Python is a general-purpose language developed by Guido van Rossum at CWI as a replacement for the ABC programming language and was first released in 1991. It’s typically used for procedural and functional programming, but also supports both imperative and OOP.
Now that we have a better understanding of these two languages, we can finally start comparing. I’ll start by weighing out which language is best suited for beginners, then move on to the juicy stuff like salary, performance, data science, web development, and which one is best for you!
Hot tip: Functional programming is more than just a paradigm, it’s a way of thinking and approaching coding as a whole. Improving your functional programming skills make you a better developer and helps you create better applications.
Both languages have excellent advantages for beginners, it just depends on what advantages you’re looking for.
Python was designed to be straightforward, and what makes it a great choice for beginners is its readability. Python is praised by industry professionals of all backgrounds for its simplistic design that reads like English, giving it an unbeatable learning curve.
It’s also a procedural language, one of the easiest paradigms to learn, and has easy error handling, further cementing its claim as a perfect beginner language.
Aside from being easy to learn, Python has an amazing standard library full of useful features, and excellent compatibility with industry-preferred third-party tools. And with a huge community of enthusiastic developers, there’s no short supply of open-source frameworks and libraries at your disposal, giving you plenty of diverse Python applications.
This is particularly handy for accessing libraries for fields like AI, back-end development, and even finance and fintech when leveraging large mathematical data sets.
Learning a new language is great, but it’s important to learn a language that’s going to earn your money back.
There are multiple ways to think about performance, but today we’re focusing on run-time performance.
Python is strong, stable, and readable, but that also means it’s slower than its competitors. Granted this doesn’t seem to affect its love and popularity, but it’s not a go-to for performance-heavy applications.
Python gets its readability from using higher levels of abstractions and has stricter rules around coding, meaning it takes longer to interpret. However, it’s these exact features that make it a safe language for large CPU programs. There’s also no compilation option, so it relies on slower interpreted functions.
When it comes to machine learning, Python’s influence is undeniable.
Python naturally fits into experimental fields. It handles machine learning algorithms and data effortlessly, and its readable syntax makes it a stable language to experiment with and quickly make changes.
Python’s also platform-independent, so you can implement your ML programs across different platforms without losing time rewriting them.
Along with the correct structural support, Python’s compatible with fantastic deep learning libraries like TensorFlow and Keras. Since it’s supported by the machine learning industry, open-source resources are abundant, as well as community forums perfect for problem-solving and peer support.
Probably the most attractive aspect though is how quickly you can get started. The demand for Python developers in ML is growing and it’s an easy language to learn, creating an ideal environment for getting involved in the field quickly.
Hot tip: Machine learning is largely the application of algorithms that use and learn from data. So, the more advanced your algorithms are, the better machine learning developer you’ll become.
Data science and machine learning require similar prerequisites, so it’s no surprise that Python does well in data science too.
Again, Python simply has the bandwidth to process and support large data sets and CPU-heavy operations, making it ideal for data science. It also has fantastic third-party support like Pandas for high-level data structure and data analysis, Matplotlib for crafting 2D charts, Statsmodel for statistical algorithms, and NumPy for processing large mathematical functions.
It’s the language for data scientists, and with that comes an enthusiastic backing from programmers and companies eager to develop new tools to enhance Python’s data science capabilities.
I said earlier that both languages support some form of web development, but one ultimately does it better.
With React.js it has modernized front-end development, taking dynamic web pages and user experiences to the next level. With Node.js, it’s become a powerful back-end scripting language that can build dynamic server-side web pages. And then there’s Angular.js, an open-source front-end framework that lets you create dynamic single applications.
As you can see, the number of resources is endless because it’s been the go-to for web development for nearly 30 years. Big or small, front or back, you can develop web applications of all sizes for any need.
Python, however, sticks to the background focusing on back-end development. Its robust and stable design makes it ideal for handling back-end CPU operations, and its readability makes coding and documenting back-end software a breeze. But, it struggles to perform at the level necessary for front-end development.
Asking which is better is like asking which came first, the chicken or the egg?
In reality, both languages have the potential to satisfy any of these cravings – machine learning, web development, modern, start-up – and both come with the promise of exciting and well-paying futures.