Do you love diving into one subject in depth instead of switching between Medium, Twitter, and five different documentation sites? Then check out the 2nd version of my book Streamlit for Data Science I just published!

Why I wrote it

If you’re new to Streamlit, it can feel tricky to know where to start. Do you follow people on Twitter? Start with your own project? What do you need to get started with the right foundations? How do you know if you’re missing a crucial piece of knowledge? There are so many resources and so little time.

Two years ago, I tried to solve this problem by writing the book I wanted to read when I started as a Streamlit developer.

I’m very proud of it! But…

Streamlit kept putting out release after release, making amazing changes. We got past the famous 1.0 version barrier with the introduction of new features like data editors, caching and visualization improvements, and component upgrades. As a result of this, the original book became increasingly outdated.

In addition, I left my job at Meta Integrity to join Streamlit as a data scientist, and just two months later, Streamlit was acquired by Snowflake. It was quite a whirlwind, to say the least.

So I wrote an updated version of the book. It includes new interviews with creators, power users, and employees, along with the experience I gained from building Streamlit apps with the Streamlit data team over the last year and a half. It also incorporates examples using the latest tools and platforms that have gained popularity, such as OpenAI and HuggingFace. Since the first version, I’ve created hundreds of apps, and this version contains all the lessons, both big and small, that I’ve learned.

A lot hasn't changed too. Streamlit is still my favorite Python library, my favorite way to show off data science work. I wrote this second version as a labor of love, and I hope that you find it as lovely to read as I did to write.

Who is it for?

This book is for data scientists and ML enthusiasts, especially those who are new to Streamlit or data science in general. You’ll get the most value from it if you already have knowledge of Python, and it’d also be helpful (though not necessary!) to have some familiarity with popular libraries like Pandas. But I highly recommend that you first explore the Streamlit documentation to see if it meets your requirements. It’s truly delightful, and I often refer to it myself. If you find all the information you need there, simply use the docs!

What is the book about?

Roughly the book contains three sections:

1. The first section gives an introduction to building basic Streamlit apps. It covers visualizations, understanding the execution model, and introduces popular libraries. By the end of this section, you’ll have a working app and an ML model that you can share with anyone!
2. The second section focuses on beauty and complex use cases. It covers Streamlit components, databases, animations, and generative AI. By the end of this section, you’ll have enough knowledge to create production-level Streamlit apps for work or for a large audience.
3. The final section is project-based and explores the use of Streamlit in a working environment. It includes interviews with power users, discusses using Streamlit for job applications, and gives you a better understanding of Streamlit’s long-term direction.

How do I get a copy?

You can always snag a copy on Amazon! Use this link to get 25% (it expires on October 31st). We're also giving away 10 copies on Twitter and LinkedIn. For a chance to win: follow @Streamlit, like this post, and tag a data nerd in the comments!

All proceeds for this copy are going to be donated to PyLadies. If you can’t afford it, shoot me a DM on Twitter and I’d be happy to send you a copy.

Can’t wait to hear your thoughts about the book.

Happy reading! 📕