Automatically visualize any dataset with a single line of code, including data quality assessment and fixes.
AutoViz is a Python library that automatically generates comprehensive visualizations for any dataset with a single line of code. It simplifies exploratory data analysis by creating insightful plots, assessing data quality, and identifying patterns without manual coding. The tool handles datasets of any size and supports multiple output formats including static images and interactive charts.
Data scientists, analysts, and researchers who need quick exploratory data analysis and visualization, particularly beginners who want an accessible tool and experts seeking an automated second opinion on their data.
AutoViz dramatically reduces the time and effort required for data visualization by automating plot generation while maintaining flexibility through customization options. Its integrated data quality assessment and fix capabilities provide additional value beyond typical visualization libraries.
Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Generates multiple insightful plots with a single function call, abstracting away complexities from libraries like Matplotlib and Bokeh, as shown in the examples with minimal code.
Includes the FixDQ() function to automatically assess and fix data quality issues, adding value beyond visualization by addressing common EDA pitfalls.
Supports various formats including PNG, SVG, JPG, interactive Bokeh charts, HTML files, and live dashboards via the chart_format parameter, allowing tailored outputs for different needs.
Handles datasets of any size using statistical sampling with configurable max_rows_analyzed and max_cols_analyzed limits, ensuring performance without crashing on big data.
Requires separate requirement files for different Python versions (e.g., requirements-py310.txt for 3.10), complicating installation and increasing risk of version conflicts in diverse environments.
Relies on statistical sampling for large datasets, which might miss subtle patterns or outliers in the full data, as admitted in the lowess regression warning for rows over 100,000.
Can generate and save numerous plots locally when using verbose=2, necessitating manual deletion of the AutoViz_Plots directory to avoid storage issues, as noted in the Tips section.