Interactive Dashboards And Data Apps With Plotly And Dash

Abstract

1. Introduction

With Plotly's Dash framework, it is now easier than ever for Python programmers to develop complete data apps and interactive dashboards. Dash apps can be used by a non-technical audience, and this will make data analysis accessible to a much wider group of people. This book will help you to explore the functionalities of Dash for visualizing data in different ways and getting the most out of it.

In this book, we also go through The World Bank's Poverty and Equity Database to build an interactive dashboard, that allows users to interactivley create the chart they want and make the comparisons they want.

Learn how to build a fully customized dashboard using a real-life dataset.

2. Objective

Interactive Dashboards and Data Apps with Plotly and Dash, published by Packt

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

4. Technology Stack

5. System Requirements

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

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• 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/PacktPublishing/Interactive-Dashboards-and-Data-Apps-with-Plotly-and-Dash.git
cd Interactive-Dashboards-and-Data-Apps-with-Plotly-and-Dash

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 PacktPublishing 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.