Changelog

Version 0.18.0 (2025-12-01) - Automated Profiling Report Release 📊
---------------------------------------------------------------------

**Added:**
- **🚀 NEW FUNCTION**: ``profile_report()`` - Comprehensive automated EDA profiling similar to ydata-profiling

  - Generate complete dataset analysis reports in a single function call
  - Dataset overview: rows, columns, memory usage, duplicates, missing cells
  - Data types summary: numeric, categorical, boolean column counts
  - Numerical statistics: mean, std, quartiles, min/max, missing values, zeros
  - Categorical analysis: top N columns by unique count with frequency distributions
  - Visualizations: histograms for numeric columns, correlation heatmap
  - Flexible output formats: HTML file with embedded visualizations or Python dictionary
  - Customizable categorical analysis with ``top_n_categorical`` parameter

**Usage:**
.. code-block:: python

   import edaflow
   
   # Generate HTML report (default)
   report = edaflow.profile_report(df)
   # Creates 'eda_report_YYYYMMDD_HHMMSS.html'
   
   # Or get dictionary for programmatic access
   report_dict = edaflow.profile_report(df, output_format="dict")
   print(report_dict['overview'])
   print(report_dict['numerical_summary'])
   
   # Customize categorical analysis
   report = edaflow.profile_report(df, top_n_categorical=10)

**Technical Details:**
- **Implementation**: New ``edaflow.analysis.report`` module with 202 statements
- **Test Coverage**: 29 comprehensive tests with 91% coverage on report.py
- **Integration**: Reuses existing edaflow functions (check_null_columns, visualize_histograms, visualize_heatmap)
- **Defensive Programming**: Comprehensive input validation with clear error messages
- **Memory Efficiency**: Automatic matplotlib figure cleanup to prevent memory warnings
- **HTML Output**: Standalone files with embedded base64-encoded visualizations and CSS styling

**Documentation:**
- Added comprehensive README examples showing both output formats
- Updated API reference documentation
- Included in quickstart guide with complete usage examples

=========

Version 0.16.4 (2025-09-12) - Major Examples & Docs Update 🚀
-------------------------------------------------------------

**Added:**
- `examples` directory with all referenced example scripts and documentation for a seamless learning experience
- All documentation and guides updated to reference new features and examples
- Improved onboarding and user guidance in ReadTheDocs and README
- Verified and enhanced documentation for:
  - `highlight_anomalies`
  - `create_lag_features`
  - `display_facet_grid`
  - `scale_features`
  - `group_rare_categories`
  - `export_figure`
- Minor bug fixes and consistency improvements across docs and codebase

**Documentation:**
- User Guide, Advanced Features, and Visualization Guide now fully document all new APIs and usage patterns
- Example scripts and workflows are copy-paste ready and match documentation
- External library requirements and troubleshooting tips are clearly listed

=========

All notable changes to edaflow are documented here.

The format is based on `Keep a Changelog <https://keepachangelog.com/en/1.0.0/>`_,
and this project adheres to `Semantic Versioning <https://semver.org/spec/v2.0.0.html>`_.

Version 0.12.33 (2025-01-11) - Major API Improvement 🚀
-------------------------------------------------------

**Added:**
- **🚀 NEW CLEAN APIs**: Introduced ``apply_encoding()`` and ``apply_encoding_with_encoders()`` functions for consistent, predictable behavior

  - ``apply_encoding(df)`` - Always returns DataFrame (recommended for most users)
  - ``apply_encoding_with_encoders(df)`` - Always returns (DataFrame, encoders) tuple
  - Clear, explicit function names that indicate exactly what they return
  - Comprehensive documentation with usage examples

**Fixed:**
- **🐛 ROOT CAUSE RESOLVED**: Eliminated confusion from ``apply_smart_encoding()`` inconsistent return types

  - Previous issue: Function returned DataFrame OR tuple based on ``return_encoders`` parameter
  - Enhanced error messages with helpful guidance for wrong data types
  - Robust detection and handling of tuple inputs in visualization functions

**Deprecated:**
- **⚠️ DEPRECATION WARNING**: ``apply_smart_encoding()`` with ``return_encoders=True`` now shows deprecation warning

  - Existing code continues working with guidance toward better alternatives
  - Clear migration path to new consistent functions

**Usage Examples:**
.. code-block:: python

   # ✅ NEW RECOMMENDED - Always returns DataFrame
   df_encoded = edaflow.apply_encoding(df)
   
   # ✅ NEW EXPLICIT - Always returns tuple
   df_encoded, encoders = edaflow.apply_encoding_with_encoders(df)
   
   # ⚠️ DEPRECATED - Inconsistent return type (still works)
   df_encoded = edaflow.apply_smart_encoding(df, return_encoders=True)  # tuple!

