‘Only in Python’ series talks about the constructs that are more specific to Python than other mainstream languages. Of course there are some overlaps with other language, so my choices would be language behavior/characteristics screams Python whenever I see it.
Python also access the class (not instance) by its own name (just like MATLAB) followed by dot operator. In C++, class-wise (not instance-wise) are always accessed through SRO, aka Scope Resolution Operator ::.
There isn’t really a concept for class method despite Python has a @classmethod decorator. The syntactic sugar merely for the use case where a static function happens to access to static members of the same class through its own class’s name.
Static methods and static data members are pretty much glorified namespaces (just like packages and modules in Python) that isn’t tied to any object instances. If a function wants to access the class-wise members (attributes/properties), simply call them by the name of the class instead of object instances of it.
Btw, C++ implicitly passes *this internally and not rely on the function prototype, aka first argument, to pass self around, so you don’t have this problem.
MATLAB does not have native support for static data member but it has static methods. (Btw, in MATLAB, attributes means the access modifiers to methods and properties block, not members of a class like in Python). It’s smart enough to figure out if a method is marked static so it’s exempt from receiving the object itself as the first argument.
Python is a little primative in this department. Calling a method from an object instances always imply passing self as the first argument in the call, whether you want it (instance methods) or not (static/class-wise methods).
If you call a method by the class name, no implict first argument was passed.
To address this mandatory self issue, Python simply use decorators (function wrappers) to wrestle the passed self to fit your purposes.
@staticmethod effectively tosses out the implied first argument (self) passed by method calls from object instances since it’s by definition not needed/wanted.
@classmethod effectively takes the implied first argument (self) and replace it with its class name, aka type(self) as cls so your function prototype expects the first argument. I said effectively because if you call the @classmethod-decorated method by class name, which self is not passed, the deocrator still produces the variable cls to match the first argument in your function prototype.
Here’s an example:
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class A:
a = 3
def__init__(self):
self.a = 42
@classmethod
defprint_a_cls(cls):
print(A.a)
print(cls.a)
# Both obj_a.print_a_cls() and A.print_a_cls() prints 3 twice
# @classmethod basically swap the passed 'self' argument and replace the first arg
# with type(self) which is the class's name A here
@staticmethod
defprint_a_static():
print(A.a)
# Both obj_a.print_a_static() and A.print_a_static() prints 3
# @staticmethod basically absorbs and discard the first argument 'self' if passed
defprint_a_instance(self):
print(self.a)
defprint_a_classwise():
print(A.a)
# obj_a.print_a_classwise()
# "TypeError: A.print_a_classwise() takes 0 positional arguments but 1 was given"
# A.print_a_classwise() prints 3
class A:
a = 3
def __init__(self):
self.a = 42
@classmethod
def print_a_cls(cls):
print(A.a)
print(cls.a)
# Both obj_a.print_a_cls() and A.print_a_cls() prints 3 twice
# @classmethod basically swap the passed 'self' argument and replace the first arg
# with type(self) which is the class's name A here
@staticmethod
def print_a_static():
print(A.a)
# Both obj_a.print_a_static() and A.print_a_static() prints 3
# @staticmethod basically absorbs and discard the first argument 'self' if passed
def print_a_instance(self):
print(self.a)
def print_a_classwise():
print(A.a)
# obj_a.print_a_classwise()
# "TypeError: A.print_a_classwise() takes 0 positional arguments but 1 was given"
# A.print_a_classwise() prints 3
class A:
a = 3
def __init__(self):
self.a = 42
@classmethod
def print_a_cls(cls):
print(A.a)
print(cls.a)
# Both obj_a.print_a_cls() and A.print_a_cls() prints 3 twice
# @classmethod basically swap the passed 'self' argument and replace the first arg
# with type(self) which is the class's name A here
@staticmethod
def print_a_static():
print(A.a)
# Both obj_a.print_a_static() and A.print_a_static() prints 3
# @staticmethod basically absorbs and discard the first argument 'self' if passed
def print_a_instance(self):
print(self.a)
def print_a_classwise():
print(A.a)
# obj_a.print_a_classwise()
# "TypeError: A.print_a_classwise() takes 0 positional arguments but 1 was given"
# A.print_a_classwise() prints 3
If you don’t use these decorators, methods without self as the first argument will work only if you call them by the class’s name. If you try to call it through an object instance, it’ll rightfully refuse to run because the call supplies self as an argument which your function isn’t ready to accept.
I have an example MapWithDefault template wrapper implementation that allows you to endow the map (during construction) with a default value to be returned when the key you’re trying to read does not exist
C++ has the at() method but it involves throwing an exception when the key is not found. However enabling exceptions is a material performance overhead burden in C++.
MATLAB
MATLAB’s hashtables are done with containers.Map() and with more modern MATLAB, dictionary objects (R2020b and on), unless you want to stick to valid MATLAB variable names as keys and exploit dynamic fieldnames.
Neither containers.Map() or dictionary have a default value mechanism when a key is not found. It will just throw an error if the key you are trying to read does not exist. Use iskey()/isKey() method to check if the key exist first and decide to read it or spit out a default value.
Python
Natively Python dictionaries will throw a Key error exception if the requested key in [] operator (aka __getitem__()) do not already exist.
