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Getting Started with Streamlit for Data Science

You're reading from   Getting Started with Streamlit for Data Science Create and deploy Streamlit web applications from scratch in Python

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Product type Paperback
Published in Aug 2021
Publisher Packt
ISBN-13 9781800565500
Length 282 pages
Edition 1st Edition
Languages
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Author (1):
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Tyler Richards Tyler Richards
Author Profile Icon Tyler Richards
Tyler Richards
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Creating Basic Streamlit Applications
2. Chapter 1: An Introduction to Streamlit FREE CHAPTER 3. Chapter 2: Uploading, Downloading, and Manipulating Data 4. Chapter 3: Data Visualization 5. Chapter 4: Using Machine Learning with Streamlit 6. Chapter 5: Deploying Streamlit with Streamlit Sharing 7. Section 2: Advanced Streamlit Applications
8. Chapter 6: Beautifying Streamlit Apps 9. Chapter 7: Exploring Streamlit Components 10. Chapter 8: Deploying Streamlit Apps with Heroku and AWS 11. Section 3: Streamlit Use Cases
12. Chapter 9: Improving Job Applications with Streamlit 13. Chapter 10: The Data Project – Prototyping Projects in Streamlit 14. Chapter 11: Using Streamlit for Teams 15. Chapter 12: Streamlit Power Users 16. Other Books You May Enjoy

Technical requirements

This chapter requires access to Streamlit Sharing, which as of this writing is in beta. You can request Streamlit Sharing access at https://streamlit.io/sharing-sign-up. They send out new admissions each week! If you are still waiting for Streamlit Sharing access and want to deploy an app immediately, feel free to move on to Chapter 8, Deploying Streamlit Apps with Heroku and AWS, where we deploy on AWS and Heroku.

This chapter also requires a free GitHub account, which can be attained at https://www.github.com. A full Primer on GitHub, along with detailed setup instructions, can be found in the section A quick primer on GitHub later in this chapter.

The code for this chapter can be found in the following GitHub repository:

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