Range Reading And Large File Analysis Agent Skill

by OpenSenseNova

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。

1k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

4851

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/OpenSenseNova/SenseNova-Skills --skill range-reading-and-large-file-analysis

The instruction itself

as written by the author

> 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。

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。

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（例如：行 40-50，特定两列）
# 模拟从非规范表格中提取数值对
data_rows = []
x_col_idx, y_col_idx = 0, 1 # 假设目标数据在第0列和第1列

for i in range(40, min(50, len(df))):
    row = df.iloc[i]
    try:
        # 清洗字符串并转换为浮点数
        val_x = float(str(row.iloc[x_col_idx]).replace(' ', ''))
        val_y = float(str(row.iloc[y_col_idx]).replace(' ', ''))
        if pd.notna(val_x) and pd.notna(val_y):
            data_rows.append((val_x, val_y))
    except (ValueError, TypeError):
        continue

analysis_df = pd.DataFrame(data_rows, columns=['target_x', 'target_y'])

Step3 执行高级统计分析与可视化。包含线性回归拟合、中英文字体配置、高分辨率图表保存及下载链接生成。

import matplotlib.pyplot as plt

# 配置中文字体（兼容不同环境）
plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False

if not analysis_df.empty:
    x = analysis_df['target_x'].values
    y = analysis_df['target_y'].values
    
    # 1. 线性拟合
    coeffs = np.polyfit(x, y, 1)
    poly_func = np.poly1d(coeffs)
    trend_line = poly_func(x)
    
    # 2. 绘图美化
    plt.figure(figsize=(10, 6), dpi=300)
    plt.scatter(x, y, color='#1f77b4', s=60, label='原始数据点', alpha=0.7)
    plt.plot(x, trend_line, color='#d62728', lw=2, label=f'趋势线: y={coeffs[0]:.4f}x+{coeffs[1]:.4f}')
    
    plt.title("数据分布与线性回归分析", fontsize=14, pad=20)
    plt.xlabel("维度 X", fontsize=12)
    plt.ylabel("维度 Y", fontsize=12)
    plt.grid(True, linestyle='--', alpha=0.5)
    plt.legend()
    
    chart_path = os.path.join(output_dir, "analysis_chart.png")
    plt.savefig(chart_path, bbox_inches='tight')
    plt.close()
    
    # 3. 结果导出
    result_path = os.path.join(output_dir, "analysis_results.csv")
    analysis_df['trend_prediction'] = trend_line
    analysis_df.to_csv(result_path, index=False, encoding='utf-8-sig')
    
    # 4. 输出下载链接
    print(f"分析图表已保存: sandbox:{chart_path}")
    print(f"结构化数据已保存: sandbox:{result_path}")
    print(f"拟合方程: y = {coeffs[0]:.4f}x + {coeffs[1]:.4f}")
else:
    print("未提取到有效数值数据，跳过可视化步骤")

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How to use it

Copy the folder

Take opensensenova/range-reading-and-large-file-analysis from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

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