DummyPy Analytics Library

Overview

DummyPy is a Python analytics library created for educational and testing purposes. This toolkit provides example statistical modeling, data analysis, and visualization capabilities designed to showcase modern Python development practices.

📚 EDUCATIONAL & DEMONSTRATION PURPOSE This software is created for learning and demonstration purposes. Feel free to use, modify, and distribute.

Quick Start

☁️ Instant Development with GitHub Codespaces

Get started immediately with a fully configured cloud development environment:

Benefits:

• 🚀 Zero Setup - Ready in 2-3 minutes
• 🔧 Pre-configured - All tools and dependencies included
• 📊 Marimo Notebooks - Interactive analytics environment on port 8080
• 🛡️ Quality Tools - Ruff, pre-commit, and testing ready to use

💻 Local Development

Core Features

• Example Statistical Models: Sample algorithms & statistics for data analysis
• Data Processing: Demonstration of data manipulation and analysis techniques
• Visualization Tools: Example plotting and data visualization capabilities

Installation & Setup

Quick Start (Linux/macOS)

# Clone the repository
git clone git@github.com:[USERNAME]/dummypy.git
cd dummypy

# Run the automated setup
make setup

Windows Users

For detailed Windows setup instructions using WSL and VS Code, see INSTALL_WINDOWS.md.

Available Commands

make help          # Show all available commands
make setup         # Create development environment
make test          # Run test suite
make lint          # Run code quality checks
make format        # Format code
make clean         # Clean up environment

Usage

import dummypy as dp

grid = dp.Grid()
grid.diff()

Development

For developers working on this project, comprehensive documentation about the CI/CD infrastructure, development workflows, and quality assurance processes is available in GITHUB_CICD_README.md.

This documentation covers:

• GitHub Actions workflows for automated testing and deployment
• Pre-commit hooks for code quality enforcement
• Dependency management with Renovate
• GitHub Codespaces cloud development environment
• Development workflow commands and best practices

Architecture

• Core Models (dummypy.models): Example statistical models
• Core Payoffs (dummypy.payoffs): Demonstration payoff functions
• Analytics (dummypy.analytics): Sample performance tracking and reporting

License

© 2025 Mark Richardson. Released under MIT License.

This software is provided for educational and demonstration purposes. Feel free to use, modify, and distribute according to the MIT License terms.

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Version: 0.1.0 Last Updated: August 2025 Classification: CONFIDENTIAL