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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

Predicting penguin species

The dataset that we will primarily use in this chapter is the same Palmer Penguins dataset that we used earlier in Chapter 1, An Introduction to Streamlit. As is typical, we will create a new folder that will house our new Streamlit app and accompanying code.

The following code creates this new folder within our streamlit_apps folder and copies the data from our penguin_app folder. If you haven’t downloaded the Palmer Penguins dataset yet, please follow the instructions in the The setup: Palmer Penguins section in Chapter 2, Uploading, Downloading, and Manipulating Data:

mkdir penguin_ml
cp penguin_app/penguins.csv penguin_ml
cd penguin_ml
touch penguins_ml.py
touch penguins_streamlit.py

As you may have noticed in the preceding code, there are two Python files here, one to create the ML model (penguins_ml.py) and the second to create the Streamlit app (penguins_streamlit.py). We will start with the penguins_ml.py file, and once we have...

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