Python Interview Questions and Answers For 2025

Python is one of the most widely used programming languages today. It is used in applications like web development, data science, artificial intelligence, and many more. Python offers simple syntax, versatility, and strong community support, which makes it the best choice for developers and top companies like Google, Meta, Amazon, Microsoft, etc. If you are preparing for the Python interview questions, you have come to the right place!

In this article, you will learn the most commonly asked Python interview questions and answers, which range from basic concepts for the freshers to advanced concepts for the experienced developers.

The Python interview questions and answers article is classified into the following categories:

Table of Contents:

• Basic Python Interview Questions for Freshers
• Python Intermediate Interview Questions
• Advanced Python Interview Questions for Experienced Professionals
• Python OOPs Interview Questions
• Python Coding Interview Questions
• How to Prepare for a Python Interview?

Basic Python Interview Questions for Freshers

If you’re starting your career in tech, learning these basic Python interview questions is essential. These questions cover all the basic topics like variables, loops, and functions that are often asked in the interview for freshers.

Going through these Python basic interview questions and understanding them helps you clear your basics and answer with confidence. They help the interviewer know that you have a strong knowledge of the core concepts of Python. Whether you are applying for an internship or entry-level roles, these questions are a great place to start your preparation.

1. What is Python, and how does its dynamic typing work?

Python is a high-level and general-purpose programming language developed by Guido van Rossum in 1991. It uses English keywords extensively, and has a simpler syntax as compared to multiple other programming languages.

It’s a dynamically typed language with capabilities of garbage collection for better memory performance. It supports multiple programming paradigms, including structured, object-oriented, and functional programming.

Dynamic typing in Python allows you to determine the type of a variable at runtime, rather than during code compilation.

Example:

Here, x can hold different types of data (first an integer, then a string) during its lifecycle. Python internally tracks the type of x and adjusts its behaviour based on the current value.

2. What are the key features of Python? How is it different from other languages?

Python is one of the most popular programming languages, especially among beginners. What sets it apart are some of its features that make it flexible and powerful to use:

• Python is a dynamic programming language, which means there is no need to declare variables before. It also automates the process of memory allocation and deallocation.
• Python is an interpreted language; therefore, it runs the code line by line, which is very helpful while debugging.
• Python is a high-level language, and there is no need to look into system-level details like memory management or compatibility.
• Python has a clean and simple syntax. The code readability is better compared to other programming languages.
• Python is across-platform language which means that the code which is written on Windows can run in other operating systems like Linux or MacOS without the need for changing the code.

3. What do you mean by Python being an Interpreted language?

Python is an interpreted language, as it allows the code to execute line by line rather than being compiled all at once. This approach simplifies debugging and makes Python more flexible, saving time for compilation.

4. What is the difference between slicing and indexing?

Here are the differences between slicing and indexing:

Here is an example representing slicing and indexing:

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

In Python, argument passing is done using “pass by object’s reference” or “call by sharing”

If the object is mutable (like a list or dictionary), changes made inside the function will affect the original object.

If the object is immutable (like an integer or string), changes that are made inside the function will not affect the original object.

Example with mutable object (list):

Example with immutable object (int):

6. What is regression in Python?

Regression is termed as a supervised machine learning algorithm technique, which is used to find the correlation between variables, and help predict the dependent variable(y) based upon the independent variable (x). It is mainly used for prediction, time series modeling, forecasting, and determining the causal-effect relationship between variables.

The Scikit library is used in Python to implement regression and all machine learning algorithms.

There are two different types of regression algorithms in machine learning:

• Linear Regression: Used when the variables are continuous and numeric.
• Logistic Regression: Used when the variables are categorical.

7. Why would you use NumPy arrays instead of lists in Python?

Here are a few reasons why NumPy arrays are used over Python lists:

• Memory efficiency: NumPy arrays consume less memory compared to regular Python lists because they store the data more compactly.
• Performance: NumPy arrays are optimized for faster numerical computations, which makes them significantly more efficient for vectorized operationsand large datasets compared to Python lists.
• Readability: NumPy arrays provide a simpler and more readable syntax for the mathematical operations, mainly for handling large datasets or multidimensional data.

8. What is the difference between %, /, //?

In Python, %, /, and // are arithmetic operators with distinct functions:

• The ‘ % ’ is the modulus operator, which returns the remainder of a division. For instance, 5 % 2 would return 1.
• The ‘ / ’ is the division operator that performs floating-point division and returns a float. For example, 5 / 2 would return 2.5.
• The ‘ // ’ is the floor division operator that performs division but rounds down the result to the nearest whole number. So 5 // 2 would return 2.

9. What are collections? What is the significance of collections in Python?

Collections refer to built-in data structures or containers in Python capable of holding collective data in a single variable. These data structures are alternatives to built-in types such as lists, dictionaries, sets, and tuples.

Let’s take an example to understand collections in Python. Suppose you have lots of books and are trying to build a library. Each book is different; some are about life lessons, others are about stories, and some explore magic. In Python, collections are like those groups of books. They help you organize a variety of items (like numbers, words, or other data) in an organized way, just like how you organize your books in your library.

The Python collection Module implements specialized container data types, providing alternatives to Python’s general-purpose built-in containers. Let’s see some of the most commonly used collection modules in Python.

