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Interactive Dashboards and Data Apps with Plotly and Dash

Interactive Dashboards and Data Apps with Plotly and Dash

By : Dabbas
4.4 (24)
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Interactive Dashboards and Data Apps with Plotly and Dash

Interactive Dashboards and Data Apps with Plotly and Dash

4.4 (24)
By: 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)
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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 an interactive KMeans clustering app

Let's now put everything together and make an interactive clustering application using our dataset. We will give users the option to choose the year, as well as the indicator(s) that they want. They can also select the number of clusters and get a visual representation of those clusters, in the form of a colored choropleth map, based on the discovered clusters.

Please note that it is challenging to interpret such results with multiple indicators because we will be handling more than one dimension. It can also be difficult if you are not an economist and don't know which indicators make sense to be checked with which other indicators, and so on.

The following screenshot shows what we will be working toward:

Figure 9.9 – An interactive KMeans clustering application

Figure 9.9 – An interactive KMeans clustering application

As you can see, this is a fairly rich application in terms of the combinations of options that it provides. As I also mentioned...

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