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Among the many use cases Python covers, data analytics has become perhaps the biggest and most significant. The Python ecosystem is loaded with libraries, tools, and applications that make the work of scientific computing and data analysis fast and convenient.
But for the developers behind the Julia language — aimed specifically at “scientific computing, machine learning, data mining, large-scale linear algebra, distributed and parallel computing”—Python isn’t fast or convenient enough. Python represents a trade-off, good for some parts of data analytics work but terrible for others.
Created in 2009 by a four-person team and unveiled to the public in 2012, Julia is meant to address the shortcomings in Python and other languages and applications used for scientific computing and data processing. “We are greedy,” they wrote. They wanted more:
Here are some of the ways Julia implements those aspirations:
Julia was designed from the start for scientific and numerical computation. Thus it’s no surprise that Julia has many features advantageous for such use cases:
Although Julia is purpose-built for data science, whereas Python has more or less evolved into the role, Python offers some compelling advantages to the data scientist. Some of the reasons “general purpose” Python may be the better choice for data science work:
This story, “Julia vs. Python: Which is best for data science?” was originally published by