• namedtuple: A tuple subclass that allows accessing elements by name and index. It provides a more readable and self-documenting structure.
• deque: A deque or double-ended queue allows adding and removing elements efficiently from both ends, which is useful for implementing queues and stacks.
• Counter: A dictionary subclass for counting hashable objects, which is used to count occurrences of elements in a collection, like a list or string.
• defaultdict: It is a dictionary subclass that provides a default value for a nonexistent key. This is useful when you want to avoid KeyError and automatically initialize missing keys.
• OrderedDict: A dictionary subclass that maintains the order of keys as they are added. In Python 3.7+, regular dictionaries maintain insertion order by default, but OrderedDict ensures this behaviour in earlier versions and provides extra methods.
• ChainMap: A class that groups multiple dictionaries or mappings into a single view.

These collections in Python help in writing clean, efficient, and readable code, and are very useful when using large datasets.

10. Explain the difference between pickling and unpickling.

11. What is Experimental Just-in-Time (JIT) Compilation in Python?

Experimental Just-in-Time (JIT) Compilation in Python is a major enhancement feature that was introduced in Python 3.14. With C,Python as the main interpreter, it is known as an upcoming performance enhancement feature. The goal of JIT is to improve the execution speed of Python code by compiling parts of it during runtime, instead of interpreting it line-by-line, and helps in handling heavy computations.

12. What are Access Specifiers in Python?

Access specifiers (also known as access modifiers) are used to control the visibility or accessibility of class attributes and methods. They help in defining how and where to access the methods of the class.

• Public members: Can be accessed from anywhere, either inside or outside the class.
• Protected members: They are denoted by a single underscore (_), indicating they should only be accessed within the class or subclasses.
• Private members: They are denoted by double underscore (__), which makes the member less accessible outside the class.

13. Is this statement true? “Python is a case-sensitive language”. Explain it with an Example

Yes, Python is a case-sensitive language as the identifiers like Function, function, and FUNCTION are not treated as the same name. For example, defining a variable as a name is not the same as NAME or Name in Python.

14. What are the differences between Lists and Tuples?

15. Explain the differences between a set and a dictionary.

Sets and dictionaries are both built-in data structures for storing and managing data.

16. Differentiate between Mutable and Immutable data types.

17. What is list comprehension?

List comprehension in Python is a concise way of creating a new list from existing data, like a list, a range of numbers and even a string. It helps in replacing the usage of for loops and makes the code more readable.

Key Features of List Comprehension:

• It allows the creation of new lists using a single-line expression.
• It acts as a simple alternative to loops for building lists.
• It can include conditions or any changes to filter or modify the elements.
• Improves code readability and performance in Python programs.

Example:

18. What is the difference between break and continue?

Here are the differences between break and continue:

Example for break statement:

Example for a continue statement:

19. What is the Pass statement?

The pass statement in Python is used when a particular statement is required syntactically, but there is no code to be added. It acts like a placeholder that helps in avoiding the errors caused by empty blocks in Python.

Key points:

• The pass statement helps in keeping a code block empty without causing any error.
• It is often used in loops, functions, classes and if statements, when the code is not ready.
• Python shows an error if a block is left empty. By using the pass statement, the error can be resolved.
• It helps in planning or testing parts of code that are written later after the execution.

Here is an example of the pass statement:

20. What do you understand by scope resolution?

The scope is the area in a program where a variable or function can be accessed. In simple terms, scope resolution determines where to use or interact with a particular variable or function.

There are two main types of scope: 

1. Global Scope

When a variable is defined outside of any function or block, it’s accessible from anywhere in the code.

Example: A book is placed on a public shelf so that anyone in the library can access it.

2. Local Scope

When a variable is defined inside a function, it can only be accessed within that function.

Example: A book kept in the individual drawer is accessed by only the owner.

21. Can we use else with For in Python?

Yes, the else clause is an integral part of the for-loop. It is executed when the loop completes normally and no condition is TRUE.

Example:

22. What are negative indexes, and why are they used?

In Python, negative indexes are used to access the elements from the end of the sequence, like lists and tuples.

• It is very helpful when you want to access the values from the end of the sequence, without knowing the exact length of the sequence.
• Negative indexes start from -1,which refers to the last item and -2 refers to the second last and so on.

Example:

23. What is PEP 8 in Python?

PEP in Python stands for Python Enhancement Proposal. It comprises a collection of guidelines that outline the optimal approach for crafting and structuring Python code to improve the readability and clarity of the code. PEP 8 mainly focuses on style conventions in Python, like indentation, naming patterns, and line length, which helps in standardising code formatting.

24. What is exception handling in Python?

Exceptional handling is a feature that helps developers handle runtime errors and exceptions efficiently without crashing the program. It consists of multiple blocks, as stated below:

• try: wraps the risky code that might raise an exception or error.
• except: wraps code that is to be executed if an error is encountered.
• else: wraps code that is to be executed if no error is encountered.
• finally: wraps code that is to be executed no matter what.

25. What are compile-time and runtime errors in Python?

Compile-time errors and runtime errors are the two errors in Python at different stages of execution of the program.

Yes, both types of errors exist in Python, but instead of using the term “compile-time error”, syntax errors are used more commonly in Python, because these errors are caught when the interpreter is going through the code line by line, and encounters a piece of code that doesn’t follow the Python syntax. Everything else is handled as runtime errors in Python.