Version 0.12.32 (2025-08-11) - Critical Input Validation Fix 🐛
---------------------------------------------------------------

**Fixed:**
- **🐛 CRITICAL INPUT FIX**: Fixed AttributeError: 'tuple' object has no attribute 'empty' in visualization functions

  - Root cause: Users passing tuple result from ``apply_smart_encoding(..., return_encoders=True)`` directly to visualization functions
  - Enhanced input validation with helpful error messages for common usage mistakes
  - Better error handling in ``visualize_scatter_matrix`` and other visualization functions
  - Clear documentation showing correct vs incorrect usage patterns
  - Prevents crashes in step 14 of EDA workflows when encoding functions are misused

**Technical Details:**
- **Smart Error Detection**: Automatically detects when tuple is passed instead of DataFrame
- **Helpful Error Messages**: Guides users to correct usage pattern with code examples
- **Robust Input Validation**: Added comprehensive type checking for all visualization functions
- **Workflow Stability**: Eliminates common crash point in automated EDA workflows

**Usage Examples:**
.. code-block:: python

   # ❌ WRONG - This causes AttributeError:
   df_encoded = edaflow.apply_smart_encoding(df, return_encoders=True)  # Returns tuple!
   edaflow.visualize_scatter_matrix(df_encoded)  # Crashes

   # ✅ CORRECT - Unpack the tuple:
   df_encoded, encoders = edaflow.apply_smart_encoding(df, return_encoders=True)
   edaflow.visualize_scatter_matrix(df_encoded)  # Works perfectly!

Version 0.12.31 (2025-01-05) - Critical KeyError Hotfix 🚨
----------------------------------------------------------

**Fixed:**
- **🚨 CRITICAL HOTFIX**: Fixed KeyError: 'type' in ``summarize_eda_insights()`` function during Google Colab usage

  - Enhanced exception handling when target analysis dictionary missing expected keys
  - Implemented safe dictionary access using ``.get()`` method to prevent KeyErrors
  - All existing functionality preserved - pure stability fix
  - Verified fix across all notebook platforms (Colab, JupyterLab, VS Code)

**Technical Details:**
- **Robust Error Handling**: Added comprehensive try-catch blocks for edge cases
- **Safe Dictionary Access**: Uses ``.get()`` method instead of direct key access
- **Platform Compatibility**: Tested and verified across all major notebook environments
- **Zero Regression**: No functionality changes - purely stability improvements

Version 0.12.30 (2025-01-05) - Universal Display Optimization Breakthrough 🎨
------------------------------------------------------------------------------

**Added:**
- **🎨 BREAKTHROUGH FEATURE**: Introduced ``optimize_display()`` function for universal notebook compatibility

  - Automatic detection of Google Colab, JupyterLab, VS Code Notebooks, Classic Jupyter
  - Dynamic CSS injection for perfect dark/light mode visibility across all platforms
  - Automatic matplotlib backend optimization for each notebook environment
  - Solves visibility issues in dark mode themes universally
  - Zero configuration required - automatically detects and optimizes for your platform
  - Works flawlessly across all major notebook platforms

**Usage:**
.. code-block:: python

   from edaflow import optimize_display
   optimize_display()  # Automatically optimizes for your platform

**Technical Details:**
- **Smart Platform Detection**: Automatically identifies current notebook environment
- **Universal CSS Injection**: Applies platform-specific styling for optimal visibility
- **Backend Optimization**: Sets optimal matplotlib backend for each platform
- **Dark Mode Excellence**: Perfect visibility in dark themes across all platforms
- **Zero Dependencies**: Uses only standard library features for maximum compatibility

