multi-file-excel-parquet-analysis
读取多 Sheet Excel 文件并统计规模，支持大文件向 Parquet 格式转换、分类数据统计及可视化报告生成。
npx skills add OpenSenseNova/SenseNova-Skills --skill multi-file-reading --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.
> **Note**: This sub-skill covers one step of the Excel analysis workflow. For the full pipeline (file reading, row counting, large-file optimization, export), see the parent workflow SKILL.md. Step1 读取 Excel 文件，遍历所有 Sheet 统计行数，评估数据规模。 ```python import pandas as pd import os file_path = "input_data.xlsx" # 替换为实际文件路径 if not os.path.exists(file_path): print(f"Error: 文件 {file_path} 不存在") else: # 获取所有 sheet 名称 xl = pd.ExcelFile(file_path) sheet_names = xl.sheet_names print("Sheet 列表:", sheet_names) total_rows = 0 for sheet in sheet_names: # 仅读取第一列以快速统计行数，避免大文件内存溢出 df_tmp = pd.read_excel(file_path, sheet_name=sheet, usecols=[0]) row_count = len(df_tmp) total_rows += row_count print(f"Sheet: {sheet}, 行数: {row_count}") print(f"总行数汇总: {total_rows}") ``` Step2 读取转换后的数据，执行分类统计分析，计算频数与占比。 ```python import pandas as pd # 读取 Parquet 文件 df_analyzed = pd.read_parquet(output_parquet) # 定义目标统计列（如 '剪裁结果'、'状态' 等） target_col = '剪裁结果' if target_col in df_analyzed.columns: # 统计各分类数量及占比 counts = df_analyzed[target_col].value_counts() percent = df_analyzed[target_col].value_counts(normalize=True) * 100 # 构建统计表格并添加总计行 summary_df = pd.DataFrame({ '分类': counts.index, '数量': counts.values, '占比(%)': percent.val
What does the multi-file-excel-parquet-analysis skill do?
读取多 Sheet Excel 文件并统计规模，支持大文件向 Parquet 格式转换、分类数据统计及可视化报告生成。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill multi-file-reading --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.