本文共 1736 字，大约阅读时间需要 5 分钟。

import jiebafrom jieba.analyse import extract_tagsfrom scipy.misc import imreadfrom wordcloud import WordCloudimport matplotlib.pyplot as pltdef generater(**kwargs):    content_name = kwargs['content_name']    content_path = './{}.txt'.format(content_name)    top_k = kwargs['top_k']    bg_name = kwargs['bg_name']    color = kwargs['color']    font_type = kwargs['font_type']    with open(content_path, mode='r', encoding='utf-8') as f:        content = f.read()    '''    根据TF/IDF提取topK个关键词    '''    tags = extract_tags(sentence=content, topK=top_k)    '''    得到关键词的词频    '''    # 全模式    words = [word for word in jieba.cut(content, cut_all=True)]    words_freq = {}    for tag in tags:        freq = words.count(tag)        words_freq[tag] = freq    '''    设置背景    scipy.misc imread()：返回的是 numpy.ndarray 也即 numpy 下的多维数组对象    '''    bg_path = './{}.png'.format(bg_name)    bg_img = imread(bg_path)    font_path = './{}.ttf'.format(font_type)    word_cloud = WordCloud(font_path=font_path, # 设置字体                           background_color=color, # 背景颜色                           max_words=top_k, # 词云显示的最多词数                           max_font_size=100, # 字体最大                           mask=bg_img, # 背景图                           )    word_cloud.generate_from_frequencies(words_freq)    plt.imshow(word_cloud)    plt.axis('off') # 不显示坐标轴    plt.show()    # 保存图片    word_cloud_img = './{}_word_cloud.jpg'.format(content_name)    word_cloud.to_file(word_cloud_img)if __name__ == '__main__':    generater(content_name=' xx xx ',              top_k=66,              bg_name='china', # 默认png              color='black',              font_type='wryh')

链接:密码: ujxc

windows 安装 wordcloud

pip install  wordcloud-1.3.1-cp36-cp36m-win_amd64.whl

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