The fields of data science & machine learning have made it possible for machines to learn from experience and derive astounding insights into how things and people function. However, realizing the potential of these technologies requires a moderate level of technical capability. Many tools have been developed to simplify procedures when working on data science and machine learning projects. For example, look at the top programming languages for Data Scientists in 2022.
Initially, conversations regarding what programming language beginners should learn for data science & machine learning were dominated by Python vs. R (you can learn more about the difference between Python and R for Data Science in a separate post). Now, things are starting to change; There is no doubt Python has been one of the most popular programming languages of recent times. According to the TIOBE Index for November 2022, Python has topped the popularity charts, but there is a new tool on the block called Julia.
As Julia continues to gather momentum, several experienced data science and machine learning practitioners may ponder whether it is worth upskilling, while beginners are asking themselves new questions such as, “should I learn Python or Julia?” In this article, we will present the distinctions between Python and Julia to help simplify the decision-making process so you can get started on advancing or enhancing your career.
What is Python?
Most people in the technology space would have heard of Python – it has been popular among developers for many years, especially since the rise of data science and machine learning. The language was conceived by Guido van Rossum and was first released in 1991 as a successor to the ABC programming language.
Python is a high-level, interpreted, object-oriented programming language with dynamic semantics – which means that its variables are dynamic objects. The Python interpreter and the extensive standard libraries that come with the programming language are freely distributed and available in source or binary form.
“Its high-level built-in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components.”
[Source: Python Documentation]
Who uses Python?
Python is a general-purpose language. Thus, you can use it for building software in a broad range of application domains across many hardware configurations and operating systems.
Example domains include:
- Data Science (Check out this Python for Data Science cheatsheet for Beginners)
- Scripting/Automation (i.e., tracking product prices and filling out forms)
- Web development
- Machine learning
- Software testing
Check out our article on What Python is Used For? 7 Real-Life Python Uses to go more in-depth with Python use cases.
Advantages of Python
Several factors contribute to why Python is extremely popular. Here are a few:
- Accessibility: Python's syntax almost mimics that of natural language, which makes it easier to read and comprehend for other developers. Consequently, developers can build projects and improve on them faster since the language is extremely simple.
- Versatility: Python is a general-purpose language, which means it can do and create several different things, from machine learning to scripting and automation of everyday tasks.
- Open-Source: Python is developed under an OSI-approved open-source license. Thus, it is free to use and distribute for all purposes, including commercial use. The combination of Python being freely distributed and its growing popularity has contributed to a strong community developing around the language, which users can tap into whenever support is required.
- Libraries: Libraries are extremely useful tools that can make developers more efficient in their jobs. At the time of writing, there are more than 137 000 Python libraries that can be used to create applications in a variety of fields.
Disadvantages of Python
Despite all of the advantages of Python, it has its fair share of naysayers for the following reasons:
- Speed: One of the main critiques of Python is its speed. Being a dynamically typed interpreted language, Python is slow compared to compiled languages such as C and Java (which can also be considered to be interpreted as it is a hybrid language).
- Memory consumption: Python is flexible to data types, which results in a large amount of memory being consumed.
- Mobile environment: Despite thriving in the development of server and desktop applications, Python is not ideal for mobile development due to its high memory consumption and slow processing speed compared to other programming languages.
- Runtime errors: Python is a dynamically typed language, which means data types are associated with values instead of variables. Many Python users have complained about this design as it raises various issues. For example, the value assigned to a variable can be changed at any time in a program, which means the variable's data type could change, thereby leading to errors at runtime.
What is Julia?
At the beginning of 2022, in the year of their tenth anniversary, Julia computing announced the programming language had been downloaded nearly 35 million times, which is nearly five times the total cumulative number of downloads as of three years prior. It is this type of growth that has brought Julia to the attention of several developers.
Note: Develop your Julia programming skills with the Introduction to Julia course.
The founders of Julia – Jeff Bezanson, Alan Edelman, Stefan Karpinski, Viral B. Shah – had one clear purpose: to improve the shortcomings of other programming languages while simultaneously incorporating their distinctive and desirable qualities. In a blog post titled Why we Created Julia, the authors brazenly state, “We are greedy: we want more” before listing several desirable yet ambitious qualities they would like to be present in Julia.
“We want a language that's open source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that's homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn, yet keeps the most serious hackers happy. We want it interactive and we want it compiled. (Did we mention it should be as fast as C?).”
[Source: Why we Created Julia]
By the time they released Julia 1.0.0 in 2018, 90% of the promises they had made six years prior had been delivered.
