Section 1: Creating Basic Streamlit Applications
This section will introduce you to the basics of Streamlit applications, data visualization in Streamlit, how to deploy applications, and how to implement models in a Streamlit application.
The following chapters are covered in this section:
- Chapter 1, An Introduction to Streamlit
- Chapter 2, Uploading, Downloading, and Manipulating Data
- Chapter 3, Data Visualization
- Chapter 4, Using Machine Learning with Streamlit
- Chapter 5, Deploying Streamlit with Streamlit Sharing
Chapter 1: An Introduction to Streamlit
Streamlit is a web application framework that helps you build and develop Python-based web applications that can be used to share analytics results, build complex interactive experiences, and illustrate new machine learning models. On top of that, developing and deploying Streamlit apps is incredibly fast and flexible, often turning application development time from days into hours.
In this chapter, we 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. 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. At the end of this chapter, you should be comfortable starting to make your own Streamlit applications!
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 3.7 (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 Sublime (https://www.sublimetext.com/3).
- 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 and test in a later section called Installing 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 a repeatable scale. One pain point for data scientists is in the process just after they have found a new insight or made a new model. What is the best way to show a dynamic result, a new model, or a complicated piece of analytics to a data scientist's colleagues? They can send a static visualization, which works in some cases but fails for complicated analyses that build on each other or on anything that requires user input. They can create a Word document (or export their Jupyter notebook as a document) that combines text and visualizations, which also doesn't work for user input and is harder to reproduce. Another option is to build out an entire web application from scratch using a framework such as Flask or Django, and then figure out how to deploy the entire app in AWS or another cloud provider. None of these options really work that well. Many are slow, don't take user input, or are suboptimal for informing the decision-making process so fundamental to data science.
Enter Streamlit. Streamlit is all about speed and interaction. It is a web application framework that helps you build and develop Python web applications. It has built-in and convenient methods for taking in user input, graphing using the most popular and powerful Python graphing libraries, and quickly deploying graphs to a web application.
I have spent the past year building Streamlit apps of all different flavors, from data projects for my personal portfolio to building quick applications for data science take-home problems, to even building mini-apps for repeatable analysis at work. I truly believe that Streamlit could be as valuable to you and your work as it has been to mine and wrote this to bring you quickly up to speed so you can accelerate your learning curve and get to building web applications in minutes and hours instead of days. If this is for you, read on! We will work in three sections, starting with an introduction to Streamlit, and ramp you up to building your own basic Streamlit applications. In part two, we'll extend this knowledge to more advanced topics such as production deployment methods and using components created by the Streamlit community for increasingly beautiful and usable Streamlit apps. And in the last part, we'll focus heavily on i...
