We all know that preparing for a Python interview can be very stressful. It is hard to know what to study because the language covers so many topics. That’s exactly why I created this guide.
Here, I have collected the most trending Python interview questions that companies actually ask and will surely continue asking in 2026. I even ask many of these as an interviewer during recruitment. This list is for everyone, both for freshers and experienced professionals. Let’s begin with the basics.
Python Fundamentals
We will start with the basic Python interview questions. These are the first things interviewers often ask to understand your foundation.
1. What is Python
Python is a very popular programming language. We call it a high-level language because it is easy for humans to read and write. Its syntax is simple and clean. This makes it a great choice for beginners. We use Python for many different things like web development, data science and automation. Many top companies like Netflix and Spotify use Python for their services.
2. Is Python an interpreted language
This is a great question. The most common Python version CPython, is both compiled and interpreted. When we run our code, Python first compiles it into a simpler form called bytecode. This bytecode is a lower-level set of instructions. Then a program called the Python Virtual Machine interprets and runs that bytecode line by line.
3. What is PEP 8
PEP 8 is the official style guide for Python code. It is a set of rules for how we should format our code. These rules make the code clean and easy to read for everyone on our team. A big part of this is using indentation correctly. Following PEP 8 helps us write professional and maintainable code.
4. What is a docstring in Python
A docstring is a documentation string. We use it to explain what a module class or function does. We write it as the very first thing inside the code block. We use triple quotes """ to write the docstring. Unlike a regular comment, Python can read the docstring at runtime. We can see it by using the help() function.
5. What are modules and packages in Python
Modules and packages help us organize our code.
- A module is just a single Python file with a .py extension. It can have functions and classes inside it.
- A package is a folder or directory. It contains multiple modules and other sub-packages. To make Python treat a folder as a package it must contain a special file named __init__.py.
6. What is slicing in Python
Slicing is a way to get a smaller part of a sequence like a list or a string. We use a simple syntax with square brackets [start:stop:step]. The start is the index of the first item we want. The stop is the index we stop before. The step is how many items to jump. A fun trick we can do is my_string[::-1] which reverses the entire string.
Data Structures We Use in Python
Data structures are the building blocks for storing data in our programs. They are a core part of the top Python interview questions.
7. What are the common built-in data types in Python
Python has several data types built-in. We can group them to make them easier to remember.
- Numeric Types
int(integers)float(decimal numbers)complex(complex numbers) - Sequence Types
str(strings)list(lists)tuple(tuples) - Mapping Type
dict(dictionaries) - Set Types
set(sets) - Boolean Type
bool(True or False)
8. What is the difference between a list and a tuple
This is one of the most popular top Python interview questions. The main difference is that lists are mutable meaning we can change them after we create them. Tuples are immutable meaning we cannot change them once they are created.
Because tuples cannot change they are a bit faster than lists. We can also use tuples as keys in a dictionary but we cannot use lists.
| Feature | List | Tuple |
| Mutability | Mutable (can be changed) | Immutable (cannot be changed) |
| Syntax | “ | (1, 2, 3) |
| Use Case | A collection of items that may need to grow or change. | A collection of items that should never change. |
9. What is the difference between a mutable data type and an immutable data type
This question is about the core concept from the last answer.
- A mutable object is one that we can change after we create it. Good examples are list, dict and set.
- An immutable object is one we cannot change. Examples are int, str and tuple.
If we have a string my_str = "hello" and we write my_str = "world" we are not changing the “hello” object. We are creating a brand new string object “world” and making our variable point to it.
10. What is the difference between a set and a dictionary
Both sets and dictionaries are collections.
- A set is a collection of unique values. It is not ordered and does not allow duplicate items.
- A dictionary is a collection of key-value pairs. Each key must be unique and we use the key to find its value.
11. What is the difference between a shallow copy and a deep copy
This is important when we have nested objects like a list inside another list.
- A shallow copy creates a new main object. But it shares the nested objects with the original. If we change the nested list it changes in both the original and the copy.
- A deep copy creates a new main object and a new copy of all nested objects. The deep copy is completely independent. We use the copy module for this.
12. What is list comprehension
List comprehension is a short and clean way to create a list from another list or sequence. It lets us write one line of code instead of a full loop. For example to get a list of squares we can write squares = [x * x for x in range(10)]. We can also add an if condition at the end to filter the list.
13. What is dictionary comprehension
This is the same idea but for creating a dictionary. We use curly braces {}. We need to provide both a key and a value. For example we can create a dictionary of squares like this squares_dict = {x: x * x for x in range(10)}.
14. How do you create an empty set in Python
This is a common trick question. We cannot use {} to make an empty set. Using {} will create an empty dictionary. To create an empty set we must use the set() function with no arguments.
Python Functions and Scope
Functions are how we organise our code into reusable blocks. Let’s look at some underrated but highly important Python interview questions related to functions and scope.
