Top 10 Python Interview Questions and Answers

As Python continues to be a popular programming language, preparing for an interview requires a solid understanding of its core concepts. Here are ten common Python interview questions along with detailed answers to help you get started.

1. What are Python’s built-in data types?

Python has several built-in data types, including:

• Numeric Types: int, float, complex
• Sequence Types: list, tuple, range
• Text Type: str
• Mapping Type: dict
• Set Types: set, frozenset
• Boolean Type: bool
• Binary Types: bytes, bytearray, memoryview

2. What is the difference between list and tuple?

• List: A mutable, ordered collection that allows duplicate elements. You can modify lists after creation (e.g., add or remove items).
• Tuple: An immutable, ordered collection that also allows duplicates. Once created, tuples cannot be changed.

3. What is a Python decorator?

A decorator is a function that modifies or enhances another function or method. Decorators are often used for logging, access control, or modifying behavior. They are applied using the @decorator_name syntax above the function definition.

4. Explain the concept of list comprehension in Python.

List comprehension is a concise way to create lists in Python. It consists of an expression followed by a for clause and optionally one or more if clauses. It provides a more readable and compact syntax for generating lists.

Example: squares = [x**2 for x in range(10)]

5. What is the difference between deepcopy and copy in Python?

• copy: Creates a shallow copy of an object. This means that if the original object contains references to other objects, the new object will reference the same objects as the original.
• deepcopy: Creates a deep copy of an object. This means that all nested objects are also copied, resulting in completely independent copies.

6. What are Python generators?

Generators are a special type of iterator that allow you to iterate over a sequence of values lazily. They are defined using a function that contains one or more yield statements. When called, the generator function returns a generator object that can be iterated over.

Example: def count_up_to(n): count = 1 while count <= n: yield count count += 1

7. What is the purpose of the with statement in Python?

The with statement is used for resource management and exception handling. It simplifies exception handling by encapsulating common preparation and cleanup tasks in context managers. For example, it is commonly used with file operations to ensure files are properly closed after their suite finishes.

Example: with open('file.txt', 'r') as file: data = file.read()

8. What is the difference between str and repr?

• str: Used to define a human-readable string representation of an object. It is called by the str() function and print().
• repr: Used to define an unambiguous string representation of an object, typically meant for debugging. It is called by the repr() function and should ideally return a string that could be used to recreate the object.

9. What are Python’s built-in exceptions?

Python has several built-in exceptions, including:

• ValueError: Raised when a function receives an argument of the right type but inappropriate value.
• TypeError: Raised when an operation or function is applied to an object of inappropriate type.
• IndexError: Raised when a sequence subscript is out of range.
• KeyError: Raised when a dictionary key is not found.

10. How do you handle exceptions in Python?

Exceptions in Python can be handled using try and except blocks. The code that might raise an exception is placed inside the try block, and the code that handles the exception is placed in the except block.

Example: try: result = 10 / 0 except ZeroDivisionError: print("You can't divide by zero!")