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


In this chapter, we started by learning how to organize our files and folders for the remainder of this book and quickly moved on to instructions for downloading Streamlit. We then built our first Streamlit application, Hello World, and learned how to run our Streamlit applications locally. Then we started building out a more complicated application to show the implications of the central limit theorem from the ground up, going from a simple histogram to accepting user input and formatting different types of text around our app for clarity and beautification.

By now, you should be comfortable with subjects such as basic data visualization, editing Streamlit apps in a text editor, and locally running Streamlit apps. We're going to dive more deeply into data manipulation in our next chapter.