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

Clustering countries by population

We will first understand this with one indicator that we are familiar with (population), and then make it interactive. We will cluster groups of countries based on their population.

Let's start with a possible practical situation. Imagine you were asked to group countries by population. You are supposed to have two groups of countries, of high and low populations. How do you do that? Where do you draw the line(s), and what does the total of the population have to be in order for it to qualify as "high"? Imagine that you were then asked to group countries into three or four groups based on their population. How would you update your clusters?

We can easily see how KMeans clustering is ideal for that.

Let's now do the same exercise with KMeans using one dimension, and then combine that with our knowledge of mapping, as follows:

  1. Import pandas and open the poverty dataset, like this:
    import pandas as pd
    poverty = pd...