Dictionary of equivalent/analogous concepts in programming languages

Common	C	C++	MATLAB	Python
Variable arguments	`<stdarg.h>` `T f(...)` Packed in `va_arg`	Very BAD! Cannot overload when signatures are uncertain.	`varargin` `varargout` Both packed as cells. MATLAB does not have named arguments	`args` (simple, stored as tuples) `*kwargs` (specify input by keyword, stored as a dictionary)
Referencing	N/A	`operator[]`	`(_)` is for references `subsindex subsassgn` `[_]` is for concat `{_}` is for (un)pack	`__getitem__() __setitem__()`
Default values	N/A	Supported	Not supported. Manage with `inputParser()` or newer `arguments`	Non-intuitive static data behavior. Stick to `None` or immutables.
Name-Value Argument Matching			Old way: `.., 'PropName', Value` and parse `varargin` Since R2021a: `Name=Value` `options` in `arguments`	`Name=Value` `**kwargs`
Major Dimension	Row	Row	Column	Row (Native/Numpy) Column for Pandas
Constness	`const`	`const`	Only in classes	N/A (Consenting adults)
Variable Aliasing	Pointers	References	NO! Rely on Copy-on-write (No in-place functions*) Handle classes under limited circumstances	References
`=` assignment	Copy one element	Values: Copy References: Bind	New Copy Copy-on-write	NO VALUES Bind references only (could be to unnamed objects)
Chained access operators	N/A	Difficult to operator overload it right	Difficult to get it right. MATLAB had some chaining bugs with `dataset()` as well.	Chains correctly natively
Assignment expressions (assignment evaluates to assigned lvalue)	`=`	`=`	N/A	Named Expression `:=`
Version Management			`verLessThan()` `isMATLABReleaseOlderThan`	`virtenv` (Virtual Environment)
Exponentiation	`<math.h>` `pow()`	`<cmath>` `pow()`	`^`	`**`
Stream (Conveyor belt mechanism. Saves memory)	I/O (std, file, sockets)	`iterator` in STL containers	MATLAB doesn’t do references. Just increment indices.	iterators (uni-directional only) `iter(): __iter__()` `next(): __next__()`
Looping	for(init, cont_cond, next)	C-style for(auto running: iterable)	for k = array to iterate	list-comp for (index, thing) in enumerate(lists)

Since MATLAB doesn’t do references, iterators (by extension generators) and functions that do in-place operations do not make sense (unless you bend it very hard with anti-patterns such as handles and dbstack).

Data Types

Common	C	C++	MATLAB	Python
Sets	N/A	`std::set`	Only set operations, not set data type	`{ , , ...}`
Dictionaries		`std::unordered_map`	– Dynamic fieldnames (qualified varnames as keys) – `containers.Map()` or `dictionary()` since R2022b	Dictionaries `{key:value}` (Native)
Heterogeneous containers			cells `{}`	lists (mutable) tuples (immutable)
Structured Heterogeneous containers			`table()` `dataset()` [Old] Mix in classes	Pandas Dataframe
Array, Matrices & Tensors			Native `[ , ; , ]`	Numpy/PyTorch
Records	struct	class (members)	dynamic field (structs) properties (class) `getfield()/setfield()`	No structs (use dicts) attribute (class) `getattr()/setattr()`
Type deduction	N/A	`auto`	Native	Native
Type extraction	N/A	`decltype()` for compile time (static) `typeid()` for RTTI (runtime)	`class()`	`type()`

Native sets operations in Python are not stable and there’s no option to use stable algorithm like MATLAB does. Consider installing orderly-set package.

Array Operations

Common	MATLAB	Python
Repeat	`repmat()`	`[] * N` `np.repeat()`
Logical Indexing	Native	List comprehension Boolean Indexing (Numpy)
Equally spaced numbers	Internally `colon()`: `start:step:end` `linspace`/`logspace`	`range(begin, past_end, step)` produces an iterator `list(range())` or `tuple(range())` iterates to realize the vector
Equally spaced indexing	MATLAB has no generators, so produced vector only	`[start:past_end:step]` is internally `slice()` which produces a slice object, not range/lists/tuple. Faster but not iterable
Shallow copy	Deep copy-on-write	Slice: `x = y[:]` `copy.copy()`
Deep copy	Deep copy-on-write	`copy.deepcopy()`

