category-statistics
提取指定类别列并统计各类别数量与占比，生成高分辨率的柱状图、饼图等组合可视化报告，适用于分类数据的分布情况分析。
npx skills add OpenSenseNova/SenseNova-Skills --skill category-statistics --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.
## Skill Steps Step1 提取目标类别数据，清洗无效标签，并统计各类别数量与占比。 ```python import pandas as pd def calculate_distribution(data, target_col='类别'): # 检查目标列是否存在 if target_col not in data.columns: raise ValueError(f'未找到指定的类别字段: {target_col}') # 提取数据，清洗无效标签（如'--'、'代码'等占位符） category_data = data[target_col].dropna().replace(['--', '代码'], pd.NA).dropna() # 统计各类别数量并计算占比 counts = category_data.value_counts() proportions = (counts / counts.sum()) * 100 # 实用技巧：生成包含总计行的统计表 # summary = counts.copy() # summary.loc['总计'] = counts.sum() return counts, proportions ``` Step2 生成基础可视化（双轴图：柱状图+占比曲线），并保存为高分辨率图片。 ```python import matplotlib.pyplot as plt def generate_and_save_basic_chart(counts, proportions, title='各类别数量分布', output_path='category_distribution.png'): # 设置中文字体避免乱码 plt.rcParams['font.sans-serif'] = ['SimHei', 'WenQuanYi Zen Hei', 'Noto Sans CJK JP', 'DejaVu Sans'] plt.rcParams['axes.unicode_minus'] = False fig, ax1 = plt.subplots(figsize=(10, 6)) # 绘制柱状图 bars = ax1.bar(counts.index, counts.values, color='skyblue', edgecolor='black') for bar in bars: height = bar.get_height() ax1.text(bar.get_x() + bar.get_width()/2., height + 0.05, f'{height}', ha='center', va='bottom', fontsize=10) ax1.set_ylabel('数量', fo
- Skill Steps
What does the category-statistics skill do?
提取指定类别列并统计各类别数量与占比，生成高分辨率的柱状图、饼图等组合可视化报告，适用于分类数据的分布情况分析。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill category-statistics --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.