Julia vs Python: Which Programming Language is Better?

Julia vs Python

Julia vs Python: Which Programming Language is Better?

In this blog, we have discussed the definition, features, and which is better Julia vs Python?

What is Python

Python is an object-oriented, high-level, multi-paradigm programming language that can be interpreted and has dynamic semantics. Guido van Rossum created the language in 1991 as a successor language to ABC. To create Python, he used all the syntax and useful features of ABC.

Python is a general-purpose language with high-level, in-built data structures, dynamic typing, dynamic binding, and many other features. Python is a great choice for Rapid Application Development, Complex and Rapid Application Development, or as a scripting language or glue language to connect components.

Key Features of Python:

  • It’s easy to learn and code
  • Python Software Foundation License – Free and Open Source
  • Object-Oriented Language
  • Dynamically Typed Language
  • GUI Programming Support
  • High-Level Multiplatform Language
  • A Portable, Extensible Language
  • Interpreted Language
  • Large Standard Library

Also read: Best Python Libraries for Every Become Python Developer

What is Julia?

Julia, an open-source high-performance, high-level, dynamically-typed programming language, was launched in 2012 and founded in 2009. Julia was designed to solve the shortcomings of other programming languages and integrate the desirable, unique features of those languages.

Julia was originally designed to be a general-purpose programming language. However, it excels in scientific and numerical computing. Multiple dispatches are the language’s central programming paradigm. It supports parallelism at three levels: Julia coroutines (green threading), multithreading, and multicore or distributed processing.

Key Features of Julia:

  • Open-source, free and MIT licensed program
  • Math friendly syntax makes it easy to learn
  • It is compiled, not interpreted which makes the process fast
  • The high-performance language that is similar to statically-typed languages
  • A language that is dynamically typed and highly extensible
  • Configured for distributed and parallel computing
  • Built-ins are quick and easy to create user-defined types
  • Interoperability and compatibility with other programming languages such as C, Python, etc.
  • Lisp-like macros, and other metaprogramming tools
  • Supports encoding via Unicode, UTF-8, etc.

Which is Better – Julia vs Python

Julia vs Python: Performance

Julia is a bit faster than Python in terms of performance. Julia is a compiled programming language, which means programs written in Julia can be executed directly as executable code.

Julia code can be used with many languages, including C, R, Python, C, R, and so on. It produces impressive, fast, and efficient results without the need for optimizations or native profiling techniques. Python cannot use some optimizations in Julia.

Projects written in other languages can be compiled once and then rewritten in Julia. This makes Julia ideal for data science and machine learning. Julia takes less time to execute large and complex code than Python.

Python is not just slow to implement, but also requires many optimization methods and libraries that enhance Julia’s performance.

Julia vs Python: Speed

Julia was developed and created to be fast. Julia fulfills the requirements of a programming language that is as fast as C. Julia is part of the Petaflop Club, which includes computing languages with a peak performance exceeding one petaflop per second.

Julia cannot be interpreted, so it uses just-in-time (JIT), compilation, and type declarations in order to execute code that requires compilation at run-time. Julia excels in complex numerical and computational functions because they can execute codes quickly. Its multiple dispatches make it easy to define data types such as arrays and numbers. Python, however, is not as fast as Julia. Nevertheless, Python can be made more efficient by using external libraries, optimization tools, and third-party JIT compilers.

Julia vs Python: Libraries

Python wins the Python vs Julia battle for libraries and packages. Julia is still in its infancy and has only a few libraries. The libraries aren’t well maintained and take a lot longer to plot data and execute it. Julia’s library is growing rapidly and can directly interface with foreign libraries such as Fortran, C++ Python, Python, R, JavaScript, etc. to handle plots.

Python, due to its long existence and popularity, has a large number of rich libraries. These libraries are also well-maintained, which makes it possible to do many additional tasks. Python is supported by many third-party libraries which make it a popular choice for programmers and developers.

Julia vs Python: Tooling Support

Any programming language should have tooling support. Python is easily ahead of Julia. Python boasts a vibrant and supportive programming community that has created amazing interfaces, tools, and support systems.

Julia, however, lacks the support and resources to resolve issues as well as debugging tools or much of the performance that Python offers.

Julia vs Python: Community

A large, active, committed community is essential for any programming language to succeed and be recognized as a leader. Python recently crossed the three-decade mark. It has built a strong community of support over this period.

The growth and development of Python have taken huge leaps forward. It is often called the fastest-growing programming language. Developers have a huge advantage because there are many resources available to solve any issues or doubts. You can get help with almost any Python-related problem.

Julia vs Python: Differences

Julia’s syntax will be easy to learn for Python users. Although they may look and feel very similar, their logic and paradigms can sometimes be quite different. It may be easier to understand Pythonistas’ potential by comparing Julia and Python.

Julia vs. Python: Data Science

Julia was specifically designed for data and has a math-friendly syntax. Python, on the other hand, was designed with a different purpose. It became more popular, and it was used for a variety of purposes. In the end, Python became a Data Science programming language. Julia is a math-oriented language. Python requires an external library such as NumPy to do statistical work.

Also read: What is Java Architecture? Components of Java Architecture

Julia vs Python: Machine Learning

The same reasoning applies to ML. Julia was created to be a fast and powerful programming language that could handle machine learning. It can also support linear algebra, as well as all equations required to create work in this area. NumPy can be used to handle math-oriented tasks in Python, but it is not a native feature.

Julia vs Python: Integration

Julia can also integrate code from Python and C, as well as use their libraries. These languages allow code to be translated into Julia. Julia can interface with Python directly and share data between the two languages.

Julia vs Python: Popularity

Python is currently the most used language for programming development. It has been around for more than 30 years. It has one of the largest developers communities for any language. This community provides support and solutions for every situation.

Julia has a small but enthusiastic community. Most support is still provided to Julia by the authors. However, the number of followers has steadily increased. There are blogs that specialize in Julia, and an expanding community of users sharing their experiences with it on many other platforms. At the time this article was written, Python was at the top of the Tiobe Index. Julia was at 36.

It is possible to see Julia’s popularity rise as it expands into other areas than Data Science. The language has recently accepted web development frameworks. This will increase the development possibilities and, therefore, the number of developers who can use it.

Conclusion: Julia vs Python

We’re confident that you’ll be able to quickly decide who wins the face-off between Julia vs Python. While Julia is making waves and making a name, Python isn’t falling behind. No matter which language you choose, there are many things to consider. Each language has its strengths as well as drawbacks.

Both Julia and Python are brighter in the future of big data, machine learning, AI, and data science. However, it is impossible to predict what might happen. Julia still has a long road ahead if it wants to be as successful in these fields as Python. Julia can only reach industry acceptance and maturity when it has a large community behind it.

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