Python 读取docx/txt根据词频生成云图
#!/usr/bin/env python # -*- coding: utf-8 -*- from wordcloud import WordCloud, STOPWORDS from imageio import imread from sklearn.feature_extraction.text import CountVectorizer import jieba import csv import docx2txt # 读docx文档 # text = docx2txt.process("file.docx") # contents = text
# outFile = open("file.txt", "w", encoding='utf-8')
# outFile.write(text)
# 获取文章内容 with open("djstl.txt",encoding='utf-8') as f: contents = f.read() print("contents变量的类型:", type(contents)) # 使用jieba分词,获取词的列表 contents_cut = jieba.cut(contents) print("contents_cut变量的类型:", type(contents_cut)) contents_list = " ".join(contents_cut) print("contents_list变量的类型:", type(contents_list)) # 制作词云图,collocations避免词云图中词的重复,mask定义词云图的形状,图片要有背景色 wc = WordCloud(stopwords=STOPWORDS.add("一个"), collocations=False, background_color="white", font_path=r"C:\Windows\Fonts\simhei.ttf", width=400, height=300, random_state=42, mask=imread('timg.jpg',pilmode="RGB")) wc.generate(contents_list) wc.to_file("ciyun.png") # 使用CountVectorizer统计词频 cv = CountVectorizer() contents_count = cv.fit_transform([contents_list]) # 词有哪些 list1 = cv.get_feature_names() # 词的频率 list2 = contents_count.toarray().tolist()[0] # 将词与频率一一对应 contents_dict = dict(zip(list1, list2)) # 输出csv文件,newline="",解决输出的csv隔行问题 with open("caifu_output.csv", 'w', newline="") as f: writer = csv.writer(f) for key, value in contents_dict.items(): writer.writerow([key, value])