// HackMT 2026 Project
A Python CLI tool I built during my first hackathon to transform Collectivity's spreadsheet data into structured, validated JSON our team could import into the application.
// The Story
Our team was building Collectivity, a website designed to securely organize digital and physical collectibles. We had entered item information into an Excel spreadsheet, but the application needed structured JSON that could be imported and used by the rest of the project.
I was worried that I wasn't going to be able to contribute much at my first-ever hackathon. Instead, I took the data problem and independently built the converter. The completed tool was used by the team to turn the spreadsheet into application-ready JSON.
// Interactive Walkthrough
This walkthrough uses the original HackMT spreadsheet data and output. It demonstrates the conversion flow in the browser; the Python CLI itself runs locally.
| Category | Sub-Category | Name | Status | Price |
|---|---|---|---|---|
| Card | Pokemon | 2005 Pokemon Japanese Play Espeon | blank | 48000 |
| Card | Yu-Gi-Oh | 2002 Gaia the Fierce Knight | blank | 4560 |
| Game Items | Console | Nintendo 64 Launch Edition | blank | 75000 |
| Game Items | Merch | blank | blank | blank |
$ python converter.py
Enter file to convert: [Collectivity workbook]
Press "Run converter.py" to continue.{
"count": 21,
"items": [
{
"category": "Card",
"sub_category": "Pokemon",
"name": "2005 Pokemon Japanese Play Espeon",
"price": 48000,
"valid": true
},
{
"category": "Game Items",
"sub_category": "Merch",
"name": null,
"price": null,
"valid": false,
"errors": ["name"]
}
]
}// How It Works
Accept a spreadsheet filename and load one worksheet into a Pandas DataFrame.
Verify that the expected nine spreadsheet columns are available before processing.
Remove fully empty rows, fill down categories, normalize prices, and convert blank cells to null.
Mark records invalid when required Category or Name values are missing and record the errors.
Write the records, timestamp, and total count to a formatted output.json file.
Challenge
I had never handled spreadsheets with Python before the hackathon. I worked through documentation and YouTube tutorials, then asked my team questions or used AI when those resources did not resolve a specific logic issue. The implementation was my own, supported by persistence and plenty of energy drinks.
What I Learned
Current Limitations
Future Improvements
// Project Repositories
See the converter implementation or explore the larger Collectivity application it supported.