Use .get(key, default) method if you want to assume a default value if the key is not found. The .get() method does not throw an exception: the default is None if not specified.
If you want C++’s default behavior of reading a new key means inserting the said key with a default, you have to explicitly import collections package and use defaultdict. I wouldn’t recommend this as the behavior is not intuitive and likely confusing in insidious ways.
There’s a simiar approach to my MapWithDefault in Python dictionaries: subclass from dict and define your own __missing__ dunder/magic method that returns a default when a key is missing, then use the parent (aka dict)’s constructor to do an explicit (type/class) conversion for existing dict object into your child class object that has __missing__ implemented.
Despite this approach is a little like my MapWithDefault, the __missing__ approach has more flexibility like allowing the default value to be dependent on the query key string, but it comes at the expense of making up one different class, not instance per different default values.
Monkey-patching instance methods is frowned upon in Python. So if you want the default value to tie to instances, the mechanism needs to be redesigned.
[TLDR] Python doesn’t have RAII. C++ and MATLAB allows RAII. You can have a proper RAII only if destructor timing is 100% controllable by the programmer.
with ... as idiom) to address the old issue of opening up a resource handler (say a file or network socket) and automatically close (free) it regardless of whether the program quit abruptly or it gracefully terminates after it’s done with the resource.
Unlike destructors in C++ and MATLAB, which registers what to do (such as closing the resource) when the program quits or right before the resource (object) is gone, Python’s Context Manager is basically rehasing the old try-block idea by creating a rigid framework around it.
It’s not that Python doesn’t know the RAII mechanism (which is much cleaner), but Python’s fundamental language design choices drove itself to a corner so it’s stuck micro-optimizing the try-except/catch-finally approach of managing opened resourecs:
Everything is seen as object in Python. Even integers have a ton of methods. MATLAB and C++ treats POD, Plain Old Data, such as integers separately from classes
Python’s garbage collector controls the timing of when the destructor of any object is called (del merely decrement the reference count). MATLAB’s garbage collector do not apply to objects so the destructor timing is guaranteed C++ has no garbage collection so the destructor timing is guaranteed and managed by the programmer.
Python cannot easily exclude garbage collecting classes (which breaks RAII) because fundamentally everything are classes (dictionaries potentially with callables) in Python.
This is one of the reasons why I have a lot of respects for MATLAB for giving a lot of consideration for corner cases (like what ’empty’ means) in their language design decisions. Python has many excellent ideas but not enough thoughts was given to how these ideas interact, producing what side effects.
Pythons documentation says out loud right what it does: with ... as ... is effectively a rigidly defined
try-except-finally
try-except-finally block:
Context Manager heavily depends on resource opener function (EXPR) to return a constructed class instance that implements __exit__ and __enter__, so if you have a C external library imported to Python, like python-ft4222, likely you have to write in your context manager in full when you write your wrapper.
Typically the destructor should check if the resource is already closed first, then close it if it wasn’t already closed. Take
io.IOBase
io.IOBase as an example:
However, this is only a convenience when you are at the interpreter and can live with the destructor called with a slight delay.
To make sure your code work reliably without timing bugs, you’ll need to explicitly close it somewhere other than at a destructor or rely on object lifecycle timing. The destructor can acts as a double guard to close it again if it hasn’t, but it should not be relied on.
The with ... as construct is extremely ugly, but it’s one of the downsides of Python that cannot be worked around easily. It also makes it difficult for users to retry acquiring a resource because one way or another retrying involves injecting the retry logic in __enter__. It’s not that much typographic savings using with ... as over try-except-finally block if you don’t plan to recycle th contextmanager and the cleanup code is a one-liner.
There are two ways to access columns in DataFrame. The preferred way is by square brackets (indexing into it like a dictionary), while it’s tempting to use the neater dot notation (treating columns like an attribute), my recommendation is don’t!
Python has dictionaries that handles arbitary labels well while it doesn’t have dynamic field names like MATLAB do. This puts DataFrame at a disadvantage developing dot notation syntax while the dictionary syntax opens up a lot of possibilities that are worth giving up dot notation for. The nature of the language design makes the dot notation very half-baked in Python and it’s better to avoid it altogether
Reason 1: Cannot create new columns with dot notation
UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
Reason 2: Only column names that doesn’t happen to be valid Python attribute names AND DataFrame do not have any method with the same name can be accessed through dot notation.
Take an example of dataframe constructed from device info dictionaries created by the package pyft4222. I added a column called 'test me' to a table converted from the dictionary of device info. The tabe T looks like this:
I tried dir() on the table and noticed:
The column name "test me" did not appear anywhere, not even mangled. It has a space in between so it’s not a valid attribute or variable name, so this column is effectively hidden from the dot notation
flags is an internal attribute of DataFrame and it was not overriden by the data column flags when called by the dot notation. This means the flags column was also shadowed in (aka hidden to) the dot notation as there were no mangled name for it either
Even more weird is that getattr() works for columns with non-qualified attribute name like test me (despite the dot notation cannot access it because of the lack of dynamic field names syntax yet test me doesn’t show up in dir()). getattr(T, 'flags') still gets the DataFrame’s internal attribute flags instead of the column called flags as expected.
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
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
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).
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)).
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 . 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
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
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.
. Pytorch is the same.