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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

Customizing the histogram by modifying its bins and using multiple histograms

We can change the number of bins through the nbins parameter. We will first see the effect of using two extreme values for the number of bins. Setting nbins=2 generates the chart in Figure 8.2:

Figure 8.2 – A histogram of the Gini indicator with two bins

Figure 8.2 – A histogram of the Gini indicator with two bins

As you can see, the values were split into two equal bins, (20, 39.9) and (40, 59.9), and we can see how many countries are in each bin. It's quite simple and easy to understand, but not as nuanced as the histogram in Figure 8.1. On the other hand, setting nbins=500 produces the chart in Figure 8.3:

Figure 8.3 – A histogram of the Gini indicator with 500 bins

Figure 8.3 – A histogram of the Gini indicator with 500 bins

It is now much more detailed, maybe more detailed than useful. When you set too many bins, it is almost like looking at the raw data.

The default number of bins resulted in the bin size being intervals of five. Now...

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