--- name: formatted-export-with-parquet description: "从多Sheet Excel文件中识别指定条件的记录,并将筛选结果以整行标红格式导出为Excel文件,适用于数据清洗、条件筛选与可视化标记场景。" metadata: "{\"nanobot\": {\"requires\": {\"pip\": [\"pandas\", \"pyarrow\", \"openpyxl\"]}}}" --- # Formatted_Export > This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md. ## Skill Steps Step1 对所有 sheet 进行扫描,通过模糊匹配定位目标列,筛选出符合条件(如空值或无效字符)的记录。 ```python empty_target_rows = [] for sheet_name, sheet_df in all_sheets.items(): target_col = None # 优先匹配目标列名(示例:包含特定关键字的列) for col in sheet_df.columns: if 'keyword1' in str(col).lower() or 'keyword2' in str(col).lower(): target_col = col break if target_col is None: # 尝试次级推断逻辑 for col in sheet_df.columns: if 'keyword3' in str(col) and ('keyword4' in str(col)): target_col = col break if target_col is None: continue # 数据清洗:筛选空值和无效字符(如空格、'nan')行 mask = sheet_df[target_col].isna() | (sheet_df[target_col].astype(str).str.strip() == '') | (sheet_df[target_col].astype(str).str.strip() == 'nan') empty_rows = sheet_df[mask].copy() if len(empty_rows) > 0: empty_rows.insert(0, '来源Sheet', sheet_name) empty_target_rows.append(empty_rows) # 合并结果 result_df = pd.concat(empty_target_rows, ignore_index=True) if empty_target_rows else pd.DataFrame() ``` Step2 将筛选出的记录导出为 Excel 文件,整行标红显示以便于视觉识别,并生成下载链接。 ```python from openpyxl import load_workbook from openpyxl.styles import PatternFill output_path = "filtered_results_highlighted.xlsx" if not result_df.empty: # 导出基础数据 result_df.to_excel(output_path, index=False) # 加载工作簿进行格式化 wb = load_workbook(output_path) ws = wb.active # 定义红色填充样式 red_fill = PatternFill(start_color="FF0000", end_color="FF0000", fill_type="solid") # 遍历所有数据行并标红(跳过表头) for row in range(2, ws.max_row + 1): for col in range(1, ws.max_column + 1): ws.cell(row=row, column=col).fill = red_fill wb.save(output_path) print(f"结果文件已保存: {output_path}") print(f"下载链接: [点击下载标红结果文件]({output_path})") else: print("未找到符合条件的记录,无需导出。") ```