Every dunder method in Python

You've just made a class. You made a __init__ method. Now what?

Python includes tons of dunder methods ("double underscore" methods) which allow us to deeply customize how our custom classes interact with Python's many features. What dunder methods could you add to your class to make it friendly for other Python programmers who use it?

Let's take a look at every dunder method in Python, with a focus on when each method is useful.

Note that the Python documentation refers to these as special methods and notes the synonym "magic method" but very rarely uses the term "dunder method". However, "dunder method" is a fairly common Python colloquialism, as noted in my unofficial Python glossary.

You can use the links scattered throughout this page for more details on any particular dunder method. For a list of all of them, see the cheat sheet in the final section.

The 3 essential dunder methods 🔑

There are 3 dunder methods that most classes should have: __init__, __repr__, and __eq__.

Operation	Dunder Method Call	Returns
T(a, b=3)	T.__init__(x, a, b=3)	None
repr(x)	x.__repr__()	str
x == y	x.__eq__(y)	Typically bool

The __init__ method is the initializer (not to be confused with the constructor), the __repr__ method customizes an object's string representation, and the __eq__ method customizes what it means for objects to be equal to one another.

The __repr__ method is particularly helpful at the the Python REPL and when debugging.

Equality and hashability 🟰

In addition to the __eq__ method, Python has 2 other dunder methods for determining the "value" of an object in relation to other objects.

Operation	Dunder Method Call	Returns
x == y	x.__eq__(y)	Typically bool
x != y	x.__ne__(y)	Typically bool
hash(x)	x.__hash__()	int

Python's __eq__ method typically returns True, False, or NotImplemented (if objects can't be compared). The default __eq__ implementation relies on the is operator, which checks for identity.

The default implementation of __ne__ calls __eq__ and negates any boolean return value given (or returns NotImplemented if __eq__ did). This default behavior is usually "good enough", so you'll almost never see __ne__ implemented.

Hashable objects can be used as keys in dictionaries or values in sets. All objects in Python are hashable by default, but if you've written a custom __eq__ method then your objects won't be hashable without a custom __hash__ method. But the hash value of an object must never change or bad things will happen so typically only immutable objects implement __hash__.

For implementing equality checks, see __eq__ in Python. For implementing hashability, see making hashable objects in Python.

Orderability ⚖️

Python's comparison operators (<, >, <=, >=) can all be overloaded with dunder methods as well. The comparison operators also power functions that rely on the relative ordering of objects, like sorted, min, and max.

Operation	Dunder Method Call	Returns
<	__lt__	Typically bool
>	__gt__	Typically bool
<=	__le__	Typically bool
>=	__ge__	Typically bool

If you plan to implement all of these operators in the typical way (where x < y would be the same as asking y > x) then the total_ordering decorator from Python's functools module will come in handy.

Type conversions and string formatting ⚗️

Python has a number of dunder methods for converting objects to a different type.

Function	Dunder Method Call	Returns
str(x)	x.__str__()	str
bool(x)	x.__bool__()	bool
int(x)	x.__int__()	int
float(x)	x.__float__()	float
bytes(x)	x.__bytes__()	bytes
complex(x)	x.__complex__()	complex
f"{x:s}"	x.__format__(s)	str
repr(x)	x.__repr__()	str

The __bool__ function is used for truthiness checks, though __len__ is used as a fallback.

If you needed to make an object that acts like a number (like decimal.Decimal or fractions.Fraction), you'll want to implement __int__, __float__, and __complex__ so your objects can be converted to other numbers. If you wanted to make an object that could be used in a memoryview or could otherwise be converted to bytes, you'll want a __bytes__ method.

The __format__ and __repr__ methods are different string conversion flavors. Most string conversions rely the __str__ method, but the default __str__ implementation simply calls __repr__.

The __format__ method is used by all f-string conversions, by the str class's format method, and by the (rarely used) built-in format function. This method allows datetime objects to support custom format specifiers.

Context managers 🚪

A context manager is an object that can be used in a with block.

Use	Dunder Method Call	Returns
with block enter	x.__enter__()	A value given to as
with block exit	x.__exit__(exc_type, exc, traceback)	Truthy/falsey value

For more on context managers see, what is a context manager and creating a context manager.

Containers and collections 🗃️

Collections (a.k.a. containers) are essentially data structures or objects that act like data stuctures. Lists, dictionaries, sets, strings, and tuples are all examples of collections.

