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 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 incorporate user input and makes reproducible results much harder. Another option still 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 everything from taking in user inputs like text and dates to showing interactive graphs using the most popular and powerful Python graphing libraries.
I have spent the past two years 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. When I started this journey, I worked at Meta (then Facebook), but after the first edition of this book was published, I loved working on Streamlit apps so much that I went to work for the Streamlit team. Soon after I moved over, the Data Cloud company Snowflake purchased Streamlit. None of this is book is sponsored by Snowflake, and I certainly do not speak for Snowflake, but I truly believe that Streamlit could be as valuable to you and your work as it has been to mine.
I wrote this book 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 ramping you up to building your own basic Streamlit applications. In Part 2, 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 interviews with power users who use Streamlit at work, in academia, and for learning data science techniques. Before we begin, we need to get Streamlit set up and discuss how this book’s examples will be structured.
