name: large-file-kpi-analysis 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。"
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)