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

Choosing between AWS, Streamlit Sharing, and Heroku

At a high level, whenever we are trying to deploy our Streamlit application such that users on the internet can see our applications, what we are really doing is renting a computer owned by someone else (such as Amazon) and giving that computer a set of instructions to start up our application. Choosing which platform to use is difficult to know how to do without either having a background in deploying systems or without trying each option out first, but there are a few heuristics that should help you out. The two most important factors for this decision are the flexibility of the system and the time it takes to get up and running. Note that these two factors directly trade off with one another. If you are using Streamlit Sharing, you cannot say "I want this to run on a macOS, and I want to add two GPUs to this app," and so on, but in return, you get a wildly simple process where you can simply point Streamlit Sharing to...