range-reading-and-large-file-analysis
读取多 Sheet Excel 文件，根据数据量动态选择处理策略，支持特定区域数据提取、大文件 Parquet 转换、统计分析及可视化图表生成。
npx skills add OpenSenseNova/SenseNova-Skills --skill range-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 针对特定 Sheet 进行数据清洗与空值统计。支持处理带空格的列名，并计算关键指标的缺失率。 ```python target_sheet = "Sheet2" target_col = "是否通过" # 示例列名，实际根据需求替换 # 读取指定 Sheet df_target = pd.read_excel(file_path, sheet_name=target_sheet) # 清洗列名：去除首尾空格 df_target.columns = [str(col).strip() for col in df_target.columns] if target_col in df_target.columns: null_count = df_target[target_col].isna().sum() print(f"'{target_col}' 列为空的数量: {null_count}") # 统计占比 stats = df_target[target_col].value_counts(dropna=False) print("分类统计结果：\n", stats) else: print(f"未找到目标列: {target_col}") ``` Step2 大文件优化处理：将 Excel 转换为 Parquet 格式以提升后续读取速度，并提取特定行/列范围的数据进行结构化转换。 ```python import numpy as np output_dir = "output_results" os.makedirs(output_dir, exist_ok=True) if is_large_file: # 转换为 Parquet 格式 parquet_path = os.path.join(output_dir, "temp_data.parquet") # 注意：大文件读取建议分块或指定关键列 df_full = pd.read_excel(file_path) df_full.to_parquet(parquet_path, engine='pyarrow', index=False) df = pd.read_parquet(parquet_path) else: df = pd.read_excel(file_path) # 提取特定区域数据（例
What does the range-reading-and-large-file-analysis skill do?
读取多 Sheet Excel 文件，根据数据量动态选择处理策略，支持特定区域数据提取、大文件 Parquet 转换、统计分析及可视化图表生成。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill range-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.