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。
npx skills add OpenSenseNova/SenseNova-Skills --skill outlier-detection --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
### 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
- Step 1 加载数据并配置环境
- Step 2 基于 IQR 方法识别异常值
- Step 3 生成多维度可视化箱线图
- Step 4 偏度与峰度分析及质量评估
- Step 5 异常值处理建议（骨架）
What does the outlier-detection-and-quality-assessment skill do?
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。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill outlier-detection --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From OpenSenseNova/SenseNova-Skills, a repository with 4,855 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.