Book Image

Getting Started with Streamlit for Data Science

By : Tyler Richards
Book Image

Getting Started with Streamlit for Data Science

By: Tyler Richards

Overview of this book

Streamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time. You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you’ll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps. By the end of this book, you’ll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.
Table of Contents (17 chapters)
Section 1: Creating Basic Streamlit Applications
Section 2: Advanced Streamlit Applications
Section 3: Streamlit Use Cases

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...