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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

Finding the optimal number of clusters

We will now see the options we have in choosing the optimal number of clusters and what that entails, but let's first take a look at the following screenshot to visualize how things progress from having one cluster to eight clusters:

Figure 9.3 – Data points and cluster centers for all possible cluster numbers

Figure 9.3 – Data points and cluster centers for all possible cluster numbers

We can see the full spectrum of possible clusters and how they relate to data points. At the end, when we specified 8, we got the perfect solution, where every data point is a cluster center.

In reality, you might not want to go for the full solution, for two main reasons. Firstly, it is probably going to be prohibitive from a cost perspective. Imagine making 1,000 T-shirts with a few hundred sizes. Secondly, in practical situations, it usually wouldn't add much value to add more clusters after a certain fit has been achieved. Using our T-shirt example, imagine if we have two people with...

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