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This article introduces the relevant knowledge of "what is Julia language". In the operation of actual cases, many people will encounter such a dilemma. Then let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

What is Julia language

Julia is a high-level programming language developed by four people at Massachusetts Institute of Technology (MIT). It is an open source, high-performance, advanced dynamic programming language for scientific computing. It is mainly used for data analysis and statistical calculation, similar to R programming language.

The main advantage of Julia is its speed, and its applications run faster than Python or R. It supports difficult tasks such as cloud computing and parallelism, which is considered to be the basis for performing big data's analysis.

Who uses Julia?

Julia is mainly used by research scientists and engineers, but it is also used by financial analysts, quantitative analysts and data scientists. Developers of the Julia language ensure that the products they develop make Julia easy to use, deploy, and extend. Julia can be installed on various platforms, such as Windows, MacOs, and Linux.

The characteristics of Julia language

The following features make julia a popular programming language:

Julia uses dynamic typing, similar to scripting, and supports interactive use well.

Julia supports advanced syntax, which makes it an efficient language for programmers.

Julia provides a rich descriptive data type language.

Julia supports multiple dispatches, which makes it easy to write object-oriented and functional programming code patterns.

Because julia is open source, all source code can be viewed publicly on GitHub.

Julia's bag.

Here are some of the most popular software packages for developers:

Interact.jl: interactive widgets, such as drop-downs, sliders, and check boxes, can easily implement julia code.

Generic Linear Algebra: used to extend the functionality of linear algebra.

Colors.jl: this is a color processing tool for Julia.

UnicodePlots.jl: scientific drawings based on unicode work in the terminal.

Nemo: computer algebra software package.

Revise: automatically updates function definitions in a running Julia session

BenchmarkTools: benchmark framework.

OhMyREPL.jl: square brackets highlight, syntax highlight, and rainbow square brackets.

StaticArrays: a framework that provides static size arrays.

Compare Julia and Python

Python is the most popular language widely used by most developers. Julia was launched in 2012 and is much younger than python. But given redmonk's ranking, many developers may use julia because it will soon become popular.

Advantages of Julia:

From the beginning, Julia was designed for numerical and scientific calculations. Not surprisingly, Julia has many such uses:

Faster by default. JIT compilation and JIT type declaration mean that it can often beat "pure" Python. With the help of external libraries, tools such as Cython and third-party JIT compiler python can be used for optimization, but the design of Julia has been speeded up from the start.

A math-friendly grammar. Users of computing languages and environments such as Matlab, R, Mathematica, and Octave are the target audiences of julia. Julia's mathematical syntax looks similar to the way mathematical formulas are written outside the computing world, which makes it easier for non-programmers to understand.

Automatic memory management. Like Python, Julia does not provide users with the details of allocating and freeing memory, but rather provides some controls on garbage collection. The idea is that if you switch to Julia, you won't lose a common convenience of Python.

Advantages of Python:

Python is a universal computing language that is easy to learn, and it has become the main language of scientific computing. A number of reasons may still make python the best choice for data science work:

Is still in its infancy. Julia is still under development and its current version runs on v1.1.0. As developers keep trying to improve performance, more features will be added in the future.

More third-party packages. The widespread use of python's custom package culture remains the language's greatest interest. The relative novelty of Julia is similar to the surrounding software culture is still very small.

The big community advantage of Python. A language has nowhere to go without the support of large, active communities around it. The julia community is enthusiastic and growing, but it is still only a small part of the python community.

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