mercury VS nbdev

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.

One example I've seen is ML/DL folks using jupyter notebooks to develop DL libraries in jupyter notebooks, see https://github.com/fastai/nbdev

- https://github.com/fastai/nbdev: Increase developer productivity by 10x with a new exploratory programming workflow.

9. Nbdev: Boost developer productivity with an exploratory programming workflow - https://nbdev.fast.ai/

See if you like nbdev way of working with data through python and jupyter. nbdev is an optional part that will create python packages from jupyter notebooks. Also even the simple tutorials are opinionated and will guide you to unit test your code and write CICD pipelines.

You probably don’t have time for this for the purposes of your task, but I will also throw in the recommendation of nbdev especially if you’re a Python person. I haven’t had a project to use it on yet, but I’ve gone through the docs and the walkthrough and it seems like a great framework for starting potential projects with all the infrastructure needed for if/when they eventually get big and need all the packaging and stuff

nbdev: jupyter notebooks -> python package

Take a look at https://github.com/fastai/nbdev - haven't used it but supposedly the whole if fast.ai library was written that way. It sounds like a natural direction in your scenario - allowing your to keep working in a familiar environment and still producing production ready code (will, at least in paper 😅)