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Table of contents
1.
Beginner-Level Python Coding Interview Questions
2.
Intermediate Level Python Coding Interview Questions
3.
Advance Level Python Coding Interview Questions
4.
Conclusion
Last Updated: Mar 27, 2024
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Interview Questions for Coding Round in Python

Author Kanak Rana
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Python is a high-level programming language recently gaining significant popularity. It is widely used for various applications, including web development, data analysis, artificial intelligence, and machine learning. Python's simplicity, readability, and a vast library of pre-existing code make it a favorite language among developers.

Regarding Python coding interviews, interviewers usually test a candidate's programming skills, knowledge of Python syntax, and ability to solve complex problems. In these interviews, candidates are often given a problem statement or scenario and are expected to write clean, efficient, and maintainable code to solve the problem.

Interview Questions for Coding Round in Python

This article will explore some common Python coding interview questions and provide tips and strategies to help you prepare and master your next interview. Whether you are new to this or an experienced programmer, this guide will help you sharpen your Python coding skills and increase your chances of landing your dream job.

Also See, Intersection in Python

Beginner-Level Python Coding Interview Questions

This section will show how to answer some easy questions with proper sentences for python coding interview questions.
 

1. What is Python, and how is Python useful?

Ans: Python is a high-level, interpreted programming language first released in 1991. It is open-source, meaning the source code is freely available for modification and distribution. 

Python's popularity is due to its simple and readable syntax, versatility, and ease of use. It has a large standard library and a vast array of third-party libraries and frameworks that allow developers to build complex applications quickly and efficiently.

Python is useful in the following ways:

  • Python is useful for various applications, including web development, scientific computing, data analysis, machine learning, artificial intelligence, and automation. It is used by organizations such as Google, NASA, and Netflix and is also widely used in academia.
     
  • One of the main advantages of Python is its simplicity and readability. The syntax is easy to understand and write, making it accessible to every programmer.
     
  • Python also strongly focuses on code readability, making it easier for people to collaborate on projects and maintain code over time.
     
  • Another advantage of Python is its versatility. It can be used for various applications, from building simple scripts to creating complex applications with graphical user interfaces. 

 

2. How do you create a function in Python? 

Ans: def keyword is used to create a function in Python, followed by the name of the function and any parameters the function will accept in parentheses. The code inside the function should be indented to show that it is a part of it. Here is an example:

def calculate_rectangle_area(length, width):
    area = length * width
    return area
    
# Call the function with arguments 5 and 10
result = calculate_rectangle_area(5, 10)

# Print the result
print(result)


Output:

50


This function takes two parameters, length and width, and calculates the area of a rectangle using the formula area = length * width. The return statement returns the value of the area.

 

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

Ans:

A list is a mutable data type in Python that uses square brackets [] to store an ordered collection of items.

On the other hand, a tuple is an immutable data type in Python that uses parentheses () to store an ordered collection of items. Tuples are faster and more memory-efficient than lists, especially for larger data collections.

Feature List Tuple
Mutable/Immutable Mutable Immutable
Syntax Square brackets [] Parentheses ()
Length Can change length Fixed length
Performance Slower for large lists Faster for large tuples
Use cases Used for dynamic data Used for static data
Example declaration fruits = ['apple', 'banana'] fruits = ('apple', 'banana')


4. What is a module in Python?

Ans: A module in Python is a file containing Python code that can be used in other Python programs.

A module is a self-contained unit of code that can include variables, functions, and classes that can be accessed and used in other Python programs. By organizing code into modules, you can avoid duplicating code across different programs and instead import and use the same code in multiple places, making it easier to maintain and reuse your code.

Python has many built-in modules that can be used for various purposes, such as working with files, network communication, data processing, and more. In addition, third-party modules can be installed and used in Python programs to extend their functionality.

To use a module in a Python program, you first need to import it using the import statement. Here is an example:

import math
result = math.sqrt(12)
print(result)


Output:

3.4641016151377544


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

Ans: Break and Continue are two keywords in Python that are used to change the flow of a loop. These keywords are used inside loops, such as for and while loops.

