Question 1

What is the difference between a list and a tuple in Python?

Accepted Answer

Lists and tuples are both sequence types in Python, but lists are mutable (can be changed after creation) while tuples are immutable (cannot be modified). Lists use square brackets and are better for collections that might change, while tuples use parentheses and are more memory-efficient. Tuples can be used as dictionary keys because they're immutable, but lists cannot.

Question 2

How does memory management work in Python?

Accepted Answer

Python uses automatic memory management with reference counting as the primary mechanism. Each object has a reference count that increases when referenced and decreases when dereferenced. When the count reaches zero, the object is deallocated. Python also has a garbage collector to handle cyclic references and memory pooling for small objects. The memory manager handles all memory allocation and deallocation automatically.

Question 3

What are decorators in Python?

Accepted Answer

Decorators in Python are functions that modify the behavior of other functions or methods. They take a function as input, add functionality to it, and return a new function. Decorators are applied using the @ symbol above function definitions and are commonly used for logging, timing, authentication, caching, and other cross-cutting concerns. They provide a clean way to extend functionality without modifying the original function.

Question 4

Explain list comprehensions and their benefits.

Accepted Answer

List comprehensions are a concise way to create lists in Python by combining a for loop, optional filtering, and element creation into a single line. They are more readable and often faster than equivalent for loops, and they follow a functional programming style. Python also supports dictionary and set comprehensions, as well as generator expressions for lazy evaluation. List comprehensions are particularly useful for transforming and filtering data.

Question 5

What are the differences between __init__ and __new__ methods in Python?

Accepted Answer

The __new__ and __init__ methods are both involved in object creation in Python, but serve different purposes. __new__ is a static method that creates and returns a new instance, while __init__ is an instance method that initializes the instance after it's created. __new__ is called first and must return an instance, while __init__ is called after and returns None. __new__ is rarely overridden except for special cases like implementing singletons or immutable types.

Question 6

How do you handle exceptions in Python?

Accepted Answer

Python handles exceptions using try-except-else-finally blocks. The try block contains code that might raise an exception, while except blocks catch and handle specific exceptions. The else block runs if no exceptions occur, and the finally block always executes, making it ideal for cleanup operations. Python also supports custom exceptions and context managers (with statements) for cleaner resource management. Best practices include catching specific exceptions and keeping try blocks small.

Question 7

What are Python generators and how do they differ from regular functions?

Accepted Answer

Generators in Python are special functions that use the yield keyword to produce a sequence of values one at a time, rather than returning all values at once. They maintain their state between calls and are memory-efficient for large datasets since they generate values on-demand. Generators differ from regular functions in that they can yield multiple values over time and remember their state between calls. They're particularly useful for processing large datasets or creating data pipelines.

Question 8

How does Python's Global Interpreter Lock (GIL) affect multi-threaded programs?

Accepted Answer

The Global Interpreter Lock (GIL) in Python is a mutex that allows only one thread to execute Python bytecode at a time. This means that for CPU-bound tasks, Python's threading doesn't provide true parallel execution. However, the GIL is released during I/O operations, making threading effective for I/O-bound tasks. For CPU-bound tasks, alternatives like multiprocessing (using multiple processes) or async I/O are often better choices.

Question 9

Explain the difference between shallow copy and deep copy in Python.

Accepted Answer

In Python, a shallow copy creates a new object but references the same nested objects as the original, while a deep copy creates a completely independent clone with copies of all nested objects. Changes to nested objects in a shallow copy affect the original, but changes to nested objects in a deep copy don't. Shallow copies are created using copy.copy() or object-specific copy methods, while deep copies use copy.deepcopy().

Question 10

What are Python metaclasses and when would you use them?

Accepted Answer

Metaclasses in Python are classes that create other classes. They allow you to customize class creation and behavior. The default metaclass is 'type', but you can create custom metaclasses to modify how classes are created. Common uses include class registration, interface enforcement, and attribute modification. Metaclasses are an advanced feature typically used in framework development, like Django's ORM, and should be used sparingly in everyday programming.

Python Interview Questions (Updated 2025)

Ready to practice Python interview questions?

Table of Contents

Top Python Interview Questions

1. What is the difference between a list and a tuple in Python?

2. How does memory management work in Python?

3. What are decorators in Python?

4. Explain list comprehensions and their benefits.

5. What are the differences between init and new methods in Python?

6. How do you handle exceptions in Python?

7. What are Python generators and how do they differ from regular functions?

8. How does Python's Global Interpreter Lock (GIL) affect multi-threaded programs?

9. Explain the difference between shallow copy and deep copy in Python.

10. What are Python metaclasses and when would you use them?

Want to test your Python knowledge in a real interview?

Related Interview Questions