Getting Started with Streamlit for Data Science
This is the code repository for Getting Started with Streamlit for Data Science, published by Packt.
Create and deploy Streamlit web applications from scratch in Python
What is this book about?
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.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
Instructions and Navigations
All of the code is organized into folders.
The code will look like the following:
import pandas as pd
penguin_df = pd.read_csv('penguins.csv')
print(penguin_df.head())
Following is what you need for this book: 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. .
With the following software and hardware list you can run all code files present in the book (Chapter 1-11).
Software and Hardware List
| Chapter | Software required | OS required |
|---|---|---|
| 1 - 11 | Python 3+ | Windows, Mac OS X, and Linux (Any) |
| 1 - 11 | Streamlit 0.81+ | Windows, Mac OS X, and Linux (Any) |
| 1 - 11 | GitHub | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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Get to Know the Author
Tyler Richards is a data scientist at Facebook, working on community integrity. Before this gig, his focus was on helping bolster the state of US elections for the nonprofit Protect Democracy. He is a data scientist and industrial engineer by training, which he gets to make use of 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. He is always looking for a new project, a new adventure.