Streamlit for Data Science E-Book

Tyler Richards

• Herausgeber: Packt Publishing
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 305

Veröffentlichungsjahr: 2023

Beschreibung

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.

Leseprobe

Streamlit for Data Science

Second Edition

Create interactive data apps in Python

Tyler Richards

BIRMINGHAM—MUMBAI

Streamlit for Data Science

Second Edition

Copyright © 2023 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Publishing Product Manager: Bhavesh Amin

Acquisition Editor: Peer Reviews: Gaurav Gavas

Project Editor: Amisha Vathare

Content Development Editor: Rebecca Robinson

Copy Editor: Safis Editing

Technical Editor: Aniket Shetty

Proofreader: Safis Editing

Indexer: Tejal Daruwale Soni

Presentation Designer: Ganesh Bhadwalkar

Developer Relations Marketing Executive: Monika Sangwan

First published: August 2021

Second edition: September 2023

Production reference: 1210923

Published by Packt Publishing Ltd.

Grosvenor House

11 St Paul’s Square

Birmingham

B3 1RB, UK.

ISBN 978-1-80324-822-6

www.packt.com

Foreword

I remember a CS professor of mine pointing out that most of the magic in Harry Potter can now be done on computers! Images dance on our digital newspapers. Cellphones swirl with memories like portable Pensieves. Computer classes are our Charms. Algorithms are our Arithmancy!

If computing departments are the new Hogwarts, then technical tomes are the new spell books. The best works brim with technical secrets and arcana and represent a totem to some branch of our magical field: Python. Algorithms. Visualization. Machine learning.

I’m therefore particularly excited and proud to share that the canonical Streamlit book, Streamlit for Data Science, has a major new version, lovingly written by one of our own, a previous Streamlit Creator and now a Streamlit data scientist, Tyler Richards.

This is a true spell book. Yes, other books teach Streamlit, but this is the first that captures the essence of Streamlit. This book demonstrates how Streamlit is transforming the very definition of data science and machine learning.

Throughout the 2010s, data science and machine learning had two basic outputs. On the one hand, you could use a notebook environment to create static analyses. On the other, you could deploy complete machine learning models into production. Streamlit opened up a new middle way between these two: interactive apps that let you play with analyses and share models interactively throughout an organization.

Streamlit for Data Science teaches you how to master this new superpower. You start by creating a basic analysis and work your way up to complete Streamlit apps with fancy graphics and interactive machine learning models. You even learn how to use LLMs like OpenAI’s GPT series!

So read on! Learn the deep secrets of Streamlit. Join our magical community. Share your apps with the world. Contribute to our gallery. Or invent your own spells with custom Components. Whether you’re a wizard-in-training looking to deploy your first machine learning project or an experienced auror, this book will turn you into a Streamlit sorcerer.

Adrien TreuilleStreamlit Co-Founder

Contributors

About the author

Tyler Richards is a data scientist at Snowflake, working on Streamlit-related projects. He joined Snowflake through the Streamlit acquisition in the Spring of 2022. Before Snowflake, his focus was on integrity measurement at Facebook (Meta), along with helping bolster the state of US elections for the nonprofit Protect Democracy. He is a data scientist and industrial engineer by training and spends his free time applying data science in fun ways, such as applying machine learning to local campus elections, creating algorithms to help P&G target Tide Pod users, and finding ways to determine the best ping pong players in friend groups. You can find out more at https://www.tylerjrichards.com/.

About the reviewer

Chanin Nantasenamat, Ph.D. is a developer advocate, YouTuber, and ex-professor with a passion for data science, bioinformatics, and content creation. After earning a B.Sc. (biomedical science) and Ph.D. (medical technology) from Mahidol University, his academic career started in 2006, and he was appointed a full professor of bioinformatics in 2018. He pioneered the use of data science and bioinformatics at Mahidol University through courses, research, mentorship, and as founding head of the Center of Data Mining and Biomedical Informatics (2013-2021). He has published more than 170 peer-reviewed research articles in the fields of biology, chemistry, and informatics. In 2021, he pivoted to tech and joined Streamlit, later acquired by Snowflake, where he works as a senior developer advocate. In his free time, he creates educational videos about data science and bioinformatics on YouTube as the Data Professor, with his channel having over 162,000 subscribers.

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Contents

Preface

Who this book is for

What this book covers

Acknowledgment

To get the most out of this book

Get in touch

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

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

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

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

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

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

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

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

Connecting to Databases

Technical requirements

Connecting to Snowflake with Streamlit

Connecting to BigQuery with Streamlit

Adding user input to queries

Organizing queries

Summary

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

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

Streamlit Power Users

Fanilo Andrianasolo

Adrien Treuille

Gerard Bentley

Arnaud Miribel and Zachary Blackwood

Yuichiro Tachibana

Summary

Other Books You May Enjoy

Index

Landmarks

Cover

Index

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