What is Python?
Python is an interpreted, object-oriented, high-level and multi-paradigm programming language with dynamic semantics. The language was created in 1991 by Guido van Rossum as a successor to his previous language ABC. He took all the useful features and syntax of ABC to create a new language; Python.
Further, Python is a general-purpose language that features high-level in-built data structures as well as dynamic typing, dynamic binding, and many more features. This makes Python convenient for use in Complex or Rapid Application Development or as a scripting or glue language that connects components.
Features of Python:
- Easy to code and learn
- Free and Open Source with a Python Software Foundation License
- Object-Oriented Language
- Dynamically Typed Language
- GUI Programming Support
- High-Level Multi-platform Language
- Extensible & Portable Language
- Interpreted Language
- Large Standard Library
What is Julia?
Founded in 2009 and launched in 2012, Julia is an open-source, high-performance, high-level, and dynamically-typed programming language. As its four creators blatantly say it, Julia was created in the name of greed; to resolve the inadequacies of other programming languages while also integrating the unique and desirable features of the same languages.
While initially designed as a general-purpose programming language, Julia greatly thrives at numerical and scientific computing. The language uses multiple dispatches as its central programming paradigm and supports parallelism in three primary levels, namely: Julia coroutines (green threading), multi-threading, and multi-core or distributed processing.
Features of Julia:
- Free, open-source and MIT licensed program
- Easy to learn with math friendly syntax
- Compiled, not interpreted which makes it fast
- High-performance language similar to statically-typed languages
- Dynamically typed and extremely extensible language
- Designed for parallel and distributed computing
- Quick and compact user-defined types as built-ins
- Interoperability with other programming languages like C, Python, etc.
- Lisp-like macros and other metaprogramming facilities
- Supports encoding via Unicode, UTF-8, etc.
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What are the points to consider before choosing between Julia vs. Python — Click here to read the detailed article.
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Which one is better?
Performance:
Performance-wise, Julia vs Python takes a twist. Julia is a compiled language which means that programs written in Julia are directly executed as executable code.
Therefore, Julia code is also universally executable with languages like Python, C, R, etc. It provides impressive, efficient, and rapid results with no need for many optimizations and native profiling techniques. Some optimization in Julia can not be used in Python.
Basically, projects from other languages can be written once and naively compiled in Julia making it ideal for machine learning and data science. The time taken by Julia to execute big and complex codes is lesser to Python’s.
Python not only takes some time to implement codes but requires several optimization methods and external libraries that highlight Julia’s performance excellence.
Speed:
Speed was one of the main objectives in the creation and development of Julia. The need for a programming language with the speed of C, and for a fact, Julia doesn’t disappoint! Interestingly, Julia is a member of the Petaflop Club which comprises computing languages that surpass a one petaflop per second peak performance.
Julia is not interpreted hence uses just-in-time (JIT) compilation and type declarations to execute codes that involve compilation at run time. Julia impresses at complex numerical and computational functions since it is designed to quickly execute codes. Further, its multiple dispatch quickly defines data types like numbers and arrays. In comparison, Python is fast but not as Julia. However, with ongoing speed Python interpreter improvements, Python can be made faster via external libraries, optimization tools and third-party JIT compilers
Libraries:
In terms of libraries and packages, Python takes the cake in Python vs Julia face off. Given its infancy, Julia has a limited number of libraries. Besides, the libraries aren’t very well maintained, taking considerably longer to plot and execute data. Regardless, Julia’s library is steadily growing, and it can directly interface with foreign libraries of Fortran, C++, Python, R, Javascript, etc. to handle plots.
In contrast, Python boasts an enormous number and rich set of libraries, mainly due to its lengthy existence and popularity. More so, these libraries are well maintained, making it easy to perform various additional tasks. Python is also supported by a significant number of third-party libraries, which makes it a favorite among developers and programmers.
Tooling Support:
Tooling support is an essential aspect of any programming language. Python easily takes the lead over edges Julia. Having a supportive and active programming community, Python brags brilliant tool support, systems, and interfaces built by its community.
However, Julia lacks substantial support and many great resources, debugging tools, or resolving issues with a performance like Python does.
Community:
For any programming language to be successful and position itself as a force, a massive, dedicated, and active community is indispensable. With Python hitting the three-decade mark recently, it has amassed a vast and supportive community over that period.
Consequently, the development and growth of Python has taken leaps forward, often branded as the fastest-growing programming language. The large Python community serves a massive advantage for developers since it allows multiple resources to resolve any problems and doubts. There’s barely any Python-related issue you cannot get assistance.
Here is the full guide where we explain the differences between Julia & Python and choosing the right language for your next project!
Conclusion:
By now, we’re sure you can easily pass judgment on who takes the crown in Julia vs Python’s face-off. Although Julia is attracting some attention and making a name for itself, Python is not falling back in the same race. Whichever language you might opt for, many factors have to be considered since each language has its strengths and drawbacks. Fast-forward to the future, both Julia and Python have a brighter future in the big data, data science, AI, and machine learning fields, and there’s no assurance of what may happen. Nevertheless, Julia has a long journey ahead should it want to match Python’s footprint in the aforementioned fields. Only with full maturity which might be years away and a mass community following can Julia increase its relevance as a programming language and achieve complete industry adoption.
Then again, with the knowledge of Julia’s abilities, Python will only improve on its weaknesses. Python will also continue to be a big player in all technology fields and a skill that is sought after regardless of Julia’s upsurge. Both languages will, however, have to share from the same plate. Overall, with both languages showing promise, the competition can only get better. For developers, Python vs Julia highly stresses the need for having more than one arrow in your programming language quiver.
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