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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

Chapter 7: Exploring Map Plots and Enriching Your Dashboards with Markdown

In this chapter, we are going to explore how to handle maps, one of the most engaging types of charts. There are many ways of creating and handling maps, as well as many types of map plots. There are also many specialized geographic and scientific applications for maps. We will mainly be focusing on two of the most common types of map plots: choropleth map plots and scatter map plots. Choropleth maps are the type of maps we are most familiar with. These are the types of maps where geographical areas are colored to indicate a country, state, district, or any arbitrary polygon on a map, and express variations in quantity among them. Most of the knowledge we established in the previous chapter can easily be adapted to scatter map plots, as they are essentially the same, with a few differences. Similar to the x and y axes, we have longitude and latitude instead, and we also have different map projections. We will...

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