Guide

8 Python Interview Questions Every Hiring Manager Should Ask

python interview questions

Introduction

Known for its versatility and ease of use, Python has become a popular programming language among developers worldwide. According to Statista, Python is 3rd most used programming language among developers worldwide as of 2023. 

python interview questions

This popularity makes Python a key skill for developers and a critical area for hiring managers to focus on during technical interviews.

Effective Python coding interviews are essential for hiring managers. They need to ask the right Python interview questions to gauge a candidate’s true ability. That’s where InterviewZen comes in. It’s a platform that helps create technical interviews tailored for software engineers.

In this article, we will explore the top 8 Python interview questions that every hiring manager should ask. We will also discuss how Interview Zen can help craft these questions, ensuring you find the right person for your Python development needs.

What is Python?

Python is a versatile and widely used programming language known for its simplicity, readability, and extensive libraries. It was created by Guido van Rossum and first released in 1991. Python has gained huge popularity and is widely used across various industries and applications. 

It was initially designed for general-purpose programming, and it has found extensive use in web development and other domains. Python’s key strength lies in its readability and ease of use. While Python is used for many applications, it is primarily known for its use in web development, data analysis, and scientific computing. 

Popular Websites that Use Python:

  1. Instagram
  2. YouTube
  3. Dropbox
  4. Reddit
  5. Pinterest
  6. Spotify

These websites handle massive amounts of data and user interactions daily, showcasing Python’s scalability and reliability for web applications.

Python is a versatile and widely adopted programming language with a strong presence in web development and various other fields. Its simplicity, integration capabilities, and compatibility with different platforms make it a popular choice for developers and organizations worldwide.

8 Python Interview Questions Every Hiring Manager Should Ask

1. Reverse Words in a Sentence

Task: Write a Python function called reverse_words, which receives a sentence as input and outputs that sentence with the words in reverse order.
Input Format: A single string represents the sentence.
Constraints:
  • The sentence will only include alphanumeric characters and spaces.
  • No leading or trailing spaces are allowed in the sentence.
  • The sentence will have at least one word.
Output Format: A string with the sentence’s words in reversed order.
Sample Input:   Python is awesome
Output: awesome is Python

Suggested Answer:

def reverse_words(sentence): 

  words = sentence.split() 

  reversed_sentence = ‘ ‘.join(reversed(words)) 

return reversed_sentence

Code Explanation:

The reverse_words function operates in a few simple steps:

  • It starts by splitting the sentence into words using the split() method. This creates a list of words.
  • Then, the function reverses the order of these words using the reversed() function.
  • The reversed words are joined back into a sentence using the join() method with a space character (‘ ‘) as the separator.
  • Finally, the function returns this reversed sentence.
Common Mistakes to Watch Out For:
  • Reversing the characters of each word instead of reversing the order of the words.
  • Failing to handle multiple spaces correctly.
  • Overlooking edge cases like sentences with a single word.
Follow-ups:
  • Can you modify the function to reverse the order of words without using built-in Python functions?
  • How would you handle punctuation or special characters if included in the sentence?
What the question tests? This interview question tests a candidate’s ability to manipulate strings and demonstrates their understanding of fundamental programming concepts like loops and conditionals.

2. Maximum Subarray Sum

Task: Create a Python function named max_subarray_sum. This function should take an array of integers as input and return the maximum sum achievable by any contiguous subarray within that array.
Input Format: The function will receive a list of integers.
Constraints:
  • The array will have a minimum length of one.
  • It may include both positive and negative integers.
Output Format: The function should return a single integer, which is the maximum sum of a contiguous subarray found in the input array.
Sample Input:   [1, 2, 3, -2, 5]
Output: 9

Suggested Answer:

def max_subarray_sum(arr): 

max_sum = arr[0] 

current_sum = arr[0] 

for i in range(1, len(arr)): 

current_sum = max(arr[i], current_sum + arr[i]) 

max_sum = max(max_sum, current_sum) 

return max_sum

Code Explanation:

The max_subarray_sum function applies Kadane’s algorithm to determine the maximum sum of a subarray within a given array:

