Tag: The Round Up

Writing poems and writing Python in Jupyter notebooks encourage thinking about similarities and differences between poetry and code, and more importantly, how one informs the other. Python makes me very aware of form, specifically how lines are best broken up. On the other hand, poetry makes me very aware of words as symbols, references and pointers.

While my long-term goal is to finish an exhaustive essay on the distinctions and connections between these two tendencies, here are some preliminary thoughts:

• Concision. Both poetry and code seek concision — the latter for optimization, and the former for impact. For both, it is a matter of building the shortest path to something intentional and sensible. In both, concision works best when balanced with a good sense of flow coming from a clarity of phrasing and/or readability.

• Tendencies. Code leans towards precision and predictability, while poetry leans towards ambiguity and surprise. While code needs to be as transparent or explicit as possible, poetry relies on the implicit, the unsaid. While a line of code usually serves a singular purpose — which can be reused elsewhere, I know, but that’s beside the point — a poetic line usually elicits multiple interpretations and meanings.

That’s it for now. I plan to add more as I delve deeper into finding ways on how to cross-pollinate these two passions.

Goodness. A month+ without update. Gross.

• A total #codercrush on Corey Schafer. Plenty easy on the eyes, and his tutorials are definitely worth watching.
• Got into a bit of a rabbit hole when reading about the difference between IPython and Jupyter Notebook. Finally had an AHA! moment when I figured that I already have Jupyter Lab with my Anaconda installation.
• Guido van Rossum gives zero farts about blog UX, meanwhile here I am waging war against WordPress’ Gutenberg blocks because of so. much. padding.

I’ve been on a mad rush to cover as much Dataquest ground as I can before the holiday-slash-Grand-O’-Family-visit week arrives, and there’s so much to cover.

Starting this issue, I’ve made an executive decision to include obsessions that are not necessarily related to programming or data science, because let’s face it, everything can be connected to either in one way or another. Besides, these obsessions might present themselves as sources of inspiration in the future. You never know.

• I’m a year late, but heard Adam Lambert’s Believe for the first time, and I believed. His choice of slowing the song down, stripping away the techno bits, and just exposing the RAW! emotion was a perfect way to honor Cher. Speaking of AI alumni, Jennifer Hudson’s Memory is probably the most beautiful rendition I’ve ever heard. She’s perfected this softer / head-y tone, which contrasts well to her belt-ier bits.
• Professing that it has helped her organize her life, a friend recently recommended Bullet Journal. It felt serendipitous; I felt an instant connection when Ryder Carroll started talking about skimming one’s life and intentionally, since I’ve been on a similar journey of streamlining.
• The shell intimidated me. The stripped-down UI made me feel like I didn’t know what I was doing, or that my laptop would explode with the wrong dot command. I feel a bit more comfortable now, having a bit of practice with psql and sqlite3, though additional non-DQ (immersive) courses seem necessary. I want to be a command line ninja.
• kennethreitz’s Principle of Polarity resonates with my growing belief that the trick in life is to understand extremes, and find footing within that spectrum.
• Anything that marries Python and web excites me. The requests Python library makes me giddy like a kid. It gets me a step closer to actualizing a few web scraping projects I’ve been conceptualizing.

Here’s another installment of my round-up. All of these links are taken from Dataquest’s Data Science online course.

• Tableau’s Color Blind 10, although discontinued, is a great color palette to utilize, since grays are perfect for non-data ink, while the different blues and oranges provide nice contrasts for highlights.
• Tidy Data as an organizing principle is something that I’d like to go back to later in my development. At its philosophical core:
  • Tidy datasets are easy to manipulate, model and visualize, and have a specific structure: each variable is a column, each observation is a row, and each type of observational unit is a table. (Hadley Wickham)
• RegExr seems like a valuable tool in learning and mastering regular expressions in a very visual way.
• Simon Holywell’s The SQL Style Guide has a few quite eye-opening tips, such as using collective names for tables and singular names for columns, and lining up root keywords to form a code river. Way to make things incredibly legible, almost sculptural. Also, extra points for referencing typographical quirks in the explanation.

At the 2.5th month mark of my monkish pursuit in relearning Python and Pythonic concepts in Data Science, I found myself wanting to closely chronicle my journey. For me, the biggest question in writing about my thoughts and ideas (related to this pursuit) has everything to do with outlining. How do I organize and clearly present my often random — and most likely novice — realizations?

The simple answer, of course, is just that; as cluttered and as close to the bone as possible. Hence, this roundup format. The idea is to notate what feels relevant and urgent. My goal is make these posts as my metaphorical version controls, my milestones, so I can someday look back and see how far I’ve come.

Let’s get right to it!

For this first-ever post, I want to ribbon cut by listing some of my online finds due to a confusing first date with Matplotlib.

• Jake VanderPlas’ Visualization with Matplotlib (Chapter 4 of his Python Data Science Handbook, via Github) was a lifesaver. It gave the conceptual overview I needed to better grasp the linguistic / syntactical quirks of Matplotlib. His prologue on the history of MPL provided some useful background on why the scripting is a little different from everyday Python.
• HTML5 makes me excited about possibly learning Javascript, or researching more on converting Python to Javascript. I’ve been thinking a lot about how to make interactive web visualizations, and this might be a great answer to it.
• r/place is such a conceptual mood. I want to be able to create something similar to it, probably in a much smaller scale in the beginning.
• Speaking of reddit, r/dataisbeautiful seems to be a serendipitous find. Haven’t explored it yet, but I think it’ll be a great future resource.