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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 8: Calculating the Frequency of Your Data with Histograms and Building Interactive Tables

All the chart types that we've explored so far displayed our data as is. In other words, every marker, whether it was a circle, a bar, a map, or any other shape, corresponded to a single data point in our dataset. Histograms, on the other hand, display bars that correspond to a summary statistic about groups of data points. A histogram is mainly used to count values in a dataset. It does so by grouping, or "binning," the data into bins and displaying the count of observations in each bin. Other functions are possible, of course, such as working out the mean or maximum, but counting is the typical use case.

The counts are represented like a bar chart, where the heights of the bars correspond to the counts (or other function) of each bin. Another important result is that we also see how data is distributed, and what shape/kind of distribution we have. Are the observations...

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