys:
            find_by_keyword(key,count)


def search():
    global results
    results={}
    if int(ch1.get()):
        print("书名搜索")
        find_by_name(var.get(),0)
    if int(ch2.get()):
        print("作者搜索")
        find_by_author(var.get(),0)
    if int(ch3.get()):
        print("关键字搜索")
        find_by_keyword(var.get(),0)
    if not int(ch1.get()) and not int(ch2.get()) and not int(ch3.get()):
        find_by_name(var.get(),0)
        find_by_author(var.get(),0)
        find_by_keyword(var.get(),0)

    if not results:
        messagebox.showerror(title="未找到",message=f"未找到{var.get()}相关内容")

    global book_list
    book_list=[]
    while len(book_list) <10:
        index=random.choice(list(results))
        book=results[index]
        if book not in book_list:
            book_list.append(book)
    lab=tk.Label(root,text="***以下是搜索结果***",font=('kaiti',16))
    lab.grid(row=2,column=0,columnspan=7)
    show(book_list)
        
        
def random_choice():
    global book_list
    book_list=[]
    while len(book_list) != 10:
        index=random.randint(1,ws.max_row)
        book = [each[index].value for each in ws.iter_cols()]
        if book not in book_list:
            book_list.append(book)
    lab=tk.Label(root,text="***猜你喜欢***",font=('kaiti',16))
    lab.grid(row=2,column=0,columnspan=7)
    show(book_list)
    

def show(book_list):
    lab=tk.Label(root,width=20,text="书名",font=('heiti',12))
    lab.grid(row=3,column=0)
    lab=tk.Label(root,width=15,text="作者",font=('heiti',12))
    lab.grid(row=3,column=1)
    lab=tk.Label(root,width=15,text="出版社",font=('heiti',12))
    lab.grid(row=3,column=2)
    lab=tk.Label(root,width=10,text="出版时间",font=('heiti',12))
    lab.grid(row=3,column=3)
    lab=tk.Label(root,width=5,text="评分",font=('heiti',12))
    lab.grid(row=3,column=4)
    lab=tk.Label(root,width=8,text="评分人数",font=('heiti',12))
    lab.grid(row=3,column=5)
    lab=tk.Label(root,width=10,text="ISBN",font=('heiti',12))
    lab.grid(row=3,column=6)
    for i,info in enumerate(book_list):
        lab=tk.Label(root,width=20,text=info[0],anchor='w',font=('songti',12))
        lab.grid(row=4+i,column=0)
        lab=tk.Label(root,width=15,text=info[1],anchor='w',font=('songti',12))
        lab.grid(row=4+i,column=1)
        lab=tk.Label(root,width=15,text=info[2],anchor='w',font=('songti',12))
        lab.grid(row=4+i,column=2)
        lab=tk.Label(root,width=10,text=info[3],anchor='w',font=('songti',12))
        lab.grid(row=4+i,column=3)
        lab=tk.Label(root,width=5,text=info[8],font=('songti',12))
        lab.grid(row=4+i,column=4)
        lab=tk.Label(root,width=8,text=info[9],font=('songti',12))
        lab.grid(row=4+i,column=5)
        lab=tk.Label(root,width=10,text=info[7],font=('songti',12))
        lab.grid(row=4+i,column=6)
        # if info[10] is None:
        #     btn=tk.Button(root,width=10,text="无封面",font=('songti',12))
        # else:
        #     btn=tk.Button(root,width=10,text="看封面",font=('songti',12),command=lambda x=song:play_song(x))
        # btn.grid(row=4+i,column=3)


root=tk.Tk()
root.title("推理小说推荐器")
root.iconphoto(True, tk.PhotoImage(file='Python/实战项目/爱心/love.png'))

