爬取豆瓣网评论最多的书籍

Posted 2020-10-31 veol

相信很多人都有书荒的时候，想要找到一本合适的书籍确实不容易，所以这次利用刚学习到的知识爬取豆瓣网的各类书籍，传送门https://book.douban.com/tag/?view=cloud。

首先是这个程序的结构，html_downloader是html下载器，html_outputer是导出到Excel表，html_parser是解析页面，make_wordcloud是制作词云，spided_main是程序入口，url_manager是URL管理器，有兴趣的童鞋可以去慕课网看paython基础爬虫课程。

主要实现思路是先请求下载需要的html，解析得到目标URL并存储到URL管理器中，再从URL管理器中获取得到URL，发送请求，解析得到需要的信息内容，导出到Excel表格，再重Excel表中获取数据进行分析得到词云。

html_downloader:

在这里我使用的是urllib.request进行请求，之前有试过用request进行请求，但是爬取了几百页就被封了ip，所以弃用request。

# -*- coding:utf8 -*-
import urllib.request
from urllib.parse import quote
import string


class HtmlDownloader(object):

    def download(self,url):
        if url is None:
            return  None
        s = quote(url, safe=string.printable) #url里有中文需要添加这一句，不然乱码
        response = urllib.request.urlopen(s)

        if response.getcode()!= 200: 
            return None

        return  response.read()  #返回内容

 

通过分析豆瓣网的结构，可以看到，我们首先传进去的是总的图书分类，但是我们需要的是每一个分类里面的图书信息。所以我们需要得到每一个分类的url，即base_url，再通过这个base_url去获取图书url，即detail_url。

url_manager:

# -*- coding:utf8 -*-

class UrlManage(object):
    def __init__(self):
        self.base_urls = set()  #基本分类的URL
        self.detail_urls = set() #详细内容页的URL
        self.old_base_urls = set() #已经爬取过的url
        self.old_detail_urls = set()#已经爬取过的url

　　#添加单个url
    def add_base_url(self,url):
        if url is None:
            return
        if url not in self.base_urls and url not in self.old_base_urls:
            self.base_urls.add(url)

    def add_detail_url(self,url):
        if url is None:
            return
        if url not in self.detail_urls and url not in self.old_detail_urls:
            self.detail_urls.add(url)
            # print(self.detail_urls)

    # 添加多个url
    def add_new_detail_urls(self, urls):
        if urls is None or len(urls) == 0:
            return
        for url in urls:
            self.add_detail_url(url)

    def add_new_base_urls(self, urls):
        if urls is None or len(urls) == 0:
            return
        for url in urls:
            self.add_base_url(url)

　　#判断是否还有url
    def has_new_detail_url(self):
        return len(self.detail_urls)!=0

    def has_new_base_url(self):
        return len(self.base_urls)!=0

　　#得到一个新的url
    def get_base_url(self):
        new_base_url = self.base_urls.pop()
        self.old_base_urls.add(new_base_url)
        return new_base_url

    def get_detail_url(self):
        new_detail_url = self.detail_urls.pop()
        self.old_detail_urls.add(new_detail_url)
        return new_detail_url

　

解析器　html_parser:

# -*- coding:utf8 -*-
import re
from urllib.parse import urlparse
from bs4 import BeautifulSoup


class HtmlParser(object):
    def soup(cont):
        soups = BeautifulSoup(cont, \'html.parser\', from_encoding=\'utf-8\')
        return soups

　　#得到具体的data数据
    def get_new_data(soup):
        dict = {}
        if (soup.select(\'.subject-list\')[0].contents):
            li = soup.select(\'.subject-list\')[0].select(\'.subject-item\')
            di = {}
            for i in li:
                bookname = i.select(\'.info\')[0].select(\'a\')[0].attrs[\'title\']  # 书名
                comment = i.select(\'.clearfix\')[0].select(\'.pl\')[0].text
                comment = re.findall(\'\\d+\', comment)[0]
                di[bookname] = comment
        if di:  # 返回的字典不为空的时候
            dict.update(di)
        return dict

    # 得到详细内容的url
    def get_detail_url(base_url):
        detail_urls = set()
        for k in range(0, 501, 20):
            if (k == 0):
                urls = base_url
                # print(urls)
            else:
                urls = base_url + \'?start={}&type=T\'.format(k)
                # print(urls)
            detail_urls.add(urls)
        return detail_urls

