Streamlit for Data Science

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 beautiful apps

Book Description

If you work with data in Python and are looking to create data apps that showcase ML models and make interactive visualizations that are easy to create, then this is the ideal book for you. Streamlit for Data Science, Second Edition, shows you how to create and deploy widgets and dashboards quickly, all within Python. This helps you create prototypes in hours instead of days!

Written by a prolific Streamlit user, and current employee who joined after the first edition was published, 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 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

1. An Introduction to Streamlit
2. Uploading, Downloading, and Manipulating Data
3. Data Visualization
4. Using Machine Learning with Streamlit
5. Deploying Streamlit with Streamlit Sharing
6. Beautifying Streamlit Apps
7. Exploring Streamlit Components
8. Deploying Streamlit Apps with Heroku and AWS
9. Connecting Streamlit to Databases
10. Improving Job Applications with Streamlit
11. The Data Project - Prototyping Projects in Streamlit
12. Using Streamlit for Teams
13. Streamlit Power Users