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 #1 – Fanilo Andrianasolo

(Tyler) Hey, Fanilo! Before we get started, do you want to introduce yourself to the readers? What's your background? What do you spend your time doing, and who do you work for?

(Fanilo) Hello, everybody! My name is Fanilo Andrianasolo, I'm from Madagascar, and I work at Worldline, which is one of the leading European companies in digital payments and transactional services. I work there as a data science and business intelligence advocate and tech lead, where I help internal product and development teams to prototype new data science use cases, architect those use cases, and then put them into production. So, most of my work is focused on integrating data analytics inside Worldline, which is a huge task because it covers multiple industries from finance, for example, fraud detection, retail, and customer analysis. And I'm also a data science advocate, so I build and present multiple talks internally or to prospective customers...