Panel Graphic Walker

A project providing a Graphic Walker Pane for use with HoloViz Panel.

Data SciencePythonMIT

Abstract

Panel Graphic Walker is an open-source Data Science project. A project providing a Graphic Walker Pane for use with HoloViz Panel. panel-graphic-walker brings the power of Graphic Walker to your data science workflow, seamlessly integrating interactive data exploration into notebooks and Panel applications. Effortlessly create dynamic visualizations, analyze datasets, and build dashboards—all within a Pythonic, intuitive interface. It is built using Python. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building a Data Science mini project or final-year project.

1. Introduction

panel-graphic-walker brings the power of Graphic Walker to your data science workflow, seamlessly integrating interactive data exploration into notebooks and Panel applications. Effortlessly create dynamic visualizations, analyze datasets, and build dashboards—all within a Pythonic, intuitive interface.

2. Objective

A project providing a Graphic Walker Pane for use with HoloViz Panel.

This project demonstrates how Python can be applied to a real-world Data Science problem.

4. Technology Stack

Python

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/panel-extensions/panel-graphic-walker.git
cd panel-graphic-walker
pip install panel-graphic-walker

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Turn the analysis into an interactive dashboard
  • Automate data refresh with a scheduled job
  • Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What is the source of the dataset and how was missing data handled?
  2. Which exploratory analysis steps revealed the most useful insight?
  3. Why were these particular charts chosen to present the data?
  4. Which statistical or ML technique supports the conclusions?
  5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by panel-extensions and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

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