How to Transition from Python to MATLAB Using Syntax, Indexing and Visualization

August 07, 2025

Alex Turner

Australia

Python

Alex Turner has over 8 years of experience in engineering and data analysis. He earned his Master’s degree from Western Sydney University, Australia, specializing in computational mathematics and programming.

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If you’ve spent time coding in Python whether through university coursework, online classes, or personal projects—then switching to MATLAB might feel both familiar and slightly different. This transition is common for university students, especially those pursuing degrees in engineering, data science, or applied mathematics, where MATLAB is often the preferred tool for simulations, data analysis, and algorithm development.

Fortunately, your Python background gives you a strong head start. While both Python and MATLAB are high-level languages, MATLAB’s matrix-first design, 1-based indexing, and built-in functions for linear algebra and visualization introduce a few unique concepts. Understanding these differences can help you adapt quickly and confidently.

This blog is designed to guide Python users through the essentials of MATLAB. You’ll explore how to write basic MATLAB code, perform array operations, index data efficiently, create visualizations, define custom functions, and implement both loops and vectorized logic. We also cover data types and even touch on object-oriented programming.

By the end of this guide, you’ll not only understand how MATLAB works you’ll be able to use it effectively to solve your MATLAB assignment or tackle real-world academic problems with confidence. Whether you're working with datasets, simulations, or custom algorithms, this blog is a practical starting point.

Learn the Basics of MATLAB Programming

How to Transition from Python to MATLAB Using Syntax, Indexing and Visualization

In MATLAB, everything revolves around matrices. That’s even reflected in its name, which stands for “Matrix Laboratory.” In contrast, Python treats data more generically as objects. This matrix-based approach influences everything in MATLAB, from calculations to visualization.

Let’s explore MATLAB using an example dataset called popcorn. This data includes popcorn yields (in cups) for different kernel brands using two types of poppers: oil and air. The first three rows are for oil poppers, and the last three are for air poppers.

load 'popcorn'; popcorn

Output:

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

Let’s calculate the yield ratio between the Gourmet and National brands:

popcorn(:,1)./popcorn(:,2)

Output:

Notice the use of ./ for element-wise division. In MATLAB, standard operators like / are used for matrix operations unless prefixed with a dot (.) to perform element-wise computations.

Index Data and Function Outputs

One major distinction from Python is that MATLAB uses 1-based indexing, meaning arrays start from index 1 instead of 0.

MATLAB allows both N-D (multi-dimensional) and linear indexing. Here's how to extract the data for batches using air poppers (rows 4 to 6):

popcorn(4:end,:)

And here’s how to select just the Gourmet and Generic columns:

popcorn(:,[1 3])

You can also use linear indexing, which treats the matrix as a single column vector:

popcorn(9)

To find all yields above 6 cups using logical indexing, try:

popcorn(popcorn > 6)

MATLAB also allows easy array expansion. If you assign a value outside the current dimensions, MATLAB fills in with zeros:

popcorn(1,4) = 0

This creates a new column. To delete it and revert to the original matrix:

popcorn(:,4) = [];

Write and Use MATLAB Functions

MATLAB includes many built-in functions, such as mean to compute averages:

brandAvg = mean(popcorn)

Output:

brandAvg = 6.25 4.75 4

You can also write your own functions in separate files. Here's a function to compare average yields from oil vs. air poppers:

function [firstHalfAvg, secondHalfAvg] = popperTypeAvg(data)
     rows1to3 = mean(data(1:3,:),'all');
     rows4to6 = mean(data(4:6,:),'all');
     firstHalfAvg = rows1to3;
     secondHalfAvg = rows4to6;
end

Save the above in a file called popperTypeAvg.m. Now call it from your main script:

[oilAvg, airAvg] = popperTypeAvg(popcorn)

Other ways to define functions include:

Local functions (within the same script)
Nested functions (within another function)
Anonymous functions (defined in one line)

Visualize Data

Let’s create a basic bar chart of the popcorn data:

bar(popcorn,'BarWidth',.75)
brandNames = ["Gourmet","National","Generic"];
legend(brandNames,'Location','bestoutside')
title('Popcorn Yield')
xlabel('Batch Number')
ylabel('Cups of Popped Popcorn')

To create multiple charts in one figure using tiledlayout:

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

This layout is helpful for comparing each brand visually.

Understand Data Types

Unlike Python, MATLAB doesn’t require explicitly defining variable types—assigning a value creates the variable.

Use whos to check variable types:

whos 'popcorn'

Output:

Name Size Bytes Class popcorn 6x3 144 double

To convert arrays into tables with named columns:

array2table(popcorn,'VariableNames',brandNames)

Logical values (true/false) in MATLAB are 1 and 0. You can use them in conditions:

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

Output:

ans = logical 1

Other supported types include char, datetime, categorical, struct, and cell.

Implement Looping Behavior

MATLAB supports loops like Python, but looping can be slower if arrays are resized dynamically. Pre-allocation helps improve speed.

Example using a for loop to find the top-performing brand in each 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
bestBrand

Instead of using loops, vectorized code is often faster and cleaner in MATLAB. Here's a vectorized version using max:

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

This produces a quick output of the best brand for each batch without looping.

Define a Custom Class

Object-oriented programming is available in MATLAB, useful for building applications or organizing code. Unlike Python, MATLAB requires each class to be defined in its own file.

Example of a simple class:

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

Save the class in a file named MyClass.m. MATLAB classes use copy-by-value behavior by default (instead of Python’s typical copy-by-reference).

Conclusion

For students familiar with Python, picking up MATLAB can feel intuitive once you get used to its matrix-focused structure and 1-based indexing. While both languages support powerful programming concepts like functions, loops, and object-oriented design, MATLAB shines in areas like numerical computing, data visualization, and matrix operations.

By understanding the key differences and similarities—like how indexing works, how to write functions, how to visualize data, and how to work with various data types—you’re well on your way to mastering MATLAB. Whether you're tackling assignments, building simulations, or analyzing data, the knowledge shared in this blog will help you write efficient, clean, and effective MATLAB code.

Keep practicing with real data, try different functions, and explore vectorization techniques to speed up your computations. With consistent effort, MATLAB will become a valuable and easy-to-use tool in your academic journey.