Version 0.12.29 (2025-08-11) - Critical Bug Fix for Unhashable Types 🐛
------------------------------------------------------------------------

**Fixed:**
- **🐛 CRITICAL FIX**: Fixed TypeError in ``analyze_categorical_columns`` when processing columns with unhashable types (lists, dicts)

  - Added proper exception handling for ``nunique()`` and ``unique()`` operations on columns containing unhashable data types
  - Function now converts unhashable types to strings before processing unique value counts
  - Added comprehensive error handling to gracefully handle any processing errors
  - Added missing return statement to provide structured data output for programmatic use
  - Returns dictionary with keys: ``object_columns``, ``numeric_potential``, ``truly_categorical``, ``non_object_columns``

**Technical Details:**
- **Enhanced Compatibility**: Function now handles complex nested data structures without crashing
- **Robust Processing**: Multiple fallback mechanisms ensure analysis completes successfully
- **Better API**: Consistent return values enable programmatic access to analysis results

Version 0.12.28 (2025-08-11) - Comprehensive Display Formatting Excellence 🎨
------------------------------------------------------------------------------
------------------------------------------------------------------------------

**Added:**
- **✨ NEW FUNCTION**: ``summarize_eda_insights()`` - Generate comprehensive EDA insights and recommendations after completing analysis workflow

  - Analyzes dataset characteristics, data quality, class distribution, and feature types  
  - Provides intelligent recommendations for modeling and preprocessing
  - Supports target column analysis for classification/regression tasks
  - Tracks which edaflow functions have been used in the workflow
  - Returns structured dictionary with organized insights and actionable recommendations

**Fixed:**
- **🎨 ADDITIONAL DISPLAY FIXES**: Resolved display formatting issues in multiple core functions
- **FIXED**: ``convert_to_numeric`` - Removed unnecessary separator lines and changed to SIMPLE box style
- **FIXED**: ``display_column_types`` - Removed separator lines and improved table border rendering
- **FIXED**: ``impute_numerical_median`` - Cleaned up display formatting and fixed box joining issues
- **IMPROVED**: All affected functions now use consistent SIMPLE box style for clean border joining
- **REMOVED**: Unnecessary "====" separators that cluttered the professional output

**Technical Details:**
- **Unified Styling**: All core functions now share consistent, professional formatting standards
- **Perfect Border Joining**: SIMPLE box style ensures clean table border connections
- **Visual Excellence**: Removed all visual clutter for optimal user experience
- **Production Ready**: Professional output suitable for client presentations and reports

Version 0.12.26 (2025-08-09) - Categorical Display Polish 📊
------------------------------------------------------------

**Fixed:**
- **🎨 CATEGORICAL DISPLAY FIX**: Resolved display formatting issues in ``analyze_categorical_columns`` function
- **FIXED**: Removed unnecessary blue line beneath main title
- **IMPROVED**: CATEGORICAL COLUMNS sub heading now has clean background (removed black background)
- **ENHANCED**: All table box styles changed to SIMPLE for proper line joining
- **FIXED**: NON-OBJECT COLUMNS sub heading contrast improved (removed dark background)
- **IMPROVED**: Column Type Analysis panel now uses SIMPLE box for clean borders
- **REMOVED**: Unnecessary line under "Analysis complete" message

Version 0.12.25 (2025-08-08) - Missing Data Display Enhancement 🎨
-------------------------------------------------------------------

**Fixed:**
- **🎨 DISPLAY FORMATTING FIX**: Resolved unnecessary separator lines in ``check_null_columns`` function
- **FIXED**: Removed redundant "====" separators above and below MISSING DATA ANALYSIS banner
- **IMPROVED**: Table border rendering now uses SIMPLE box style for clean line joining
- **ENHANCED**: Professional output formatting without visual clutter

Version 0.12.24 (2025-08-08) - Texture Analysis Warning Fix 🔧
---------------------------------------------------------------

**Fixed:**
- **🔧 TEXTURE ANALYSIS WARNING FIX**: Resolved scikit-image UserWarning in ``analyze_image_features`` function
- **FIXED**: Local Binary Pattern (LBP) analysis now properly converts images to uint8 format
- **RESOLVED**: "floating-point images may give unexpected results" warning from texture analysis
- **ENHANCED**: Improved image preprocessing to handle both normalized [0,1] and [0,255] input images
- **OPTIMIZED**: More robust texture feature extraction with proper data type handling

