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. However, robust 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.
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.
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.
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.
There’s a Python library for everything:
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; it’s your developers’ time. Reducing your time-to-market should therefore 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.
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:
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.
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.
There are many 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:
The reason Python developers are able to understand Golang without much trouble is because the design of Python and the 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 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 near future.
As artists use different brushes to create a magnificent masterpiece, software developers might want to work in several programming languages to achieve an optimal result.
Many pieces of software you use every day are based on multiple programming languages.
Python is a great general-purpose language. Due to easy syntax, it gained popularity not only among software engineers but also with data scientists and academic researchers.
Its simplicity is best for overcoming complex problems and make it the most popular choice for machine learning and data processing.
In terms of web development, Python is typically applied to build the backend and for server-side scripting.
However, with the help of optimized C code (using tools like NumPy or Cython), Python software developers can take advantage of C modules that speed up the execution of code.
Expectations for Instagram are high—the software has to be able to process massive amounts of data, from traveling chickens to miserable geography. Simplicity and practicality play a crucial role here. These might be the reasons why the entire Instagram servers run on Python—software engineers can quickly understand code and debug if necessary.
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.
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.
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.
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.
Both Python and Ruby allow developers to reach similar results when building web apps. While web development is what Ruby is primarily used for, Python is capable of much more.
Other than their use cases, the two languages also differ in their philosophies and approaches to solving problems. The use of Ruby has been declining over the past decade, whereas Python’s popularity has skyrocketed.
Both languages are:
In short, Python enjoys much higher adoption rates among developers than Ruby.
GitHub’s Octoverse has found that Ruby’s popularity has been declining by the year. It went from ranking 5th in 2014 to being 10th four years later.
Python, on the other hand, has been growing exponentially. Stack Overflow has referred to it as the “fastest-growing major programming language.”
Ruby’s flexible syntax allows developers to come up with highly creative solutions. This has led some to describe the language as “magical.” Conversely, Python focuses on clear and simple solutions.
The approach to solving problems is the greatest difference between Python and Ruby. While the former features simple, singular solutions, the latter usually offers more than one way to get something done.
Although this may be seen as an advantage of Ruby, it could in fact compromise readability and simplicity as well as make errors more difficult to debug.
Unless the project you’re working on requires you to use Ruby, going with Python is the smarter choice.
Anything you can do with Ruby, you can do with Python. However, the rule doesn’t apply the other way around. There are plenty of areas—such as academia, science, machine learning, or data analysis—where Python has a clear advantage over Ruby.
Despite the fact that the popularity of Ruby has been declining, the language still has plenty to offer when it comes to web development. There have been voices that the language is going obsolete, though this doesn’t seem to be the case for now.
However, given the plethora of Python’s use cases, choosing between Python and Ruby is a no-brainer. Python’s dynamic growth, application in many different industries, and ease of use clearly make it the better pick.
PHP is a widely used open-source language that’s become the default web server technology. Often called a “scripting language for the web,” PHP powers about 80% of website servers.
Although the language is often used for “traditional” web projects that don’t need plenty of calculations or latest features, it has been applied across the board, from user authentication through database support to real-time applications.
PHP is extremely easy for a beginner to learn, yet it offers a lot of advanced features that lead to top-notch results.
Even though Python is currently used predominantly for web development, this area was not intended to be its chief focus at the start.
The range of Python use cases is wide and keeps on growing. It can be applied successfully in machine learning, data analytics, statistics, science, academia, and the Internet of Things.
In comparison with other programming languages, Python is very easy to learn, clear to read, and simple to write in.
From data analytics through machine learning models to powerful web apps, there is little that Python can’t do. In terms of versatility, it beats PHP hands down.
Since there have been fewer releases of Python than PHP, it tends to be more organized, secure, and easier to maintain.
Given its application in areas such as artificial intelligence, machine learning, and the Internet of Things, as well as a vast array of uses that remain beyond the scope of PHP, Python has enjoyed a huge popularity spike in recent years.
Even though PHP has traditionally been more popular than Python, it’s been gradually losing traction of late.
PHP comes with more out-of-the-box features than Python. The latter does, however, make use of plenty of libraries to make up for that slight inconvenience.
Ease of installation
PHP is easier to install on any platform than Python, unless you stick exclusively to Linux.
Even though both Python and PHP are mature, well-established languages that enjoy widespread use and support, they couldn’t be more different from each other when it comes to their syntax and philosophy.
However, when faced with a choice between the two, the decision should always come down to the individual requirements of your project.
If you’re after a website, a blog, or a simple web service, the end result will most likely be the same whether you go with Python, PHP, or any other leading web development technology. You shouldn’t be able to notice any difference in terms of performance, speed, or user design.
However, if the scope of your project is more varied and includes, for instance, machine learning, data analytics, or the Internet of Things, you should pick Python.
All in all, you can’t go wrong with Python. Since it’s great for web development and has plenty of other uses, you won’t need to worry about not being able to expand the scope of your project in the future.
Plus, with its popularity skyrocketing in the last few years, Python is bound to stick around for a long time—unlike PHP.
These days, data science is an integral part of a growing number of people’s jobs. The increased availability of data, the importance of analytics-driven decisions, and powerful computing in business make data science a huge deal in the tech world.
When it comes to tools for data science work, Python and R are two of the most popular tech stack choices.
Both are flexible, open-source programming languages with new libraries and tools added to their catalogs frequently that are oriented toward data science and enjoy large support communities. That makes it a challenge to pick one out of the two for your data analytics.
However, Python takes a more general approach to data science, whereas R is primarily used for statistical analysis. Therefore, anyone interested in leveraging these languages for their data science project should know the key differences and benefits of using one language over the other.
Let’s take a look at the main features and advantages of Python and R.
R was developed by academics and statisticians. Because of this, the programming language offers one of the richest ecosystems for performing data analysis.
With R, you’ll be able to find a library for almost any analysis you wish to perform by leveraging the 12,000 packages available in CRAN, an open-source repository. In fact, the wide variety of libraries is what makes R the top choice for statistical analysis, particularly for specialized analytical work.
Another factor that makes R a cutting-edge language is the output it generates. R features impressive tools that are efficient in communicating the results. Rstudio includes the library knitr that was written by Xie Yihui, which makes communicating your findings in the form of a presentation or document seamless and almost trivial.
Python can do almost all the tasks as R—engineering, data wrangling, feature selection, web scraping, app development, and so much more. Basically, Python code is easier to maintain and stronger than R. Hence, Python is often used to deploy and implement machine learning at a large scale.
A few years back, Python didn’t have a lot of data analysis and machine learning libraries, but the language is catching up fast. Currently, it features some truly cutting-edge APIs for machine learning and artificial intelligence. You can often do most data science jobs using five Python libraries: NumPy, SciPy, Seaborn, Pandas, and scikit-learn.
Python really stands out when it comes to making replicability and accessibility easier than R. In fact, Python is your best bet if you’re looking to use the results of your analysis in an application or website.
Since R was developed by academics and scientists, it’s designed to solve statistical problems and also works well for machine learning and data science. Hence, it’s fair to say the language is the right tool for data science, thanks to its powerful communication libraries. Additionally, R also contains many packages that can be used to perform time series analysis, data mining, and panel data.
Conversely, if you’re a beginner in the field of data science and wish to learn how the algorithm works and deploys the model, you should ideally start learning Python.
Python comes with influential libraries for math, statistics, and artificial intelligence, making it a key player in machine learning. The programming language also features fantastic libraries that help greatly with manipulating matrices or coding algorithms.
Python is fairly easy to work with if you’re looking to build a model from scratch, allowing you to later switch to the functions from the machine learning libraries. Similarly, you can also use R and Python together when going into data analysis.
R is excellent when you’re focusing on statistical methods. Still, Python is the better choice if you want to do more than statistics—deployment and reproducibility, for instance.
To put it briefly, R and Python are basically on par these days when applied to data science. You can even use a combination of both languages if you need. However, Python can be used in many more areas, such as web development, network programming, and software prototyping. Ultimately, this makes Python a much more versatile choice.
Thank you for reading our comparisons of Python to other programming languages. We hope our 15+ 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.