diff --git a/testAny.py b/testAny.py
new file mode 100644
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--- /dev/null
+++ b/testAny.py
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+"""
+this script can identify xml tags competency within a column of selected Dataframe.
+It will collect the complete and incomplete tags into 2 separate columns.
+"""
+
+import pandas as pd
+from lxml import etree
+import re
+
+data = {
+    'Response': [
+        '<tag1>content1</tag1> some text here',
+        '<tag2>content2<tag3>content3</tag3></tag2> more text',
+        "<tag4 content=\"attribute\">content4</tag4> additional text",
+        '<tag5>incomplete</tag5> extra text',
+        '</tag6> miscellaneous text',
+        # ... 其他数据
+    ]
+}
+# df = pd.DataFrame(data)
+
+# 正则表达式匹配完整的XML标签
+complete_tag_pattern = re.compile(r'<(\w+)[^>]*>.*?</\1>')
+
+# 正则表达式匹配不完整的XML标签
+incomplete_tag_pattern = re.compile(r'<(\w+)[^>]*>.*?$')
+
+# 正则表达式匹配所有开放标签和闭合标签
+open_tag_pattern = re.compile(r'<(\w+)[^>]*>')
+close_tag_pattern = re.compile(r'</(\w+)>')
+
+
+# 函数：使用正则表达式提取标签，并分类完整和不完整的标签
+def classify_tags(xml_string):
+    # 找到所有开放标签和闭合标签
+    open_tags = set(open_tag_pattern.findall(xml_string))
+    close_tags = set(close_tag_pattern.findall(xml_string))
+
+    # 确定不完整的标签：只有开放标签或只有闭合标签的
+    incomplete_tags = list(open_tags - close_tags) + list(close_tags - open_tags)
+
+    # 确定完整的标签：既有开放标签又有闭合标签的
+    complete_tags = [f"<{tag}></{tag}>" for tag in open_tags.intersection(close_tags)]
+
+    # 将标签列表转换为字符串
+    complete_tags_str = ', '.join(complete_tags) if complete_tags else ''
+    incomplete_tags_str = ', '.join(
+        [f"<{tag}>" if tag in open_tags else f"</{tag}>" for tag in incomplete_tags]) if incomplete_tags else ''
+
+    return complete_tags_str, incomplete_tags_str
+
+
+def check_df_tags(defaultDf=pd.DataFrame(data)):
+
+    # 应用函数到DataFrame
+    df['CompleteTags'], df['IncompleteTags'] = zip(*df['Response'].apply(classify_tags))
+
+    return df
+
+
+# 正则表达式匹配非XML标签的英文字符
+non_tag_english_chars_pattern = re.compile(r'>[^<>]*<|<[^<>]*>')
+
+
+# 函数：提取非XML标签的英文字符
+def extract_english_chars(response):
+    # 移除XML标签
+    non_tags_content = re.sub(non_tag_english_chars_pattern, '', response)
+
+    # 提取英文字符
+    english_chars = re.findall(r'[A-Za-z]+', non_tags_content)
+
+    # 将提取的英文字符转换为字符串
+    english_chars_str = ' '.join(english_chars) if english_chars else ''
+
+    return english_chars_str
+
+
+def check_df_english(defaultDf=pd.DataFrame(data)):
+    # 应用函数到DataFrame
+    df['EnglishChars'] = df['Response'].apply(extract_english_chars)
+
+    return df
+
+if __name__ == '__main__':
+    df = pd.read_excel('test_response0529_160times.xlsx')
+    checked_df = check_df_tags(df)
+    checked_df = check_df_english(checked_df)
+    # 保存结果到Excel文件
+    checked_df.to_excel('output.xlsx', index=False)
+