Both keywords are used to change the flow of a loop, but they have different effects on the loop:
 

  • Break - It exits the loop entirely and continues with the next statement after the loop.
     
for i in range(10):
   if i ==5:
       break
   print(i)


Output:

0
1
2
3
4

 

  • Continue- It skips the current iteration and moves on to the next iteration of the loop.
     
for i in range(10):
   if i == 5:
       continue
   print(i)


Output:

0
1
2
3
4
6
7
8
9

 

6. How do you iterate over a list in Python? 

Ans: Let's consider the following code to explain iteration over a list in Python.

numbers = [1, 2, 3, 4, 5]
for num in numbers:
   if num % 2 == 0:
       print(num, "is even")
   else:
       print(num, "is odd")


Output:

1 is odd
2 is even
3 is odd
4 is even
5 is odd


Explanation: We have a list called numbers containing five integers. We use a for loop to iterate over the list, and on each iteration, we assign the current item in the list to the variable num. Then, we check if the number is even or odd using the modulus operator (%) and print out the result.

The 'for' loop iterates over the list from the first to the last item and executes the indented code block once for each item.

 

7. What is a dictionary in Python? 

Ans: A dictionary is a collection of key-value pairs that store and retrieve data. In other words, a dictionary is like a map that connects keys to values.

Example: 
 

student = {
    "name": "Jaideep",
    "age": 22,
    "major": "Computer Science"
}
print(student["name"])  
print(student["age"])   
print(student["major"])


Output:

Jaideep
22
Computer Science

 

8. Write a Python factorial program without using if-else, for, and ternary operators.

Ans: We can use a recursive function that calculates the factorial of a given number without using if-else, for, and ternary operators:

The function recursively calls itself until it reaches the base case of n=1, at which point it returns 1. Each recursive call multiplies the current value of n by the result of the previous call, effectively calculating the factorial.

def factorial(n):
   return (n==1) or (n * factorial(n-1))
print(factorial(4))


Output:

24

 

9. Write a Python code to check the Armstrong number.
Ans:

number = int(input("Enter a number: "))
order = len(str(number))
sum = 0
temp = number

while temp > 0:
 digit = temp % 10
 sum += digit ** order
 temp //= 10
 
if number == sum:
  print(number, "is an Armstrong number")
else:
  print(number, "is not an Armstrong number")


Output:

Enter a number: 34
34 is not an Armstrong number

Intermediate Level Python Coding Interview Questions

This section will show some intermediate questions with answers. It will help you to prepare to answer python coding interview questions.
 

10. What is the difference between a Mutable datatype and an Immutable data type?

Ans: Mutable data types are those whose values can be changed after creation. When you modify a mutable object, it changes its value in place without creating a new object. Any other references to the object will also see the change.

On the other hand, immutable data types are those whose values cannot be changed after creation. When you modify an immutable object, you create a new object with the modified value. Any other references to the original object will not see the change.

difference between a Mutable datatype and an Immutable data type

 

11. What is the difference between '==' and 'is' in Python? 

Ans: In Python, the == operator and the is operator are used to compare two objects. However, they have different meanings and are used in different contexts.

The == operator compares the values of the objects, while the is operator checks whether the two objects are the same, i.e., whether they have the same identity.

== operator is operator
It compares the values of two objects. It checks whether two objects are the same object.
It returns True if the values of two objects are equal. It returns True if two variables reference the same object in memory.
It returns False if the values of two objects are not equal. It returns False if two variables do not reference the same object in memory.
It is used for value comparison. It returns False if two variables do not reference the same object in memory.
Examples: a == b, x == 42. Examples: a is b, x is None.

Example:

a = [4, 2, 1]
b = [4, 2, 1]
c = a
print(a == b)
print(a is b)
print(a is c)


Output:

True
False
True


Explanation:

  • True - the values of the objects are the same.
  • False - the objects have different identities.
  • True - both variables reference the same object in memory.


12. What is the difference between a shallow copy and a deep copy in Python?

Ans: The difference between a shallow copy and a deep copy in python are as follows:

Features  Shallow Copy Deep Copy
Definition A shallow copy creates a new object but references the same memory addresses as the original object for the nested objects. A deep copy creates a new object with new memory addresses for the main object and any nested objects.
Syntax new_object = old_object.copy() or new_object = list(old_object) new_object = copy.deepcopy(old_object) or new_object = list(old_object).
Changes to nested objects Any changes to the nested objects will reflect in both the original and copied objects. Changes made to the nested objects in the copied object will not reflect in the original object.
Speed It is faster than a deep copy because it does not create a new object for nested objects. Slower than a shallow copy because it creates a new object for each nested object.
Memory Usage Less memory usage because it shares memory addresses for nested objects. More memory usage because it creates new memory addresses for each nested object.

