Python and Julia are popular programming languages widely used in scientific and technical computing communities. Both languages have their strengths and weaknesses, and choosing the right one for your needs can be difficult for a new developer. In this article, we will compare Python and Julia to help you decide which language is right for you.
We will assume that you have been exposed to Python and are interested in learning some basic information about how Julia compares and why Julia may be interesting to use in some areas of scientific computing.
Python
Python is a high-level, general-purpose programming language that was first released in 1991. It is an interpreted language, meaning it is executed line by line rather than being compiled into machine code like some other languages. This makes Python easy to learn and use, as it is more similar to natural language than many other programming languages.
One of the main advantages of Python is its large and active community of users. Many libraries, frameworks, and tools are available for Python, making it easy to perform a wide range of tasks, including scientific computing, data analysis, machine learning, and web development. Python is also widely used in academia and industry, which means that there are many resources and job opportunities available for Python programmers.
On the downside, Python is not the fastest language available, as it is interpreted rather than compiled. This means it can be slower than other languages when running large, complex programs. However, in many cases, Python’s simplicity and ease of use can make up for this.
Julia
Julia is a high-level, high-performance programming language that was first released in 2012. It is specifically designed for scientific computing and data analysis. It aims to provide the performance of compiled languages like C and Fortran with the ease of use of interpreted languages like Python.
One of the main advantages of Julia is its speed. It is designed to be fast, and in many cases it can be faster than both Python and compiled languages like C and Fortran. This makes it a good choice for applications where performance is critical, such as large-scale data analysis or machine learning.
Julia also has a strong focus on numerical accuracy, which makes it a good choice for scientific computing tasks that require precise results. It also has a rich set of libraries and tools for scientific computing and data analysis, which makes it easy to perform complex tasks. In addition, Julia is very user-friendly, with a syntax that is similar to other popular programming languages like Python and R. This makes it accessible to a wide range of users, including those who may be new to programming.
However, Julia is a relatively new language; its community is not as large or well-established as the Python community. This means there may be fewer resources and job opportunities available for Julia programmers, and it may be more difficult to find help when you are stuck on a problem.
Despite these challenges, Julia has a growing and active community of users, and it is quickly gaining popularity. As more people learn and use Julia, the community will only continue to grow and become more robust. In addition, Julia’s focus on speed, numerical accuracy, and user-friendliness make it a strong contender in the data analysis and scientific computing space. In that area, it may eventually become as popular as Python is now.
Visual Studio Code integrations for Julia
If you are using Visual Studio Code (VS Code) as your development environment, you may already be comfortable with the variety of well-established extensions available for Python but may not be familiar with those for Julia.
Several extensions available through VSCode can help you work with Julia. The most popular Julia extension for VS Code is the “Julia for VSCode” extension, which provides syntax highlighting, code completion, and integrated documentation for Julia. It includes features like a Julia REPL (read-eval-print loop) terminal, debugger, and formatter.
Other popular Julia extensions for VS Code include “Julia Extension Pack”, which includes the “Julia for VSCode” extension along with other useful tools and libraries, and “Julia Syntax Highlighting”, which provides syntax highlighting for Julia code.
Using a Julia extension can make it easier to work with Julia in VS Code, as it provides many of the features and tools you would expect in a dedicated Julia development environment. It is worth noting that while these extensions can be very useful, they are not required to use Julia in VS Code, and you can still use the standard VS Code features to work with Julia if you prefer.
Sample Code
Here is a simple scientific program that calculates the distance of a star from the amount of redshift of light in both Python and Julia:
# Scientific calculation in Python
import math
# Define constants
c = 299792458 # speed of light in m/s
H0 = 74.03 # Hubble constant in km/s/Mpc
# Define function to calculate distance
def distance(z):
return (c * z) / H0
# Calculate distance for redshift of 1.0
result = distance(1.0)
print(result)
# Scientific calculation in Julia
# Define constants
c = 299792458 # speed of light in m/s
H0 = 74.03 # Hubble constant in km/s/Mpc
# Define function to calculate distance
function distance(z)
return (c * z) / H0
end
# Calculate distance for redshift of 1.0
result = distance(1.0)
println(result)
While this example represents only a small subset of the functionality of each language, aside from obvious similarities, we can see a couple of significant differences;
Unlike Python, Julia does not rely on indentation to indicate code blocks. For this reason, Julia is more flexible with whitespace. However, functions and loops must include an explicit ‘end’ keyword.
Also, we can see that the math library must be imported in the Python version but not in the Julia version. This is because the equivalent of the math library for Python is built-in to Julia.
Julia has a number of built-in libraries that are not available in Python without importing them first. Here are a few examples:
LinearAlgebra: This library provides functions for linear algebra operations, such as matrix multiplication, singular value decomposition, and eigenvalue decomposition.Statistics: This library provides functions for statistical analysis, such as mean, median, mode, variance, and correlation.FFTW: This library provides functions for fast Fourier transform (FFT) operations, which are commonly used in signal processing and scientific computing.IterativeSolvers: This library provides functions for solving systems of linear equations using iterative methods, such as the conjugate gradient method and the Jacobi method.SpecialFunctions: This library provides functions for special mathematical functions, such as the gamma function and the Bessel functions.
While there are advantages and disadvantages to building these libraries into Julia, they should not be of significant consequence. The fact that these and other libraries are built into Julia can make coding a little easier. While this can also reduce your control level compared to imported libraries in Python, using the standard built-in libraries may cause Julia code to be a little more readily understood than the Python equivalent.
Conclusion
In conclusion, Python and Julia are excellent choices for new developers interested in scientific computing and data analysis. Python is a well-established language with a large and active community, and it is easy to learn and use. It is a good choice for beginners and for tasks that do not require the highest performance levels.
Julia is a newer language that is specifically designed for scientific computing and data analysis. It is faster than Python, and other interpreted languages and strongly focuses on numerical accuracy. It is a good choice for tasks that require high performance or precise results. Still, it may be more challenging for beginners due to its relatively lower (compared to Python!) resources and community support. However, that is not to say those things are lacking for Julia.
Ultimately, the choice between Python and Julia will depend on your specific needs and goals. If you are starting out as a programmer, Python may be the better choice due to its simplicity and large community. Julia may be the better choice if you are more experienced and need the highest performance levels. Python and Julia are powerful tools that can help you achieve your goals as a developer regardless of which language you choose.