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An Introduction to Streamlit
Streamlit is the fastest way to make data apps. It is an open-source Python library that helps you build web applications to be used for sharing analytical results, building complex interactive experiences, and iterating on top of new machine learning models. On top of that, developing and deploying Streamlit apps is incredibly fast and flexible, often reducing the application development time from days to hours.
In this chapter, we will start out with the Streamlit basics. We will learn how to download and run demo Streamlit apps, how to edit demo apps using our own text editor, how to organize our Streamlit apps, and finally, how to make our very own apps. Then, we will explore the basics of data visualization in Streamlit. We will learn how to accept some initial user input, and then add some finishing touches to our own apps with text. By the end of this chapter, you should be comfortable with starting to make your own Streamlit apps!
In particular, we will cover the following topics:
- Why Streamlit?
- Installing Streamlit
- Organizing Streamlit apps
- Streamlit plotting demo
- Making an app from scratch
Before we begin, we will start with the technical requirements to make sure we have everything we need to get started.
Technical requirements
Here are the installations and setup required for this chapter:
- The requirements for this book are to have Python 3.9 (or later) downloaded (https://www.python.org/downloads/) and have a text editor to edit Python files in. Any text editor will do. I use VS Code (https://code.visualstudio.com/download).
- Some sections of this book use GitHub, and a GitHub account is recommended (https://github.com/join). Understanding how to use Git is not necessary for this book but is always useful. If you want to get started, this link has a useful tutorial:https://guides.github.com/activities/hello-world/.
- A basic understanding of Python is also very useful for this book. If you are not there yet, feel free to spend some time getting to know Python better using this tutorial (https://docs.python.org/3/tutorial/) or any other of the freely and readily available tutorials out there, and come back here when you are ready. We also need to have the Streamlit library installed, which we will do in a later section calledInstalling Streamlit.
Why Streamlit?
Data scientists have become an increasingly valuable resource for companies and nonprofits over the course of the past decade. They help make data-driven decisions, make processes more efficient, and implement machine learning models to improve these decisions at scale. One pain point for data scientists is the process just after they have found a new insight or made a new
