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

Chapter 1: An Introduction to Streamlit

Streamlit is a web application framework that helps you build and develop Python-based web applications that can be used to share analytics results, build complex interactive experiences, and illustrate new machine learning models. On top of that, developing and deploying Streamlit apps is incredibly fast and flexible, often turning application development time from days into hours.

In this chapter, we start out with the Streamlit basics. We will learn how to download and run demo Streamlit apps, how to edit demo apps using our own text editor, how to organize our Streamlit apps, and finally, how to make our very own. Then, we will explore the basics of data visualization in Streamlit. We will learn how to accept some initial user input, and then add some finishing touches to our own apps with text. At the end of this chapter, you should be comfortable starting to make your own Streamlit applications!

In particular, we will cover the following topics:

  • Why Streamlit?
  • Installing Streamlit
  • Organizing Streamlit apps
  • Streamlit plotting demo
  • Making an app from scratch

Before we begin, we will start with the technical requirements to make sure we have everything we need to get started.