Top 60 Python Interview Questions and Answers [2024]

Freshers-level Python Interview Questions

Here are some essential Python interview questions for freshers. These questions cover fundamental concepts across various Python technologies, helping you build a solid foundation in the language. By preparing for these questions, you can enhance your understanding and effectively showcase your Python skills during interviews.

1. What Is Python?

Python is a widely-used, high-level programming language known for its simplicity and readability. Created by Guido van Rossum in 1991, Python emphasizes code readability and enables developers to express concepts in fewer lines of code. It is used in various domains, such as system scripting, web development, game development, software development, and complex mathematics.

2. What Are Some Popular Applications of Python in the World of Technology?

This is one of the commonly asked Python interview questions. Python is utilized in several areas, including:

• System Scripting: Automating tasks and scripts in operating systems.
• Web Development: Python web frameworks like Django, Flask, and Pyramid enable robust web app development.
• Game Development: Creating web-based and stand-alone games.
• Software Development: Used to build desktop applications and software tools.
• Complex Mathematics: Python libraries like NumPy and SciPy are popular for scientific computing.

3. What Are the Benefits of Using Python as a Programming Language?

Python offers several advantages:

• Object-oriented and high-level programming language.
• Dynamically typed and extensive support through libraries.
• Open-source with a large community for constant development.
• Portability across different operating systems.
• Highly interactive and easy to learn for beginners.

4. Is Python a Compiled or an Interpreted Language?

Python is both partially compiled and partially interpreted. The code is first compiled into bytecode, which is then interpreted by the Python Virtual Machine (PVM) based on the underlying platform.

5. What Does the ‘#’ Symbol Do in Python?

The # symbol is used for adding comments in Python. Everything written after the # on a line is ignored by the interpreter, allowing developers to include explanations or notes within the code. This feature helps ensure code readability and maintainability.

6. How Are Arguments Passed in Python—by Value or by Reference?

In Python, arguments are passed by object reference, meaning the function receives a reference to the object itself. However, the behavior varies:

• Mutable objects: Can be modified inside the function.
• Immutable objects: Cannot be modified inside the function.

7. What Is the Difference Between a Set and a Dictionary in Python?

Below is the difference between a set and a dictionary in Python:

• A set is an unordered collection of unique elements.
• A dictionary is an unordered collection of key-value pairs, where each key is unique.

This level of basic question is often asked in many of the Python interview questions.

8. What Is List Comprehension in Python?

List comprehension is a concise way to create lists. It allows you to generate a new list by applying an expression to each element in an existing iterable. Example:

my_list = [i for i in range(1, 10)]

Mastering concepts like list comprehension is essential for Python developers as it is often asked in most of the Python interview questions.

9. What Does the Pass Statement Do in Python?

The pass statement is a placeholder used to indicate that no action is to be performed. It's often used in empty function definitions or loops.

10. What Is the Difference Between / And // Operators in Python?

This difference is important as it is often highlighted in most of the fresher-level Python interview questions.

• The / operator performs floating-point division and returns a float.
• The // operator performs floor division and returns the largest integer less than or equal to the result.

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11. How is Exception Handling Done in Python?

Python uses the try, except, and finally blocks for exception handling:

• try block contains the code that might raise an exception.
• except block handles the exception if one occurs.
• finally block runs after the try block, regardless of whether an exception was raised.

Having a basic understanding of handling exceptions is important for freshers and it’s often asked in most of the Python interview questions.

12. What Is the Swapcase Function in Python?

The swapcase() method returns a copy of the string where all uppercase letters are converted to lowercase and vice versa. Example:

string = "WOw"

print(string.swapcase())  # Output: "wOW”

13. What Is the Difference Between a for Loop and a while Loop in Python?

For and while are the two most commonly used loops in Python programming language, and its difference is often asked in most of the Python interview questions.

• A for loop is used to iterate over a sequence (like a list, tuple, or string) and is typically used when the number of iterations is known.
• A while loop repeats as long as a condition is true and is used when the number of iterations is not predefined.

14. Can We Pass a Function as an Argument in Python?

Yes, in Python, functions are first-class objects, meaning you can pass functions as arguments to other functions.

15. What Are the Differences Between Break, Continue, and Pass in Python?

Understanding these control flow statements is important for any fresher starting with Python, and it’s often highlighted in most of the Python interview questions.

• break: Terminates the loop entirely and exits.
• continue: Skips the current iteration and moves to the next one.
• pass: A placeholder used to indicate an empty block of code, doing nothing.

16. What Are Python’s Built-In Data Types?

These data types are important for any fresher starting with Python and it’s often highlighted in most of the Python interview questions.

• Numeric: int, float, complex.
• Sequence types: string, list, tuple, range.
• Mapping types: dictionary.
• Set types: set, frozenset.
• Boolean: bool.
• Binary types: bytes, bytearray.

17. How Do You Floor a Number in Python?

Python’s math module provides a floor() function that returns the largest integer less than or equal to a given number. Example:

import math print(math.floor(3.7)) # Output: 3

18. What Is the Purpose of the __init__() Function in Python?

In Python, the __init__() function is a special method that is automatically called when an object of a class is created. This method is commonly featured in many Python interview questions. It is used to initialize the attributes of the object or perform any setup tasks needed during the object's creation. By using __init__(), you can assign initial values to properties or execute other necessary operations at the time of instantiation.

19. What Is the Difference Between a List and a Tuple in Python?

The key difference between a list and a tuple is that lists are mutable, meaning their contents can be changed, whereas tuples are immutable and cannot be modified after creation. Lists are defined using square brackets [], while tuples use parentheses ()

Due to their immutability, tuples are often faster than lists in certain operations like indexing, slicing, and iteration. This is an important distinction to keep as a fresher, and it's often highlighted in most of the Python interview questions.

20. What Is the Difference Between While and for Loops in Python?

The while and for loops are both used to iterate over sequences of values in Python, but they differ in structure and usage. A while loop repeatedly executes a block of code as long as a specified condition is true. For example, the following while loop prints numbers from 0 to 9:

num = 0

while num < 10:
    print(num)
    num +=1

In this example, the loop checks if num is less than 10, printing the value and incrementing it by 1 until the condition becomes false.

On the other hand, a for loop iterates directly over a sequence, such as a list, tuple, or string. For example, you can iterate through a list of testing frameworks and print each framework:

testing_frameworks = ["Selenium", "Playwright", "Behave", "Pytest"]
for framework in testing_frameworks:
    print(framework)

In summary, while loops are condition-based, whereas for loops are typically used to iterate over predefined sequences or ranges, making them suitable for different programming scenarios. This is a common topic in Python interview questions.

21. Write a Function That Checks if a Number Is Prime or Not

Here’s a function that checks if a number is prime or not. This is a common question in many Python interview questions to test your understanding of loops and conditions.

def is_prime(num):
    # 0 and 1 are not prime numbers
    if num < 2:
        return False
    # 2 is prime
    if num == 2:
        return True

    # check if num is divisible by any number from 2 to num-1
    for i in range(2, num):
        if num % i == 0:
            return False
        # if num is not divisible by any number from 2 to num-1, then it is prime
        else:
            return True

print(is_prime(8))  # Returns False
print(is_prime(131))  # Returns True

Explanation:

• Prime numbers: A prime number is only divisible by 1 and itself, and it must be greater than 1.
• The function first checks if the number is less than 2 (not prime) or equal to 2 (which is prime).
• The loop runs from 2 to sqrt(num) + 1 to efficiently check for divisors.
• If no divisor is found, the function returns True, indicating that the number is prime.

22. Write a Program to Reverse a String Using Slicing

Here’s a program to reverse a string using slicing. This is a common question in many Python interview questions to test your understanding of string manipulation and slicing.

def reverse_str(s):
    return s[::-1]

print(reverse_str("Pytest"))
print(reverse_str("Playwright"))
print(reverse_str("Selenium testing with Python "))

Explanation:

You can reverse a string in Python using slicing with the syntax [::-1]. Here's a breakdown:

• The first colon: represents the start of the slice, and since there's no index before it, it defaults to the beginning of the string.
• The second colon: represents the end of the slice, and since no index is specified, it defaults to the end of the string.
• The -1 means the slice moves backward, essentially reversing the string by starting at the end and moving towards the beginning.

This method efficiently reverses the string and is often asked in Python interview questions to assess your understanding of Python's powerful slicing capabilities.

23. What Is the Difference Between ‘is’ and ‘==’ in Python?

Both operators are used to compare objects, but they serve different purposes and are often explored in Python interview questions to test your understanding of object comparison.

The == operator checks if the values of two objects are equal. It returns True if the values are equal and False if they are different. For example:

x = [1,2,3,4]
y = [1,2,3,4]
print(x == y) # Returns True

The is operator checks whether two objects point to the same memory location, i.e., whether they are the same object. In this case, the comparison will return False because although x and y have the same content, they are different objects in memory.

x = [1,2,3,4]
y = [1,2,3,4]
print(x is y) # Returns False

For comparing values, you should generally use the equality operator == or !=, unless you're comparing to None, in which case is is preferred.

24. How Do You Handle Errors and Exceptions in Python?

Handling errors and exceptions in Python is an essential topic often covered in Python interview questions. Python uses try-except blocks to catch and handle errors that occur during runtime. The try block tests a section of code for potential errors, and the except block contains the code to handle those errors. Additionally, the else block executes code if no errors occur, while the finally block always runs, regardless of whether an exception was raised or not.

The general syntax for handling errors is:

try:
    # code that might raise an exception
except TypeError:
    # code to handle TypeError
else:
    # code to execute if no exception is raised
finally:
    # code to execute whether an exception is raised or not

Here’s an example to demonstrate how to handle an exception:

def divide_by(num1, num2):
    try:
        result = num1 / num2
    except ZeroDivisionError:
        print("Error: Cannot divide a number by zero.")
    else:
        return result
    finally:
        print("Division complete.")

print(divide_by(10, 5)) # prints 2.0 and "Division complete."
print(divide_by(10, 0)) # prints "Error: Cannot divide a number by zero." and "Division complete."

In this example, the divide_by function attempts to divide two numbers, num1 and num2. If num2 is 0, a ZeroDivisionError is raised, and the except block handles this by printing an error message. If no exception occurs, the else block executes and returns the result.

Finally, the finally block ensures that a message indicating the completion of the division is printed, regardless of whether an exception occurred.

25. How To Open and Read a File in Python?

It is the most commonly asked Python interview question about how to open and read a file in Python. The process involves using the built-in open() function, which is used to open files in various modes, such as read mode ("r"). Here's a simple example of how to open a file and read its contents:

# Open the file in read mode
file = open("file.txt", "r")

# Read the contents of the file
contents = file.read()

# Close the file
file.close()

# Print the contents of the file
print(contents)

After opening the file with the open() function, we use the read() method to retrieve the content and store it in a variable. Once done, it's important to close the file using close(), which doesn't require any parameters.

f = open(r"C:\myfilesile.txt", "r")

Additionally, you can read the entire file line by line by iterating through the file:

file = open("file.txt", "r")

for line in file:
    print(line)

file.close()

26. What Is the Difference Between a Module and a Package in Python?

Understanding the difference between a module and a package is crucial, as it is often asked in most of the Python interview questions.

A module is a Python file with a .py extension that contains a collection of functions, classes, or global variables. Modules are used to organize code into reusable components.

For instance, you can create a module named division.py:

Example of a Module:

def divide_by_2(num):
    result = num / 2
    print(result)

To use the module, create another file, module_test.py, and import it:

import division

division.divide_by_2(10) # output will be 5.0

A package, on the other hand, is a directory that contains multiple modules and an __init__.py file. The __init__.py file signals to Python that the directory should be treated as a package. Packages allow better structuring of related modules under a single namespace.

For example, the unittest package is commonly used for writing and executing unit tests:

import unittest

While a module represents a single Python file, a package is a collection of modules organized in a directory structure. Understanding their distinct roles in organizing and reusing Python code is a frequent topic in Python interviews.

27. What Is a Dictionary in Python and How Can You Access the Elements of a Dictionary?

A dictionary in Python is a collection of data organized as key-value pairs. It is ordered (starting from Python 3.7), modifiable, and does not allow duplicate keys. Dictionaries are defined using curly braces , with each key-value pair separated by a colon :.

Here is an example of a dictionary of Python testing frameworks with their name and definitions as key-value pairs.

frameworks_dict = {
    "unittest": "standard testing framework for unit tests",
    "pytest": "popular Python testing framework ",
    "Behave": "Python testing framework for Behavior Driven Development",
    "lettuce": "executes automated Python tests using plain-text descriptions"
}

To access a value in the dictionary, use its corresponding key inside square brackets [ ]. For example, to access the definition of pytest:

print(frameworks_dict["pytest"])

A new value can be assigned to an existing key using the syntax below.

frameworks_dict["lettuce"] = "similar framework to Behave"

To add a new key-value pair to the dictionary:

To add a new key-value pair to the dictionary:

Dictionaries are a versatile and commonly used data structure in Python, particularly in scenarios where data needs to be organized for fast retrieval using keys.

28. Write a Function That Returns the Minimum and Maximum Values in a List

Below is the Python function that returns the minimum and maximum values from a list:

def find_min_max(lst):
    # assign the min and max variables with the first element of the list.
    min_elem = lst[0]
    max_elem = lst[0]

    # loop through the list, updating min and max variables where necessary
    for elem in lst:
        if elem < min_elem:
            min_elem = elem
        if elem > max_elem:
            max_elem = elem

    # return the min and max elements
    return min_elem, max_elem

# example
lst = [23, 52, 11, 91, 102, 46, 8, 77]
min_elem, max_elem = find_min_max(lst)
print("Minimum element:", min_elem) # output is 8
print("Maximum element:", max_elem) # output is 102

Explanation:

• Initialization: The function starts by assigning the first element of the list to both min_elem and max_elem.
• Iteration: The function iterates through the list. For each element:
• If the element is smaller than min_elem, it updates min_elem.
• If the element is larger than max_elem, it updates max_elem.
• Return Values: Once the loop completes, the function returns the smallest (min_elem) and largest (max_elem) values in the list.

This approach ensures that the list is scanned in one pass, making it efficient with a time complexity of O(n).

29. Write a Function That Removes Duplicates From a List

Here is the Python function using the set data structure to remove duplicate values from a list:

def remove_duplicates(lst):
    # create an empty set to store unique elements
    # sets only store unique elements, so duplicates will be automatically removed
    new_set = set()

    # loop through the list, adding each element to the set
      for item in lst:
        new_set.add(item)

    # convert the set back to a list
    return list(new_set)

# For example
lst = [1, 2, 3, 2, 1, 4, 5, 3, 6]
new_lst = remove_duplicates(lst)
print("Original list:", lst)
print("List with duplicates removed:", new_lst) 

Explanation:

• Using a Set:
• Sets automatically eliminate duplicates because they store unique values.
• The function initializes an empty set unique_set to collect unique elements from the input list.
• Iteration:
• The function loops through the input list lst, adding each element to the set using the add() method.
• Conversion:
• After all elements are added to the set, it is converted back into a list using list().

The Python interview questions discussed above are fundamental and essential for any fresher to understand, as they form the foundation for mastering Python programming concepts and practices. Developing a strong grasp of these basics is vital for building a solid skill set and excelling in interviews.

As you advance, you will come across intermediate-level Python interview questions that delve deeper into the language's features and applications. Tackling these will broaden your knowledge, improve your problem-solving skills, and prepare you to handle more complex challenges, enhancing your expertise in Python programming.

Intermediate-Level Python Interview Questions

These Python interview questions cover advanced topics and are ideal for candidates with experience in Python programming. They are designed to evaluate your ability to solve complex problems, optimize code, and apply Python effectively in various scenarios, helping you further enhance your expertise in the language.

30. What Is the Difference Between Append() and Extend() in Python?

The append() and extend() methods are essential Python list operations and are commonly featured in Python interview questions. Both are used to add elements to a list, but they differ in how they handle the addition.

The append() method is used to add a single element to the end of a list. It takes the element as its argument and treats it as a single entity, even if it's an iterable. For example:

lst = [1,2,3,4]
lst.append(5)
print(lst)

If you append an iterable like a list, the entire iterable is added as one element:

lst.append([6, 7])
print(lst)  # Output: [1, 2, 3, 4, 5, [6, 7]]

On the other hand, the extend() method is used to add multiple elements to the end of a list. It takes an iterable as its argument and adds each element of the iterable to the list individually. For example:

frameworks = ["Selenium", "Playwright"]
more_frameworks = ["Jest", "Appium", "Cypress"]
frameworks.extend(more_frameworks)
print(frameworks)

Unlike append(), using extend() with an iterable like a list unpacks its elements and adds them individually.

Understanding the differences between these methods is crucial for solving real-world scenarios efficiently, and questions about them are often included in Python interview questions to assess your grasp of list operations and optimization techniques.

31. What Is a Namespace in Python?

A namespace in Python is a fundamental concept often discussed in Python interview questions. It refers to a mapping of unique names to objects. You can think of a namespace as a dictionary where the keys are names (such as variables, methods, or classes) and the values are the actual objects. This structure allows Python to organize and access objects efficiently, avoiding naming conflicts.

There are four types of namespaces in Python:

• Built-in namespaces: Contain names of built-in functions and types like print() or int.
• Global namespaces: Include names defined at the top level of a Python program or module.
• Enclosing namespaces: Created for nested functions, encompassing the outer function's variables.
• Local namespaces: Created when a function is invoked, containing function parameters and local variables.

Local namespaces are temporary and exist only during the execution of a function. Once the function returns, its namespace is destroyed. In contrast, global namespaces persist until the Python interpreter terminates.

Below is an example illustrating how namespaces work:

# global_var is in the global namespace
global_var = 5

def local_function():
    # local_var is in the local namespace
    local_var = 10

    def inner_function():
        # inner_var is in the nested local namespace
        inner_var = 15
        print("Inner variable: ", inner_var)

    print("Local variable: ", local_var)
    inner_function()

# print the value of the global variable
print("Global variable: ", global_var)

# call the local function and print local and nested local variables
local_function()

In this example:

• global_var belongs to the global namespace with a value of 5.
• Inside local_function, local_var is part of the local namespace.
• The nested inner_function introduces a separate local namespace containing inner_var.

When the script executes, Python first looks for global_var in the global namespace and prints its value. Next, local_function is called, where Python searches its local namespace to find and print local_var. Finally, inner_function is invoked to print inner_var from its own local namespace.

Understanding namespaces is crucial for managing variable scope, avoiding naming collisions, and organizing code effectively. Questions about namespaces are a common part of Python interview questions to evaluate your grasp of scoping and memory management.

32. How Do Continue, Break, and Pass Work in Python?

In Python, continue, break, and pass are control flow statements that manage how loops and conditional statements execute. These keywords are often included in Python interview questions to assess the understanding of flow control in loops and conditionals. Here's how they work:

Continue:

The continue statement skips the remainder of the current loop iteration and proceeds to the next iteration. It is commonly used to skip specific conditions during a loop.

# loop from 1 to 5
for i in range(1, 6):
    # If i is equal to 3, continue to the next iteration without printing
    if i == 3:
        continue
    else:
        # print value of i
        print(i)

In this example, when i == 3, the continue statement skips the print statement for that iteration, and the loop proceeds with the next number.

Break:

The break statement exits the loop immediately, regardless of the loop's condition. If used in a nested loop, it only terminates the innermost loop.

s = 'LambdaTest'
# Using for loop
for letter in s:
    print(letter)
    # break the loop as soon it sees 'b'
    if letter == 'b':
        break
print("Exits the for loop")

# Using while loop
i = 0
while True:
    print(s[i])
    # break the loop as soon it sees 'b'
    if s[i] == 'b':
        break
    i += 1
print("Exits of while loop")

Pass:

The pass statement acts as a placeholder where a statement is syntactically required but no operation is performed. It is useful for creating empty functions, classes, or loops during code development.

# PASS STATEMENT
s = "LambdaTest"

# Empty loop
for i in s:
    pass
 
# Empty function
def function():
    pass
 
# No error will be raised
function()
 
# Pass statement
for i in s:
    if i == 'b':
        pass
        print('Nothing happens. Pass executed')

The pass statement ensures the code doesn't raise errors in scenarios where actual logic isn't yet implemented.

33. Write a Program That Finds the Least Common Multiple of Two Given Numbers

def calculate_lcm(num1, num2):  
    # selecting the greater number  
    if num1 > num2:  
        max_num = num1  
    else:  
        max_num = num2  
    while(True):  
        if max_num % num1 == 0 and max_num % num2 == 0:  
            lcm = max_num
            break  
        max_num += 1  
    return lcm    
 
# taking input from users  
num1 = int(input("Enter first number: "))  
num2 = int(input("Enter second number: "))  
# printing the result for the users  
print("The L.C.M. of ", num1," and ", num2, " is ", calculate_lcm(num1, num2))

• Identifying the Starting Point: The function starts with the larger of the two numbers (max(num1, num2)) since the LCM cannot be smaller than either number.
• Iterative Check: The while loop continuously checks if the current max_num is divisible by both num1 and num2 using the modulus operator (%). If true, this value is the LCM.
• Breaking the Loop: Once the LCM is found, it is stored in the lcm variable, and the break statement exits the loop.
• Returning the Result: The LCM is returned, and the program prints it using a formatted string for clarity.

34. Write a Python Function That Counts the Number of Vowels in a String

Below is an example of a function to count the number of vowels in a string:

def count_vowels(string):
    # Define a set of vowels
    vowels = set("aeiouAEIOU")
    # Initialize a counter for vowels
    vowel_count = 0
    # Iterate over each character in the string
    for char in string:
        # Check if the character is a vowel
        if char in vowels:
            # If char is a vowel, increment the vowel count
            vowel_count += 1
    # Return the total number of vowels
    return vowel_count

# Test the function
string = input("Enter a string: ")
print("The number of vowels is:", count_vowels(string))

Explanation:

• Input: The function takes a string as input and counts the number of vowels.
• Vowel Definition: Vowels are defined in a set for efficient membership checking (includes both uppercase and lowercase).
• Counter Initialization: A vowel_count variable is initialized to 0 to track the number of vowels.
• Iteration: The function loops through each character in the string.
• Check and Count: An if statement checks if the character is a vowel (present in the set). If true, vowel_count is incremented.
• Return: The function returns the total count of vowels after the loop completes.

This function is commonly asked in Python interview questions related to string manipulation and optimization.

35. Write a Python Program That Displays the Fibonacci Sequence up to a Specified Number of Terms

Below is a Python program that displays the Fibonacci Sequence up to a specified number of terms. The Fibonacci sequence is a series of numbers where each number is the sum of the two preceding ones.

def fibonacci_sequence(num):
    # Check if num input is 0 or 1
    if num == 0:
        # return empty list
        return []
    elif num == 1:
        # return 0
        return [0]
    # Initialize the sequence with the first two numbers
    sequence = [0, 1]
    # Append new numbers to the sequence until it has num terms
    while len(sequence) < num:
        # Calculate the next number as the sum of the previous two numbers
        next_num = sequence[-1] + sequence[-2]
        # Append the next number to the sequence
        sequence.append(next_num)
    # Return the complete sequence
    return sequence

# Test the function
n = int(input("Enter the number of terms: "))
print("The Fibonacci sequence:", fibonacci_sequence(n))

The fibonacci_sequence function takes an integer input, num, which represents the number of terms the Fibonacci sequence should display. The function checks if num is 0 (returning an empty list) or 1 (returning only the first Fibonacci number, 0). If num is greater than 1, the sequence is initialized with the first two Fibonacci numbers: 0 and 1.

The function uses a while loop to add new numbers to the sequence until it contains the specified number of terms. The next number in the sequence is computed by summing the last two numbers using the indices [-1] and [-2], which represent the last and second-to-last elements in the sequence.

This approach is commonly asked in Python interview questions, as it’s important for a Python developer to have understanding of basic loops and sequence manipulation.

36. What Is a Lambda Function, and How Is It Used?

A lambda function in Python is a small, anonymous function defined using the lambda keyword. It is often used for creating simple functions in a single line of code without needing to define a full function using the def keyword.

Lambda functions are typically used when you need a quick function for a short period, often passed as an argument to higher-order functions like map(), filter(), or sorted().

lambda arguments: expression

• arguments: These are the parameters that the function takes.
• expression: This is a single expression that gets evaluated and returned when the function is called.

Here is an example where a lambda function doubles a number:

# Using a lambda function to double a number
lambda_double = lambda x: x*2
print(lambda_double(5)) # output is 10

In this example, x is the argument, and x * 2 is the expression that will be evaluated and returned.

Lambda functions are often used when passing functions as arguments to other functions. For instance, you can use a lambda function in combination with the map() function to apply a transformation to each element in a list.

numbers = [1, 2, 3, 4, 5]
double_numbers = map(lambda_double, numbers)
print(list(double_numbers))

In this example, the lambda function lambda x: x * 2 is passed as an argument to map(), which applies this function to each element of the numbers list.

Lambda functions are often encountered in Python interview questions, where the goal is to test your understanding of functional programming concepts and concise function definitions.

37. How Are the Functions Map(), Filter(), and Reduce() Used in Python?

In Python, map(), filter(), and reduce() are high-order functions, meaning they take other functions as arguments and return a new function or value. These functions are useful for processing iterables like lists, tuples, and strings. Let's explore each function in detail:

The map() function applies a given function to each item in an iterable (like a list, tuple, or string) and returns an iterable (of type map). You can use it with user-defined functions or lambda functions.

map(function, iterable)

• function: A function to apply to each element.
• iterable: An iterable (e.g., list, tuple, string) whose elements are passed to the function.

def square_num(num):
    return num**2
numbers = [1, 2, 3, 4, 5]
squared_nums = map(square_num, numbers)
print(tuple(squared_nums))

Here, the map() function applies the square_num function to each element in the numbers list, and the result is a new iterable containing the squared numbers. The output is converted to a tuple for printing.

The filter() function applies a given function that returns a boolean value (True or False) to each item in an iterable. It returns an iterable containing only the items for which the function returned True.

def is_even(num):
    return num % 2 == 0

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
even_numbers = filter(is_even, numbers)
print(list(even_numbers))

The even_numbers list contains only the elements that are even. We can also use a lambda expression with the filter() function.

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
odd_numbers = filter(lambda x: x % 2 == 1, numbers)
print(set(odd_numbers))

Here, the filter() function filters out the odd numbers, leaving only the even ones. The output is converted to a list to print the results.

The reduce() function applies a function cumulatively to the items of an iterable, so that the result of each step is passed as the first argument to the next step, reducing the iterable to a single value. The function is defined in the functools module, so you must import it before using it.

Syntax:

from functools import reduce
reduce(function, iterable)

Example:

import functools
numbers = [1, 3, 5, 6, 2]
# using reduce() to compute maximum element from numbers
max_num = functools.reduce(lambda a, b: a if a > b else b, numbers)
print(f"The maximum element of the list is {max_num}")
# using reduce() to compute sum of numbers
sum = functools.reduce(lambda a, b: a+b, numbers)
print(f"The sum of the list elements is {sum}")

Here, the reduce() function is used to find the maximum number in the list and also to compute the sum. In both cases, the function is applied cumulatively across the elements of the list.

38. Differentiate Between Deep() and Shallow Copy() in Python

In Python, the terms shallow copy and deep copy refer to different ways of copying complex objects, especially those that contain other objects like lists or class instances. Understanding the distinction between the two is important, and it often comes up in Python interview questions as it’s related to object handling.

Shallow Copy

A shallow copy creates a new compound object but does not recursively copy the objects inside it. Instead, it copies references to the nested objects (i.e., the child objects). This means that if you modify a nested object inside the copied compound object, the change will reflect in the original object because both the original and copied objects share references to the same nested elements.

original_list = [[1, 2, 3], 4, 5, 6, 7, 8, 9]
shallow_copy = original_list.copy()

# changing an element in the copied object
shallow_copy[0][1] = "changed"
print("The original list:", original_list)
print("The copied list:", shallow_copy)

Here, you can see that modifying shallow_copy affects original_list because both share the same reference to the inner list [1, 2, 3].

Deep Copy

A deep copy, on the other hand, creates a new compound object and recursively copies all the objects inside it. This means that the child objects are completely duplicated and stored in the copied object, rather than just being referenced. Changes to the deep copy will not affect the original object, as they no longer share references to the nested elements.

import copy

original_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
deep_copy = copy.deepcopy(original_list)
# changing an element in the copied object
deep_copy[0][1] = "altered"
print(original_list)
print(deep_copy)

In this case, when the deep_copy is modified, the original original_list remains unchanged because the nested objects were fully copied, not just referenced.

39. What Is the Difference Between Split() and Join() in Python?

The split() and join() functions in Python are used for manipulating strings, but they perform opposite tasks.

str.split(separator, maxsplit)

• separator: This is the delimiter that determines where the split happens. If no separator is provided, whitespace is used by default.
• maxsplit: This specifies the maximum number of splits to make. If not specified, the string is split as many times as possible.

Example:

string = "Python, is cool"
char = string.split(',')
print(char)

In this example, the string is split at the comma ,, creating a list with two substrings: "Python" and " is cool".

The join() function is used to link a list of strings using a delimiter into a single string. It has the following syntax:

"delimiter".join(iterable)

• delimiter: This is the string that will be placed between each element of the iterable.
• iterable: This can be a string, list, or tuple that contains the elements to be joined together.

Example:

list = ['l', 'a', 'm', 'b', 'd', 'a', 'T', 'e', 's', 't']
new_str = " ".join(list)
print(new_str)

The join() method joins the list of characters into a single string using the space “ ” delimiter between each character.

40. Write a Recursive Function To Calculate the Sum of Numbers From 0 to 10

Recursion is a technique where a function calls itself to solve problems that can be broken down into smaller, similar subproblems. In Python, recursion is useful for tasks that involve repetitive patterns.

Here's an example of a recursive function that calculates the sum of numbers from 0 to 10:

def recursive_addition(num):
    # base case
    if num == 0:
        return 0
    # recursive case
    else:
        return num + recursive_addition(num-1)

result = recursive_addition(10)
print(result)

The recursive_addition() function takes an integer num and recursively adds all the numbers between num and 0. The base case (if num == 0) stops the recursion, and the recursive case returns the sum of num and the result of the next recursive call. This is a classic example that may appear in Python interview questions to test understanding of recursion.

41. What Are *args and **kwargs in Python?

In Python, *args and **kwargs are used to pass a variable number of arguments to a function. These syntaxes allow you to handle functions with an arbitrary number of positional or keyword arguments.

*args

The *args syntax allows a function to accept a variable number of non-keyword arguments, which are collected into a tuple. Here's an example of how it works:

def add(*nums):
    sum = 0
    for num in nums:
        sum += num
    return sum

print("Sum:", add(1,2))
print("Sum:", add(1,2,3,4,5))
print("Sum:", add(10,20,30,40,50,60,70,80,90))

**kwargs

The syntax **kwargs stands for “any number of keyword arguments.” It passes a variable number of keyword arguments to a function. All the arguments will be collected in a dictionary that maps the keyword to a value passed alongside it.

For example, let’s create a function that returns the total number of employees at a startup.

def startup(**employees):
    total_emps = 0
    for num in employees.values():
        total_emps += num
    return total_emps

print("Total employees at the startup are:", startup(frontend=2, backend=3, ux=2, cto=1))

These concepts—*args and **kwargs—are commonly featured in Python interview questions, as they help assess the flexibility of a candidate's coding approach in handling different function arguments.

42. Explain the Concept of a Decorator in Python

A decorator in Python is a design pattern that allows you to modify the behavior of a function or class method without changing its structure. It wraps a function and can add functionality before or after the original function executes. The decorator pattern is helpful in extending functionality in a clean, reusable way. Here's an example of a simple decorator:

For example, we can demonstrate a decorator that modifies a function to return a string in uppercase when the function is called.

# Decorator function takes a function as its parameter
def uppercase_decorator(func):
    # The wrapper function
    def wrapper():
        uppercase = func().upper()
        return uppercase
    return wrapper

# Decorated function
@uppercase_decorator
def LambdaTest():
    return "Mobile apps and cross browser testing cloud"

print(LambdaTest())

In this example, the uppercase_decorator modifies the LambdaTest() function to return its result in uppercase. The concept of decorators is often asked in Python interview questions as it demonstrates higher-order functions and function wrapping.

43. What Is a Virtual Environment in Python, and What Are Its Benefits?

A virtual environment in Python is an isolated directory containing its own Python interpreter and libraries. It ensures that your project dependencies are kept separate from the system-wide Python environment, preventing version conflicts between different projects. Here's how you can create a virtual environment:

python -m venv <venv_name>

Benefits of using a virtual environment:

• Isolation: Keeps your project dependencies isolated.
• Portability: You can easily replicate environments across different machines.
• Security: Reduces the risk of system-wide security vulnerabilities by isolating dependencies.

44. What Is a Closure in Python?

A closure in Python refers to a function that retains access to variables from its enclosing scope, even after the outer function has finished execution. Closures are useful for encapsulating data and functions that rely on it.

Here's an example:

def calculate():
    num = 1
    def increment():
        nonlocal num
        num += 1
        return num
    return increment

new_num = calculate()

print(new_num())
print(new_num())
print(new_num())

In this example, the increment() function is a closure that has access to the num variable from the calculate() function. Each time new_num() is called, it increments num. Understanding closures is vital for solving problems in Python and is often asked in Python interview questions as it relates to encapsulation and data retention.

45. What Is a Generator in Python?

A generator is a function that returns an iterator, yielding items one at a time. This is memory-efficient since it doesn’t store all values in memory, unlike a list. You can iterate over a generator using the next() function or a for loop.

Let us look at an example of a generator in Python:

def generator_example(n):
    num = 0
    while num < 3:
        yield num
        num += 1

nums = generator_example(3)

print("using next() to print values")
print(next(nums))
print(next(nums))
print(next(nums))

print("using a for loop to print values")
for num in generator_example(3):
    print(num)

The generator_example() function creates an iterator object with values that are less than three. The nums = generator_example(3) assigns the function’s result. Generators are frequently covered in Python interview questions as they relate to understanding memory efficiency and iterable creation.

The intermediate-level Python interview questions listed above are designed to help both beginners and those with some experience prepare effectively for interviews.

As you progress in your Python development career, you will encounter more challenging questions that are particularly relevant for experienced developers. These questions will help you deepen your understanding and expertise in various Python concepts and technologies, ensuring you're well-prepared for more advanced roles.

Experienced-Level Python Interview Questions

Here, the focus shifts to advanced topics essential for experienced Python developers. By exploring these advanced Python interview questions, you will gain a comprehensive understanding of complex Python concepts and optimization strategies. This preparation equips you to handle intricate programming scenarios and build high-performance applications effectively.

46. What Is the Purpose of conftest.py in Pytest?

The conftest.py file in pytest defines fixture functions, configurations, and hooks, making them accessible to multiple test files in a test suite. Fixtures defined in conftest.py can be used across the suite without explicit imports, as pytest automatically discovers them. If there are nested test directories, each can have its own conftest.py to define specific fixtures.

import pytest

@pytest.fixture
def framework_fixture():
    framework_name = "pytest"
    return framework_name

This fixture can be accessed by test files as shown below:

def test_framework_name(framework_fixture):
    assert framework_fixture == "pytest"

def test_framework_length(framework_fixture):
    assert len(framework_fixture) == 6

To execute these tests, use the following command:

python -m pytest test_framework_name.py test_framework_length.py

This demonstrates how the conftest.py fixture is passed as a parameter, ensuring a clean and modular setup in Python interview questions about Pytest.

47. How Do You Launch a Web Browser Using Selenium WebDriver in a Pytest Script?

Selenium WebDriver is an API that automates browsers for testing web applications. Using Selenium with pytest involves setting up the WebDriver and creating fixtures for reusable configurations.

