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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

Iterative improvement

So far, we have been almost purely in production mode with this app. Iterative improvement is all about editing the work we have already done and organizing it in a way that makes the app more usable and, frankly, nicer to look at. There are a few improvements that we can shoot for here:

  • Beautification via animation
  • Organization using columns and width
  • Narrative building through text and additional statistics

Let’s start by using animations to make our apps a bit prettier!

Beautification via animation

In Chapter 7, Exploring Streamlit Components, we explored the use of various Streamlit Components; one of these was a component called streamlit-lottie, which gives us the ability to add animation to our Streamlit applications. We can improve our current app by adding an animation to the top of our current Streamlit app using the following code. If you want to learn more about Streamlit Components, please head back...

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