It’s overtaking JavaScript as the No. 1 programming language in the world, and it’s the name on everyone’s lips in every tech-development meeting. But you still can’t help picturing a snake whenever you hear the name “Python.”
Feel out of the loop? Or even a bit silly?
Have no fear: We’re experts in programming languages, so we’ll explain the deal with this vitally important one, as well as noting how it found inspiration in the truly ridiculous.
What is Python?
Python is one of the most popular programming languages around (No. 1 or No. 2 in 2022, depending on who’s doing the polling), and forms the building block of many of the platforms and apps you use from giants in the tech industry.
When clients want examples of Python software in widespread use, we point to some of the biggest names around: Google, Meta, IBM, Disney, Venmo, Spotify and Dropbox. Ever wonder why Netflix suggests you binge-watch 12 hours of Christmas programming based on your previous viewing choices? The Netflix recommender system is written in Python — and is an excellent example of one of Python’s strengths.
Fun facts about Python
It was created all the way back in 1991, and it was named not the snake, but the legendary British comedy troupe Monty Python. Most programmers use the latest version of Python, Python 3.0, which was released on Dec. 3, 2008. Python’s creator, Guido van Rossum, has said he doesn’t expect to ever make a Python 4.
What is Python good at?
Python’s based on natural rules of human language and is remarkably similar to regular English, meaning that it’s easier for developers to both write and read. That, in turn, means that it’s much easier to find and fix errors than in most other code, and that it’s got an easy learning curve for new programmers. Because of its ease of use in repetitive math-based tasks, our developers have found again and again that it’s great for getting through huge amounts of data, and is thus the language of choice for many machine learning algorithms, like Netflix’s recommender.
Just like with any “real” human language, one of the things that programming languages have to do before it can have a “conversation” or get anything done is to define its terms, or declare types and variables. In JavaScript, before they begin on the “meat” of a line of code, developers have to spend a lot of time defining certain terms — and they have to do this every time. Python, however, is dynamically typed, which means these definitions are built right into the language. The result is our programmers have found they don’t have to spend all that time on definitions, so they can start on the working part of a code right off the bat — and cutting development time down considerably.
In general, we’ve found that Python’s code is typically three to five times shorter than the equivalent JavaScript code or as much as 10 times shorter than it would be in C++. Python also has a fast and efficient debugging cycle, which means that programmers spend a lot of time actually programming rather than waiting for a cycle to complete.
Python is impressively flexible and easy to apply to almost any programming situation, which goes a long way toward explaining its popularity. It’s used extensively in science, accounting, quality assurance and data analysis. It has also proved a robust solution for all sorts of automations, software testing, machine learning, the cloud and more.
Python already has a comprehensive standard library, but thanks to being open-source and having legions of fans and users, there are vast numbers of deep, free-to-use third-party Python libraries for almost every conceivable use you can envision. Additionally, Python is platform-agnostic, meaning that if you program it to use in Windows you don’t normally need to tweak the code or rewrite it to use on MacOS.
What’s Python not so good at?
In the experience of our developers, one of the tradeoffs of saving developers time defining terms is that Python can be less efficient at reading code, meaning that a Python-based script will often run more slowly than, say, one written in JavaScript.
Python isn’t seen as much on smartphones because speed is even more of the essence than on desktops — and JavaScript, we’ve found, tends to be faster on mobile. And though Python’s dynamic typing makes for pithier, cleaner and faster code in the actual coding part of the process, it can lead to run-time errors later on.
And because Python, unlike Java, doesn’t currently have strong database-access layers, which are the apps that give an app access to persistent storage, it’s not as often the top choice for huge enterprise applications.
When do you use Python?
We like Python’s flexibility and ease of use with modules, which make it a common solution for a programming language that can bind together programming modules made from other, faster languages, like JavaScript. Our developers have also found it great for building websites, AI and machine learning, automations, data analysis or visualization, financial and accounting software. Its legendary versatility means that Python’s basically a viable option for an incredibly diverse range of projects.