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

Creating and deploying apps from private repositories

One great feature of the Streamlit for Teams product is the ability to use Streamlit Sharing from private GitHub repositories. This works exactly the same as how we learned in Chapter 5, Deploying Streamlit with Streamlit Sharing, but from a private rather than a public repository. To make this change, you will need to have access to Streamlit Teams or get access from the Streamlit team (they might just let you try it out if you ask nicely!). 

To create a private GitHub repo, head over to and make sure to click the Private rather than Public option, as shown in the next screenshot: 

Figure 11.4 – Private repository on GitHub

And after we add our current code to our GitHub repository, we can deploy on Streamlit Sharing just as we normally would, by going over to and following the directions for one-click deployment. I have deployed this...