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.
中文简介正在准备中。当前显示英语简介。
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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查看安装方法安装前可以查看交给 AI 代理的指令内容。
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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Compare counts and shares for two categories in Excel data. The skill cleans the selected columns, calculates differences and percentages, and produces a summary spreadsheet plus bar and pie charts.
中文简介正在准备中。当前显示英语简介。
Analyze Excel data using custom categories, count distributions, and compare groups with charts. Score text using its length, technical terms, numbers, and selected complex concepts, then visualize difficulty levels and the relationship between text length and scores.
中文简介正在准备中。当前显示英语简介。
Count categories in a chosen data column after removing missing values and placeholder labels. Calculate each category’s share and create high-resolution charts combining counts, percentages, pie charts, and horizontal bars for distribution reports.
中文简介正在准备中。当前显示英语简介。
Clean category and numeric fields in structured data, compare groups with counts and cross-tabulations, and produce charts, Excel results, and a Markdown report. Export visuals as high-resolution PNG and SVG files for use in reports.
中文简介正在准备中。当前显示英语简介。
Filter Excel data using candidate column names, RGB color values, text prefixes, and regular expressions, which match text patterns. Calculate totals and averages, then export results to Excel with highlighted rows and a download link.
中文简介正在准备中。当前显示英语简介。
Filter rows from Excel or previously converted Parquet files, a format designed for efficient data storage. Summarize matching records by group with counts, percentages, and totals, then export the summary to Excel.
中文简介正在准备中。当前显示英语简介。