var=tk.StringVar()
var.set('')
lab=tk.Label(root,width=45,text="*****粗制滥造推理小说推荐器!请随意输入:*****",font=('songti',12))
lab.grid(row=0,column=0,columnspan=4)
ent=tk.Entry(root,width=10,textvariable=var,font=('kaiti',14))
ent.grid(row=0,column=4,columnspan=2)
btn=tk.Button(root,width=10,text="搜索",font=('heiti',10),command=search)
btn.grid(row=0,column=6)

book_list=[]
random_choice()

btn_random=tk.Button(root,width=10,text="随机推荐",font=('heiti',10),command=random_choice)
btn_random.grid(row=1,column=6)
l=tk.Label(root,text="请选择搜索选项:",font=('songti',10))
l.grid(row=1,column=0)
ch1=tk.IntVar()
ch_continue=tk.Checkbutton(root,text="书名",font=('heiti',10),variable=ch1,onvalue=1,offvalue=0)
ch_continue.grid(row=1,column=1)
ch2=tk.IntVar()
ch_continue=tk.Checkbutton(root,text="作者",font=('heiti',10),variable=ch2,onvalue=1,offvalue=0)
ch_continue.grid(row=1,column=2)
ch3=tk.IntVar()
ch_continue=tk.Checkbutton(root,text="关键字",font=('heiti',10),variable=ch3,onvalue=1,offvalue=0)
ch_continue.grid(row=1,column=3)

root.mainloop()

完成效果

image

image

image

image

image

image

标签:url,list,豆瓣,爬虫,book,import,推理小说,txt
From: https://www.cnblogs.com/xianmasamasa/p/16867823.html

相关文章

  • 爬虫-破译百度翻译
    爬取一整张页面的局部数据抓取ajkx包这一步出现了一些问题,和老师的不一样,直接输入没有sug包,但是一个一个字母输入可以得到sug包 代码如下:importrequestsimportjs......
  • Python 爬虫之Beautiful Soup
    网络爬虫(又被称为网页蜘蛛,网络机器人,在FOAF社区中间,更经常的称为网页追逐者),是一种按照一定的规则,自动地抓取万维网信息的程序或者脚本。另外一些不常使用的名字还有蚂蚁、......
  • 最新抖音数据分析app爬虫
    我们提供封装好的抖音数据采集接口,实时采集,接口稳定。长期维护使用便宜接口使用详情请参考接口地址:github访问地址:github.com/ping0206guo…全部支持的接口如下,并且......
  • 【C#爬虫】使用C# 进行bing翻译爬取
    ​ 首先我们打开bing翻译页面并将浏览器控制台打开​编辑然后在文本框随便输入看右侧工作台中网络里面请求的变化经过多次输入​编辑可以看到每次输入字符后都会......
  • Selenium + Jsoup 抓取豆瓣演员图片
    依赖<dependency><groupId>org.jsoup</groupId><artifactId>jsoup</artifactId><version>1.13.1</version></d......
  • 爬虫基础
    一.爬虫基础网络爬虫(Crawler)又被称为网页蜘蛛(Spider),网络机器人,它是一种按照一定的规则,自动的抓取万维网信息的程序或者脚本名词解释URL:UniformResourceLocator,即统......
  • py爬虫数据到本地Excel表格
    效果图需要爬取的网页和内容程序目的:根据​​公众号文章​​中的内容,爬取文章的标题、发布时间、责任人署名、文章链接,将这个python程序打包成为exe文件,在运行exe文件时可以......
  • python爬虫,爬取51job 智联 58同城
    口182480171有源码和lun文词云图 ......
  • 盘点一个Python网络爬虫中请求参数的一个小坑
    大家好,我是皮皮。一、前言国庆期间在Python白银交流群【空翼】问了一个Python网络爬虫的问题,提问截图如下:二、实现过程这里【瑜亮老师】指出,一般情况下都是data=jso......
  • 爬虫-requests模块(1)爬取搜狗首页页面数据
    requests模块:python中原生的一款基于网络请求的模块,功能非常强大,简单便捷,效率高作用:模拟浏览器发请求如何使用:(request编码的使用流程)请求url发起请求获取响应数据持......