Python

In 2024, I was in the middle of a transition period in my life, moving from Auckland to Melbourne. I had a lot going on, and one of the outlets I leaned on was working on personal projects. One of those was a charting library called “charted.” The gimmick was that I wanted to make it a completely zero-dependency Python library, beautiful SVG graphs you can use directly instead of having to lean on third-party libraries.

Modern spatial applications face unique challenges when dealing with large geospatial datasets, real-time mapping interfaces, and complex spatial queries. In other words, it can often be an extreme pain in the ass to get things working smoothly. I’ve spent an embarrassing amount of time wrestling with spatial web applications. What started as a small side-project turned into a career, and finally into what I can only describe as a love-hate relationship with anything involving coordinates and polygons.

I was working on an NBA related project the other week and I had decided I wanted to create my own API wrapper (for fun) as part of the project. The primary source of data I was looking to use was the stats.nba.com API, our biggest challenge in using this endpoint is the fact they do not want to use it.