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Interactive Dashboards and Data Apps with Plotly and Dash

Interactive Dashboards and Data Apps with Plotly and Dash

By : Dabbas
4.4 (24)
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Interactive Dashboards and Data Apps with Plotly and Dash

Interactive Dashboards and Data Apps with Plotly and Dash

4.4 (24)
By: Dabbas

Overview of this book

Plotly's Dash framework is a life-saver for Python developers who want to develop complete data apps and interactive dashboards without JavaScript, but you'll need to have the right guide to make sure you’re getting the most of it. With the help of this book, you'll be able to explore the functionalities of Dash for visualizing data in different ways. Interactive Dashboards and Data Apps with Plotly and Dash will first give you an overview of the Dash ecosystem, its main packages, and the third-party packages crucial for structuring and building different parts of your apps. You'll learn how to create a basic Dash app and add different features to it. Next, you’ll integrate controls such as dropdowns, checkboxes, sliders, date pickers, and more in the app and then link them to charts and other outputs. Depending on the data you are visualizing, you'll also add several types of charts, including scatter plots, line plots, bar charts, histograms, and maps, as well as explore the options available for customizing them. By the end of this book, you'll have developed the skills you need to create and deploy an interactive dashboard, handle complexities and code refactoring, and understand the process of improving your application.
Table of Contents (18 chapters)
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1
Section 1: Building a Dash App
6
Section 2: Adding Functionality to Your App with Real Data
11
Section 3: Taking Your App to the Next Level

Expanding your data manipulation and preparation skills

If you have read any introductory text on data science, you will have probably been told that data scientists spend the majority of their time cleaning data, reformatting it, and reshaping it.

As you have read this book, you will have probably seen this in action!

We saw several times how much code and mental effort, and importantly, domain knowledge, goes into just getting our data into a certain format. Once we have our data in a standardized format, for example, a long form (tidy) DataFrame, then our lives become easier.

You might want to learn more pandas and NumPy for a more complete set of techniques on reshaping your data however you want. As mentioned at the beginning of the chapter, learning new pandas techniques without a practical purpose in mind can help a lot in expanding your imagination. Learning regular expressions can help a lot in text analysis, because text is typically unstructured, and finding and...

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