 

13. How are arguments passed by value or by reference in Python?

Ans: In Python, arguments are generally passed by reference, but how it works can be confusing.

When you pass an object to a function in Python, a reference to that object is passed to the function. This means that the function can modify the object, and those modifications will be reflected in the calling code.

Example:
 

def modify_list(my_list):
  my_list.append(4)

# create a list
my_list = [1, 2, 3]

# call the modify_list function
modify_list(my_list)

# print the list
print(my_list)


Output:

[1, 2, 3, 4]


Explanation: In this example, the ‘modify_list’ function modifies the original list by appending the value 4. When the function returns, the modified list is still accessible in the calling code.

However, there are some cases where it appears that Python is passing arguments by value. For example, when you pass an integer or a string to a function and modify it within it.

In conclusion, arguments in Python are generally passed by reference, which means that modifications made to objects within a function can affect the original object in the calling code. However, the behavior can vary depending on the type of object being passed since immutable objects like strings, and integers cannot be modified in place.

 

14. How to convert a list into a set?

Ans: To convert a list into a set in Python, you can use the built-in set() function. This function takes an iterable object (such as a list) as input and returns a new set object that contains all the unique elements in the iterable.
 

my_list = [1, 2, 3, 3, 4, 4, 5]
my_set = set(my_list)
print(my_set)


Output: 

{1, 2, 3, 4, 5}


Explanation:

In the above code, we first define a list my_list containing duplicate elements. We then pass this list to the set() function to create a new set object, my_set, that contains only the unique elements of the original list.

Note that sets are unordered collections of unique elements, so the order of the elements in the original list may not be preserved in the resulting set.

 

15. How can you create Empty NumPy Array In Python?

Ans: You can create an empty NumPy array in Python using the numpy.empty() function. This function creates an array of a specified size and shape but with uninitialized entries. 

Here's an example:

import numpy as np
# Create an empty array with shape (3, 4)
empty_arr = np.empty((3, 4))
print(empty_arr)


This code will output an empty array of shape (3, 4), which means it has 3 rows and 4 columns, but with no values assigned to any of its entries:


Output:

[[1.86780114e-316 0.00000000e+000 5.31998820e-317 6.90266114e-310]
[6.90266150e-310 5.31998820e-317 6.90266114e-310 6.90266114e-310]
[6.90266114e-310 6.90266115e-310 5.31998820e-317 6.90266083e-310]]


As you can see, the array entries are uninitialized, containing whatever values were already in the memory space where the array was created. If you want to create an empty array with initialized entries, you can use the numpy.zeros() function instead.

 

16. What are Pickling and Unpickling? 

Ans: Pickling and unpickling are processes used in Python to serialize and deserialize objects. Serialization converts an object into a byte stream, which can be stored or transmitted over a network. Deserialization transforms a sequence of bytes, typically stored in a file or transmitted over a network, back into an object in memory that can be manipulated and used by a program.

  • Pickling converts a Python object hierarchy into a byte stream using the pickle module. This byte stream can be saved to a file or sent over a network. The pickle module can handle most Python objects, including complex data types such as lists, sets, and dictionaries.
     
  • Unpickling is the reverse process of pickling. It involves reading a byte stream and reconstructing the original Python object hierarchy. This is done using the pickle.load() function.

 

17. Write a code snippet to get an element, delete an element, and update an element in an array.

Ans: 

import numpy as np
# Create an array
arr = np.array([1, 2, 3, 4, 5])

# Get an element
element = arr[2]
print("Element at index 2:", element)

# Delete an element
arr = np.delete(arr, 3) # Delete element at index 3
print("Array after deleting element at index 3:", arr)

# Update an element
arr[1] = 6 # Update element at index 1 to 6
print("Array after updating element at index 1:", arr)


Output:
 

Element at index 2: 3
Array after deleting element at index 3: [1 2 3 5]
Array after updating element at index 1: [1 6 3 5]
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Advance Level Python Coding Interview Questions

 

This section will show how to answer the hard-level python coding interview questions for your dream job.

 

18. Given two lists, generate a list of pairs (one element from each list)with the help of the zip function.

Ans: Using the Zip function, you can generate a list of pairs from two lists.

We define lists list1 and list2 with values [1, 2, 3] and ['a', 'b', 'c'], respectively. We then use the zip() function to generate a new list of pairs, where the first element of each pair comes from list1 and the second element of each pair comes from list2.

