Exoplanets Data Analysis and Visualization

This repository contains a data analysis and visualization project developed as part of the Data Visualization course in the Master of Science in Data Science program at the University of Luxembourg.

The project focuses on exploratory analysis and visual storytelling using exoplanet and solar system datasets, combining static plots, animated visualizations, and interactive elements to study planetary properties, orbital behavior, and habitability indicators.

One of the visualizations produced in this project was later used by the University of Luxembourg to represent the Data Science program in official communication.

Project Overview

The main goals of this project are:

• To explore exoplanet and solar system datasets using data-driven visualization
• To highlight relationships between planetary characteristics such as distance, temperature, and rotation period
• To design clear and engaging static and animated visualizations
• To demonstrate best practices in scientific visualization and exploratory data analysis

The analysis and visualizations are implemented primarily in a Jupyter Notebook.

Key Visualizations

The project includes multiple visual outputs:

• Distribution and clustering of exoplanets
• Discovery trends over time and distance
• K-Means clustering of planetary features
• Animated visualization of planets orbiting a star
• Identification and visualization of habitable zones

Animation Notes

The planet orbiting star animation is computationally expensive.

To keep the repository lightweight and the notebook responsive:

• The default animation uses a reduced number of planets and frames
• Full-resolution animation parameters are documented in the notebook comments

Generating the full animation may take 30–40 minutes, depending on hardware.

The animation is generated in code, saved to the plots/ directory, and then displayed in a Markdown cell.
If the animation does not render immediately inside the notebook, restarting the notebook interface usually resolves the issue.

How to Run

1. Install dependencies:

pip install -r requirements.txt

2. Ensure the datasets are present in the data/ directory.

3. Open and run:

main.ipynb

Academic Context

• Course: Data Visualization
• Program: Master of Science in Data Science
• Institution: University of Luxembourg
• Year: 2024

This project was developed as part of coursework and is shared for educational and reference purposes.

Author

Anton Zaitsev