But what exactly is Julia?
The best way to think of it is as a high-level, dynamic, fast, and easy-to-use programming language containing features well suited for numerical analysis and computational science. You can learn more about The Rise of Julia in a separate article.
Who uses Julia?
One of Julia's primary goals was to develop a language that would allow developers to write code that is concise, high-level, generic, and abstract, similar to mathematical formulas, while also being able to generate the low-level machine code common to static languages quickly.
Despite being designed specifically with technical and scientific users in mind, Julia is also considered to be a general-purpose language, which means it is broadly applicable across application domains and lacks specialized features.
These features have made Julia extremely attractive to developers in fields that involve:
- Numerical Computing
- Machine Learning
- Web Development
Check out our article What is Julia Used For? 10 Applications of Julia Programming to go more in-depth with Julia's use cases.
Advantages of Julia
- Speed: One of the main appeals of Julia is undoubtedly its speed. Julia is fast. It uses just-in-time (JIT) compilation, which means there is less chance of code being interpreted multiple times, so there is less overhead. Also, Julia consistently outperforms existing data programming languages such as Python, R, and Matlab in benchmarking tests, which comes down to the fact that Julia is a compiled programming language instead of an interpreted one.
- Accessibility: Julia has a clear syntax – cleaner than C++ – that is simple to use and easy to learn. It is dynamically typed, which is generally more succinct than statically typed languages.
- Purposeful: Much of the day-to-day work of the creators of Julia included scientific computing, machine learning, data mining, large-scale linear algebra, and distributed & parallel computing. Thus, Julia was designed with scientific computing in mind and contains a robust selection of packages that enable additional use cases in the fields of science, mathematics, statistics, and machine learning.
Disadvantages of Julia
Despite the blossoming introduction Julia has received thus far, the language is far from perfect and presently contains some significant limitations.
- Age: Julia is a new language. Therefore, the language is not as popular or supported as other languages like Python and R.
- The time to first plot problem: There are still some issues with Julia’s JIT compiler, which results in a noticeable lag when attempting to run code from some packages, for the first time. The authors state, “Because of the way Julia works under the hood, this is a difficult problem to solve, but much progress has been made in the past few years to reduce this compilation time.” [Source: Julia Documentation].
- Small ecosystem: As expected from a new technology, Julia’s community and ecosystem are still in their early stages. Although Julia has plenty of unique solutions and packages, they are dwarfed when compared to the likes of Python and R’s ecosystem – both of which have an ecosystem that has been evolving for the past 20+ years.
Python vs. Julia: Key Differences
Python boasts an established, large community that contributes to the language and supports other Python developers. The language is also extremely versatile and has several use cases, thereby living up to the name of a general-purpose programming language. On the contrary, despite also being considered a general-purpose programming language, Julia is considerably less versatile than Python – however, it is a great tool for scientific programming.
One of the main factors that make Julia so appealing is how fast it is; Julia trumps Python in speed and performance. This is because Julia is a compiled language written on its base, whereas Python is an interpreted language meaning each line must be reprocessed, resulting in slower execution.
TIOBE Index rank (Dec 2022)
Python has a large, well-developed community
Julia’s community is newer and much smaller than Python
Slower in some areas
Faster in some areas
Easy to Medium
Julia vs Python - Which to Choose?
It is too early to explicitly state that Julia will dethrone Python in certain domains, such as data science and machine learning. Though both languages boast easy-to-read and easy-to-learn syntaxes, they each have their respective advantages. For instance, Python has a large established community and a vast array of frameworks and libraries, as well as being extremely versatile. Julia, on the other hand, is an excellent tool for scientific computing that is growing in popularity due to its speed.
For seasoned professionals, learning a new program language may be a worthwhile investment if the benefits of the language are significant since acquiring the skill takes time, which may hamper productivity. Beginners starting their journey should pay attention to job boards to see what skills are in demand and equip themselves accordingly.
Another way to determine which language to choose is by considering the type of programs you intend on building. For programs that are not extremely large, Python may be able to generate results faster than Julia since it is interpreted instead of compiled. However, if you plan on building programs with CPU-heavy computations should probably lean more toward Julia.
Python & Julia Courses
Mastering the Pandas .explode() Method: A Comprehensive Guide
Python NaN: 4 Ways to Check for Missing Values in Python
Seaborn Heatmaps: A Guide to Data Visualization
Test-Driven Development in Python: A Beginner's Guide
Exponents in Python: A Comprehensive Guide for Beginners