This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md.
Step1 Clean text fields, use regular expressions to extract pure Chinese characters (filtering out numbers, special symbols, etc.).
import re
def extract_chinese(text):
if pd.isna(text):
return text
# 仅保留 Unicode 中文字符范围
chinese_chars = re.findall(r'[一-龥]', str(text))
cleaned = ''.join(chinese_chars)
return cleaned if cleaned else ''
clean_col = 'target_clean_column' # placeholder example, such as 'recipient' if clean_col in df.columns: df[clean_col] = df[clean_col].apply(extract_chinese)
Step2 动态模糊匹配列名，并统计该列中特定值的数量。
```python
# 动态查找包含特定关键字的列
keyword = 'type'
target_val = 'varchar'
target_col = next((col for col in df.columns if keyword in str(col).lower()), None)
total_target_count = 0
details = []
if target_col is not None:
# 忽略大小写和首尾空格进行匹配
mask = df[target_col].astype(str).str.lower().str.strip() == target_val
count = mask.sum()
total_target_count += count
if count > 0:
details.append({
'sheet': target_sheet,
'target_count': count,
'total_rows': len(df)
})
print(f"{'='*50}")
print(f"匹配列 '{target_col}' 中值为 '{target_val}' 的总数: {total_target_count}")
print(f"{'='*50}")
for detail in details:
print(f" {detail['sheet']}: {detail['target_count']} 个匹配项 (共 {detail['total_rows']} 行)")
Step3 Save the cleaned and processed data as Excel, and output the file size and download link.
output_path = "/mnt/data/cleaned_data_output.xlsx"
df.to_excel(output_path, index=False)
file_size = os.path.getsize(output_path)
print(f"清洗后的数据已保存至: {output_path}")
print(f"文件大小: {file_size} 字节")
# 生成标准下载链接格式
print(f"下载链接: sandbox:{output_path}")