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

Predicting penguin species

The dataset that we will primarily use in this chapter is the same Palmer's Penguins dataset that we used in Chapter 1, An Introduction to Streamlit. As is typical, we will create a new folder that will house our new Streamlit app and accompanying code. The following code creates this new folder within our streamlit_apps folder and copies the data from our penguin_app folder. If you haven't downloaded the Palmer's Penguins data yet, please follow the instructions in the The Setup: Palmer's Penguins section in Chapter 2, Uploading, Downloading, and Manipulating Data:

mkdir penguin_ml
cp penguin_app/penguins.csv penguin_ml 
cd penguin_ml 

As you may have noticed in the preceding code, there are two Python files here, one to create the ML model ( and the second to create the Streamlit app ( We will start with the file, and once we have...