group_col = '分组字段'
# value_counts 统计与占比计算
counts = df[group_col].value_counts()
proportions = (counts / counts.sum() * 100).round(2)
# 交叉分析 (crosstab)，包含总计行
cross_analysis = pd.crosstab(df[group_col], df['Mapped_Category'], margins=True, margins_name='总计')
# 多维度聚合统计
stats = df.groupby(group_col)[value_col].agg(['sum', 'mean', 'min', 'max']).round(2)
# 多维度评分/分级算法结构
df['Score'] = df[value_col] * 1.5 # 示例计算逻辑
df['Grade'] = pd.cut(df['Score'], bins=[0, 50, 80, 100], labels=['C', 'B', 'A'])
# 导出结构化结果
output_excel_path = 'analysis_result.xlsx'
df.to_excel(output_excel_path, index=False)
# 生成可点击的下载链接
print(f"分析结果已保存，下载链接：[下载结果数据](sandbox:{output_excel_path})")
output_img_path = 'comprehensive_chart.png'
# 中英文字体配置与图表美化
plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans', 'WenQuanYi Zen Hei']
plt.rcParams['axes.unicode_minus'] = False
fig, axes = plt.subplots(2, 2, figsize=(15, 12))
fig.suptitle('多维度数据分布综合分析', fontsize=16, fontweight='bold')
# 饼图：分布比例
colors = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99']
axes[0, 0].pie(counts.values, labels=counts.index, autopct='%1.1f%%', colors=colors, startangle=90)
axes[0, 0].set_title('分组选项分布比例')
# 柱状图：交叉分类分布
plot_data = cross_analysis.drop('总计', axis=0, errors='ignore').drop('总计', axis=1, errors='ignore')
plot_data.plot(kind='bar', ax=axes[0, 1], color=colors[:len(plot_data.columns)])
axes[0, 1].set_title('不同分组下分类分布')
axes[0, 1].tick_params(axis='x', rotation=45)
# 箱线图：数值分布
df.boxplot(column=value_col, by=group_col, ax=axes[1, 0])
axes[1, 0].set_title('不同分组下数值分布')
# 直方图：频数分布
for grp in df[group_col].dropna().unique():
subset = df[df[group_col] == grp]
axes[1, 1].hist(subset[value_col].dropna(), alpha=0.7, label=str(grp), bins=8)
axes[1, 1].legend()
axes[1, 1].set_title('数值分布直方图')
plt.tight_layout()
# 高分辨率图像导出
plt.savefig(output_img_path, format='png', dpi=300)
plt.savefig(output_img_path.replace('.png', '.svg'), format='svg')
plt.close()
report = [
"# 数据综合分析报告\n",
"## 1. 关键发现",
f"- 数据集共包含 {len(df)} 条有效记录。",
]
for idx, val in proportions.items():
report.append(f"- 分组 '{idx}' 的占比为 {val}%。")
report.extend([
"\n## 2. 交叉分析汇总",
cross_analysis.to_markdown(),
"\n## 3. 聚合统计指标",
stats.to_markdown(),
f"\n## 4. 可视化分析\n\n",
"**结论**: 各类别在数据中呈现特定分布特征，详细明细与评分定级结果请参考上方下载链接获取完整附件。"
])
report_content = '\n'.join(report)
print(report_content)
- How do I install chart-embedded-export?
- Download skills/sn-da-excel-workflow/capability/excel-result-export/chart-embedded-export/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 chart-embedded-export live?
- The publisher keeps it at https://github.com/OpenSenseNova/SenseNova-Skills.
- Which agents can use chart-embedded-export?
- Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.