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outlier-detection-and-quality-assessment

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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。

skills/sn-da-excel-workflow/capability/excel-data-cleaning/outlier-detection/SKILL.md

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### 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)
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