Multi File Excel Parquet Analysis Agent Skill
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。
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/OpenSenseNova/SenseNova-Skills --skill multi-file-excel-parquet-analysisThe 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 读取 Excel 文件，遍历所有 Sheet 统计行数，评估数据规模。
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。
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.values.round(2)
})
# 添加总计行
total_row = pd.DataFrame([['总计', summary_df['数量'].sum(), 100.0]], columns=summary_df.columns)
summary_df = pd.concat([summary_df, total_row], ignore_index=True)
print("统计摘要:\n", summary_df)
else:
print(f"未找到目标列: {target_col}")
Step3 生成可视化饼图并保存分析报告，提供结果下载链接。
import matplotlib.pyplot as plt
# 配置中文字体（实战技巧：防止图表乱码）
plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False
if target_col in df_analyzed.columns:
# 绘制饼图
plt.figure(figsize=(10, 7), dpi=100)
plot_data = df_analyzed[target_col].value_counts()
plt.pie(plot_data, labels=plot_data.index, autopct='%1.1f%%', startangle=90, colors=plt.cm.Paired.colors)
plt.title(f'{target_col} 分布占比')
# 保存图表
chart_output = "analysis_pie_chart.png"
plt.savefig(chart_output, bbox_inches='tight')
# 保存统计结果为 Excel
report_output = "analysis_report.xlsx"
summary_df.to_excel(report_output, index=False)
print(f"分析图表已保存: {chart_output}")
print(f"统计表格已保存: {report_output}")
# 生成下载链接（用于报告展示）
print(f"下载链接: {os.path.abspath(report_output)}")
Other skills for the same job
different authors, same section of the catalogue
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.
Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.
MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
How to use it
Take opensensenova/multi-file-excel-parquet-analysis from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.