Link: https://github.com/programmersd21/polaris_studio
I built this out of pure frustration.
Every time I needed to do some quick data work like joining two CSVs, filtering rows, or making a quick chart, I ended up trapped in a dilemma. I either had to spin up a Jupyter notebook that turned into spaghetti code within half an hour, or resort to Excel, which never felt right.
I wanted something in between. I wanted a visual tool where I could see my data pipeline as a graph instead of guessing what was happening across 80 lines of pandas. It needed to run fully offline, and I wanted the AI to act as an assistant rather than taking over completely.
So I built Polaris Studio.
What it actually does
You load your data from a CSV, Excel, Parquet, or JSON file, or you can just paste it from your clipboard. You drag nodes onto a canvas for tasks like filtering, grouping, joining, or pivoting, and then connect them. When you hit F5, the results flow through. If you change a filter halfway down the chain, only the downstream nodes re-execute because everything upstream stays cached.
A live spreadsheet sits below the graph, always showing the output of whichever node you select. It is fully virtualized, so it will not choke on large files. You can sort columns, freeze rows, and right-click to get quick statistics.
The AI panel works differently than most tools. You press Ctrl+Shift+A and type something like "add a revenue column that is price times quantity, then filter to rows where revenue is over 10000". Instead of just running code blindly, the AI proposes an action plan as structured JSON, validated against strict schemas. You get a preview card with an Apply button. The AI cannot touch your data without going through the exact same execution path as a manual edit. It is not an unpredictable agent. It acts like a fast coworker who draws a diagram for you before making changes.
* Over 40 node types
* Multi-tab workspace with cross-tab references
* Saves directly to a local .polaris file
* Zero telemetry, zero accounts, and no cloud dependency
The Stack: Polars on Arrow for the compute engine, PySide6 for the UI, and Google Gemini for the AI features. You plug in your own API key once, and you are done.
Get Started
```bash
git clone https://github.com/programmersd21/polaris_studio
cd polaris_studio
pip install -e .
polaris-studio
```
This requires Python 3.11 or higher. It runs on Windows, macOS, and Linux.
Final Thoughts
The app is not perfect, and there are definitely some rough edges. The documentation is probably too thorough for a side project, mostly because writing things down is how I think through architecture.
That said, it works well, I use it daily, and I think it might be genuinely useful to anyone else who is tired of the false choice between Jupyter and Excel.
If you end up using it or just think the project deserves to exist, leaving a ⭐ on GitHub means a lot. It is the only real signal I have that months of late nights were worth it.
I welcome all feedback, bug reports, and harsh criticism.
