pie-chart-data-analysis
对多Sheet Excel或CSV数据进行分类汇总统计，自动识别关键字段并生成包含占比、数值及美化饼图的可下载分析报告。
npx skills add OpenSenseNova/SenseNova-Skills --skill pie-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.
Step1 读取文件并统计所有 Sheet 的行数，确认数据规模以决定处理策略。 ```python import pandas as pd file_path = input_file total_rows = 0 sheet_names = [] try: if file_path.endswith('.xlsx'): excel_file = pd.ExcelFile(file_path) sheet_names = excel_file.sheet_names # 统计所有工作表总行数 for sheet in sheet_names: df_tmp = pd.read_excel(file_path, sheet_name=sheet) total_rows += len(df_tmp) elif file_path.endswith('.csv'): df = pd.read_csv(file_path) total_rows = len(df) else: raise ValueError("不支持的文件格式，仅支持 .xlsx 或 .csv") except Exception as e: raise RuntimeError(f"文件读取失败: {e}") is_large_file = total_rows >= 10000 ``` Step2 自动识别分类列与数值列，执行数据清洗与格式转换。 ```python import re # 加载首个有效数据集 if file_path.endswith('.xlsx'): df = pd.read_excel(file_path, sheet_name=sheet_names[0]) else: df = pd.read_csv(file_path) # 1. 识别数值目标列（如：金额、支出、得分、数量） target_keywords = ['金额', '支出', '造价', '经费', '数量', '得分'] target_cols = [col for col in df.columns if any(k in col for k in target_keywords)] target_col = target_cols[0] if target_cols else df.select_dtypes(include=['number']).columns[0] # 2. 识别分类列（支持正则匹配中文序号或特定分类标识） category_pattern = re.compile(r'[一二三四五六七八九十百]+|地区|类别|类型|状态') category_cols = [col for col in df.columns if category_pattern.search(col)
What does the pie-chart-data-analysis skill do?
对多Sheet Excel或CSV数据进行分类汇总统计，自动识别关键字段并生成包含占比、数值及美化饼图的可下载分析报告。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill pie-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.