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Streamlit for Data Science

You're reading from   Streamlit for Data Science Create interactive data apps in Python

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781803248226
Length 300 pages
Edition 2nd 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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Table of Contents (15) Chapters Close

Preface 1. An Introduction to Streamlit 2. Uploading, Downloading, and Manipulating Data FREE CHAPTER 3. Data Visualization 4. Machine Learning and AI with Streamlit 5. Deploying Streamlit with Streamlit Community Cloud 6. Beautifying Streamlit Apps 7. Exploring Streamlit Components 8. Deploying Streamlit Apps with Hugging Face and Heroku 9. Connecting to Databases 10. Improving Job Applications with Streamlit 11. The Data Project – Prototyping Projects in Streamlit 12. Streamlit Power Users 13. Other Books You May Enjoy
14. Index

Getting started with Streamlit Community Cloud

Streamlit Community Cloud is Streamlit’s answer to a fast deployment process and is certainly my first recommendation to deploy your Streamlit applications. When I discovered Streamlit in the summer of 2020, I remember deploying an app locally and loving the library, but then quickly being disappointed in having to use AWS to deploy my app. Then, the Streamlit team reached out to me and asked if I wanted to try out a product they were working on, which is now called Streamlit Community Cloud. I thought that there was no way that it was all that simple. We only need to push our code to a GitHub repository and point Streamlit to the said repository, and it takes care of the rest.

There are times when we care about “the rest,” such as when we want to configure the amount of storage space or memory available, but often, letting Streamlit Community Cloud handle deployment, resourcing, and sharing makes our development...

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