statistical-distribution-and-outlier-analysis

npx skills add OpenSenseNova/SenseNova-Skills --skill histogram-visualization --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Step 1 加载数据并进行预处理，配置中文字体与环境参数 ```python 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 ```python # 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