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

Interview #2 – Johannes Rieke

(Tyler) Hey, Johannes! Before we get started, do you want to give us a quick intro to yourself? Where have you worked in the past, what do you do, what is your background?

(Johannes) Hello! I'm from Germany, and currently living in Berlin. Well, as you know, I'm currently working at Streamlit and have been for the past 2 months but my background actually is in physics. So I did physics in my undergrad and I somehow got into neuroscience. I took a couple courses, did a few projects, and really loved it, especially the combination with computer science, doing simulations of nerve cells, the brain, all that kind of stuff. I got super interested in that. I decided to do my master's in computational neuroscience, which is kind of a combination of neuroscience on the one hand, but also machine learning on the other. In that program, I did a lot of stuff in all kinds of different areas of machine learning, like medical imaging, natural...