Editor Syntax

Common	C	C++	MATLAB	Python
Commenting	`/* ... */` `//` (only for newer C)	`//` (single line) `/* ... */` (block)	`%` (single line) (Block): `%{ ... %}`	`#` (single line) `"""` or `'''` is docstring which might be undersirably picked up
Reliable multi-line commenting (IDE)			Ctrl+(Shift)+`R`(Windows), `/` (Mac or Linux)	[Spyder]: Ctrl+`1`(toggle), `4`(comment), `5`(uncomment)
Code cell (IDE)			`%%`	[Spyder]: `# %%`
Line Continuation	`\`	`\`	`...`	`\`
Console Precision			`format`	`%precision` (IPython)
Clear variables			`clear` / `clearvars`	`%reset -sf` (IPython)

Macros only make sense in C/C++. This makes code less transparent and is frowned upon in higher level programming languages. Even its use in C++ should be limited. Use inline functions whenever possible.

Python is messy about the workspace, so if you just delete

Object Oriented Programming Constructs

Common	C++	MATLAB	Python
Getters Setters	No native syntax. Name mangle (prefix or suffix) yourself to manage	Define methods: `get.x` `set.x`	Getter: `@property def x(self): ...` Setter: `@x.setter def x(self, value): ...`
Deleters	Members can’t be changed on the fly	Members can’t be changed on the fly	Deleter (removing attributes dynamically by `del`)
Overloading (Dispatch function by signature)	Overloading	Overload only by first argument	`@overload` (Static type) `@singledispath @multipledispatch`
Initializing class variables	Initializer Lists Constructor	Constructor	Constructor
Constructor	`ClassName()` Does not return (`*this` is implicit)	`obj=ClassName(...)` MUST output the constructed object	`__init__(self, ...)` Object to be constructed is 1st argument
Destructor	`~ClassName()`	`delete()`	`__del__()`
Special methods	Special member functions	(no name) method that control specific behaviors	Magic/Dunder methods
Operator overloading	`operator`	operator methods to define	Dunder methods
Resource Self-cleanup	RIAA	`onCleanup()`: make a dummy object with cleanup operation as destructor to be removed when it goes out of scope	`with` Context Managers
Naming for the object itself	Class: (class’s own name by SRO `::`) Instance: `*this`	Class: (class’s own name) Instance: `obj` (or any output name defined in constructor)	Class: `cls` Instance: `self` (Recommended PEP8 names)

Python allows adding members (attributes) on the fly with setattr(), which includes methods. MATLAB’s dynamicprops allows adding properties (data members) on the fly with addprop

onCleanup() does not work reliably on Python because MATLAB’s object destructor time is deterministic (MATLAB specifically do not garbage collect user objects to avoid this mess. It only garbage collects PODs) while Python leaves it up to garbage collector.

*this is implicitly passed in C++ and not spelled out in the method declaration. The self object must be the first argument in the instance method’s signature/prototype for both MATLAB and Python.

Functional Programming Constructs

Common	C++	MATLAB	Python


Function as variable	Functors (Function Objects) `operator()`	Function Handle	Callables (Function Objects) `__call__()`
Lambda Syntax	Lambda `[capture](inputs) {expr} -> optional trailing return type`	Anonymous Function `@(inputs) expr`	Lambda `lambda inputs: expr`
Closure (Early binding): an instance of function objects	Capture `[]` only as necessary. Early binding `[=]` is capture all.	Early binding ONLY for anonymous functions (lambda). Late binding for function handles to loose or nested functions.	Late binding* by default, even for Lambdas. Can capture `Po` through default values `lambda x,P=Po: x+P` (We’re relying users to not enter the captured/optional input argument)

Concepts of Early/Late Binding also apply to non-lambda functions. It’s about when to access (usually read) the ‘global’ or broader scope (such as during nested functions) variables that gets recruited as a non-input variable that’s local to the function itself.

An instance of a function object is not a closure if there’s any parameter that’s late bound. All lambdas (anonymous functions) in MATLAB are early bound (at creation).

