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 multiple scatter traces in a single plot

We will mostly be focusing on using Plotly Express as much as possible, because of its convenience, and the other advantages previously discussed in Chapter 4, Data Manipulation and Preparation - Paving the Way to Plotly Express. It's still very important to know how to work with Figure objects as you will encounter many situations where you will need to work with them, especially when you have a lot of customizations to make. Also, keep in mind that although the most important chart types are supported by Plotly Express, not all of them are.

Let's extend the preceding chart with traces of other countries and compare the two approaches. We start with the graph_objects module's Figure object:

  1. Create a countries list to filter with:
    countries = ['Argentina', 'Mexico', 'Brazil']
  2. Create a subset of poverty, which we will call df, where the values of the Country Name...