50+ Interview Questions on Python Programming Language

What are the most analytic and fundamental interview questions on Python?

If you are seeking answers to similar questions, you have reached your destination. Hiring has always been challenging for every business, as the employees must be good at what they do.
 

And, if you are hiring for a new technology like Python, it is undoubtedly a tremendous pain in the head. Hence, we are here with some questions you can ask respective candidates and analyse their tech-centric knowledge effectively.

However, you can also leverage HyreSnap Interview as a Service platform to recruit best-in-market candidates. We have a team of 450+ tech experts who execute the interview process and provide you with an AI-generated performance report to check every candidate's performance.

If not, these questions will help you check the Python knowledge of your shortlisted candidates:

Preparing for an interview as an interviewer is crucial to ensure a smooth and effective interview process. Here are some steps you can follow to prepare for an interview as an interviewer:

Review the job description

Familiarise yourself with the requirements, responsibilities, and qualifications mentioned in the job description. This will help you assess candidates' suitability for the role.

Understand the team and company

Understand the team structure, company culture, and values. This knowledge will help you evaluate candidates based on their organisational fitness.

Identify the key skills and competencies

Determine the essential skills and competencies required for the position. This will help you frame relevant interview questions and assess candidates effectively.

Prepare a structured interview plan

Create a clear interview plan that includes the order and duration of each interview section, the specific questions you'll ask, and the evaluation criteria you'll use.

Develop a list of interview questions

Prepare a set of well-thought-out interview questions that assess candidates' technical skills, problem-solving abilities, and relevant experience. Consider using a mix of behavioral, technical, and situational questions.

Conduct a resume review

Review candidates' resumes before the interview to gain insights into their educational background, work experience, and achievements. Note down any areas you'd like to explore further during the interview.

Research the candidates

Spend some time researching the candidates before the interview. Look at their LinkedIn profiles, portfolios, and any other publicly available information to better understand their background and experience.

Familiarise yourself with legal and ethical guidelines

Understand the legal and ethical considerations related to interviewing, such as non-discrimination laws and confidentiality obligations. Ensure you follow these guidelines throughout the interview process.

Coordinate logistics

Confirm the interview schedule, location (in-person or virtual), and any technical requirements well in advance. Ensure all necessary arrangements are made to provide a smooth interview experience for the candidates.

Practise active listening

Develop your active listening skills to ensure you fully understand the candidates' responses. Take notes during the interview to capture important details and impressions.

Create a scoring or evaluation system

Define an evaluation system to assess candidates consistently and fairly. This can include rating scales, specific criteria, or a scoring rubric to quantify the evaluation process.

Collaborate with other interviewers

If multiple interviewers are involved, collaborate with them to align on interview objectives, questions, and evaluation criteria. This promotes a consistent evaluation process and allows for a comprehensive assessment of candidates.

Consider real-world scenarios

Prepare scenario-based questions or exercises that simulate real-world situations candidates may encounter in the role. This helps assess their problem-solving abilities and how they apply their skills in practical scenarios.

Anticipate follow-up questions

Prepare follow-up questions to delve deeper into specific areas based on candidates' responses. This allows you to gather more information and better understand their thought process.

Stay updated on industry trends

Stay informed about the latest trends, technologies, and practices relevant to the position and industry. This lets you ask informed questions and assess candidates' knowledge and adaptability.

Be mindful of bias

Be aware of unconscious biases that may influence your judgement during the interview. Maintain objectivity and evaluate candidates based on their qualifications, skills, and potential rather than personal factors.

Plan for candidate engagement

Prepare strategies to engage candidates during the interview, such as providing an overview of the company, team, and growth opportunities. This helps create a positive candidate experience and showcases the company's culture.

Practise effective communication

Brush up on your communication skills to ensure clarity and professionalism during the interview. Pay attention to your tone, body language (if applicable), and responsiveness.

What is Python?

A: Python is a high-level, interpreted programming language known for its simplicity and readability. It emphasizes code readability and has a large standard library.

What are the key features of Python?

A: The key features of Python include its simplicity, readability, extensive library support, dynamic typing, automatic memory management, and cross-platform compatibility.

Explain the difference between Python 2 and Python 3.

A: Python 2 and Python 3 are two major language versions. Python 3 introduced several backward-incompatible changes and syntax improvements compared to Python 2. Python 3 is the recommended version for new projects.

