🐍 Python, classes, tuples

Wednesday, March 21, 2018 :: Tagged under: engineering. ⏰ 4 minutes.

Hey! Thanks for reading! Just a reminder that I wrote this some years ago, and may have much more complicated feelings about this topic than I did when I wrote it. Happy to elaborate, feel free to reach out to me! 😄

The song for this post is Classical Thump, by Victor Wooten.

During my "learn as many languages as quickly as possible" phase, I never got to Python. For most of its use cases I chose Ruby or Racket. But then I started work at Reonomy, built on Flask. Then I left to work at ClassPass, using Pyramid and Flask. Then I left to work at Lyft; my team uses Flask. I couldn't get away with winging it anymore.

Favorite resources

The lowest-effort thing I did was install vim-flake8 and set it so I'd run PEP8 validations on Python. That way, even if my code was bad, it wasn't bad bad. With a lot of Googling for syntax and Stack Overflow answers, I was producing code that structurally looked like Scheme (but with way less function-mangling), or Ruby (with way fewer objects).

The next "big break" for Python-specific knowledge was literaly called "Beyond PEP8." The full title was Beyond PEP8: Best practices for intelligible code, by Raymond Hettinger:

It clearly demonstrated the Python aesthetic and how well it could work when it does. In particular, I enjoyed the example of iterating on a block of Python code that looked fine to my eye, and learning what assumptions from other languages I was baking into it.

After that, one talk that spoke to me was Stop Writing Classes, by Jack Diederich, which I've watched many times and shared all around:

By talk standards, it's short! But it showed me, again, something Python shines at: many problems don't inherently need design patterns. This goes back to an old narrative Peter Norvig surfaced, stating "Design patterns are bug reports against your programming language" in his presentation in 1998, where he showed many design patterns from languages like Java or C++ don't make sense in languages like Common Lisp.

If I had to succinctly describe Python, it's a language that tries to eliminate the need to use formal design patterns. This doesn't mean Python programs don't have recipes or designs, just that the ones the language ships with cover a large number of very common programming cases. Per the above video, you absolutely can use objects, but there will be many cases when a function will do, or tuples, or a dictionary.

These days, I tend to use a namedtuples for compound data, and enums for sum types. I don't love that everything is effectively typed Optional, but Python 3 has type annotations, and projects like mypy help as well. If absolutely necessary, I'll write with objects. If you're using 3.7 or above, dataclasses fit this need very well 😄

Grinds my gears

I also enjoyed How Python was shaped by leaky internals, by Armin Ronacher:

This is lovely, because sometimes you need to override or dig deep into some Python internal to modify its existing behavior, or even understand the behavior of your system. Ruby gets a lot of hate for monkey patching, but the secret is Python lets you futz with its internals also, they just furrow their brows more. With all that said, it does grind my gears how this can bite you.

Many of the things that people love about Python are also what makes it not my favorite language: with one exception, most of my startup experiences were after a company received funding, and the initial code was written by rockstars who'd burnt out, been purged, or got promoted to work on new shiny things, and I had to maintain their codebase. Inevitably when I attribute specific frustrations during these times to Python, folks say things like "you can write shitty code in any language" or "a poor craftsman blames their tools" and I get all that, but in 2018, the statement "guarantees help you reason about large systems" shouldn't be controversial, and Python gives you few of them.

Here's Khan Academy spending 2 months moving files around. Think of how many blog posts are written about companies moving from Python 2 to Python 3. 2to3 and six and similar facilitate, but that it's still such a challenge, this late in the game, with blessed tools, tells you something about how easy it is to reason about these codebases. Not all programs need to have easily-understood behavior and correctness semantics! But it's something I like, especially in roles that have me performing the code equivalent of viscera cleanup detail.

Oh, and I hate GIL and wish Python had a better story for concurrent and parallel computations.

All that said

I actually rather enjoy Python, especially on 3, especially with few abstractions. I'd almost always prefer static types, but if you want to fly without them, this is the one to beat (until I try Elixir, idk).

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