# 分类统计与占比
stats_summary = df_clean.groupby('group_col')['y_val'].agg(['count', 'mean', 'std', 'min', 'max'])
stats_summary['percentage'] = (stats_summary['count'] / stats_summary['count'].sum()) * 100
# 添加总计行
total_row = pd.DataFrame(df_clean[['y_val']].agg(['count', 'mean']).T)
total_row.index = ['Total']
# 交叉分析示例
pivot_table = pd.pivot_table(df_clean, values='y_val', index='group_col', columns='category_col', aggfunc='count', fill_value=0)
import matplotlib.pyplot as plt
import matplotlib
from scipy import stats
import numpy as np
# 字体配置：优先使用 SimHei 或 DejaVu Sans 确保中文显示
matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'WenQuanYi Zen Hei', 'DejaVu Sans']
matplotlib.rcParams['axes.unicode_minus'] = False
x = df_clean['x_val'].values
y = df_clean['y_val'].values
# 线性回归计算
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
line = slope * x + intercept
plt.figure(figsize=(12, 8), dpi=300)
# 散点图：添加随机抖动 (jitter) 避免点重叠
jitter_x = x + np.random.normal(0, 0.01, size=len(x))
plt.scatter(jitter_x, y, alpha=0.6, edgecolors='w', label='Data Points')
# 趋势线
plt.plot(x, line, color='red', linestyle='--', linewidth=2,
label=f'Trend: y={slope:.4f}x+{intercept:.4f}\n$R^2$={r_value**2:.4f}, p={p_value:.4e}')
# 数据点标注 (实战技巧：仅标注极值或特定点)
for i, (xi, yi) in enumerate(zip(x, y)):
if i % (len(x)//5 or 1) == 0: # 抽样标注避免拥挤
plt.annotate(f'({xi:.2f}, {yi:.2f})', (xi, yi), textcoords="offset points", xytext=(5,5), fontsize=8)
plt.xlabel('Dimension X')
plt.ylabel('Dimension Y')
plt.title('Statistical Distribution & Trend Analysis')
plt.grid(True, linestyle=':', alpha=0.6)
plt.legend()
output_img = 'analysis_plot.png'
plt.savefig(output_img, bbox_inches='tight')
plt.show()
output_csv = 'cleaned_analysis_data.csv'
# 使用 utf-8-sig 确保 Excel 打开中文不乱码
df_clean.to_csv(output_csv, index=False, encoding='utf-8-sig')
print(f"Visualization saved to: {output_img}")
print(f"Data exported to: {output_csv}")
# 打印回归关键指标供快速参考
print(f"R-squared: {r_value**2:.6f}, P-value: {p_value:.6f}")
- How do I install excel-statistical-viz-large-file?
- Download skills/sn-da-excel-workflow/capability/excel-data-visualization/scatter-plot-visualization/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 excel-statistical-viz-large-file live?
- The publisher keeps it at https://github.com/OpenSenseNova/SenseNova-Skills.
- Which agents can use excel-statistical-viz-large-file?
- Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.