How do you comment in Python?

A: In Python, single-line comments start with a hash symbol (#). Multi-line comments are enclosed within triple quotes (''' or """).

What is PEP 8?

A: PEP 8 is the official style guide for Python code. It provides guidelines on formatting code to enhance readability and maintain consistency across projects.

How do you declare a variable in Python?

A: Variables in Python are dynamically typed and do not require explicit declaration. You can assign a value to a variable using the assignment operator (=).

What are the different data types in Python?

A: Python supports several built-in data types, including integers, floats, strings, booleans, lists, tuples, dictionaries, and sets.

Explain the concept of list comprehension.

A: List comprehension is a concise way to create lists in Python. It allows you to generate a new list by iterating over an existing sequence and applying a condition or transformation to each element.

How do you handle exceptions in Python?

A: Exceptions in Python can be handled using the try-except block. The code that may raise an exception is placed within the try block, and the corresponding exception handling code is placed within the except block.

What is the purpose of the "self" parameter in Python class methods?

A: The "self" parameter refers to the instance of the class and is used to access instance variables and methods within the class.

Explain the difference between a shallow copy and a deep copy.

A: In Python, a shallow copy creates a new object that references the original data, while a deep copy creates a completely independent copy of the original data.

What are decorators in Python?

A: Decorators are a way to modify the behavior of a function or class without directly changing its source code. They allow you to add functionality before and after the function or class.

How do you open and close a file in Python?

A: To open a file in Python, you can use the built-in open() function. Once you are done with the file, it is essential to close it using the close() method to free up system resources.

Explain the concept of a generator in Python.

A: A generator is a unique function that returns an iterator object. It allows you to generate a sequence of values without storing them in memory all at once.

What is the difference between a function and a method in Python?

A: In Python, a function is a block of reusable code that performs a specific task. A method, on the other hand, is a function that is associated with an object and can access its data.

Explain the Global Interpreter Lock (GIL) in Python and its impact on multi-threading.

A: The Global Interpreter Lock (GIL) is a mechanism in CPython (the reference implementation of Python) that allows only one thread to execute Python bytecode at a time. This limits the ability of Python threads to utilize multiple CPU cores effectively, making CPU-bound multi-threaded programs slower. However, it does not impact I/O-bound or asynchronous tasks as they can release the GIL.

What are Python decorators, and how can they be used to enhance function behaviour?

A: Python decorators are a way to modify the behavior of functions or classes. They take a callable object as input and return a modified version of it. Decorators can be used for tasks such as adding additional functionality, logging, input validation, and authorization.

Explain the difference between deep copy and shallow copy in Python.

A: In Python, a shallow copy creates a new object that references the original data, whereas a deep copy creates a completely independent copy of the original data. In a deep copy, changes made to the copy do not affect the original, while in a shallow copy, changes may affect the original object.

What is the difference between a generator and normal functions in Python?

A: A generator function returns an iterator object using the "yield" keyword, allowing for lazy evaluation and memory-efficient processing of large datasets. Normal functions, on the other hand, execute entirely and return a value before exiting.

How does Python handle memory management?

A: Python uses automatic memory management through a technique called garbage collection. It keeps track of no longer referenced objects and automatically deallocates their memory.

Explain the concept of name mangling in Python.

A: Name mangling is a mechanism in Python that prefixes class attributes with a double underscore (e.g., "__attribute") to make them private. This provides a way to avoid accidental name clashes in subclasses.

How can you profile and optimise Python code for performance?

A: Python provides built-in profiling tools such as cProfile and line_profiler. You can improve performance by identifying bottlenecks and optimising critical sections of code. Techniques include algorithmic optimization, caching, using built-in functions, and utilising libraries written in C/C++ for computationally intensive tasks.

Explain the difference between a shallow copy and a view in NumPy arrays.

A: In NumPy, a shallow copy creates a new array object that references the original data, while a view creates a new array object that shares the same data buffer with the original array. Modifying the data in a shallow copy affects the original array, whereas modifying a view creates a new array.

How do you handle circular imports in Python?

A: Circular imports occur when two or more modules depend on each other. To handle this, you can restructure your code to eliminate circular dependencies, move imports to local scopes within functions, or use techniques such as lazy imports or import-time checking.

