MATLAB to Python Migration Guide 2026: The Complete Walkthrough

MathWorks ended perpetual MATLAB licenses in January 2026. Every seat is now a recurring subscription — $2,190+/year for the base product, more with toolboxes. For a team of ten engineers, that's $30,000+ annually just to keep existing code running.

Python has been the dominant scientific computing language for years. NumPy, SciPy, matplotlib, pandas, scikit-learn, PyTorch — the Python scientific stack covers everything MATLAB does, and more. The migration cost is real but it's a one-time investment, not a recurring fee.

This guide gives you the complete picture: environment, syntax, the major gotchas, a toolbox-by-toolbox replacement map, and a realistic strategy for migrating code that's actually used in production.

MATLAB ships as a single package. Python requires assembling the stack. Here's what you actually need:

`bash # Install Miniconda (lightweight Anaconda) # Download from: https://docs.conda.io/en/latest/miniconda.html

# Create an environment matching your MATLAB toolbox usage conda create -n matlab-migration python=3.12 conda activate matlab-migration

# Core scientific stack (replaces MATLAB + Statistics + Optimization) pip install numpy scipy matplotlib pandas scikit-learn

# Toolbox-specific packages pip install scikit-image # Image Processing Toolbox pip install python-control # Control System Toolbox pip install sympy # Symbolic Math Toolbox pip install pywavelets # Wavelet Toolbox

# Development tools pip install jupyter notebook # MATLAB-like interactive workflow pip install spyder # MATLAB-like IDE (optional) `

Recommended editor: VS Code with the Python extension, or JupyterLab for exploratory work. Spyder is the closest to the MATLAB desktop environment if your team needs the familiar layout during transition.

1. Indexing: 1-based vs 0-based

This is the biggest source of bugs in MATLAB migrations. Every array index shifts by 1.

`matlab % MATLAB v = [10, 20, 30, 40]; first = v(1); % 10 last = v(end); % 40 slice = v(2:3); % [20, 30] `

`python # Python v = np.array([10, 20, 30, 40]) first = v[0] # 10 last = v[-1] # 40 slice = v[1:3] # [20, 30] — end is exclusive `

2. Matrix multiply: * vs @

`matlab C = A * B; % matrix multiply D = A .* B; % element-wise multiply `

`python C = A @ B # matrix multiply D = A * B # element-wise multiply (NumPy default) `

3. Semicolons: output suppression vs statement separation

In MATLAB, ; at the end of a line suppresses console output. In Python, there's no console output by default — semicolons are optional and have no effect in scripts.

4. Logical NOT: ~ vs not / ~

`matlab if ~isempty(x) % MATLAB logical not `

`python if len(x) > 0: # Python — explicit check if not x: # works for lists; careful with NumPy arrays if x.size > 0: # safest for NumPy arrays `

5. Boolean short-circuit: &&/|| vs and/or

`matlab if a > 0 && b > 0 % MATLAB scalar short-circuit `

`python if a > 0 and b > 0: # Python — works for scalars # For NumPy arrays, use np.logical_and(a, b) or (a > 0) & (b > 0) `

6. String quotes: single vs double

`matlab s = 'hello'; % MATLAB: single quotes for char s = "hello"; % MATLAB R2016b+: double quotes for string object `

`python s = 'hello' # Python: both work, single preferred by convention s = "hello" `

| MATLAB Toolbox | Python replacement | Install | |---|---|---| | Core (matrices, linear algebra) | NumPy + SciPy | pip install numpy scipy | | Statistics Toolbox | scipy.stats + pandas | pip install scipy pandas | | Signal Processing Toolbox | scipy.signal | included with scipy | | Image Processing Toolbox | scikit-image | pip install scikit-image | | Optimization Toolbox | scipy.optimize | included with scipy | | Control System Toolbox | python-control | pip install python-control | | Symbolic Math Toolbox | SymPy | pip install sympy | | Deep Learning Toolbox | PyTorch or Keras/TensorFlow | pip install torch | | Curve Fitting Toolbox | scipy.optimize + scipy.interpolate | included with scipy | | Parallel Computing Toolbox | joblib, multiprocessing, or dask | pip install joblib | | Wavelet Toolbox | PyWavelets | pip install pywavelets |

For functions with no direct equivalent, use scipy.special, statsmodels, or search PyPI. The Python ecosystem is larger than MATLAB's — most niche functions have been implemented.

Don't try to migrate everything at once. Use this workflow:

Step 1: Inventory your codebase

Run a directory scan to list all .m files and their sizes. Sort by lines of code — migrate the simplest functions first to build confidence and test coverage.

Step 2: Use the converter for first-pass conversion

Paste each .m file into the [converter at mtopython.com/convert](/convert). The converter produces a Python file with: - All registry-mapped functions replaced with correct NumPy/SciPy equivalents - 0-based indexing applied - Imports injected - # TODO: flags on anything that needs manual attention

Step 3: Fix the flagged items

Each # TODO: flag identifies a construct the converter couldn't translate automatically — OOP patterns, eval() calls, complex nargin logic. Address these manually.

Step 4: Write tests before the conversion, verify after

The most reliable migration strategy: capture the MATLAB output for a set of representative inputs, then verify the Python function produces the same values. Even simple numerical assertions catch most bugs.

Step 5: Migrate in layers, not all at once

Keep the MATLAB version running in production until the Python version is validated. Call Python from MATLAB (or vice versa) during the transition if needed.

1-based to 0-based indexing is the source of 80% of migration bugs. Here's every pattern you need to know:

`matlab % MATLAB indexing patterns v(1) → v[0] % first element v(end) → v[-1] % last element v(end-1) → v[-2] % second to last v(2:5) → v[1:5] % elements 2,3,4,5 (MATLAB inclusive both ends) v(1:2:end) → v[0::2] % every other element from start A(i, j) → A[i-1, j-1] % 2D matrix element A(:, 2) → A[:, 1] % second column A(end, :) → A[-1, :] % last row `

For loops: MATLAB for i = 1:n is typically converted to for i in range(1, n+1): (keeping the 1-based variable) so array indexing with A[i-1] is still correct. Alternatively, rewrite to for i in range(n): and update all index expressions to A[i].

The converter chooses the 1-based loop variable approach to minimize the number of index changes throughout the function body.

Some MATLAB patterns don't map cleanly and need judgment calls:

nargin / optional arguments:

`matlab function y = foo(x, n) if nargin < 2, n = 10; end y = x + n; end `

`python def foo(x, n=10): # Python default argument — cleaner return x + n `

eval() and feval(): Avoid these in Python too — use a dispatch dict or getattr.

global variables: Refactor to pass parameters explicitly. MATLAB global state causes the same bugs in Python.

fprintf: Map to Python print() for stdout, or f-strings:

`matlab fprintf('Value: %.3f\n', x); `

`python print(f'Value: {x:.3f}') `

Handle classes (@myClass): Convert to Python classes using class. MATLAB handle semantics (reference behaviour) maps to normal Python object references.

The migration from MATLAB to Python in 2026 is well-trodden territory. The ecosystem is mature, the toolboxes are mapped, and the converter does the mechanical work.

Typical migration results: - 69.8% of real-world MATLAB files compile to valid, flag-free Python on the first pass (measured against 923 files from public research repositories) - Most remaining issues are OOP patterns, eval() usage, or toolbox functions requiring manual mapping - Average review time: 20–30 minutes per 200-line function for flagged items

Paste your MATLAB code into the [free converter at mtopython.com/convert](/convert) to get Python output, a full compatibility report, and a checklist of items needing manual review. Free for 50 lines; file upload for full migrations.