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

Preparing data with scikit-learn

scikit-learn is one of the most widely used and comprehensive machine learning libraries in Python. It plays very well with the rest of the data-science ecosystem libraries, such as NumPy, pandas, and matplotlib. We will be using it for modeling our data and for some preprocessing as well.

We now have two issues that we need to tackle first: missing values and scaling data. Let's see two simple examples for each, and then tackle them in our dataset. Let's start with missing values.

Handling missing values

Models need data, and they can't know what to do with a set of numbers containing missing values. In such cases (and there are many in our dataset), we need to make a decision on what to do with those missing values.

There are several options, and the right choice depends on the application as well as the nature of the data, but we won't get into those details. For simplicity, we will make a generic choice of replacing...