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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

San Francisco Trees – a new dataset

We’re going to be working with all sorts of graphs in this chapter, so we’re going to need a new dataset that has much more info, especially dates and locations. Enter SF Trees. The department of public works in SF has a dataset (cleaned by the wonderful folks in the R community who run Tidy Tuesday, a weekly event where people publish interesting visualizations of new data each week) of every tree planted and maintained in the city of SF. They cleverly call this dataset EveryTreeSF – Urban Forest Map and update it every day. I have selected a random set of 10,000 trees with complete info and placed this data in the main GitHub repository under the trees folder (I’m not as clever as the data engineer in SF’s DPW, I know). The GitHub repo can be found at https://github.com/tylerjrichards/streamlit_apps. If you would like to download the full dataset, the link is here: https://data.sfgov.org/City-Infrastructure...

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