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Large Excel Analysis And Formatting Agent Skill

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。

997 tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
4851
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/OpenSenseNova/SenseNova-Skills --skill large-excel-analysis-and-formatting

The instruction itself

1 sections, as written by the author

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)

Step2 提取目标数据进行分组汇总分析,并识别出最大值及其对应的分类项。

# 占位示例:根据实际数据集替换列名
group_col = '分类列名'  # 如 '控股类型'
target_col = '目标数值列'  # 如 '建筑业总产值'

# 假设 df 已清洗并包含所需列,进行汇总分析
summary = df.groupby(group_col)[target_col].sum().reset_index()

# 识别最大值及其对应的分类
max_idx = summary[target_col].idxmax()
max_type = summary.loc[max_idx, group_col]
print(f"最高产值类型: {max_type}")

Step3 使用 openpyxl 将分析结果写入新的Excel文件,配置表头样式、边框、列宽,并对满足特定条件(如最大值)的行进行绿色高亮标注,最后生成下载链接。

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}")

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How to use it

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Take opensensenova/large-excel-analysis-and-formatting from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

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