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

Hosting and promotion

Our final step is to host this app on Streamlit Sharing. To do this, we need to perform the following steps:

  1. Create a GitHub repository for this work.
  2. Add a requirements.txt file.
  3. Use 1-click deployment on Streamlit Sharing to deploy.

We already covered this extensively in Chapter 5, Deploying Streamlit with Streamlit Sharing, so give it a shot now without instruction. If you get stuck, head over to Chapter 5, Deploying Streamlit with Streamlit Sharing, to find the exact instructions.