python面对对面编程对三国演义，水浒传，红楼梦人物关系与出场频率进行文本分析，数据可视化

python对文本进行分析和数据可视化，主要运用到了jieba，worldcloudmatplotlib,nxwworkx,pandas库，其他库在代码中给出。
1.首先准备好这三本名著
2.准备好停词词库

代码如下：

import matplotlib.pyplot as plt  
import matplotlib  
import networkx as nx  
import tkinter as tk  
import tkinter.ttk as ttk  
import pandas as pd  
matplotlib.rcParams['font.sans-serif']=['SimHei']  
  
  
class Text:    
    ###################获取词频###########################################  
     #读取文本文件数据  
    def __getText(self):                           
        with open(self.path,"r",encoding="UTF-8") as f:              
            self.__text=f.read()  
              
    #获取停用词  
    def __stopwordslist(self):                     
        self.stopwords=[line.strip() for line in open(self.stoppath,'r',encoding="UTF-8").readlines()]  
          
    #获取词频                                 
    def __wordFreq(self,filepath,topn,text):        
        words=jieba.lcut(text.strip())  
        counts={}  
        self.__stopwordslist()     
        for word in words:  
            if len(word)==1:  #删除长度为1的字符  
                continue  
            elif word not in self.stopwords:  
                if word=="凤姐儿":  
                    word="凤姐"  
                elif word=="林黛玉" or word=="林妹妹" or word=="黛玉笑":  
                    word="黛玉"  
                elif word=="宝二爷":  
                    word="宝玉"  
                elif word=="袭道人":  
                    word="袭人"  
                counts[word]=counts.get(word,0)+1  
        items=list(counts.items())  
        items.sort(key=lambda x:x[1],reverse=True)           #获得词频  
        with open(filepath[:-4]+"_词频.txt","w") as f:  
            for i in range(topn):  
                word,count=items[i]  
                f.writelines("{}\t{}\n".format(word, count))  
                  
    ##################画人物出场频率图############################  
    def __drawbar(self):  
        with open(self.path1,"r") as f:  
            self.name=f.readlines()  
        self.dic={}  
        for item in self.name:  
            item=item.split("\t")  
            self.dic[item[0]]=int(item[1])  
        x=[]          #用来存储人物名称  
        y=[]          #用来存储出场次数  
        for item in self.dic:             
            x.append(item)  
            y.append(self.dic[item])  
      ##用pandas库画图###  
        dic={}  
        dic["名字"]=x  
        dic["次数"]=y  
        df=pd.DataFrame(dic)  
        df=df.set_index("名字")   
        df.plot(kind='bar',title=self.val1.get()+"人物出场频率图",figsize=(15,15))  
        plt.savefig(self.val1.get()+"人物出场频率图.jpg")  
        plt.show()  
          
    ####################################开始画词云############   
    def __wordCloud(self,path):      
        plt.figure(figsize=(15,15))               
        with open (path,"r") as f:  
            text=f.read()  
            wcloud=wordcloud.WordCloud(background_color="white",width=1000,max_words=500,font_path=r"C:\Windows\Fonts\simhei.ttf",  
                                       height=860,margin=2).generate(text)  
            wcloud.to_file("{}cloud.png".format(self.val1.get()))  
            plt.imshow(wcloud)  
            plt.axis('off')  
            plt.show()  
                              
    ###################找到文本中的人物关系################################  
    def __getrelations(self):  
          
        if self.val1.get()=='红楼梦':  
            self.Names=["宝玉","凤姐","贾母","王夫人","老太太","袭人","贾琏","平儿","宝钗","薛姨妈",  
                    "探春","鸳鸯","贾政","晴雯","湘云","刘姥姥","邢夫人","贾珍","紫鹃","香菱",  
                    "尤氏","薛蟠","贾赦"]  
        if self.val1.get()=='水浒传':  
            self.Names=["宋江","李逵","吴用","公孙胜","关胜","林冲","秦明","呼延","花荣","柴进",  
                        "燕青","朱仝","鲁智深","武松","董平","秦明","李俊","卢俊义","晁盖","戴宗"]  
        if self.val1.get()=="三国演义":  
            self.Names=["刘备","刘禅","关公","张飞","赵云","诸葛亮","徐遮","马良","黄忠","玄德","曹丕","孙权"  
                        ,"司马懿","周瑜","孔明","卢布","周瑜","袁绍","马超","魏延","姜维","马岱","庞德"]  
              
