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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

Summary

This has been by far the most technical of our chapters so far, so congratulations on making it through! Deploying applications is notoriously difficult and time-consuming, and requires skills from software engineering and DevOps, along with often requiring experience with version control software (such as Git) and UNIX-style commands and systems. This is part of the reason why Streamlit Community Cloud is such a crucial innovation, but in this chapter, we have learned how to push the edge of Streamlit deployment by renting our own virtual machines and deploying apps on Hugging Face Spaces and Heroku. We have also learned how to figure out what the right deployment strategy is before starting out, which will save hours or days of work (nothing is worse than finishing the deployment of an app and finding out you need to use another platform!).

Next, we’ll move on to learning how to query from databases inside our Streamlit apps.

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