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

Streamlit visualization use cases

Some Streamlit users are relatively experienced Python developers with well-tested workflows in visualization libraries of their choice. For these users, the best path forward is the one we've taken so far, which is to create our graphs in our library of choice (Seaborn, Matplotlib, Bokeh, and so on) and then use the appropriate Streamlit function to write this to the app. 

Other Streamlit users will have less experience in Pythonic graphing, and especially for these users, Streamlit offers a few built-in functions. We'll start with built-in libraries and move on to learning how to import the most popular and powerful libraries for our Streamlit apps.