26. What are loops in Python? How do you write a nested for-loop program?

The loop is a sequence of instructions that gets executed repeatedly until an exit condition is reached.

There are two main types of loops in Python:

For Loop: This Loop iterates over a sequence like lists, strings, tuples or any other iterable object, and is used when the number of iterations is known in advance.

Example:

27. What is the method to write comments in Python?

Python comments are statements used by the programmer to increase the readability of the code. You can define a single comment by using the # symbol. Another way of commenting is to use the docstrings (strings enclosed within triple quotes).

28. Do we need to declare variables with respective data types in Python?

No, Python is a dynamically typed language, and the interpreter automatically identifies the data type of a variable based on the type of value assigned. This makes coding easy and efficient for beginners, and also, the data type can be changed later, which is executed without any error in Python.

29. What does len() do in Python?

The len() function is a built-in method in Python that is used to get the length of sequences such as lists, strings, and arrays.

Example:

30. What is the difference between append() and extend() methods?

Both the append() and extend() methods are used to add elements at the end of a list.

• Append(element):Adds the given element at the end of the list that calls this append() method
• Extend(another-list):Adds the elements of another list at the end of the list that calls this extend() method

31. What is a dictionary in Python?

A Dictionary is one of the collective data types available in Python. It has a few characteristics:

• It can store a set of heterogeneous elements(elements with different data types).
• It stores the elements in the form of a key-value pair.
• No order is maintained while storing the elements, i.e., indexing and slicing are not applicable.
• Keys can’t be collective datatypes except for strings.

Syntax:

Variable_name = {key1 : value1, key2 : value2, ………}

Example:

32. What are functions in Python?

Functions are entities in Python that increase code reusability and decrease code base size. These are reusable blocks of code that can be invoked by just making a function call. A function consists of two parts:

• Function Definition:It is where you define what the function will perform.
• Function Call:It is used to invoke the function.

33. What are the common built-in data types in Python?

Here is the set of built-in data types in Python:

Universal Data type:

• Integer is referred to as int
• Float is referred to as float
• Boolean is referred to as bool

Collective Data Type:

• Lists
• String
• Tuple
• Dictionary
• Set

34. What is type conversion in Python?

Type conversion, also known as type casting, is the process of converting a variable from one data type to another. This is done by passing the variable as a parameter in the data type class function.

For Example:

35. How to comment with multiple lines in Python?

To comment multiple lines in Python, every line should be prefixed the line with #. To do so, select the lines that you want to comment and press CTRL + / for Windows and Linux operating systems and Cmd + / for macOS. This shortcut comments the selected lines. To remove the comments, use the same shortcut again.

Python Intermediate Interview Questions

36. What are generators and decorators?

Generators and Decorators are two methods used in Python to modify functions in a way that the code becomes more readable and efficient. However, they are used for completely different purposes.

Generators

• Generators are special functions in Python that return an iterator object. You can iterate over the values one at a time using this iterator.
• They use the yield method instead of the return method, and it is usually used in functions.
• Useful when dealing with large datasets, infinite sequences, or lazy evaluation.
• Generator functions remember their state between each yield, which is very useful for pausing and resuming the execution.

Here is an example representing the use of generators:

Decorators

• Decorators are the higher-order functions in Python used to modify or enhance an existing function or class.
• They extend the behaviour or properties of the existing functions or classes without changing the code.
• Decorator functions take a function as an argument, add some behaviour to it, and return the new functionwith modified or extended behaviour.
• Useful in cases like logging, authentication, and measuring execution time.

Here is an example of a decorator:

37. Differentiate between sorted() and sort()

The sort() and sorted() are the two built-in methods used to sort objects in Python. However, their features differ in some aspects that we’ve covered below.

Here is an example differentiating sorted() and sort():

38. What is the method for generating random numbers in Python?

The Random Module is used to generate a random number in Python. You can generate random integers, floating-point numbers and also help in selecting a random number from the given list. The random module has to be imported before using it, which can be done using the import random in Python. For example, it can be used in the dice roll game to select the random numbers between 1 to 6.

Example:

39. What is an event loop in Python and how does it manage asynchronous tasks?

An event loop is typically the core of Python asyncio framework. It controls asynchronous operations by scheduling and executing coroutines, tasks, and I/O-bound operations without blocking the primary thread.

• • The event loop generally keeps looking for prepared tasks and executes them when it is possible.
  • It also allows non-blocking I/O by switching tasks while waiting for operations such as file access or network requests.

Example:

import asyncio

async def say_hello():
    print("Hello")
    await asyncio.sleep(1)
    print("World")

asyncio.run(say_hello())

40. How do you handle exceptions in asynchronous coroutines?

You can handle exceptions in asynchronous coroutines by simply using try-except blocks within async def functions. With asyncio.gather(), use return_exceptions=True to prevent unhandled exceptions.

Example:

41. What is lambda in Python and what are the advantages of using lambda function??

Lambda function is called an anonymous function. They are used for writing quick and simple functions without using the def keyword. Here are a few characteristics of a lambda function:

Key points:

• It is also known as a “single-line function.”
• Lamba  function can accept any number of parameters but is limited to a single statement.
• Function definition is stored within a variable.
• A function call is made using the variable name.
• It is written in a single line using the lambda keyword.
• It helps in improving the readability of the Python code.

