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

What this book covers

Chapter 1, An Introduction to Streamlit, teaches the very basics of Streamlit by creating your first app.

Chapter 2, Uploading, Downloading, and Manipulating Data, looks at data; data apps need data! We'll learn how to use data efficiently and effectively in production applications.

Chapter 3, Data Visualization, teaches how to use all your favorite Python visualization libraries in Streamlit apps. There's no need to learn new visualization frameworks!

Chapter 4, Using Machine Learning with Streamlit, covers machine learning. Ever wanted to deploy your new fancy machine learning model in a user-facing app in hours? Start here for in-depth examples and tips. 

Chapter 5, Deploying Streamlit with Streamlit Sharing, looks at the one-click deploy feature that Streamlit comes with. We'll learn how to remove friction in the deployment process here! 

Chapter 6, Beautifying Streamlit Apps, looks at the features that Streamlit is chock-full of to make gorgeous web apps. We'll learn all the tips and tricks in this chapter. 

Chapter 7, Exploring Streamlit Components, teaches how to leverage the thriving developer ecosystem around Streamlit through open source integrations called Streamlit Components. Just like LEGO, only better. 

Chapter 8, Deploying Streamlit Apps with Heroku and AWS, teaches how to deploy your Streamlit applications using AWS and Heroku as an alternative to Streamlit Sharing. 

Chapter 9, Improving Job Applications with Streamlit, will help you to prove your data science chops to employers using Streamlit apps through everything from apps for resume building to apps for take-home sections of interviews.

Chapter 10, The Data Project – Prototyping Projects in Streamlit, covers making apps for the Streamlit community and others, which is both fun and educational. We'll walk through some examples of projects and you'll learn how to start your own. 

Chapter 11, Using Streamlit for Teams, teaches how to deploy private Streamlit repositories and enforce user authentication using the Streamlit product Streamlit for Teams.

Chapter 12, Streamlit Power Users, provides more information on Streamlit, which is already extensively used for such a young library. Learn from the best with in-depth interviews with the Streamlit founder, data scientists, analysts, and engineers.