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

Creating a DataTable

Technically, dash_table is a separate package, as mentioned at the beginning of the chapter, and can be installed separately. It is installed automatically with Dash, the correct, up-to-date version, which is the recommended approach.

Many times, displaying tables, especially if they are interactive, can add a lot of value to users of our dashboards. Also, if our dashboards or data visualizations are not sufficient for users, or if they want to run their own analysis, it is probably a good idea to allow them to get the raw data for that. Finally, the DataTable component allows its own data visualization through custom coloring, fonts, sizes, and so on. So, we have another way to visualize and understand our data through tables. We will explore a few options in this chapter, but definitely not all of them.

Let's see how we can create a simple DataTable in a simple app using a DataFrame:

  1. Create a subset of poverty containing only countries, from...