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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

Multi-page apps

Our Streamlit apps thus far have all been single pages, where all or nearly all the information in the app has been visible to us with a simple scroll. However, Streamlit also has multi-page functionality. Multi-page apps are a powerful tool for creating apps that are not limited to one page of content and can extend the user experience that comes with Streamlit. For example, the Streamlit data team currently primarily builds multi-page apps, having a new app for each project or team that they are creating Streamlit apps for.

For our first multi-page app, we’re going to focus on separating the map section of the trees app from the rest of the graphs in two separate apps. The way Streamlit creates multi-page apps is it looks in the same directory as our Streamlit app for a folder called pages and then runs each Python file inside the pages folder as its own Streamlit app. To do this, create a new folder inside pretty_trees called pages, and then put a file...

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