Example:

42. What is PYTHONPATH?

PYTHONPATH is an environment variable in Python that specifies a list of directories where the Python interpreter looks for modules and packages to import. By default, Python searches for modules in the current directory and the standard library directories, but you can extend this search path using the PYTHONPATH variable.

43. What are Python namespaces?

Python namespaces ensure the names assigned to objects within a program are unique and can be used without conflict. In Python, namespaces have a unique name for each object in Python.

Here is a list of namespaces in Python:

• The Local Namespace is specific to a function and contains the names defined within that function. It is created temporarily when the function is called, and is cleared once the function finishes executing.
• The Global Namespaceincludes names from imported modules or packages that are used in the current project. It is created when the package is imported into the script and remains accessible throughout the script’s execution.
• The Built-in Namespacecomprises the built-in functions provided by Python’s core, as well as specific names dedicated to various types of exceptions.

44. What does *args and **kwargs mean?

In Python, *args and **kwargs are used in the definitions of functions to allow a variable number of arguments to be passed. These are helpful when the number of inputs that the function might get is unknown.

1. *args – Positional Arguments

• Allows a function to accept any number of positional arguments.
• The extra arguments are stored in a tuple.
• Useful when you want to handle multiple values without specifying them one by one.

Example:

2. **kwargs – Keyword Arguments

• Allows a function to accept any number of keyword arguments (in key-value format).
• The extra arguments are stored in a dictionary.

Example:

45. How do you copy an object in Python?

In Python, copying an object means creating a new object with the same values as the original. There are three main ways to copy an object in Python, and each works differently in terms of memory and reference.

1. Shallow Copy

A shallow copy helps in creating a new outer object, but if the outer object contains nested elements like a list inside another list, then it copies only the references of the object, but not the actual data. A shallow copy creates a new outer object, but if the object contains changes to nested elements, it affects both the original and the copy.

2. Deep Copy

A deep copy creates a completely new object that includes all nested elements. Changes in the copied object will not affect the original object in any way. This method creates an entirely independent copy of the original object, including all nested objects, which means no references are shared between the original and the copied object. Any modifications in the deep copy do not affect the original object, and vice versa.

46. What is file handling in Python? What are the various file-handling operations in Python?

File handling is a technique that handles file operations such as the process of opening, reading, writing, and manipulating files on the file system. Python file handling operations can be categorized into the following categories:

• ‘r’:  Read (default mode).
• ‘w’: Write (creates a new file or truncates an existing file).
• ‘a’:  Append (adds content to the end of an existing file).
• ‘b’:  Binary mode (used for binary files).

47. What do you understand about iterators in Python?

The Iterators in Python are objects that allow us to traverse through a collection (such as lists, tuples, dictionaries, or sets). They use the __iter__() and __next__() methods to retrieve the next element, until there are no methods left. Iterators are commonly used in for loops, and can be created for custom objects. They promote efficient memory usage and enable the lazy evaluation of elements in Python.

48. How can you randomize the items of a list in Python?

To randomise the items in a list in Python, we use the shuffle() from the random module. Here is an example:

49. Differentiate between NumPy and SciPy.

NumPy and SciPy are two different libraries extensively used in Python. Here are the differences between NumPy and SciPy:

50. What is the difference between range & xrange?

The range() and xrange() are used to iterate in a for loop for a fixed number of times. Functionality-wise, both these functions are the same. The difference comes when talking about Python version support for these functions and their return values

51. What Python libraries are most commonly used in AI/ML projects, and how do they differ?

Python libraries that are most commonly used in AI/ML projects are:

Each library generally plays a role in the ML pipeline: from data prep (Pandas) to model training (Scikit-learn, TensorFlow, PyTorch).

52. How do you load and preprocess data for machine learning in Python?

In Python loading and preprocessing data is very important step in any machine learning pipeline. Basically, structured and clean data enhance the accuracy and performance of model. 

Here’s how to do it effectively in Python:

1. Load Data Using Pandas or Numpy

import pandas as pd

# Load from CSV
df = pd.read_csv("data.csv")

2. Explore the Data

Use methods like .head(), .info(), .describe() to inspect structure and find out the missing or inconsistent values:

print(df.head())
print(df.info())
print(df.describe())

3. Handle Missing Values

# Fill missing values
df.fillna(method='ffill', inplace=True)

# Or drop missing rows
df.dropna(inplace=True)

4. Encode Categorical Variables

Convert non-numeric features into numeric format:

# Label encoding
df['Gender'] = df['Gender'].map({'Male': 0, 'Female': 1})

# One-hot encoding
df = pd.get_dummies(df, columns=['Country'])

5. Scale and Normalize Features

Use sklearn.preprocessing for standardization:

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
df[['age', 'income']] = scaler.fit_transform(df[['age', 'income']])

6. Split the Data

from sklearn.model_selection import train_test_split

X = df.drop("target", axis=1)
y = df["target"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

53. How do you identify missing values and deal with missing values in a Dataframe?

For identifying the missing values in the Pandas DataFrame, the isnull() and isna() functions can be used.

 missing_count=data_frame1.isnull().sum() 

1. Replace the  missing values with 0

df['col_name'].fillna(0)

2. Replace the missing values with the mean value of that column

<br>
df['col_name'] = df['col_name'].fillna((df['col_name'].mean()))<br>

54. What does [::-1] do?

It is a slicing notation that is used to reverse the sequence. Let’s break it down the syntax for slicing is [start: stop: step]. We know that if start and stop are not mentioned, then it takes the default value of start and stop. So the slicing notation is [0:n:-1] (n refers to the last index), which means traverse the whole data, but with step -1, i.e., reverse order (negative indexing). This is one of the quickest and most efficient ways to reverse a string in Python.

