Python vs. Other Programming Languages
Building software is like building a house.
In both cases, what you need the most is a strong foundation.
Use a weak foundation, and you will struggle with expansion, suffer costly repairs, or possibly be forced to rebuild the whole thing from scratch down the line.
But use a strong foundation, and you will scale up smoothly, the upkeep will be a breeze, and your project will be built to last.
If the house is your software, then the foundation is your programming language.
We’re going to show you why Python is one of the best programming languages out there and explain the many reasons you should consider choosing it for your software project.
But as good as it may be, Python isn’t the only programming language worth its salt. There are many other Python alternatives to choose from, including:
We’re also going to show you how Python compares to these programming languages, as impartially and informatively as we can. We’ll let you decide which of them is the best choice for your software.
Let’s start with Python, then move on to the others.
The popularity of Python in 2018
Since 2012, Python has been consistently growing in popularity, and the trend is likely to continue, if not increase, in the future.
Per the Stack Overflow Developer Survey 2018:
The demand and support for Python are also on the rise, and if projections are to be believed, Python will overtake Java in the coming years and claim the top spot.
Is Python’s popularity a good thing?
Yes, very much so. While generally what is popular isn’t always the best, in the case of programming languages the popularity pays off.
Thanks to Python’s popularity, you’re likely to find a ready-made solution to any problem you may be experiencing. The community of Python enthusiasts is strong and they are working tirelessly on improving the language every day.
Python also has a number of corporate sponsors, pushing to popularize the language further still. Among them are tech giants such as Google, which itself is using Python.
Fast development speed
Python is designed to be accessible. This makes writing Python code very easy and developing software in Python very fast.
What does that mean for your development team? Less time wasted struggling with the language and more time spent building your product.
Numerous libraries and frameworks
There’s a Python library for everything:
- data visualization,
- machine learning,
- data science,
- natural language processing,
- complex data analysis.
From NumPy to TensorFlow—you name it, Python has it.
The same is true for frameworks, which help get your project off the ground and save you time and effort.
There’s a variety of frameworks to choose from, depending on your needs, such as:
One of the biggest criticisms of Python is the runtime, relatively slow when compared to other languages. However, there’s a workaround to this specific challenge.
When performance takes priority, Python gives you the ability to integrate other, higher-performing languages into your code. Cython is a good example of such a solution. It optimizes your speed without forcing you to rewrite your entire code base from scratch.
Besides, the priciest resource isn’t CPU time, but rather your developers’ time. Therefore, reducing your time-to-market should always take precedence over fast runtime execution.
Python is intuitive to read, because it resembles actual English. This makes the language effortless to decipher and maintain.
Additionally, Python has a clear syntax and doesn’t require as many lines of code as Java or C to give you comparable results.
What are the benefits of Python’s high readability?
Python’s simplicity is particularly helpful in reading code—yours or someone else’s. Because Python code has fewer lines and mimics English, reviewing it takes a lot less time. This is a major benefit.
Reducing the time you need to spend on code review is invaluable, since the productivity of your developers should be your top priority.
Scalability is unpredictable. You never know when your user numbers surge and you find yourself prioritizing the ability to scale over anything else.
That’s why Python is such an optimal choice, with its reliability and scalability. Some of the biggest players on the web, like YouTube, have bet on Python for that very reason.
Why Python? Because:
- it’s popular, fast, readable, intuitive, simple, clear, and scalable;
- it offers a ton of useful frameworks and libraries;
- it enjoys a vast and ever-growing community of supporters and enthusiasts.
Python is so flexible and readable that it can be understood without any prior knowledge of the language—the same is true for Golang. Neither of the two requires so much as reading one tutorial to follow their code.
Go in particular is easy to find your way around. Within the first 24 hours of being introduced to Go, you’re able to start making changes to software written in it.
Similarities between Python and Golang
The main similarity between Python and Golang lies in high-level types.
Go’s slices and maps resemble Python’s lists and dicts, only statically typed.
Also, enumerate in Python functions as range in Golang.
And… this is where the similarities end.
Differences between Python and Golang
There are much more differences than similarities between Python and Go, some of them likely to shock Python developers.
For instance, Golang doesn’t have try-except, instead allowing functions to return an error type along with a result. Therefore, you need to check whether an error was returned before you use a function.
The greatest difference between the two languages, however, lies in typing. Python is dynamically typed, while Go is statically typed. Python is also an interpreted language, as opposed to Golang, which is a compiled language.
Go has a number of other surprises in store for Python developers to learn, including:
- channels (sending messages between goroutines),
- defer (replacing try-finally),
- structs (compound types).
High readability of both Python and Golang
The reason Python developers are able to understand Golang without much trouble is because the design of Python and design of Go are based on similar principles.
If we compare the Zen of Python with the guiding principles of Golang, we notice that both languages prioritize simplicity and minimizing clutter:
- Python values readability, while the clean syntax of Go leads precisely to high readability.
- “Simple is better than complex” for Python, and the same is achieved thanks to the orthogonality of Golang.
- The static typing of Go aligns with the rule of “explicit is better than implicit” in Python.
Which language is better: Python or Golang?
Python is an excellent choice for data science and the web. Meanwhile, because Golang is compiled and statically typed, its performance is much faster than that of an interpreted and dynamically typed language like Python.
