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large-file-kpi-analysis

Extract key metrics from spreadsheet data, check whether recorded values match calculations after unit conversion, and rank selected metrics from highest to lowest. Export the ranking or validation results to an Excel file for download.

OriginalShow the original description

根据数据量自动选择读取策略(大文件转Parquet),提取关键指标进行单位一致性验证与排序分析,并输出可下载的结果表格。

Works withExcel
See how to install

Good for

  • Check calculations involving different units
  • Rank business metrics from highest to lowest
  • Export validation results to Excel

Files included (1)

  • SKILL.md2.5 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Skill Steps

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

Step1 提取关键指标,进行物理量/指标的单位一致性验证计算,并对核心业务指标进行降序排列。

# 1. 物理量/指标单位一致性验证与计算 (保留公式结构示例)
col_numerator = 'numerator_col'  # 示例:Mx (kN·m)
col_denominator = 'denominator_col' # 示例:Wx (cm³)
col_target = 'target_col' # 示例:sigma (MPa)

if col_numerator in data.columns and col_denominator in data.columns and col_target in data.columns:
    # 单位换算示例:统一到标准单位后计算
    data['den_converted'] = data[col_denominator] * 1e-6
    data['num_converted'] = data[col_numerator] * 1e3
    data['calc_result_pa'] = data['num_converted'] / data['den_converted']
    data['calc_result_mpa'] = data['calc_result_pa'] / 1e6
    
    # 容差验证
    tolerance = 1e-6
    data['is_valid'] = abs(data['calc_result_mpa'] - data[col_target]) < tolerance
    print("单位一致性验证通过率:", data['is_valid'].mean() * 100, "%")

# 2. 提取关键指标并降序排列
group_col = 'group_col' # 示例:开发区名称
metric_col = 'metric_col' # 示例:实际到帐外资额

result_df = pd.DataFrame()
if group_col in data.columns and metric_col in data.columns:
    result_df = data[[group_col, metric_col]].copy()
    result_df = result_df.sort_values(metric_col, ascending=False).reset_index(drop=True)

Step2 将分析与验证结果整理为最终的数据框,保存为 Excel 文件,并生成可供下载的链接。

output_path = 'analysis_result.xlsx'

# 确定最终输出的数据框
if not result_df.empty:
    result_df_final = result_df
elif 'calc_result_mpa' in data.columns:
    result_df_final = data[[col_numerator, col_denominator, col_target, 'calc_result_mpa', 'is_valid']].copy()
    result_df_final.columns = ['分子指标', '分母指标', '目标比对值', '计算结果', '是否一致']
else:
    result_df_final = data.head(100) # 默认输出前100行作为示例

# 保存为Excel文件
result_df_final.to_excel(output_path, index=False, engine='openpyxl')
print(f"分析结果已保存至: {output_path}")

# 生成下载链接
print(f"下载链接: [点击下载分析结果](./{output_path})")

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