From f57bf172a144e5bd87d3ad91ec0a221f1f897eff Mon Sep 17 00:00:00 2001
From: pioc37juv <1245880206@qq.com>
Date: Wed, 18 Sep 2024 19:50:12 +0800
Subject: [PATCH] =?UTF-8?q?Delete=20'=E8=AF=8D=E4=BA=91=E5=9B=BE.py'?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 词云图.py | 40 ----------------------------------------
 1 file changed, 40 deletions(-)
 delete mode 100644 词云图.py

diff --git a/词云图.py b/词云图.py
deleted file mode 100644
index edd2ecf..0000000
--- a/词云图.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import os
-
-import matplotlib.pyplot as plt
-import pandas as pd
-from wordcloud import WordCloud
-
-
-def generate_wordcloud_from_excel(file_path):
-
-    # 1、读取相应文件
-    df = pd.read_excel(file_path)
-
-    # 2、确认是否包含'词汇'和'频率'两列
-    if '词汇' not in df.columns or '频率' not in df.columns:
-        print("excel文件中缺少 '词汇' 或 '频率' 列。")
-        return
-
-    # 3、将词汇和频率转换为字典
-    content = dict(zip(df['词汇'], df['频率']))
-
-    # 4、生成词云图
-    wc = WordCloud(font_path='simhei.ttf', background_color='white', width=1371, height=771)  # 生成参数
-    wc.generate_from_frequencies(content)  # WordCloud库中的generate_from_frequencies方法用于根据提供的词频数据生成词云。‌这个方法需要一个字典作为输入，其中键是单词，值是对应的词频。
-
-    # 5、保存词云图到指定位置
-    output_folder = 'output'  # 指定输出文件夹
-    if not os.path.exists(output_folder):  # 如果文件夹不存在就创建它
-        os.makedirs(output_folder)
-    output_path = os.path.join(output_folder, '词云图.png')  # 生成完整的文件路径
-    wc.to_file(output_path)
-
-    # 6、显示词云图
-    plt.imshow(wc, interpolation='bilinear')
-    plt.axis('off')  # 不显示坐标轴
-    plt.show()
-
-
-if __name__ == '__main__':
-    file_path = 'output\\高频词.xlsx'  # 可替换为任意excel文件路径
-    generate_wordcloud_from_excel(file_path)