excel-bar-chart-visualization
读取多工作表Excel文件，自动处理合并单元格与数据清洗，进行交叉分组统计并生成带总计行的结果表，最后绘制支持中英文字体的美化柱状图，适用于多维度数据汇总与可视化分析。
npx skills add OpenSenseNova/SenseNova-Skills --skill bar-chart-visualization --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: 数据合并与清洗 ```python combined_df = pd.concat(data_frames, ignore_index=True) # 数据清洗：使用正则表达式统一命名 if '题型' in combined_df.columns: combined_df['题型'] = combined_df['题型'].astype(str).str.replace('判', '判断题', regex=False) # 处理合并单元格技巧1：前向填充 if '流程描述' in combined_df.columns: combined_df['流程描述'] = combined_df['流程描述'].fillna(method='ffill') # 处理合并单元格技巧2：通过逻辑判断与手动映射还原完整名称 group_col = '项目阶段' target_col = '控制要点' if group_col in combined_df.columns and target_col in combined_df.columns: project_stages, control_points = [], [] current_stage = None for _, row in combined_df.iterrows(): stage = row[group_col] point = row[target_col] if pd.notna(point) and point != target_col: if pd.notna(stage): current_stage = stage project_stages.append(current_stage) control_points.append(point) combined_df = pd.DataFrame({ group_col: project_stages, target_col: control_points }) ``` ### Step2: 交叉分析与分类映射 ```python # 分类映射函数骨架 if group_col in combined_df.columns: stage_mapping = { '碎片值1': '标准分类A', '碎片值2': '标准分类A', '碎片值3': '标准分类B', '异常值': '其他' } combined_df
- Skill Steps
- Step1: 数据合并与清洗
- Step2: 交叉分析与分类映射
- Step3: 统计结果输出与下载
- Step4: 图表绘制与美化
What does the excel-bar-chart-visualization skill do?
读取多工作表Excel文件，自动处理合并单元格与数据清洗，进行交叉分组统计并生成带总计行的结果表，最后绘制支持中英文字体的美化柱状图，适用于多维度数据汇总与可视化分析。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill bar-chart-visualization --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.