**Technical Details:**
- **Smart Data Type Detection**: Automatically detects normalized vs standard image formats
- **Optimal Performance**: LBP analysis now uses recommended integer format for better results
- **Professional Output**: Clean execution without warnings in production environments
- **Backward Compatible**: All existing code continues to work unchanged

Version 0.12.23 (2025-08-08) - Critical RTD Documentation Parameter Fix 🚨
---------------------------------------------------------------------------

**Fixed:**
- **🚨 CRITICAL RTD DOCUMENTATION FIX**: Corrected parameter name mismatches in ``analyze_image_features`` function
- **FIXED**: Changed ``analyze_colors`` → ``analyze_color`` in quickstart.rst documentation (3 instances)
- **FIXED**: Changed ``bins`` → ``bins_per_channel`` in RTD documentation examples
- **RESOLVED**: TypeError when users followed RTD documentation examples exactly
- **ENHANCED**: Documentation now matches actual function signature perfectly
- **TESTED**: Created comprehensive test suite to prevent future parameter mismatches

**Impact:**
- **User Experience**: Eliminated TypeError when following documentation examples
- **Documentation Quality**: RTD examples now work correctly out-of-the-box
- **Professional Standards**: Maintained edaflow's reputation for accurate documentation

Version 0.12.22 (2025-08-08) - Google Colab Compatibility & Clean Workflow 🌟
------------------------------------------------------------------------------

**Fixed:**
- **🔧 GOOGLE COLAB COMPATIBILITY**: Fixed KeyError in ``apply_smart_encoding`` documentation examples
- **FIXED**: Removed hardcoded 'target' column assumption in documentation examples
- **FIXED**: Updated quickstart.rst and README.md with flexible column handling
- **RESOLVED**: Documentation examples now work in Google Colab, Jupyter, and all environments
- **ENHANCED**: More robust ML encoding workflow that adapts to user datasets

**Enhanced:**
- **📚 CLEAN WORKFLOW**: Removed redundant print statements from documentation examples
- **IMPROVED**: Professional rich-styled output eliminates need for manual formatting
- **MODERNIZED**: Documentation examples now showcase rich styling capabilities
- **CREATED**: Google Colab compatibility test suite for validation

**Impact:**
- **Universal Compatibility**: Documentation works across all Python environments
- **Modern Presentation**: Clean, professional output using rich styling
- **Educational Value**: Enhanced learning experience for users across platforms

Version 0.12.3 (2025-08-06) - Complete Positional Argument Compatibility Fix 🔧
--------------------------------------------------------------------------------

**CRITICAL BUG FIX:**

**Fixed:**
- **CRITICAL**: Resolved TypeError when calling ``visualize_image_classes(image_paths, ...)`` with positional arguments
- **Positional Arguments**: Function now properly handles legacy positional argument usage from Jupyter notebooks
- **Backward Compatibility**: Complete support for all three usage patterns:
  1. ``visualize_image_classes(path, ...)`` - Positional (deprecated, shows warning)  
  2. ``visualize_image_classes(image_paths=path, ...)`` - Keyword deprecated (shows warning)
  3. ``visualize_image_classes(data_source=path, ...)`` - Recommended (no warning)

**Improved:**
- **User Experience**: Clear deprecation warnings guide users toward recommended ``data_source=`` syntax
- **Function Architecture**: Refactored to wrapper function pattern for robust argument handling
- **Error Messages**: Enhanced error messages provide clear guidance for parameter usage
- **Documentation**: Updated all examples to show modern ``data_source=`` syntax

**Technical Details:**
- **Implementation**: Split function into public wrapper and internal implementation
- **Argument Handling**: Proper detection and mapping of positional arguments to correct parameters
- **Warning System**: Contextual warnings for different deprecated usage patterns
- **Testing**: Comprehensive test suite validates all backward compatibility scenarios

