Update get_wordcloud_pic.py

get_wordcloud_pic.py

@@ -5,36 +5,36 @@ import matplotlib.pyplot as plt
 from PIL import Image
 import numpy as np
 
-# 1. 读取停用词表
+# 读取停用词表
 def load_stopwords(file_path):
     with open(file_path, 'r', encoding='utf-8') as f:
         stopwords = set(line.strip() for line in f)
     return stopwords
 
-# 2. 过滤停用词
+# 过滤停用词
 def remove_stopwords(words_list, stopwords):
     return [word for word in words_list if word not in stopwords and len(word) > 1]
 
-# 3. 读取Excel文件并提取弹幕内容
+# 读取Excel文件并提取弹幕内容
 file_path = "danmu_data.xlsx"
 df = pd.read_excel(file_path)
 comments = df['danmu'].astype(str)
 text = ' '.join(comments)
 
-# 4. 使用 jieba 分词
+# 使用 jieba 分词
 words = jieba.cut(text, cut_all=False)
 
-# 5. 加载停用词表
+# 加载停用词表
 stopwords_file = "D://edge//stop.txt"  # 替换为实际路径
 stopwords = load_stopwords(stopwords_file)
 
-# 6. 去除停用词
+# 去除停用词
 filtered_words = remove_stopwords(words, stopwords)
 
-# 7. 将过滤后的词汇重新拼接为一个字符串
+# 将过滤后的词汇重新拼接为一个字符串
 words_list = ' '.join(filtered_words)
 
-# 8. 加载形状图片并生成词云
+# 加载形状图片并生成词云
 mask = np.array(Image.open("D://edge//kk.png"))
 
 wordcloud = WordCloud(
@@ -47,10 +47,10 @@ wordcloud = WordCloud(
     height=600
 ).generate(words_list)
 
-# 9. 显示词云图
+# 显示词云图
 plt.imshow(wordcloud, interpolation='bilinear')
 plt.axis("off")
 plt.show()
 
-# 10. 保存词云图
+# 保存词云图
 wordcloud.to_file("filtered_wordcloud.png")