Book Image

Getting Started with Streamlit for Data Science

By : Tyler Richards
Book Image

Getting Started with Streamlit for Data Science

By: Tyler Richards

Overview of this book

Streamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time. You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you’ll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps. By the end of this book, you’ll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.
Table of Contents (17 chapters)
Section 1: Creating Basic Streamlit Applications
Section 2: Advanced Streamlit Applications
Section 3: Streamlit Use Cases


In this chapter, we've learned how to get started with Git and GitHub on the command line, how to debug apps on Streamlit Sharing, how to use Streamlit Secrets to use private data on public apps, and how to deploy our apps quickly using Streamlit Sharing. This completes part one of this book! Congratulations for making it to this point. The next section will use all of part one as a building block for more advanced topics such as more complicated formatting and beautification of our Streamlit apps and using valuable open source community-built add-ons called Streamlit components. 

In the next chapter, we will cover beautifying Streamlit apps through themes, columns, and many more features.