Python词云

#coding:utf-8
__author__ = 'Administrator'
import jieba    #分词包
import numpy    #numpy计算包
import codecs   #codecs提供的open方法来指定打开的文件的语言编码，它会在读取的时候自动转换为内部unicode 
import pandas   
import matplotlib.pyplot as plt
%matplotlib inline

from wordcloud import WordCloud#词云包

第二部：导入分好词的西游记txt文件：

file=codecs.open(u"西游记.txt",'r','utf-8')
content=file.read()
file.close()
jieba.load_userdict(u"红楼梦分词.txt")
segment=[]
segs=jieba.cut(content)
for seg in segs:
    if len(seg)>1 and seg!='
':
        segment.append(seg)

第三部：统计分词结果并去掉停用词：

segmentDF=pandas.DataFrame({'segment':segment})
segmentDF.head()
stopwords=pandas.read_csv("stopwords.txt",index_col=False,quoting=3,sep="	",names=['stopword'])#quoting=3全不引用
stopwords.head()
segmentDF=segmentDF[~segmentDF.segment.isin(stopwords.stopword)]
wyStopWords=pandas.Series(['之','其','或','亦','方','于','即','皆','因','仍','故','尚','呢','了','的','着','一'
                           ,'不','乃','呀','吗',
                           '咧','啊','把','让','向','往','是','在','越','再',
                           '更','比','很','偏','别','好','可','便','就','但','儿','又','也','都','我','他','来','" "'])
segmentDF=segmentDF[~segmentDF.segment.isin(wyStopWords)]

第四部：统计词频：

segStat=segmentDF.groupby(by=['segment'])['segment'].agg({"计数":numpy.size})
segStat=segStat.reset_index().sort(columns="计数",ascending=False)
segStat

第五步：显示词云

wordcloud=WordCloud(font_path="simhei.ttf",background_color="black")
wordcloud=wordcloud.fit_words(segStat.head(1000).itertuples(index=False))
plt.imshow(wordcloud)

第六步：自定义词云形状

from scipy.misc import imread
import matplotlib.pyplot as plt
from wordcloud import WordCloud,ImageColorGenerator
bimg=imread('3.jPG')
wordcloud=WordCloud(background_color="white",mask=bimg,font_path='C:WindowsFontssimhei.ttf')
wordcloud=wordcloud.fit_words(segStat.head(39769).itertuples(index=False))
bimgColors=ImageColorGenerator(bimg)
plt.axis("off")
plt.imshow(wordcloud.recolor(color_func=bimgColors))
plt.show()