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 Hugging Face and OpenAI models, and creating apps for job interviews. Key Features Create machine learning apps with random forest, Hugging Face, and GPT-3.5 turbo models Gain an insight into how experts harness Streamlit with in-depth interviews with Streamlit power users Discover the full range of Streamlit’s capabilities via hands-on exercises 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, integrating Hugging Face and OpenAI models into your apps, 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 this 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, Hugging Face Spaces, and Heroku Integrate Streamlit with Hugging Face, OpenAI, 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 Machine Learning and AI with Streamlit Deploying Streamlit with Streamlit Community Cloud Beautifying Streamlit Apps Exploring Streamlit Components Deploying Streamlit Apps with Hugging Face and Heroku Connecting to Databases Improving Job Applications with Streamlit The Data Project – Prototyping Projects in Streamlit Streamlit Power Users Cover Copyright Page Contributors Table of Contents Preface Chapter 1: An Introduction to Streamlit Technical requirements Why Streamlit? Installing Streamlit Organizing Streamlit apps Streamlit plotting demo Making an app from scratch Using user input in Streamlit apps Finishing touches – adding text to Streamlit Summary Chapter 2: Uploading, Downloading, and Manipulating Data Technical requirements The setup – Palmer’s Penguins Exploring Palmer’s Penguins Flow control in Streamlit Debugging Streamlit apps Developing in Streamlit Exploring in Jupyter and then copying to Streamlit Data manipulation in Streamlit An introduction to caching Persistence with Session State Summary Chapter 3: Data Visualization Technical requirements San Francisco Trees – a new dataset Streamlit visualization use cases Streamlit’s built-in graphing functions Streamlit’s built-in visualization options Plotly Matplotlib and Seaborn Bokeh Altair PyDeck Configuration options Summary Chapter 4: Machine Learning and AI with Streamlit Technical requirements The standard ML workflow Predicting penguin species Utilizing a pre-trained ML model in Streamlit Training models inside Streamlit apps Understanding ML results Integrating external ML libraries – a Hugging Face example Integrating external AI libraries – an OpenAI example Authenticating with OpenAI OpenAI API cost Streamlit and OpenAI Summary Chapter 5: Deploying Streamlit with Streamlit Community Cloud Technical requirements Getting started with Streamlit Community Cloud A quick primer on GitHub Deploying with Streamlit Community Cloud Debugging Streamlit Community Cloud Streamlit Secrets Summary Chapter 6: Beautifying Streamlit Apps Technical requirements Setting up the SF Trees dataset Working with columns in Streamlit Exploring page configuration Using Streamlit tabs Using the Streamlit sidebar Picking colors with a color picker Multi-page apps Editable DataFrames Summary Chapter 7: Exploring Streamlit Components Technical requirements Adding editable dataframes with streamlit-aggrid Creating drill-down graphs with streamlit-plotly-events Using Streamlit Components – streamlit-lottie Using Streamlit Components – streamlit-pandas-profiling Interactive maps with st-folium Helpful mini-functions with streamlit-extras Finding more Components Summary Chapter 8: Deploying Streamlit Apps with Hugging Face and Heroku Technical requirements Choosing between Streamlit Community Cloud, Hugging Face, and Heroku Deploying Streamlit with Hugging Face Deploying Streamlit with Heroku Setting up and logging in to Heroku Cloning and configuring our local repository Deploying to Heroku Summary Chapter 9: Connecting to Databases Technical requirements Connecting to Snowflake with Streamlit Connecting to BigQuery with Streamlit Adding user input to queries Organizing queries Summary Chapter 10: Improving Job Applications with Streamlit Technical requirements Using Streamlit for proof-of-skill data projects Machine learning – the Penguins app Visualization – the Pretty Trees app Improving job applications in Streamlit Questions Answering Question 1 Answering Question 2 Summary Chapter 11: The Data Project – Prototyping Projects in Streamlit Technical requirements Data science ideation Collecting and cleaning data Making an MVP How many books do I read each year? How long does it take for me to finish a book that I have started? How long are the books that I have read? How old are the books that I have read? How do I rate books compared to other Goodreads users? Iterative improvement Beautification via animation Organization using columns and width Narrative building through text and additional statistics Hosting and promotion Summary Chapter 12: Streamlit Power Users Fanilo Andrianasolo Adrien Treuille Gerard Bentley Arnaud Miribel and Zachary Blackwood Yuichiro Tachibana Summary PacktPage Other Books You May Enjoy Index 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 Hugging Face and OpenAI models, and creating apps for job interviews. Key Features* Create machine learning apps with random forest, Hugging Face, and GPT-3.5 turbo models * Gain an insight into how experts harness Streamlit with in-depth interviews with Streamlit power users * Discover the full range of Streamlit’s capabilities via hands-on exercises to effortlessly create and deploy well-designed apps Book DescriptionIf 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, integrating Hugging Face and OpenAI models into your apps, 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 this 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, Hugging Face Spaces, and Heroku * Integrate Streamlit with Hugging Face, OpenAI, and Snowflake * Beautify Streamlit apps using themes and components * Implement best practices for prototyping your data science work with Streamlit Who this book is forThis 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 Contents1. An Introduction to Streamlit 2. Uploading, Downloading, and Manipulating Data 3. Data Visualization 4. Machine Learning and AI with Streamlit 5. Deploying Streamlit with Streamlit Community Cloud 6. Beautifying Streamlit Apps 7. Exploring Streamlit Components 8. Deploying Streamlit Apps with Hugging Face and Heroku 9. Connecting to Databases 10. Improving Job Applications with Streamlit 11. The Data Project – Prototyping Projects in Streamlit 12. Streamlit Power Users