55. What are f-strings in Python, and why are they preferred?

F-strings are a modern and efficient way of creating a string in Python. They are defined by adding an ‘f’ before the string, and placing the expressions or the variable inside the curly braces. The f-strings are automatically evaluated by Python and include the result in the final string.   The f strings make it easier to read and write the string, compared to the older string formatting methods like % formatting or the str.format(). The f-strings also help in complex string formatting in Python.

Advanced Python Interview Questions for Experienced Professionals

As you grow and become experienced in your career, interviewers start focusing more on logic, key concepts, and writing clean and efficient code. These advanced Python interview questions help you get a clear understanding of topics like memory handling, multithreading, generators, and advanced Python features. Practicing these questions and answers will help in sharpening your problem-solving approach and answer confidently in your interview.

56. What is the purpose of using super() in Python classes?

The super() function in Python is used to call methods from a parent class within a subclass. It is commonly used in object-oriented programming for ensuring that the parent class has been properly initialized, which helps in inheritance. The super() function helps in making the code simpler by avoiding the need to call the parent class again, when there is multiple inheritance.   Example:

57. Explain the use of yield in Python with an example.

The yield keyword is used to create a generator function, which is used to return an iterator that helps in yielding one value at a time, instead of running the whole list at once. Using the yield keywords helps in making the memory more efficient, mainly when working on large datasets.

Example:

58. What is multi-threading in Python?

Multithreading is a technique where the processors execute multiple threads concurrently within a single process. A thread is the smallest unit of a CPU’s execution that can run independently. By using multiple threads, a program can perform several tasks at the same time. This improves performance, especially for I/O-bound operations such as reading files, handling user inputs, and other similar tasks.

Example:

59. How is memory managed in Python?

Memory management is handled by the Python memory manager in Python. The process of automatic memory management is done in Python by the Garbage Collector and Heap Space.

• • Garbage collection is a built-in feature in Python that identifies and clears circular references automatically. Circular References are when two objects refer to each other, but aren’t needed by the program.

• • A private heap memory contains all the Python objects and data structures.

• • Every object in Python has a reference count, which tracks how many variables or objects refer to that object. When the reference count drops to zero, Python frees up the memory for that object.

• • Python uses a system of memory pools to optimize the allocation and deallocation of small objects. It improves performance, which helps in improving efficiency.

60. Does Python support multiple inheritance?

Yes, Python supports multiple inheritance, which means a class can inherit from more than one parent class. In multiple inheritance, a class can have multiple base classes, and it inherits attributes and methods from all of them. This helps in increasing the reusability and the flexibility of the code. It is also confusing in cases like method resolution order (MRO), where multiple inheritance can result in an issue while searching a path.

Example:

61. Does Python support Switch Case?

No, Python does not have a built-in switch statement like some other languages (such as C or Java). Instead, Python developers typically use Match and Case, if-elif-else chains, or dictionary mappings to achieve similar functionality, as these approaches help in writing cleaner and more readable code, compared to the traditional switch case method.

62. What is a Walrus Operator?

The Walrus Operator (:=) is a new feature introduced in Python 3.8. It allows you to assign a value to a variable and return the value within a single expression. This is known as an assignment expression, and is very helpful in making the Python code simpler, more readable, especially when the unwanted variables need to be avoided. The walrus operators are very helpful in loops and conditional statements in Python. Example:

63. How do you securely store API keys and secrets in a Python application?

Storing API keys and secrets securely in a Python is really important if you want to protect sensitive information and prevent hackers from getting access. Below are the best steps to follow for securely handling secrets:

1. Use Environment Variables

Rather than directly embedding secrets in your Python files, save them as environment variables and then retrieve them with the os module.

import os

api_key = os.getenv("API_KEY")

How to set environment variables:

On Linux/macOS:

export API_KEY='your_api_key_here'

On Windows:

set API_KEY=your_api_key_here

2. Use a .env File with python-dotenv (for local development)

Create a .env file to keep your secrets, then load it securely using python-dotenv.

# .env
API_KEY=your_api_key_here

Install the library:

pip install python-dotenv

Then use it in your code:

from dotenv import load_dotenv
import os

load_dotenv()
api_key = os.getenv("API_KEY")

3. Use Secret Management Services (for Production)

For production environments, use secret managers like:

• AWS Secrets Manager
• Azure Key Vault
• Google Secret Manager
• HashiCorp Vault

These services:

• Store secrets in encrypted form.
• Control access via IAM policies.
• Rotate secrets automatically.

64. Why isn’t all memory deallocated when Python exits?

In Python, some objects that are referred to the other objects in a cyclic manner or are linked to global variables may not get deleted automatically when the program ends. Some portions of memory remain out of reach since they were reserved by the C library. The cleaning process of Python automatically attempts to release all the objects before program termination.

