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

Controlling the look and feel of the table (cell width, height, text display, and more)

There are numerous options available to modify how your tables look, and it's always good to consult the documentation for ideas and solutions. The potentially tricky part is when you have combinations of options. In some cases, these might modify each other and not be displayed exactly the way you want. So, it is always good to isolate the options as much as possible when debugging.

In Figure 8.13, we displayed only three columns and the first few rows. We will now see how to display more columns and enable users to explore more rows:

  1. Modify df to include all columns that contain Income share:
    df = poverty[poverty['year'].eq(2000)&poverty['is_country']].filter(regex='Country Name|Income share')
  2. Place the DataTable in a dbc.Col component with the desired width, 7 in this case. The table automatically takes the width of the container it is in,...