Python is one of the oldest and most popular programming languages. It is also the most preferred language among Data Scientists, Data Analysts, Machine Learning Engineers, and in the field of Artificial Intelligence. Being an open-source language it is easy to use and has flexible coding features.
In fact, it is among the top 20the languages of the year in 2021, as mentioned in the TIOBE index. But Julia is a new buzz in the IT world, which is mainly known for its high speed and is gaining popularity among Data Scientists and Statisticians. Now, the questions arise – Which one is better? Which language to use between Julia vs Python?
It is a little difficult to decide among these two languages as both programming languages have their own advantages. Apart from their benefits, it depends upon the programming needs of the programmer that helps to decide which language to use. But for newbies, who want to know which language to learn between Julia vs Python, we have elaborated in this article which language is better and which language you should learn and use. But before getting into the comparison, let us first try to understand what these languages are.
Python is an object-oriented and interpreted language. It is a flexible coding language that helps programmers to write the code dynamically using a few lines. It is a very quick and efficient programming language that comes with features, such as dynamic typing, high-level data structures, and dynamic binding. All these features make Python a rapid application development scripting language and popular in the community.
Features of Python
Below are some unique features of Python:
Julia is a programming language developed by four MIT students. It is a programming language with flexible coding syntax similar to Python and a high execution speed similar to C language. Julia is an open-source language, which is mainly used for Data Analysis and Statistical Computation. It can even be used for Big Data and Cloud Computing. Julia has more execution speed than both Python and R.
Features of Julia
Below are the unique features of the Julia programming language:
Now you have understood what these languages are. Let us compare these two languages on different parameters and decide which language to learn or which language is better for you between Julia vs Python. Below is the comparison between Julia programming language vs Python. Check out the comparisons and decide which language you want to learn or use.
The execution speed is a crucial factor while writing code. Julia has execution speed as fast as C language. It was developed with the intent to create a language that is fast. Julia is not an interpreted language that makes its execution speed faster. In Julia, code is compiled using the LLVM framework. Julia solves the performance problems that provide speed without handcrafted profiling and optimization techniques. Julia is a perfect choice to solve Big Data, Cloud Computing, Data Analysis, and Statistical Computing-based problems. Clearly, Julia is better than Python if we compare Julia vs Python speed and performance.
Community support is of utmost importance for any programming language. Large community support means multiple resources to solve problems. Julia is a new language and has a community, which is smaller in size but is ever-growing and quite enthusiastic. On the other hand, Python is an age-old language and thus has massive community support. Comparing the community support between Julia vs Python, Python is more advantageous because it has large community support and while in Julia it is nascent. The large community support of Python is helpful in getting more resources who can resolve the issues and solve coding-based doubts.
Python has a rich set of libraries that helps Python coding easily by simply importing these libraries and using their functionalities. Julia has not a large set of libraries, which is a disadvantage of Julia as compared to Python. In addition to that, many third-party libraries support Python. Julia has another disadvantage in terms of libraries that packages are not properly maintained. Julia can interface with libraries in C language, but it takes time to plot the data at the initial stage. As Julia is a new language, it needs more mature libraries to grow.
Both Julia and Python are dynamically typed languages, and coders do not need to declare the variables specifically before using them in the code. But Julia is both static as well as dynamic typed language, and coders can use Julia in both ways as per their requirement. This gives Julia an edge over Python.
Python is an easy to read and coding-friendly language that makes it a versatile language. The versatility of Python makes it fit for automation, web scripting, web development, and many other programming tasks. Python is the first choice for developers as it can perform the activities and saves time of development using its rich set of libraries and frameworks. Julia is great to use for solving scientific programming problems, but Python is more versatile than Julia.
Both Julia and Python can run the operations parallelly. The methods used in Python need data serialization and deserialization between threads. On the other hand, Julia has more refined and parallel techniques. In addition to that, Julia has less top-heavy parallelization syntax than used in Python, which limits the use of Julia as a programming language.
Any programmer will prefer a programming language that offers great tooling support for any software development project. In the case of tooling support, Python wins over Julia. It is because Python offers great tooling support, and tooling support work is still in progress in the case of Julia. Thus, Julia does not support as many tools as provided by Python for debugging and performance issues resolving. Moreover, Julia is a new language with native APIs, so there is more possibility of an unsafe interface in the case of Julia.
In terms of working with the shell, Julia is a much better language. It is because Julia is well-integrated with the shell. The variables used in Julia can easily be exported to the shell in the form of an environment variable. Shell commands can be used to see the content of a file and edit that. All in all, Julia provides a very easy way to integrate and work with the shell.
Code conversion is simple and well supported in the case of Julia. Code written in Python or C can be easily converted to Julia, while the opposite is not true. Code conversion from Python to C or from C to Python is not easy. In fact, Julia can easily interface with the libraries which are written in C or Fortran. The Pycall library allows sharing the Julia code with Python too.
Julia has more of the scientific community as Julia helps to solve mathematical programming problems. The community of Julia is different from that of Python, which is more of an application programming community. In terms of ease of use for data science, Julia is better. It is because the syntax of Julia is more like mathematical formulae, and programmers find Julia easy to use for coding and to solve mathematical operations. However, Python is more user-friendly than Julia, but the scientific community people prefer using Julia over Python.
Julia is not an interpreted but compiled language. It uses the LLVM framework for the compilation that increases the speed of the execution but shows problems in recompiling the code. On the other hand, Python does not need compilation but is an interpreted language.
Julia was developed specifically to get a faster programming language that can perform Machine Learning tasks and Mathematical Computations fast. With many advantages in its bucket, Julia has gained popularity recently among Data Scientists and Machine Learning Engineers. But talking about Julia vs Python, Python is preferred because Python being the oldest language has a huge active Python community and a rich set of libraries and tooling support.
However, the faster computation and easy code conversion are some points that make Julia a tough competition between Julia vs Python. But Python is also getting better in terms of speed with time. All in all, Julia has many advantages over Python, but Python is the first choice among programmers, Data Scientists, and students because Julia is still growing. If you are working on a project which is heavy on Mathematics, Julia is the language for you.
We hope you are clear about which one to choose between Julia vs Python. You can learn both these languages at Jigsaw Academy. They have live sessions and online courses created by industry experts. Explore the courses available for you to explore at Jigsaw and give yourself a promising career.
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