# Dynamic Percentage And Large File Analysis

根据文件行数动态切换大文件处理策略（Parquet转换），通过逐行扫描或列匹配提取关键指标并计算占比、均值等统计量，最终输出结构化Excel报告及可视化图表。

- Repository: https://github.com/OpenSenseNova/SenseNova-Skills (6002 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-10-29
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add OpenSenseNova/SenseNova-Skills --skill dynamic-percentage-and-large-file-analysis -g -a claude-code -y`
Codex: `npx skills add OpenSenseNova/SenseNova-Skills --skill dynamic-percentage-and-large-file-analysis -g -a codex -y`
Cursor: `npx skills add OpenSenseNova/SenseNova-Skills --skill dynamic-percentage-and-large-file-analysis -g -a cursor -y`
Gemini CLI: `npx skills add OpenSenseNova/SenseNova-Skills --skill dynamic-percentage-and-large-file-analysis -g -a gemini-cli -y`

## Description

根据文件行数动态切换大文件处理策略（Parquet转换），通过逐行扫描或列匹配提取关键指标并计算占比、均值等统计量，最终输出结构化Excel报告及可视化图表。

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## Skill Steps

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

Step1 在数据中动态定位关键字段，通过逐行扫描匹配关键词提取数值，并进行条件筛选与占比计算。
```python
key_values = {}
target_col = None
value_col = 'target_value_col'

# 动态查找目标分类列
for col in df_analysis.columns:
    if 'keyword1' in col.lower() or 'keyword2' in col.lower():
        target_col = col
        break

# 通用字段查找逻辑：逐行扫描匹配关键词并提取首个正数
for idx, row in df_analysis.iterrows():
    row_str = str(row.values)
    if '指标A' in row_str and '指标A' not in key_values:
        for val in row.values:
            if isinstance(val, (int, float)) and val > 0:
                key_values['指标A'] = val
                break
    if '指标B' in row_str and '指标B' not in key_values:
        for val in row.values:
            if isinstance(val, (int, float)) and val > 0:
                key_values['指标B'] = val
                break

# 条件筛选与统计
if target_col and '特定类别' in df_analysis[target_col].unique():
    df_filtered = df_analysis[df_analysis[target_col] == '特定类别']
    if value_col in df_filtered.columns:
        df_filtered[value_col] = pd.to_numeric(df_filtered[value_col], errors='coerce')
        avg_val = df_filtered[value_col].mean()
        print(f"特定类别平均值 = {avg_val:.2f}")

# 计算占比
if '指标A' in key_values and '指标B' in key_values:
    percentage = (key_values['指标A'] / key_values['指标B']) * 100
    print(f"指标A占指标B的百分比: {percentage:.2f}%")
```

Step2 将计算结果保存为结构化表格文件（.xlsx），并在输出中提供可追溯的下载链接。
```python
output_path = "output_analysis_result.xlsx"
os.makedirs(os.path.dirname(output_path), exist_ok=True)

result_data = {
    '项目': ['指标A', '指标B', '占比'],
    '数值': [key_values.get('指标A', 0), key_values.get('指标B', 0), f"{percentage:.2f}%" if 'percentage' in locals() else "N/A"]
}
df_result = pd.DataFrame(result_data)

with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
    df_result.to_excel(writer, sheet_name='汇总结果', index=False)

The rest: https://github.com/OpenSenseNova/SenseNova-Skills/blob/HEAD/skills/sn-da-excel-workflow/capability/excel-data-statistics/percentage-calculation/SKILL.md