What is the purpose of the "yield" keyword in Python?

A: The "yield" keyword is used in generator functions to define a sequence of values to be returned lazily, one at a time, rather than all at once. It allows efficient memory usage and supports iteration over large or infinite sequences.

Explain the concept of metaclasses in Python.

A: Metaclasses are classes that define the behaviour of other classes. They allow you to customise class creation and control the behaviour of class objects. Metaclasses are defined by subclassing the "type" metaclass or using class decorators.

How does Python manage memory in the context of large objects and circular references?

A: Python uses a combination of reference counting and garbage collection to manage memory. For large objects, Python uses memory allocation techniques specific to the underlying platform. Circular references are handled through the garbage collection process, where unreachable objects are detected and freed.

Explain the concept of method overloading and method overriding in Python.

A: Method overloading refers to the ability to define multiple methods with the same name but different parameters within a class. Python does not support method overloading directly. 

Method overriding, on the other hand, occurs when a subclass defines a method with the same name as a method in its superclass, thereby replacing the inherited implementation.

What are descriptors in Python? Provide an example of their usage.

A: Descriptors are a powerful Python feature that allows you to customise attribute access and modification. They are implemented through the use of descriptor classes with "get," "set," and "delete" methods. An example could be a descriptor that converts an attribute's value to uppercase when accessed or modified.

Explain the Global Interpreter Lock (GIL) and its impact on Python's concurrency model.

A: The Global Interpreter Lock (GIL) is a mechanism in CPython that allows only one thread to execute Python bytecode at a time. This limits the ability to achieve true parallelism using threads. However, Python provides alternatives such as multiprocessing, asyncio, and multi-threading for I/O-bound and concurrent tasks.

What are the differences between a shallow copy and a deep copy in Python? When would you use each?

A: A shallow copy creates a new object that references the original data, while a deep copy creates a completely independent copy of the original data. Shallow copies are helpful when creating a new object that shares data with the original, while deep copies are necessary when you need a new object with a separate copy of the data.

Explain the differences between "is" and "==" comparison operators in Python.

A: The "is" operator checks if two variables refer to the same object in memory, while the "==" operator checks if the values of two variables are equal. "is" compares object identity, whereas "==" compares object equality.

How does Python handle memory management in the context of circular references between objects?

A: Python uses a combination of reference counting and garbage collection to handle circular references. 

The reference counting mechanism detects and deallocates objects when their reference count reaches zero. For objects involved in circular references, Python employs a garbage collector that detects and collects cyclically referenced objects.

Explain the method resolution order (MRO) concept in Python multiple inheritance.

A: Method Resolution Order (MRO) is the order in which Python searches for methods in a class hierarchy with multiple inheritance. 

The MRO follows the C3 linearization algorithm, which provides a consistent and predictable order for method lookup. It ensures that each method in the inheritance hierarchy is called once and only once.

How would you remove duplicates from a list in Python?

A: One way to remove duplicates from a list is by converting it to a set and then back to a list: unique_list = list(set(original_list)).

How can you read data from a CSV file in Python?

A: You can use the CSV module in Python to read data from a CSV file. Here's an example:

import csv

with open('data.csv', 'r') as file:

    csv_reader = csv.reader(file)

    for row in csv_reader:

        print(row)

How can you sort a dictionary by its values in descending order?

A: You can use the sorted() function with the items() method and a lambda function as the key parameter. Here's an example:

my_dict = {'a': 5, 'b': 2, 'c': 8}

sorted_dict = dict(sorted(my_dict.items(), key=lambda x: x[1], reverse=True))

How can you find the index of an element in a list?

A: You can use the index() method to find the index of an element in a list. Here's an example:

my_list = [1, 2, 3, 4, 5]

index = my_list.index(3)

How would you reverse a string in Python?

A: One way to reverse a string is by using string slicing with a step of -1. Here's an example:

my_string = 'Hello, World!'

reversed_string = my_string[::-1]

How can you concatenate two lists in Python?

A: You can use the + operator to concatenate two lists. Here's an example:

list1 = [1, 2, 3]

list2 = [4, 5, 6]

concatenated_list = list1 + list2

How would you find the maximum and minimum values in a list?

