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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

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,...
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