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🍖 前言

Python基于jieba和wordcloud绘制词云图

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🎶一、实现过程

1. 读取文本信息，采用jieba进行分词统计
2. wordcloud配合matplotlib进行词云绘制（可以选择一张词云的背景轮廓图，本实例选择一张动物轮廓）
3. 将词云图保存到本地、展示出来

✨二、代码展示

以下是组件源码（未包含自定义的材质类）：

import jieba
from wordcloud import WordCloud
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np# 读取文本文件
def readText(file_path):with open(file_path, 'r', encoding='utf-8') as f:text = f.read()return text# 对文本进行分词
def segmentText(text):# 使用jieba进行分词，精确模式words = jieba.cut(text)# 过滤掉单个字符的词filtered_words = [word for word in words if len(word) > 1]return filtered_words# 统计词频
def countWords(words):word_counts = {}for word in words:if word in word_counts:word_counts[word] += 1else:word_counts[word] = 1# 按词频降序排序word_counts = dict(sorted(word_counts.items(), key=lambda x: x[1], reverse=True))return word_counts# 生成词云
def generateWordcloud(word_counts, background_image_path):# 读取背景图片background_image = Image.open(background_image_path)mask = np.array(background_image)# 创建WordCloud对象wordcloud = WordCloud(font_path='simhei.ttf',background_color='white',max_words=200,mask=mask)wordcloud.generate_from_frequencies(word_counts)return wordcloud# 显示词云
def showWordcloud(wordcloud):plt.figure(figsize=(10, 6))plt.imshow(wordcloud, interpolation='bilinear')plt.axis('off')plt.savefig('wordcloud.png')plt.show()# 输出词频前十名
def outputTop10(word_counts):top10 = dict(list(word_counts.items())[:10])for word, count in top10.items():print(f'{word}: {count}')if __name__ == "__main__":text = readText('yourTxt.txt')words = segmentText(text)word_counts = countWords(words)background_image_path = 'yourImg.jpg'wordcloud = generateWordcloud(word_counts, background_image_path)showWordcloud(wordcloud)outputTop10(word_counts)

🏀三、运行结果

运行成功展示:

🏆四、知识点提示

1.采用jieba进行分词，去除长度为1的分词
2.wordcloud配合matplotlib进行词云图的绘制
3.文本路径和图片路径需要替换掉你自己的