A: You can use the max() and min() functions to find the maximum and minimum values in a list. Here's an example:

my_list = [3, 1, 5, 2, 4]

max_value = max(my_list)

min_value = min(my_list)

How can you check if a string is a palindrome in Python?

A: One way to check if a string is a palindrome is by comparing it with its reverse. Here's an example:

def is_palindrome(string):

    return string == string[::-1]

result = is_palindrome('radar')

How would you find the factorial of a number in Python?

A: You can use recursion or a loop to find the factorial of a number. Here's an example using a loop:

def factorial(n):

    result = 1

    for i in range(1, n+1):

        result *= i

    return result

factorial_value = factorial(5)

How can you check if a string contains only digits?

A: You can use the isdigit() method to check if a string contains only digits. Here's an example:

def contains_only_digits(string):

    return string.isdigit()

result = contains_only_digits('12345')

How would you calculate the sum of all numbers in a list using a list comprehension?

A: You can use a list comprehension along with the sum() function to calculate the sum of all numbers in a list. Here's an example:

my_list = [1, 2, 3, 4, 5]

sum_of_numbers = sum([num for num in my_list])

How can you find the most frequent element in a list?

A: You can use the max() function with the key parameter and the list.count() method to find the most frequent element in a list. Here's an example:

my_list = [1, 2, 3, 3, 3, 4, 5, 5]

most_frequent = max(set(my_list), key=my_list.count)

How would you merge two dictionaries in Python?

A: You can merge two dictionaries using the update() method. Here's an example:

dict1 = {'a': 1, 'b': 2}

dict2 = {'c': 3, 'd': 4}

merged_dict = dict1.copy()

merged_dict.update(dict2)

How can you find the second-largest number in a list?

A: You can use the sorted() function to sort the list in descending order and then access the second element. Here's an example:

my_list = [5, 2, 8, 1, 9]

second_largest = sorted(my_list, reverse=True)[1]

How would you check if a string is an anagram of another string?

A: You can sort both strings and compare them. If they are anagrams, they will have the same characters in the same order. Here's an example:

def is_anagram(str1, str2):

    return sorted(str1) == sorted(str2)

result = is_anagram('listen', 'silent')

How can you find the common elements between two lists?

A: You can use the set intersection (&) operator to find the common elements between two lists. Here's an example:

list1 = [1, 2, 3, 4]

list2 = [3, 4, 5, 6]

common_elements = list(set(list1) & set(list2))

How would you convert a string to a list of characters?

A: You can use list comprehension to convert a string to a list of characters. Here's an example:

my_string = "Hello"

char_list = [char for char in my_string]

Explain the concept of generators in Python and provide an example of their usage.

A: Generators are functions that use the "yield" keyword to produce a sequence of values lazily. They allow efficient memory usage and support iteration over large or infinite sequences. An example is a generator that yields the Fibonacci sequence:

def fibonacci():

    a, b = 0, 1

    while True:

        yield a

        a, b = b, a + b

What is the purpose of the ‘yield from’ statement in Python? Provide an example.

A: The 'yield from' statement in Python delegates the iteration control to another generator or iterator. It allows you to flatten nested structures and write more concise generator functions. Here's an example that demonstrates the usage of 'yield from':

def subgenerator():

    yield 'a'

    yield 'b'

def maingenerator():

    yield from subgenerator()

    yield 'c'

for item in maingenerator():

    print(item)  # Output: 'a', 'b', 'c'

How can you create and work with threads in Python? Provide an example.

A: Threads in Python can be created and managed using the ‘threading’ module. Here's an example that demonstrates the creation of a thread:

import threading

def worker():

    print('Worker thread')

thread = threading.Thread(target=worker)

thread.start()

These questions will help you understand every candidate's capabilities in the Python programming language. However, if you do not have an expert by your side, we recommend having a look over HyreSnap Interview as a Service platform.

Our platform works by the collective power of the machine as well as human intelligence. 

Features of HyreSnap Interview as a Service platform:

AI-Report Generation

450+ Tech Experts

Hasslefree Recruitment

Interview Assistance

AI job matcher

24X7 chat support

Mock tests

Key Takeaways
 

Recruitment is not a piece of cake, even for expert recruiters. A wrong recruit can affect the growth of your entire business negatively. Hence, if you want expert assistance, contact us at info@hyresnap.com.