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

# Clone and enter the repository
git clone git@github.com:markrichardson/dummyrepo.git
cd dummyrepo

# Install the development environment
make install

# Run the test suite and quality checks
make test
make fmt

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:markrichardson/dummyrepo.git
cd dummyrepo

# Run the automated setup
make install

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 install       # Create development environment
make test          # Run test suite
make fmt           # Run pre-commit hooks and linting
make clean         # Clean up environment

Usage

import dummypy as dp

# Build a grid and compute element-wise differences
grid = dp.Grid()
grid.diff()

# Vanilla European option payoffs at expiry
dp.call_payoff([80.0, 100.0, 130.0], strike=100.0)  # -> [ 0.,  0., 30.]
dp.put_payoff([70.0, 100.0, 130.0], strike=100.0)   # -> [30.,  0.,  0.]

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

• Grid (dummypy.things): Example grid data structure built on pandas DataFrames
• Payoffs (dummypy.payoffs): Vanilla European option payoff functions

License

© 2026 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: 0.1.5 Last Updated: June 2026 Classification: Public (MIT License)