For this blog post, I’m trying something different. This is a jupyter notebook that I’m using to study some data, and just dumping my brain out in text. If I can easily export this to a format that works with hugo, this might become a common occurrence. For this one, I’m leaving the code in. There isn’t that much of it, but I think it’s fun to show how much visualization per line of code you can get with seaborn and pandas....
I worked for a few years in the intersection between data science and software engineering. On the whole, it was a really enjoyable time and I’d like to have the chance to do so again at some point. One of the least enjoyable experiences from that time was to deal with big CSV exports. Unfortunately, this file format is still very common in the data science space. It is easy to understand why – it seems to be ubiquitous, present everywhere, it’s human-readable, it’s less verbose than options like JSON and XML, it’s super easy to produce from almost any tool....