    # 得到所有的baseurl
    def get_all_base_urls(soup):
        links = soup.select(\'.tagCol\')[0].select(\'a\')
        base_urls = set()
        for link in links:
            new_full_url = \'https://book.douban.com{}\'.format(link.attrs[\'href\'])
            # HtmlParser.get_detail_url(new_full_url)
            base_urls.add(new_full_url)
        return base_urls


    def parser(cont):
        soup = BeautifulSoup(cont, \'html.parser\', from_encoding=\'utf-8\')
        base_urls = HtmlParser.get_all_base_urls(soup)
        return base_urls

　　

spided_main:

# -*- coding:utf8 -*-
from douban_spider2 import url_manager, html_downloader, html_parser, html_outputer

class SpiderMain(object):
    def __init__(self):
        self.urls = url_manager.UrlManage()
        self.downloader = html_downloader.HtmlDownloader()
        self.htmlparser = html_parser.HtmlParser
        self.outputer = html_outputer.HtmlOutputer()

    def craw(self,root_url):
        count = 1
        dictdata = {}
        cont = self.downloader.download(root_url)
        base_urls = self.htmlparser.parser(cont)
        self.urls.add_new_base_urls(base_urls)
        while self.urls.has_new_base_url():
            try:
                base_url = self.urls.get_base_url()
                detail_urls = self.htmlparser.get_detail_url(base_url)
                self.urls.add_new_detail_urls(detail_urls)
            except:
                print(\'craw failed\')

        while self.urls.has_new_detail_url():
            try:
                detail_url = self.urls.get_detail_url()
                print (\'crow %d : %s\'%(count,detail_url))
                html_cont = self.downloader.download(detail_url)
                soup = self.htmlparser.soup(html_cont)
                dict = self.htmlparser.get_new_data(soup)
                dictdata.update(dict)
                if count == 1000:    #因为之前有被封过ip,所以这里先爬取前1000条detail_url的内容
                    break

                count = count + 1
            except:
                print (\'craw failed\')

        self.outputer.output_excel(dictdata)


#程序入口
if __name__=="__main__":  
    url = \'https://book.douban.com/tag/?view=cloud\'  
    obj_spider = SpiderMain()
    obj_spider.craw(url)

　　

html_outputer:

# -*- coding:utf8 -*-
import xlwt  #写入Excel表的库

class HtmlOutputer(object):
    def __init__(self):
        self.datas =[]

    def output_excel(self, dict):
        di = dict
        wbk = xlwt.Workbook(encoding=\'utf-8\')
        sheet = wbk.add_sheet("wordCount")  # Excel单元格名字
        k = 0
        for i in di.items():
            sheet.write(k, 0, label=i[0])
            sheet.write(k, 1, label=i[1])
            k = k + 1
        wbk.save(\'wordCount.xls\')  # 保存为 wordCount.xls文件　　

 

导出的Excel表格格式为，一共导出15261条记录

 

 

make_wordcloud:

# -*- coding:utf8 -*-
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import xlrd
from PIL import Image,ImageSequence
import numpy as np

file = xlrd.open_workbook(\'wordCount.xls\')
sheet = file.sheet_by_name(\'wordCount\')
list = {}
for i in range(sheet.nrows):
    rows = sheet.row_values(i)
    tu = {}
    tu[rows[0]]= int(rows[1])
    list.update(tu)
print(list)

image= Image.open(\'./08.png\')
graph = np.array(image)
wc = WordCloud(font_path=\'./fonts/simhei.ttf\',background_color=\'white\',max_words=20000, max_font_size=50, min_font_size=1,mask=graph, random_state=100)
wc.generate_from_frequencies(list)
plt.figure()
# 以下代码显示图片
plt.imshow(wc)
plt.axis("off")
plt.show()

 

爬过的坑：

当定义的类有构造函数时候，调用时一定要加上括号，如 f =  html_downloader.HtmlDownloader().download()，而不是 f=  html_downloader.HtmlDownloader.download()，不然就会一直报错，类似于TypeError: get_all_base_urls() takes 1 positional argument but 2 were given。

 

 

生成词云的背景图片我选用的是

最后的做出由15261本书形成的词云

       本次爬虫只是针对图书类热门评论而做出的词云，可以看到涵盖所有分类的书籍里最热门评论的有解忧杂货店，白夜行等，据此我们可以选取比较热门的图书进行阅读，也可以根据此结果再做进一步的分析，获取热门书籍中的评论进行分析人们对于某本书的评价关键词，从而进一步的了解这本图书所描述的内容。