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.
You may also like: Go Top Programming Languages in 2020 from Authentic Surveys.
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.
Initially released in 1991, Python is an interpreted, general-purpose programming language that has multiple uses ranging from web applications to data analysis. This means that Python can be seen in complex websites such as YouTube or Instagram, in cloud computing projects such as OpenStack, in Machine Learning, etc. (basically everywhere!)
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.
A huge advantage of Python is the wide selection of libraries and frameworks it offers. Your time-to-market will improve if you leverage them, since you won’t be coding features manually.
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, 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.
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 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:
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 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.
Because of that, writing in Node.js means you’re using the same language on the frontend and the backend.
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.
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.
The universality and versatility of Python are among the top reasons why the language is a great fit for trending technologies such as data science
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.
This requires more flexibility and higher understanding of the project from your developers.
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.
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
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
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.
Whether it’s artificial intelligence or machine learning, Python’s design and features give it an advantage over all other languages for these relatively new purposes.
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.” Converselty, 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 could be seen as an advantage of Ruby, it could actually 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.
You may also like: Top Python IDEs and Code Editors.
Thank you for reading our comparisons of Python to other programming languages. If you enjoyed this article, please share it with others who may enjoy it as well.!
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Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
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Alright is a python wrapper that helps you automate WhatsApp web using python, giving you the capability to send messages, images, video, and files to both saved and unsaved contacts without having to rescan the QR code every time.
I was looking for a way to control and automate WhatsApp web with Python; I came across some very nice libraries and wrappers implementations, including:
So I tried
pywhatkit, a well crafted to be used, but its implementations require you to open a new browser tab and scan QR code every time you send a message, no matter if it’s the same person, which was a deal-breaker for using it.
I then tried
which is based onyowsupand require you to do some registration with
yowsupbefore using it of which after a bit of googling, I got scared of having my number blocked. So I went for the next option.
I then went for WebWhatsapp-Wrapper. It has some good documentation and recent commits so I had hoped it is going to work. But It didn’t for me, and after having a couple of errors, I abandoned it to look for the next alternative.
PyWhatsapp by shauryauppal, which was more of a CLI tool than a wrapper, surprisingly worked. Its approach allows you to dynamically send WhatsApp messages to unsaved contacts without rescanning QR-code every time.
So what I did is refactoring the implementation of that tool to be more of a wrapper to easily allow people to run different scripts on top of it. Instead of just using it as a tool, I then thought of sharing the codebase with people who might struggle to do this as I did.
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Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…
You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).
Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.
class AnyClass: def __init__(): print("Init called on its own") obj = AnyClass()
The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.
The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.
Init called on its own
Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,
class AnyClass: def __init__(self, var): self.some_var = var def __add__(self, other_obj): print("Calling the add method") return self.some_var + other_obj.some_var obj1 = AnyClass(5) obj2 = AnyClass(6) obj1 + obj2
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