The blog post "Elliptical Python Programming" explores techniques for writing concise and expressive Python code by leveraging language features that allow for implicit or "elliptical" constructs. It covers topics like using truthiness to simplify conditional expressions, exploiting operator chaining and short-circuiting, leveraging iterable unpacking and the * operator for sequence manipulation, and understanding how default dictionary values can streamline code. The author emphasizes the importance of readability and maintainability, advocating for elliptical constructions only when they enhance clarity and reduce verbosity without sacrificing comprehension. The goal is to write Pythonic code that is both elegant and efficient.
This project introduces "SHORTY," a C++ utility that aims to make lambdas more concise. It achieves this by providing a macro-based system that replaces standard lambda syntax with a shorter, more symbolic representation. Essentially, SHORTY allows developers to define and use lambdas with fewer characters, potentially improving code readability in some cases by reducing boilerplate. However, this comes at the cost of relying on macros and introducing a new syntax that deviates from standard C++. The project documentation argues that the benefits in brevity outweigh the costs for certain use cases.
HN users largely discussed the potential downsides of Shorty, a C++ library for terser lambdas. Concerns included readability and maintainability suffering due to excessive brevity, especially for those unfamiliar with the library. Some argued against introducing more cryptic syntax to C++, preferring explicitness over extreme conciseness. Others questioned the practical benefits, suggesting existing lambda syntax is sufficient and the library's complexity outweighs its advantages. A few commenters expressed mild interest, acknowledging the potential for niche use cases but emphasizing the importance of careful consideration before widespread adoption. Several also debated the library's naming conventions and overall design choices.
The blog post "What makes code hard to read: Visual patterns of complexity" explores how visual patterns in code impact readability, arguing that complexity isn't solely about logic but also visual structure. It identifies several patterns that hinder readability: deep nesting (excessive indentation), wide lines forcing horizontal scrolling, fragmented logic scattered across the screen, and inconsistent indentation disrupting vertical scanning. The author advocates for writing "calm" code, characterized by shallow nesting, narrow code blocks, localized logic, and consistent formatting, allowing developers to quickly grasp the overall structure and flow of the code. The post uses Python examples to illustrate these patterns and demonstrates how refactoring can significantly improve visual clarity, even without altering functionality.
HN commenters largely agree with the article's premise that visual complexity hinders code readability. Several highlight the importance of consistency in formatting and indentation, noting how deviations create visual noise that distracts from the code's logic. Some discuss specific patterns mentioned in the article, like deep nesting and arrow anti-patterns, offering personal anecdotes and suggesting mitigation strategies like extracting functions or using guard clauses. Others expand on the article's points by mentioning the cognitive load imposed by inconsistent naming conventions and the helpfulness of visual aids like syntax highlighting and code folding. A few commenters offer alternative perspectives, arguing that while visual complexity can be a symptom of deeper issues, it isn't the root cause of hard-to-read code. They emphasize the importance of clear logic and good design over purely visual aspects. There's also discussion around the subjective nature of code readability and the challenge of defining objective metrics for it.
Summary of Comments ( 14 )
https://news.ycombinator.com/item?id=43643292
HN commenters largely discussed the practicality and readability of the "elliptical" Python style advocated in the article. Some praised the conciseness, particularly for smaller scripts or personal projects, while others raised concerns about maintainability and introducing subtle bugs, especially in larger codebases. A few pointed out that some examples weren't truly elliptical but rather just standard Python idioms taken to an extreme. The potential for abuse and the importance of clear communication in code were recurring themes. Some commenters also suggested that languages like Perl are better suited for this extremely terse coding style. Several people debated the validity and usefulness of the specific code examples provided.
The Hacker News post "Elliptical Python Programming" (https://news.ycombinator.com/item?id=43643292) sparked a discussion with several interesting comments, primarily focusing on the readability and maintainability implications of the coding style advocated in the article.
One of the most compelling threads revolves around the trade-off between conciseness and clarity. Several commenters express concern that while the "elliptical" style might appear elegant and reduce code length, it could significantly hinder readability, especially for those unfamiliar with the specific idioms or tricks employed. This reduced readability could lead to increased difficulty in debugging and maintaining the codebase over time. One commenter specifically points out that code is read far more often than it is written, emphasizing the importance of prioritizing readability over conciseness.
Another key point raised is the potential for misuse and abuse of these techniques. While some elliptical constructs can be genuinely helpful in reducing boilerplate, the concern is that excessive use or application in inappropriate contexts could lead to obfuscated and difficult-to-understand code. The consensus seems to be that these techniques should be used judiciously and only when they genuinely improve clarity rather than detract from it.
Several commenters discuss the specific examples presented in the article, debating their merits and drawbacks. Some of the examples are considered more acceptable than others, with the more controversial ones involving complex nested comprehensions or unconventional uses of operators.
The idea of implicit context also arises in the discussion. Commenters point out that while some elliptical constructs rely on implicit context, excessive reliance on implicit information can make the code harder to reason about. Explicitly stating the context, even if it adds a bit of verbosity, can often improve clarity and maintainability.
Finally, the discussion touches on the importance of coding style guides and team conventions. Even if some developers find elliptical Python acceptable, the consensus is that consistency within a codebase is paramount. Adopting a consistent style, even if it's not everyone's preferred style, is crucial for collaboration and long-term maintainability. Therefore, teams should carefully consider the trade-offs before incorporating highly elliptical styles into their projects.