Introduction
Python has existed since the decade of 1990, and now it is one of the most popular programming languages in the world. The reason Python became so popular is that it offered simplicity and allowed programmers to read its code easily.. This language is also relatively easy to learn, so it is not surprising that there is more of 7 millions Python coders in the world.
Python is used by both small startups and well-known companies such as Netflix, Spotify, Google e Instagram. It is also quite popular in data science and machine learning..
But nevertheless, the world of programming evolves rapidly and programmers always want more. More efficiency, more application areas, more flexibility, etc. This is exactly what motivated the creators of Julia, another programming language that was introduced in 2012. According to the developers, his goal was to create a language that was as usable as Python, had the same computational capabilities as Matlab and was as fast as C.
As a result, Julia became the language of choice for many programmers and, but nevertheless, many people can't decide if they should learn Julia or Python.
This is not an easy choice., and even the developers using Julia claim that Python would be your first choice if they didn't use Julia. Both languages have certain advantages and disadvantages, so we decided to address all the differences between these languages to help you answer the Julia contra Python question.
What is Julia?
Julia is a programming language created specifically for data science, complex linear algebra, data mining and machine learning. The creators of this language wanted to address the disadvantages of Python and other programming languages, offering a more convenient tool. Is Julia better than Python?? Good, it certainly has some cool features that make people choose Julia for data science.
- Interactivity
Julia has an interactive command line called REPL (Read Eval Print Loop) to help programmers easily add quick commands and scripts.
- Julia is compiled and not interpreted
Offers faster runtime performance. Why is Julia so fast?? Use the LLVM framework for just-in-time compilation (JIT). Thanks to this approach, Julia can offer the same speed as C.
- Simple syntax
Like python, Julia has a simple but powerful syntax.
- An opportunity to call C libraries, Fortran and Python
Julia can work directly with various external libraries. For instance, you can use the PyCall library to interact with code written in Python, and even exchange data between Julia and Python.
- Julia combines the advantages of static and dynamic writing
Julia allows you to specify types for variables and also allows you to create type hierarchies so that general cases can handle specific types of variables. “For instance, you can create a function that accepts integers without specifying their signature or length”, explica Brenda Wilkins, software developer on a copywriting service review website. Choose the writer.
- Julia includes a full debugger
From Julia debug suite you can run code in a local REPL so you can check variables, results and add breakpoints.
- Multiple offices
Julia has multiple express dispatches. This feature makes the functions expandable. What's more, polymorphic dispatch allows developers to apply function definitions as properties of a structure.
Now that we have considered the main features of this language, Let's think about what makes it a better choice for data scientists compared to Python and try to find the answer for Python vs Julia.
Julia Advantages
- The syntax is optimized for math.
Julia was intended for users of languages and scientific environments such as R, Octave, Matlab y Mathematica. As a result, the syntax of this language is similar to the formulas used by non-programmers, which makes this language easier for mathematicians to learn.
- Speed
Type declarations and JIT compilation allow Julia to beat non-optimized Python when it comes to speed. Of course, you can make python faster by using third party compilers and external libraries, but Julia was already designed to be faster.
Python advantages
But nevertheless, Piton also offers great benefits for data scientists. Although this language was not created for data science, evolved rapidly. Let's take a look at the advantages of the Python language to try to resolve the debate between Python and Julia.
- Less startup overhead
Although Python may run slower than Julia, its execution time is less heavy, so Python programs generally take less time to start working, which provides some first results. Julia's JIT compilation also reduces startup speed. Although the developers work on this problem, Python still starts faster.
- Zero-based matrix indexing
In many languages, including C and Python, the first elements of the arrays are accessed with a zero. For instance, and Python, the first character of a string is a string[0]. When using Julia, must deal with indexing 1, because this approach is often used in various scientific applications, and Julia was intended for a similar audience. Fortunately, there is an experimental feature allowing zero indexing support, but the default indexing can be inconvenient for people with programming experience.
- Python is more popular
Julia has an enthusiastic community that is constantly growing, but it's still far from the python community in terms of size.
- More third-party packages
One of the main benefits of Python is the variety of third-party packages. There is not much software built around Julia. Libraries like Knet and Flux make Julia a good choice for machine learning, but PyTorch and TensorFlow are mainly used for various tasks.
- Python is getting faster
First, improved Python interpreter, including enhancements to multicore and parallel processing. It's easier to make Python faster. For instance, the mypyc project translate Python into native C, which is much more convenient than Cython. This approach provides four times better performance or even more impressive results when it comes to pure math tasks..
Final notes
Julia was created specifically for scientific calculations and machine learning, which is why it is so popular with professionals in these areas. Julia beats Python in terms of speed, as well as being convenient and easy to use. But nevertheless, Python is still a great programming language with certain advantages. Has a thriving community and offers faster startup speed.
If you want to learn data science or work in this area, you should analyze the benefits of both languages and think about what is especially important to you. Thus, will be able to answer the Python vs Julia dilemma. Both languages are relatively easy to learn and have a lot in common, so the right choice depends on your specific goals and preferences.
About the Author
Anna Medina
Anna likes to write since her college years. When he graduated from the Department of Interpreters, realized that the translation was not as interesting as the writing. She trains her skills now working as a freelance writer on different topics. He always does his best in posts and articles.
