Python vs. Java: Comparing the Pros, Cons, and Use Cases

Originally written by Adam Stempniak

Some battles seem unwinnable and have apparently been raging forever.

Star Wars vs. Star Trek. Cats vs. dogs. Apple vs. Samsung.

But when it comes to software development, different conflicts tend to come up:

Quality vs. time. Time vs. cost. Cost vs. quality.

These concerns play a key role in choosing the programming language for your project, which is one of the first major decisions you have to make.

As a Python software house, we are intimately familiar with the challenge of contrasting Python with other languages:

Python vs. Golang. Python vs. Node.js. Python vs. Java.

In this article, we’ll focus on the last one.

Granted, such comparisons aren’t as set in stone as they may appear. It’s usually a little more complicated than a cut-and-dry list of pros and cons.

For example, building an MVP in Java can take months, while Python gets you the same results in weeks. And yet, Java is still popular with big banks and fintechs. Are they justified in their choice?

There’s a lot more to the story. Each language has different use cases, and you should look at what matters most to you when you make your choice.

Without further ado, let’s break down in detail how Python compares to Java.

1. Interpreted vs. compiled and dynamic vs. static

The differences between Python and Java start at the most basic level.

Python is an interpreted language, while Java is a compiled language.

Interpreted languages convert human-readable code to machine-readable code on the go, as the program executes commands, making it easier to revise or debug.

Conversely, compiled languages must translate source code into machine code before run time, making the code harder to revise or debug.

What’s more, Python is dynamically typed, while Java is statically typed.

Even though code translation has nothing to do with type-checking, their definitions are mirror images of one another: dynamic typing means checking types during run time, while static typing means checking types before execution.

The resulting difference is noticeable in compilation time. Generally speaking, we could say that Python launches faster and runs slower, while Java launches slower and runs faster.

It’s also a conflict between flexibility and reliability, respectively. Python and Java have opposing approaches to when errors are detected and how strict each language is about allowing variables to change types.

2. Entry point: Python gets you started faster

One of my colleagues once said something that stuck with me:

“You can learn the basics of Python over a weekend and start coding.”

While slightly exaggerated, this statement isn’t far from the truth.

Python has a low entry point and is very user-friendly, making it the perfect choice for junior developers and programming newcomers.

Getting started on Python quickly is only one side of the coin, though; it takes much more time to learn how to use it well.

Python can give you the same functionalities as Java, but only if your developers have more experience in it, which can be inconvenient in some cases.

With Java, there is a definite learning curve and the entry point is high. It takes a lot of time to start writing in it and get to know it well, and each API is a different story.

However, once your developers put in the work, you will end up with higher-quality code from day one. So the time Java demands is well spent, but it will take a while before you see the results.

In short, it takes weeks to get started on Python, and months to get started on Java.

3. Stability: Java is slightly more stable

Java always requires more code. The language is designed in such a way that everything needs to be defined from the get-go.

This means you obviously need more time to review code written in Java, because there’s simply more of it—not to mention fixing all the potential issues you may find. When it’s bad enough, you’re actually better off rewriting the whole thing from scratch, rather than burning time and money to debug it all.

But this code volume doesn’t necessarily have to be a bad thing.

Certainly, the more code you have, the more complicated it gets—but if you write it well, you get more robust and stable software that crashes less. It may not matter as much for smaller-scale projects, but it’s a very different story for large ones that process a lot of data of all kinds.

This perceived stability is the reason large companies see Java as a strong language that gives them order and security. Big players like banks or fintech businesses usually don’t think twice before settling on Java due to its seemingly superior stability, without considering other options.

While Java may have traditionally been the go-to language for corporations, we should ask ourselves: can we really say that Java is the enterprise solution?

Not quite. Python is also well equipped to handle large-scale software products; otherwise, tech giants like Dropbox, Instagram, or Google wouldn’t have chosen it for their tech stack. Actually, there’s a particularly strong case to be made for using Python in fintech. All in all, it would be inaccurate to say that Python gives you an unstable product.

So why the preconception that Java is better for corporations?

Other than sheer code volume—which isn’t always an advantage in and of itself—Java is seen as enterprise-friendly because of the strong library support it enjoys.

Java offers plenty of libraries that help you perform various tasks common in enterprise applications. Examples include:

• Drools (a Business Rule Engine),
• Lucene (a search engine),
• Hadoop (a Big Data tool).

Libraries matter. It’s the same reason Python is AI/ML-friendly—more on that later.

The bottom line is that performance is a sum total of a plethora of factors, mostly involving your code environment and external support. That being said, the stability scale is slightly tipped in Java’s favor.

4. Speed: Python is perfect for building an MVP fast

Python is known for its speed and famously easy to write in, making development really fast. If you’re pressed for time and looking to meet a deadline, you should go with Python.

Building an MVP with medium-quality code written in Python, then refactoring it later is a perfectly valid solution. Sometimes, you even start off by doing a mockup to see how your product is going to work before you decide on the actual language. Python is an ideal choice for that purpose.

Time is decidedly in favor of Python. It can take months to build an MVP with Java, while with Python you can get to that stage in a matter of weeks.

In line with this, Java projects usually take years—a year-long project is considered small—while for Python it is perfectly normal to have projects that require only months of work.

What’s more, Java usually requires larger development teams. Python demands fewer developers, and sometimes even one will suffice, helping you lower the total cost of your project.

For all of these reasons, Python is a great choice for startups. If developing an MVP as fast as possible is your top priority, Python won’t let you down.

5. Resources: Java requires a larger investment than Python

Another reason why Java is considered the language of corporations, is because development in it demands a large budget and a lot of time. It’s a sizable investment all around.

Python is more cost-effective than Java, which is why small- and medium-sized projects prefer it. For most use cases, it’s a perfect fit.

Mind you, just because Java is more stable and expensive doesn’t mean Python is unreliable or a lower tier language. Far from it.

Writing some projects in Java can be overkill—form over content, if you will—but large companies with resources to spare often choose it over other languages simply because it is the pricier solution, and thus better in their eyes.

The logic there is debatable, but it’s not like those corporations suffer for it.

If you have plenty of time and a generous budget at your disposal, there’s no reason not to go with Java. You will end up with a product of highly comparable quality to Python, though your development will be longer and slower.

6. Trending technologies: Python is the best choice for AI/ML

There are no two ways about it: Python has no equal when it comes to trending technologies.

The easy architecture of Python makes it a perfect fit for artificial intelligence, while Python’s simplicity and clarity gives it the necessary edge over other languages to effectively design the complex internal logic of machine learning.

Writing either in Java would require much more code, slowing down development and losing focus of the task at hand in the process.

But the main reason Python has been adopted as the go-to solution for trending technologies is the strong support it offers with its wide variety of ready-made libraries. Whatever technical novelty you’re after, there’s a Python tool out there to help you out.

The race for quicker implementation of AI or ML is only picking up speed, and there’s every indication that Python will not only stay in the leader’s seat, but also continue to become more popular and widely used.

7. Key takeaways

If your top priority is development speed, go with Python; if your top priority is stability over all else, go with Java.

Generally speaking, Java is better equipped to handle complex tasks. On the other hand, Python is clearer, easier, and simpler—to read, write, and modify.

Keep in mind that this is an oversimplification. Your choice should always depend on your individual needs, your budget, and the type of project you have in the works.

However, if the current trends continue and the language keeps growing in popularity, one thing is certain: Python is the future.

Does that mean we will soon see a day when one language emerges victorious, and the other fades into obscurity?

Unlikely.

What’s far more likely is that the battle will carry on for years to come, like so many others of the same kind.

It’s up to you to decide: which side are you on?