chart-embedded-export
从结构化数据中提取分类分布执行清洗与统计，生成多维度交叉分析、高分辨率对比图表及包含下载链接的完整分析报告，适用于大文件处理与嵌入式可视化场景。
npx skills add OpenSenseNova/SenseNova-Skills --skill chart-embedded-export --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 > This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md. Step1 执行数据清洗，处理合并单元格，使用正则表达式清理文本，并建立分类映射函数骨架。 ```python target_col = '分类字段' value_col = '数值字段' # 合并单元格处理 (向下填充还原) df[target_col] = df[target_col].ffill() # 数据清洗：正则去除特殊字符、去空、类型转换 df[target_col] = df[target_col].astype(str).str.replace(r'[^\w\s]', '', regex=True).str.strip() df[value_col] = pd.to_numeric(df[value_col], errors='coerce') df = df.dropna(subset=[target_col, value_col]) # 分类映射函数骨架 def map_category(val): if 'A类特征' in str(val): return 'Category_A' elif 'B类特征' in str(val): return 'Category_B' return 'Other' df['Mapped_Category'] = df[target_col].apply(map_category) ``` Step2 进行多维度统计与交叉分析，计算分类占比并生成包含总计行的交叉表。 ```python group_col = '分组字段' # value_counts 统计与占比计算 counts = df[group_col].value_counts() proportions = (counts / counts.sum() * 100).round(2) # 交叉分析 (crosstab)，包含总计行 cross_analysis = pd.crosstab(df[group_col], df['Mapped_Category'], margins=True, margins_name='总计') # 多维度聚合统计 stats = df.groupby(group_col)[value_col].agg(['sum', 'mean', 'min', 'max']).round(2) ``` Step3 执行业务逻辑计算（如多维度评分与分级），将结果导出为 Excel 并生成沙盒下载链接。 ```pytho
- Skill Steps
What does the chart-embedded-export skill do?
从结构化数据中提取分类分布执行清洗与统计，生成多维度交叉分析、高分辨率对比图表及包含下载链接的完整分析报告，适用于大文件处理与嵌入式可视化场景。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill chart-embedded-export --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.