Which one should I learn, python or julia?

Julia is a high-performance dynamic high-level programming language for scientific computing. Its syntax is similar to other scientific computing languages. In many cases it has performance comparable to compiled languages. Julia is a flexible, dynamic language suitable for scientific and numerical computing, with performance comparable to traditional statically typed languages.

A group of advanced Matlab users with extensive programming experience in various languages ​​are dissatisfied with the existing scientific computing programming tools - these software are very specific to their areas of expertise. Great, but terrible in other areas. (Recommended learning: Python video tutorial)

What they want is an open source software that is as fast as C language and as dynamic as Ruby; it must be as dynamic as Lisp True homoiconicity with mathematical notations as familiar as Matlab; as versatile as Python, as handy in statistical analysis as R, as natural as Perl in string processing, and as powerful as Matlab in linear algebra Computing power, the ability to glue the language like a shell, easy to learn without boring real hackers; also, it should be interactive and compiled at the same time...

The project is about Started in mid-2009, it is currently (February 2012) 90% away from the release of version 1.0. You can download and try it out on the source code home page.

Currently, the Julia Chinese community is growing rapidly, and question and answer platforms such as Julia language programming development have been established.

This is his official introduction: "We want an open source language with a liberal license that has the speed of C and the flexibility of Ruby. We want an identity Languages, ranging from real macros like Lisp to familiar mathematical notation like Matlab. We want a language that is as usable for general programming as Python, as easy for statistics as R, and as natural for string processing as Perl , a language as powerful as Matlab for linear algebra, and as good at gluing programs together as the shell. It's easy to learn, but it can make serious hackers fall in love with it. We want it to be interactive and compilable. ”

Julia can call everything in Python (JuliaPy/PyCall.jl), and can call most of R, so even if there is a historical burden, you don’t have to worry too much, unless your task is urgent, that is, a month Something to come up with. Because although the learning curve of Julia is smooth, it takes a certain amount of time to use Julia to write code with good performance and clean abstraction. The simplicity of Python is an advantage, but it also brings disadvantages.

In addition, the Julia community has never said that it will give up Python because there is no silver bullet. Only in the field of scientific computing, Julia is currently a more appropriate solution because it is designed for scientific computing. student, but in other areas Julia has almost no advantages. So you also have pyjulia to help you use Julia in Python. Of course, we may expect that in the future we will often use Julia directly without calling Python.

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