Updated April 18, 2023
Difference Between Julia vs Python
Julia and Python is a programming language that works for data analysts and big data. Julia and Python are high-level languages used for statistical computation and machine learning. Python and Julia are general purposes and easy programming language that works for cloud computing. Julia is a high-performance and fast programming language used to create applications and scientific computing.
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Julia is open source, high-level technology that supports parallel, concurrent, and distributed computing. Python is a popular, easy, and general-purpose programming language that works for small and large software projects. Python is a high-level interpreted language used for data computing and machine learning. Julia and Python is an open-source and functional programming technology for software development and data management system.
Head to Head Comparison Between Julia vs Python (Infographics)
Below are the top differences between Julia vs Python
Key differences between Julia vs Python
Given below are some of the differences between Julia vs Python:
- Julia is a compiled programming language, whereas Python is an interpreted language.
- The Julia language is faster than the python programming language. But, on the other hand, python is more user-friendly than the Julia language.
- Python is a functional and object-oriented programming language. Therefore, Julia is a functional programming language.
- Python has matured library support, whereas Julia supports actively develop libraries.
- Julia converts code easily into other languages. Unfortunately, python has the difficult process of converts code into another language.
- The python arrays are started from 0 to N-1. The Julia arrays are started from 1 to N.
- Julia is a simple syntax for numerical computing than the python programming language. Hence, Julia is easier and simple code than python language.
- People prefer Julia’s programming language for data science programming. Whereas the python language prefers clean code to be small and large-scale applications.
- Julia is a better performance in Big Data, machine learning, Cloud Computing, data science, Data Analysis, and Statistical Computing.
Comparison table between Julia vs Python
- Julia and Python are open-source and functional programming works for data analysis.
- Julia and Python are easy, clean, and powerful typing languages in modern technology.
- Julia and Python languages have their features, functions, and availability for different applications.
- The table below helps to understand Julia and the python language’s functionality for development and other purposes.
| Features | Julia | Python |
| Definition | Julia is a high-performance and fast programming language used by data scientists and scientific computing.
|
Python is a high-level, interpreted language used to create applications, data scientists, and machine learning.
|
| Type | Julia is a compiled language. | Python is an Interpreted language. |
| Designed by | The Julia language was designed by four designers. The designers are Jeff bezanson, Stefan karpinski, Alan Edenlman, and Viral B. Shah. | The python language was designed by Guido van Rossum. |
| Developer | This language was developed by designers and teams. | This language was developed by the python software foundation. |
| First release | The Julia programming language first-time release in 2012. | The python programming language first-time release in 1991. |
| File extension | The file extension of Julia is “.jl” after the filename. | The file extensions of the python are .py, .pyi, .pyc, .pyd, .pyo, .pyw, .pyz after file name. |
| Implement languages | This programming language implements by Julia, C, C++, scheme, and LLVM languages. | This programming language implements by CPython. |
| Typing discipline | The typing disciplines of Julia are dynamic, optional, nominative, and parametric. | The typing disciplines of Python are duck, strong, dynamic, and gradual. |
| License | MIT provides a license to the Julia language. | The Python software foundation provides licenses to the Python language. |
| Website | Website link: https://julialang.org/ | Website link: https://www.python.org/ |
| Operating system | The Julia language supports Windows, Linux, macOS, and FreeBSD operating systems. | The Python language supports Windows, Linux, and macOS operating systems. |
| Syntax | The Julia language shows a basic program for print output.
println(“Hello Julia programmer”) |
The python language shows a basic program for print output.
print(“Hello Python programmer “) |
| Parameters | This language supports multiple paradigms. The paradigms are functional, procedural, and multistage. | This language supports multiple paradigms. The paradigms are functional, procedural, object-oriented, structured, and reflective. |
| Influence | This language is mainly influenced by C, C++, Dylan, Lisp, Lua, MATLAB, Perl, Ruby, Python, and mathematical. | This language is mainly influenced by C, C++, Java, Perl, ABC, Ada, CLU, and Dylan technology. |
| speed | Julia is better for speed and performance than python language. | Python is slower than Julia for speed and performance. |
| Libraries | Julia does not have enough library sources. | Python has better library sources to write code and create applications easily. |
| Code conversion | Julia easily converts code from other language codes. | Python is difficult to convert code from other language code. |
| Company uses languages | The following companies use Julia language.
|
The following are some companies that use the python language.
|
| Community | Julia is a new programming language; therefore, the community size is small. Hence community is continuously growing. | The python language is old technology; therefore, the community has large size. It supports a programmer in solving errors. |
| Versatility | Julia is less versatile than python language. This language is easy for scientific coding. | Python is a simple and user-friendly language. Python is a popular language because of its versatility. |
| Parallelism | Julia supports parallel operations. This works on serialization and deserialization of multiple threads. | Python supports parallel operations. This works on serialization and deserialization of multiple threads. |
| Tooling support | Julia does not easily support tooling like API. | Python supports better tooling like API. |
| Uses | This language uses for Big Data, machine learning, Cloud Computing, data science, Data Analysis, and Statistical Computing. | This language uses for Software projects, web applications, Big Data, machine learning, Cloud Computing, data science, and Data Analysis. |
Conclusion
- Julia and Python are most useful and helpful for data scientist developers.
- Julia is demanded because of the speed. Hence, Python is popular because of its versatility.
- Julia and Python are user-friendly, simple, and clean programming languages.
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