Book Image

Streamlit for Data Science - Second Edition

By : Tyler Richards
3.3 (3)
Book Image

Streamlit for Data Science - Second Edition

3.3 (3)
By: Tyler Richards

Overview of this book

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.
Table of Contents (15 chapters)
13
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Index

What this book covers

Chapter 1, An Introduction to Streamlit, teaches the very basics of Streamlit by creating your first app.

Chapter 2, Uploading, Downloading, and Manipulating Data, looks at data; data apps need data! You’ll learn how to use data efficiently and effectively in production applications.

Chapter 3, Data Visualization, teaches how to use all your favorite Python visualization libraries in Streamlit apps. There’s no need to learn new visualization frameworks!

Chapter 4, Machine Learning and AI with Streamlit, covers machine learning. Ever wanted to deploy your new fancy machine learning model in a user-facing app in hours? Start here for in-depth examples and tips, including working with Hugging Face and OpenAI models.

Chapter 5, Deploying Streamlit with Streamlit Community Cloud, looks at the one-click deploy feature that Streamlit comes with. You’ll learn how to remove friction in the deployment process here!

Chapter 6, Beautifying Streamlit Apps, looks at the features that Streamlit is chock-full of to make gorgeous web apps. You’ll learn all the tips and tricks in this chapter.

Chapter 7, Exploring Streamlit Components, teaches how to leverage the thriving developer ecosystem around Streamlit through open-source integrations called Streamlit Components. Just like LEGO, only better.

Chapter 8, Deploying Streamlit Apps with Hugging Face and Heroku, teaches how to deploy your Streamlit applications using Hugging Face and Heroku as an alternative to Streamlit Community Cloud.

Chapter 9, Connecting to Databases, will help you add data from production databases into your Streamlit apps, which expands the possible apps you can make.

Chapter 10, Improving Job Applications with Streamlit, will help you to prove your data science chops to employers using Streamlit apps through everything from apps for resume building to apps for take-home sections of interviews.

Chapter 11, The Data Project – Prototyping Projects in Streamlit, covers making apps for the Streamlit community and others, which is both fun and educational. You’ll walk through some examples of projects and learn how to start your own.

Chapter 12, Streamlit Power Users, provides more information on Streamlit, which is already extensively used for such a young library. Learn from the best with in-depth interviews with the Streamlit founder, data scientists, analysts, and engineers.