Python or Julia? What’s Your Best Bet for Data Science
Both Python and Julia are considered as strong programming languages for data science professionals. While Python is a much older language and Julia the most recent one to become the preferred language for data science
Python! An admirable programming language and an excellent pick by tech giants like Google, Amazon, and Facebook are taking the data science industry by storm. Founded by the great Dutch programmer Guido van Rossum, Python is heralded to be a high level, powerful, and dynamic programing language used for the development of applications such as web apps, scientific computing, and prominently used for data analysis.
Whereas Julia officially entered the industry only in 2018, although it was unveiled to the programmers in 2012.
Julia was founded by Jeff Bezanson, Deepak Vinchhi, Viral B Shah, Alan Edelman, and Keno Fischer. The programming language was developed in an attempt to go far beyond just being between the developers’ stage, rather they wanted this language to be an expert in every aspect.
What’s the best pick for data science?
Although debates might say certain factors demonstrate Julia to be better than Python. But let us not get into the critics yet. Both Julia and Python have been established as great programming languages for data science professionals.
Why Julia?
Julia, a multiparadigm and primarily functional programming language was specifically built for machine learning and statistical programming.
Though new in the technology market, Julia is well-known for its quirky and unique features. And the coolest features include Julia’s multiple dispatches. We may further speak about the unique features as we move further ahead.
The invention of Julia by a four-member team was to address every kind of uncertainties and shortcomings Python programming language faces.
A reply by the four-person team said, they were just being greedy and that they wanted more. Here’s what they said,
“We want a language that’s an open-source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that’s homoiconic, with true macros like Lisp, but with 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 gluing 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. ”
Not to forget, they wanted it to be faster than C!
The inspiration that led developers and programmers to choose Julia as one of their data scientist skills .
• Straightforward syntax — the syntax is similar to Python programming and is quite expressive and powerful.
• It is compiled and not interpreted — Julia’s just-in-time (JIT) compiler with the LLVM compiler framework is deemed perfect for faster runtime performance. Also, it can be easily matched with the speed of C.
• An add-on benefit, in combination with dynamic typing and static typing, Julia is awesome — with this combination, you can easily be able to specify types such as “unsigned 32-bit integer. ” All in all, you also get the opportunity to create hierarchies of types allowing general cases to handle variables that have a specific type.
• Interactive programing language — Julia has a REPL (read-eval-print loop), a command-line which is almost the same as Python offers. Commands and quick one-off scripts ca easily be punched in.
• Supports metaprogramming — one of the best features about Julia is the fact that it can also generate other Julia programs and modify its code.
• Julia can easily call C, Python, and even Fortran libraries — Julia offers the feasibility of having an interface with Python through PyCall library through which data can be shared between both Python and Julia.
Python or Julia?
Well, Python has been around for a much longer time than Julia and has always been the most preferred choice by data science professionals and other developers. Based on several surveys, Python topped to be the first programming language for developers if Julia wasn’t around.
• Julia was majorly designed for numerical and scientific computation and it is purpose — built for data science. While Python was more or less evolved into the data science role. However, both Python and Julia are crucial languages that need to be pinned down in the data science skills list.
• Julia is superfast — the JIT compilation simply means it can easily beat an unoptimized Python via orders of magnitude. However, Python will need the help of libraries.
• Automatic memory management — Julia eases the burden of the user with details by offering manual control over the garbage collection, unlike Python.
• Possess full-featured debugger — Julia 1.1 introduced the debugging suite. This suite allows the execution of code in local REPL and helps you step through the results for further inspection of variables and adding breakpoints in the code.
• Julia has a math-friendly syntax — Julia offers syntax that can be readily available for non-programmers to pick it on.
• Superior parallelism — when math and scientific computing thrives together one can make full use of the resources on a machine. Both Python and Julia can run parallel operations. But Python’s method of running parallel operations requires data to be serialized between nodes whereas Julia’s method of parallel operation is much simpler and is less top-heavy as compared to Python.
Talking of Python, this programming language has been around for over the past 30 years. Julia, though still young, has undergone quite a churn in the technology industry.
There are yet but a dozen multiple reasons why you shouldn’t add both these languages in your kitty. As far as we’ve seen Julia’s popularity is gaining much attention in the data science industry and there’s no specific reason why this programming language shouldn’t be added in your arsenal. While you decide on which languages to start getting your hands on, it is also recommended for you to start considering a few credible data science platforms available online. In case you need to learn both these programming languages.
Overall, Julia could be much more fun language to write and perhaps provide more viability in the data science realm. However, it depends on your choice of language and the one you’re comfortable working with.
