mcpbeat

Large File Conditional Formatting

opensensenova/large-file-conditional-formatting

根据Excel总行数自动切换Parquet加速读取,计算特定维度的时间序列平均值,并使用openpyxl输出带有条件格式(如低于均值标绿)和自定义样式的分析报告。

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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-file-conditional-formatting

The instruction itself

1 sections, as written by the author

Skill Steps

> Note: This sub-skill covers one step of the Excel analysis workflow. For the full pipeline (file reading, row counting, large-file optimization, export), see the parent workflow SKILL.md.

Step1 读取文件并统计所有 sheet 的行数,汇总后打印总行数,用于判断是否需要大文件加速。

import pandas as pd
import openpyxl

file_path = "input_data.xlsx"

# 获取所有sheet名称
wb = openpyxl.load_workbook(file_path, read_only=True)
sheet_names = wb.sheetnames
print("Sheet列表:", sheet_names)
print("Sheet数量:", len(sheet_names))

# 统计每个sheet的行数
total_rows = 0
for name in sheet_names:
    df_temp = pd.read_excel(file_path, sheet_name=name, header=None)
    rows = len(df_temp)
    total_rows += rows
    print(f"Sheet '{name}': {rows} 行")

print(f"\n总行数 = {total_rows}")

Step2 提取目标实体的时间序列数据,计算平均值,并构建包含比较结果的结构化 DataFrame。

target_entity = 'Target_Entity' # 占位示例,如 'US'

# 提取目标行数据 (假设第0列为实体名称)
target_row = df[df[0] == target_entity]

# 提取时间标签和对应数值 (假设第6行为表头,1:10列为数据)
time_labels = df.iloc[6, 1:10].tolist()
target_values = target_row.iloc[0, 1:10].tolist()
target_values_numeric = [float(v) for v in target_values]

# 计算平均值
avg_value = sum(target_values_numeric) / len(target_values_numeric)

# 构建结果 DataFrame
result_data = {
    '时间维度': time_labels,
    '指标数值': target_values_numeric,
    '是否低于平均值': [v < avg_value for v in target_values_numeric]
}
result_df = pd.DataFrame(result_data)

Step3 使用 openpyxl 将分析结果保存为 Excel 文件,应用精细的样式控制(加粗标题、边框、居中对齐),并对低于平均值的行进行条件格式填充(标绿)。

from openpyxl import Workbook
from openpyxl.styles import PatternFill, Font, Alignment, Border, Side

wb = Workbook()
ws = wb.active
ws.title = "指标分析报告"

# 定义样式
green_fill = PatternFill(start_color="92D050", end_color="92D050", fill_type="solid")
header_fill = PatternFill(start_color="4472C4", end_color="4472C4", fill_type="solid")
header_font = Font(bold=True, color="FFFFFF")
thin_border = Border(
    left=Side(style='thin'), right=Side(style='thin'),
    top=Side(style='thin'), bottom=Side(style='thin')
)

# 设置主标题
ws.merge_cells('A1:D1')
ws['A1'] = f"目标实体指标分析 - 平均值: {avg_value:.2f}"
ws['A1'].font = Font(bold=True, size=14)
ws['A1'].alignment = Alignment(horizontal='center')

# 设置表头
headers = ['时间维度', '指标数值', '与平均值比较', '是否标绿']
for col, header in enumerate(headers, 1):
    cell = ws.cell(row=3, column=col, value=header)
    cell.fill = header_fill
    cell.font = header_font
    cell.alignment = Alignment(horizontal='center')
    cell.border = thin_border

# 写入数据并应用条件格式
for i, row_data in result_df.iterrows():
    row_num = i + 4
    time_label = row_data['时间维度']
    value = row_data['指标数值']
    below_avg = row_data['是否低于平均值']
    
    # 写入各列数据
    ws.cell(row=row_num, column=1, value=time_label).alignment = Alignment(horizontal='center')
    ws.cell(row=row_num, column=2, value=value).alignment = Alignment(horizontal='center')
    
    diff = value - avg_value
    ws.cell(row=row_num, column=3, value=f"{diff:+.2f}").alignment = Alignment(horizontal='center')
    ws.cell(row=row_num, column=4, value="是" if below_avg else "否").alignment = Alignment(horizontal='center')
    
    # 添加边框并根据条件标绿整行
    for col in range(1, 5):
        cell = ws.cell(row=row_num, column=col)
        cell.border = thin_border
        if below_avg:
            cell.fill = green_fill

# 调整列宽
ws.column_dimensions['A'].width = 15
ws.column_dimensions['B'].width = 20
ws.column_dimensions['C'].width = 18
ws.column_dimensions['D'].width = 12

output_path = "output_report.xlsx"
wb.save(output_path)
print(f"分析报告已保存至: {output_path}")

How to use it

Copy the folder

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

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