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

Chapter 6: Beautifying Streamlit Apps

Welcome to Section 2 of the book! In Section 1, Creating Basic Streamlit Applications, we focused on the basics – visualization, deployment, and data munging, all the topics that are crucial to getting started with Streamlit. In this part of the book, the purpose is to explore Streamlit through more complex applications and use cases, with the intent of turning you into an expert Streamlit user. 

Throughout this chapter, we'll work with elements including sidebars, columns, colors, and themes to extend our ability to make beautiful Streamlit applications. By the end of this chapter, you should feel much more comfortable creating applications that are better than the average Minimum Viable Product (MVP). We'll start by learning about columns and move on to the rest of the elements discussed, weaving each into the main Streamlit app for the chapter. 

Specifically, in this chapter, we will cover the following topics...