AutoViz is an open-source Python library designed for automated exploratory data analysis (EDA) and visualization. It enables users to generate insightful visualizations from any dataset, regardless of size, with a single line of code, making it accessible for both beginners and experts. The library also includes functionality for assessing and fixing data quality issues.

How It Works

AutoViz automates the process of creating multiple visualizations by analyzing data patterns, trends, and relationships. It supports various chart formats including static (PNG, SVG, JPG) via Matplotlib, interactive Bokeh charts, and server-based dashboards. For large datasets, it employs statistically valid sampling to maintain performance. The library also offers data quality assessment and correction capabilities through its FixDQ() function.

Quick Start & Requirements

• Install via pip: pip install autoviz
• For specific Python versions, use requirements.txt (Python < 3.10), requirements-py310.txt (Python 3.10), or requirements-py311.txt (Python 3.11+).
• Usage example:

  from autoviz import AutoViz_Class
  AV = AutoViz_Class()
  AV.AutoViz(filename="your_file.csv", depVar="your_target_variable", chart_format="bokeh")
  

• Official documentation and examples are available on GitHub.

Highlighted Details

• Visualizes any dataset size using statistically valid sampling.
• Supports static, interactive (Bokeh), and server-based chart formats.
• Includes a FixDQ() function for data quality assessment and correction.
• Customizable through arguments like chart_format, verbose, max_rows_analyzed, and max_cols_analyzed.

Maintenance & Community

The project is actively maintained by @AutoViML and collaborators. Contributions are welcomed via pull requests on GitHub.

Licensing & Compatibility

AutoViz is released under the Apache License, Version 2.0. This license permits commercial use and linking with closed-source projects.

Limitations & Caveats

The project is not an official Google project and is provided without warranty. Users are advised to select the appropriate requirements file based on their Python version for optimal compatibility.