--- tags: - devops - l2 - flashcard-deck - python-oop --- [Portal](../../../../library/portal/index.md) | **Level:** [L2: Operations](../../../../library/portal/levels.md) | **Topics:** [Python Automation](../../../../library/portal/topics.md) | **Domain:** DevOps & Tooling id category difficulty tags question answer source_path python-oop/2b0107af8c3d python-oop hard python, oop, metaclasses What are metaclasses and when would you use them? A metaclass is the class of a class — it controls how classes themselves are created. The default metaclass is `type`.\n\nclass Meta(type):\n def __new__(mcs, name, bases, namespace):\n # modify class before creation\n namespace['created_by'] = 'Meta'\n return super().__new__(mcs, name, bases, namespace)\n\nclass MyClass(metaclass=Meta):\n pass\n\nprint(MyClass.created_by) # 'Meta'\n\nUse cases: ORMs (Django models), API registration, enforcing interfaces. In practice, __init_subclass__ or class decorators cover most use cases more simply. interview-qa-gap-analysis python-oop/8645045c9bb7 python-oop hard python, oop, design-patterns, singleton Implement the Singleton pattern in Python (three ways) 1. Module-level instance (Pythonic):\n_instance = MyClass()\n\n2. __new__ override:\nclass Singleton:\n _instance = None\n def __new__(cls):\n if cls._instance is None:\n cls._instance = super().__new__(cls)\n return cls._instance\n\n3. Metaclass:\nclass SingletonMeta(type):\n _instances = {}\n def __call__(cls, *args, **kw):\n if cls not in cls._instances:\n cls._instances[cls] = super().__call__(*args, **kw)\n return cls._instances[cls]\n\nThe module-level approach is simplest and most Pythonic. Use __new__ for classes that need lazy initialization. interview-qa-gap-analysis python-oop/c97e9afa1f95 python-oop medium python, oop, functional, currying What is currying in Python? Currying transforms a function with multiple arguments into a sequence of functions each taking one argument.\n\nfrom functools import partial\n\ndef multiply(x, y):\n return x * y\n\ndouble = partial(multiply, 2)\nprint(double(5)) # 10\n\n# Manual currying:\ndef curry_multiply(x):\n def inner(y):\n return x * y\n return inner\n\ntriple = curry_multiply(3)\nprint(triple(5)) # 15\n\nfunctools.partial is the standard way to do partial application in Python. True currying (auto-currying) isn't built-in but libraries like toolz provide it. interview-qa-gap-analysis python-oop/68ca466362f6 python-oop hard python, oop, mro, inheritance Explain Python's Method Resolution Order (MRO) MRO determines the order in which base classes are searched when calling a method. Python uses the C3 linearization algorithm.\n\nclass A: pass\nclass B(A): pass\nclass C(A): pass\nclass D(B, C): pass\n\nprint(D.__mro__) # (D, B, C, A, object)\n\nRules: (1) children before parents, (2) left-to-right order preserved, (3) each class appears once. Use ClassName.mro() or ClassName.__mro__ to inspect. super() follows the MRO, not just the immediate parent — critical for cooperative multiple inheritance. interview-qa-gap-analysis python-oop/456161a3d461 python-oop medium python, oop, performance, slots What is __slots__ and when should you use it? __slots__ restricts instance attributes to a fixed set, replacing the per-instance __dict__ with a more memory-efficient structure.\n\nclass Point:\n __slots__ = ('x', 'y')\n def __init__(self, x, y):\n self.x = x\n self.y = y\n\np = Point(1, 2)\np.z = 3 # AttributeError!\n\nBenefits: ~40% less memory per instance, slightly faster attribute access. Use when creating millions of instances. Drawbacks: no dynamic attributes, complications with multiple inheritance, no __dict__ for introspection. Not needed for most classes. interview-qa-gap-analysis python-oop/6a0485f1987b python-oop hard python, oop, descriptors, protocols What are descriptors in Python? A descriptor is any object that defines __get__, __set__, or __delete__. They control attribute access on classes.\n\nclass Validator:\n def __set_name__(self, owner, name):\n self.name = name\n def __get__(self, obj, objtype=None):\n return obj.