15. What are *args and **kwargs
We use *args and **kwargs to let a function accept any number of arguments.
- *args (arguments) collects all extra positional arguments into a tuple.
- **kwargs (keyword arguments) collects all extra keyword arguments into a dictionary.
The names args and kwargs are just a convention but the asterisks * and ** are the important part.
16. What is a lambda function
A lambda function is a small anonymous function. Anonymous means it does not have a name. It can have many arguments but it can only have one single expression. We do not use lambda functions for complex tasks. We use them for short simple tasks like sorting a list based on a custom rule.
17. What is Scope in Python
Scope is the area in our code where a variable can be seen and used. If a variable is defined inside a function it has local scope and we can only use it inside that function. If it is defined outside all functions it has global scope and we can use it anywhere. Python does not have block scope. This means a variable inside an if block can still be used after the if block ends.
18. What is the LEGB rule in Python
LEGB is the rule Python follows to find a variable. It is the order of scopes that Python checks.
- L stands for Local. This is the area inside a function.
- E stands for Enclosing. This is the area of any outer functions.
- G stands for Global. This is the main part of our script.
- B stands for Built-in. These are the names Python always knows like
printorlen.
Object-Oriented Programming (OOP) in Python
OOP is a way to structure our programs using classes and objects. Many Python jobs require this knowledge.
19. What is a class and an object
A class is a blueprint for creating things. For example, we can have a class called Dog that describes what all dogs have.
An object is the actual instance we create from that blueprint. my_dog = Dog() creates a new object from the Dog class.
20. What is the init method
The __init__ method is a special method in a class. It is a constructor. It is automatically called when we create a new object from a class. We use this method to initialize the object’s starting values or attributes like self.name = "Buddy".
21. What is the use of self in Python
self is a special word we use as the first parameter in a class method. It is a reference to the object itself. We use self to access the object’s own data and methods like self.name. It is how the object’s methods can find and use the object’s data.
22. What is inheritance
Inheritance is when a new class (the child class) gets all the methods and properties from an old class (the parent class). This helps us reuse code. We call this an “is-a” relationship. For example, a Car class can inherit from a Vehicle class because a Car is-a Vehicle.
23. What is the super() function used for
The super() function is used in a child class. It lets us call a method from the parent class. We most often use it inside the __init__ method. We call super().__init__() to make sure the parent’s setup code runs first before we add the child’s new properties.
Advanced Python Concepts
These questions test deeper knowledge of how Python works. They are often asked in more experienced roles.
24. What is the difference between an iterator and a generator
An iterator is an object that lets us get items one at a time. It uses a next() method to get the next item. A generator is a special function that helps us create an iterator easily.
25. What is a decorator in Python
A decorator is a function that takes another function as an argument. It adds some new behavior to that function and then returns the modified function. This lets us change what a function does without changing the function’s code itself. We use the @ symbol as a shortcut to apply a decorator.
26. What is the Global Interpreter Lock (GIL)
The GIL is a lock that is part of Python. It ensures that only one thread can run Python bytecode at any single moment. Even if we have a computer with many cores, Python’s GIL will not let our threads run in parallel at the same time. This was done to make Python’s memory management simple and safe. This lock can slow down programs that do a lot of math, but it does not affect programs that wait for networks or files.
27. How is memory managed in Python
Python handles memory for us automatically. We do not have to free memory ourselves. The main system it uses is called reference counting. Python counts how many variables point to an object. When that count drops to zero, Python removes the object and frees its memory. Python also has a garbage collector that cleans up more complex cases.
28. What is break continue and pass in Python
These are three keywords we use inside loops.
- break This keyword exits the loop immediately. The loop stops completely.
- continue This keyword skips the rest of the current loop. It continues to the next loop iteration.
- pass This keyword does nothing. It is a placeholder. We use it when Python syntax needs a line of code, but we do not want to do anything like in an empty function.
Python Exception Handling
Errors happen. These questions check if you know how to handle them gracefully.
29. What is exception handling
Exception handling is a way to manage errors that happen while our program is running. We use try and except blocks. We put our “risky” code in the try block. If an error happens, the except block “catches” it. This stops our program from crashing and lets us handle the problem safely.
30. What is the difference between errors and exceptions
Errors are usually serious problems that we cannot handle. A SyntaxError is a good example. This means our code is written incorrectly and cannot even run.
Exceptions are problems that happen during runtime. A ZeroDivisionError is an exception. Our code is valid but the data caused a problem. We can catch and handle exceptions.
31. What is the purpose of the finally clause in Python
The code in a finally block always runs. It runs if the try block succeeds. It runs if an error happens, and the except block runs. We use finally for important cleanup actions. A perfect example is closing a file to make sure it is always closed no matter what.
Common Python Coding Questions
Let us practice a few of the top Python interview questions that involve writing code. Interviewers ask these to see our logic.
32. How can you concatenate two lists in Python
We have two main ways to join two lists.
