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Changelog

All notable changes to the Complete Journey Python package are documented here.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

[0.1.0] - 2025-01-11

🎉 Major Documentation & CI/CD Release

This release represents a significant enhancement from a basic data package to a comprehensive, production-ready toolkit for retail analytics education and exploration.

Added

📚 Complete Documentation Ecosystem

  • Comprehensive MkDocs documentation with Material theme
  • 7 Analysis Cookbooks with detailed business insights:
  • Dataset Summary Analysis: High-level overview of all 8 datasets
  • Top Selling Products: Product performance and ranking analysis
  • Shopping Frequency Analysis: Customer behavior and visit patterns
  • Coupon Analysis: Promotional effectiveness and redemption patterns
  • Traffic Patterns: Store visit timing and seasonal trends
  • Demographic Product Analysis: Purchase behavior by income/age/family structure
  • Market Basket Analysis: Product associations and cross-selling opportunities
  • Interactive Jupyter notebooks with visualizations and strategic recommendations
  • User guide with getting started tutorials and dataset exploration
  • Complete API reference documentation
  • Professional about section with changelog and license

🔧 Production CI/CD Infrastructure

  • GitHub Actions workflows for automated testing across Python 3.8-3.11
  • Automated documentation deployment to GitHub Pages
  • Package building and installation verification
  • Code quality checks with flake8 linting
  • Test coverage reporting with Codecov integration
  • Dependabot configuration for automated dependency updates

📊 Enhanced Package Metadata

  • Professional README with GitHub Actions status badges
  • Comprehensive usage examples and business use cases
  • Links to live documentation and cookbook examples
  • CLAUDE.md guidance for AI-assisted development

Changed

  • Documentation Structure: Streamlined user-guide/datasets.ipynb to focus on data structure overview
  • Version Badges: Updated all documentation to reflect new version
  • Package Description: Enhanced with comprehensive business applications and use cases

Technical Improvements

  • Coverage: 10,501+ lines of new documentation and analysis content
  • Quality Assurance: All PRs must pass comprehensive testing pipeline
  • Automation: Documentation automatically updates with code changes
  • Professional Standards: GitHub Actions status badges show project health

Business Value

  • Educational Content: 7 comprehensive analysis notebooks with real insights
  • Strategic Applications: Business recommendations for marketing, merchandising, and operations
  • Multiple Approaches: Different analysis frameworks with experimentation guidance
  • Production Ready: Complete CI/CD pipeline ensures reliability

[0.0.3] - 2025-01-10

Added

  • Modern pyproject.toml configuration following PEP 621 standards
  • Comprehensive type hints throughout the codebase
  • Enhanced test suite with 9 test cases covering all functionality
  • Professional README with badges and comprehensive documentation
  • Comprehensive .gitignore following Python best practices

Changed

  • BREAKING: Replaced deprecated pkg_resources with modern importlib.resources
  • Updated Python requirement to >=3.8 (from >=3.6)
  • Enhanced function docstring with detailed examples and parameter descriptions
  • Modernized development workflow with black, isort, mypy integration

Removed

  • Legacy setup.py and setup.cfg files
  • Deprecated pytest-runner dependency

Fixed

  • Eliminated deprecation warnings from pkg_resources
  • Improved error handling and edge case coverage

[0.0.2] - Previous Release

Added

  • Fixed missing dependency on pyarrow

Changed

  • Improved package metadata

[0.0.1] - Initial Release

Added

  • Initial package structure with get_data() function
  • Support for loading 8 Complete Journey datasets:
  • transactions: 1.47M purchase records
  • demographics: Household characteristics
  • products: Product metadata
  • campaigns: Marketing campaigns
  • campaign_descriptions: Campaign metadata
  • promotions: Product placement data
  • coupons: Coupon information
  • coupon_redemptions: Coupon usage records
  • Parquet format for efficient data storage and loading
  • Basic documentation and usage examples

Technical Details

  • Python 3.6+ support
  • pandas and pyarrow dependencies
  • Package data included in distribution

Contributors

  • James Cunningham - Original package author
  • Brad Boehmke - Package co-author and contributor
  • Claude Code AI - Package modernization and documentation

Data Attribution

This package provides Python access to the Complete Journey dataset:

Original Data Source:

  • Provider: 84.51°
  • Original R Package: completejourney by Bradley Boehmke
  • License: Available for research and educational purposes

Python Package:

  • License: MIT License
  • Repository: GitHub

For detailed technical changes, see the Git commit history.