# 配置多子图布局
num_cols = len(target_cols)
cols_per_row = 3
rows = (num_cols + cols_per_row - 1) // cols_per_row
fig, axes = plt.subplots(rows, cols_per_row, figsize=(18, 5 * rows))
fig.suptitle('数据分布与异常值检测箱线图', fontsize=16, fontweight='bold')
axes_flat = axes.flatten()
# 遍历绘制每个维度的分布
for i, col in enumerate(target_cols):
ax = axes_flat[i]
# 绘制箱线图并美化
sns.boxplot(y=df[col].dropna(), ax=ax, color='skyblue', width=0.4,
flierprops=dict(marker='o', markerfacecolor='red', markersize=5, alpha=0.5))
ax.set_title(f'列: {col}', fontsize=12)
ax.grid(True, linestyle='--', alpha=0.6)
# 嵌入实时统计标注
stats = df[col].describe()
stats_text = f'均值: {stats["mean"]:.2f}\n中位数: {stats["50%"]:.2f}\n标准差: {stats["std"]:.2f}'
ax.text(0.05, 0.95, stats_text, transform=ax.transAxes, fontsize=9,
verticalalignment='top', bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))
# 隐藏多余的子图
for j in range(i + 1, len(axes_flat)):
axes_flat[j].axis('off')
plt.tight_layout(rect=[0, 0.03, 1, 0.95])
output_path = 'outlier_analysis_report.png'
plt.savefig(output_path, dpi=300, bbox_inches='tight')
plt.show()
# 分析分布形态以辅助清洗决策
print("=== 数据分布形态分析报告 ===")
quality_analysis = []
for col in target_cols:
data = df[col].dropna()
skewness = data.skew()
kurtosis = data.kurtosis()
# 判定分布特征
skew_type = "右偏 (Positive)" if skewness > 0.5 else "左偏 (Negative)" if skewness < -0.5 else "对称"
kurt_type = "尖峰 (Leptokurtic)" if kurtosis > 1 else "平峰 (Platykurtic)" if kurtosis < -1 else "正态趋向"
quality_analysis.append({
'字段': col,
'偏度': round(skewness, 3),
'峰度': round(kurtosis, 3),
'分布形态': skew_type,
'峰度特征': kurt_type
})
analysis_df = pd.DataFrame(quality_analysis)
print(analysis_df.to_string(index=False))
# 导出分析结果
# analysis_df.to_csv('data_quality_report.csv', index=False)
def handle_outliers(df, col, method='cap'):
"""
异常值处理骨架函数
method: 'cap' (盖帽法), 'drop' (删除), 'none' (保留)
"""
data = df[col].copy()
Q1 = data.quantile(0.25)
Q3 = data.quantile(0.75)
IQR = Q3 - Q1
lower = Q1 - 1.5 * IQR
upper = Q3 + 1.5 * IQR
if method == 'cap':
df[col] = df[col].clip(lower=lower, upper=upper)
elif method == 'drop':
df = df[(df[col] >= lower) & (df[col] <= upper)]
return df
# 示例：对特定列应用盖帽法处理
# df = handle_outliers(df, 'target_col', method='cap')
- How do I install outlier-detection-and-quality-assessment?
- Download skills/sn-da-excel-workflow/capability/excel-data-cleaning/outlier-detection/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 outlier-detection-and-quality-assessment live?
- The publisher keeps it at https://github.com/OpenSenseNova/SenseNova-Skills.
- Which agents can use outlier-detection-and-quality-assessment?
- Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.