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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

Interview #3 – Adrien Treuille

(Tyler) Hey, Adrien! Thanks for being willing to be interviewed for this. Before we really get started, do you want to tell me a little bit about yourself? I know you were a professor at Carnegie Mellon, and before that you were working with protein folding. You've also worked on self-driving cars, and now are the founder of Streamlit. So how do you introduce yourself?

(Adrien) First of all, when I was a professor, this whole Python data stack was kind of new. NumPy was certainly pre 1.0, and there was kind of this revelation that there was this amazing library called NumPy, all of a sudden, that made Python as good as MATLAB, and then after a while, way was better than MATLAB. That was the beginning of Python becoming the dominant language of numerical computation, and then ultimately machine learning. Python was a scripting language, a sysadmin language, or maybe a CS 101 language. All of a sudden it had this massive, new, super important...

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