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。
Install
npx skills add OpenSenseNova/SenseNova-Skills --skill excel-bar-chart-visualization -g -a claude-code -y- Repository
- OpenSenseNova/SenseNova-Skills6k
- Category
- Engineering
- Updated
- 6d ago
- License
- MIT
- Context cost
- Lightabout 955 tokens
- Runs scripts
- No, instructions only
What it does
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。
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
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
# 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[f'{group_col}_合并'] = combined_df[group_col].map(stage_mapping).fillna('其他')
grouped_stats = combined_df.groupby(f'{group_col}_合并')[target_col].count().sort_values(ascending=False)
elif '题目分类' in combined_df.columns and '题型' in combined_df.columns:
# 交叉分析 crosstab/pivot
grouped_stats = combined_df.groupby(['题目分类', '题型']).size().unstack(fill_value=0)
else:
grouped_stats = combined_df.groupby(combined_df.columns[0]).size()
Step3: 统计结果输出与下载
import tempfile
import os
output_path = os.path.join(tempfile.gettempdir(), "统计结果.xlsx")
