Andrew Montalenti

A few years back, I published The Elements of Python Style, a popular Python code style guide. Since publishing it, friends of mine in the Python community have wondered if I might consider adding a section about package installation, dependency management, and other similar “standard tooling” recommendations.

This is a reasonable request, since Python lacks much in the way of standard tooling. I took a stab at this in a pull request here, but then abandoned my attempt. The length of this “section” started to approach the length of the overall style guide itself! So, I gave up on that. I decided to turn the section into this blog post here, instead. Then, I’ll share one thought about how emerging programming language communities, such as Zig’s, could learn from the Python experience.

On “Standard” Tools

There’s a zoo of tooling options out there, and no “standard” Python tooling beyond the python executable, and, perhaps, pip (for installing packages, which was semi-formalized in Python 3.x with PEP 453 and PEP 508). Here, we’ll discuss an opinionated (yet uncontroversial) approach to “standard” tooling with Python.

Continue reading How Python programmers can uncontroversially approach build, dependency, and packaging tooling (+ a note on Zig)

I’m always surprised to learn that a friend who has used Linux for a long time, in both server and desktop contexts, might not have heard of GNU parallel.

If you use GNU parallel together with pv (pipe viewer), UNIX shell pipelines, and Python fileinput module, you get a pretty powerful parallel job running framework with testable independent pieces. It’s one of my favorite tricks when writing tools for my own benefit.

Let’s break it down.

Continue reading GNU parallel is underrated

A few years back, I wrote Clojonic: Pythonic Clojure, which compares Clojure to Python, and concluded:

My exploration of Clojure so far has made me realize that the languages share surprisingly more in common than I originally thought as an outside observer. Indeed, I think Clojure may be the most “Pythonic” language running on the JVM today (short of Jython, of course).

That said, as that article discussed, Clojure is a very different language than Python. As Rich Hickey, the creator of Clojure, put it in his “A History of Clojure”:

Most developers come to Clojure from Java, JavaScript, Python, Ruby and other OO languages. [… T]he most significant […] problem  [in adopting Clojure] is learning functional programming. Clojure is not multiparadigm, it is FP or nothing. None of the imperative techniques they are used to are available. That said, the language is small and the data structure set evident. Clojure has a reputation for being opinionated, opinionated languages being those that somewhat force a particular development style or strategy, which I will graciously accept as meaning the idioms are clear, and somewhat inescapable.

There is one area in which Clojure and Python seem to have a gulf between them, for a seemingly minor (but, in practice, major) technical reason. Clojure, being a JVM language, inherits the JVM’s slow start-up time, especially for short-lived scripts, as is common for UNIX CLI tools and scripts.

As a result, though Clojure is a relatively popular general purpose programming language — and, indeed, one of the most popular dynamic functional programming languages in existence — it is still notably unpopular for writing quick scripts and commonly-used CLI tools. But, in theory, this needn’t be the case!

Continue reading Learning about babashka (bb), a minimalist Clojure for building CLI tools

James Bennett, a long-time Python developer, blogger, and contributor to Django, recently wrote a nice post about the “end” of Python 2.x, entitled “Variations on the Death of Python 2.” It’s a great read for anyone who, like me, has been in the Python community a long time.

I’ve been a Python user since the early 2.x days, first discovering Python in a print copy of Linux Journal in the year 2000, where a well-known open source developer and advocate described his transition from Perl to Python. He wrote:

I was generating working code nearly as fast as I could type. When I realized this, I was quite startled.

An important measure of effort in coding is the frequency with which you write something that doesn’t actually match your mental representation of the problem, and have to backtrack on realizing that what you just typed won’t actually tell the language to do what you’re thinking. An important measure of good language design is how rapidly the percentage of missteps of this kind falls as you gain experience with the language. When you’re writing working code nearly as fast as you can type and your misstep rate is near zero, it generally means you’ve achieved mastery of the language.

But that didn’t make sense, because it was still day one and I was regularly pausing to look up new language and library features!

This was my first clue that, in Python, I was actually dealing with an exceptionally good design.

Python’s wonderful design as a language has always been a source of inspiration for me. I even wrote “The Elements of Python Style”, as an ode to how good Python code, to me, felt like good written prose. And, of course, many of my personal and professional projects are proudly Python Powered.

Thus, I was always a little worried about the Python 2 to 3 transition. I was concerned that this one big risk, taken on by the core team, could imperil the entire language, and thus the entire community. Perl 5 had embarked on a language schism toward Perl 6 (now Raku), and many believe that both communities (Perl 5 and Raku) became weaker as a result.

But, here we are in 2020, and Python 2 is EOL, and Python 3 is here to stay. A lot of the internet debates about Python 2 vs Python 3 (like this flame war on lobste.rs) now seem to boil down to this question: was Python 3 a good idea, in retrospect?

Continue reading Python 3 is here and the sky is not falling