In the early days of working for SHARCNET, my colleague and I decided to standardize how cluster metrics were computed across our internal data frames. As mentioned in a previous post, part of the solution was pandas. The second part was to figure out how to deploy the package for others to contribute to, as well as install on their own specific HPC clusters. Some quick searching of course revealed PyPi and pip were the way to go. ...
Eventually I got to the point in data analytics where keeping things in lists, or list of lists was no longer quite cutting it. My processing was slowly starting to grind to a halt, and things were getting way too abstract. I decide to call up a friend who had worked in the business longer than me and they suggested “pandas”. I was vaguely familiar as users/clients had used it in the past. A “DataFrame” did sound like it would take care of a lot of my problems after reading the documentation casually… ...
Back when I first got hired at SHARCNET, I used a lot of Python. I mean a lot. What this meant is that I quickly became the lightning rod for all Python related questions (and commentary). During a fun Friday chat, a colleague remarked that Python was on average 40x slower than C++. I defended my current language of choice saying it was better than that, surely. To make a long story short, I was wrong. It really is about 40x slower depending on the problem. Determined to prove myself capable, and my language of choice a bit more defensible, I decided to look into ways to make Python faster. ...