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Interactive Dashboards and Data Apps with Plotly and Dash: Harness the power of a fully fledged frontend web framework in Python – no JavaScript required
Build web-based, mobile-friendly analytic apps and interactive dashboards with Python 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. This Plotly Dash book is for data professionals and data analysts who want to gain a better understanding of their data with the help of different visualizations and dashboards – and without having to use JS. Basic knowledge of the Python programming language and HTML will help you to grasp the concepts covered in this book more effectively, but it's not a prerequisite.
364 pages, Paperback
Published May 21, 2021
20 people are currently reading
32 people want to read
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Displaying 1 - 2 of 2 reviews
May 29, 2021
Disclaimer: I have been delivered a copy of the book before the publication. I have read the book, and I have promised to write my honest opinions 100%.
Overview:
A complete Data Science and Visualization fest with high-level clarity and quality. Even if you don't know to code, this book can teach you everything step by step from how a computer works to how an interactive app can be created on the back-end and front-end side. And, an analytical mind for data visualization, interpretation, and manipulation will be presented by Elias Dabbas. Aggregating data while grouping the data samples, and giving the flexibility to change parameters with interactive Dash is a powerful skill that can be learned from this book. I recommend everyone this journey that will start with a simple Pandas Data Frame and a simple HTML Element, and end by using machine learning for data sample clustering and visualization with Dash that shows every angle for data manipulation.
What I Like:
First of all, even if you do something wrong, you have all of the code. You can check what the author tells in his book, and also what he has written in the code. And, the author incentives you to write your own code to follow his lead. In other ways, it is like learning in a classroom without being a classroom.
Also, the book makes everything complex easy to understand. Structures the different phases of coding and introduces every necessary concept to the reader. Every section of the book completes a missing part for the full picture of data science and visualization in an interactive way.
I have learned lots of new ways and methods of the Pandas library, and I have learned that creating a web app for the users is not so hard with a dash.
It includes machine learning for clustering the samples, and it made me excited. Because, this book shows the reader how to give a decision authority to a machine, even if you don't know anything, it will guide you for the necessary steps.
The author provides ready-to-go Jupyter Notebook examples with different versions for every chapter. Even if you can't implement the code blocks, still you can check the right code structure to understand.
The author makes every concept easier to understand, and every coder from every level will find something new to learn from this book.
What I Don't Like:
In some of the code blocks, there were some minor errors, at the beginning section of the book.
Some of the chapters are really longer than others, and it is a completely normal situation, but also if you have stuck for one of these chapters, it might stall you for a time.
If you don't have enough time or if you give a longer break than 3 days to book, you will need to repeat some latest sections, and finding where were you at your Jupyter notebook might not be easy.
Adding different code blocks from the book to your Jupyter Notebook might not be easy while reading an offline book, but also, the Author provides you ready-to-go Jupyter Notebook examples for every lecture. And, having some numeric labels for code blocks from the Jupyter notebook examples to the code blocks of the book would make the following easier.
What I Would Like To See
The book's name explains and defines itself perfectly. So, the book has shown what it promised to me.
Overview:
A complete Data Science and Visualization fest with high-level clarity and quality. Even if you don't know to code, this book can teach you everything step by step from how a computer works to how an interactive app can be created on the back-end and front-end side. And, an analytical mind for data visualization, interpretation, and manipulation will be presented by Elias Dabbas. Aggregating data while grouping the data samples, and giving the flexibility to change parameters with interactive Dash is a powerful skill that can be learned from this book. I recommend everyone this journey that will start with a simple Pandas Data Frame and a simple HTML Element, and end by using machine learning for data sample clustering and visualization with Dash that shows every angle for data manipulation.
What I Like:
First of all, even if you do something wrong, you have all of the code. You can check what the author tells in his book, and also what he has written in the code. And, the author incentives you to write your own code to follow his lead. In other ways, it is like learning in a classroom without being a classroom.
Also, the book makes everything complex easy to understand. Structures the different phases of coding and introduces every necessary concept to the reader. Every section of the book completes a missing part for the full picture of data science and visualization in an interactive way.
I have learned lots of new ways and methods of the Pandas library, and I have learned that creating a web app for the users is not so hard with a dash.
It includes machine learning for clustering the samples, and it made me excited. Because, this book shows the reader how to give a decision authority to a machine, even if you don't know anything, it will guide you for the necessary steps.
The author provides ready-to-go Jupyter Notebook examples with different versions for every chapter. Even if you can't implement the code blocks, still you can check the right code structure to understand.
The author makes every concept easier to understand, and every coder from every level will find something new to learn from this book.
What I Don't Like:
In some of the code blocks, there were some minor errors, at the beginning section of the book.
Some of the chapters are really longer than others, and it is a completely normal situation, but also if you have stuck for one of these chapters, it might stall you for a time.
If you don't have enough time or if you give a longer break than 3 days to book, you will need to repeat some latest sections, and finding where were you at your Jupyter notebook might not be easy.
Adding different code blocks from the book to your Jupyter Notebook might not be easy while reading an offline book, but also, the Author provides you ready-to-go Jupyter Notebook examples for every lecture. And, having some numeric labels for code blocks from the Jupyter notebook examples to the code blocks of the book would make the following easier.
What I Would Like To See
The book's name explains and defines itself perfectly. So, the book has shown what it promised to me.
February 4, 2024
As stated in the last chapter of the book, I followed a bottom-up approach, attempting to understand a piece of technology, i.e., Plotly, and came across this resource. The book begins with simple chapters to initiate the utilization of Dash, a Python-based web framework / a web interface to create web apps. Then, gradually, the author leads us towards Plotly, covering different types of charts (bar, histograms, scatter plots, maps), and emphasizing the importance of data manipulation skills. Along with covering the basics of incorporating machine learning (unsupervised clustering), multi-page web app creation, and finally deploying the app on a server, the book offers a comprehensive glimpse of everything. Lastly, the author provides good resources to follow up on these topics and more.
I thoroughly enjoyed the book and found its teachings to be a valuable resource – something I can engage with during my free time or wield as a tool in my arsenal. 😄
PS: Since I was reading the book years after its release, most of the packages the author used have been updated. To stay current, I opted to use the latest packages available, which was initially tricky.
I thoroughly enjoyed the book and found its teachings to be a valuable resource – something I can engage with during my free time or wield as a tool in my arsenal. 😄
PS: Since I was reading the book years after its release, most of the packages the author used have been updated. To stay current, I opted to use the latest packages available, which was initially tricky.
Displaying 1 - 2 of 2 reviews