Apart from various scientific works Python does, data analytics has the highest majority. The Python environment is filled up with tools, packages, applications etc., which makes developers to accomplish their scientific and analytics work in an efficient way. But, the Julia programming language which is primarily designed to provide the workforce to scientific computing, ML, mathematical computations, data analysis and mining, parallel and distributed computing involved application, faster than Python. This language is in trend for some part of work these days, but for few people, it’s terrible.
Python, which is one of the most popular languages after C++, has been used by data scientists and developers from the past 30 years. While, Julia- whose journey was marked in public since 2012, has emerged as a possible alternative to Python. It is catching quickly and now showing its power in numerous ways.
Last month, when 1.0 release of the Julia programming was uncovered in front of the developers across the globe, one of the Julia founder says that- “No matter, which language you choose from C++, Java, R, Python, Matlab etc., for your scientific, engineering or mathematical work- Julia is the fastest and easy among them.”
Julia was developed by a team of four in 2009 to address the issues unsolved by Python and other programming languages. The main concern was fixing the complexity faced in data processing and scientific programming.
Julia uses the LLVM compiler framework to pace up the compiling as fast as C compiler. It follows just-in-time (JIT) approach to compile written program. On comparing its syntax with Python- they’re almost similar. But, Julia expresses more. Julia also allows you to specify variable types such as signed 32-bit integer. You can create types hierarchy for dealing various kinds of variables. Say, you can define a function which contains integers without knowing length or sign. So, you can directly use it without typing the whole context.
Through Julia’s interface, developers can directly use external libraries written in other programming languages such as in Fortran or C. You can also use Python code with it by using PyCall library and share data between both. One most significant benefit of Julia is that it supports metaprogramming. It can generate and modify various Julia programs.
On comparing Python with Julia which is specially designed for scientific and mathematical computations, you’ll get to observe various advantages of Julia over Python. Let’s explore them one by one:
As I’ve discussed before, Julia has JIT compilation with type declarations facility. It means Julia’s routine magnitude is better than Python’s unoptimised compilation which declares Julia faster than Python. Python can achieve fast compilation with the help of external libraries or third-party compilers like PyPy. It can also optimise well with applications like Cython. But, Julia is straight-forward by default.
Julia is designed to target the scientific users and data scientists who are currently working on R, Python, Octave, Matlab etc. In Julia, writing mathematical syntax is as easy as you are writing an equation that’s why non-programmers have also started loving it.
Developers can measure garbage collection manually in Julia programming. This way, they get rid of memory allocation and freeing it up. The main reason behind it was that developers don’t want to compromise this convenient method also offered by Python.
Executing maths and scientific programs require complete resources available for a given machine with the high configuration such as various cores. Both Python and Julia support the parallelism in execution. But, Julia’s light syntax eliminates the high use of computing resources.
Thus, in trending tug of war between Python and Julia- Julia seems to be a clear winner. There’s no doubt that Python is also used in advanced computing work like in ML and AI projects, but for data science, Julia is the best. Python libraries like PyTorch, Keras, TensorFlow, Gensim, Theano are the major player in data science related works such as d scalable statistical semantics. If you are also interested in learning their implementation, you can join the Data Science with Python course available over the internet. Here, you will get to understand the statistical and computational measurements, creating desired models and perform unified and efficient methods on them.