Streamlit for Data Science: Create interactive data apps in Python 2nd Edition

Streamlit for Data Science by Tyler Richards is a comprehensive guide to building interactive data apps in Python using the Streamlit library. The book is well-written and easy to follow, even for beginners. It covers all aspects of Streamlit development, from the basics of creating a simple app to more advanced topics such as deploying apps to the web and using Streamlit with machine learning models.The book is divided into two parts. The first part introduces Streamlit and its core features, such as widgets, charts, and layout. It also includes several tutorials on how to build common types of data apps, such as dashboards and machine learning demos.The second part of the book covers more advanced topics, such as deploying Streamlit apps to the web, using Streamlit with machine learning models, and customizing the look and feel of Streamlit apps. It also includes a chapter on best practices for developing and deploying Streamlit apps.Overall, Streamlit for Data Science is an excellent resource for anyone who wants to learn how to build interactive data apps in Python using Streamlit. It is well-written, comprehensive, and covers a wide range of topics, from the basics to more advanced features.

My company uses Streamlit routinely to deploy data products for our clients and to prototype various ideas that we have. This book has been a very useful resource in making us aware of how to leverage various app deployment options when we would like to scale further, how to further customize our apps to give a more bespoke look when deployed, and gave us more best-practice examples of how to write our Streamlit applications.Really appreciate Tyler's work and we will continue to use this book as a reference into the future.

Bought it for my cousins, one is a data engineer and one is swe and both love it. Highly recommend it

Just finished reading Tyler Richards’s new Streamlit book by Packt and I must say, it's an interesting read!Although I don't work with machine learning on a daily basis, reading this book felt like a throwback to my grad school days when I was first introduced to the world of data science and machine learning.Here are the top 5 things that stood out for me:1. The introduction to the Streamlit framework was easy to understand, even for someone completely new to it. I was able to connect the dots with my experiences working with Jupyter notebooks back in the day.2. All the code files are easily accessible through a single link on Github, which includes every example discussed in the book. This made it easy to follow along and test out the concepts.3. As someone coming from the data visualization world, I was impressed by the wide range of options integrated into the framework for easy visualizations.4. The book covers interaction with OpenAI modules, which has become increasingly relevant. There is a gradual move through chapters into ML, AI, and then web deployment.5. The most important piece that made me happy was the demo on how Streamlit can be utilized to stand out in job interviews.Whether you're looking to brush up on your knowledge or dive into the field, I highly recommend giving this book a read!

As I am getting off the ground with Streamlit, I wanted to have a solid guidance manual to help me come up to speed. Unfortunately, I my view is that this book is average in that regards.It's not bad, just that as a beginner I learned more by watching a set of 30 minute Streamlit introduction training videos offered free on the internet. I think the book could be really good if the book content was redesigned by an instructional design person and novice streamlit user.

I am a full stack developer and do a lot of frontend work in my day job. I also write some code in my free time and because I just want results and don't want to build a a whole application to do it, most of the time I just settle for getting results from the terminal.Then I found Streamlit and it is almost as easy as doing just that and this book taught me more about it. Not that you need to know that much to get started. It is almost as easy as just using the command line and you get interactive apps instead of needing to editing variables in your Python script or learn how to use a library like argparse.I recommend this book who wants to build interactive web apps that run out data without having to learn a frontend framework. Or even for frontend developers who want to play around with data, machine learning, and AI, because it will save you a lot of time.

While I’ve used Python over the years for various solutions both at work and on personal projects this book served as my introduction to Streamlit. Tyler did an excellent job of providing examples with step-by-step instructions that even the most junior developer could follow. I particularly enjoyed chapter 4 when we began to explore Machine Learning models and building an application that captured user inputs and produced graphs as outputs. The book also provides examples of how to deploy Streamlit applications via various methods such as StreamLit Community Cloud, Amazon Web Services, Heroku, or Hugging Face Spaces. I highly recommend this book to anyone who is interested in expanding toolset related to data science or just looking to learn a new technology.