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。
python# 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。
pythonoutput_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})")
