Streamlit is a Python library for creating interactive data applications in the web browser. By combining Streamlit widgets, actions and plots with Python's data ecosystem you can create simple interactive tools for collecting data, peforming analysis and exploring datasets.
Streamlit includes built-in support for Pandas dataframes, plots and Matplotlib figures (among others) allowing you to display your data, analysis and plots directly in the browse. By connecting buttons, inputs and other widgets to your logic you can dynamically update the display in response to user input.
This tutorial will take you through the basics of getting a Streamlit application up and running, explore common UI features such as widgets, layouts and actions.
Looking for something else? I also have a PyQt6 tutorial, PySide6 tutorial, PyQt5 tutorial and PySide2 tutorial.
This track consists of 3 tutorials. Keep checking back as I'm adding new tutorials regularly — last updated .
Getting started with Streamlit
Building data apps with Streamlit & Python
In this short tutorial we'll take our first steps building data applications with Streamlit. We'll introduce simple Streamlit widgets, actions and layouts.
Martin Fitzpatrick PhD