mcpbeat

Group By Analysis

opensensenova/group-by-analysis

对多 Sheet 的 Excel 文件进行行数统计、大文件 Parquet 转换预处理、数据清洗及分组聚合分析,并生成带样式标记的统计表与可视化图表。

913 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 group-by-analysis

The instruction itself

as written by the author

Step1 对数据进行清洗与预处理,包括处理合并单元格、正则过滤以及分类映射。

import re

# 1. 处理合并单元格:向前填充
target_col = 'category_column'
df[target_col] = df[target_col].ffill()

# 2. 正则清洗:去除无效字符或筛选特定格式
def clean_text(text):
    if pd.isna(text): return text
    return re.sub(r'[^\w\s]', '', str(text)).strip()

df[target_col] = df[target_col].apply(clean_text)

# 3. 分类映射函数骨架
def map_categories(value):
    mapping = {
        'example_key_1': 'Group_A',
        'example_key_2': 'Group_B'
    }
    return mapping.get(value, 'Others')

df['group_tag'] = df[target_col].apply(map_categories)

Step2 执行分组统计,计算频数、占比,并添加总计行。

group_col = 'group_tag'
value_col = 'value_column'

# 分组聚合:计数与求和
summary = df.groupby(group_col)[value_col].agg(['count', 'sum']).reset_index()

# 计算占比
total_sum = summary['sum'].sum()
summary['percentage'] = (summary['sum'] / total_sum).map(lambda x: f"{x:.2%}")

# 添加总计行
total_row = pd.DataFrame({
    group_col: ['Total'],
    'count': [summary['count'].sum()],
    'sum': [total_sum],
    'percentage': ['100.00%']
})
summary_final = pd.concat([summary, total_row], ignore_index=True)

print(summary_final)

Step3 生成可视化柱状图,配置中文字体、数值标签及网格美化。

import matplotlib.pyplot as plt

# 配置中文字体支持
plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False

plt.figure(figsize=(10, 6), dpi=100)
bars = plt.bar(summary[group_col], summary['sum'], color='#4472C4')

# 添加数值标签
for bar in bars:
    height = bar.get_height()
    plt.text(bar.get_x() + bar.get_width()/2., height,
             f'{height:,.0f}', ha='center', va='bottom', fontsize=10)

plt.title("Distribution Analysis", fontsize=14)
plt.xlabel(group_col)
plt.ylabel("Values")
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()

chart_path = "analysis_chart.png"
plt.savefig(chart_path)

Step4 使用 openpyxl 生成带样式和条件格式的 Excel 报告,并提供下载。

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

output_path = "analysis_report.xlsx"
wb = Workbook()
ws = wb.active
ws.title = "Summary Report"

# 定义样式
header_style = {
    "fill": PatternFill(start_color="4472C4", end_color="4472C4", fill_type="solid"),
    "font": Font(bold=True, color="FFFFFF"),
    "alignment": Alignment(horizontal="center"),
    "border": Border(left=Side(style="thin"), right=Side(style="thin"), top=Side(style="thin"), bottom=Side(style="thin"))
}

highlight_style = PatternFill(start_color="00B050", end_color="00B050", fill_type="solid")

# 写入数据并应用样式
for r_idx, row in enumerate(summary_final.values, 2):
    for c_idx, value in enumerate(row, 1):
        cell = ws.cell(row=r_idx, column=c_idx, value=value)
        # 示例:对最大值所在行进行绿色标记
        if value == summary['sum'].max():
            cell.fill = highlight_style

# 自动调整列宽
for col in ws.columns:
    max_length = max(len(str(cell.value)) for cell in col)
    ws.column_dimensions[col[0].column_letter].width = max_length + 2

wb.save(output_path)
print(f"Download link: {output_path}")

How to use it

Copy the folder

Take opensensenova/group-by-analysis from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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