This is the code repository for Interactive Dashboards and Data Apps with Plotly and Dash, published by Packt.
Harness the power of a fully fledged frontend web framework in Python – no JavaScript required
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.
This book covers the following exciting features:
- Find out how to run a fully interactive and easy-to-use app
- Convert your charts to various formats including images and HTML files
- Use Plotly Express and the grammar of graphics for easily mapping data to various visual attributes
- Create different chart types, such as bar charts, scatter plots, histograms, maps, and more
- Expand your app by creating dynamic pages that generate content based on URLs
Learn how to build a fully customized dashboard using a real-life dataset.
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.
By the end of the book, you will have built and deployed this Plotly Dash app online, which is available for you to test and play with.
Although every chapter builds on previous chapters, each new functionality is first built as a mini independent app, and works on its own. These can be found in the JupyterLab notebooks in each of the respective chapters.
You also get a $100 hosting credit from Linode (to be consumed within 60 days of creating your account), so you can test it on an actual server and share it with the world.
Watch here the evolution of the Dash app that we build, going through chapters one by one.
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, chapter02.
The code will look like the following:
import plotly.express as px
gapminder = px.data.gapminder()
gapminder
Following is what you need for this book: This Plotly Dash book is for data professionals and data analysts who want to gain a better understanding of their data with the help of different visualizations and dashboards. Basic to intermediate-level knowledge of the Python programming language will help you to grasp the concepts covered in this book more effectively.
You will need a system with a good internet connection and an AWS account.
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Click on the following link to see the Code in Action: [https://bit.ly/3vaXYQJ]
Elias Dabbas
is an online marketing and data science practitioner. Combining both fields, he produces open source software for building dashboards and data apps, as well as software for online marketing. He is the author and maintainer of advertools, a Python library that provides various digital marketing tools, with a focus on SEO, SEM, crawling, and text analysis.