mercury VS awesome-jupyter

I'm build dashboards in Jupyter Lab. My plotting libraries are Altair, matplotlib, seaborn, Plotly - all work well in notebook.

My favorite is Altair. It provides interactivity for charts, so you can move/zoom your plots and have tooltips. It is much lighter than Plotly after saving the notebook to ipynb file. Altair charts looks much better than in matplotlib. One drawback, that exporting to PDF doesn't work. To serve notebook as dashboard with code hidden, I use Mercury framework, you can check example https://runmercury.com/tutorials/vega-altair-dashboard/

disclaimer: I'm author of Mercury framework https://github.com/mljar/mercury

Web App is using two open-source packages that I've created:

- MLJAR AutoML - Python package for AutoML on tabular data https://github.com/mljar/mljar-supervised

- Mercury - framework for converting Jupyter Notebooks into Web App https://github.com/mljar/mercury

You can run Web App locally. What is more, you can adjust notebook's code for your needs. For example, you can set different validation strategies or evalutaion metrics or longer training times. The notebooks in the repo are good starting point for you to develop more advanced apps.

The GitHub repository https://github.com/mljar/mercury

We are working on open-source web framework Mercury that converts Python notebooks to Web Apps.

It is very opinionated:

- it has no callbacks - we automatically re-execute cells below updated widget

- it has no layout widgets, all input widgets are always in the left sidebar

Thanks to above decisions you don't need to change notebook's code to have web app and fit to the framework.

The simplicity of the framework is very important to us. We also care about deployment simplicity. That's why we created a shared hosting service called Mercury Cloud. You can deploy notebook by uploading a file.

The GitHub repository https://github.com/mljar/mercury

Documentation https://RunMercury.com/docs/

Mercury Cloud https://cloud.runmercury.com

Analysis was done in Jupyter Notebook with Python 3.10, Pandas, Matplotlib, wordcloud and Mercury framework.

Analysis was done in Jupyter Notebook with Python 3.10 kernel, Pandas, Matplotlib, wordcloud and Mercury framework to share notebook as a web application with widgets and code hidden. Gif created in Canva.

What are some Software Development methods for reducing errors:

1. AUTOMATED TESTS; test assertions

To write spreadsheet tests:

A. Write your own test assertion library for their macro language; write assertEqual() in VBscript and Apps Script.

B. Use another language with a test library and a test runner; e.g. Python and the `assert` keyword, unittest.TestCase().assertEqual() or pytest.

C. Test the spreadsheet GUI with something like AutoHotKey.

From https://news.ycombinator.com/item?id=35896192 :

> The Scientific Method is testing, so testing (tests, assertions, fixtures) should be core to any scientific workflow system.

> awesome-jupyter#testing: https://github.com/markusschanta/awesome-jupyter#testing

> ml-tooling/best-of-jupyter lists papermill/papermill under "Interactive Widgets/Visualization" https://github.com/ml-tooling/best-of-jupyter#interactive-wi...

https://github.com/markusschanta/awesome-jupyter#hosted-note...

In the open source world, look at the sort of stuff you can learn for free...... https://github.com/markusschanta/awesome-jupyter

> Rendering/Publishing/Conversion https://github.com/markusschanta/awesome-jupyter#renderingpu... :

> ContainDS Dashboards - JupyterHub extension to host authenticated scripts or notebooks in any framework (Voilà, Streamlit, Plotly Dash etc)

Streamlit lists Bokeh, Jupyter Voila , Panel, and Plotly Dash as Alternative dashboard approaches: