Create, deploy, and test your Python applications, analyses, and models with ease using Streamlit
Key Features
• Learn how to showcase machine learning models in a Streamlit application effectively and efficiently
• Become an expert Streamlit creator by getting hands-on with complex application creation
• Discover how Streamlit enables you to create and deploy apps effortlessly
Book Description
Streamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time.
You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you'll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps.
By the end of this book, you'll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.
What you will learn
• Set up your first development environment and create a basic Streamlit app from scratch
• Explore methods for uploading, downloading, and manipulating data in Streamlit apps
• Create dynamic visualizations in Streamlit using built-in and imported Python libraries
• Discover strategies for creating and deploying machine learning models in Streamlit
• Use Streamlit sharing for one-click deployment
• Beautify Streamlit apps using themes, Streamlit Components, and Streamlit sidebar
• 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 create web apps using Streamlit. Whether you're a junior data scientist looking to deploy your first machine learning project in Python to improve your resume or a senior data scientist who wants to use Streamlit to make convincing and dynamic data analyses, this book will help you get there! Prior knowledge of Python programming will assist with understanding the concepts covered.
Author(s): Tyler Richards
Edition: 1
Publisher: Packt Publishing
Year: 2021
Language: English
Commentary: Vector PDF
Pages: 282
City: Birmingham, UK
Tags: Amazon Web Services; Data Science; Python; Web Applications; Data Visualization; Deployment; Heroku; Prototyping; Streamlit
Cover
Title Page
Copyright and Credits
Contributors
Table of Contents
Preface
Section 1: Creating Basic Streamlit Applications
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
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
Summary
Chapter 4: Using Machine Learning with Streamlit
The standard ML workflow
Predicting penguin species
Utilizing a pre-trained ML model in Streamlit
Training models inside Streamlit apps
Understanding ML results
Summary
Chapter 5: Deploying Streamlit with Streamlit Sharing
Technical requirements
Getting started with Streamlit Sharing
A quick primer on GitHub
Deploying with Streamlit Sharing
Debugging Streamlit Sharing
Streamlit Secrets
Summary
Section 2: Advanced Streamlit Applications
Chapter 6: Beautifying Streamlit Apps
Technical requirements
Setting up the SF Trees dataset
Working with columns in Streamlit
Exploring page configuration
Using the Streamlit sidebar
Picking colors with Color Picker
Utilizing Streamlit themes
Summary
Chapter 7: Exploring Streamlit Components
Technical requirements
Using Streamlit Components – streamlit-embedcode
Using Streamlit Components – streamlit-lottie
Using Streamlit Components – streamlit-pandas-profiling
Finding more components
Summary
Chapter 8: Deploying Streamlit Apps with Heroku and AWS
Technical requirements
Choosing between AWS, Streamlit Sharing, and Heroku
Deploying Streamlit with Heroku
Setting up and logging in to Heroku
Cloning and configuring our local repository
Deploying to Heroku
Deploying Streamlit with AWS
Selecting and launching a virtual machine
Installing the necessary software
Cloning and running your app
Long-term AWS deployment
Section 3: Streamlit Use Cases
Chapter 9: 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 10: 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 11: Using Streamlit for Teams
Analyzing hypothetical survey costs using Streamlit for Teams
Setting up a new Streamlit app folder
Illustrating the representativeness of the sample
Calculating the cost of the sample
Using interaction to show trade-offs
Creating and deploying apps from private repositories
User authentication with Streamlit
Summary
Chapter 12: Streamlit Power Users
Interview #1 – Fanilo Andrianasolo
Interview #2 – Johannes Rieke
Interview #3 – Adrien Treuille
Interview #4 – Charly Wargnier
Summary
Other Books You May Enjoy
Index