        f=open(self.path,'r',encoding="UTF-8")  
        s=f.read()  
        self.relations={}  
        self.lst_para=s.split('\n')  #安段落划分  
        for text in self.lst_para:  
            for name1 in self.Names:  
                if name1 in text:  
                    for name2 in self.Names:  
                        if name2 in text and name1 !=name2 and (name2,name1) not in self.relations:  
                            self.relations[(name1,name2)]=self.relations.get((name1,name2),0)+1  
        self.maxRela=max([v for k,v in self.relations.items()]) #取最大共现次数  
        self.relations={k:v/self.maxRela for k,v in self.relations.items()}      
          
    ###########################画出人物关系图###################################################  
    def __getmap(self):  
        plt.figure(figsize=(15,15))  
        self.G=nx.Graph()  
        #根据relations的数据想G中添加边  
        for k,v in self.relations.items():  
            self.G.add_edge(k[0],k[1],weight=v)  
        #筛选权重大于0.6的边  
        self.elarge=[(u,v) for (u,v,d) in self.G.edges(data=True) if d['weight']>0.6]  
        #筛选权重大于0.3但小于0.6的边  
        self.emidle=[(u,v) for (u,v,d) in self.G.edges(data=True) if (d['weight']>0.3) & (d['weight']<=0.6)]  
        #筛选权重小于0.3的边  
        self.esmall=[(u,v) for (u,v,d) in self.G.edges(data=True) if d['weight']<=0.3]  
        #设置图形布局  
        self.pos=nx.circular_layout(self.G)  
        nx.draw_networkx_nodes(self.G,self.pos,alpha=0.6,node_size=800)  
        #alpha是透明度，width是连接线的宽度  
        nx.draw_networkx(self.G, self.pos,edgelist=self.elarge,width=2.5,alpha=0.9,edge_color='g')  
        nx.draw_networkx(self.G, self.pos,edgelist=self.emidle,width=1.5,alpha=0.6,edge_color='y')  
        nx.draw_networkx(self.G, self.pos,edgelist=self.esmall,width=1,alpha=0.2,edge_color='b',style='dashed')  
        nx.draw_networkx_labels(self.G,self.pos,font_size=12)  
        plt.axis('off')  
        plt.title("{}主要人物关系网络".format(self.val1.get()))  
        plt.show()  
          
     ################上面函数的调用#####################     
    def __getvar(self,event):  
        self.path=self.val1.get()+".txt"            #文本路径  
        self.__getText()     #读取文本文件数据   
        self.__wordFreq(self.path,20,self.__text)  #获取20个词的词频  
        self.path1=self.val1.get()+'_词频.txt'      #词频路径  
        self.__wordCloud(self.path1)  #画词频云图  
        self.__getrelations()                       #获得文本中人物关系  
        self.__getmap()                             #画出人物关系图  
        self.__drawbar()  
          
      #####################构造函数，GUI界面的设计#####################    
    def __init__(self):          
        self.window=tk.Tk()  
        self.window.geometry("400x400")  
        self.window.title("文本选择器")  
        self.txt=["红楼梦","水浒传","三国演义"]  
        self.val1=tk.StringVar()  
        self.cb=ttk.Combobox(self.window,textvariable=self.val1)  
        self.cb['value']=self.txt  
        self.cb.current()          
        self.cb.place(x=100,y=60)  
        self.cb.bind("<>",self.__getvar)  
        self.stoppath="C:/Users/Alison/Desktop/PyExp3/stop_words.txt"   #停词文本路径  
        self.lab1=tk.Label(self.window,text="请选择你想处理的文本",font="楷体").place(x=100,y=30)  
        self.window.mainloop()         
  
def main():  
    txt=Text()  
      
if "__name__"==main():  
    main()  

运行结果如下：




因为需要分析整个文本，分析，话云图等问题，其实程序运行得还是比较慢的，这是一个没有改进的地方，希望大家多多指教！！