Although Python have a lot to offer for Data Science projects, Julia is not that much behind at all. Julia have much better features as compared to Python in some aspects like Speed, Memory management etc. These features make Julia quite appealing for the Data Science Projects.
Let’s have a look at what each of these two programming languages, Julia/Python, have for offering.
What is Python?
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python’s simple, easy to learn syntax emphasises readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.
Typical features of language are: –
- Easy to code – Have quite simple syntax
- High-level Language – No issues about Memory management, garbage collection as these are built into the language itself
- Highly portable – Can be easily ported for different devices either it be Mac or Windows
- External libraries/packages/modules – Python offers a lot of third party stuff which can be used for doing specific things. Most of these third party packages are available at https://pypi.org/
What is Julia?
Julia is not that old a programming language, it’s development started in 2009 and was made available publicly in 2012. It was developed by Jeff Bezanson, Alan Edelman, Stefan Karpinski and Viral B. Shah.
“We want a language that’s 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.”— Core Developers of Julia
Typical features of Language are: –
- Syntactical Expressions
- Meta programming
- Parallel Computing
Python vs Julia: One on One feature comparison
|Speed of Execution||Python is an interpreted programming language, which make it slower. But speed can be used by using only external libraries like CPython.||Julia’s JIT compilation and type declarations makes it quite fast programming language.||Julia wins in this aspect|
|Syntax for Mathematical Operations||Python have quite simpler syntax for writing mathematical formula’s for Data Science project.||Julia too have quite simpler syntax but it’s easier/simpler as compared to Python. Because the syntax of writing maths expressions is quite similar as a person would write in real world.||Julia wins owing to easier syntax|
|Memory Management||Memory management in Python involves a private heap containing all Python objects and data structures. |
The management of this private heap is ensured internally by the Python memory manager.
The Python memory manager has different components which deal with various dynamic storage management aspects, like sharing, segmentation, preallocation or caching.
As Python being Higher level programming language, Programmer doesn’t need to care that much about Memory Management. Which is a plus point specifically for Data Science Projects.
Python helps to just focus on Data Science not memory management.
|Python, Julia doesn’t burden the user with the details of allocating and freeing memory, and it provides some measure of manual control over garbage collection. The idea is that if you switch to Julia, you don’t lose one of Python’s common conveniences.||Both languages win for this aspect|
|Parallelism||If dealing with large datasets then computing times really matter. For example – If processing a dataset of 1TB and it’s taking 24 hours that would not be viable situation. |
In order to reduce this 24 hours to as little as 2 or 3 hours Parallelism can be used.
Check out how this can be done in Python https://docs.python.org/3/library/multiprocessing.html
|In Python’s approach for Parallelism data need to serialised, deserialised between threads while that’s not the case in Julia.|
Check out how this can be done in Julia
|Julia wins in this aspect|
|Native Machine Learning Libraries||In order to do Machine Learning part of Data Science Project, need to build up the whole data science ecosystem by installing number of third party libraries. |
Extra Learning Required For Integrating These Libraries With Python.
|Julia Developers are working on developing Flux, which is a native Machine Learning Libraries. Built into language itself, thus does not need to learn about library integration etc.||Julia wins in this case|
|Maturity/Community||Python have got a lot of users all across the world, with one of largest community.|
Almost 1.5 million questions have been asked about Python on StackOverFlow.
Thus if you encounter any error while development, then there are higher chances that you would find a solution easily.
|Julia’s community is not that big and it still developing.|
May be it will become as big as that of Python in next ten years or so.