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

The Global Interpreter Lock (GIL) is a mechanism in the standard Interpreter of Python that allows only one thread to execute a bytecode at a time. This helps in making the process of memory management simpler by preventing the race conditions, and also helps in proper multithreading, for the tasks that are bound to the CPU.   GIL is also very helpful for the IO-bound tasks like reading files or making network requests, where the threads are used, as the GIL is reassessed during the waiting. GIL is one of the main reasons for Python being among the best choices for CPU-bound multithreaded operations.

66. How does Python handle type hinting?

Type handling is very useful in Python, which helps in adding additional information to the functions and variables, and specifying the kind of data expected. It does not affect the execution of the Python code, but helps others to understand the functions or the variables. It also allows tools like linkers and IDEs that help in catching errors in the code, and improve the process of debugging. Static type checkers like ‘mypy’ are used in large projects, which helps in improving the readability of the code and reducing errors. The type hints follow the Python PEP 484 format.

67. What is monkey patching in Python?

Monkey patching in Python helps in changing or adding code to the program that is already running. The method in a class or a module can be replaced or updated easily without the need to change the source code. This is mainly used for testing and fixing small issues in the libraries. Monkey patching should be used carefully when dealing with large projects, as the changes can be harder to trace and debug.

68.  How do you handle large files efficiently in Python?

It is better to avoid loading the entire file into memory when working on large files, which may be time-consuming. Instead, loops can be used for better file handling, which reads the file line by line using the for line in file, which uses very little memory, and is efficient for text files. When working with binary files like PDF, images mmap module can be used, which helps in accessing the file content directly from the disk. Also, the chunk-based reading and the file buffering can be used for better file handling.

69. What is duck typing in Python?

Duck typing in Python refers to focusing on what the object can do without the need for the type of the object. The term ‘duck’ comes from the saying: “If it looks like a duck and quacks like a duck, it’s probably a duck.” Python follows this by only considering the behaviour and the methods of the object without checking the actual type of the object. For example: When an object is looped over, it is treated as an iterable, even if the object is not in list in Python. This makes Python more flexible and efficient, but the code must be checked properly to avoid runtime errors.

70. How to add values to a Python array?

In Python, adding elements to an array can be easily done with the help of extend(), append(), and insert() functions.

Consider the following example:

71. What are Python closures, and how are they useful?

The closure in Python is created when a function within another function (nested function) captures a variable from the outer function. These captured variables are stored even after the outer function’s execution is finished, which allows the inner functions to use these variables later.   The closure helps in maintaining the state of the program without the need to use global variables and classes. They are very helpful in the decorators, callbacks, and factory functions, where storing and reusing the data is very important.

72. What is the difference between is and == operators in Python?

Example:

73. Explain the use of the ‘with’ statement and its syntax.

The ‘with’ statement in Python is used for managing resources efficiently and helps in file handling operations. It helps in simplifying the code by automatically opening and closing the files, even if there is an error during the execution. For opening a file with open(“filename”, “mode”) as file: is used, and the file is automatically closed.   With statements is a better approach than manually opening and closing a file using open() and close(). It also helps in preventing memory leakage and file access errors, which makes it the best choice for file handling operations.   Here is the syntax:

with open("filename", "mode") as file_var:

74. How can the ternary operators be used in Python?

The ternary operator is the operator used to show the conditional statements in Python. This consists of the boolean true or false values, along with a statement that has to be checked.

Syntax:

[on_true] if [condition] else [on_false]<br>
x, y = 10, 20<br>
count = x if x < y else y<br>
print(count)

Explanation: The above expression is evaluated as if x<y else y, in this case, if x<y is true, then the value is returned as count=x, and if it is incorrect, then count=y will be stored into the result.

75. What are the applications of Python?

Python is used in web development, data analysis, machine learning, automation, desktop applications, game development, and IoT. It’s easy syntax and large library support make it ideal for a wide range of real-world applications. It’s also popular in scientific computing and cybersecurity. Python’s flexibility and community support make it a preferred choice across industries.

76. What is a Docstring in Python?

A docstring is a special multi-line string used to describe what a function, class, or module does. It’s written right after the definition using triple quotes (“”” “”” or ”’ ”’) and helps others understand the purpose of your code.

Example:

77. What is dictionary comprehension?

Dictionary comprehension in Python is a quick and clear way to create a dictionary using existing data like a list, tuple, or range. It works just like list comprehension but builds a dictionary instead of a list.

Key Features:

• Builds a dictionary using a single-line expression.
• Allows adding conditions or modifying values and keys.
• Makes the code shorter, cleaner, and easier to read

Example:

Python OOPs Interview Questions

78. What are the four pillars of OOP? Explain each.

Here are the four main pillars of OOPs:

• • Inheritance allows us to inherit the behaviours or properties from the parent class to the child class.
  • Polymorphism means “many forms”, and in programming, functions with the same name that can be executed on many objects or classes for different behaviour.
  • Encapsulation is the practice of bundling the data (attributes) and methods (functions) that operate on the data into a single unit (class), and restricting direct access to some of the object’s components.
  • Abstraction is the process of hiding implementation details and showing only essential features of an object.

• 79. What is a class and an object in Python?

