Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Getting Started with Streamlit for Data Science

You're reading from   Getting Started with Streamlit for Data Science Create and deploy Streamlit web applications from scratch in Python

Arrow left icon
Product type Paperback
Published in Aug 2021
Publisher Packt
ISBN-13 9781800565500
Length 282 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Tyler Richards Tyler Richards
Author Profile Icon Tyler Richards
Tyler Richards
Arrow right icon
View More author details
Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Creating Basic Streamlit Applications
2. Chapter 1: An Introduction to Streamlit FREE CHAPTER 3. Chapter 2: Uploading, Downloading, and Manipulating Data 4. Chapter 3: Data Visualization 5. Chapter 4: Using Machine Learning with Streamlit 6. Chapter 5: Deploying Streamlit with Streamlit Sharing 7. Section 2: Advanced Streamlit Applications
8. Chapter 6: Beautifying Streamlit Apps 9. Chapter 7: Exploring Streamlit Components 10. Chapter 8: Deploying Streamlit Apps with Heroku and AWS 11. Section 3: Streamlit Use Cases
12. Chapter 9: Improving Job Applications with Streamlit 13. Chapter 10: The Data Project – Prototyping Projects in Streamlit 14. Chapter 11: Using Streamlit for Teams 15. Chapter 12: Streamlit Power Users 16. Other Books You May Enjoy

Chapter 3: Data Visualization

Visualization is fundamental to 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 the most popular Python graphing libraries (Matplotlib and Seaborn) in our examples. This chapter will focus on extending that ability to a broad range of Python graphing libraries, along with including some graphing functions native to Streamlit.

By the end of this chapter, you should feel comfortable with using Streamlit's native graphing functions, and also using Streamlit's 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 Trees – A new dataset
  • Streamlit's built-in graphing...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime