Top Basic Python Interview Questions and Answers for Freshers – Easy to Reply 2025

Below are some basic Python interview questions along with their answers that are suitable for freshers. These questions cover fundamental concepts and are easy to understand.

1. What is Python?

• Answer: Python is a high-level, interpreted programming language known for its simplicity and readability. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming. Python is widely used for web development, data analysis, artificial intelligence, scientific computing, and more.

2. What are the key features of Python?

• Answer:
  • Easy to Learn and Use: Python has a simple syntax that is easy to understand, making it ideal for beginners.
  • Interpreted Language: Python code is executed line by line, which makes debugging easier.
  • Dynamically Typed: You don’t need to declare the type of variable; it is determined at runtime.
  • Extensive Libraries: Python has a rich set of libraries and frameworks for various applications.
  • Platform Independent: Python code can run on different platforms like Windows, macOS, Linux, etc., without modification.

3. What are Python Indentations?

• Answer: Indentation in Python refers to the spaces or tabs used at the beginning of a line of code to define the structure of the code. Unlike other languages that use braces {} to define blocks of code, Python uses indentation to indicate the scope of loops, functions, classes, and conditionals. Proper indentation is crucial in Python as it affects the logic and flow of the program.

   if 5 > 2:
       print("Five is greater than two!")  # This is indented

4. What are Python Lists?

• Answer: A list in Python is a mutable, ordered collection of items. Lists are defined by enclosing items in square brackets [], separated by commas. Lists can contain items of different data types, and you can add, remove, or change items after the list is created.

   my_list = [1, 2, 3, "apple", "banana"]

5. What is the difference between append() and extend() methods in Python lists?

• Answer:
  • append(): Adds a single element to the end of the list.
  • extend(): Adds multiple elements (from an iterable) to the end of the list.

   list1 = [1, 2, 3]
   list1.append(4)        # list1 becomes [1, 2, 3, 4]
   list1.extend([5, 6])   # list1 becomes [1, 2, 3, 4, 5, 6]

6. What is a Python Dictionary?

• Answer: A dictionary in Python is an unordered collection of key-value pairs. Dictionaries are defined by enclosing key-value pairs in curly braces {}, with keys and values separated by a colon :. Keys must be unique and immutable (e.g., strings, numbers), while values can be of any data type.

   my_dict = {"name": "Alice", "age": 25}

7. What is the difference between == and is in Python?

• Answer:
  • ==: Compares the values of two objects to check if they are equal.
  • is: Checks if two objects refer to the same memory location (i.e., if they are the same object).

   a = [1, 2, 3]
   b = [1, 2, 3]
   print(a == b)  # True, because the values are the same
   print(a is b)  # False, because they are different objects in memory

8. What are Python Tuples?

• Answer: A tuple in Python is an immutable, ordered collection of items. Tuples are defined by enclosing items in parentheses (), separated by commas. Once a tuple is created, you cannot change its elements.

   my_tuple = (1, 2, 3, "apple")

9. What is the difference between a list and a tuple?

• Answer:
  • List: Mutable (can be modified after creation), defined with square brackets [].
  • Tuple: Immutable (cannot be modified after creation), defined with parentheses ().

   list_example = [1, 2, 3]
   tuple_example = (1, 2, 3)

10. What is a lambda function in Python?

• Answer: A lambda function is a small, anonymous function defined using the lambda keyword. It can have any number of arguments but only one expression. Lambda functions are often used for short, throwaway functions.

   add = lambda x, y: x + y
   print(add(5, 3))  # Output: 8

11. What is the purpose of the __init__ method in Python?

• Answer: The __init__ method is a special method in Python classes that is automatically called when a new instance of the class is created. It is used to initialize the object’s attributes.

   class Person:
       def __init__(self, name, age):
           self.name = name
           self.age = age

   person = Person("Alice", 25)

12. What is the difference between break and continue in Python?

• Answer:
  • break: Exits the loop immediately, stopping further iterations.
  • continue: Skips the rest of the code inside the loop for the current iteration and moves to the next iteration.

   for i in range(5):
       if i == 3:
           break  # Loop stops when i is 3
       print(i)

   for i in range(5):
       if i == 3:
           continue  # Skips the print statement when i is 3
       print(i)

13. What is a Python module?

• Answer: A module in Python is a file containing Python code, such as functions, classes, or variables. Modules help in organizing code by grouping related functionality together. You can import a module using the import statement.

   import math
   print(math.sqrt(16))  # Output: 4.0

14. What is the difference between deepcopy and shallow copy?

• Answer:
  • Shallow Copy: Creates a new object but inserts references to the original objects. Changes to mutable objects inside the copy will affect the original.
  • Deep Copy: Creates a new object and recursively copies all objects found in the original. Changes to the copy do not affect the original.

   import copy
   list1 = [1, [2, 3]]
   list2 = copy.copy(list1)       # Shallow copy
   list3 = copy.deepcopy(list1)   # Deep copy

15. What is the purpose of the if __name__ == "__main__": statement?

• Answer: This statement is used to check whether the Python script is being run as the main program or being imported as a module. If the script is run directly, the code inside this block will execute. If the script is imported as a module, the code inside this block will not run.

   if __name__ == "__main__":
       print("This script is being run directly")

Certainly! Here are 10 more basic Python interview questions with easy-to-understand answers for freshers:

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16. What is the difference between range() and xrange() in Python?

• Answer:
  • range(): Returns a list of numbers in Python 2 and a sequence object in Python 3. It generates all numbers at once and consumes more memory.
  • xrange(): (Python 2 only) Returns a generator-like object that produces numbers on-the-fly, making it more memory-efficient for large ranges.
  • In Python 3, xrange() is removed, and range() behaves like xrange() in Python 2.

   # Python 3
   for i in range(5):
       print(i)  # Output: 0, 1, 2, 3, 4

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17. What is a Python Set?

• Answer: A set is an unordered collection of unique elements. Sets are defined using curly braces {} or the set() function. They do not allow duplicate values and are useful for operations like union, intersection, and difference.

   my_set = {1, 2, 3, 3, 4}
   print(my_set)  # Output: {1, 2, 3, 4}

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18. What is the difference between remove() and discard() in Python sets?

• Answer:
  • remove(): Removes a specified element from the set. If the element does not exist, it raises a KeyError.
  • discard(): Removes a specified element from the set if it exists. If the element does not exist, it does nothing (no error).

   my_set = {1, 2, 3}
   my_set.remove(2)  # Removes 2
   my_set.discard(4)  # Does nothing

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19. What is the purpose of the pass statement in Python?

• Answer: The pass statement is a placeholder in Python that does nothing. It is used when a statement is syntactically required but no action is needed. It is often used in empty functions, classes, or loops.

   def my_function():
       pass  # Placeholder for future code

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20. What is the difference between *args and **kwargs in Python?

• Answer:
  • *args: Allows a function to accept a variable number of positional arguments as a tuple.
  • **kwargs: Allows a function to accept a variable number of keyword arguments as a dictionary.

   def my_function(*args, **kwargs):
       print(args)  # Output: (1, 2, 3)
       print(kwargs)  # Output: {'a': 4, 'b': 5}

   my_function(1, 2, 3, a=4, b=5)

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21. What is a Python Generator?

• Answer: A generator is a special type of iterator that generates values on-the-fly using the yield keyword. It does not store all values in memory at once, making it memory-efficient for large datasets.

   def my_generator():
       yield 1
       yield 2
       yield 3

   for value in my_generator():
       print(value)  # Output: 1, 2, 3

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22. What is the difference between sorted() and sort() in Python?

• Answer:
  • sorted(): Returns a new sorted list from the elements of any iterable. It does not modify the original iterable.
  • sort(): Sorts the list in-place (modifies the original list) and returns None.

   my_list = [3, 1, 2]
   sorted_list = sorted(my_list)  # Returns a new sorted list
   my_list.sort()  # Sorts the original list

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

• Answer: The with statement is used for resource management, such as file handling. It ensures that resources are properly released after use, even if an exception occurs. It is commonly used with file operations.

   with open("file.txt", "r") as file:
       content = file.read()
   # File is automatically closed after the block

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24. What is the difference between isinstance() and type() in Python?

• Answer:
  • isinstance(): Checks if an object is an instance of a specific class or a tuple of classes. It also considers inheritance.
  • type(): Returns the exact type of the object. It does not consider inheritance.

   class Parent:
       pass

   class Child(Parent):
       pass

   obj = Child()
   print(isinstance(obj, Parent))  # True
   print(type(obj) == Parent)      # False

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25. What is the purpose of the zip() function in Python?

• Answer: The zip() function combines multiple iterables (e.g., lists, tuples) into a single iterable of tuples. It pairs elements from each iterable based on their positions.

   list1 = [1, 2, 3]
   list2 = ["a", "b", "c"]
   zipped = zip(list1, list2)
   print(list(zipped))  # Output: [(1, 'a'), (2, 'b'), (3, 'c')]

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26. What is the difference between str() and repr() in Python?

• Answer:
  • str(): Returns a user-friendly string representation of an object, meant for readability.
  • repr(): Returns a more detailed and unambiguous string representation of an object, often used for debugging.

   x = "Hello"
   print(str(x))   # Output: Hello
   print(repr(x))  # Output: 'Hello'

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27. What is the purpose of the enumerate() function in Python?

• Answer: The enumerate() function adds a counter to an iterable and returns it as an enumerate object. It is useful for getting both the index and value of elements in a loop.

   my_list = ["apple", "banana", "cherry"]
   for index, value in enumerate(my_list):
       print(index, value)
   # Output:
   # 0 apple
   # 1 banana
   # 2 cherry

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These questions and answers should give freshers a solid foundation for Python interviews.

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1. Introduction to Python: What is Python?
2. Key Features That Make Python Stand Out
3. Understanding Python Indentation: Why It Matters
4. Python Lists: A Beginner’s Guide
5. append() vs extend(): What’s the Difference?
6. Python Dictionaries: Storing Data as Key-Value Pairs
7. == vs is: Understanding the Difference
8. Python Tuples: Immutable and Ordered Collections
9. Lists vs Tuples: When to Use Which?
10. Lambda Functions: Simplifying Code with Anonymous Functions
11. The __init__ Method: Initializing Objects in Python
12. break vs continue: Controlling Loop Execution
13. Python Modules: Organizing Code for Reusability
14. deepcopy vs shallow copy: Copying Objects in Python
15. if __name__ == "__main__": Explained
16. range() vs xrange(): Iterating Efficiently in Python
17. Python Sets: Unordered Collections of Unique Elements
18. remove() vs discard(): Managing Elements in Sets
19. The pass Statement: A Placeholder in Python
20. *args and **kwargs: Handling Variable-Length Arguments
21. Python Generators: Efficient Iteration with yield
22. sorted() vs sort(): Sorting Data in Python
23. The with Statement: Simplifying Resource Management
24. isinstance() vs type(): Checking Object Types
25. The zip() Function: Combining Iterables in Python
26. str() vs repr(): String Representations in Python
27. The enumerate() Function: Looping with Index and Value

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