Julia vs Python for Data Science in 2022
Comparing Programming Languages for Data Science
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This article compares Julia to Python in terms of general performance, package availability and adoption and gives guidance on whether you should consider learning it.
Know Your Programming Languages for Data Science
In 2021 Python achieved #1 ranking in the TIOBE Index of programming languages for the second year in a row. This should come as no surprise: over the past decade, Python has become a household name in the data science industry.
Even if you are currently working in Python, that is no reason to lean back and trust Python to continue to be the dominant programming language for data science in the years to come. Rapid innovation in the world of programming means it is advisable to stay tuned to the latest developments in competing languages. One language you should certainly keep an eye on is Julia, a language specifically designed for data science and machine learning applications.
Why You Should Care About Julia for Data Science
Julia is a high-level, general-purpose programming language explicitly designed for high-performance numerical computation. It is often referred to as a language with the simplicity of Python and the speed of C.
Since its open-source launch in 2012, Julia has grown to a community of over 1000 maintainers with 34.8 million downloads. The StackOverflow Trends data shows a steady increase in the volume of questions with the “Julia” tag since then.
Julia boasts a number of features and capabilities that you should be aware of if you are currently using Python or want to be aware of the latest developments in programming languages for data science or machine learning:
- Julia’s performance matches statically-typed languages like C
- Julia supports native mathematical expressions
- Julia was designed for parallel and distributed computing
- Julia has a built-in package manager that enables easily reproducible…
