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)
1
Section 1: Creating Basic Streamlit Applications
7
Section 2: Advanced Streamlit Applications
11
Section 3: Streamlit Use Cases

Chapter 2: Uploading, Downloading, and Manipulating Data

So far in this book, we have exclusively used simulated data in our Streamlit apps. This was useful for getting a good background in some of the basics of Streamlit, but most data science is not done on simulated data, but on real-world datasets that data scientists already have, or on datasets provided by users.

This chapter will focus on the world of data in Streamlit apps, covering everything you will need to know to bring datasets to life using Streamlit. We will cover data manipulation, using user imported data, flow control, debugging Streamlit apps, and speeding up our data applications using caching through an example dataset called Palmer's Penguins.

In particular, we will cover the following topics:

  • The setup – Palmer's Penguins
  • Debugging Streamlit apps
  • Data manipulation in Streamlit