**Notes:**
- **Zero Breaking Changes**: All existing code continues to work unchanged
- **Jupyter Notebook Fix**: Resolves the specific TypeError reported in Jupyter notebook usage
- **Migration Path**: Users can migrate at their own pace with clear guidance

Version 0.12.2 (2025-08-06) - Documentation Refresh Release 📚
---------------------------------------------------------------

**Documentation:**
- **PyPI Description**: Refreshed PyPI package description with latest feature updates
- **Changelog Display**: Fixed PyPI changelog display showing current version information
- **Version Alignment**: Ensured all documentation reflects current v0.12.2 capabilities

Version 0.12.1 (2025-08-05) - Enhanced Computer Vision EDA 🖼️
--------------------------------------------------------------

**Enhanced Functions:**
- **visualize_image_classes()**: Now supports both directory paths and pandas DataFrames as data sources
  - **DataFrame Support**: Pass image metadata as DataFrame with 'image_path' and 'class' columns
  - **Directory Support**: Continues to support organized folder structure (folder = class)
  - **Flexible Input**: Unified interface for different dataset organizations
  - **Error Handling**: Clear error messages guide proper usage for both input types

**Improvements:**
- **Data Source Flexibility**: Switch between directory-based and metadata-based workflows
- **DataFrame Integration**: Perfect for datasets with existing metadata and annotations
- **Backward Compatibility**: Maintains all existing directory-based functionality
- **User Experience**: Enhanced error messages and parameter validation

Version 0.10.0 (2025-08-05) - Image Quality Assessment Release 🔍
----------------------------------------------------------------

**Major New Feature: Comprehensive Image Quality Assessment**

**NEW Functions:**
- **assess_image_quality()**: Complete automated quality assessment for image datasets

**Key Capabilities:**
- **Corruption Detection**: Automatically identify unreadable or damaged image files
- **Brightness Analysis**: Flag overly dark or bright images with statistical thresholds  
- **Contrast Assessment**: Detect low-contrast images that might impact training
- **Blur Detection**: Use Laplacian variance to identify potentially blurry images
- **Color Analysis**: Distinguish between grayscale and color images, detect mixed modes
- **Dimension Consistency**: Find unusual aspect ratios and size outliers using statistical methods
- **Artifact Detection**: Identify compression artifacts and unusual patterns
- **Quality Scoring**: Statistical quality scoring system (0-100) for overall dataset health
- **Automated Recommendations**: Actionable suggestions for dataset improvement
- **Production Integration**: Quality gates with customizable thresholds for ML pipelines
- **Scalable Analysis**: Sampling support for efficient processing of large datasets

**Enhanced Capabilities:**
- Expanded from 15 to 16 comprehensive EDA functions
- Extended computer vision capabilities with production-ready quality assessment
- Added scipy optimization for advanced blur detection algorithms
- Comprehensive statistical analysis with detailed reporting
- Educational focus on image quality standards and best practices

**Perfect For:**
- Medical and scientific imaging with strict quality requirements
- Production ML pipelines with automated data validation
- Research and development with dataset quality monitoring
- Educational purposes for learning image quality assessment

Version 0.9.0 (2025-08-05) - Computer Vision EDA Release 🖼️
------------------------------------------------------------

**Added**
~~~~~~~~~
* **NEW**: ``visualize_image_classes()`` function for comprehensive image classification dataset analysis
* **NEW**: Computer Vision EDA workflow support with class-wise sample visualization
* **NEW**: Directory-based and DataFrame-based image dataset analysis capabilities  
* **NEW**: Automatic class distribution analysis with imbalance detection
* **NEW**: Image quality assessment with corrupted image detection
* **NEW**: Statistical insights for image datasets (balance ratios, sample counts, warnings)
* **NEW**: Professional grid layouts for image sample visualization with smart sizing
* **NEW**: Technical image information display (dimensions, file sizes, aspect ratios)
* **NEW**: Comprehensive documentation for computer vision EDA workflows

**Enhanced**
~~~~~~~~~~~~
* Complete EDA suite now includes 15 functions (expanded from 14)
* Added Pillow dependency for robust image processing and visualization
* Extended edaflow's educational philosophy to computer vision domains
* Professional documentation with computer vision examples and workflows
* Updated package metadata and dependencies for image processing capabilities

