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

Chapter 8: Calculating the Frequency of Your Data with Histograms and Building Interactive Tables

All the chart types that we've explored so far displayed our data as is. In other words, every marker, whether it was a circle, a bar, a map, or any other shape, corresponded to a single data point in our dataset. Histograms, on the other hand, display bars that correspond to a summary statistic about groups of data points. A histogram is mainly used to count values in a dataset. It does so by grouping, or "binning," the data into bins and displaying the count of observations in each bin. Other functions are possible, of course, such as working out the mean or maximum, but counting is the typical use case.

The counts are represented like a bar chart, where the heights of the bars correspond to the counts (or other function) of each bin. Another important result is that we also see how data is distributed, and what shape/kind of distribution we have. Are the observations...