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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

Chapter 9: Letting Your Data Speak for Itself with Machine Learning

While making histograms we got a glimpse of a technique that visualizes aggregates, and not data points directly. In other words, we visualized data about our data. We will take this concept several steps further in this chapter, by using a machine learning technique to demonstrate some options that can be used to categorize or cluster our data. As you will see in this chapter, and even while using a single technique, there are numerous options and combinations of options that can be explored. This is where the value of interactive dashboards comes into play. It would be very tedious if users were to explore every single option by manually creating a chart for it.

This chapter is not an introduction to machine learning, nor does it assume any prior knowledge of it. We will explore a clustering technique called KMeans clustering and use the sklearn machine learning package. This will help us in grouping our data...

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