__dict__.get(self.name)\n def __set__(self, obj, value):\n if not isinstance(value, int):\n raise TypeError(f'{self.name} must be int')\n obj.__dict__[self.name] = value\n\nclass Order:\n quantity = Validator()\n\nProperty, classmethod, staticmethod are all implemented as descriptors. Data descriptors (with __set__) take priority over instance __dict__; non-data descriptors don't. interview-qa-gap-analysis python-oop/ef86a0f80a4f python-oop medium python, oop, abc, abstract What is the ABC module and how do you use abstract classes? The abc module provides Abstract Base Classes — classes that can't be instantiated and enforce method implementation in subclasses.\n\nfrom abc import ABC, abstractmethod\n\nclass Shape(ABC):\n @abstractmethod\n def area(self) -> float:\n ...\n\n @abstractmethod\n def perimeter(self) -> float:\n ...\n\nclass Circle(Shape):\n def __init__(self, r):\n self.r = r\n def area(self):\n return 3.14159 * self.r ** 2\n def perimeter(self):\n return 2 * 3.14159 * self.r\n\nShape() # TypeError: Can't instantiate abstract class\nCircle(5).area() # 78.5\n\nUse ABCs to define interfaces and ensure subclasses implement required methods. Also supports @abstractproperty (deprecated — use @property + @abstractmethod). interview-qa-gap-analysis python-oop/50ac0061c201 python-oop medium python, oop, dataclasses, namedtuple Dataclasses vs namedtuples — when to use which? Both reduce boilerplate for data-holding classes but differ in mutability and features.\n\nfrom dataclasses import dataclass\nfrom typing import NamedTuple\n\n@dataclass\nclass PointDC:\n x: float\n y: float\n\nclass PointNT(NamedTuple):\n x: float\n y: float\n\nNamedTuple: immutable, hashable, tuple-compatible, lighter memory. Use for simple records, dict keys, function returns.\n\nDataclass: mutable by default (frozen=True for immutable), supports default_factory, __post_init__, inheritance, field metadata. Use for domain objects needing methods or validation.\n\nRule of thumb: NamedTuple for simple data, dataclass for everything else. interview-qa-gap-analysis python-oop/2e8dd56c78fe python-oop medium python, oop, encapsulation, name-mangling Explain name mangling with double underscores Python mangles attributes starting with __ (double underscore) by prepending _ClassName to prevent accidental override in subclasses.\n\nclass Parent:\n def __init__(self):\n self.__secret = 42\n\nclass Child(Parent):\n def __init__(self):\n super().__init__()\n self.__secret = 99 # different attribute!\n\nc = Child()\nprint(c._Parent__secret) # 42\nprint(c._Child__secret) # 99\n\nThis is NOT access control — it's name collision avoidance. Single underscore (_name) is the convention for 'private'. Use __ only when you need to prevent subclass attribute clashes. interview-qa-gap-analysis python-oop/5b46a384b579 python-oop hard python, oop, metaclasses, init-subclass What is __init_subclass__ and how does it replace metaclasses? __init_subclass__ is a hook called when a class is subclassed, added in Python 3.6. It covers many metaclass use cases more simply.\n\nclass Plugin:\n registry = {}\n def __init_subclass__(cls, name=None, **kwargs):\n super().__init_subclass__(**kwargs)\n Plugin.registry[name or cls.__name__] = cls\n\nclass PDF(Plugin, name='pdf'):\n pass\n\nclass CSV(Plugin, name='csv'):\n pass\n\nprint(Plugin.registry) # {'pdf': PDF, 'csv': CSV}\n\nThis auto-registers subclasses without metaclasses. Use for plugin systems, validation on subclass creation, or enforcing class-level constraints. Simpler to understand and compose than metaclasses. interview-qa-gap-analysis python-oop/e6cf7e2faa98 python-oop medium python, oop, super, inheritance How does super() work in Python 3? super() returns a proxy object that delegates method calls to the next class in the MRO.\n\nclass A:\n def greet(self):\n return 'A'\n\nclass B(A):\n def greet(self):\n return 'B->' + super().greet()\n\nclass C(A):\n def greet(self):\n return 'C->' + super().greet()\n\nclass D(B, C):\n def greet(self):\n return 'D->' + super().greet()\n\nD().greet() # 'D->B->C->A'\n\nPython 3 super() needs no args (uses __class__ cell). It follows MRO, not parent — crucial for cooperative multiple inheritance. Always call super().