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Streamlit for Data Science - Second Edition

You're reading from  Streamlit for Data Science - Second Edition

Product type Book
Published in Sep 2023
Publisher Packt
ISBN-13 9781803248226
Pages 300 pages
Edition 2nd Edition
Languages
Author (1):
Tyler Richards Tyler Richards
Profile icon Tyler Richards

Table of Contents (15) Chapters

Preface An Introduction to Streamlit Uploading, Downloading, and Manipulating Data Data Visualization Machine Learning and AI with Streamlit Deploying Streamlit with Streamlit Community Cloud Beautifying Streamlit Apps Exploring Streamlit Components Deploying Streamlit Apps with Hugging Face and Heroku Connecting to Databases Improving Job Applications with Streamlit The Data Project – Prototyping Projects in Streamlit Streamlit Power Users Other Books You May Enjoy
Index

Developing in Streamlit

In the first option, we write our code directly in Streamlit as we’re experimenting and exploring exactly what our application will do. We’ve basically been taking this option already, which works very well if we have less exploration work and more implementation work to do.

Pros:

  • What you see is what you get – there is no need to maintain both IPython and Python versions of the same app.
  • Better experience for learning how to write production code.

Cons:

  • A slower feedback loop (the entire app must run before feedback).
  • A potentially unfamiliar development environment.
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