  • It begins by setting both max_sum and current_sum to the first element of the array.
  • As the function iterates through the array, it updates current_sum. This is either by adding the current element to it or starting a new subarray sum with the current element.
  • max_sum is then updated at each step to reflect the maximum sum found so far.
  • The function ultimately returns max_sum, which is the highest sum of any contiguous subarray in the array.
Common Mistakes to Watch Out For:
  • Failing to initialize max_sum and current_sum properly can lead to incorrect results.
  •  Not correctly accounting for cases where all elements in the array are negative.
  • Implementing more complex algorithms when a simpler approach, like Kadane’s algorithm, is sufficient.
Follow-ups:
  • How would you modify the function if all elements in the array are negative?
  • Can you optimize the function to run with constant space complexity?
  • Besides Kadane’s algorithm, what other methods could be used to solve this problem?
What the question tests? This question tests a candidate’s ability to apply Kadane’s algorithm correctly, showcasing their skills in implementing efficient algorithms. It also evaluates their understanding of array manipulation, including how to handle and perform operations on array elements.

3. Merge Intervals

Task: Write a Python function named merge_intervals. This function should accept a list of intervals as input and return a new list where overlapping intervals are combined into single intervals.
Input Format: The input will be a list of intervals. Each interval is represented by a list with two integers: the start and end points of the interval.
Constraints:
  • The list of intervals provided will not be empty.
  • The start and end points of each interval are integers.
Output Format: The output should be a list of merged intervals. Each merged interval is represented as a list with two elements: the start and end points of the interval.
Sample Input:   [[1, 3], [2, 6], [8, 10], [15, 18]]
Output: [[1, 6], [8, 10], [15, 18]]

Suggested Answer:

def merge_intervals(intervals): 

intervals.sort(key=lambda x: x[0]) 

merged = [] 

for interval in intervals: 

if not merged or merged[-1][1] < interval[0]: 

merged.append(interval) 

else: merged[-1][1] = max(merged[-1][1], interval[1]) 

return merged

Code Explanation

The merge_intervals function begins by sorting the intervals based on their start points. This step is crucial for efficiently identifying overlapping intervals.

  • An empty list named merged is initialized. This list will eventually contain the merged intervals.
  • The function then iterates through each interval in the sorted list.
  • For each interval, the function checks if it overlaps with the last interval in the merged list. There are two scenarios:
    • If merged is empty or if the current interval does not overlap with the last interval in merged, the current interval is simply appended to the merged list.
    • If there is an overlap, the function updates the endpoint of the last interval in merged to be the maximum of the two interval’s endpoints.
  • After processing all intervals, the function returns the merged list. This list now contains intervals that have been merged wherever there was an overlap, ensuring no overlaps remain.
Common Mistakes to Watch Out For:
  • Not sorting the intervals by their start points, which is essential for efficiently identifying overlaps.
  • Mismanaging the logic to determine and handle overlapping intervals.
  • Altering the original list of intervals instead of working with a separate list for merged intervals.
Follow-ups:
  • How would you solve this problem if you couldn’t sort the intervals first?
  • What is the time and space complexity of your solution?
  • How does your function handle intervals that are entirely contained within another interval?
What the question tests? This question assesses the developer’s ability to tackle complex problems by merging overlapping intervals in a list. It’s a great way to gauge a candidate’s proficiency in identifying patterns, breaking down problems, and designing effective solutions using programming constructs like loops, conditionals, and list manipulation.

4. Balanced Parentheses Checker

Task: Write a Python function named is_balanced that checks if a string of parentheses, brackets, and curly braces is balanced. A string is balanced if each opening symbol has a corresponding closing symbol and the pairs are properly nested.
Input Format: A single string consisting of parentheses (), brackets [], and curly braces {}.
Constraints:
  • The string may contain any number of characters.
  • It can include (, ), [, ], {, } symbols only.
Output Format: Return True if the string is balanced and False otherwise.
Sample Input:   “{[()()]}”
Output: True

Suggested Answer:

def is_balanced(s):

    stack = []

    mapping = {‘)’: ‘(‘, ‘}’: ‘{‘, ‘]’: ‘[‘}

    for char in s:

        if char in mapping:

            top_element = stack.pop() if stack else ‘#’

            if mapping[char] != top_element:

                return False

        else:

            stack.append(char)

    return not stack

Code Explanation:

  • The function uses a stack to keep track of opening symbols.
  • It iterates through each character in the string.
  • If it encounters a closing symbol, it checks if the last symbol in the stack matches the corresponding opening symbol. If not, the string is unbalanced.
  • If the stack is empty at the end of the iteration, the string is balanced.
Common Mistakes to Watch Out For:
  • Not considering edge cases such as empty strings or strings with only one type of symbol.
  • Mismanaging the stack operations (push/pop).
Follow-ups:
  • How would you extend this function to handle other types of symbols, like angle brackets < and >?
  • Can you improve the function to handle strings with characters other than parentheses, brackets, and braces?
What the question tests? This question assesses the candidate’s understanding of stack data structures and ability to apply them to solve a common programming problem. It also tests their knowledge of string manipulation and attention to detail, particularly in handling edge cases.

