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):

Elias 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

Understanding the Figure object

Plotly is a fully fledged data visualization system, which ships with more than 50 types of charts out of the box (for example, bar charts, scatter plots, and histograms). It supports 2D and 3D visualizations, ternary plots, maps, and more. The available options for customizing almost any aspect of your charts are very detailed and can be overwhelming. This, as they say, is a good problem to have!

We use charts to uncover certain characteristics of our data or the relationships between different datasets. However, pure data visualization would be meaningless if we didn't know what is being visualized. Imagine a rectangle that has a bunch of dots on it with clear patterns. It would still be meaningless if you didn't know what the x axis represented, for example. If you have different shapes and colors in a plot, then they would mean nothing without a legend. Usually, titles and annotations are also needed to give us context around the data...