Categorical Comparison Analysis Agent Skill
对两类分类数据进行对比分析，统计数量差异与比例关系并生成可视化图表。
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/OpenSenseNova/SenseNova-Skills --skill categorical-comparison-analysisThe instruction itself
1 sections, as written by the authorcategorical-comparison-analysis
> This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md.
Step1 读取文件并统计所有 sheet 的总行数，评估是否需要进行大文件优化处理。
import pandas as pd
from pandas import read_excel
from pathlib import Path
# 统计所有 sheet 的行数以决定处理策略
file_path = "input_data.xlsx"
sheet_names = pd.ExcelFile(file_path).sheet_names
total_rows = 0
for sheet in sheet_names:
# 仅读取行索引以快速计数
df_tmp = read_excel(file_path, sheet_name=sheet, usecols=[0])
total_rows += len(df_tmp)
print(f"Total rows across all sheets: {total_rows}")
Step2 提取对比维度的分类信息，执行数据清洗，包括去除空值、处理合并单元格填充以及排除非数据行。
# 定义目标列名
target_col_a = "category_a_column"
target_col_b = "category_b_column"
# 处理合并单元格（ffill）并清洗数据
df[target_col_a] = df[target_col_a].ffill()
df[target_col_b] = df[target_col_b].ffill()
# 排除标题行占位符（如 '代码'、'名称'）及空值
exclude_val = "代码"
data_a = df[target_col_a].dropna()
data_a = data_a[data_a != exclude_val]
data_b = df[target_col_b].dropna()
data_b = data_b[data_b != exclude_val]
Step3 统计分类数量，计算差异值与占比，生成多维度对比统计表。
count_a = len(data_a)
count_b = len(data_b)
total_count = count_a + count_b
difference = abs(count_a - count_b)
# 计算占比
ratio_a = (count_a / total_count) * 100 if total_count > 0 else 0
ratio_b = (count_b / total_count) * 100 if total_count > 0 else 0
# 构建统计摘要
summary_df = pd.DataFrame({
"分类名称": ["类别A", "类别B"],
"数量": [count_a, count_b],
"占比": [f"{ratio_a:.2f}%", f"{ratio_b:.2f}%"]
})
print(summary_df)
print(f"数量差异: {difference}")
Step4 配置中文字体并生成可视化图表（柱状图与饼图），美化输出效果。
import matplotlib.pyplot as plt
# 中文字体配置
plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))
labels = ['类别A', '类别B']
counts = [count_a, count_b]
colors = ['#3498db', '#e74c3c']
# 柱状图美化
bars = ax1.bar(labels, counts, color=colors, alpha=0.8, edgecolor='black')
ax1.set_title('分类数量对比', fontsize=14)
ax1.grid(axis='y', linestyle='--', alpha=0.6)
for bar in bars:
height = bar.get_height()
ax1.text(bar.get_x() + bar.get_width()/2., height + 0.1, f'{int(height)}',
ha='center', va='bottom', fontweight='bold')
# 饼图美化
ax2.pie(counts, labels=labels, colors=colors, autopct='%1.1f%%', startangle=140, explode=(0.05, 0))
ax2.set_title('分类比例分布', fontsize=14)
output_img = "/mnt/data/comparison_analysis_chart.png"
plt.tight_layout()
plt.savefig(output_img, dpi=300, bbox_inches='tight')
plt.show()
Step5 将分析结果导出为 Excel 文件，并生成可供下载的链接。
from IPython.display import FileLink
output_path = "/mnt/data/analysis_report.xlsx"
with pd.ExcelWriter(output_path) as writer:
summary_df.to_excel(writer, sheet_name='统计摘要', index=False)
# 如果有明细数据也可在此导出
print(f"分析报告已生成")
display(FileLink(output_path, result_html_prefix="下载分析报告: "))
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How to use it
Take opensensenova/categorical-comparison-analysis from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.