You're reading from Interactive Dashboards and Data Apps with Plotly and Dash Harness the power of a fully fledged frontend web framework in Python – no JavaScript required

Product type Paperback

Published in May 2021

Publisher Packt

ISBN-13 9781800568914

Length 364 pages

Edition 1st Edition

Languages

JavaScript

Tools

Dash

Concepts

Data Visualization

Author (1):

Dabbas

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Table of Contents (18) Chapters

Preface

1. Section 1: Building a Dash App

2. Chapter 1: Overview of the Dash Ecosystem FREE CHAPTER

3. Chapter 2: Exploring the Structure of a Dash App

4. Chapter 3: Working with Plotly's Figure Objects

5. Chapter 4: Data Manipulation and Preparation, Paving the Way to Plotly Express

6. Section 2: Adding Functionality to Your App with Real Data

7. Chapter 5: Interactively Comparing Values with Bar Charts and Dropdown Menus

8. Chapter 6: Exploring Variables with Scatter Plots and Filtering Subsets with Sliders

9. Chapter 7: Exploring Map Plots and Enriching Your Dashboards with Markdown

10. Chapter 8: Calculating the Frequency of Your Data with Histograms and Building Interactive Tables

11. Section 3: Taking Your App to the Next Level

12. Chapter 9: Letting Your Data Speak for Itself with Machine Learning

13. Chapter 10: Turbo-charge Your Apps with Advanced Callbacks

14. Chapter 11: URLs and Multi-Page Apps

15. Chapter 12: Deploying Your App

16. Chapter 13: Next Steps

17. Other Books You May Enjoy

Technical requirements

We will focus on using the Figure object from the graph_objects module of the plotly package. Later in the chapter, we will utilize the other packages that we have been using to improve our app and add an interactive chart to it. As a reminder, the packages that we will use are Dash, Dash HTML Components, Dash Core Components, Dash Bootstrap Components, JupyterLab, Jupyter Dash, and pandas.

The packages can be individually installed by running pip install <package-name>, but it would be better to install the exact same versions that we use here, to reproduce the same results. You can install them all by running one command, pip install –r requirements.txt, from the root folder of the repository. The latest version of the poverty dataset can be downloaded from this link: https://datacatalog.worldbank.org/dataset/poverty-and-equity-database. However, as with the packages, if you want to reproduce the same results, you can access the dataset from...