Top 20 Python Interview Questions

📌 Example question on this topic

Question: What data types does Python support and how to create variables with these types?

Answer: Python supports several built-in data types: numbers - integers and floating point, strings, lists, tuples, sets, dictionaries, and booleans.

Example code with different data types:

# Numbers

number = 42

float_num = 3.14

# Lines

text = «Hello»

# Lists

my_list = [1, 2, 3]

# Tuples

my_tuple = (1, 2, 3)

# Sets

my_set = {1, 2, 3}

# Dictionaries

my_dict = {«key»: «value»}

# Boolean type

is_active = True

📌 Example question on this topic

Question:How to use the ‘for’ and ‘while’ loops in Python. And when should I use each one?

Answer:‘for’ loops are used to iterate over elements in collections, such as lists or strings. For example: for item in items: print(item). The while loop is used when you need to repeat actions until a certain condition is met: while condition: do_something(). It is important to be able to control the repetition process and use loop exit conditions correctly.

📌 Example question on this topic

Question: How to correctly use the ‘if’, ‘elif’ and ‘else’ constructs in Python?

Answer: The if, elif, and else statements help control program logic by allowing code to be executed based on conditions.

Code example:

if condition1:

do_something()

elif condition2:

do_something_else()

else:

do_default_thing()

It is important to construct conditions correctly to avoid errors in the logic of the program.

📌 Example question on this topic

Question: how to work with dictionary items in Python?

Answer: 

# Create a dictionary

user = {‘name’: ‘John’, ‘age’: 25}

# Add an element

user[‘city’] = ‘New York’

# Changing element

user[‘age’] = 26

# Removing an element

del user[‘name’]

# Removing using pop()

city = user.pop(‘city’)

print(user) # Outputs: {‘age’: 26}

📌 Example question on this topic

Question:How do I define and call functions in Python?

Answer:To create a function, use the ‘def’ keyword, then call it by name. For example:

def greet(name):

return f»Hello, {name}!»

print(greet(«Maria»))

📌 Example question on this topic

Question: Why is indentation important in Python?

Answer: Indentation in Python determines the structure of a program. Incorrect code formatting can lead to errors. Strict indentation rules must be followed to maintain the readability and correctness of the program.

📌 Sample question on this topic

Question: What is the difference between a list, tuple, and set in Python?

Answer: 

# List — mutable, ordered, with duplicates

numbers_list = [1, 2, 2, 3]

numbers_list[0] = 5 # Elements can be modified

# Tuple — immutable, ordered, with duplicates

numbers_tuple = (1, 2, 2, 3)

# numbers_tuple[0] = 5 # Elements cannot be modified

# Set — mutable, unordered, no duplicates

numbers_set = {1, 2, 2, 3} # Will be {1, 2, 3}

numbers_set.add(4) # Can add elements

print(numbers_list) # [5, 2, 2, 3]

print(numbers_tuple) # (1, 2, 2, 3)

print(numbers_set) # {1, 2, 3, 4}

📌 Example question on this topic

Question:How do I convert a dictionary to a set in Python?

Answer: Python's built-in methods are used to convert a dictionary to a set. When converting to a set, only dictionary keys are preserved.

Code example:

my_dict = {‘a’: 1, ‘b’: 2, ‘c’: 3}

my_set = set(my_dict)

print(my_set) # Output: {‘a’, ‘b’, ‘c’}

To get a set of values:

values_set = set(my_dict.values())

print(values_set) # Output: {1, 2, 3}

📌 Example question on this topic

Question: In what case would you use merge sort instead of quicksort?

Answer: Merge sort makes sense to use when sorting stability is important. Namely, when you need to preserve the order of identical elements. Sorting is also suitable for large data that does not fit in RAM, since it works on the principle of dividing the data into parts and processing them. It has the same worst-case complexity as the average-case sort — O(n log n), unlike quicksort, which has O(n^2) worst-case complexity.

