Python
Description
I remember the first time that someone showed me Python code. I was knee-deep in a Java project, drowning in boilerplate and semicolons, and a friend pulled up a script that did the same thing in about twelve lines. No curly braces, no type declarations, no public static void main nonsense. I thought it was fake. It looked too clean to actually run. That was 2016, and I have not voluntarily opened an IDE for Java since.
Python is a general-purpose programming language developed by Guido van Rossum, who began work on it in the late 1980s and released the first version in 1991. The name is from Monty Python, not the snake, which is still surprising to people. Van Rossum designed it with readability as a core principle — the idea that code should look almost like plain English and that there should be one obvious way to do things. Whether the community has actually stuck to that philosophy is debatable, but the intent influenced a language that is still one of the easiest to pick up from scratch.
The reason Python exploded in the last decade has a lot to do with timing. When data science and machine learning went from being a niche academic interest to the hottest career path on LinkedIn, Python was already sitting there with NumPy, pandas and scikit-learn ready to go. Then TensorFlow and PyTorch decided to use Python as their main interface and that was pretty much game over. If you wanted to do anything with neural networks, you were writing Python. The language did not become popular because it was the fastest or the most elegant — it became popular because the libraries people needed happened to be built for it, and by the time alternatives caught up, the ecosystem was too massive to ignore.
That ecosystem is Python’s best feature and worst bug. Need to build a web app? Django and Flask. Automate boring spreadsheet tasks? openpyxl, pandas. Scrape websites? BeautifulSoup and Scrapy. Talk to an API? requests does it in 3 lines. The standard joke is that there is a Python library for everything, and it is barely a joke. I once discovered a package specifically for generating fake restaurant reviews, which seemed like a sign that maybe we have gone too far.
But installing and managing all those packages is where the pain begins. Anyone who tells you Python dependency management is fine has either never dealt with it seriously or suppressed the trauma. At some point, you will encounter a situation where two packages require different versions of the same dependency and your entire environment will break. Virtual environments help — venv, virtualenv, conda, poetry, pipenv — and the fact that I just listed five different tools for roughly the same problem tells you everything about the state of things. I spent an entire afternoon last year debugging a project that worked perfectly on my laptop but crashed on a colleague’s machine because our NumPy versions were three minor releases apart.
The other elephant in the room is speed. Python is slow. Not slow in the sense that it makes a difference when you are writing a script to rename files or pull data from a CSV, but genuinely slow when you are doing anything computationally intensive in pure Python. The reason that this doesn’t kill the language is that most of the heavy lifting is done in C or Fortran under the hood — NumPy, pandas, and PyTorch are all wrappers around compiled code. So you write Python, but the actual number crunching is at near-native speed. As long as you don’t go outside the bounds of vectorized operations and don’t write nested for-loops over large arrays, you’re good. The moment you break that pattern, you feel the interpreter tax immediately.
The Global Interpreter Lock — the infamous GIL — has something to do with this. It prevents true multithreading in CPython, which is the default Python implementation almost everyone uses. If you need parallelism, you either go with multiprocessing, which creates separate processes with their own memory overhead, or you switch to asyncio for I/O-bound tasks. There have been attempts to eliminate the GIL over the years, and PEP 703 added an experimental free-threaded build in Python 3.13, but as of this writing it is still optional, and most production code has not switched to it. For web servers that deal with concurrent requests, frameworks such as FastAPI utilize patterns such as asynchronous that avoid the problem well enough. For CPU-bound parallel work, most people reach for C extensions or just accept the limitation.
What I really love about Python is how little friction there is between having an idea and testing it. I can open a terminal, type python3 and start playing around in the interactive shell in less than a second. No compilation step, no project scaffolding, no XML configuration file. For prototyping, exploratory data analysis, or simply figuring out how an API works, nothing else comes close to that speed of iteration. It is the reason Python is the dominant language in Jupyter notebooks and why it is the default language for teaching introductory computer science at most universities — including MIT, which switched from Scheme to Python years ago.
The downside of all this accessibility is that Python code in the wild can be absolutely terrible. Because the barrier to entry is so low, you get a lot of scripts written by people who learned just enough to automate their specific task and never thought about error handling, documentation, or structure. I have inherited production codebases where every variable was named x, y, or temp, and the only comment in eight hundred lines was “# fix this later” dated three years ago. The language does not impose discipline the way something like Rust or even TypeScript does, and in large teams that permissiveness can create real maintenance headaches.
Python is free, open-source, and runs on basically everything — Windows, macOS, Linux, even Raspberry Pi. There is no licensing cost, no subscription, no enterprise tier. You download it from python.org and you go. That zero-cost entry point, combined with the library ecosystem and the gentle learning curve, is why it consistently ranks at or near the top of every programming language popularity index year after year. It is not the best tool for every job — you would not build a high frequency trading engine or a game engine in Python — but for the vast majority of tasks that programmers actually face on a daily basis, it gets the work done with less suffering than most alternatives. And frankly, some days that is all you can ask for.