A class is a blueprint for creating objects. It contains member functions, attributes, etc., that get instantiated when the object is called. On the other hand, an object is nothing but an instance of a class, possessing state, identity, and functionality, and is used to call class members. Let’s take a look at a simple example:

80. What are constructors?

Constructors are called when an object of a class is created. They are used to initialize the objects and assign values to their properties.

81. What is abstraction?

One of the pillars of object-oriented programming is abstraction. Abstraction is a very simple process, where only the necessary details are shown, and the background computations or processes stay hidden. To simplify, let’s try to understand abstraction with an example: Let’s say you visit a motor showroom to buy your new car. The dealer will take you for a quick ride to show you the features of the car. The noticeable thing here is that you will be shown the entire car, but you will not be able to figure out how the actual combustion works. This is exactly how abstraction works: only the necessary details are shown, and internal functionality is hidden from the users.

Here’s a Python example that demonstrates abstraction:

82. How does inheritance work in Python?

Inheritance is one of the four pillars of Object-Oriented Programming. Inheritance allows one class to inherit aspects like attributes and methods from another class.

• • The class that inherits is called the child class or subclass.
  • The class from which the attributes are inherited is called the parent class or superclass.
  • On performing inheritance, the child class gets access to all the attributes and methods of the parent class.
  • The child class can override, extend, or modify the methods of the parent class, promoting code reuse.

• Here is an example of inheritance:

83. What is the difference between abstraction and encapsulation?

Abstraction and encapsulation are both a part of the four pillars of object-oriented programming, but they serve different purposes.

Here is an example representing abstraction:

84. What is method overriding? How is it different from method overloading?

Method overriding and method overloading are two ways to achieve polymorphism in object-oriented programming, but they work in different ways.

Here is an example representing method overloading in Python:

Here is an example representing method overriding in Python:

85. What are class methods and static methods? How are they different from instance methods?

Class methods, Static methods, and Instance methods are three different methods that follow different rules to interact with the class and its instances.  

Class Methods

• Python class methods deal with the class itself.
• They can modify the class-level attributes, but not the objects or their instances.
• Class methods take “cls” as their first parameter rather than “self”, which refers to the class itself.
• Class methods are defined using the decorator @classmethod. This tells Python that the method works with the class rather than an instance.

Example:

Static Methods:

• Python static methods do not depend on any instance or class-specific data.
• They are normal functions that are included inside the class for better code readability.
• Static methods are defined using the @staticmethod decorator.
• They do not require any keyword like self or cls as their first parameter.
• Useful for implementing logic for a class, but requires no access to the class or instance data.

Example:

Instance Methods

• Python Instance methods take the self keyword as their first parameter.
• They are used to access instance-specific data (attributes)
• They work with the individual object’s data.

86. What are Python metaclasses, and how do they work?

The metaclasses in Python help in controlling the creation of classes. There is a built-in type metaclass in Python; custom metaclasses in Python can be defined using the inject methods or by enforcing the coding standards. Metaclasses are very helpful when multiple classes need to follow a particular set of rules. They make it easy for developers to enforce these rules and are mainly used in Python frameworks and library designs.

87. What are the different types of inheritance in Python?

Inheritance is a major pillar in the OOPs concept. There are five types of inheritance available in Python. All of them are listed below:

• Single Inheritance: A Situation where a class inherits properties from one superclass.
• Multiple Inheritance: A Situation where a class inherits properties from multiple superclasses
• Multilevel Inheritance: This is a scenario where a class inherits properties from a superclass, which itself inherits from another superclass. It forms a chain of inheritance across multiple levels
• Hierarchical Inheritance: A Situation where multiple classes are inheriting properties from a single superclass
• Hybrid Inheritance: A Situation where different types of inheritance are used.

88. What is the use of slots in Python classes?

The __slots__ in a Python class help in limiting the attributes of an object. This prevents Python from creating a separate __dict__ for each instance, which helps in making the classes memory efficient and faster. This is very helpful when dealing with multiple objects. But __slot__ cannot be used to add new attributes to the instances, which reduces the flexibility. It is mainly used to improve the performance and make the class’s memory efficient.

Python Coding Interview Questions

89. Write a program to find the greater of the two numbers.

The greater than operator (>) is used to compare the two numbers and find which one is greater.

90. Write a program to check even and odd numbers using shorthand if-else statements.

The modulus function (%)  is used to find whether the number is even or odd by dividing the number by 2.

91. How to remove values from a Python array?

Elements can be removed from the Python array using the pop() or remove() methods. pop(): This function will return the removed element. remove(): It will not return the removed element.

Consider the following example :

92. Write a program in Python to find the largest and second-largest element in a list using Python.

Here, the problem statement says that we have to find out the largest and second-largest elements from a list.

93. Write a program to produce the Fibonacci series in Python.

The below code produces a star triangle-

94. Write a program in Python to produce a Star triangle

The Fibonacci series refers to a series where an element is the sum of two elements before it.

95. Write a program in Python to check if a number is prime

The code below is used to check if a number is prime or not.

