large-excel-analysis-and-formatting

by OpenSenseNova5.8k

用于处理多Sheet大型Excel文件，支持大文件Parquet格式转换提速，并使用openpyxl生成带条件高亮和自定义样式的格式化Excel报告及下载链接。

Install

The skill file stays in the publisher's repository.

Paste the copied command in a terminal. It installs this SKILL.md into that agent.

npx skills add OpenSenseNova/SenseNova-Skills --skill large-excel-reading -a cursor -y

Download skill skills/sn-da-excel-workflow/capability/excel-reading/large-excel-reading/SKILL.md on GitHub

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Questions

How do I install large-excel-analysis-and-formatting?: Download skills/sn-da-excel-workflow/capability/excel-reading/large-excel-reading/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does large-excel-analysis-and-formatting live?: The publisher keeps it at https://github.com/OpenSenseNova/SenseNova-Skills.
Which agents can use large-excel-analysis-and-formatting?: Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.

name: large-excel-analysis-and-formatting description: "用于处理多Sheet大型Excel文件，支持大文件Parquet格式转换提速，并使用openpyxl生成带条件高亮和自定义样式的格式化Excel报告及下载链接。"

Skill Steps

Step1 读取Excel文件，统计所有Sheet的总行数。若数据量过大（如≥1万行），则转换为Parquet格式以显著提升后续读取和分析效率。

import pandas as pd

file_path = "input.xlsx"
xls = pd.ExcelFile(file_path)
total_rows = 0

# 统计所有 sheet 的总行数
for name in xls.sheet_names:
    df_temp = pd.read_excel(file_path, sheet_name=name, header=None)
    total_rows += len(df_temp)

print(f"总行数: {total_rows}")

# 大文件处理：超过阈值转换为 Parquet 提升效率
if total_rows >= 10000:
    parquet_path = "/mnt/data/temp.parquet"
    # 此处以读取第一个sheet为例，实际可根据需求合并多个sheet
    df = pd.read_excel(file_path, sheet_name=0)
    df.to_parquet(engine='pyarrow', path=parquet_path)
    df = pd.read_parquet(parquet_path)
else:
    df = pd.read_excel(file_path, sheet_name=0)

from openpyxl import Workbook from openpyxl.styles import PatternFill, Font, Alignment, Border, Side wb = Workbook() ws = wb.active ws.title = "分析报告" # 样式定义 header_fill = PatternFill(start_color="4472C4", end_color="4472C4", fill_type="solid") header_font = Font(name="微软雅黑", bold=True, color="FFFFFF", size=12) highlight_fill = PatternFill(start_color="00B050", end_color="00B050", fill_type="solid") highlight_font = Font(name="微软雅黑", bold=True, color="FFFFFF", size=12) normal_font = Font(name="微软雅黑", size=11) center_align = Alignment(horizontal="center", vertical="center") thin_border = Border( left=Side(style="thin"), right=Side(style="thin"), top=Side(style="thin"), bottom=Side(style="thin") ) # 写入表头并应用样式 headers = [group_col, target_col] for col, header in enumerate(headers, 1): cell = ws.cell(row=1, column=col, value=header) cell.fill = header_fill cell.font = header_font cell.alignment = center_align cell.border = thin_border # 写入数据并进行条件高亮 for row_idx, row_data in enumerate(summary.itertuples(index=False), 2): type_name, value = row_data[0], row_data[1] cell_type = ws.cell(row=row_idx, column=1, value=type_name) cell_value = ws.cell(row=row_idx, column=2, value=value) # 基础样式 for cell in [cell_type, cell_value]: cell.alignment = center_align cell.border = thin_border cell.font = normal_font # 命中最大值条件时高亮整行 if type_name == max_type: cell_type.fill = highlight_fill cell_type.font = highlight_font cell_value.fill = highlight_fill cell_value.font = highlight_font # 调整列宽 ws.column_dimensions['A'].width = 18 ws.column_dimensions['B'].width = 25 # 保存文件 output_path = "/mnt/data/formatted_analysis_report.xlsx" wb.save(output_path) print(f"文件已保存至: {output_path}") # 提供下载链接 download_link = f"sandbox:{output_path}" print(f"下载链接: {download_link}")