Python is an object-oriented, high-level, multi-paradigm, interpreted programming language with dynamic semantics. Guido van Rossum invented the language in 1991 as a successor to his prior language ABC. He used all of ABC’s helpful features and syntax to build Python, a new language.
Python is also a general-purpose language with built-in high-level data structures, as well as dynamic typing, dynamic binding, and many other capabilities. Python is so well-suited for usage in Complex or Rapid Application Development, as well as as a scripting or glue language that connects components.
- Simple to code and learn
- The Python Software Foundation License makes it free and open source.
- It is an Object-Oriented Programming Language
- Python has GUI Programming Support
- Python is Extensible & Portable
- High-Level Multi-Platform Language
- Python has large standard libraries which are in-built(built-in methods).
Julia is a computer language that is used for scientific computing and mathematical programming. Julia is a mix of C and Python, however, it does not literally copy any of the characteristics of either language. This combo combines the fast execution speed of C with the flexibility of Python code development.
Julia was created by a team of four people at MIT. It is a dynamic, high-level programming language that is open-source and used for statistical calculation and data analysis. Julia was created primarily for its programming speed; it executes code significantly faster than Python and R. Julia assists with big data analytics by handling complex tasks such as cloud computing and parallelism, both of which are critical in studying Big Data.
Python vs Julia
|Developed By||Python was developed by Python Software Foundation||Julia was developed by a team of four people at MIT|
|Speed||Python, on the other hand, is fast but slower than C.||Julia is faster than Python, with an execution performance that is comparable to that of C.|
|Interpreted or complied?||Python is an Interpreted language||Julia has Complied|
|Community||Python has been around for a long time and has a huge programming community. As a result, it is considerably easier to get your issues fixed online.||Julia is a new language with a small community, therefore tools for resolving issues and problems are limited.|
|Code Conversion||It is extremely tough to create Python code by converting C code or vice versa.||Julia codes are simply developed by converting C or Python code.|
|Paradigm||OOP, POP, and Functional Paradigms||Functional|
|companies that use the language||Google, Facebook, Spotify, Quora, Netflix, and Reddit are just a few examples.||Amazon, Apple, Disney, Ford, Google, and NASA are a few examples that use Julia|
|Library support||Python, on the other hand, has a number of libraries, making it easier to accomplish a variety of additional jobs.||Julia has a limited number of libraries to work with. It may, however, interfere with C and Fortran libraries used to handle plots.|
|Type System||Python is dynamically typed, which allows for the generation of variables without the need for type declaration. It differs from Julia only in that it is not statically typed.||Julia is also a dynamically typed programming language that allows developers to create variables without having to declare their types. Julia also has the advantage of Static typing.|
|Python arrays have a 0 index. Arrays are generally indexed from 0 in every language.||Julia arrays are 1-indexed, which means they begin with 1-n rather than 0-n. It may present issues for programmers who are accustomed to utilizing other languages.|
|Development||Matured ( version – v3.9.2 )||Actively Developed (version – v1.5.4)|
Julia vs Python performance takes a turn. Julia is a compiled language, which means that Julia programmes are directly executed as executable code.
As a result, Julia code is also universally executable with languages such as Python, C, R, and others. It produces outstanding, efficient, and quick results without the need for numerous optimizations and native profiling approaches. Some Julia optimizations cannot be applied in Python.
Essentially, Julia allows projects built in other languages to be written once and naively compiled, making it perfect for machine learning and data research. Julia’s time to execute large and complex codes is less than Python’s.
Python not only takes time to implement codes, but it also necessitates a number of optimization methods and additional libraries, highlighting Julia’s performance perfection.
When writing code, execution speed is an important factor. Julia’s execution speed is comparable to that of the C programming language. It was created with the intention of creating a rapid language. Julia is not an interpreted language that accelerates execution. The LLVM framework is used to build code in Julia. Julia solves performance issues that require speed without the use of 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.
Julia supports functional programming and Type hierarchy out of the box. Python enables us to be more creative in how we solve our software. Python allows for Functional, Object-Oriented, and Procedural Programming.
Reusability of Code
Julia’s code re-usability is one of its most important features. Although code re-usability is an important characteristic of Object-Oriented Programming, Julia’s type system and multiple-dispatch are more effective for code re-usability.
Python offers a large library that simplifies Python coding by simply importing these libraries and use their functions. Julia lacks a huge library set, which is a disadvantage when 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, like a new language, needs more developed libraries in order to grow.
Python is a fairly popular programming language (Top 3 in 2021). It has a lot of community support, with people from all walks of life coming up with creative ways to help and support the community. Every year, the international Python programming language community hosts a number of conferences (PyCons). PyCons are held all around the world, with the majority of them organized by volunteers from local Python communities. In such community events, you may expect to see people ranging from software professionals to researchers to students.
Julia is also a very welcoming community, with individuals from various walks of life. Julia is still ascending the popularity ladder, so don’t expect a massive community like Python, but she does have a supportive one.
A programming language that provides excellent tools support for any software development project is preferred by any programmer. Python outperforms Julia in terms of tooling support. It’s because Python provides excellent tooling support, whereas Julia’s tooling support is still in the works.
As a result, Julia does not have as many tools for debugging 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.