Julia Language Introduction
Julia is a high-level open-source programming language, developed by a group of 4 people at MIT. Julia is a dynamic, high-performance programming language that is used to perform operations in scientific computing. Similar to R Programming Language, Julia is used for statistical computations and data analysis. Julia was built mainly because of its speed in programming, it has much faster execution as compared to Python and R.
Julia provides support for big data analytics by performing complex tasks such as cloud computing and parallelism, which play a fundamental role in analyzing Big Data.
To gain such features and compatibility, Julia draws upon the lineage of mathematical programming languages but also adopts much from many other popular dynamic languages, which include Perl, Python, Lua, Lisp, and Ruby.
Why Julia Programming Language?
Julia has many reasons for being widely used for Data Analytics. Few of the reasons are:
- Easy to Start: Julia is a high-level language so it is closer to other popular programming languages like Python, C, R, etc. Thus it becomes very easy to learn Julia for anyone, especially for Python and C programmers.
- Open-source: Julia is fully open-source and free, hence it can be downloaded and worked upon easily.
- Integrated Language: Julia is built for scientific computations just like in Python, R, and MATLAB alongside it has its roots in the general-purpose programming.
- Faster Execution: Julia is known to be a combination of Python and C programming language. Hence, it has a very high execution speed of C as compared to Python, R, and MATLAB.
- Fewer Lines of Code: Julia provides the flexibility of writing fewer lines of codes as in Python.
Beginning with Julia Programming
Finding a Compiler: There are various online IDEs such as TutorialsPoint, repl.it, etc. which can be used to run Julia programs without installing.
Jupyter Notebook can also be used to run Julia programs offline. Here’s How to install Jupyter Notebook for Julia in Windows?
Programming in Julia: Since Julia is a lot similar to other widely used languages syntactically, it is easier to code and learn in Julia. Programs can be written in Julia in any of the widely used text editors like Notepad++, gedit, etc. or on any of the text-editors. After writing the program, save the file with the .jl extension.
Writing our first program:
Just type in the following code after you start the interpreter.
[print(“Hello World!”)]: To print something on the console, print() function is used. To give a new line after the print statement, println() is used.
Comments are used for explaining code and are used in a similar manner as in Python. Compilers ignore the comment entries and do not execute them. Comments can be of a single line or multiple lines.
- Single line Comments:
# Single line comment
- Multi line comments:
#= Multi line comments =#
Features of Julia
There are multiple features that make Julia different from other languages.
- Julia is a dynamically typed language which makes it interactive to use.
- Julia is an open-source language and hence all source codes are easily available online.
- Julia can work on Python, C and Fortran libraries by directly calling them.
- Julia is flexible to use, because it allows writing fewer lines of codes as compared to C.
- Julia is just-in-time compiled and hence it can even approach the execution speed of C.
- Julia can handle complex data analytics very easily.
There are many advantages of Julia over Python and C. Few of them are:
- Julia is faster as compared to Python, because of its just-in-time(JIT) compilation.
- Julia is highly compatible with mathematical computations.
- Julia allocates memory to variables automatically, like Python.
- Julia is a combination of both dynamic and static typed language.
- Julia is 1-indexed language, which means that its array indexing starts from 1, unlike other languages in which it starts from 0. This might cause problems to adopt new habits of writing codes.
- Julia, as compared to Python, is very new. Hence, people still prefer python over Julia.
- Matrices in Julia are accessed column-wise, whereas Python matrices are accessed row-wise. This can create problems in taking design decisions on how to go through matrices effectively in memory.
- Dictionaries in Julia are hashed differently than dictionaries in Python, which can make the execution slower in multiple cases.
Application of Julia:
- Julia can be used for major scientific computations which involve Big Data.
- Julia can be used for Web Programming with the help of packages.
- Machine Learning computations can also be performed easily with the help of Julia.
- With the help of ARM support, Julia will soon be used on Android Smartphones.