One of the trending debates in the scientific and computing community is that is MATLAB vs. Python. Often, people in the scientific community talk about transitioning from MATLAB to Python.
While MATLAB is a robust computing environment for mathematical or technical computing operations involving arrays, matrices, and linear algebra, Python is also gaining popularity in the computing area. This is because Python incorporates MATLAB’s computational power and facilitates speedy and easy development of scientific applications. Check out our data science courses to learn more about MATLAB and Python.
In this article, we’ll explore the differences between MATLAB and Python.
MATLAB vs. Python: What are they?
MATLAB is both a commercial numerical computing environment and programming language. In fact, it is one of the most advanced and well-designed programming language for computing. In the late 1970s, Cleve Moler began the development of MATLAB. It is a multi-paradigm computing environment and language developed by MathWorks.
It is an excellent tool for matrix manipulations, data plotting, implementing algorithms, and developing user interfaces. Even though MATLAB is designed primarily for numerical computation functions, it allows for symbolic computation using the MuPAD symbolic engine.
Python is an open-source, high-level, general-purpose programming language. It was developed by Guido van Rossum and released in 1991. Simplicity lies at the core of Python, and hence, it uses the OOP approach to help developers write precise and logical code for small and large projects.
Python supports multiple programming paradigms, such as procedural programming, OOP, and functional programming. Apart from its neat syntax and code readability features, Python’s best aspect is that it comes equipped with a host of standard libraries for accomplishing different programming and computing tasks.
MATLAB vs. Python: The key differences
Let’s look at some of the main differences between MATLAB and Python:
MATLAB is closed-source software and a proprietary commercial product. Thus, you need to purchase it to be able to use it. For every additional MATLAB toolbox you wish to install and run, you need to incur extra charges. The cost aspect aside, it is essential to note that since MATLAB is specially designed for MathWorks, its user base is quite limited. Also, if MathWorks were to ever go out of business, MATLAB would lose its industrial importance.
Unlike MATLAB, Python is an open-source programming language, meaning it is entirely free. You can download and install Python and make alterations to the source code to best suit your needs. Due to this reason, Python enjoys a bigger fan following and user base. Naturally, the Python community is pretty extensive, with hundreds and thousands of developers contributing actively to enrich the language continually. As we stated earlier, Python offers numerous free packages, making it an appealing choice for developers worldwide.
The most notable technical difference between MATLAB and Python lies in their syntax. While MATLAB treats everything as an array, Python treats everything as a general object. For instance, in MATLAB, strings can either be arrays of strings or arrays of characters, but in Python, strings are denoted by a unique object called “str.” Another example highlighting the difference between MATLAB and Python’s syntax is that in MATLAB, a comment is anything that starts after the percent sign (%). In contrast, comments in Python typically follow the hash symbol (#).
MATLAB boasts of having an integrating development environment. It is a neat interface with a console located in the center where you can type commands, while a variable explorer lies on the right, you’ll find a directory listing on the left.
On the other hand, Python does not include a default development environment. Users need to choose an IDE that fits their requirement specifications. Anaconda, a popular Python package, encompasses two different IDEs – Spyder and JupyterLab – that function as efficiently as the MATLAB IDE.
Programming languages are usually accompanied by a suite of specialized tools to support a wide range of user requirements, from modeling scientific data to building ML models. Integrated tools make the development process easier, quicker, and more seamless.
Although MATLAB does not have a host of libraries, its standard library includes integrated toolkits to cover complex scientific and computational challenges. The best thing about MATLAB toolkits is that experts develop them, rigorously tested, and well-documented for scientific and engineering operations. The toolkits are designed to collaborate efficiently and also integrate seamlessly with parallel computing environments and GPUs. Moreover, since they are updated together, you get fully-compatible versions of the tools.
As for Python, all of its libraries contain many useful modules for different programming needs and frameworks. Some of the best Python libraries include NumPy, SciPy, PyTorch, OpenCV Python, Keras, TensorFlow, Matplotlib, Theano, Requests, and NLTK. Being an open-source programming language, Python offers the flexibility and freedom to developers to design Python-based software tools (like GUI toolkits) for extending the capabilities of the language.
Despite having an active community and excellent standard packages, Python fails to match up to MATLAB in one particular area – the Simulink Toolbox. This toolbox extends MATLAB’s capabilities for signal processing and modeling in a graphical interface. Python lacks a graphical interface that can perform these advanced functions.
Overall, both MATLAB and Python are excellent tools. While one is designed for specific tasks (MATLAB), another can perform a wide variety of generic operations.
If you’re interested to learn more about MATLAB, machine learning, and its relevant topics, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.
If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
What are the major differences between MATLAB and Python?
Python is a high-level language, it is more user friendly, more readable and more portable. MATLAB is a low-level language and not good at some algorithms such as bioinformatics. MATLAB has the function of the matrix, and Python can use NumPy, and the library can achieve similar results. MATLAB has very strong mathematical calculation ability, Python is difficult to do. Python has no matrix support, but the NumPy library can be achieved. MATLAB is particularly good at signal processing, image processing, in which Python is not strong, and performance is also much worse.
Is MATLAB better than Python for machine learning?
It depends on your goals and resources. If you want to focus on machine learning, Python has its own libraries as well (e.g. Scikit-learn), which are very powerful, and there are also some libraries built by the community (e.g. PyBrain). MATLAB is focused more on numerical computing, so if you're mostly interested in theoretical aspects of machine learning, then MATLAB could be the better choice. It's also worth mentioning that the most popular machine learning frameworks (e.g. Scikit-learn) are written in Python.
Which is faster, MATLAB or Python?
According to this benchmark, MATLAB is faster than Python. But this benchmark is not done on a real time algorithm. So, we guess it is difficult to use numbers as a definitive answer. There are two very different ways to measure speed in this world. First, there is the speed at which an algorithm solves a problem. The second type is the speed at which a program runs. The former is better measured with something like Numerical Recipes or similar. The latter is better measured with some production code.