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

Working with columns in Streamlit

In all of our apps prior to this point, we have viewed each Streamlit task as a top-down experience. We output text as our title, collect some user input below, and then put our visualizations below that. However, Streamlit allows us to format our app into dynamic columns using the st.beta_columns() feature. As of now, the columns feature is in beta (hence the beta_ in the function name), but the feature should be out of beta at some point in 2021, where it will be called st.columns()

We can divide our Streamlit app into multiple columns of variable lengths, and then treat each column as its own unique space in our app to include text, graphs, images, or anything else we would like. 

The syntax for columns in Streamlit uses with notation, which you may already be familiar with for use cases such as resource management and dealing with opening and writing to files in Python. The easiest way to think about with notation in Streamlit...