# Outlier Detection And Quality Assessment

执行全面的异常值检测与数据质量评估，利用 IQR 方法识别异常值并结合偏度、峰度分析数据分布特征，适用于非正态分布数据的预处理阶段。

- Repository: https://github.com/OpenSenseNova/SenseNova-Skills (6002 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-10-29
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add OpenSenseNova/SenseNova-Skills --skill outlier-detection-and-quality-assessment -g -a claude-code -y`
Codex: `npx skills add OpenSenseNova/SenseNova-Skills --skill outlier-detection-and-quality-assessment -g -a codex -y`
Cursor: `npx skills add OpenSenseNova/SenseNova-Skills --skill outlier-detection-and-quality-assessment -g -a cursor -y`
Gemini CLI: `npx skills add OpenSenseNova/SenseNova-Skills --skill outlier-detection-and-quality-assessment -g -a gemini-cli -y`

## Description

执行全面的异常值检测与数据质量评估，利用 IQR 方法识别异常值并结合偏度、峰度分析数据分布特征，适用于非正态分布数据的预处理阶段。

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

### Step 1 加载数据并配置环境
```python
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

# 设置中英文字体以支持可视化显示 (SimHei 或 WenQuanYi)
plt.rcParams['font.sans-serif'] = ['SimHei', 'WenQuanYi Zen Hei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False

# 加载数据
file_path = 'data.xlsx'  # 替换为实际文件路径
df = pd.read_excel(file_path)

# 基础信息检查
print(f"数据形状: {df.shape}")
print(f"数据类型:\n{df.dtypes}")
print(df.head())
```

### Step 2 基于 IQR 方法识别异常值
```python
# 自动筛选数值型列进行分析
target_cols = df.select_dtypes(include=[np.number]).columns.tolist()
outlier_summary = []

for col in target_cols:
    data = df[col].dropna()
    if data.empty:
        continue
        
    # 四分位距计算 (IQR)
    Q1 = data.quantile(0.25)
    Q3 = data.quantile(0.75)
    IQR = Q3 - Q1
    lower_bound = Q1 - 1.5 * IQR
    upper_bound = Q3 + 1.5 * IQR
    
    # 识别异常值
    outliers = data[(data < lower_bound) | (data > upper_bound)]
    
    outlier_summary.append({
        'target_col': col,
        'outlier_count': len(outliers),
        'outlier_ratio': f"{(len(outliers)/len(data)*100):.2f}%",
        'lower_limit': lower_bound,
        'upper_limit': upper_bound,
        'sample_values': outliers.values.tolist()[:5]  # 保留前5个示例
    })

outlier_df = pd.DataFrame(outlier_summary)
print("\n=== 异常值统计汇总 ===")
print(outlier_df.to_string(index=False))
```

### Step 3 生成多维度可视化箱线图
```python
# 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()

The rest: https://github.com/OpenSenseNova/SenseNova-Skills/blob/HEAD/skills/sn-da-excel-workflow/capability/excel-data-cleaning/outlier-detection/SKILL.md