5. Find All Anagrams in a String

Task: Write a function called find_anagrams that takes two strings, s and p, and returns the start indices of all anagrams of p in s. An anagram of a word is a permutation of its letters.
Input Format: Two strings: s (the main string) and p (the string of which anagrams are to be found).
Constraints:
  • Both strings will consist of lowercase English letters only.
  • The length of both strings will be at least 1.
Output Format: A list of integers representing the start indices of the anagrams of p found in s.
Sample Input:   s = “cbaebabacd”, p = “abc”
Output: [0, 6]

Suggested Answer:

def find_anagrams(s, p):

    from collections import Counter

    p_count = Counter(p)

    s_count = Counter(s[:len(p) – 1])

    result = []

    for i in range(len(p) – 1, len(s)):

        s_count[s[i]] += 1   # include a new char in the window

        if s_count == p_count:  # compare counters

            result.append(i – len(p) + 1)  # append the starting index

        s_count[s[i – len(p) + 1]] -= 1  # remove the oldest char in the window

        if s_count[s[i – len(p) + 1]] == 0:

            del s_count[s[i – len(p) + 1]]  # clean up the counter

    return result

Code Explanation:

  • The function uses collections.Counter to count characters in p and in a sliding window of s.
  • It iterates through s, updating the character count for the current window.
  • If the character counts of the window and p match, the starting index of that window is added to the result list.
  • The function maintains the size of the sliding window by adding a new character and removing the oldest one in each iteration.
Common Mistakes to Watch Out For:
  • Failing to handle edge cases such as empty strings or strings of different lengths.
  • Incorrectly managing the sliding window or character counts.
Follow-ups:
  • How would the function change if case sensitivity is important?
  • Can you optimize the function to improve its time complexity?
What the question tests? This question examines the candidate’s ability to use advanced data structures (like Counters) and algorithms (sliding window technique). It also tests their skills in string manipulation and attention to detail, especially in maintaining the correct window size and character counts.

6. Serialize and Deserialize a Binary Tree

Task: Create two Python functions, serialize and deserialize. serialize converts a binary tree into a string representation, and deserialize converts that string back into the original binary tree structure.
Input Format for Serialize: The root node of a binary tree.
Input Format for Deserialize: A string representing the serialized binary tree.
Constraints:
  • The binary tree can have any number of nodes.
  • The tree nodes contain integer values.
Output Format for Serialize: A string representing the serialized form of the binary tree.
Output Format for Deserialize: The root node of the binary tree that has been reconstructed from the serialized string.
Sample Input:       1

   / \

  2   3

     / \

    4   5

Suggested Answer:

class TreeNode:

    def __init__(self, val=0, left=None, right=None):

        self.val = val

        self.left = left

        self.right = right

def serialize(root):

    “””Encodes a tree to a single string.”””

    def helper(node):

        if not node:

            vals.append(“null”)

            return

        vals.append(str(node.val))

        helper(node.left)

        helper(node.right)

    vals = []

    helper(root)

    return ‘,’.join(vals)

def deserialize(data):

    “””Decodes your encoded data to tree.”””

    def helper():

        val = next(vals)

        if val == “null”:

            return None

        node = TreeNode(int(val))

        node.left = helper()

        node.right = helper()

        return node

    vals = iter(data.split(‘,’))

    return helper()

Code Explanation for serialize:

  • The serialize function converts a binary tree into a string representation using preorder traversal (root-left-right).
  • It begins at the root and processes each node before its children. For each node, it adds the node’s value to the string.
  • When encountering a null (or empty) node, which signifies the absence of a child in the tree, it adds the placeholder “null” to the string. This is crucial for preserving the tree structure in the string.
  • The resulting string is a comma-separated list of node values and “null” placeholders, reflecting the tree’s structure.

