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

Section 3: Streamlit Use Cases

Now that we understand how to create and deploy Streamlit apps, this section will focus on the various use cases of Streamlit. We'll go through practical examples of complicated projects and interview power users to find out everything that they can do using Streamlit. 

The following chapters are covered in this section:

  • Chapter 9, Improving Job Applications with Streamlit
  • Chapter 10, The Data Project – Prototyping Projects in Streamlit
  • Chapter 11, Using Streamlit for Teams
  • Chapter 12, Interviews with Power Users