python interview questions and answers 2024

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Question 1.
What is the difference between list comprehension and generator expression in Python?
Answer.
List comprehension and generator expressions are both constructs in Python that allow you to create sequences from iterables, but they differ in how they construct and store those sequences.
List Comprehension:
List comprehension returns a list.
It constructs the entire list in memory before returning it.
It is denoted by square brackets `[]`.
Generator Expression:
Generator expression returns a generator object.
It generates values on-the-fly as they are needed.
It is denoted by parentheses `()`.
Here's a breakdown of the differences:
Memory Usage: List comprehension constructs the entire list in memory, which can consume a lot of memory for large sequences. Generator expression, on the other hand, yields values one at a time, using minimal memory.
1. Lazy Evaluation:
List comprehension evaluates the entire expression and stores the result in memory before returning the list. Generator expression generates values lazily, only when they are requested, which can be more memory-efficient.
2. Iterability:
Lists comprehensions are iterables. Once constructed, you can iterate over them multiple times. Generator expressions are iterators. Once exhausted (all values have been yielded), they cannot be reused.
In summary, use list comprehensions when you need to construct a list and plan to iterate over it multiple times or need random access to its elements. Use generator expressions when you want to conserve memory, especially for large datasets, or when you only need to iterate over the sequence once.
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Question 2.
Explain the GIL or (Global Interpreter Lock) in Python.
Answer.
The Global Interpreter Lock or (GIL) in Python is a mutex that ensures only one thread executes Python bytecode at a time, regardless of the number of cores in a CPU. This means that even in multi-threaded environments, only one thread can execute Python code while others are blocked. The GIL was introduced to simplify memory management and ensure thread safety by preventing conflicts in memory access.
Question 3.
What are decorators in Python?
Answer.
In Python, decorators are a powerful tool for dynamically modifying the behavior of functions or methods. They allow you to add functionality to existing code without modifying it. Decorators are defined using the `@decorator_name` syntax, placed before the function or method definition.
Question 4.
Explain the difference between shallow copy and deep copy in python.
Answer.
When working with objects in Python, particularly those containing nested structures, it's crucial to understand the concept of copying. There are two primary methods for creating copies of objects: shallow copy and deep copy. Each offers distinct advantages and considerations depending on the desired outcome.
1. Shallow Copy:
A shallow copy creates a new object, but instead of copying the elements themselves, it copies references to the original objects. This means that the new object references the same sub-objects (or elements) as the original object.
2. Deep Copy:
A deep copy, on the other hand, creates a completely new object with its own copy of the elements. It recursively copies all the sub-objects as well, so changes made to the elements in the deep copy will not affect the original object and vice versa.
Question 5.
What is the purpose of `__init__` method in Python classes?
Answer.
The `__init__` method in Python classes serves as a constructor. It's called automatically when a new instance of the class is created. The primary purpose of `__init__` is to initialize the instance's attributes and perform any other setup that's necessary for the object to function correctly.
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