TL;DR: I made Nod, a JupyterLab extension to pop open a Jupyter Notebook from anywhere in your Python codebase. I’m working to make Nod the best it can be, and I need your help: I’m looking for participants in my paid (60–100 USD) user study to see how folks use Nod in their own work. Please sign up if you’re interested!

You’re working in a large Python project and code is spread over countless files, classes, and function calls. You begin writing a new function, but the arguments are complex and questions abound: What exactly does that class method return? When I join these two tables, what does the output look like? How do I even construct the inputs to this function?

You think: This would be so much easier if I could just write this function in a notebook!

And so you embark on a quest to recreate your program state in a Jupyter notebook — copying out code snippets, JSON values, and parameters, piecing them back together in a blank notebook. Even if it works eventually, you’ve still spent 15 minutes preparing a one-off environment just so you can finally start programming.

If you’re like me, this experience is all too familiar. It’s clear that a notebook is the right tool for the job, but the cost of recreating a small piece of your program in a notebook makes it rarely worth the effort.

So I built Nod: Notebooks-on-Demand. Nod is a JupyterLab extension that lets you call notebook() from anywhere in your Python program to get access to a notebook with all your variables in scope, on just the part of the program you care about. If you find notebooks helpful but need to write code in Python files, or are working in a large Python codebase and just wish you could open these five lines of code in a notebook with all the right state, Nod might be helpful!

https://blog.jupyter.org/announcing-jupyter-builder-a-standalone-build-system-for-jupyterlab-extensions-c3d4cc06fa95

"Authors of MyST Markdown can now embed interactive JavaScript widgets directly in content using the new {anywidget} directive.

You can now add any javascript interactivity to your MyST Markdown website or Jupyter Book."

All thanks to this extension: https://github.com/marimo-team/marimo-jupyter-extension

While working on a small ML project, I wanted to make the initial data validation step a bit faster.

Instead of going column by column to check missing values, correlations, distributions, duplicates, etc., I generated an automated profiling report from the dataframe.

It gave a pretty detailed breakdown:

• Missing value patterns
• Correlation heatmaps
• Statistical summaries
• Potential outliers
• Duplicate rows
• Warnings for constant/highly correlated features

I still dig into things manually afterward, but for a first pass it saves some time.

Curious....do you prefer fully manual EDA or using profiling tools for the initial sweep?

Github link...

more...

Hey y'all 🙋‍♂️

I've been experimenting with an interactive equation set solver for sympy.

A previous version was implemented in tkinter.

This version is for JupyterLite.

The notebook I use in the video is here:

https://github.com/dharmatech/github-pages-test/blob/main/README.md

What We're Looking For:

• 2+ years of professional Python development experience.
• Strong proficiency in English (both written and spoken) for clear communication.
• Ready for a technical 1-on-1 interview via zoom call

How to Apply:

1. Must have your LinkedIn profile visible and up-to-date.
2. Please comment "LinkedIn profile" below OR send a Direct Message (DM). Please only reach out if you meet the experience requirement and are available for an interview.

Looking forward to connecting with qualified candidates!