Executable pseudocode: Python, and how a hobby project ate the world

It began as a Christmas-break side project in Amsterdam and became the default language of AI, science, and the working programmer. A field guide to what Python actually is, why it won, the honest costs of its design — and what its numbers really say in 2026.

There is a party trick you can play with Python: show a page of it to someone who has never programmed and ask what it does. More often than not, they can tell you. for student in class_list: print(student.name) reads like the instruction it is. That property — code that reads almost like the pseudocode you'd scribble on a whiteboard — is not an accident, and it is the single biggest reason a modest scripting language from 1991 now sits at the centre of artificial intelligence, scientific computing, and the world's introductory programming courses.

This guide is the first of three on languages that shaped how programmers think — Python, then Prolog, then Lisp. Python goes first because it's the one you'll most likely touch this week.

01A Christmas project in Amsterdam

Python's origin is unusually well documented, because its creator wrote it down. In December 1989, Guido van Rossum — then a researcher at CWI, the Dutch national centre for mathematics and computer science — was looking for a “hobby” programming project to keep himself occupied over the Christmas break. He wanted a scripting language that would sit between the shell and C: higher-level than C, more programmable than the shell, and pleasant for the Amoeba operating-system project he worked on. He borrowed heavily from ABC, an earlier teaching language developed at CWI, keeping what he loved (readable syntax, indentation) and fixing what he didn't (rigidity, no extensibility).¹ The first public release, version 0.9.0, went out in February 1991.¹

And the name has nothing to do with snakes. Van Rossum was reading the published scripts of Monty Python's Flying Circus and wanted a name that was short, unique, and slightly mysterious; the official FAQ confirms it, and it's why the documentation is littered with spam, eggs, and knights who say Ni.²

02The design that won: readability as a feature

Most languages treat readability as a virtue; Python treats it as a constraint. Indentation isn't decoration — it is the block structure, which means the code's visual shape can never lie about its logic. The language's philosophy is codified, half-seriously, in “The Zen of Python”: Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Readability counts.³ Pair that with an official style guide, PEP 8, that most of the ecosystem actually follows,⁴ and Python code from strangers tends to look like code from colleagues.

Two more design choices did quiet, decades-long work. First, the “batteries included” standard library: JSON, HTTP, sqlite, zip files, email — useful programs get written without installing anything. Second, Python is a superb glue language: its C API lets performance-critical code live in compiled extensions while Python orchestrates from above. That's not a footnote; it is the architecture of the entire scientific stack. NumPy — the array library underneath nearly all Python data science — is C doing the arithmetic with Python doing the thinking, and it has become foundational enough that its design was written up in Nature.⁵

The language also learned to bend without breaking. Dynamic typing makes small programs fast to write; for large codebases, optional type hints (PEP 484, 2015) bolted a gradual type system onto the language without changing its runtime character⁶ — a pragmatic compromise very much in Python's temperament.

03Growing pains, honestly told

Python's history has two famous scars, and both are instructive. The first is the 2-to-3 transition: Python 3.0 (2008) fixed deep design mistakes — notably text-versus-bytes — at the cost of breaking existing code, and the community took over a decade to migrate. Python 2 was finally sunset on 1 January 2020, with the core team's blunt explanation: “we became concerned we couldn't security-patch both versions with the volunteers we have.”⁷ The lesson the industry drew — never split a language ecosystem in two — has shaped every language migration since.

The second scar is the GIL, the Global Interpreter Lock: a design that made the interpreter simple and single-thread fast, but meant one Python process could only execute one thread of Python bytecode at a time — painful in a many-core world. After decades of “you can't remove it,” PEP 703 was accepted, and Python 3.13 shipped an experimental free-threaded build with the GIL genuinely optional.⁸⁹ A thirty-year-old language re-architecting its concurrency story rather than calcifying is, frankly, the healthiest sign you can ask for.

Python's superpower was never speed. It was that the time from “idea” to “working program” is shorter than in almost anything else — and most programs are bottlenecked on the human, not the machine.

04The numbers in 2026, read carefully

Popularity claims deserve scrutiny, because different rulers measure different things. On the TIOBE index (search-engine visibility), Python hit an all-time record for any language — 26.98% in July 2025 — and still leads in July 2026, though it has cooled to around 19% as the index's compilers debate whether the AI-driven spike is plateauing.¹⁰ In Stack Overflow's 2025 developer survey (what working developers say they use), JavaScript remains the most-used language overall at 62.3%, but Python posted the survey's biggest jump — roughly seven percentage points year-on-year — on the back of AI and data work.¹¹ And on GitHub (what code actually gets pushed), Python overtook JavaScript as the most-used language in 2024, ending a ten-year reign¹² — before TypeScript, in turn, took the top spot in 2025 as typed languages surged in the agent-assisted-coding era.¹³

Read together, the honest summary is: Python is either the first or second most important language in the world depending on your ruler, its growth engine is AI and data, and the one frontier it doesn't own is the typed, large-team application codebase — which is exactly the gap type hints are trying to close.

05Why AI chose Python

It's tempting to say Python got lucky with the AI boom. It's more accurate to say Python spent twenty years becoming the language AI would need. Scientists adopted it early because it read like their notation and glued to their Fortran and C libraries; Nature was telling researchers to “pick up Python” back in 2015, years before the generative-AI wave.¹⁴ By the time deep learning arrived, the ecosystem — NumPy's arrays,⁵ Jupyter's notebooks, and eventually PyTorch's tensors¹⁵ — was already where the researchers were. The frameworks do their heavy lifting in C++ and CUDA; Python is the humane control panel on top. GitHub's own analysis credits exactly this dynamic for Python's 2024 surge: the generative-AI boom pulled a wave of data scientists and researchers — not just traditional software engineers — into programming, and they arrived speaking Python.¹²

06Where OcxlyDev lands

We use Python daily — the verification harnesses, sitemap checkers and build scripts behind this very site are Python — and our advice tracks our practice. Choose Python when the problem is exploratory, data-shaped, glue-shaped, or when the team's time matters more than the CPU's: which is most problems. Reach for something else when you need predictable low latency on a hot path, tight memory, or the compile-time guarantees a large team leans on — and even then, Python often remains the right orchestrator around that faster core. If you're starting from zero, start here: the official tutorial¹⁶ plus a real small project beats any course. And keep an eye on free-threaded Python — if it matures, the language's oldest asterisk quietly disappears.