learning python 3: look I drew an owl

So! I know it’s been a long time since my last post. A lot has been happening since then, like an entire career I thought I had transitioned out of. But interestingly enough, I did learn how to Python.

How did I finally learn how to Python? It was a little like that meme about drawing an owl:

Source: Know Your Meme

I was going to document my process of learning the language but I decided to sign up for Python classes instead. It was much more fun that way.

And it turns out, there wasn’t much to learning Python. Once you know how variables, loops, objects and all that work for one language, you can for the most part generalize it over other languages—kind of like learning French makes it easier to learn Spanish and vice-versa (only even easier! Programming languages are the easiest to learn of all human languages and don’t let anyone tell you different).

The hard part, if it could be called hard, is learning how to use the different libraries, but even there, you don’t really need to know how the libraries work the way they do, only how to use them. Just like you don’t need to understand what goes on at a sausage factory to know how to cook sausages.

(Which brings us to NLTK. I ultimately found it really boring. The problem with most work being done in the digital humanities is that it doesn’t really have much to do with how I read or analyze literature. I recently read Macroanalysis: Digital Methods and Literary History by Matthew Lee Jockers, which does a helpful survey of literary analysis techniques using digital methods, and I was fascinated by how little of the field I found fascinating. A lot of it made me go, “So what?” Bird’s eye views, statistical information on meaningless generalizations for the most part. The real work seems to be: how to make all this uninteresting data interesting. And that can be interesting, just not to me.)

In the end, it was much like drawing an owl. You take in a little bit of the basics, and all the other automated processes and libraries fill in the rest. And that’s where software engineering seems to be going.