📌 Example question on this topic

Question: What are the main differences between bubble sort and quicksort?

Answer: Bubble sort is a simple algorithm that compares pairs of adjacent elements and swaps them if they are in the wrong order. This continues until the entire array is sorted.

Sorting has a time complexity of O(n^2), which makes it inefficient for large arrays. Quicksort, on the other hand, uses the divide-and-conquer principle, breaking the array into subarrays and sorting them recursively. Its average time complexity is O(n log n), which makes it suitable for large data.

💡 A module is a file with the .py extension that contains Python code that can be used in other programs.

💡 A package is a directory containing modules and special-purpose files.

📌 Example question on this topic

Question:What is encapsulation in Python and how is it implemented?

Answer:Encapsulation is a principle that involves hiding the internal state of an object and restricting access to its data. In Python, encapsulation is achieved using private methods and attributes.

Example code with encapsulation:

class Employee:

def __init__(self, salary):

self.__salary = salary

@property

def salary(self):

return self.__salary

@salary.setter

def salary(self, value):

if value > 0:

self.__salary = value

worker = Employee(5000)

print(worker.salary)

worker.salary = 6000

📌 Example of a question for this topic

Question:How to implement polymorphism in Python?

Answer:Polymorphism allows objects of different classes to handle calls to methods of the same name in their own way. In Python, this is achieved by using methods with the same name in different classes.

Example code:

class Bird:

def fly(self):

return «Flying»

class Airplane:

def fly(self):

return «Soaring through the sky»

def perform_fly(entity):

print(entity.fly())

bird = Bird()

airplane = Airplane()

perform_fly(bird) # Outputs «Flying»

perform_fly(airplane)

📌 Example question on this topic

Question: What classes and methods would you create to implement a simple library management program?

Answer: A Book class that contains attributes: book title, author, and ISBN. A Library class would manage a collection of books, with methods for adding, deleting, and searching for books. It is important to apply encapsulation so that methods can interact only with the necessary attributes.

SQLite is a built-in database that does not require a separate server to run. Suitable for small projects and simple testing.

PostgreSQL is a powerful database with support for various functions. The psycopg2 library is used to work with it.

📌 Example question on this topic

Question: How do I connect to a PostgreSQL database using Python?

Answer: Using the psycopg2 library to interact with PostgreSQL from Python. The connection is established through the connect() function, where the connection parameters are passed.

Code example:

import psycopg2

try:

connection = psycopg2.connect(

dbname=»your_dbname»,

user=»your_username»,

password=»your_password»,

host=»localhost»,

port=»5432″

)

cursor = connection.cursor()

cursor.execute(«SELECT version();»)

db_version = cursor.fetchone()

print(f»Connected to: {db_version}»)

except Exception as error:

print(f»Error: {error}»)

finally:

if connection:

cursor.close()

connection.close()

📌 Example of a question on this topic

Question: What security measures should be taken when working with databases to protect data from SQL injection attacks?

Answer: Three methods - stored procedures, access rights restrictions, and input data validation.

• Stored procedures are pre-created and validated queries that are called through the API: cursor.callproc(‘get_user’, [user_id]).

• Access Rights Restriction — each user is given only the necessary privileges in the database.

• Input Data Validation — checking and cleaning all data received from the user.

Parallelismis a technique in which several tasks are executed simultaneously to speed up the process.

Multithreadingis the principle of parallel execution of several tasks within a single application with a shared memory space. In operating systems, threads run simultaneously due to fast switching between them at the processor level.

📌 Example question on this topic

Question:What is the difference between the threading and multiprocessing modules in Python? When should they be used?

Answer:The threading module in Python is used to work with threads. It helps create and manage them, synchronize and organize their interactions. It is suitable for tasks that can be performed in parts and copes well with I/O tasks.