__init__() in __init__ for MI to work correctly. interview-qa-gap-analysis python-oop/081437da9063 python-oop easy python, oop, classmethod, staticmethod What are class methods and static methods? @classmethod receives the class as first arg (cls), @staticmethod receives no implicit arg.\n\nclass Date:\n def __init__(self, year, month, day):\n self.year, self.month, self.day = year, month, day\n\n @classmethod\n def from_string(cls, s):\n y, m, d = map(int, s.split('-'))\n return cls(y, m, d) # works with subclasses too\n\n @staticmethod\n def is_valid(s):\n parts = s.split('-')\n return len(parts) == 3\n\nDate.from_string('2026-04-01') # creates Date instance\nDate.is_valid('2026-04-01') # True\n\nUse classmethod for alternative constructors (factory methods). Use staticmethod for utility functions that don't need class/instance state but logically belong to the class. interview-qa-gap-analysis python-oop/72cc239bd880 python-oop medium python, oop, property, descriptors Explain Python's property decorator @property creates managed attributes with getter/setter/deleter methods.\n\nclass Temperature:\n def __init__(self, celsius):\n self._celsius = celsius\n\n @property\n def fahrenheit(self):\n return self._celsius * 9/5 + 32\n\n @fahrenheit.setter\n def fahrenheit(self, value):\n self._celsius = (value - 32) * 5/9\n\nt = Temperature(100)\nprint(t.fahrenheit) # 212.0\nt.fahrenheit = 32\nprint(t._celsius) # 0.0\n\nProperties are descriptors under the hood. Use them to add validation, computed attributes, or to migrate from public attributes to managed access without breaking the API. interview-qa-gap-analysis python-oop/ad06304f4e6f python-oop easy python, oop, duck-typing, protocols What is duck typing and how does Python use it? 'If it walks like a duck and quacks like a duck, it's a duck.' Python checks behavior (methods/attributes) rather than type.\n\ndef get_length(obj):\n return len(obj) # works with str, list, dict, any object with __len__\n\nget_length('hello') # 5\nget_length([1,2,3]) # 3\n\nProtocol classes (Python 3.8+) formalize this:\nfrom typing import Protocol\n\nclass Sized(Protocol):\n def __len__(self) -> int: ...\n\ndef f(x: Sized) -> int:\n return len(x)\n\nDuck typing enables polymorphism without inheritance. The typing.Protocol class adds optional static checking while preserving runtime duck typing. interview-qa-gap-analysis python-oop/82faad897e9a python-oop easy python, oop, dunder, string-representation What is __repr__ vs __str__? __repr__ is for developers (unambiguous), __str__ is for users (readable).\n\nclass Point:\n def __init__(self, x, y):\n self.x, self.y = x, y\n def __repr__(self):\n return f'Point({self.x}, {self.y})' # eval-able if possible\n def __str__(self):\n return f'({self.x}, {self.y})'\n\np = Point(1, 2)\nrepr(p) # 'Point(1, 2)'\nstr(p) # '(1, 2)'\nprint(p) # calls __str__: (1, 2)\n\nIf only one is defined, implement __repr__ — str() falls back to __repr__, but not vice versa. f-strings and print() call __str__; the REPL and containers call __repr__. interview-qa-gap-analysis --- ## Wiki Navigation ### Prerequisites - Python Flashcards *(CLI)* (flashcard_deck, L1) ### Related Content - Perl Flashcards *(CLI)* (flashcard_deck, L1) — Python Automation - Python Algorithms Flashcards *(CLI)* (flashcard_deck, L2) — Python Automation - [Python Async & Concurrency](../../../../library/topics/python-async-concurrency/index.md) (Topic Pack, L2) — Python Automation - Python Concepts Flashcards *(CLI)* (flashcard_deck, L0) — Python Automation - [Python Debugging](../../../../library/topics/python-debugging/index.md) (Topic Pack, L1) — Python Automation - [Python Drills](../../../../library/drills/python_drills.md) (Drill, L0) — Python Automation - Python Exercises (Quest Ladder) *(CLI)* (Exercise Set, L0) — Python Automation - Python Flashcards *(CLI)* (flashcard_deck, L1) — Python Automation - [Python Packaging](../../../../library/topics/python-packaging/index.md) (Topic Pack, L2) — Python Automation - [Python for Infrastructure](../../../../library/topics/python-infra/index.md) (Topic Pack, L1) — Python Automation