Python is my bread-and-butter, and I love it.
More experienced people in the field tend to be Python-evangelists anyway.
On the one hand, the language was evolving rapidly and adding extremely interesting new features.
The development team at Microsoft took a different approach: they created TypeScript by expanding JavaScript.
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TypeScript, on the other hand, is compiled.
This provides an extra layer of safety because programmers get information about possible bugs before execution time.
The key difference between JavaScript and TypeScript, however, is the jot down checker.
Upon compilation, every TypeScript-program gets checked on whether the data types are consistent with one another.
This might sound trivial to do manually.
There are a few other differences, like the fact that TypeScript has anonymous functions and asynchronous functions.
Asynchronous programming ispossible in Pythonand Javascript, but in TypeScript its built in from the core.
That doesnt mean that TypeScript isnt a staple in the general programming world.
Among data scientists, however, its never been particularly popular.
You could conclude that TypeScript may not be a good match for data science.
If you happen to work in one of these areas, its worth giving TypeScript a shot.
And if you dont, who knows where youll land next?
The field is moving fast.
You have a competitive advantage if you’re able to look beyond your nose.
But these syntactical hiccups arent the reason why programmers love TypeScript so much.
But for building large Microsoft-style applications, this seems to work extremely well.
That being said, TypeScript still only has a fraction of the popularity that JavaScript has.
This could be attributed to the age: TypeScript is eight years old.
JavaScript is three times as old!
And Python is also an oldie in the field with its thirty years.
Despite its young age, there are fields where TypeScript is inevitable.
This adds to its popularity.
You compile it once, and it works everywhere.
When that first came out, it almost seemed like magic.
As its built on JavaScript, TypeScript shares that magic.
Python does, too!
Granted, it has a slightly different implementation since it uses an interpreter instead of a virtual machine.
But that doesnt change the fact that in terms ofrun-me-anywhere, TypeScript and Python are on par.
Features like generics and static typing make it easier to do functional programming in TypeScript than in Python.
On the other hand, Python has beenadding moreand more features of functional programming, too.
That leaves parallel programming and asynchronous programming on the table.
In TypeScript, all libraries are asynchronous from the core.
You might already be considering writing your next project in TypeScript rather than Python.
Whether thats a good idea depends on many other factors, though.
First of all, TypeScript doesnt have a straightforward way of doinglist comprehensions.
This can be frustrating when dealing with large arrays, for example.
Second, there are no native matrix operations in TypeScript.
Python has NumPy, as well as a host of other tools, that make them easy.
So if your project is heavy in linear algebra, you might want to stay away from TypeScript.
Third, if youre not too familiar with JavaScript, youre almost guaranteed to get some moments of confusion.
Although, if Im honest, encountering these phenomena can be quite amusing, too…
Finally, youll want to take into account that programming isnt a solitary occupation.
There is an enormous community for Python in data science that offers support and advice.
But at this point in time, TypeScript isnt that popular among data scientists.
So you might not be able to find that many helpful answers to your questions on StackOverflow and elsewhere.
Especially if it involves some parallel or asynchronous programming.
Sure, some languages are more fun or more intuitive than others.
Starting with a well-loved language like TypeScript or Python is therefore not a bad idea.
But at the end of the day, you shouldnt stick to one language like to a religion.
Programming languages are tools.
Which tool is best for you depends on what youre trying to do with it.
At the moment, Python is huge for data science.
But in a rapidly evolving field, it’s crucial that you be able to look past your nose.
As your tasks are changing, so might your tools.
Whats interesting is that these areas intersect with data sciencemore oftenthan one thinks.
Node, in particular, is gaining more and more traction among data scientists.
Of course, this doesnt mean that you should dabble with a dozen languages at a time.
There is enormous value in knowing one language really well.
So dont hesitate to try something new when you feel like it.
Why not with TypeScript?
This article was written byAri Jouryand was originally published onTowards Data Science.
