Streamlit for Data Science: Create interactive data apps in Python

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.

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Streamlit for Data Science: Create interactive data apps in Python

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.

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Streamlit for Data Science: Create interactive data apps in Python

Streamlit for Data Science: Create interactive data apps in Python

Streamlit for Data Science: Create interactive data apps in Python

Streamlit for Data Science: Create interactive data apps in Python

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Overview

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.


Product Details

ISBN-13: 9781803232959
Publisher: Packt Publishing
Publication date: 09/29/2023
Sold by: Barnes & Noble
Format: eBook
Pages: 300
File size: 15 MB
Note: This product may take a few minutes to download.

About the Author

Tyler Richards is a senior data scientist at Snowflake, working on a variety of Streamlit-related projects. Before this, he worked on integrity as a data scientist for Meta and non-profits like Protect Democracy. While at Facebook, he launched the first version of this book and subsequently started working at Streamlit, which was acquired by Snowflake early in 2022.

Table of Contents

Table of Contents
  1. 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
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