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Key benefits

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

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.

Who is this book 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.

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

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Frequently bought together

Causal Inference and Discovery in Python

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Machine Learning Engineering with Python

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N/A Feb 28, 2024

really accurate, without code sampling problems

Feefo Verified review

David G Jan 10, 2024

Bought it for my cousins, one is a data engineer and one is swe and both love it. Highly recommend it

Amazon Verified review

Matvey Osipov Oct 01, 2023

Streamlit simplifies the way you build web applications using Python. There is really no "back end" work needed to use Streamlit as everything you want to make a web application allowing users to enter data, upload files, navigate tabs, etc is already built in and just needs to be called on and configured/formatted to your liking, all you need to know is Python (mainly libraries involving data and visualization: Pandas, Matplotlib, Seaborn, etc). It is a great way to show off your data science/analyst skills and share useful data with others, especially potential employers!This book provides more than enough to get started with Streamlit and also gives you a huge amount of practical code patterns for each topic discussed. I would highly recommend this book to users looking to learn how to use Streamlit and see how easy it is deploy their Streamlit application to share with others.

Mackenzie Patel Oct 20, 2023

"Streamlit for Data Science: Create interactive data apps in Python" is an excellent resource for data scientists and Python enthusiasts. The author, Tyler Richards, provides a comprehensive guide to building and deploying data apps using Streamlit. The book is well-structured, starting with basic concepts and gradually moving to advanced topics, making it suitable for both beginners and experienced developers. Plus, I loved the SF trees example! Well done Tyler.

Robert Oct 01, 2023

Disclaimer: This review is based on an early review copy of the book.Having worked with Shiny in R, I was naturally curious about creating interactive data apps in Python. Before reading this book, I was unaware of Steamlit. After reading the book, I find the library is valuable and something I will study more in the future. It's a great tool to have in your visualization toolbox.As for the book, Tyler Richardson has done an excellent job introducing the audience to Streamlit and provides many great examples of how to create an interactive data app. The book is written for those with previous experience with Python, so he immediately jumps into how to use Streamlit. The chapters discuss using Streamlit for data visualizations, machine learning, and how to connect to Snowflake and BigQuery. He also includes an interview with five knowledgeable Streamlit power users, giving some insight into how it's used in the wild.I look forward to learning and using this library in the future.

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

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