The more proper way (without creating an extra optional argument that’s not supposed to be used, aka defaults overridden) to convert late binding to early binding (by capturing variables) is called partial application, where you freeze the parameters (to be captured) by making them inputs to an outer layer function and return a function object (could be lambda) that uses these parameters.

The same trick (partial application) applies to bind (capture) variables in simple/nested function handles in MATLAB which do behave the same way (early binding) like anonymous functions (lambda).

Currying is partial application one parameter at a time, which is tedious way to stay faithful to pure functional programming.

List comprehension is a shorthand syntax for transform/map() and copy_if/remove_if/filter() in one shot, but not accumulate/reduce(). MATLAB and C/C++ does not have listcomp, but listcomp is not specific to Python. Even Powershell has it.

Listcomp syntax, if wrapped in round brackets like (x**x for x in range(5)), gives a generator. Wrapping in square bracket is the shortcut of casting the generator into a list, so [x**x for x in range(5)] is the same as list(x**x for x in range(5)).

Coroutines / Asynchronous Programming

MATLAB natively does not support coroutines.

Common	C++20	Python
Generators	Input Iterators	Functions that `yield value_to_spit_out_on_next` (Implicitly return a generator/functor with `iter` and `next`)
Coroutines		Functions that `value_accepted_from_outside = yield` Send value to the continuation by `g.send(user_input)` `async`/`await` (native coroutines)

Matrix Arrays

The way Numpy requires users to specify matrices with a bracket for every row drives me nuts. Not only there’s a lot of typing, the superfulous brackets reinforce C’s idea of row-major which is horrendous to people with a proper math background who see matrices as column-major $\mathbf{A}_{r,c}$ . Pytorch is the same.

Once you are trained in APL/MATLAB’s matrix world-view, you’ll discover going back to the world where matrices aren’t first class citizens is clumsy AF.

With Python, you lose the clutter free readability where your MATLAB code is one step away from the matrix equations in your scientific computing work, despite a lot of the features that addresses frequent use patterns are implemented earlier in Python than MATLAB.

Don’t believe those who haven’t lived and breathed MATLAB tell you Python is strictly superior. No it isn’t. They just didn’t know what they were missing as they haven’t made the intellectual leap in MATLAB yet. Python is very convenient as a swiss-army knife but scientific computing is an afterthought in Python’s language design.

The only way to use MATLAB-like semi-colon to change rows only works for np.matrix() type, which they plan to deprecate. For now one can cast matrix into array like np.array(np.matrix(matrix_string)).

Even numpy’s ndarray (or matrix to be deprecated) are CONCEPTUALLY equivalent to a matrix of cells in MATLAB. There isn’t native numerical matrices like in MATLAB that doesn’t have the overhead of unpacking arbitrary data types. You don’t want to do numerical matrices in MATLAB with cell matrices as it’s insanely slow.

You get away without the unpacking penalty in Numpy if all the contents of the ndarray happens to have the same dtype (such as numerical), aka known to be uniform. In other words, MATLAB’s matrices are uniform if it’s formed by [] and heterogeneous if formed by {}, while for Python [] is context-dependent, kept track of by dtype.

Concept	MATLAB	Numpy
Construction	`[8,9;6,4]`	`np.array([[8,9],[6,4]])`
Size by dimension	`size()`	`A.shape`
Concatenate within existing dimensions	`[A;B]` or `vertcat()` `[A,B]` or `horzcat()` `cat(dim, A, B, ...)`	`np.vstack()` `np.hstack()` `np.concatenate(list, dim)`
Concatenate expanding to 3D (expand in last dimension)	`cat(3, A, B, ...)`	`np.dstack()` ‘d’ for depth (3rd dimension)
Concatenate expanding dimensions	`cat(newdim, A, B, ...)` then `permute()`	`np.stack([A, ..], expand_at_axis)` `np.array([A, ..])` expands at first dimension as outermost bracket refers to first dimension
Tiling	`repmat()`	`np.tile()`
Fill with same value	`repmat()`	`np.full()`
Fill with ones/zeros	`ones(), zeros()`	`np.ones(), np.zeros()`
Fill minicking another array’s size	`repmat(x, size(B)) ones(x, size(B))` `zeros(x, size(B))`	`np.full_like(B, x)` `np.ones_like(B)` `np.zeros_like(B)`
Preallocate	Any of the above (Must be initialized)	`np.empty()` `np.empty_like()` UNINITIALIZED

repelem() is just repmat() with the repetition by axes vector expanded out as variable input arguments one per dimension. Using ones vector to broadcast a singleton instead of repmat() is horrendously inefficient and non-intuitive.