To get started, install Selenium and the webdriver-manager module:

pip install -U selenium

pip install webdriver-manager

Steps to launch a Chrome browser:

import pytest
from selenium import webdriver
from selenium.webdriver.chrome.service import Service as ChromeService
from webdriver_manager.chrome import ChromeDriverManager

@pytest.fixture
def open_browser():
    driver = webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()))
    yield driver
    driver.quit()

Run the test using:

python -m pytest test_google.py -v -s

This setup is essential for Python developers and it’s often asked in most of the Python interview questions, as it demonstrates Selenium WebDriver integration with pytest to automate browser interactions.

Pytest also integrates with most of the cloud testing platforms like LambdaTest to help you scale Selenium Python testing across various environments.

By integrating with LambdaTest, you can test your web applications across a wide range of browsers and operating systems in isolated, cloud-hosted environments. LambdaTest is an AI-powered test execution platform that allows you to run manual and automated tests at scale across 3000+ browsers and OS combinations.

This platform ensures compatibility without the overhead of managing physical test infrastructure, making both virtual environments and cloud testing complementary in modern development workflows.

48. What Are Marks in Pytest?

In pytest, markers are decorators used to add metadata or attributes to test functions, allowing categorization such as slow tests, high-priority tests, or tests to skip. Built-in markers like pytest.mark.skip can skip specific tests.

To view all built-in markers, use the terminal command:

pytest --markers

For example, the pytest.mark.skip marker can skip a test:

import pytest
from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager

@pytest.mark.skip(reason="Skip loading the LambdaTest homepage")
def test_LambdaTest_homepage():
    service = Service(ChromeDriverManager().install())
    browser = webdriver.Chrome(service=service)
    browser.get("https://www.LambdaTest.com/")
    browser.quit()

Execute the test with:

pytest -v test_markers.py

To categorize tests for specific purposes, create custom markers. Register them in pytest.ini to avoid warnings. For example:

File - pytest.ini

[pytest]
markers =
    chrometest: To run specifically on Google Chrome

Use the custom marker in a test:

import pytest
from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys

@pytest.mark.chrometest
def test_search_LambdaTest_on_google():
    service = Service(ChromeDriverManager().install())
    driver = webdriver.Chrome(service=service)
    driver.get("http://www.google.com")
    search_bar = driver.find_element(By.NAME, "q")
    search_bar.send_keys("LambdaTest")
    search_bar.send_keys(Keys.RETURN)
    assert "LambdaTest" in driver.title
    driver.quit()

Run tests marked as chrometest with:

pytest -v -m chrometest

Markers are a crucial topic in Python interview questions, as they allow better test organization and flexibility in Pytest.

49. What Are Pytest Plugins, and How Do You Install Them?

Pytest plugins extend the framework's functionality by providing additional features like parallel testing, code coverage, and HTML reporting.

Popular plugins include:

• pytest-xdist: Parallel test execution.
• pytest-cov: Code coverage reports.
• pytest-html: HTML reports for test results.

To install a plugin, use:

pip install pytest-xdist

Plugins are valuable for optimizing testing workflows and frequently appear in Python interview questions related to Pytest.

50. What Is a Unit Test in Python?

A unit test validates the functionality of a single code component, such as a function or method. It ensures correctness, supports early bug detection, and simplifies code refactoring. Python's unittest module provides a structured approach to creating unit tests with features like fixtures, test suites, and assertions.

51. What Is Behavior-Driven Development Testing in Python?

Behavior-Driven Development (BDD) uses natural language to define application behavior, fostering collaboration among developers, testers, and stakeholders. In Python, BDD is implemented using the Behave framework, which employs Gherkin syntax in .feature files.

Install Behave using:

pip install Behave

BDD and its application through frameworks like Behave often form an integral part of Python interview questions on advanced testing methodologies.

52. How Is BDD Different From Test-Driven Development (TDD)?

Focus:

• BDD emphasizes application behavior from a user's perspective, focusing on expected outcomes and interactions.
• TDD ensures the correctness of individual units of code by writing tests that validate specific functionality.

Participants:

• BDD involves developers, QA testers, and non-technical stakeholders to create scenarios in human-readable language.
• TDD primarily involves developers writing and maintaining unit tests.

Language:

• BDD uses domain-specific languages like Gherkin, making scenarios understandable to all stakeholders.
• TDD tests are written in the programming language of the codebase.

Outcome:

• BDD creates documented scenarios that describe behavior in simple language.
• TDD produces a suite of unit tests to ensure correct functionality.

53. What Is a Feature File in Behave?

A feature file in Behave defines application behavior using the Gherkin language and has a .feature extension. It serves as both documentation and automated test input for verifying application behavior.

Main components include:

• Feature: Describes the software functionality.
• Background: Common preconditions for all scenarios in the file.
• Scenario: Individual test cases outlining expected behavior.
• Scenario Outline: Parameterized scenarios for testing different inputs.
• Tags: Metadata for grouping or filtering tests.

54. What Is the Purpose of the “Given-When-Then” Structure in Behave Scenarios?

The Given-When-Then structure in Gherkin provides a clear format for defining test cases:

• Given: Sets up preconditions.
• When: Describes actions or triggers.
• Then: Specifies expected outcomes.

This structure enables readable scenarios and generates step definitions for automation.

55. What Are Step Definitions and How Are They Implemented in Behave?

Step definitions are Python functions that provide the implementation of steps in a feature file. They use decorators (@given, @when, @then) to link steps to their logic. These decorator accepts a string phrase used in the scenario step it implements.

Let’s look at an example of a scenario where a user clicks on a checkbox and asserts that the checkbox is clicked.

