How to Switch from Python to MATLAB Using Matrix Operations and Data Visualization

August 04, 2025

Dr. Ryan Caldwell

Australia

Dr. Ryan Caldwell has over 9 years of experience in computational programming and numerical analysis. He earned his Ph.D. from Murdoch University, Australia, specializing in applied mathematics and MATLAB-based modeling.

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If you’ve worked with Python, whether through a programming course, data science project, or self-learning, you already have a strong starting point for learning MATLAB. Since both languages share key concepts like arrays, indexing, and function creation, your prior experience will help ease the transition. However, MATLAB is built around matrix and numerical operations, which makes it especially powerful for tasks involving engineering, simulations, and data analysis.

As more technical disciplines adopt MATLAB for computation and modeling, Python users often find themselves needing to learn this new environment. Understanding the differences in syntax, logic, and functionality can significantly improve your ability to write efficient code and complete your MATLAB assignment with confidence.

In this guide, we explore MATLAB’s key features through comparisons with Python. You’ll learn how MATLAB handles data structures, plotting, indexing, loops, and user-defined functions. We’ll also show how to write cleaner, faster code by taking advantage of MATLAB’s matrix-first approach and vectorization capabilities.

By the end, you’ll have a clearer understanding of how to apply what you already know in Python to solve problems using MATLAB. Whether you're working on projects, lab tasks, or need to complete your MATLAB assignment, these insights will make your experience smoother and more effective.

Learn the Basics of MATLAB Programming

The most important distinction between MATLAB and Python is in the way data is structured and manipulated. MATLAB is fundamentally matrix-oriented—its name stands for “matrix laboratory.” Unlike Python, which treats everything as a general object, MATLAB’s primary data structure is the matrix.

How to Switch from Python to MATLAB Using Matrix Operations and Data Visualization

Let’s look at a dataset example called popcorn, which includes yield data from a study involving popcorn brands and popper types. The columns represent the brands—Gourmet, National, and Generic. The first three rows are from oil popper batches, and the last three from air poppers. The yield is measured in cups.

load 'popcorn';
popcorn
popcorn = 
5.5     4.5    3.5
5.5     4.5     4
6       4      3
6.5      5      4
7       5      5
7       5     4.5

Now, calculate the ratio between the yields of Gourmet and National brands:

popcorn(:,1)./popcorn(:,2)
ans = 
1.22
1.22
1.5
1.3
1.27
1.4

In MATLAB, operations like division default to matrix-level math, whereas Python (especially using NumPy) often does element-wise operations by default. To perform element-wise math in MATLAB, use a dot before the operator (e.g., ./).

Index Data and Function Outputs

Indexing in MATLAB starts at 1, which differs from Python’s 0-based indexing. MATLAB uses N-D indexing for multidimensional arrays and is inclusive at both bounds.

To view specific rows and columns:

popcorn(4:end,:) popcorn(:,[1 3])

MATLAB supports linear indexing:

popcorn(9)

And also logical indexing:

popcorn(popcorn > 6)

To expand arrays, MATLAB automatically resizes them and fills missing elements with zeros:

popcorn(1,4) = 0

To remove a column:

popcorn(:,4) = []

Write and Use MATLAB Functions

MATLAB has a wide library of built-in functions and also allows you to define your own easily. Let’s use the built-in mean function:

brandAvg = mean(popcorn)

To define a custom function that compares oil and air popper yields:

function [firstHalfAvg, secondHalfAvg] = popperTypeAvg(data)
firstHalfAvg = mean(data(1:3,:),'all');
secondHalfAvg = mean(data(4:6,:),'all');
end

Call the function as follows:

[oilAvg, airAvg] = popperTypeAvg(popcorn)

You can also define local, nested, or anonymous functions depending on your use case.

Visualize Data

MATLAB has powerful built-in plotting tools. To visualize popcorn yields:

bar(popcorn, 'BarWidth', 0.75)
brandNames = ["Gourmet", "National", "Generic"];
legend(brandNames, 'Location', 'bestoutside')
title('Popcorn Yield')
xlabel('Batch Number')
ylabel('Cups of Popped Popcorn')

To create multiple subplots in a single figure:

tiledlayout(1, size(popcorn,2))
for i = 1:size(popcorn,2)
nexttile
bar(popcorn(:,i))
title(brandNames(i))
xlabel('Batch Number')
ylabel('Cups of Popped Popcorn')
end

Understand Data Types

MATLAB uses double as its default numeric type and doesn’t require explicit variable declarations. To inspect data types:

whos 'popcorn'

To convert arrays to tables with labeled columns:

array2table(popcorn, 'VariableNames', brandNames)

MATLAB supports logical data as well:

(brandAvg(1)>brandAvg(2)) && (brandAvg(1)>brandAvg(3))

You can explore other data types like categorical, char, datetime, cell, and struct using built-in functions and documentation.

Implement Looping Behavior

Loops in MATLAB are common but should be optimized using pre-allocation to avoid dynamic resizing. Here’s how to find the best-performing brand per batch:

bestBrand = zeros(6,1);
bestBrand = categorical(bestBrand);
for i = 1:6
if popcorn(i,1) == max(popcorn(i,:))
bestBrand(i) = 'Gourmet';
end
if popcorn(i,2) == max(popcorn(i,:))
bestBrand(i) = 'National';
end
if popcorn(i,3) == max(popcorn(i,:))
bestBrand(i) = 'Generic';
end
end

But this can be vectorized for better performance:

[maxVal, index] = max(popcorn, [], 2); brandNames(index)

This method reduces lines of code and improves execution speed.

Define a Custom Class

MATLAB supports object-oriented programming. Here's a simple class definition structure:

classdef MyClass
properties
prop
end
methods
function obj = MyClass(val)
obj.prop = val;
end
end
end

Classes in MATLAB allow for encapsulation of both properties and methods, similar to Python, but require being saved in files named after the class. You can define properties as constant, private, or dependent for more control.

Conclusion

This guide should give you a solid transition from Python to MATLAB, covering everything from basic syntax and functions to data visualization and object-oriented programming. MATLAB’s matrix-centric design, built-in plotting tools, and engineering-specific capabilities make it especially suitable for university assignments and technical applications.

If you're a student needing help with MATLAB assignments, knowing these concepts can go a long way. Practice with real datasets, make use of MATLAB’s documentation, and leverage this foundational knowledge for advanced projects.

For more hands-on practice, consider working through the free MATLAB Onramp course or explore the examples in the MATLAB documentation to reinforce your learning.