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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

Understanding the role of data manipulation skills

In practical situations, we rarely have our data in the format that we want; we usually have different datasets that we want to merge, and often, we need to normalize and clean up the data. For these reasons, data manipulation and preparation will always play a big part in any data visualization process. So, we will be focusing on this in this chapter and throughout the book.

The plan for preparing our dataset is roughly the following:

  1. Explore the different files one by one.
  2. Check the available data and data types and explore how each can help us categorize and analyze the data.
  3. Reshape the data where required.
  4. Combine different DataFrames to add more ways to describe our data.

Let's go through these steps right away.

Exploring the data files

We start by reading in the files in the data folder:

import os
import pandas as pd
pd.options.display.max_columns = None
os.listdir('data&apos...
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