Feature: Click checkbox  
  Scenario: User clicks checkbox
      Given User opens playground webpage
      Then User clicks on Single Checkbox Demo
      Then User verifies checkbox is clicked

Step Definitions File:

from behave import *
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.chrome.service import Service as ChromeService
from webdriver_manager.chrome import ChromeDriverManager

@given('User opens playground webpage')
def open_homepage(context):
    context.driver = webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()))
    context.driver.get("https://www.lambdatest.com/selenium-playground/checkbox-demo")

@then('User clicks on Single Checkbox Demo')
def click_checkbox(context):
    checkbox = context.driver.find_element(By.XPATH, "//input[@id='isAgeSelected']")
    checkbox.click()

@then('User verifies checkbox is clicked')
def verify_text(context):
    element = WebDriverWait(context.driver, 5).until(EC.visibility_of_element_located((By.CSS_SELECTOR, "#txtAge")))
    assert element.text == "Checked", "Checkbox verification failed"

Run the tests with the following command:

behave clickcheckbox.feature

56. What Is the Difference Between Background and Scenario Outline in Behave Feature Files?

In Behave, a background is a keyword that allows you to specify common steps across multiple scenarios within a feature file. It reduces repetition by providing a shared context for every scenario and maintaining that each scenario starts in the same state. A background is executed before each scenario in the feature file.

The background ensures that the user must be logged in to the ecommerce store in both scenarios. It avoids repeating the login process steps in each scenario, making the tests cleaner and more readable.

57. What Are Hooks in Behave Framework?

Hooks in Behave are a feature that allows you to execute code at different points during test execution. They are similar to event handlers and help set up preconditions or perform cleanup tasks, such as before and after steps, scenarios, or features.

These hooks in Behave are defined in a file named environment.py, located in the features directory alongside the steps folder, where Behave is configured to recognize them.

Some common hooks in Behave include:

• before_all(context): Runs before the entire test suite starts.
• before_feature(context, feature): Runs before every feature.
• before_scenario(context, scenario): Runs before every scenario.
• before_step(context, step): Runs before every step.
• before_tag(context, tag): Runs before every tag.
• after_all(context): Runs after the test suite is completed.
• after_feature(context, feature): Runs after every feature.
• after_scenario(context, scenario): Runs after every scenario.
• after_step(context, step): Runs after every step.
• after_tag(context, tag): Runs after every tag.

They are a crucial concept to understand as a Python developer and they are often asked in most of the Python interview questions related to the Behave framework.

58. What Is the Function of the Behave.ini File in Behave?

The Behave.ini file serves as a configuration file for setting up various preferences and settings to control how Behave executes BDD tests. It eliminates the need to specify command-line arguments each time you run tests.

The file can be located in one of the following locations:

• Current working directory.
• Home directory.
• %APPDATA% directory on Windows.

The configuration file must begin with the [Behave] label. Here is an example of a Behave.ini file:

[Behave]
tags = @smoke
format = pretty
dry_run = false

The [Behave] label indicates that the settings apply to Behave. The tags = @smoke configuration specifies that only scenarios with the @smoke tag will run. The format = pretty option ensures the output is more readable. The dry_run = false option indicates that Behave will execute all scenarios matching the specified configurations.

Understanding the purpose of the Behave.ini file is essential for Python developers, and it is often asked in many of the Python interview questions on the Behave framework.

59. What Are Tags in Behave?

Tags in Behave are annotations that add context or allow the grouping of test cases for selective execution or filtering. A tag begins with the "@" symbol followed by a keyword and can be applied at the feature or scenario level. Multiple tags can be assigned to the same feature or scenario for greater flexibility in test organization.

Here’s an example feature file with tags describing login and logout behaviors for the LambdaTest e-commerce web application:

Feature: User authentication
    @login
    Scenario: User login
        Given a user navigates to the login page
        When the user enters valid email and password credentials
        Then the user is directed to account page

    @logout
    Scenario: User logout
        Given a user is logged in
        When the user clicks on the logout button
        Then the user should be logged out

Tags are helpful for managing test execution by including or excluding specific features or scenarios.

To execute the scenario with the @logout tag:

behave features\tagsdemo.feature --tags=@logout

To exclude the scenario with the @login tag:

behave features\tagsdemo.feature --tags="~@login"

Tags make it easy to focus on specific test cases during execution and streamline test management.

60. How Do You Pass Data To Step Definitions in Behave?

In Behave, data defined in scenario steps can be passed as parameters to step definitions. There are several ways to pass data to step functions in Behave:

Passing Data Defined in Scenario Steps:

You can directly pass the data from the scenario steps as arguments to the step definition functions. This is the most common way to pass data. Behave automatically maps the text in the scenario steps to the parameters defined in the step definition.

Example:

Scenario: User logs in with valid credentials
Given the user enters username "test_user" and password "password123"

The corresponding step definition:

@given('the user enters username "{username}" and password "{password}"')
def step_impl(context, username, password):
    print(f'Username: {username}, Password: {password}')

Using Context Variables:

Behave provides a context object that can be used to share data between steps. The context is useful when you need to store data that is shared across multiple steps in a scenario or feature.

Example:

@given('the user sets the username to "test_user"')
def step_impl(context):
    context.username = "test_user"

In subsequent steps, you can access the context variable:

@when('the user enters the username')
def step_impl(context):
    print(f'Username: {context.username}')

Using Scenario Outlines:

Scenario outlines allow you to run the same scenario with different sets of data. The data is provided through a table in the Examples section and passed to the step definitions as parameters.

Example:

Scenario Outline: User logs in with different credentials
Given the user enters username "<username>" and password "<password>"

Examples:
  | username  | password    |
  | user1     | pass123     |
  | user2     | pass456     |

The corresponding step definition:

@given('the user enters username "{username}" and password "{password}"')
def step_impl(context, username, password):
    print(f'Username: {username}, Password: {password}')

These methods enable you to pass and manage data efficiently while writing step definitions in Behave.

Preparing for Python interviews requires a strong grasp of foundational concepts, practical applications, and the ability to articulate differences and advantages across Python's features. By focusing on questions frequently asked in interviews, such as data types, control flow, exception handling, and object-oriented programming, freshers can build confidence and demonstrate their technical proficiency effectively.

As Python continues to be a sought-after skill across industries like web development, data science, and automation, mastering these interview questions will help you align with market demands and stand out in a competitive job landscape. Start your preparation today and ensure you're ready to impress with your Python expertise in 2024!

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Citations:

https://devguide.python.org/getting-started/