Preface

Data scientists and machine learning engineers throughout the 2010s have primarily produced static analyses. We create documents to inform decisions, filled with plots and metrics about our findings, or about the models we create. Creating complete web applications that allow users to interact with analyses is cumbersome, to say the least! Enter Streamlit, a Python library for creating web applications built with data folks in mind at every step.

Streamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes in Python in hours instead of days.

This book takes a hands-on approach to help 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 this 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 and work-related data-focused web applications, and will learn about more complicated topics such as using Streamlit Components, beautifying your apps, and the quick deployment of your new apps.