Book Image

Getting Started with Streamlit for Data Science

By : Tyler Richards
Book Image

Getting Started with Streamlit for Data Science

By: Tyler Richards

Overview of this book

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.

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Aug, 2021 | 282 pages
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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Share Your Thoughts
1
Section 1: Creating Basic Streamlit Applications
Section 1: Creating Basic Streamlit Applications
Free Chapter
2
Chapter 1: An Introduction to Streamlit
Chapter 1: An Introduction to Streamlit
Technical requirements
Why Streamlit?
Installing Streamlit
Organizing Streamlit apps
Streamlit plotting demo
Making an app from scratch
Summary
3
Chapter 2: Uploading, Downloading, and Manipulating Data
Chapter 2: Uploading, Downloading, and Manipulating Data
Technical requirements
The setup – Palmer's Penguins
Debugging Streamlit apps
Data manipulation in Streamlit
Summary
4
Chapter 3: Data Visualization
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
Summary
5
Chapter 4: Using Machine Learning with Streamlit
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
6
Chapter 5: Deploying Streamlit with Streamlit Sharing
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
Summary
7
Section 2: Advanced Streamlit Applications
Section 2: Advanced Streamlit Applications
8
Chapter 6: Beautifying Streamlit Apps
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
9
Chapter 7: Exploring Streamlit Components
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
10
Chapter 8: Deploying Streamlit Apps with Heroku and AWS
Chapter 8: Deploying Streamlit Apps with Heroku and AWS
Technical requirements
Choosing between AWS, Streamlit Sharing, and Heroku
Deploying Streamlit with Heroku
Deploying Streamlit with AWS
11
Section 3: Streamlit Use Cases
Section 3: Streamlit Use Cases
12
Chapter 9: Improving Job Applications with Streamlit
Chapter 9: Improving Job Applications with Streamlit
Technical requirements
Using Streamlit for proof of skill data projects
Improving job applications in Streamlit
Summary
13
Chapter 10: The Data Project – Prototyping Projects in Streamlit
Chapter 10: The Data Project – Prototyping Projects in Streamlit
Technical requirements
Data science ideation
Collecting and cleaning data
Making an MVP
Iterative improvement
Hosting and promotion
Summary
14
Chapter 11: Using Streamlit for Teams
Chapter 11: Using Streamlit for Teams
Analyzing hypothetical survey costs using Streamlit for Teams
Creating and deploying apps from private repositories
User authentication with Streamlit
Summary
15
Chapter 12: Streamlit Power Users
Chapter 12: Streamlit Power Users
Interview #1 – Fanilo Andrianasolo
Interview #2 – Johannes Rieke
Interview #3 – Adrien Treuille
Interview #4 – Charly Wargnier
Summary
Why subscribe?
16
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Deploying Streamlit with Heroku

Heroku is slightly faster and simpler than AWS, and more cumbersome than Streamlit Sharing. But if you have run out of your Streamlit Sharing repositories, or need some more compute than Sharing has to offer but require fewer configuration options than the infinite ones provided by AWS, then Heroku is the place for you. One other win is that you can get custom URLs for your apps with Heroku, which Streamlit Sharing does not support (yet!). To deploy our Streamlit apps on Heroku, we need to do the following:

  1. Set up and log in to Heroku.
  2. Clone and configure our local repository.
  3. Deploy to Heroku.

Let's look at each of these steps in detail!

Setting up and logging in to Heroku

In the Technical requirements section of this chapter, we covered how to download Heroku and create an account. Now, we need to log in to our Heroku from our command line by running the following command and logging in when prompted:

heroku login...
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