python爬取《三国演义》小说&统计词频&生成词云图
python爬取《三国演义》小说&统计词频&生成词云图
注意点：
-
爬取小说正文时用的正则表达式涉及到多行匹配。需要开启多行模式(?s)
源代码练习
import re import requests import jieba import jieba.analyse import codecs from collections import Counter import wordcloud import matplotlib.pyplot as plt # 要爬取的网址url url = 'http://www.janpn.com/book/sanguoyanyi2.html' def get_content(url): # 先获取二进制数据，再进行编码 txt = requests.get(url).content.decode('utf-8') # print(txt) book_title = re.compile(r'<h3 class="bookTitle">(.+)</h3>') # m1.findall(txt)得到的是一个数组，取m1.findall(txt)[0] # print(m1.findall(txt)) # book_chapters_re = re.compile(r'<li><a href="(.+\.html)">([第|回].+)</a></li>') # book_chapters_re = re.compile(r'<ul class="panel-body panel-chapterlist"><li><a href="(.+)">(.*)</a></li></ul>') # 一定要注意 要使用非贪婪模式 来匹配特定结尾.html book_chapters_re = re.compile(r'<li><a href="(.*?\.html)".*?>(.+?)</a></li>') book_chapters = book_chapters_re.findall(txt) # 开启多行模式 正文是很多段落的 book_content_re = re.compile(r'(?s)<div.*?id="htmlContent">(.*?)</div>') # 过滤正文的\r\n m3 = re.compile(r'\r\n') # 过滤正文的 m4 = re.compile(r' ') # 过滤正文的<br /> m5 = re.compile(r'<br />') print(book_chapters) # print(book_chapters) with open('三国演义.txt','a') as f: for i in book_chapters: print([i[0],i[1]]) # print(book_chapters) print(i[0]) i_url = i[0] print("正在下载--->%s" % i[1]) # 根据每个章节的url，先获取二进制，再编码 content_html = requests.get(i_url).content.decode('utf-8') # 匹配正文 content = book_content_re.findall(content_html)[0] print(content) content = m3.sub('',content) content = m4.sub('',content) content = m5.sub('',content) print(content) f.write('\n'+i[1]+'\n') f.write(content) # ================================================= # 创建停用词列表 def stopwordlist(): stopwords = [line.strip() for line in open('../结巴分词/hit_stopwords.txt',encoding='UTF-8').readline()] return stopwords # 对句子进行中文分词 并 去停用词 def seg_depart(sentence): print('正在分词') sentence_depart = jieba.cut(sentence.strip()) # 创建一个停用词列表 stopwords = stopwordlist() # 输出结果为outstr outstr = '' # 去停用词 for word in sentence_depart: if word not in stopwords: if word != '\t': outstr += word outstr += ' ' return outstr # 读取文件并生成词云图 filepath = '三国演义.txt' def create_word_cloud(filepath): #读取文件内容 content = codecs.open(filepath,'r','gbk').read() # 去停用词 content = seg_depart(content) # 结巴分词 wordlist = jieba.cut(content) wl = ' '.join(wordlist) print(wl) # 配置词云图 wc = wordcloud.WordCloud( # 这只背景颜色 background_color='white', # 设置最大显示的词数 max_words=100, # 设置字体路径 font_path = 'C:\Windows\Fonts\msyh.ttc', # height=1200, width=1600, # 设置字体最大值 max_font_size=300, # 设置有多少种随机配色方案 random_state=50 ) # 生成词云图 myword = wc.generate(wl) # 展示词云图 plt.imshow(myword) plt.axis('off') plt.show() # ================================================= # 开启爬虫 # get_content(url) # 生成词云图 create_word_cloud(filepath) # =================================================== # 读取文件 词频统计 def count_from_file(filepath,top_limit=0): with codecs.open(filepath,'r','gbk') as f: content = f.read() # 将多个空格替换为一个空格 content = re.sub(r'\s+',r' ',content) content = re.sub(r'\.+',r' ',content) # 去停用词 content = seg_depart(content) return count_from_str(content) def count_from_str(content,top_limit=0): if top_limit<=0: top_limit=100 # 提取文章的关键词 tags = jieba.analyse.extract_tags(content,topK=100) print("关键词：") print(tags) words = jieba.cut(content) counter = Counter() for word in words: if word in tags: counter[word]+=1 return counter.most_common(top_limit) # ===================================== print("打印词频统计") # 打印词频统计 result = count_from_file(filepath) print(result) def test(url): # 开启多行匹配模式 因为如果涉及到换行符 就要用多行 book_content_re = re.compile(r'(?s)<div.*?id="htmlContent">(.*?)</div>') content_html = requests.get(url).content.decode('gbk') print(content_html) content = book_content_re.findall(content_html) print(content) # test("http://www.janpn.com/book/171/171182/35225767.html")
