Interactive data visualization in Python transforms static charts into dynamic tools for exploration. Using Matplotlib with ipympl in JupyterLab allows zooming, panning, and real-time updates.

Python’s visualization ecosystem—featuring Matplotlib, Seaborn, and Plotly—turns raw datasets into clear, engaging stories. From precise static figures to interactive dashboards, each tool serves a ...

Abstract: The integration of Augmented Reality (AR) technology into education has the potential to revolutionize the way programming languages, such as Python, are taught. This research explores the ...

Prerequisite: Introduction to Python for Absolute Beginners or some experience using Python. You’ve cleaned and analyzed your data, now learn how to visualize it. Visualizing data is critical for both ...

In this Folium tutorial, we build a complete set of interactive maps that run in Colab or any local Python setup. We explore multiple basemap styles, design rich markers with HTML popups, and ...

Visualization has become one of the most important training tools for athletes in bobsleigh, luge and skeleton at the 2026 Milan Cortina Olympics. With limited access to real tracks, competitors rely ...

Abstract: Designing multiscale visualizations, particularly when the ratio between the largest scale and the smallest item is large, can be challenging, and designers have developed many approaches to ...

This project serves as a complete portfolio demonstrating my journey from Python fundamentals to advanced data analytics techniques. It includes hands-on examples of data cleaning, exploration, ...