Operation	Dunder Method Call	Return Type	Implemented
len(x)	x.__len__()	integer	Very common
iter(x)	x.__iter__()	iterator	Very common
for item in x: ...	x.__iter__()	iterator	Very common
x[a]	x.__getitem__(a)	any object	Common
x[a] = b	x.__setitem__(a, b)	None	Common
del x[a]	x.__delitem__(a)	None	Common
a in x	x.__contains__(a)	bool	Common
reversed(x)	x.__reversed__()	iterator	Common
next(x)	x.__next__()	any object	Uncommon
x[a]	x.__missing__(a)	any object	Uncommon
operator.length_hint(x)	x.__length_hint__()	integer	Uncommon

The __iter__ method is used by the iter function and for all forms of iteration: for loops, comprehensions, tuple unpacking, and using * for iterable unpacking.

While the __iter__ method is necessary for creating a custom iterable, the __next__ method is necessary for creating a custom iterator (which is much less common). The __missing__ method is only ever called by the dict class on itself, unless another class decides to implement __missing__. The __length_hint__ method supplies a length guess for structures which do not support __len__ so that lists or other structures can be pre-sized more efficiently.

Also see: the iterator protocol, implementing __len__, and implementing __getitem__.

Callability ☎️

Functions, classes, and all other callable objects rely on the __call__ method.

Operation	Dunder Method Call	Return Type
x(a, b=c)	x.__call__(a, b=c)	any object

When a class is called, its metaclass's __call__ method is used. When a class instance is called, the class's __call__ method is used.

For more on callability, see Callables: Python's "functions" are sometimes classes.

Arithmetic operators ➗

Python's dunder methods are often described as a tool for "operator overloading". Most of this "operator overloading" comes in the form of Python's various arithmetic operators.

There are two ways to break down the arithmetic operators:

• Mathematical (e.g. +, -, *, /, %) versus bitwise (e.g. &, |, ^, >>, ~)
• Binary (between 2 values, like x + y) versus unary (before 1 value, like +x)

The mathematical operators are much more common than the bitwise ones and the binary ones are a bit more common than the unary ones.

These are the binary mathematical arithmetic operators:

Operation	Left-Hand Method	Right-Hand Method	Description
x + y	__add__	__radd__	Add / Concatenate
x - y	__sub__	__rsub__	Subtract
x * y	__mul__	__rmul__	Multiply
x / y	__truediv__	__rtruediv__	Divide
%	__mod__	__rmod__	Modulo
x // y	__floordiv__	__rfloordiv__	Integer division
**	__pow__	__rpow__	Exponentiate
x @ y	__matmul__	__rmatmul__	Matrix multiply

Each of these operators includes left-hand and right-hand methods. If x.__add__(y) returns NotImplemented, then y.__radd__(x) will be attempted. See arithmetic dunder methods for more.

These are the binary bitwise arithmetic operators:

Operation	Left-Hand Method	Right-Hand Method	Description
x & y	__and__	__rand__	AND
x | y	__or__	__ror__	OR
x ^ y	__xor__	__rxor__	XOR
x >> y	__rshift__	__rrshift__	Right-shift
x << y	__lshift__	__rlshift__	Left-shift

These are Python's unary arithmetic operators:

Operation	Dunder Method	Variety	Description
-x	__neg__	Mathematical	Negate
+x	__pos__	Bitwise	Affirm
~x	__invert__	Bitwise	Invert

The unary + operator typically has no effect, though some objects use it for a specific operation. For example using + on collections.Counter objects will remove non-positive values.

Python's arithmetic operators are often used for non-arithmetic ends: sequences use + to concatenate and * to self-concatenate and sets use & for intersection, | for union, - for asymmetric difference, and ^ for symmetric difference. Arithmetic operators are sometimes overloaded for more creative uses too. For example, pathlib.Path objects use / to create child paths.

In-place arithmetic operations ♻️

Python includes many dunder methods for in-place operations. If you're making a mutable object that supports any of the arithmetic operations, you'll want to implement the related in-place dunder method(s) as well.

Operation	Dunder Method Call	Returns
x += y	x.__iadd__(y)	Typically self
x -= y	x.__isub__(y)	Typically self
x *= y	x.__imul__(y)	Typically self
x /= y	x.__itruediv__(y)	Typically self
x %= y	x.__imod__(y)	Typically self
x //= y	x.__ifloordiv__(y)	Typically self
x **= y	x.__ipow__(y)	Typically self
x @= y	x.__imatmul__(y)	Typically self
x &= y	x.__iand__(y)	Typically self
x |= y	x.__ior__(y)	Typically self
x ^= y	x.__ixor__(y)	Typically self
x >>= y	x.__irshift__(y)	Typically self
x <<= y	x.__ilshift__(y)	Typically self

All of Python's binary arithmetic operators work in augmented assignment statements, which involve using an operator followed by the = sign to assign to an object while performing an operation on it.

