pivot-table-generator

majiayu000's avatarfrom majiayu000

Generate pivot tables from CSV/Excel with aggregations, filters, and automatic chart creation.

5stars🔀1forks📁View on GitHub🕐Updated Jan 11, 2026

When & Why to Use This Skill

The Pivot Table Generator is a high-performance Claude skill designed to automate complex data analysis by transforming raw CSV and Excel files into structured pivot tables. It eliminates manual spreadsheet work by providing instant data aggregation, multi-level grouping, and advanced filtering. With built-in support for automatic chart creation and multiple export formats, it enables users to convert large datasets into actionable visual insights and professional reports in seconds.

Use Cases

  • Sales Performance Analysis: Automatically aggregate total revenue, average deal size, and lead counts by region and product line to identify growth opportunities.
  • Financial Reporting: Process monthly expense exports to create categorized spending summaries with multi-level grouping for departmental budget tracking.
  • Inventory Management: Analyze stock levels across multiple warehouses by filtering for specific categories and calculating min/max stock requirements.
  • Marketing Campaign Tracking: Generate pivot charts from campaign data to visualize conversion rates and ROI across different platforms and demographics.
  • Data Cleaning & Pre-processing: Use filtering and aggregation features to summarize large datasets before exporting them for further specialized analysis.
namepivot-table-generator
descriptionGenerate pivot tables from CSV/Excel with aggregations, filters, and automatic chart creation.

Pivot Table Generator

Create pivot tables with aggregations and visualizations.

Features

  • Multiple Aggregations: Sum, mean, count, min, max
  • Filtering: Filter data before pivoting
  • Grouping: Multi-level row/column grouping
  • Charts: Auto-generate pivot charts
  • Export: Excel, CSV, HTML output

CLI Usage

python pivot_table_generator.py --data sales.csv --rows region --columns product --values amount --agg sum

Dependencies

  • pandas>=2.0.0
  • numpy>=1.24.0
  • matplotlib>=3.7.0
  • openpyxl>=3.1.0