Code Explanation for deserialize:

  • The deserialize function reconstructs the binary tree from its string representation.
  • It uses an iterator to step through each element of the string (split by commas). The iterator ensures that each node is processed in the same preorder sequence.
  • For each non-“null” element, the function creates a new tree node and recursively constructs its left and right children in the preorder sequence.
  • The function handles “null” elements by returning None, effectively reconstructing the tree’s original structure, including its empty branches.
Common Mistakes to Watch Out For:
  • Incorrect tree traversal order in serialization and deserialization leading to wrong tree structure.
  • Not properly handling null/empty nodes.
Follow-ups:
  • How would you handle serialization for trees with data types other than integers?
  • Can you optimize the storage for the serialized string, especially for large trees?
What the question tests? This question tests the candidate’s understanding of binary trees, tree traversal algorithms, and their ability to design algorithms for complex data structures serialization and deserialization.

7. Check if String is a Palindrome

Task: Write a Python function called is_palindrome that checks if a given string is a palindrome. A palindrome is a word, phrase, number, or other sequences of characters that reads the same forward and backward (ignoring spaces, punctuation, and capitalization).
Input Format: A single string.
Constraints:
  • The string may contain alphanumeric characters, spaces, and punctuation.
  • The string can be empty or have any length.
Output Format: A boolean value: True if the string is a palindrome, and False otherwise.
Sample Input:   “A man, a plan, a canal: Panama”
Output: true

Suggested Answer: 

def is_palindrome(s):

    filtered_chars = [c.lower() for c in s if c.isalnum()]

    return filtered_chars == filtered_chars[::-1]

Code Explanation:

  • The function first filters out non-alphanumeric characters and converts all characters to lowercase.
  • It then checks if the list of filtered characters reads the same forwards and backwards.
Common Mistakes to Watch Out For:
  • Not considering cases with spaces, punctuation, and varying capitalizations.
  • Incorrectly comparing characters at mirrored positions.
Follow-ups:
  • Can you optimize the function to check for palindromes in place without creating a new string or list?
  • How would you extend this function to handle multi-word phrases?
What the question tests? This interview question tests the candidate’s ability to manipulate strings, specifically their proficiency in handling character filtering, string reversal, and case insensitivity. It also assesses their understanding of basic programming constructs like loops and conditions.

8. Find the First Non-Repeating Character in a String

Task: Write a Python function named first_non_repeating_char that finds the first non-repeating character in a given string and returns its index. If there is no such character, return -1.
Input Format: A single string.
Constraints:
  • The string consists of only lowercase English letters.
  • The string can be empty or have any length.
Output Format: An integer representing the index of the first non-repeating character. If there is no non-repeating character, return -1.
Sample Input:   “loveleetcode”
Output: 2 (The first non-repeating character is ‘v’, which is at index 2.)

Code Explanation:

  • The function first iterates over the string, creating a dictionary (frequency) to store the count and index of each character.
  • It then iterates over the frequency dictionary to find the first character with a count of 1.
  • The index of this character is returned, or -1 if no non-repeating character is found.
Common Mistakes to Watch Out For:
  • Not correctly handling characters that appear more than once.
  • Failing to consider the order of characters in the string.
Follow-ups:
  • How would you modify the function to handle strings with upper and lower case letters?
  • Can the function be optimized to use less memory?
What the question tests? This interview question tests a candidate’s ability to manipulate strings and use data structures like dictionaries. It also demonstrates their understanding of iteration and conditionals in Python.

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Interview Zen allows hiring managers to review recorded interviews at varying speeds, like 1x, 2x, or even 5x. This functionality provides valuable insights into how the candidate processed the question, how quickly they worked, and how much they modified their code throughout the assessment. This feature lets you better understand the candidate’s technical skills and problem-solving abilities.

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Conclusion

The questions discussed in this guide assess a candidate’s technical skills, problem-solving abilities, and understanding of Python’s nuances. From string manipulation to data structures and algorithms, each question is designed to assess a candidate’s proficiency in Python comprehensively.

Remember, the goal of a technical interview is not just to test a candidate’s current knowledge but also to gauge their ability to learn, adapt, and apply their skills to real-world problems. Using these carefully crafted questions, hiring managers can gain deep insights into the capabilities and potential of their candidates.

We encourage you to leverage Interview Zen for your Python interviewing needs. This online coding platform simplifies the process of creating and managing technical interviews, making it easier to evaluate candidates effectively. 

With its focus on code-related questions and practical assessments, Interview Zen is an invaluable tool for any technical recruiter or hiring manager looking to streamline their interview process and find the perfect fit for their Python development roles.

So, take these questions, adapt them to your specific needs, and use Interview Zen to bring efficiency and precision to your hiring process. Happy interviewing!

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