Statistical Distribution And Outlier Analysis
执行数值型数据的分布分析与异常值检测，支持通过正则表达式从文本中提取误差项并生成高分辨率的箱线图与直方图报告。
Install
npx skills add OpenSenseNova/SenseNova-Skills --skill statistical-distribution-and-outlier-analysis -g -a claude-code -y- Repository
- OpenSenseNova/SenseNova-Skills6k
- Category
- Engineering
- Updated
- 6d ago
- License
- MIT
- Context cost
- Lightabout 1,054 tokens
- Runs scripts
- No, instructions only
What it does
执行数值型数据的分布分析与异常值检测，支持通过正则表达式从文本中提取误差项并生成高分辨率的箱线图与直方图报告。
Step 1 加载数据并进行预处理，配置中文字体与环境参数
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import re
# 设置中文字体，兼容不同环境
plt.rcParams['font.sans-serif'] = ['SimHei', 'WenQuanYi Zen Hei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False
# 加载数据并处理合并单元格
file_path = 'input_data.xlsx'
df = pd.read_excel(file_path)
df.ffill(inplace=True) # 处理可能的合并单元格空值
# 统一重命名列名以便于程序化处理
original_columns = df.columns.tolist()
df.columns = [f'col_{i+1}' for i in range(df.shape[1])]
print(f"数据形状: {df.shape}")
print(f"原始列映射: {dict(zip(df.columns, original_columns))}")
Step 2 生成多子图箱线图，直观展示各维度数据的分布特征与统计量
# 计算子图布局
num_cols = len(df.columns)
rows = (num_cols + 2) // 3
fig, axes = plt.subplots(rows, 3, figsize=(18, 5 * rows))
fig.suptitle('数据分布维度分析', fontsize=16, fontweight='bold')
axes_flat = axes.flatten()
for i, column in enumerate(df.columns):
data_series = df[column].dropna()
if pd.api.types.is_numeric_dtype(data_series):
axes_flat[i].boxplot(data_series, patch_artist=True,
boxprops=dict(facecolor='lightblue', alpha=0.7),
medianprops=dict(color='red', linewidth=2))
stats = data_series.describe()
axes_flat[i].set_title(f'{column} (n={len(data_series)})', fontsize=12)
axes_flat[i].text(0.05, 0.95, f'均值: {stats["mean"]:.2f}\n中位数: {stats["50%"]:.2f}',
transform=axes_flat[i].transAxes, verticalalignment='top',
bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))
axes_flat[i].grid(True, alpha=0.3)
plt.tight_layout(rect=[0, 0.03, 1, 0.95])
output_path = 'individual_boxplots.png'
plt.savefig(output_path, dpi=300, bbox_inches='tight')
plt.show()
Step 3 执行异常值检测算法，计算四分位距（IQR）并生成统计报告
analysis_results = []