Augmented assignments on mutable objects are expected to mutate the original object, thanks to the mutable object implementing the appropriate dunder method for in-place arithmetic.

When no dunder method is found for an in-place operation, Python performs the operation followed by an assignment. Immutable objects typically do not implement dunder methods for in-place operations, since they should return a new object instead of changing the original.

Built-in math functions 🧮

Python also includes dunder methods for many math-related functions, both built-in functions and some functions in the math library.

Operation	Dunder Method Call	Returns
divmod(x, y)	x.__divmod__(y)	2-item tuple
divmod(x, y)	y.__rdivmod__(x)	2-item tuple
abs(x)	x.__abs__()	float
sequence[x]	x.__index__()	int
round(x)	x.__round__()	Number
math.trunc(x)	x.__trunc__()	Number
math.floor(x)	x.__floor__()	Number
math.ceil(x)	x.__ceil__()	Number

Python's divmod function performs integer division (//) and a modulo operation (%) at the same time. Note that, just like the many binary arithmetic operators, divmod will also check for an __rvidmod__ method if it needs to ask the second argument to handle the operation.

The __index__ method is for making integer-like objects. This method losslessly converts to an integer, unlike __int__ which may perform a "lossy" integer conversion (e.g. from float to int). It's used by operations that require true integers, such as slicing, indexing, and bin, hex, and oct functions (example).

Attribute access 📜

Python even includes dunder methods for controlling what happens when you access, delete, or assign any attribute on an object!

Operation	Dunder Method Call	Returns
x.missing	x.__getattr__("missing")	Attribute value
x.anything	x.__getattribute__("anything")	Attribute value
x.thing = value	x.__setattr__("thing", value)	None
del x.thing	x.__delattr__("thing")	None
dir(x)	x.__dir__()	List of strings

The __getattribute__ method is called for every attribute access, while the __getattr__ method is only called after Python fails to find a given attribute. All method calls and attribute accesses call __getattribute__ so implementing it correctly is challenging (due to accidental recursion). See __getattr__ versus __getattribute__ for examples demonstrating the difference between these two methods.

The __dir__ method should return an iterable of attribute names (as strings). When the dir function calls __dir__, it converts the returned iterable into a sorted list (like sorted does).

The built-in getattr, setattr, and delattr functions correspond to the dunder methods of the same name, but they're only intended for dynamic attribute access (not all attribute accesses).

Metaprogramming 🪄

Now we're getting into the really unusual dunder methods. Python includes many dunder methods for metaprogramming-related features.

Implemented on	Operation	Dunder Method Call	Returns
Metaclasses	class T: ...	type(base).__prepare__()	mapping
Metaclasses	isinstance(x, T)	T.__instancecheck__(x)	bool
Metaclasses	issubclass(U, T)	T.__subclasscheck__(U)	bool
Any class	class U(T): ...	T.__init_subclass__(U)	None
Any class	(Called manually)	T.__subclasses__()	list
Any class	class U(x): ...	x.__mro_entries__([x])	tuple
Any class	T[y]	T.__class_getitem__(y)	an item

The __prepare__ method customizes the dictionary that's used for a class's initial namespace. This is used to pre-populate dictionary values or customize the dictionary type (silly example).

The __instancecheck__ and __subclasscheck__ methods override the functionality of isinstance and issubclass. Python's ABCs use these to practice goose typing (duck typing while type checking).

The __init_subclass__ method allows classes to hook into subclass initialization (example). Classes also have a __subclasses__ method (on their metaclass) but it's not typically overridden.

Python calls __mro_entries__ during class inheritance for any base classes that are not actually classes. The typing.NamedTuple function uses this to pretend it's a class (see here).

The __class_getitem__ method allows a class to be subscriptable (without its metaclass needing a __getitem__ method). This is typically used for enabling fancy type annotations (e.g. list[int]).

Descriptors 🏷️

Descriptors are objects that, when attached to a class, can hook into the access of the attribute name they're attached to on that class.

