# Excel Large File Processing And Cleaning

> 读取多 sheet Excel 文件，动态识别目标列进行统计，并使用正则清洗文本字段提取中文字符，最终输出标准化 Excel 文件。

- **Type:** Skill
- **Install:** `agentstack add skill-opensensenova-sensenova-skills-structured-header-reading`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [OpenSenseNova](https://agentstack.voostack.com/s/opensensenova)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [OpenSenseNova](https://github.com/OpenSenseNova)
- **Source:** https://github.com/OpenSenseNova/SenseNova-Skills/tree/main/skills/sn-da-excel-workflow/capability/excel-reading/structured-header-reading

## Install

```sh
agentstack add skill-opensensenova-sensenova-skills-structured-header-reading
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Skill Steps

> This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md.

Step1 文本字段清洗，使用正则表达式提取纯中文字符（过滤数字、特殊符号等）。
```python
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 = '目标清洗列' # 占位示例，如'收货人'
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 将清洗和处理后的数据保存为 Excel，并输出文件大小与下载链接。
```python
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}")
```

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [OpenSenseNova](https://github.com/OpenSenseNova)
- **Source:** [OpenSenseNova/SenseNova-Skills](https://github.com/OpenSenseNova/SenseNova-Skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-opensensenova-sensenova-skills-structured-header-reading
- Seller: https://agentstack.voostack.com/s/opensensenova
- Browse the marketplace: https://agentstack.voostack.com/browse

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