System ProgrammingJulia Vs Python: Which Is A better Programming Language?

Julia Vs Python: Which Is A better Programming Language?

Python is one of the most well-known and widely used programming languages. It is also the language of choice for Data Scientists, Data Analysts, Machine Learning Engineers, and those working in Artificial Intelligence. It is simple and includes versatile coding options because it is an open-source language.

In fact, according to the TIOBE index, it is one of the top 20 languages of the year in 2021. However, Julia is a new buzz in the IT sector, recognized primarily for its speed, and is gaining appeal among Data Scientists and Statisticians. Now the issue is, which one is superior? Which language should I choose, Julia or Python?

It is tough to choose between these two programming languages because each has its advantages. Aside from their merits, the programming demands of the programmer aid to determine which language to utilize. However, for beginners who want to know which language to study between Julia and Python, we have elaborated on which language is superior and which language you should learn and utilize in this post. But, before we go into the comparison, let us first try to define these languages.

What is Python?

Python is one of the most widely used programming languages in the world. It is a high-level, interpreted, general-purpose, multi-paradigm language introduced in 1991. It has many libraries and tools specialized in web and software development, Artificial Intelligence (AI), and Machine Learning (ML). If you want to programme something, you’ll most likely do so in Python.

Python is popular among developers because of its strength, adaptability, and understandable syntax that is simple to comprehend and master. Almost 70% of developers say they use Python to build high-performance AI and ML algorithms for Natural Language Processing and sentiment analysis. Python, along with R, is the language of choice for Data Science.

Python’s adaptability stems from the large number of external libraries created by its large developer community. Python relies on several of these modules to handle mathematical and scientific operations in Data Science. Some of the most popular include NumPy, TensorFlow, PyTorch, Pandas, and Maplotlib.

Another solid argument for choosing Python is that it supports standard data formats such as CSV and JSON files and the ability to communicate with SQL databases.

Features of Python

  • It is a high-level programming and developer-friendly language that is simple to learn and use.
  • It is a free and open-source language that can be freely downloaded online.
  • The language supports classes, polymorphism, encapsulation, and other object-oriented concepts.
  • Python is a language that may be extended, and its code can be written and compiled in C or C++.
  • It is an interpreted language that does not need to be compiled. The lines are performed line by line, making code debugging simple.
  • Because this is a dynamically typed language, variables do not need to be declared before usage, and the data type of variables is determined at run-time.
  • Python includes a large set of libraries that may be used to simplify coding in Python by simply importing them. As a result, developers do not need to rewrite that exact code.

What is Julia?

Julia, a newcomer to the programming language world, was designed in 2012 to meet the demands of the Data Science and Machine Learning communities for a speedier, math-oriented language, and it had its first stable version in 2018.

Julia is a computer language that combines the most delicate features of existing programming languages while using contemporary hardware’s Concurrent, Parallel, and Distributed Computing capabilities.

Julia is a dynamic, high-level, high-performance programming language designed primarily for technical computing that has a syntax similar to Python. Because linear algebra is an essential component of this language, it is commonly utilized in Machine Learning, Data Science, data mining, numerical analysis, and any mathematical purpose.

Julia’s simplicity, excellent performance, and speed are its selling features for dealing with complicated data models. However, the potential of converting Science’s formulaic language to code is a deal-breaker for scientists: Julia supports the usage of Greek letters, allowing for the direct use of mathematical formulae in the code rather than translating such recipes into coding language.

Features of Julia

  • Julia provides an interactive command line and a Read Eval Print Loop (REPL) that aids in adding prompt commands.
  • Julia is a compiled language with a just-in-time (JIT) compilation. Julia’s high-speed execution is due to using the LLVM framework for collection.
  • Julia’s syntax is simple and basic.
  • It can simply import and utilize external libraries to communicate with Fortran, C, and Python programmes.
  • Julia supports both static and dynamic typing. You may declare the variable before using it, or you can write a function that accepts variables without saying them.
  • It includes a debugger, which lets programmers set breakpoints and inspect the results.
  • Julia provides meta-programming, which allows Julia programmes to generate Julia applications.
  • Julia’s syntax is similar to mathematical equations, making it simple to use for individuals working on mathematics-based coding.

Julia vs Python

You now understand what these languages are. Let us compare these two languages on several aspects to determine which language to learn or which is preferable for you between Julia and Python. The following is a comparison between the Julia programming language and Python. Examine the comparisons to determine which language you wish to study or use.

  • Popularity

Python is now at the top of the most popular programming languages list. It has been in operation for over 30 years and has amassed one of the largest developer communities for any language, offering solutions and help for each potential issue.

Julia has a modest but devoted community, and the majority of support is still supplied by the writers, even though the number of followers has constantly been increasing. Julia-specific blogs and a growing community share their expertise on utilizing it across many different platforms. Python dominated the Tiobe Measure, the most well-known monthly popularity index of programming languages, at the time of writing, while Julia was ranked 36th.

