Book Image

Interactive Dashboards and Data Apps with Plotly and Dash

By : Elias Dabbas
Book Image

Interactive Dashboards and Data Apps with Plotly and Dash

By: Elias Dabbas

Overview of this book

Plotly's Dash framework is a life-saver for Python developers who want to develop complete data apps and interactive dashboards without JavaScript, but you'll need to have the right guide to make sure you’re getting the most of it. With the help of this book, you'll be able to explore the functionalities of Dash for visualizing data in different ways. Interactive Dashboards and Data Apps with Plotly and Dash will first give you an overview of the Dash ecosystem, its main packages, and the third-party packages crucial for structuring and building different parts of your apps. You'll learn how to create a basic Dash app and add different features to it. Next, you’ll integrate controls such as dropdowns, checkboxes, sliders, date pickers, and more in the app and then link them to charts and other outputs. Depending on the data you are visualizing, you'll also add several types of charts, including scatter plots, line plots, bar charts, histograms, and maps, as well as explore the options available for customizing them. By the end of this book, you'll have developed the skills you need to create and deploy an interactive dashboard, handle complexities and code refactoring, and understand the process of improving your application.
Table of Contents (18 chapters)
1
Section 1: Building a Dash App
6
Section 2: Adding Functionality to Your App with Real Data
11
Section 3: Taking Your App to the Next Level

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...