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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

Making an MVP

Looking at our data, we can start by asking a basic question: what are the most interesting questions I can answer with this data? After looking at the data and thinking about what information I would want from my Goodreads reading history, here are a few questions that I have thought of:

  • How many books do I read each year?
  • How long does it take for me to finish a book that I have started?
  • How long are the books that I have read?
  • How old are the books that I have read?
  • How do I rate books compared to other Goodreads users?

We can take these questions, figure out how to modify our data to visualize them well, and then make the first attempt at creating our product by printing out all of the graphs.

How many books do I read each year?

For the first question about books read per year, we have the Date Read column with the data presented in the format of yyyy/mm/dd. The following code block will do the following:

    ...
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