5 Reasons Why Julia is Better Than Python for Data Science and Machine Learning

Julia is a multi-paradigm programming language designed especially for machine learning and statistical programming. Python is another multi-paradigm programming language used for machine learning, however, Python is widely regarded as an object-oriented language. In contrast, Julia is more grounded in the functional paradigm. Despite the fact that Julia is not as popular as Python, there are several advantages to adopting Julia for Data Science that make it a superior option in many instances.

1 - Speed:

It is difficult to discuss Julia without mentioning her quickness. Julia takes great pleasure in her speed. In contrast to Python, Julia is a compiled language that is mostly developed in its own base. In contrast to traditional programming languages, Julia is compiled during runtime, unlike other compiled languages such as C. Julia, when written correctly, may be as fast as C and occasionally even quicker. Julia utilizes the Just In Time (JIT) compiler and builds extremely quickly, however it compiles more like an interpreted language than a standard low-level compiled language such as C or Fortran.

2- Multiple Dispatch

Multiple dispatch is one of the best characteristics of the Julia programming language, which is a typed language with its own quirks and features. Mostly, Julia’s multiple dispatch is quick. Using Julia’s polymorphic dispatch also enables the application of function definitions as attributes of a struct. This obviously makes inheritance possible within Julia.

In addition, utilizing multiple dispatches in Julia makes a function extensible. This is a significant advantage for package extensions as if a method is explicitly imported, the user can modify it. It would be simple to import your method directly and expand it to forward structs to a new function.

3- Versatility

You may have noted that I mentioned Python’s versatility as a benefit over Julia, and this is true; there are many things that can be accomplished with Python but not with Julia. Obviously, this only applies to native speakers, as the versatility we’re discussing here is language versatility. Code written in Julia may be executed in R, Latex, Python, and C. This means that typical Data Science projects may be written once and built-in Julia natively from another language or simply by submitting strings.

PyCall and RCall are also significant. Given that Julia’s packages are a significant drawback, it’s quite helpful to have access to Python and R whenever necessary. PyCall is very well implemented into Julia and is definitely sincerely well-done, and very usable.

4- Designed specifically for Machine Learning

In contrast to Python, Julia was designed for use in statistics and machine learning. Python was designed in the early 1990s as a simple object-oriented programming language, but it has evolved significantly since then. Given Python’s history and the large variety of uses for Python as a result of its popularity, choosing a language designed expressly for high-level statistical work might provide several advantages.

I believe this will favor Julia over Python in linear algebra. Vanilla Python struggles with linear algebra, but vanilla Julia breezes through it. This is because Python was never intended to support the matrices and equations necessary for machine learning. Python is by no means a poor language, especially when coupled with NumPy, but in terms of a no-package experience, Julia feels far better suited to these types of mathematics. Julia’s operand system is far more similar to R’s than Python’s, which is a significant advantage. The majority of linear algebra is faster and simpler to do.

5- Package Manager

Julia’s Pkg package manager is light years ahead of Python’s Pip package manager. Pkg includes its own REPL and Julia package, through which it is possible to construct, add, delete, and instantiate packages. This is particularly easy due to Pkg’s integration with Git. Pkg is a delight to use over Python’s Pip any day. Pkg makes updating and installing packages effortless.

Conclusion

It makes little difference if you use R, Julia, Python, or Scala. It is crucial to remember, however, that every language has its flaws, and that no language will ever be “perfect.” This is especially true if you have diverse programming skills, including machine learning, GUIs, and APIs. Hence, Julia is one of my favorite programming languages, along with Python. Python offers superior packages, and as a result, if the project is small enough, I’ll generally go towards Python. But, for data-sets with millions of observations, it might be difficult to even read the data with Python.