Dont get me wrong.
Pythons popularity is still backed by a rock-solid community of computer scientists, data scientists and AI specialists.
Since data scientists and AI specialists deal with lots of mathematical problems, Julia is the winner for them.
And even upon critical scrutiny, Julia has upsides that Python cant beat.
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In this sense, Guido van Rossum created Python in the late 1980s to improve ABC.
In contrast, Python is quite pragmatic.
Python still kept the good features of ABC: Readability, simplicity, and beginner-friendliness for example.
But Python is far more robust and adapted to real life than ABC ever was.
We want the speed of C with the dynamism of Ruby.
Something that is dirt simple to learn, yet keeps the most serious hackers happy.
We want it interactive and we want it compiled.
To some extent, Python can do this, too but Python somehow grew into the job.
In contrast,Julia was built precisely for this stuff.
From the bottom up.
Apart from Julia, only C, C++ and Fortran arein the club right now.
Community
With its more than 30 years of age, Python has an enormous and supportive community.
There is hardly a Python-related question that you cant get answered within one Google search.
In contrast, the Julia community is pretty tiny.
And this can turn into programmer-relationships that are beyond value.
Code conversion
You dont even need to know a single Julia-command to code in Julia.
Needless to say, this makes it extremely easy to patch up the weaknesses of your Python code.
Or to stay productive while youre still getting to know Julia.
Libraries
This is one of the strongest points of Python its zillion well-maintained libraries.
Julia doesnt have many libraries, and users have complained that theyre not amazingly maintained (yet).
Dynamic and static types
Python is 100% dynamically typed.
While this is extremely beginner-friendly, it also introduces a whole host of possible bugs.
Since the Julia-creators also wanted it to be easy to learn, Julia fully supports dynamical typing.
This doesnt mean that Julia is unpopular rather, its naturally taking some time to get adopted by programmers.
Think about it would you really want to write your whole code in a different language?
No, youd rather try a new language in some future project.
This creates a time lag that every programming language faces between its release and its adoption.
As more and more people adopt Julia, youll already have gained enough experience to answer their questions.
Also, your code will be more durable as more and more Python code is replaced by Julia.
The industry and investors didnt believe in it, and many technologies were clunky and hard to use.
Similarly, Julia is still very niche now.
But when it grows, the big winners will be those who adopted it early.
But youre increasing your chances.
Think about it: Most programmers out there have Python on their CV.
And in the next few years, well see even more Python programmers on the job market.
Slowly at first, but inevitably.
Because lets be honest, what distinguishes you from any other Pythonista out there?
But there wont be that many Julia-programmers out there, even in three years time.
With Julia-skills, not only are you showing that you have interests beyond the job requirements.
In other words, youre fit for the job.
You and the other Julia programmers are future rockstars, and you know it.
About two and a half years ago, we set out to create the language of our greed.
Its not complete, but its time for a 1.0 release the language weve created is called Julia.
Python is still insanely popular.
But if you learn Julia now, that could be your golden ticket later on.
In this sense: Bye-bye Python.
Edit: Ive given a talk on Julia vs. Python!
It was hosted byHatchpad, and the video ishere.
This article was written byAri Jouryand was originally published onTowards Data Science.
you could read ithere.
