以下以《紅樓夢》為例進行設計。

在制作詞云圖及統計之前，需要下載python的幾個庫，wordcloud、jieba以及imageio等，我的操作系統為Windows10，IDE環境為idle，下載方式就直接搜索cmd，打開命令提示符窗口，輸入pip install wordcloud等庫進行下載即可。

像這樣，就下載成功了

要對名著進行開展，必不可少的就是這些名著的電子書，安裝好庫就要進行對電子書的下載，這個鏈接可以下載《紅樓夢》的txt電子書：

紅樓夢txt下載|紅樓夢txt全集下載-紅樓夢百度云下載-TXT下載站?www.txtxzz.com

這是我用到的背景圖

以下為我具體的操作代碼，具體的注釋我都加在了里面：

import jieba import wordcloud from imageio import imread# 1、進行詞云分析，即詞云圖的制作 def ciyun():mask = imread("林黛玉.png") # 打開詞云背景圖tf = open('紅樓夢.txt','rt',encoding = 'utf-8') # 打開《林黛玉》txt文檔txt = ''for line in tf.readlines():for j in ",.“”？：《》--！":line.replace('',j)txt += linejieba_cut = jieba.lcut(txt) # 利用jieba對文檔進行全文分詞c = wordcloud.WordCloud(width = 1200,font_path = 'msyh.ttc',height = 800,background_color='white',mask=mask) # 進行背景、畫布大小、顏色等處理c.generate(' '.join(jieba_cut))c.to_file('紅樓夢.png')tf.close() ciyun() # 2、出場統計的制作 excludes = {"什么","一個","我們","那里","你們","如今","說道","知道","起來","姑娘","這里","出來","他們","眾人","自己","一面","只見","怎么","奶奶","兩個","沒有","不是","不知","這個","聽見","這樣","進來","咱們","告訴","就是","東西","襲人","回來","只是","大家","只得","老爺","丫頭","這些","不敢","出去","所以","不過","的話","不好","姐姐","探春","鴛鴦","一時","不能","過來","心里","如此","今日","銀子","幾個","答應","二人","還有","只管","這么","說話","一回","那邊","這話","外頭","打發","自然","今兒","罷了","屋里","那些","聽說","小丫頭","不用","如何"}# 將這些會干擾的詞匯列出并且刪除，以免影響最后的結果 txt = open("紅樓夢.txt","r",encoding='utf-8').read() # 打開《紅樓夢》txt電子書 words = jieba.lcut(txt) # 利用jieba進行全文分詞 paixv = {} for word in words:if len(word) == 1: # 如果分割的長度是一，可能是語氣詞之類的，所以刪除continueelse:paixv[word] = paixv.get(word,0) + 1for word in excludes: del(paixv[word]) # 如果列出的干擾詞匯在分完詞后的所有詞匯中那么刪除items = list(paixv.items()) # 將字典轉換為列表 items.sort(key=lambda x:x[1],reverse = True) # 將列表進行降序排列for i in range(20): # 打印出前20個出場最多的人物名word,count = items[i]print("{0:<10}{1:>5}".format(word,count))# 3、字頻統計的制作 import os import codecs import jieba import pandas as pd from wordcloud import WordCloud from scipy.misc import imread import matplotlib.pyplot as plt os.chdir("/Users/Zhaohaibo/Desktop")class Hlm(object): def Zipin(self, readdoc, writedoc): # readdoc:要讀取的文件名，writedoc:要寫入的文件名word_lst = []word_dict = {} exclude_str = "，。！？、（）【】<>《》=：+-*—“”…" with open(readdoc,"r") as fileIn ,open(writedoc,'w') as fileOut:# 添加每一個字到列表中：for line in fileIn:for char in line:word_lst.append(char)# 用字典統計每個字出現的個數： for char in word_lst:if char not in exclude_str:if char.strip() not in word_dict: # strip去除各種空白word_dict[char] = 1else :word_dict[char] += 1# 排序x[1]是按字頻排序，x[0]則是按字排序lstWords = sorted(word_dict.items(), key=lambda x:x[1], reverse=True) # 輸出結果 (前100)print ('字符t字頻')print ('=============')for e in lstWords[:100]:print ('%st%d' % e)fileOut.write('%s, %dn' % e)# 詞頻表（DataFrame格式）def Cipin(self, doc): # doc：要讀取的文件名wdict = {}f = open(doc,"r")for line in f.readlines():words = jieba.cut(line)for w in words:if(w not in wdict):wdict[w] = 1else:wdict[w] += 1 # 導入停用詞表stop = pd.read_csv('stoplist.txt', encoding = 'utf-8', sep = 'zhao', header = None,engine = 'python') # sep:分割符號（需要用一個確定不會出現在停用詞表中的單詞）stop.columns = ['word'] stop = [' '] + list(stop.word) # python讀取時不會讀取到空格。但空格依舊需要去除。所以加上空格； 讀取后的stop是series的結構，需要轉成列表for i in range(len(stop)):if(stop[i] in wdict):wdict.pop(stop[i])ind = list(wdict.keys())val = list(wdict.values())ind = pd.Series(ind)val = pd.Series(val)data = pd.DataFrame()data['詞'] = inddata['詞頻'] = valreturn data

最后的結果截圖為：

詞云圖：

出場統計：

字頻統計：

有點多就只截一部分

以上便為《紅樓夢》的詞云分析及字頻統計、出場統計。主要是為了記錄一下我昨天的課程設計作業，代碼有借鑒。

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