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

Getting started with Streamlit Sharing

Streamlit Sharing is Streamlit's answer to a fast deployment process and is certainly my first recommendation for deploying your Streamlit applications. I remember the first time I deployed an app on Streamlit Sharing, I thought that there was no way that it was all that simple. We only need to push our code to a Github repository, point Streamlit to said repository, and it takes care of the rest. There are times when we care about "the rest," such as when we want to configure the amount of storage space or memory available, but often, letting Streamlit Sharing handle deployment, resourcing, and link creation makes our development significantly easier.

The goal here will be to take the Palmer's penguins ML app we have already created and deploy it using Streamlit Sharing. Before we get started, Streamlit Sharing runs using GitHub. If you are already familiar with Git and GitHub, feel free to skip over this section and make...