Python Vs Julia

Python vs Julia

Python is a general-purpose computing language that is easy to learn, and that has developed into a leading language for scientific computing. Some of the reasons Python may still be the better choice for data science work:

  • Julia arrays are 1-indexed. This might seem like an obscure issue, but it’s a potentially jarring one. In most languages, Python and C included, the first element of an array is accessed with a zero—e.g., string[0] in Python for the first character in a string. Julia uses 1 for the first element in an array. This isn’t an arbitrary decision; many other math and science applications, like Mathematica, use 1-indexing, and Julia is intended to appeal to that audience. It’s possible to support zero-indexing in Julia with an experiment but 1-indexing by default may stand in the way of adoption by a more general-use audience with ingrained programming habits.
  • Julia is still young. The Julia language has been under development since 2009, and has undergone a fair amount of feature churn along the way. It still doesn’t have a 1.0 release, although the developers are
  • Python has far more third-party packages. The breadth and usefulness of Python’s culture of third-party packages remains one of the language’s biggest attractions. Again, Julia’s relative newness means the culture of software around it is still small. Some of that is offset by the ability to use existing C and Python libraries, but Julia needs libraries of its own to thrive.
  • Python’s huge community is a huge advantage. A language is nothing without a large, devoted, and active community around it. Python enjoys just such a community right now. The community around Julia is enthusiastic and growing, but it is still only a fraction of the size of the Python community.
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