# Excel Conditional Filtering Optimization

> 根据多维数值条件筛选 Excel 数据并导出结果，支持大规模数据的自动性能优化处理。

- **Type:** Skill
- **Install:** `agentstack add skill-opensensenova-sensenova-skills-range-filtering`
- **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-data-filtering/range-filtering

## Install

```sh
agentstack add skill-opensensenova-sensenova-skills-range-filtering
```

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

## About

# Excel_Conditional_Filtering_Optimization

> **Note**: This sub-skill covers one step of the Excel analysis workflow. For the full pipeline (file reading, row counting, large-file optimization, export), see the parent workflow SKILL.md.

Step1 读取 Excel 文件中所有工作表的数据，统计各表行数并汇总，用于评估数据规模。
```python
import pandas as pd

file_path = "input_data.xlsx"

# 读取所有 sheet，统计行数
xls = pd.ExcelFile(file_path)
print("Sheet names:", xls.sheet_names)

total_rows = 0
sheet_details = []
for sheet in xls.sheet_names:
    df_temp = pd.read_excel(file_path, sheet_name=sheet)
    row_count = len(df_temp)
    sheet_details.append({"sheet": sheet, "rows": row_count})
    total_rows += row_count

print(f"Sheet details: {sheet_details}")
print(f"Total rows across all sheets: {total_rows}")
```

Step2 对目标数据进行清洗，处理表头偏移，并将关键列转换为数值类型以确保计算准确。
```python
# 读取目标数据表
target_sheet = 'Sheet1'
df = pd.read_excel(file_path, sheet_name=target_sheet, header=0)

# 处理可能的子表头或空行偏移（示例：跳过第一行）
# df = df.iloc[1:].reset_index(drop=True)

# 统一设置列名（根据实际业务逻辑调整占位符）
# df.columns = ['col_1', 'col_2', 'col_3', 'target_id', 'val_a', 'val_b', 'val_c']

# 强制转换数值列，处理非数值数据为 NaN
numeric_cols = ['val_a', 'val_b', 'val_c', 'target_id']
for col in numeric_cols:
    if col in df.columns:
        df[col] = pd.to_numeric(df[col], errors='coerce')

# 处理合并单元格（如有）
# df = df.ffill()
```

Step3 执行多维度条件筛选逻辑，提取符合特定数值特征的唯一记录。
```python
# 筛选逻辑：例如 val_a, val_b, val_c 同时满足特定阈值（如均为 0）
mask = (df['val_a'] == 0) & (df['val_b'] == 0) & (df['val_c'] == 0)
filtered_df = df[mask][['target_id', 'val_a', 'val_b', 'val_c']]

# 提取唯一编号并去除空值
result = filtered_df.drop_duplicates().dropna(subset=['target_id']).reset_index(drop=True)
```

Step4 将筛选后的结果保存为新的 Excel 文件，并生成下载链接。
```python
output_path = "filtered_analysis_result.xlsx"

# 格式化输出列名
result.columns = ['Target_Index', 'Value_A', 'Value_B', 'Value_C']

# 导出文件
result.to_excel(output_path, index=False)

# 打印结果摘要与下载路径
print(f"Filtered records count: {len(result)}")
print(f"Result saved to: {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-range-filtering
- Seller: https://agentstack.voostack.com/s/opensensenova
- Browse the marketplace: https://agentstack.voostack.com/browse

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