From Complex Scientific Notebook to User-Friendly Web Application
2023-08-15 , Aula

Learn how to show your work with the MERCURY framework. This open-source tool perfectly matches your computed notebook (e.g., written in Jupyter Notebook). Without knowledge of frontend technologies, you can present your results as a web app (with interactive widgets), report, dashboard, or report. Learn how to improve your notebook and make your work understandable for non-technical mates. Python only!


Mercury is a tool that lets you add interactive widgets to your Jupyter Notebook. With these widgets, you can easily turn your notebook into a web application for creating dashboards and presentations. You can even schedule automatic updates. Mercury also provides a way to control who can access your notebooks with a built-in authentication module. Best of all, it's free and open-source.

The tutorial will include the following:

  1. Start with Jupyter Notebook.
  2. How to start with MERCURY (installing and setting up the needed environment).
  3. Overview of the features as downloading results as PDF, restricting authentication, showing/hiding code.
  4. Add widgets to your notebook. Select the right widgets.
  5. Set up a web app with MERCURY.
  6. Deploy and share your web with others.

It would be great if you will have install Mercury before tutorial. Please check installation instructions in our repository https://github.com/mljar/mercury


Abstract as a tweet:

From Complex Scientific Notebook to User-Friendly Web Application #datascience #python #webapp

Category [Data Science and Visualization]:

Data Analysis and Data Engineering

Expected audience expertise: Domain:

none

Expected audience expertise: Python:

some

Public link to supporting material:

https://runmercury.com/

Project Homepage / Git:

https://github.com/mljar/mercury

Software engineer trying to make data science tools easier to use for everyone. Working on open source tools: mljar-supervised and mercury.

Lawyer, a graphic designer with a passion for promoting data science tools. Open source enthusiast. From 2019, Executive Director at MLJAR - (the best open-source AutoML available).