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This article mainly introduces "Why Julia is better than Python". In daily operation, I believe many people have doubts about why Julia is better than Python. The editor consulted all kinds of materials and sorted out simple and easy-to-use methods of operation. I hope it will be helpful to answer the question of "Why Julia is better than Python"! Next, please follow the editor to study!

Julia is a multi-paradigm functional programming language, which is mainly used for machine learning and statistical programming.

Python is another multi-paradigm programming language for machine learning, although Python is generally thought of as object-oriented.

On the other hand, Julia is more based on the functional paradigm. While Julia is certainly not as popular as Python, there are some huge benefits to using Julia in data science, making it a better choice in many cases of Python.

1. Extensive

Python has a wide range of applications. Many things can be done with Python, but not with Julia. Of course, this is only the local language, because the versatility we are talking about now refers to the versatility of the language. Julia code is generic executable code in R, Latex, Python, and C, which means that a typical data science project may be written only once and compiled native to Julia from another language in the wrapper, or just send strings.

PyCall and RCall are also pretty big deals. Given that one of the serious drawbacks of Julia is actually packages, it's convenient to call Python and R when needed. PyCall has been well implemented in Julia, and it has done very well and is very useful.

two。 Multiple dispatch

Julia is a very uniquely typed language with its own quirks and features, but one of the coolest features is the multi-dispatch of Julia. First and foremost, Julia's multi-dispatch is very fast. In addition, multi-dispatch using Julia makes it possible to apply function definitions as properties of structures.

Not only that, multi-dispatch using Julia makes the function extensible. This is a big benefit for package expansion, because users can change the import method whenever it is displayed. It is easy to explicitly import the method and extend it to route the structure to the new function.

3. Speed

It's hard to talk about Julia without talking about speed. Julia prides itself on its speed. Julia is different from Python, Python is a compiled language, it is mainly written on its own basis. However, unlike other compiled languages such as C #, Julia is compiled at run time, while traditional languages are compiled before execution.

Julia, especially when well written, can be as fast as C, and sometimes even faster than C. Julia uses an just-in-time (JIT) compiler, which compiles very quickly, although it compiles more like an interpreted language than a traditional low-level compiled language like C or Fortran.

4. Package Manager (Package Manager)

The first thing to say is that Julia's Pkg package manager is the whole world on top of Python's Pip package manager. Pkg comes with its own REPL and Julia packages from which you can build, add, delete, and instantiate packages. This is particularly convenient because of the connection between Pkg and Git. It's easy to update, it's always easy to add packages, and in general, Pkg is always available on Python's Pip.

5. Application in machine learning

Unlike Python, Julia is used for statistics and machine learning. Python was created in the early 1990s as a simple object-oriented language, although it has changed a lot since then. Given the history of Python and the wide use of Python (because it is very popular), using Julia, a language specially designed for advanced statistical work, can show many benefits.

Another aspect that Julia has a slight advantage over Python is linear algebra. Vanilla Python can pass through linear algebra, but vanilla Julia can leap over linear algebra. This is, of course, because Python never intended to support all the matrices and equations in machine learning. This is not the downside of Python, especially on NumPy, but for an experience without packages, Julia finds this kind of math more popular. Julia's Operand system is closer to R than Python's, which is a big advantage. Most linear algebra is faster and easier to do. A vector dot product equation (dot-product equation) is shown below to further illustrate this point:

Python-> y = np.dot (array1,array2) R-> y = array1. * array2 at this point, the study of "Why Julia is better than Python" is over, hoping to solve everyone's doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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