Scan multiple Excel sheets for rows with missing or invalid entries in a target column. Combine matching records with their source sheet names, then export an Excel file with every extracted data row filled red for review.
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。
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This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md.
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。
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。
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。")
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