Operation	Dunder Method Call	Returns
class T: x = U()	T.x.__set_name__(T, 'x')	None
t.x	T.x.__get__(t, T)	The value
t.x = y	T.x.__set__(t, y)	None
del t.x	T.x.__delete__(t)	None

The descriptor protocol is mostly a feature that exists to make Python's property decorator work, though it is also used by a number of third-party libraries.

Buffers 💾

Implementing a low-level memory array? You need Python's buffer protocol.

Operation	Dunder Method Call	Returns
memoryview(x)	x.__buffer__(flags)	memoryview
del memoryview(x)	x.__release_buffer__(m)	None

The __release_buffer__ method is called when the buffer that's returned from __buffer__ is deleted.

Python's buffer protocol is typically implemented in C, since it's meant for low level objects.

Asynchronous operations 🤹

Want to implement an asynchronous context manager? You need these dunder methods:

• __aenter__: just like __enter__, but it returns an awaitable object
• __aexit__: just like __exit__, but it returns an awaitable object

Need to support asynchronous iteration? You need these dunder methods:

• __aiter__: must return an asynchronous iterator
• __anext__: like __next__ or non-async iterators, but this must return an awaitable object and this should raise StopAsyncIteration instead of StopIteration

Need to make your own awaitable object? You need this dunder method:

• __await__: returns an iterator

I have little experience with custom asynchronous objects, so look elsewhere for more details.

Construction and finalizing 🏭

The last few dunder methods are related to object creation and destruction.

Operation	Dunder Method Call	Returns
T(a, b=3)	T.__new__(T, a, b=3)	New instance (x)
T(a, b=3)	T.__init__(x, a, b=3)	None
del x	x.__del__()	None

Calling a class returns a new class instance thanks to the __new__ method. The __new__ method is Python's constructor method, though unlike constructors in many programming languages, you should almost never define your own __new__ method. To control object creation, prefer the initializer (__init__), not the constructor (__new__). Here's an odd __new__ example.

You could think of __del__ as a "destructor" method, though it's actually called the finalizer method. Just before an object is deleted, its __del__ method is called (example). Files implement a __del__ method that closes the file and any binary file buffer that it may be linked to.

Library-specific dunder methods 🧰

Some standard library modules define custom dunder methods that aren't used anywhere else:

• dataclasses support a __post_init__ method
• abc.ABC classes have a __subclasshook__ method which abc.ABCMeta calls in its __subclasscheck__ method (more in goose typing)
• Path-like objects have a __fspath__ method, which returns the file path as a string
• Python's copy module will use the __copy__ and __deepcopy__ methods if present
• Pickling relies on __getnewargs_ex__ or __getargs__, though __getstate__ and __setstate__ can customize further and __reduce__ or __reduce_ex__ are even lower-level
• sys.getsizeof relies on the __sizeof__ method to get an object's size (in bytes)

Dunder attributes 📇

In addition to dunder methods, Python has many non-method dunder attributes.

Here are some of the more common dunder attributes you'll see:

• __name__: name of a function, classes, or module
• __module__: module name for a function or class
• __doc__: docstring for a function, class, or module
• __class__: an object's class (call Python's type function instead)
• __dict__: most objects store their attributes here (see where are attributes stored?)
• __slots__: classes using this are more memory efficient than classes using __dict__
• __match_args__: classes can define a tuple noting the significance of positional attributes when the class is used in structural pattern matching (match-case)
• __mro__: a class's method resolution order used when for attribute lookups and super() calls
• __bases__: the direct parent classes of a class
• __file__: the file that defined the module object (though not always present!)
• __wrapped__: functions decorated with functools.wraps use this to point to the original function
• __version__: commonly used for noting the version of a package
• __all__: modules can use this to customize the behavior of from my_module import *
• __debug__: running Python with -O sets this to False and disables Python's assert statements

Those are only the more commonly seen dunder attributes. Here are some more:

• Functions have __defaults__, __kwdefaults__, __code__, __globals__, and __closure__
• Both functions and classes have __qualname__, __annotations__, and __type_params__
• Instance methods have __func__ and __self__
• Modules may also have __loader__, __package__, __spec__, and __cached__ attributes
• Packages have a __path__ attribute
• Exceptions have __traceback__, __notes__, __context__, __cause__, and __suppress_context__
• Descriptors use __objclass__
• Metaclasses use __classcell__
• Python's weakref module uses __weakref__
• Generic aliases have __origin__, __args__, __parameters__, and __unpacked__
• The sys module has __stdout__ and __stderr__ which point to the original stdout and stderr versions

Additionally, these dunder attributes are used by various standard library modules: __covariant__, __contravariant__, __infer_variance__, __bound__, __constraints__. And Python includes a built-in __import__ function which you're not supposed to use (importlib.import_module is preferred) and CPython has a __builtins__ variable that points to the builtins module (but this is an implementation detail and builtins should be explicitly imported when needed instead). Also importing from the __future__ module can enable specific Python feature flags and Python will look for a __main__ module within packages to make them runnable as CLI scripts.

