Interview Questions
Python uses an automatic memory management system known as garbage collection. It automatically manages the allocation and deallocation of memory for objects in the heap. Python keeps track of all objects and frees memory that is no longer in use (garbage collection) to make it available for future use.
Decorators are a powerful and flexible feature in Python used to modify the behavior of functions or classes. They allow you to wrap another function or class, adding functionality before or after the original code. They are denoted by @decorator_name and are often used to add logging, timing, access control, etc., without modifying the original function or class.
Dictionaries in Python are implemented using hash tables. They allow you to store key-value pairs where keys are unique within a dictionary. This enables fast lookups, insertions, and deletions of items.
Inheritance allows one class (subclass/child class) to inherit the attributes and methods of another class (superclass/parent class). It promotes code reuse and allows you to create a hierarchy of classes.
Iterators: Iterators are objects that implement the iterator protocol, which consists of the methods __iter__() and __next__(). They allow iteration over a sequence of elements.
Generators: Generators are a special type of iterator that simplifies the creation of iterators. They are created using the yield keyword and allow you to iterate over a set of items without constructing the entire sequence upfront, saving memory and improving performance.
These answers provide a solid foundation for understanding key concepts in Python interviews. Make sure to practice coding these examples to solidify your understanding and be well-prepared for your interview!
The Global Interpreter Lock (GIL) is a mutex (or lock) that allows only one thread to execute Python bytecode at a time. This means that even on multi-core systems, Python threads can't fully utilize multiple cores because threads are limited to run on a single core at any given time due to the GIL. However, it is important to note that the GIL only affects threads that are executing Python bytecode (e.g., running Python code). It does not prevent multi-threading in general, as non-Python code (e.g., I/O-bound tasks, certain libraries written in C) can still run concurrently.
Python's magic methods are special methods that start and end with double underscores (often called dunder methods). They allow you to emulate behavior for built-in operations on objects. Examples include __init__() for object initialization, __str__() for string representation of objects, __len__() for getting the length of objects, __getitem__() for indexing, and __add__() for addition.
Both __str__() and __repr__() are methods used to provide string representations of objects in Python, but they serve different purposes:
__str__() is used to find the "informal" or readable string representation of an object. It is intended to provide a user-friendly output.
__repr__() is used to find the "official" or unambiguous string representation of an object. It should ideally be a valid Python expression that can recreate the object when evaluated.
Both __getattr__() and __getattribute__() are methods in Python used to access attributes of an object, but they differ in their behavior:
__getattr__(self, name): This method is called when trying to access an attribute that does not exist in an object. It allows you to define behavior for attribute access that isn't directly present.