Julia’s popularity is expected to increase as it expands outside Data Science. The language just began accepting web development frameworks, which will broaden the field of development options and, as a result, the number of developers working with it.

  • Speed

When writing code, the execution speed is critical. Julia’s execution speed is comparable to that of the C programming language. It was created to create a rapid language. Julia is not an interpreted language that accelerates execution. The LLVM framework is used to build programmes in Julia. Julia handles performance issues that require speed without manual profiling and optimization approaches. Julia is an excellent solution for challenges involving Big Data, Cloud Computing, Data Analysis, and Statistical Computing. When we compare Julia versus Python’s speed and performance, it is clear that Julia is superior.

  • Community

Community support is critical for every programming language. A large community of support suggests many resources available to tackle challenges. Julia is a new language with a small but rapidly developing and active community. On the other hand, Python is a long-standing language with a large community of users. When comparing community support between Julia and Python, Python is more favorable since it has significant community support, whereas Julia’s community support is fledgling. Python’s extensive community support is beneficial in obtaining additional resources that can handle difficulties and answer coding-related questions.

  • Libraries

Python offers an extensive library that simplifies Python coding by simply importing these libraries and using their functions. Julia lacks a vast library collection, which is a disadvantage compared to Python. Furthermore, Python is supported by a large number of third-party libraries. Another problem with Julia’s libraries is that packages are not adequately maintained. Julia can communicate with libraries written in C, although plotting data takes time at first. Julia, being a new language, needs more developed libraries to flourish.

  • Dynamically Typed

Julia and Python are both dynamically typed languages, so coders do not need to declare variables explicitly before using them in code. On the other hand, Julia is both a static and a dynamically typed language, and coders may use it in either mode according to their needs. This provides Julia with an advantage over Python.

  • Versatility

Python is a flexible language since it is easy to read and code. Python’s flexibility makes it suitable for automation, web scripting, development, and other programming jobs. Python is the preferred choice for developers since it can conduct tasks and reduce development time by utilizing many libraries and frameworks. Julia is excellent for handling scientific programming challenges, while Python is more adaptable.

  • Parallelism

Julia and Python can both conduct the operations concurrently. Python techniques need data serialization and deserialization across threads. Julia, on the other hand, employs more sophisticated and parallel approaches. Furthermore, Julia has less top-heavy parallelization grammar than Python, limiting its applicability as a programming language.

  • Tooling Support

A programming language that provides excellent tools support for any software development project is preferred by any programmer. Python outperforms Julia in terms of tools support. Python provides excellent tooling support, whereas Julia’s tooling support is still working. As a result, Julia does not have as many tools for diagnosing and addressing performance issues as Python does. Furthermore, because Julia is a novel language with native APIs, there is a greater risk of an unsafe interface in the case of Julia.

  • Working with Shell

Julia is a much superior language for working with the shell. Julia’s integration with the body is the reason behind this. Julia variables may be readily exported to the surface in the form of an environment variable. Shell commands can be used to view and alter the contents of a file. Overall, Julia makes it extremely simple to connect and operate with the shell.

  • Code Conversion

In the case of Julia, code conversion is straightforward and widely supported. While code developed in Python or C can be readily transferred to Julia, the inverse is not valid. It is challenging to convert code from Python to C or C to Python. Julia can readily connect with libraries written in C or Fortran. The Julia code may also be shared with Python via the Pycall module.

  • Ease of use for Data Science

Julia has a more significant following in the scientific world since she assists in solving mathematical programming difficulties. Julia’s community differs from Python’s, more of an application programming community. Julia is superior in terms of data science usability. Julia’s syntax is more like mathematical equations, and programmers find Julia simple to use for coding and solving mathematical tasks. Although Python is more user-friendly than Julia, the scientific community prefers Julia over Python.

  • Compiled and Interpreted

Julia is a compiled language rather than an interpreted one. It collects using the LLVM framework, which boosts execution speed but causes issues when recompiling the code. On the other hand, Python is an interpreted language that does not require compilation.

Julia was created mainly to provide a quicker programming language capable of doing Machine Learning tasks and Mathematical Computations quickly. Julia has lately acquired popularity among Data Scientists and Machine Learning Engineers due to its many benefits. However, when comparing Julia vs Python, Python is chosen since it is the oldest language and has a large active Python community and a comprehensive collection of libraries and tools support.

However, Julia versus Python is a brutal fight because of its speedier processing and more accessible code translation. On the other hand, Python improves in terms of speed with time. Overall, Julia offers numerous benefits over Python, but Python is the preferred language among programmers, data scientists, and students since Julia is still in its early stages. Julia is the language to use if you’re working on a project that requires a lot of math.

Also Read: 8 Python Libraries for Data Scientists in 2022

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