Project Overview
At Datraxa, we conducted a comparative study to benchmark three leading Python libraries that automate Exploratory Data Analysis (EDA) — Pandas Profiling (now YData Profiling), SweetViz, and AutoViz.
The goal was to understand how each tool performs when generating fast, detailed, and interactive EDA reports for datasets of different sizes and complexities.
We used popular real-world datasets — including Titanic, Housing Prices, and NYC Taxi Trips — to evaluate each library’s speed, usability, and quality of insights.
🔍 Key Highlights
- One-Line EDA Reports
Tested how quickly each tool can create a full EDA report using just a single command. - Feature Comparison
Compared data alerts, correlation analysis, visual quality, and summary insights across all three tools. - Performance Benchmarking
Measured runtime, memory usage, and report generation speed for both small and large datasets. - Trade-Off Analysis
Identified which tool works best depending on the dataset size, analysis depth, and project goals.
⚙️ Technologies Used
- Python
- Pandas Profiling (YData Profiling)
- SweetViz
- AutoViz
- Jupyter Notebooks
📈 Results & Learnings
This benchmark created a clear decision framework for selecting the best EDA automation tool based on project needs.
- 🟢 SweetViz — Best for visually rich and interactive EDA reports.
- 🔵 Pandas Profiling — Most detailed tool with comprehensive alerts and metrics.
- 🟣 AutoViz — Fastest library for large datasets with efficient summaries.
Each tool has unique strengths, making this study a useful guide for data analysts, machine learning engineers, and students who want to speed up their data exploration process.
Datraxa — Simplifying data exploration through smart automation.