And that's just most of the dunder attribute names you'll find floating around in Python. 😵

Every dunder method: a cheat sheet ⭐

This is every Python dunder method organized in categories and ordered very roughly by the most commonly seen methods first. Some caveats are noted below.

Category	Operation	Dunder Method Call	Returns
Object Creation	x = T(a, b)	x.__init__(a, b)	None
Object Creation	x = T(a, b)	T.__new__(T, a, b)	New instance (x)
Finalizer	del x (ish)	x.__del__()	None
Comparisons	x == y	x.__eq__(y)	Typically bool
Comparisons	x != y	x.__ne__(y)	Typically bool
Comparisons	x < y	x.__lt__(y)	Typically bool
Comparisons	x > y	x.__rt__(y)	Typically bool
Comparisons	x <= y	x.__le__(y)	Typically bool
Comparisons	x >= y	x.__ge__(y)	Typically bool
Hashability	hash(x)	x.__hash__()	int
Conversions	repr(x)	x.__repr__()	Always str
Conversions	str(x)	x.__str__()	Always str
Conversions	bool(x)	x.__bool__()	Always bool
Conversions	int(x)	x.__int__()	Always int
Conversions	float(x)	x.__float__()	Always float
Conversions	bytes(x)	x.__bytes__()	Always bytes
Conversions	complex(x)	x.__complex__()	Always complex
Conversions	format(x, s)	x.__format__(s)	Always str
Context Managers	with x as c:	x.__enter__()	The c object
Context Managers	with x as c:	x.__exit__()	Truthy/falsey value
Collections	len(x)	x.__len__()	int
Collections	iter(x)	x.__iter__()	An iterator
Collections	x[a]	x.__getitem__(a)
Collections	x[a] = b	x.__setitem__(a, b)	None
Collections	del x[a]	x.__delitem__(a)	None
Collections	a in x	x.__contains__(a)	bool
Collections	reversed(x)	x.__reversed__()	An iterator
Collections	next(x)	x.__next__()	Next iterator item
Collections	x[a]	x.__missing__(a)
Collections		x.__length_hint__()	int
Arithmetic	x + y	x.__add__(y)
Arithmetic	x + y	y.__radd__(x)
Arithmetic	x - y	x.__sub__(y)
Arithmetic	x - y	y.__rsub__(x)
Arithmetic	x * y	x.__mul__(y)
Arithmetic	x * y	y.__rmul__(x)
Arithmetic	x / y	x.__truediv__(y)
Arithmetic	x / y	y.__rtruediv__(x)
Arithmetic	x % y	x.__mod__(y)
Arithmetic	x % y	y.__rmod__(x)
Arithmetic	x // y	x.__floordiv__(y)
Arithmetic	x // y	y.__rfloordiv__(x)
Arithmetic	x ** y	x.__pow__(y)
Arithmetic	x ** y	y.__rpow__(x)
Arithmetic	x @ y	x.__matmul__(y)
Arithmetic	x @ y	y.__rmatmul__(x)
Arithmetic	x & y	x.__and__(y)
Arithmetic	x & y	y.__rand__(x)
Arithmetic	x | y	x.__or__(y)
Arithmetic	x | y	y.__ror__(x)
Arithmetic	x ^ y	x.__xor__(y)
Arithmetic	x ^ y	y.__rxor__(x)
Arithmetic	x >> y	x.__rshift__(y)
Arithmetic	x >> y	y.__rrshift__(x)
Arithmetic	x << y	x.__lshift__(y)
Arithmetic	x << y	y.__rlshift__(x)
Arithmetic	-x	x.__neg__()
Arithmetic	+x	x.__pos__()
Arithmetic	~x	x.__invert__()
Math functions	divmod(x, y)	x.__divmod__(y)	2-item tuple
Math functions	abs(x)	x.__abs__()	float
Math functions		x.__index__()	int
Math functions	round(x)	x.__round__()	Number
Math functions	math.trunc(x)	x.__trunc__()	Number
Math functions	math.floor(x)	x.__floor__()	Number
Math functions	math.ceil(x)	x.__ceil__()	Number
Assignment	x += y	x.__iadd__(y)	Typically self
Assignment	x -= y	x.__isub__(y)	Typically self
Assignment	x *= y	x.__imul__(y)	Typically self
Assignment	x /= y	x.__itruediv__(y)	Typically self
Assignment	x %= y	x.__imod__(y)	Typically self
Assignment	x //= y	x.__ifloordiv__(y)	Typically self
Assignment	x **= y	x.__ipow__(y)	Typically self
Assignment	x @= y	x.__imatmul__(y)	Typically self
Assignment	x &= y	x.__iand__(y)	Typically self
Assignment	x |= y	x.__ior__(y)	Typically self
Assignment	x ^= y	x.__ixor__(y)	Typically self
Assignment	x >>= y	x.__irshift__(y)	Typically self
Assignment	x <<= y	x.__ilshift__(y)	Typically self
Attributes	x.y	x.__getattribute__('y')
Attributes	x.y	x.__getattr__('y')
Attributes	x.y = z	x.__setattr__('y', z)	None
Attributes	del x.y	x.__delattr__('y')	None
Attributes	dir(x)	x.__dir__()	An iterable
Descriptors	class T: x = U()	T.x.__set_name__(T, 'x')	None
Descriptors	t.x	T.x.__get__(t, T)
Descriptors	t.x = y	T.x.__set__(t, y)	None
Descriptors	del t.x	T.x.__delete__(t)	None
Class stuff	class U(T): ...	T.__init_subclass__(U)	None
Class stuff	class U(x): ...	x.__mro_entries__([x])	tuple
Class stuff	T[y]	T.__class_getitem__(y)
Metaclasses	class T: ...	type(base).__prepare__()	dict/mapping
Metaclasses	isinstance(x, T)	T.__instancecheck__(x)	bool
Metaclasses	issubclass(U, T)	T.__subclasscheck__(U)	bool
Async	await x (ish)	x.__await__()	An iterator
Async	async with x:	x.__aenter__()	An awaitable
Async	async with x:	x.__aexit__()	An awaitable
Async	async for a in x:	x.__aiter__()	An awaitable
Async	async for a in x:	x.__anext__()	An awaitable
Buffers	memoryview(x)	x.__buffer__(flags)	memoryview
Buffers	del memoryview(x)	x.__release_buffer__(m)	None

