User Guide
==========

Comprehensive guides for using edaflow effectively in your data analysis and machine learning workflows.

.. toctree::
   :maxdepth: 2

   learning_path
   data_quality
   visualization
   ml_guide
   ml_workflow
   advanced_features
   best_practices

Overview
--------

The edaflow User Guide is organized into five main sections:

**Data Quality & Cleaning**
~~~~~~~~~~~~~~~~~~~~~~~~~~~
Learn how to assess data quality, handle missing values, convert data types, and prepare your data for analysis.

* Missing data analysis and visualization
* Categorical data insights and type conversion
* Data imputation strategies
* Outlier detection and handling

**Visualization & Analysis**
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Explore edaflow's comprehensive visualization capabilities for understanding your data.

* Distribution analysis with boxplots and histograms
* Interactive visualizations with Plotly
* Correlation and relationship analysis
* Advanced scatter matrix analysis

**Machine Learning Workflows**
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Master the complete ML pipeline with edaflow's comprehensive machine learning functions.

* ML experiment setup and data validation
* Multi-model comparison and ranking systems
* Hyperparameter optimization strategies
* Performance visualization and model artifacts
* Complete workflow examples and best practices

**Advanced Features**
~~~~~~~~~~~~~~~~~~~~
Discover advanced features and customization options for power users.

* Custom thresholds and parameters
* Integration with other libraries
* Performance optimization tips
* Extension and customization

**Best Practices**
~~~~~~~~~~~~~~~~~~
Learn recommended workflows and best practices for effective EDA and ML.

* Recommended EDA workflow
* Memory and performance considerations
* Jupyter notebook integration
* Troubleshooting common issues

New Features & Advanced Visualization
------------------------------------

edaflow now supports:
- Faceted visualizations with `display_facet_grid`
- Feature scaling with `scale_features`
- Grouping rare categories with `group_rare_categories`
- Exporting figures with `export_figure`

See the Visualization Guide and Advanced Features for details and examples.

External Library Requirements
----------------------------
Some features require additional libraries (seaborn, scikit-learn, statsmodels). See the Visualization Guide for installation instructions and troubleshooting tips.

Getting Started
---------------

If you're new to edaflow, we recommend starting with the :doc:`../quickstart` guide, then exploring each section of this user guide based on your specific needs.

For complete function documentation, see the :doc:`../api_reference/index`.