So should you choose one over the other? We don’t think so.
The most optimal approach is to use Python and Go together. Microservices or serverless are likely the best ways to go about it. When code performance is your top priority, consider writing the code in Golang and using Python for everything else.
The design similarities between Python and Golang make transitioning from one to the other seamless and enjoyable.
Hopefully, we will see more and more projects combining the two languages in the nearest future.
Comparing Python and Node.js
Because of that, writing in Node.js means you’re using the same language on the frontend and the backend.
Advantages of Python over Node.js
This is not the case with Python, since it’s easier to use for less experienced developers. The mistakes made by them will have less of a negative impact on development.
Lower entry point
Frameworks such as Django are mature, increase the quality of your code, and speed up the process of writing it—all without the need to lean on highly skilled developers.
Node.js is mostly used for the web, while the applications of Python are far greater.
Python doesn’t have that problem, which is why it’s simpler and easier to use. It also makes the language faster to write in, although Node.js is anything but slow.
Advantages of Node.js over Python
More flexible developers
This requires more flexibility and higher understanding of the project from your developers.
Less opinionated ecosystem
Packages for Node.js are often simple and designed for one purpose only. This pushes developers to think more carefully about what they want to use and when they want to use it.
Because of this, Node.js requires your developers to be more advanced. Writing Python code in Django isn’t anywhere near as demanding.
Fast growth and large community
Since 2012, Python has been consistently praised for its great community and support—and rightly so. But the days of its huge frameworks and libraries advantage are over now.
Python, on the other hand, doesn’t pose that risk, since it introduces substantial changes very slowly. The language is a perfect fit for trending technologies such as machine learning or data science, with its top-notch experts and library support.
Performance and speed
Node.js may struggle with executing a lot of tasks at once. If the code isn’t written very well, your product will perform poorly and work slowly.
This may also happen with Python, but Python frameworks such as Django come with ready-made solutions to help your software withstand high load.
It’s yet another example of Python making life easier for your developers.
Your team composition is everything—the number one factor to consider when deciding on the programming language for your software product.
Granted, this argument is invalid if you happen to have full-stack developers with both languages; however, those are hard to come by, so you usually have to keep this in mind.
All things considered, the scale is tipped in Python’s favor in one regard: it is much friendlier for junior or inexperienced developers. Furthermore, you generally shouldn’t choose Node.js if you don’t have an advanced team on hand.
But the real difference lies in your development team, not the language. They are what decides your project’s success or failure, so you should go with whichever option works better for them.
Interpreted and dynamically typed vs. compiled and statically typed
Python is an interpreted and dynamically typed language, whereas Java is a compiled and statically typed language.
Python code doesn’t need to be compiled before being run. Java code, on the other hand, needs to be compiled from code readable by humans to code readable by the machine.
Simply put, this generally means that Python has faster launch time and slower run time, while Java has slower launch time and faster run time.
For Python, the entry point is famously low, which is why it’s perfect for newbies and junior developers. The language is extremely user-friendly.
Conversely, Java has a high entry point with a clear learning curve. Learning how to write in Java—not to mention mastering it—is a significant time investment.
In a nutshell, getting started on Python takes weeks, while getting started on Java takes months.
There is a preconception that Java is the enterprise solution for software development.
Corporations consider Java to be a strong, robust language because of its large code volume. They believe it makes the language more stable and secure than, for instance, Python.
However, the notion isn’t entirely correct. Python also has what it takes to handle software products for big businesses—fintech, in particular.
To call Python unstable would be unfair and false. So why the prejudice in Java’s favor?
It’s not as much code volume as it is enterprise-friendly library support. These libraries are the actual reason why Java really is a little more stable than Python for corporate purposes.
Building an MVP with Java can take months because of its high code complexity and volume. Consequently, projects written in Java often go on for years and demand more developers on the team.
Python doesn’t have any of these problems, thanks to its lightning-fast development speed. You can build an MVP with Python in mere weeks, finish the whole project in a matter of months, and use only a handful of developers for the job.
Beating deadlines is Python’s specialty. If time is your number one concern—especially if you’re a startup—look no further.
Development in Java is a bigger investment all around; it requires more time and money. If you have a lot of those on your hands, you should be perfectly satisfied with Java.
Python is less expensive, which is why for most projects it’s the preferred choice. Remember, just because something costs more doesn’t automatically make it better.
No programming language is better suited for trending technologies than Python.
The main reason why Python’s been adopted as the go-to language for trending technologies is its extensive AI/ML library support.
Furthermore, there’s every indication that this trend will continue in the future.
Python is clear to read, easy to write, and simple to modify. So if it’s development speed you care about the most, go with Python.
Java, on the other hand, is perfectly suited to handle really complicated tasks. Therefore, if you value software stability above anything else, you might be better off with Java.
Thank you for reading our comparisons of Python to other programming languages. We hope our 13 years of experience writing software in Python have helped answer all the questions you may have had in the matter.
And if you do decide that Python is the right choice for your software project’s tech stack, maybe we could also interest you in outsourcing your Python software development?
We’ll be updating this page several times in the near future, since there are many more technologies worth looking into with regard to how they compare to Python. Stay tuned.