The above table has a slight but consistent untruth. Most of these dunder methods are not actually called on an object directly but are instead called on the type of that object: type(x).__add__(x, y) instead of x.__add__(y). This distinction mostly matters with metaclass methods.

I've also purposely excluded library-specific dunder methods (like __post_init__) and dunder methods you're unlikely to ever define (like __subclasses__). See those below.

Category	Operation	Dunder Method Call	Returns
Dataclasses	x = T(a, b)	T.__post_init__(a, b)	None
Copying	copy.copy(x)	x.__copy__()	New object
Copying	copy.deepcopy(x)	x.__deepcopy__(memo)	New object
Pickling	pickle.dumps(x)	x.__getnewargs__()	A 2-item tuple
Pickling	pickle.dumps(x)	x.__getnewargs_ex__()	A 2-item tuple
Pickling	pickle.dumps(x)	x.__getstate__()	A meaningful state
Pickling	pickle.dumps(x)	x.__reduce__()	A 2-6 item tuple
Pickling	pickle.dumps(x)	x.__reduce_ex__(4)	A 2-6 item tuple
Pickling	pickle.loads(b)	x.__setstate__(state)	None
pathlib	os.fspath(x)	p.__fspath__()	str or bytes
sys	sys.getsizeof(x)	x.__sizeof__()	int (size in bytes)
Class stuff	None?	x.__subclasses__()	Subclasses iterable
ABCs	issubclass(U, T)	T.__subclasshook__(U)	bool

So, Python includes 103 "normal" dunder methods, 12 library-specific dunder methods, and at least 52 other dunder attributes of various types. That's over 150 unique __dunder__ names! I do not recommend memorizing these: let Python do its job and look up the dunder method or attribute that you need to implement/find whenever you need it.

Keep in mind that you're not meant to invent your own dunder methods. Sometimes you'll see third-party libraries that do invent their own dunder method, but this isn't encouraged and it can be quite confusing for users who run across such methods and assume they're "real" dunder methods.