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

Using callback functions with maps

What we have done so far was done with one indicator, and we used this indicator to select the desired column from the dataset. We can easily create a dropdown to allow users to choose any of the available indicators and let them explore the whole dataset. The year variable is already interactive and part of the chart, as used by the animation_frame parameter. So, this can become the first exploratory interactive chart that users start with on our app, to help them get an overview of the available metrics and how they are changing in time.

Setting this up is straightforward, as we did several times. We will implement it, and after that, we will see how to use the Markdown component to add context around/about the map chart and the chosen indicator.

Let's do the necessary steps to implement this functionality independently in JupyterLab:

  1. Create a Dropdown component, where the available options are the column names of poverty, using...