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Interactive Dashboards and Data Apps with Plotly and Dash

You're reading from   Interactive Dashboards and Data Apps with Plotly and Dash Harness the power of a fully fledged frontend web framework in Python – no JavaScript required

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Product type Paperback
Published in May 2021
Publisher Packt
ISBN-13 9781800568914
Length 364 pages
Edition 1st Edition
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Author (1):
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Elias Dabbas Elias Dabbas
Author Profile Icon Elias Dabbas
Elias Dabbas
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Building a Dash App
2. Chapter 1: Overview of the Dash Ecosystem FREE CHAPTER 3. Chapter 2: Exploring the Structure of a Dash App 4. Chapter 3: Working with Plotly's Figure Objects 5. Chapter 4: Data Manipulation and Preparation, Paving the Way to Plotly Express 6. Section 2: Adding Functionality to Your App with Real Data
7. Chapter 5: Interactively Comparing Values with Bar Charts and Dropdown Menus 8. Chapter 6: Exploring Variables with Scatter Plots and Filtering Subsets with Sliders 9. Chapter 7: Exploring Map Plots and Enriching Your Dashboards with Markdown 10. Chapter 8: Calculating the Frequency of Your Data with Histograms and Building Interactive Tables 11. Section 3: Taking Your App to the Next Level
12. Chapter 9: Letting Your Data Speak for Itself with Machine Learning 13. Chapter 10: Turbo-charge Your Apps with Advanced Callbacks 14. Chapter 11: URLs and Multi-Page Apps 15. Chapter 12: Deploying Your App 16. Chapter 13: Next Steps 17. Other Books You May Enjoy

Creating a DataTable

Technically, dash_table is a separate package, as mentioned at the beginning of the chapter, and can be installed separately. It is installed automatically with Dash, the correct, up-to-date version, which is the recommended approach.

Many times, displaying tables, especially if they are interactive, can add a lot of value to users of our dashboards. Also, if our dashboards or data visualizations are not sufficient for users, or if they want to run their own analysis, it is probably a good idea to allow them to get the raw data for that. Finally, the DataTable component allows its own data visualization through custom coloring, fonts, sizes, and so on. So, we have another way to visualize and understand our data through tables. We will explore a few options in this chapter, but definitely not all of them.

Let's see how we can create a simple DataTable in a simple app using a DataFrame:

  1. Create a subset of poverty containing only countries, from...
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