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)
1
Section 1: Creating Basic Streamlit Applications
7
Section 2: Advanced Streamlit Applications
11
Section 3: Streamlit Use Cases

Interview #3 – Adrien Treuille

(Tyler) Hey, Adrien! Thanks for being willing to be interviewed for this. Before we really get started, do you want to tell me a little bit about yourself? I know you were a professor at Carnegie Mellon, and before that you were working with protein folding. You've also worked on self-driving cars, and now are the founder of Streamlit. So how do you introduce yourself?

(Adrien) First of all, when I was a professor, this whole Python data stack was kind of new. NumPy was certainly pre 1.0, and there was kind of this revelation that there was this amazing library called NumPy, all of a sudden, that made Python as good as MATLAB, and then after a while, way was better than MATLAB. That was the beginning of Python becoming the dominant language of numerical computation, and then ultimately machine learning. Python was a scripting language, a sysadmin language, or maybe a CS 101 language. All of a sudden it had this massive, new, super important...