96. Write a Python program to count the total number of lines in a text file.

This Python program defines a function that counts the total number of lines in a text file. It opens the file and counts each line using a generator expression for efficiency, and includes error handling for missing or inaccessible files. The function returns the line count, and the example shows how to use it by passing the file path.

def file_count(fname):
    with open(fname) as f:
        for i, _ in enumerate(f):
            pass
    return i + 1

print("Total number of lines in the text file:", file_count("file.txt"))

97. Write an async function that fetches data from multiple URLs concurrently.

For fetching multiple URLs concurrently you can simply use aiohttp and asyncio as it results in improved performance for I/O related tasks.

Note: aiohttp is typically not a built-in library in Python so you need to install it separately using terminal by command:

pip install aiohttp

Example code for async function that fetches data from multiple URLs concurrently:

98. Write a program in Python to execute the Bubble sort algorithm.

99. Write a program to reverse a list using Enumerate in Python.

Reverse a list using the enumerate() function in Python by iterating over the original list and creating a new list with the elements in reverse order.

100. Write a Python program to print a list of primes in a given range.

In this program, we aim to list all prime numbers within a given range.

101. Write a Python program to check whether the given input is an Armstrong number.

An Armstrong number is a number that equals the sum of its digits, each raised to the power of the total number of digits in the number. For example, 153 is an Armstrong number because 1³+5³+3³=153

102. What is the easiest way to calculate percentiles when using Python?

The easiest and most efficient way to calculate percentiles in Python is to use NumPy arrays and their functions.

103. Write a Python program to check whether a given string is a palindrome without using an iterative method.simp

A palindrome is a word, phrase, or sequence that reads the same backwards as forward, e.g., madam.

104. Write a Python program that will reverse a string without using the slicing operation or reverse() function.

105. How do you find the middle element of a linked list in one pass? Write a Program to demonstrate it.

Here, the main catch in the question is “one pass”. What does it mean? It means you get to traverse the list only once, and there’s no coming back to a node after you’ve traversed it. But, to calculate the middle element, you will require the length of the entire LinkedList, right? So this is where the Two Pointer Technique, also known as the Slow and Fast Pointer method, comes into play and helps in finding the middle element in just one traversal.   You will have 2 pointers, one moving twice as fast as the other. In other words, the fast pointer will skip a node and jump to the second node when the slow pointer moves one step forward. The main logic behind having two pointers is that, at the time the fast pointer reaches the end of the list, the slow pointer will be at the middle node. Now, the question here is, how do we know that the fast pointer has reached the end of the list? This is implemented through the logic “while fast and fast.next”, which means that we will iterate through the list, only if our fast pointer is not null. So, the moment the fast pointer reaches the null node, the condition remains unsatisfied, and the loop is terminated.

106. You are given a singly linked list that stores integer values in ascending order. Your task is to determine the time complexity for performing an insertion operation on the numeric value 6 in the given LinkedList.

The two main steps involved in an insertion operation in a singly LinkedList are traversing and inserting.

Step 1: Traversing and Comparing

• Traversing: Firstly, you need to find the right place to insert the element, which is between 5 and 8 in our case. Starting from the “head” node, you will traverse node by node till you find the location. The time complexityfor this operation will depend on the number of nodes O(n).
• Comparing: As you traverse through the nodes, you have to compare each node with the element you want to insert. If the element is smaller than the current node element, you will insert the new node; otherwise, move to the next node. This process is repeated until you reach the end of the list, and it will have a complexity of O(1).

Step 2: Inserting

Once you have located the position where you want to insert, you will adjust the pointers in a way so that your previous node, which is the node containing 5, will have the next pointer pointing to the new node containing 6. You then need to point the next pointer of your new node to the node containing 8. This operation also requires a constant time, i.e. O(1)   So the overall time complexity of inserting an element in the LinkedList at a random position is O(n).

How to Prepare for a Python Interview?

Preparing for a Python interview is not just about writing efficient Python code. It is more about understanding the logic for solving the problem, justifying the logic behind it, and explaining how it can be implemented in a real situation. By following the right steps, even the advanced and difficult questions become easier to handle. Here are some helpful tips to prepare for your Python interview:

1. Understand the Basics: Start with variables, data types, loops, and functions, which are often the first questions asked.
2. Practice Coding Daily: Use platforms like LeetCode or HackerRank to improve your coding logic and speed.
3. Study OOP Concepts: Learn about classes, inheritance, polymorphism, and how they apply in Python.
4. Learn Built-in Functions: Know how and when to use functions like map(), filter(), zip(), and enumerate().
5. Explore Advanced Topics: Understand decorators, generators, error handling, and memory management.
6. Understand Memory Management: Know how Python handles memory using reference counting, garbage collection, and private heap space.
7. Prepare for System Design: For backend roles, learn how to design scalable APIs and handle databases.
8. Review Real Projects: Talk about projects you’ve worked on, your role, and the challenges you solved.
9. Practice Mock Interviews: Simulate interviews with friends or use online tools to build confidence.
10. Stay Updated: Keep learning about the latest Python updates and trends in the tech industry.

Please take a moment to watch the video on Python interview questions.

Conclusion

To crack a Python interview, it’s important to have a clear understanding of the core concepts of Python. Through this article, you have explored a wide range of important Python topics like the datatypes, functions, OOPs, coding logic, and the advanced level concepts, which are most frequently asked in the interviews. Mastering these key topics of Python and practicing them regularly helps boost your confidence and improves your accuracy in solving problems in the interview.

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Python Interview Questions and Answers – FAQs