**Technical Features**
~~~~~~~~~~~~~~~~~~~~~~
* **Flexible Input Support**: Both directory structures and DataFrame-based workflows
* **Quality Assessment**: Automatic detection of corrupted images and data quality issues
* **Statistical Analysis**: Comprehensive class balance analysis with actionable warnings
* **Professional Visualization**: Smart grid layouts with customizable sampling strategies
* **Educational Integration**: Maintains edaflow's core principle of teaching through analysis
* **Production Ready**: Robust error handling and validation for real-world datasets

Version 0.8.6 (2025-08-05) - PyPI Changelog Display Fix
--------------------------------------------------------

**Fixed**
~~~~~~~~~
* **CRITICAL**: Fixed PyPI changelog not displaying latest releases (v0.8.4, v0.8.5)
* **DOCUMENTATION**: Updated README.md changelog section that PyPI displays instead of CHANGELOG.md
* **PYPI**: Synchronized README.md changelog with comprehensive CHANGELOG.md content
* **ENHANCED**: Ensured PyPI users see complete version history and latest features

Version 0.8.5 (2025-08-05) - Code Organization and Structure Improvement
--------------------------------------------------------------------------

**Changed**
~~~~~~~~~~~
* **REFACTORED**: Renamed ``missing_data.py`` to ``core.py`` to better reflect comprehensive EDA functionality
* **ENHANCED**: Updated module docstring to describe complete suite of analysis functions
* **IMPROVED**: Better project structure with appropriately named core module containing all 14 EDA functions
* **FIXED**: Updated all imports and tests to reference the new core module structure
* **MAINTAINED**: Full backward compatibility - all functions work exactly the same

Version 0.8.4 (2025-08-05) - Comprehensive Scatter Matrix Visualization Release
--------------------------------------------------------------------------------

**Added**
~~~~~~~~~
* **NEW**: ``visualize_scatter_matrix()`` function with advanced pairwise relationship analysis
* **NEW**: Flexible diagonal plots: histograms, KDE curves, and box plots
* **NEW**: Customizable upper/lower triangles: scatter plots, correlation coefficients, or blank
* **NEW**: Color coding by categorical variables for group-specific pattern analysis
* **NEW**: Multiple regression line types: linear, polynomial (2nd/3rd degree), and LOWESS smoothing
* **NEW**: Comprehensive statistical insights: correlation analysis, pattern identification
* **NEW**: Professional scatter matrix layouts with adaptive figure sizing
* **NEW**: Full integration with existing edaflow workflow and styling consistency

**Enhanced**
~~~~~~~~~~~~
* Complete EDA visualization suite now includes 14 functions (from 13)
* Added scikit-learn and statsmodels dependencies for advanced analytics
* Updated package metadata and documentation for scatter matrix capabilities

**Technical Features**
~~~~~~~~~~~~~~~~~~~~~~
* **Matrix Customization**: Independent control of diagonal, upper, and lower triangle content
* **Statistical Analysis**: Automatic correlation strength categorization and reporting  
* **Regression Analysis**: Advanced trend line fitting with multiple algorithm options
* **Color Intelligence**: Automatic categorical/numerical variable handling for color coding
* **Performance Optimization**: Efficient handling of large datasets with smart sampling suggestions
* **Error Handling**: Comprehensive validation with informative error messages
* **Professional Output**: Publication-ready visualizations with consistent edaflow styling

Version 0.8.3 (2025-08-04) - Critical Documentation Fix Release
----------------------------------------------------------------

**Fixed**
~~~~~~~~~
* **CRITICAL**: Updated README.md changelog section that PyPI was displaying instead of CHANGELOG.md
* **PYPI**: Fixed PyPI changelog display by synchronizing README.md changelog with main CHANGELOG.md
* **DOCUMENTATION**: Ensured consistent changelog information across all package files

Version 0.8.2 (2025-08-04) - Metadata Enhancement Release
----------------------------------------------------------

**Fixed**
~~~~~~~~~
* **METADATA**: Enhanced PyPI metadata to ensure proper changelog display
* **PYPI**: Forced PyPI cache refresh by updating package metadata
* **LINKS**: Added additional project URLs for better discoverability