- We can use the
+operator. This creates a new list. For examplelist_c = list_a + list_b. - We can use the
.extend()method. This changes the first list in place and does not create a new list. For examplelist_a.extend(list_b).
33. How do you check if a string is a palindrome
A palindrome is a word that reads the same forward and backward like ‘madam’ or ‘racecar’. The simplest way to check this in Python is to use slicing. We can check if my_string == my_string[::-1]. If this is true, the string is a palindrome.
34. How do you reverse a string in Python
We can use the same slicing trick from the last question. The fastest and cleanest way to reverse a string in Python is reversed_string = my_string[::-1]. This creates a new string that is the reverse of the original.
35. How do you find duplicates in a list
A good way to find duplicates is to use a set to keep track of the items we have already seen. We can loop through our list. For each item, we check if it is already in our seen set. If it is, we know it is a duplicate. If it is not, we add it to the seen set and continue.
36. Write a program for the FizzBuzz problem
This is a very common first coding question. We need to loop from 1 to 100. The trick is to check for both 3 and 5 first. We check if the number is divisible by 15. If it is, we print ‘FizzBuzz’. If not, we then check if it is divisible by 3 and print ‘Fizz’. If not, we then check if it is divisible by 5 and print ‘Buzz’. If it is none of those, we print the number itself.
37. What is the difference between loc and iloc in Pandas
This is a question about the Pandas library.
- loc is used to get data by its label or name.
- iloc is used to get data by its integer position or index.
So loc is for labels and iloc is for numbers.
38. What is NumPy
NumPy is a very important Python library. We use it for scientific computing and working with large arrays of numbers. It is very fast for mathematical operations.
39. What is Pandas
Pandas is another very popular library used for data analysis. It is built on top of NumPy. It gives us powerful and easy-to-use data structures. The two main ones are the Series and the DataFrame.
40. How do you read a CSV file into a DataFrame using Pandas
We use the built-in function pd.read_csv(). A simple example is my_dataframe = pd.read_csv("my_file.csv"). This will read the CSV file and put all the data into a new DataFrame.
41. What is the difference between a DataFrame and a Series in Pandas
A Series is a single column of data. It is like a list but with labels.
A DataFrame is a full table with rows and columns. It is like a spreadsheet or an SQL table. A DataFrame is just a collection of Series.
42. How do you handle missing data in Pandas
Missing data is very common. We have two main choices.
- We can drop the missing data. We can remove any row that has a missing value using
df.dropna(). - We can fill the missing data. We can replace the missing values with another value like zero or the average using
df.fillna(0).
43. What is a KeyError in Python
A KeyError is an exception. It happens when we try to get a key from a dictionary, but that key does not exist. To avoid this error, we can first check if the key is in the dictionary. Or we can use the .get() method, which returns None instead of crashing.
44. What are unit tests in Python
A unit test is a small piece of code that tests one unit of our main code, like a single function. We write tests to prove that our function works correctly. Python has a built-in library called unittest that helps us write and run these tests.
45. What are global protected and private attributes in Python
This is about naming conventions in a class.
- Global attributes are available everywhere.
- Protected attributes start with one underscore like
_name. This is a convention. It tells other developers “please do not use this attribute directly”. - Private attributes start with two underscores like
__name. Python changes this name to make it hard to access from outside the class.
46. What is the difference between Python Arrays and lists
This is different from a NumPy array. Python has its own array module.
- A list is built-in and can store items of different data types.
- An Array from the array module can only store items of the same data type like all integers. Arrays use less memory but lists are more flexible.
47. How can you make a Python Script executable on Unix
We need to do two things to make a script run like a command on Unix or Linux.
- Add a “shebang” line at the very top of the file
#!/usr/bin/env python3. - Go to the terminal and change the file’s permission to be executable
chmod +x my_script.py.
48. What is the difference between a for loop and a while loop
We use these loops for different reasons.
- A for loop is used when we know how many times we want to loop. We use it to iterate over a sequence like a list or a range.
- A while loop is used when we do not know how many times. It will keep looping as long as a certain condition is true.
49. How do you replace string space with a given character in Python
We can use the string’s .replace() method. For example my_string = "hello world". If we write new_string = my_string.replace(" ", "_") the new_string variable will be "hello_world".
50. Write a Python function to determine if a given string is a palindrome
This is a more advanced version of the simple palindrome check. A real-world string might have spaces and capital letters like “A man a plan a canal Panama”. Our function must first clean this string. We would make it all lowercase. Then we would remove all spaces and punctuation. After cleaning, we get ‘amanaplanacanalpanama’. Finally, we check if this clean string is equal to its reverse.
Conclusion
I hope this guide of 50 questions is helpful for your interview preparation. We have covered fundamentals, data structures, OOP, and common coding problems. Remember that mastering these Python interview questions is about understanding the ideas, not just memorising the answers. I am sure with practice and a good understanding of these topics, you will do great.
Thank you for preparing with us. I wish you the best of luck!
