Streamlit for Data Science: Create interactive data apps in Python, 2nd Edition

An easy-to-follow and comprehensive guide to creating data apps with Streamlit, including how-to guides for working with cloud data warehouses like Snowflake, using pretrained models on Hugging Face, and even creating apps for job interviews!
Key Features

Learn how to showcase machine learning models with Streamlit quickly and effectively

Work your way through hands-on exercises to become an expert Streamlit creator

Discover the full range of Streamlit’s capabilities to effortlessly create and deploy well-designed apps

Book Description
If you work with data in Python and are looking to create data apps that showcase ML models and make beautiful, interactive visualizations, then this is the ideal book for you. Streamlit for Data Science, Second Edition, shows you how to create and deploy data apps quickly, all within Python. This helps you create prototypes in hours instead of days!
Written by a prolific Streamlit user and senior data scientist at Snowflake, this fully updated second edition builds on the practical nature of the previous edition with exciting updates, including connecting Streamlit to data warehouses like Snowflake, deploying Streamlit on Hugging Face, and connecting and building apps on top of Streamlit databases. Plus, there is a totally updated code repository on GitHub to help you practice your newfound skills.
You'll start your journey with the fundamentals of Streamlit and gradually build on the foundation by working with machine learning models and producing high-quality interactive apps. The practical examples of both personal data projects and work-related, data-focused web applications will help you get to grips with more challenging topics such as Streamlit Components, beautifying your apps, and quick deployment.
By the end of this book, you'll be able to create dynamic web apps in Streamlit quickly and effortlessly.
What you will learn

Set up your first development environment and create a basic Streamlit app from scratch

Create dynamic visualizations using built-in and imported Python libraries

Discover strategies for creating and deploying machine learning models in Streamlit

Deploy Streamlit apps with Streamlit Community Cloud, AWS, and Heroku

Integrate Streamlit with Hugging Face and Snowflake

Beautify Streamlit apps using themes and components

Implement best practices for prototyping your data science work with Streamlit

Who This Book Is For
This book is for data scientists and machine learning enthusiasts who want to get started with creating data apps in Streamlit. It is terrific for junior data scientists looking to gain some valuable new skills in a specific and actionable fashion and is also a great resource for senior data scientists looking for a comprehensive overview of the library and how people use it. Prior knowledge of Python programming is a must, and you’ll get the most out of this book if you’ve used Python libraries like Pandas and NumPy in the past.
Table of Contents

An Introduction to Streamlit

Uploading, Downloading, and Manipulating Data

Data Visualization

Using Machine Learning with Streamlit

Deploying Streamlit with Streamlit Sharing

Beautifying Streamlit Apps

Exploring Streamlit Components

Deploying Streamlit Apps with Heroku and AWS

Connecting Streamlit to Databases

Improving Job Applications with Streamlit

The Data Project – Prototyping Projects in Streamlit

Using Streamlit for Teams

Streamlit Power Users