1. 讀入數據，刪除NAN，用jieba分詞 df = pd.read_csv("./data/entertainment_news.csv", encoding='utf-8') df df = df.dropna() df content=df.content.values.tolist() content #jieba.load_userdict(u"data/user_dic.txt") segment=[] for line in content:try:segs=jieba.lcut(line)for seg in segs:if len(seg)>1 and seg!='\r\n':segment.append(seg)except:print linecontinue2. 去掉停用詞 words_df=pd.DataFrame({'segment':segment}) #words_df.head() stopwords=pd.read_csv("data/stopwords.txt",index_col=False,quoting=3,sep="\t",names=['stopword'], encoding='utf-8')#quoting=3全不引用 #stopwords.head() words_df=words_df[~words_df.segment.isin(stopwords.stopword)] words_df3. 統計計數words_stat=words_df.groupby(by=['segment'])['segment'].agg({"計數":numpy.size})words_stat=words_stat.reset_index().sort_values(by=["計數"],ascending=False) words_stat.head()4. 繪圖wordcloud=WordCloud(font_path="data/simhei.ttf",background_color="white",max_font_size=80) word_frequence = {x[0]:x[1] for x in words_stat.head(1000).values} wordcloud=wordcloud.fit_words(word_frequence) plt.imshow(wordcloud)

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