Multiprocessing, on the other hand, creates separate processes that use their own memory. The module is suitable for tasks that require the use of all processor cores and where the security of process isolation is important.

Example code for threading:

import threading

def print_numbers():

for i in range(5):

print(f’Number: {i}’)

thread = threading.Thread(target=print_numbers)

thread.start()

thread.join()

Example code for multiprocessing:

from multiprocessing import Process

def print_numbers():

for i in range(5):

print(f’Number: {i}’)

process = Process(target=print_numbers)

process.start()

process.join() 

📌 Example question on this topic

Question: What difficulties can arise when using threading in Python? 

Answer: The main difficulty is that multiple threads access shared resources, which can lead to synchronization issues. Sometimes this causes errors - data races, which complicate debugging and lead to unpredictable program behavior.

📌 Example question on this topic

Question: What is the difference between NumPy and pandas? Give an example of their use.

Answer: NumPy is a library for working with multidimensional arrays, matrices, and mathematical functions. Pandas is used to work with tabular data and provides data structures and functions for manipulating it efficiently.

For example, NumPy is often used for numerical operations and linear algebra. And pandas makes it easy to import, analyze, and process data from tables.

Code example:

import numpy as np

import pandas as pd

# NumPy example

array = np.array([1, 2, 3, 4])

print(«NumPy array:», array * 2) # Multiply all array elements by 2

# pandas example

data = {‘Name’: [‘John’, ‘Anna’, ‘Peter’], ‘Age’: [28, 24, 35]}

df = pd.DataFrame(data)

print(«DataFrame:\n», df)

📌 Example question on this topic

Question: What is the advantage of Flask over Django and in what situation would you use Flask?

Answer:Flask is a lightweight web framework that provides a minimal set of tools for building web applications. It is ideal for creating small and simple prototypes where only basic functionality is needed.

Django, on the other hand, provides a comprehensive solution with various built-in functions, which makes it more suitable for larger and more complex projects.

Flask code example:

from flask import Flask

app = Flask(__name__)

@app.route(‘/’)

def hello_world():

return ‘Hello, World!’

if __name__ == ‘__main__’:

app.run()

📌 Example question on this topic

Question: how to create a time series with pandas and calculate the moving average average?

Answer: The pandas package is suitable for working with time series. You can create a time series using pd.date_range(). To calculate the moving average, you can use the .rolling() method.

Code example: 

import pandas as pd

# Creating a time series

dates = pd.date_range(start=’2023-01-01′, periods=5)

data = [10, 20, 15, 25, 30]

series = pd.Series(data, index=dates)

# Calculation of the moving average average

rolling_mean = series.rolling(window=2).mean()

print(«Series:\n», series)

print(«Rolling average:\n», rolling_mean)

📌 Example question on this topic

Question:How can I use unittest to test a function that returns the square of a number?

Answer:

import unittest

def square(x):

return x * x

class TestSquareFunction(unittest.TestCase):

def test_square(self):

self.assertEqual(square(2), 4)

self.assertEqual(square(-3), 9)

self.assertEqual(square(0), 0)

if __name__ == ‘__main__’:

unittest.main()

📌 Example question on this topic

Question: How can I test that a function raises an exception in pytest?

Answer: Pytest offers a special way to test that a function raises exceptions using the pytest.raises construct.

Example code:

import pytest

def divide(x, y):

if y == 0:

raise ValueError(«Cannot divide by zero!»)

return x / y

def test_divide_zero():

with pytest.raises(ValueError, match=»Cannot divide by zero!»):

divide(10, 0)

📌 Example question on this topic

Question: How can I debug a function using Python's built-in tools?

Answer: Python provides the pdb module for built-in debugging. With it, you can step through code line by line, view variable values, and monitor program flow.

Code example:

def buggy_function(x, y):

import pdb; pdb.set_trace() # Setting a breakpoint

result = x + y

return result

buggy_function(4, ‘2’) # Suspected error due to addition of a number and a string