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

Technical requirements

This chapter requires access to Streamlit Sharing, which as of this writing is in beta. You can request Streamlit Sharing access at They send out new admissions each week! If you are still waiting for Streamlit Sharing access and want to deploy an app immediately, feel free to move on to Chapter 8, Deploying Streamlit Apps with Heroku and AWS, where we deploy on AWS and Heroku.

This chapter also requires a free GitHub account, which can be attained at A full Primer on GitHub, along with detailed setup instructions, can be found in the section A quick primer on GitHub later in this chapter.

The code for this chapter can be found in the following GitHub repository: