From MATLAB to Python: An Engineer’s Metamorphosis
Take a look at the significant points of MATLAB and Python.
For modern computing, Python is taking over almost every domain. There is a clear answer to the question of which one is a better programming option, MATLAB or Python. However, it would be good to look at the significant features of both and see the differences.
A historical context
It would be worthwhile to examine the historical context of both languages before comparing their structural and operational features.
What is MATLAB?
MATLAB started as a scientific computing tool for students in the 1970s. The founder, Cleve Moler, a mathematician and computer programmer, developed it as a hobby based on his PhD work. In the beginning, it was developed as a matrix calculator. Later, Moler added more mathematical functionalities and distributed it among students across various universities. The first public release was made in 1979 as a free version.
MATLAB is a proprietary tool. MathWorks Inc. was founded in 1984 to develop MATLAB for commercial purposes. It was initially sold to universities, but later, the students working on MATLAB brought it to industry.
MATLAB is a high-level programming language mainly used for numerical computing, data analysis, engineering simulations and analysis, and visualization. In fact, to get started with MATLAB, we don’t need much of a programming background. It’s a fairly intuitive language, especially if someone has a basic understanding of matrices.
What is Python?
Guido van Rossum ideated and implemented Python in 1989, first as a hobby programming project. Version 1.0 was released in 1994, and since then, there have been many version releases. Python has become one of the most popular programming languages among different computing communities.
Python, an open-source programming language, has seen immense growth. People can create their own packages, and it’s being used for almost everything. Developers have created an ecosystem of packages, making use of Python in extensive application areas. As developers create more libraries, Python is being used for web development, machine learning, data science, cloud computing, and more.
The most important characteristic of Python for programmers is that it is simple, concise, clean, readable, and easy to use, all with an elegant syntax.
Fun fact: Its name came from the comedy series Monty Python’s Flying Circus.
Being free and easy to learn and use makes it a popular choice among hobbyist programmers, open-source developers, professional developers, engineers, and data scientists. Companies such as Netflix, Dropbox, Spotify, and many more use it.
On the other hand, MATLAB is mostly used in academia, research labs, and organizations. Its main audience is related to the engineering, maths, and physics domains. However, many of these are now adopting Python.
Language comparison
Open-source vs. proprietary: Python being open-source, it becomes a cost-effective choice for the users. A vibrant community of developers keeps on introducing and creating new libraries, tools, and frameworks for different applications that are freely available. On the other hand, MATLAB is proprietary of MathWorks. We cannot add new features to the language. The developers' community prefers open-source languages and tools. Open-source languages are more cost-effective, reproducible, customizable, and transparent. There is always a lot of help available from the developers' community. Even in universities, students and researchers in engineering fields are moving toward Python instead of MATLAB now.
Strongly typed vs. dynamically typed: MATLAB is a
strongly typed A strongly typed programming language is one in which variable types are strictly enforced and conversions between different types require explicit instructions. dynamically typed A dynamically typed programming language is one in which variable types are determined at runtime rather than being explicitly declared. Matrix structure for numerical computations: MATLAB is specifically designed for numerical computing purposes. It uses the default data structure of matrices for all variables, which is helpful for many mathematical computations and writing code in fewer lines. Python, on the other hand, offers a number of data structures, such as lists, tuples, dictionaries, sets, etc.
Reusable, modular, and flexible: Both are reusable, modular, flexible, and have simple syntax. However, Python offers a wide variety of implementations.
IDE: MATLAB has a standard IDE included in the package. Python doesn’t have a standard IDE like MATLAB does. There are several options available:
Visual Code Studio
PyCharm
Jupyter
Spyder
Libraries and toolboxes
One of the selling features of MATLAB remained its rich collection of toolboxes for different domains, ranging from feedback control systems to machine learning and financial engineering. Python’s ever-expanding library ecosystem has enabled it to address a wide range of application areas and offer a diverse array of algorithmic implementations. Python libraries have become not only a good alternative to MATLAB toolboxes, rather they extend vast support to different domains.
Below is a brief introduction to some of the toolboxes of MATLAB and libraries in Python.
MATLAB toolboxes
Here are some of the most popular MATLAB toolboxes:
Simulink: It is used in modeling, simulating, and analyzing dynamic systems. e.g. control systems and communication systems.
Signal processing toolbox: It is used in signal understanding, filtering, spectral analysis, and feature extraction of signals.
Image processing toolbox: It is used in analyzing, processing, and manipulating digital images.
Optimization toolbox: It is used in linear, quadratic, and nonlinear programming.
Statistics and machine learning toolbox: It is used in hypothesis testing, regression analysis, and clustering.
These toolboxes are developed by the domain experts. We can also use third-party libraries from the MATLAB file exchange.
Python libraries
Here are some of the most popular Python libraries:
NumPy: It is used for mathematical operations. It offers a large collection of functions that can be used for scientific computations.
pandas: It is used for data analysis and manipulation. It offers data structures for efficiently storing and querying large datasets.
Matplotlib: It is used for data visualization using various plots, such as charts, graphs, and histograms. Several other data visualization libraries exist, such as Seaborn, Bokeh, ggplot, Plotly, etc.
Scikit-learn: It is a popular machine learning library that provides a range of algorithms for classification, regression, and clustering.
TensorFlow: It is an open-source library for machine learning developed by Google. It is widely used for deep learning applications.
Django: It is a web framework for building web applications.
Flask: It is a micro web framework for building web applications.