A rock-solid culture of computer scientists, data scientists, and AI experts also support Python’s success. But if you’ve ever been with these people at a dinner table, you already know how much they rant about Python’s vulnerabilities. There is plenty to be pissed off over, from being late to having unnecessary checking, to generating runtime errors amid the previous testing. Therefore, other languages are being followed by more and more programmers, with Julia, Go, and Rust being the top players.
For mathematical and technological activities, Julia is fine, while Go is great for modular applications, and Rust is the top pick for programming systems. Julia is the winner for them, as data scientists and AI specialists work with a lot of mathematical problems. And Julia has upsides, even after critical scrutiny, that Python cannot beat.
Why Python is not the programming language of the future?
Everyone as an individual develops a modern programming language and they want to preserve the positive characteristics of old languages and correct the poor ones. In this context, in the late 1980s, to develop ABC, Guido van Rossum developed Python. Although its rigidity made it easy to instruct, it was difficult to use in real life and the libraries made a boom python, the latter was too ideal for a programming language. Python, in comparison, is rather tactical.
Python kept the good features of ABC: Readability, simplicity, and beginner-friendliness for example. But Python is far more robust and adapted to real life than ABC ever was. ABC paved the way for Python, which is paving the way for Julia. Photo by David Ballew on Unsplash
The developers of Julia, in the same way, tend to retain the good bits of other languages and ditch the bad ones. Julia, though, is even more ambitious: instead of eliminating one language, its aims to beat both of them. This is how Julia’s creators say it:
“We are greedy: we want more. We want an open-source language, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that’s homo-iconic, with true macros like Lisp, but with an obvious, familiar mathematical notation like MATLAB. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as MATLAB, as good at glueing programs together as the shell. Something that is dirt simple to learn yet keeps the most serious hackers happy. We want it interactive and we want it compiled.”
Julia needs to combine all the upsides that actually exist and not trade them off in other languages for the downsides. And while Julia is a young language, many of the targets set by the developers have already been accomplished.
What Julia developers are loving
From basic machine learning applications to massive supercomputer simulations, Julia can be used for anything. Python may do this, too, to some degree, but Python has somehow evolved into the work.
Julia, on the other hand, was designed specifically for these things. Up from the bottom.
The developers of Julia tried to render a language as fast as C, but what they’ve developed is much faster. While in recent years, Python has become easier to speed up, its performance is still a far cry from what Julia can do. Julia also joined the Petaflop Group in 2017, a small language club that can reach one petaflop per second at high-performance levels. Apart from Julia, right now, only C, C++ and Fortran are in the club.
Python has an immense and supportive society for its more than 30 years of age. There is hardly a Python-related query that within one Google search you will not get answered. The Julia Group, by comparison, is small. Although this means that you may need to search a little deeper to find an answer, you may be able to connect up again and again with the same people. And this can transform into beyond-value programmer-relationships.
4. Code conversion
In Julia, you don’t even need to know a single Julia-code order. Inside Julia, you can not only use Python and C language. In Python, you can also use Julia!
This makes patching the vulnerabilities in your Python code incredibly simple. Or to remain successful when you are still getting to know Julia.
This is one of Python’s best aspects, its well-maintained libraries of zillions. Julia does not have many libraries, and users have complained that they are not (yet) maintained impressively.
But the number of libraries they already have is remarkable when you remember that Julia is a very young language with a small amount of money. Apart from the fact that Julia’s number of libraries is increasing, for instance, it can also communicate with C and Fortran libraries to manage plots.
6. Dynamic and static types
Python is dynamically typed at 100 per cent. This means that at runtime, the programme determines if, for example, a value is a float or an integer. While this is incredibly beginner-friendly, a whole host of potential glitches are also added. This means that, in all possible cases, you need to verify Python code, which is quite a stupid job that takes a lot of time. Since the Julia-creators have wanted it to be quick to read, dynamic typing is completely supported by Julia. But you can add static forms if you wish, in comparison to Python, for example, in the way they are present in C or Fortran. This will save you a lot of time: You can assign the type anywhere it makes sense, instead of making reasons for not checking the code.
While all these stuff sounds fantastic, it is important to bear in mind that, relative to Python, Julia is still short. The number of questions on Stack Overflow is one reasonably decent metric: currently, Python is tagged about twenty more frequently than Julia! This does not indicate that Julia is controversial, but it takes some time to get adopted by programmers, inevitably. Think about it, would you actually like to write in a foreign language the whole code? No, in some hypothetical project, you would rather try a different language. This causes a time gap between its publication and its acceptance that every programming language faces. But you’re spending in the future if you implement it today, which is simple because Julia makes a massive amount of language translation. You’ll also have the experience to answer their questions as more and more people support Julia. Even, as more and more Python coding is substituted by Julia, the code will be more durable.
Python is still insanely popular. But if you learn Julia now, that could be your golden ticket later. In this sense: Bye-bye Python. Hello Julia!
Author Bio: Arpit Bhushan Sharma (B.Tech, 2016–2020) Electrical & Electronics Engineering, KIET Group of Institutions Ghaziabad, Uttar Pradesh, India. | Patent Analyst — Lakshmikumaran & Sridharan Attorney | Microsoft Student Partner (Beta)| Student Member R10 IEEE | Student Member PELS/PES | Voice: +91 8445726929 | E-mail: email@example.com
If you really like the article, please do the honour for help and motivating me:
1. Is Julia better than C++ and python both?
Julia, an excellent choice for numerical computing and it takes lesser time for big and complex codes. Julia undoubtedly beats Python and C++ in the performance and speed category.
2. Is Julia as fast as C?
Julia prides itself on being so fast. Julia can be as swift and sometimes even quicker than C, especially when written well. Julia uses the compiler Just In Time (JIT) which compiles extremely easily, but it compiles more like an interpreted language than a compiled conventional low-level language such as C, or Fortran.
3. Is it worth learning Julia for the future aspect?
It is certainly worth learning Julia, particularly if you are disappointed with the success of the most common languages, such as Python or R. It seems like it is really now if you are interested in using Julia for web creation, but you are also going to be an early adopter.