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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

Data Visualization

Visualization is a fundamental tool for the modern data scientist. It is often the central lens used to understand items such as statistical models (for example, via an AUC chart), the distribution of a crucial variable (via a histogram), or even important business metrics.

In the last two chapters, we used two popular Python graphing libraries (Matplotlib and Altair) in our examples. This chapter will focus on extending that ability to a broad range of Python graphing libraries, including some graphing functions native to Streamlit.

By the end of this chapter, you should feel comfortable using Streamlit’s native graphing functions and visualization functions to place graphs made from major Python visualization libraries in your own Streamlit app.

In this chapter, we will cover the following topics:

  • San Francisco (SF) Trees – a new dataset
  • Streamlit’s built-in graphing functions
  • Streamlit’s built-in...
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