Version 0.8.1 (2025-08-04) - Changelog Formatting Release
----------------------------------------------------------

**Fixed**
~~~~~~~~~
* Updated changelog dates to current date format
* Removed duplicate changelog header that was causing PyPI display issues
* Improved changelog formatting for better PyPI presentation

Version 0.8.0 (2025-08-04) - Statistical Histogram Analysis Release
--------------------------------------------------------------------

**Added**
~~~~~~~~~
* **NEW**: ``visualize_histograms()`` function with advanced statistical analysis and skewness detection
* Comprehensive distribution analysis with normality testing (Shapiro-Wilk, Jarque-Bera, Anderson-Darling)
* Advanced skewness interpretation: Normal (\|skew\| < 0.5), Moderate (0.5-1), High (≥1)
* Kurtosis analysis: Normal, Heavy-tailed (leptokurtic), Light-tailed (platykurtic)
* KDE curve overlays and normal distribution comparisons
* Statistical text boxes with comprehensive distribution metrics
* Transformation recommendations based on skewness analysis
* Multi-column histogram visualization with automatic subplot layout

**Enhanced**
~~~~~~~~~~~~
* Updated Complete EDA Workflow to include 12 functions (from 9)
* Added histogram analysis as Step 10 in the comprehensive workflow
* Enhanced README documentation with detailed histogram function examples
* Comprehensive test suite with 7 test scenarios covering various distribution types

**Fixed**
~~~~~~~~~
* Fixed Anderson-Darling test attribute error and improved statistical test error handling

Version 0.7.0 (2025-08-03) - Comprehensive Heatmap Visualization Release
-------------------------------------------------------------------------

**Added**
~~~~~~~~~
* **NEW**: ``visualize_heatmap()`` function with comprehensive heatmap visualizations
* Four distinct heatmap types: correlation, missing data patterns, values, and cross-tabulation
* Multiple correlation methods: Pearson, Spearman, and Kendall
* Missing data pattern visualization with threshold highlighting
* Data values heatmap for detailed small dataset inspection
* Cross-tabulation heatmaps for categorical relationship analysis

**Enhanced**
~~~~~~~~~~~~
* Complete EDA workflow now includes 11 steps with comprehensive heatmap analysis
* Updated package features to highlight new heatmap visualization capabilities

Version 0.6.0 (2025-08-02) - Interactive Boxplot Visualization Release
-----------------------------------------------------------------------

**Added**
~~~~~~~~~
* **NEW**: ``visualize_interactive_boxplots()`` function with full Plotly Express integration
* Interactive boxplot visualization with hover tooltips, zoom, and pan functionality
* Statistical summaries with emoji-formatted output for better readability
* Customizable styling options (colors, dimensions, margins)
* Smart column selection for numerical data

**Enhanced**
~~~~~~~~~~~~
* Complete EDA workflow now includes 10 steps with interactive final visualization
* Added plotly>=5.0.0 dependency for interactive visualizations

Version 0.5.1 (2024-01-14) - Documentation Enhancement
-------------------------------------------------------

**Fixed**
~~~~~~~~~
* Updated PyPI documentation to properly showcase handle_outliers_median() function
* Ensured PyPI page displays the complete 9-step EDA workflow including outlier handling
* Synchronized local documentation improvements with PyPI display

Version 0.5.0 (2025-08-04) - Outlier Handling Release
------------------------------------------------------

**Added**
~~~~~~~~~
* ``handle_outliers_median()`` function for automated outlier detection and replacement
* Multiple outlier detection methods: IQR, Z-score, and Modified Z-score
* Complete outlier analysis workflow integration with boxplot visualization
* Median-based outlier replacement for robust statistical handling
* Flexible column selection with automatic numerical column detection

**Fixed**
~~~~~~~~~
* Dtype compatibility improvements to eliminate pandas FutureWarnings
* Enhanced error handling and validation for numerical column processing

Earlier Versions
----------------

For complete version history, see the `GitHub Releases <https://github.com/evanlow/edaflow/releases>`_ page.

.. note::
   This changelog covers the major releases. For detailed commit history and minor updates, 
   visit the `GitHub repository <https://github.com/evanlow/edaflow>`_.