The zip() function takes multiple iterables as arguments and returns an iterator that aggregates elements from each iterable into tuples. In this case, we pass in list1 and list2 as arguments to zip(). We then convert the resulting iterator to a list using the list() function.
 

list1 = [1, 2, 3]
list2 = ['a', 'b', 'c']
pairs = list(zip(list1, list2))
print(pairs)


Output:

[(1, 'a'), (2, 'b'), (3, 'c')]

 

19. What is DataFrame?

Ans: A data frame is a data structure used in programming and data analysis, often in the context of working with data in a tabular format. It is a two-dimensional table-like structure, with rows representing observations or cases and columns representing variables or attributes.

In Python, data frames are typically created using panda's library, which provides a DataFrame object that can be used to store and manipulate data in tabular format. Data frames can be created from various sources, including CSV files, Excel spreadsheets, SQL databases, etc.
 

import pandas as pd
data = {'name': ['Raghav', 'Raju', 'Charu', 'Lokesh'],
       'age': [25, 30, 35, 40],
       'city': ['New York', 'London', 'Paris', 'Dubai']}
df = pd.DataFrame(data)
print(df)


Output:

name    age   city
0   Raghav   25    New York
1    Raju    30    London
2   Charu    35     Paris
3  Lokesh    40     Dubai

 

20. What is the Global Interpreter Lock (GIL) in Python?

Ans: The Global Interpreter Lock (GIL) is a mechanism in Python that ensures only one thread executes Python bytecode at a time. This means that multiple threads can exist within a Python process but cannot execute Python bytecode in parallel.

The GIL is implemented in CPython, the default and most widely used implementation of the Python programming language. It is a design choice made to simplify memory management and improve performance by preventing conflicts that can occur when multiple threads access the same objects or data structures simultaneously.

While the GIL provides a certain level of safety and simplicity, it can limit the performance of CPU-bound tasks that are parallelizable, as only one thread can execute at a time. However, the GIL does not necessarily impact performance for I/O-bound tasks, which often rely on external resources such as disk or network, and therefore do not heavily use the CPU.

There have been attempts to work around the limitations of the GIL, such as using multiprocessing, which allows for parallel execution across multiple processes or using other implementations of Python, such as Jython or IronPython, that do not have a GIL. However, these solutions come with their trade-offs and may only sometimes be suitable for some use cases.

 

21. What is a lambda function?

Ans: A lambda function is a small, anonymous function that can be defined on the fly and used wherever a regular function is expected. It is created using the keyword lambda followed by a list of arguments, a colon, and the expression that the function will return when called.

Lambda functions are useful when you must define a simple function that will only be used in one place. However, they can be difficult to read and understand when they become too complex or are used in many places in your code.

Generally, it's a good practice to use regular named functions for more complex operations or functions that will be reused in multiple places.

Here we take a lambda function that takes two arguments and returns their sum:
 

sum = lambda a, b: a + b
print(sum(3, 4))


Output:

7

 

22. What is the swapcase function in Python?

Ans: The swapcase() function is a built-in method for strings that returns a new string where all uppercase characters are converted to lowercase, and all lowercase characters are converted to uppercase. The original string is not modified.

The swapcase() function can be useful when you need to quickly and easily change the case of a string. For example, you might use it to normalize user input for a case-insensitive search or to format text for display in a particular way.


Example:
 

my_string = "cODINg nINJAs"
new_string = my_string.swapcase()
print(new_string)


Output:

CodinG NinjaS


Explanation: 

The original string my_string contains both uppercase and lowercase characters. The swapcase() function is called on the string, which returns a new string where all the uppercase characters are converted to lowercase, and all the lowercase characters are converted to uppercase. The resulting string is then stored in the new_string variable.

 

23. Write a Python function that takes a list of integers and finds the list's longest increasing subsequence (LIS). The LIS is the longest subsequence of the list in which the elements are in increasing order.

Ans: You can use dynamic programming to find the longest increasing subsequence of a list of integers. Here's one way to write the longest_increasing_subsequence() function using dynamic programming:

def longest_increasing_subsequence(nums):
   n = len(nums)
   
   # Initialize a dynamic programming table with ones.
   
   dp = [1] * n
   
   # Iterate over each element of nums and fill in the dp table.
   
   for i in range(1, n):
       for j in range(i):
       
           # If nums[i] is greater than nums[j], update dp[i]
           
           if nums[i] > nums[j]:
               dp[i] = max(dp[i], dp[j] + 1)
               
   # Initialize an empty list to hold the LIS and find its maximum length.
   
   lis = []
   max_len = max(dp)
   
