insightfulpy-v0.1.8

Changelog

[0.1.8] - 2025-08-05

Added

• help system with four distinct help functions:
  • help() - function overview with categories
  • quick_start() - Step-by-step guide for immediate use
  • examples() - Practical usage examples for common scenarios
  • list_all() - Complete function listing organized by category
• function categorization:
  • BASIC_FUNCTIONS - Essential functions for getting started
  • VISUALIZATION_FUNCTIONS - Core plotting and visualization tools
  • ADVANCED_FUNCTIONS - Complex analysis and multi-dataset operations
  • STATISTICAL_FUNCTIONS - Statistical calculation utilities
• documentation with mermaid diagrams for clear visualization of workflows
• PyPI-optimized README with professional formatting and clear installation instructions

Enhanced

• Streamlined __init__.py with intuitive function organization
• Improved user experience with logical function grouping
• package structure following Python packaging standards
• Better accessibility for users of all skill levels

Changed

• Updated package metadata for better PyPI presentation
• Refined function imports for cleaner namespace management
• Enhanced code documentation and examples
• Improved help system navigation

Technical

• Maintained backward compatibility with all existing functions
• Preserved original EDA functionality without modifications
• Updated development dependencies and build configuration
• Enhanced project structure for maintainability

insightfulpy-v0.1.7

Changelog

All notable changes to the InsightfulPy project will be documented in this file.

[0.1.7] - 2025-02-28

Changed

• Major architecture refactoring: consolidated utility functions from utils.py into main eda.py module.
• Package optimization: reduced package size by removing redundant examples.
• Documentation: updated README.md with improved documentation.

Removed

• Removed example/ directory (14,344 lines).
• Removed example/example.html (10,899 lines).
• Removed example/example.ipynb (3,445 lines).
• Removed separate insightfulpy/utils.py module (36 lines); functions moved to main EDA module for better organization.

Internal

• Preserved all statistical functions (calc_stats, iqr_trimmed_mean, mad) in eda.py.
• Improved package structure with a single main module approach.

[0.1.6] - 2025-02-23

Added

• Added essential project files: .gitignore, LICENSE (MIT License), MANIFEST.in, NOTICE.txt, comprehensive README.md.
• Added comprehensive example files: example/example.html with detailed usage, and example/example.ipynb Jupyter notebook demonstrations.

Changed

• Updated to version 0.1.6.

[0.1.5] - 2025-02-23

Internal

• Minor release with minor improvements.
• Released 22 minutes after 0.1.4, reflecting a rapid iteration cycle.

[0.1.4] - 2025-02-20

Fixed

• Resolved merge conflicts and stabilized codebase.
• Cleaned up old directory structures: removed legacy example/ directory.
• Removed old InsightfulPy/ directory (case-sensitive rename).

Changed

• Updated README.md with latest information.
• Stabilized project architecture.

[0.1.3] - 2025-02-06

Internal

• Development release with continued iteration.
• Released 4 minutes after 0.1.2, indicating active development.

[0.1.2] - 2025-02-06

Fixed

• Fixed package naming conventions to comply with PEP 625.
• Corrected version bump implementation.

Internal

• Released 2 hours 47 minutes after 0.1.1.

[0.1.1] - 2025-02-06

Internal

• Early development improvements.
• Released 17 minutes after 0.1.0.

[0.1.0] - 2025-02-06

Added

• Initial public release of InsightfulPy.
• Core exploratory data analysis (EDA) toolkit, including:
  • Advanced visualization and statistical tools.
  • Support for numerical and categorical data analysis.
• Features:
  • Numerical Data Analysis: statistical summaries (mean, median, standard deviation, skewness, kurtosis), normality tests (Shapiro-Wilk, Kolmogorov-Smirnov), outlier detection (IQR method), box plots, KDE plots, scatter plots.
  • Categorical Data Analysis: summary statistics (unique values, mode, frequency distribution), high-cardinality variable identification, bar charts, pie charts, categorical heatmaps.
  • Data Quality Checks: missing value detection and visualization, infinite value detection, mixed data type identification, cross-dataset column profile comparison.
  • Utility Functions: calc_stats() (comprehensive statistics calculator), iqr_trimmed_mean() (robust mean calculation with outlier removal), mad() (mean absolute deviation calculation).
• Dependencies: pandas, numpy, matplotlib, seaborn, researchpy, tableone, missingno, scipy, tabulate.

Contributors

• dhaneshbb – Primary author and maintainer

License

This project is licensed under the MIT License – see the LICENSE file for details.

Development Timeline Summary:
Total development period: 22 days (2025-02-06 to 2025-02-28).
Release frequency: 8 versions in 22 days (average: 1 release every 2.75 days).
Development pattern: Rapid iteration with major architectural improvements.

Key Milestones:

• Feb 6: Initial release and rapid early iterations (4 versions in one day).
• Feb 20: Stabilization and cleanup phase.
• Feb 23: Feature addition with comprehensive examples.
• Feb 28: Architecture optimization and production readiness.

Package Evolution:

• v0.1.0–0.1.2: Initial development and naming fixes.
• v0.1.3–0.1.4: Stabilization and structure cleanup.
• v0.1.5–0.1.6: Feature expansion with examples and documentation.
• v0.1.7: Production optimization with architectural improvements.