Streamlit for Data Science - Second Edition: Create interactive data apps in Python Tyler Richards Author

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!Learn how to showcase machine learning models with Streamlit quickly and effectivelyWork your way through hands-on exercises to become an expert Streamlit creatorDiscover the full range of Streamlit’s capabilities to effortlessly create and deploy beautiful appsIf 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.Set up your first development environment and create a basic Streamlit app from scratchCreate dynamic visualizations using built-in and imported Python librariesDiscover strategies for creating and deploying machine learning models in StreamlitDeploy Streamlit apps with Streamlit Community Cloud, AWS, and HerokuIntegrate Streamlit with Hugging Face, and SnowflakeBeautify Streamlit apps using themes and componentsImplement best practices for prototyping your data science work with StreamlitThis 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.An Introduction to StreamlitUploading, Downloading, and Manipulating DataData VisualizationUsing Machine Learning with StreamlitDeploying Streamlit with Streamlit SharingBeautifying Streamlit AppsExploring Streamlit ComponentsDeploying Streamlit Apps with Heroku and AWSConnecting Streamlit to DatabasesImproving Job Applications with StreamlitThe Data Project – Prototyping Projects in StreamlitUsing Streamlit for TeamsStreamlit Power Users