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

The setup – Palmer's Penguins

For this chapter, we'll be using a delightful dataset about Arctic penguins that comes from the work of Dr. Kristen Gorman (https://www.uaf.edu/cfos/people/faculty/detail/kristen-gorman.php) and the Palmer Station, Antarctica LTER (https://pal.lternet.edu/).

Dataset acknowledgment

Data from the Palmer LTER data repository was supported by the Office of Polar Programs, NSF Grants OPP-9011927, OPP-9632763, and OPP-0217282.

This data is a common alternative to the famous Iris datasets and includes data on 344 individual penguins with 3 species represented. The data can be found in the GitHub repository for this book (https://github.com/tylerjrichards/streamlit_apps), in the penguin_app folder entitled penguins.csv.

As we've discussed before, Streamlit apps run from inside our Python script. This sets the base directory to the location of the Python file with our Streamlit app, which means we can access any other files that...