   # Backtrack from the maximum value of dp to find the LIS.
   
   i = dp.index(max_len)
   lis.append(nums[i])
   for j in range(i-1, -1, -1):
       if nums[j] < nums[i] and dp[j] == dp[i] - 1:
           lis.append(nums[j])
           i = j
           
   # Return the LIS in reverse order.
   
   return lis[::-1]
nums = [10, 22, 9, 33, 21, 50, 41, 60]
print(longest_increasing_subsequence(nums))


Output: 

[10, 22, 33, 41, 60]

 

24. Write a function that takes a binary tree as input and returns the maximum path sum of any path in the tree.

Ans:

class TreeNode:
   def __init__(self, val=0, left=None, right=None):
       self.val = val
       self.left = left
       self.right = right
def max_path_sum(root):

   # Define a helper function to calculate the maximum path sum recursively.
   
   def helper(node):
       nonlocal max_sum
       if not node:
           return 0
       # Recursively calculate the maximum path sum from the left and right subtrees.
       
       left_path_sum = helper(node.left)
       right_path_sum = helper(node.right)
       
       # Update the maximum path sum if the current path is better.
       
       max_sum = max(max_sum, node.val, node.val + left_path_sum + right_path_sum, node.val + left_path_sum, node.val + right_path_sum)
       
       # Return the maximum path sum from the current node.
       
       return max(node.val, node.val + left_path_sum, node.val + right_path_sum)
       
   # Initialize the maximum path sum to the minimum possible value.
   
   max_sum = float('-inf')
   
   # Recursively calculate the maximum path sum.
   
   helper(root)
   return max_sum
   
# Get user input to create a binary tree.

root_val = int(input("Enter the value of the root node: "))
root = TreeNode(root_val)

if input("Does the root node have a left child? (y/n) ") == 'y':
   left_val = int(input("Enter the value of the left child: "))
   root.left = TreeNode(left_val)
   
if input("Does the root node have a right child? (y/n) ") == 'y':
   right_val = int(input("Enter the value of the right child: "))
   root.right = TreeNode(right_val)
   
# Calculate the maximum path sum.

max_sum = max_path_sum(root)
print("The maximum path sum is:", max_sum)


Output:
 

Enter the value of the root node: 5
Does the root node have a left child? (y/n) y
Enter the value of the left child: 3
Does the root node have a right child? (y/n) y
Enter the value of the right child: 3
The maximum path sum is: 11

 

25. Given a list of intervals representing different meetings' start and end times, write a Python function to find the minimum number of meeting rooms required to hold all the meetings.

Ans:

Constraints:

  • You may assume that each meeting starts and ends within the same day, so the start time is always less than the end time.
  • You may assume that the list of intervals is non-empty and contains at least one meeting.


Code
 

import heapq
def min_meeting_rooms(meetings):

   # Sort the meeting intervals by their start times.
   
   meetings.sort(key=lambda x: x[0])
   
   # Initialize a priority queue to store the end times of the currently scheduled meetings.
   
   end_times = []
   for meeting in meetings:
       start, end = meeting
       
       # If the earliest scheduled meeting has already ended by the current 
       # meeting's start time, remove it from the priority queue.
       
       if end_times and end_times[0] <= start:
           heapq.heappop(end_times)
           
       # Add the current meeting's end time to the priority queue.
       
       heapq.heappush(end_times, end)
       
   # The size of the priority queue at any point in time represents 
   # the number of currently scheduled meetings that require separate rooms.
   
   return len(end_times)
   
# Get user input for the meeting intervals.

n = int(input("Enter the number of meetings: "))
meetings = []
for i in range(n):
   start = int(input("Enter the start time of meeting %d: " % (i+1)))
   end = int(input("Enter the end time of meeting %d: " % (i+1)))
   meetings.append((start, end))
   
# Calculate the minimum number of meeting rooms required.

min_rooms = min_meeting_rooms(meetings)

# Print the output.

print("The minimum number of meeting rooms required is:", min_rooms)


Output:

Enter the number of meetings: 4
Enter the start time of meeting 1: 1
Enter the end time of meeting 1: 3
Enter the start time of meeting 2: 7
Enter the end time of meeting 2: 9
Enter the start time of meeting 3: 11
Enter the end time of meeting 3: 13
Enter the start time of meeting 4: 15
Enter the end time of meeting 4: 1
The minimum number of meeting rooms required is: 1

Conclusion

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