Heterogeneous Data Structures

Heterogeneous Data Structures are typically column major as it is a concept that derives from Structs of Arrays (SoA) and people typically expect columns to have the same data type from spreadsheets.

While Pandas offers a lot of useful features that I’ve easily implemented with wrappers in MATLAB, the indexing syntax of Pandas/Python is awkward and confusing. It’s due to the nature that matrix is a first-class citizen in MATLAB while it’s an afterthought in Python.

Python does not have the { } cell pack/unpack operator in MATLAB, so in Pandas, you select the Series object (think of it as a supercharged list with conveniences such as handling missing values and keeping track of row/column labels) then call its .values attribute.

However, Pandas is a lot more advanced than MATLAB in terms of using multiple columns as keys and have more tools to exploit multi-key row names (row names not mandatory in MATLAB but mandatory in Pandas). In the old days I had to write my own MATLAB function with unique(.., 'rows') exploit its index output to build unique keys under the hood.

Concept	MATLAB	Python (Pandas Dataframe)

Rows	Observations (`dataset()`) Row (`table()`)	Rows index
Columns	Variables	Columns
Select rows/columns	`T(rows, cols)`	`T.loc[r, col_name]` `T.iloc[r,c]` Caveats: – single index (not wrapped in list) have content extracted – `iloc` on LHS cannot expand table but `loc` can, but it can only inject 1 row
Extract one column	`T{:, c}`	`T[c].values`
Extract one entry	`T{r, c}`	`T.at[r,col_name]` `T.iat[r,c]` Faster than `loc/iloc`
Show first few rows	`T(1:5, :)`	`T.head()`

Ordinal	`categorical()` `ordinal()`	`Categorical()` `Index()`
Getting column names/labels	`T.Properties.VariableNames` (returns `cellstr()` only)	`T.columns` (returns `Index()` or `RangeIndex()`)
Getting row names/labels	`T.Properties.RowNames`	`T.index`


Move columns by name	`movevars()` since R2023a
Rename columns	`renamevars()` since R2020a	`T.rename(columns={source:target})`
Rename rows	`T.Properties.RowNames`	`T.rename(index={source:target})`
Reorder or partial selection	`T[rows, cols]`	`T.reindex(columns=..., index=...)` New labels will autofill by `NaN`
Select columns	`T[:, cols]`	`T[list_of_cols]`

Blindly concatenate columns of 2 tables	`[T1 T2]` If you defined optional rownames, they must match. You can delete it with `T.Properties.RowNames = {}`	Pandas assign row indices (labels) by default. Mismatched row labels do not combine in the same row. Consider `reset_index()` or overwrite the row indices of one table with another, like `pd.concat([T1, T2.set_index(T1.index)]`


Format export	`writetable()`	`.to_*()`

MATLAB tables does not support ranging through column names (such as 'apple':'grapes') yet Pandas DataFrame support it. I don’t think it’s fine to use it in the interpreter to poke around, but this is just asking for confusing logic bugs when the columns are moved around and the programmer has a false sense of security knowing exactly what’s where because they are using only names.

Dataframe is a little smarter than MATLAB’s table() in terms of managing column names and indices as it’s tracked with Index() type which is the same idea as MATLAB’s ordinal() ordered categorical type, where uniques names are mapped to unique indices and it’s the indices under the hood. This is how 'apple':'grapes' can work in Python but not MATLAB.

MATLAB T.Properties.VariableNames is a little clumsy. I usually implement a consistent interface called varnames() that’d output the same cellstr() headings whether it’s struct, dataset or table objects.

MATLAB’s table() by default do not make up row names. Pandas make up row names by default sequentially.

MATLAB table() do requires qualified string characters as variable names. Dataframe doesn’t care what labels you use as long as Index() takes it. It can get confusing because you can have a number 1 and ‘1’ as column headers at the same time and they look the same when displayed in the console.

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