Top 70 Python Interview Questions & Answers: Ultimate Guide 2025

Basic python interview questions often cover topics like data types, loops, functions, and object-oriented concepts. Many candidates find these questions confusing or struggle to give clear answers under pressure. 

This article provides simple, easy-to-understand python interview questions and answers to help you prepare confidently.

Take your Python skills to the next level and unlock career opportunities in data science, AI, and more. Explore our online data science courses and start building your future today!

Python Interview Questions for Freshers

This section covers common python interview questions for experienced/freshers, focusing on topics like basic syntax, data types, loops, functions, and object-oriented programming. Going through these python interview questions and answers helps build confidence and prepares you for real interviews. 

You’ll gain a clear understanding of essential Python concepts and improve your chances of cracking python top interview questions to land that first job.

Understanding how and when to apply key Python concepts in different python coding interview questions is essential. It shows you can write clean, efficient code that’s easy to maintain, something interviewers look for in strong candidates. Here are three programs that can help you:

• Masters in Data Science Degree
• Executive Diploma in Data Science & AI with IIIT-B
• Executive Diploma in Machine Learning and AI with IIIT-B 

Q1) What is the difference between a module and a package in Python?

How to Answer:
Begin by defining what a module is in Python and its purpose.
Explain what a package is and how it relates to modules.
Describe the structural differences, such as files and folders.
Highlight how modules and packages are used in organizing code.
Conclude by noting the importance of understanding both for managing large Python projects.

Sample Answer:
A module in Python is a single file containing Python code—like functions,classes, or variables—that you can import and use in other programs. A package, on the other hand, is a folder containing multiple related modules, usually with an __init__.py file that allows Python to treat the folder as a package. Modules help break code into manageable files, while packages organize these modules into a hierarchy. Knowing the difference between module and package helps in structuring Python projects clearly and makes code reusable and maintainable.

Also Read: Python Collections Module: Counter, ChainMap, Deque & Tuple 

Q2) What are the built-in types available in Python?

How to Answer:
Start by explaining what built-in types mean in Python.
Mention the two categories: mutable and immutable types.
Give examples of each type.
Explain why this distinction matters when programming.
Finish by noting their significance in Python programming.

Sample Answer:
Built-in types in Python are predefined data types provided by the language. They fall into two categories: mutable types, which can be changed after creation, such as lists, sets, and dictionaries; and immutable types, which cannot be altered once created, like strings, tuples, and numbers. Understanding this distinction is important because it affects how data is stored and manipulated. These types form the foundation of working with data in Python.

Also Read: Difference Between Mutable and Immutable in Python: Key Concepts Explained

Q3) What is lambda function in Python?

How to Answer:
Define what a lambda function is and why it’s called anonymous.
Explain that it is a small inline function with a single expression.
Mention common use cases like passing functions as arguments.
Provide a simple example to illustrate.
Conclude with its advantages in concise coding.

Sample Answer:
A lambda function in Python is a small, anonymous function defined with the lambda keyword that can take any number of arguments but contains only one expression. It’s often used for short, throwaway functions, especially as arguments to higher-order functions like map or filter. For example, lambda x, y: x + y returns the sum of two values. Lambdas help write concise and readable code when a full function definition isn’t necessary.

Q4) What is meant by namespace?

How to Answer:
Start by defining a namespace as a system that holds names and their corresponding objects.
Explain its role in avoiding naming conflicts by keeping names unique within a scope.
Mention the main types of namespaces in Python: local, global, and built-in.
Describe how namespaces work like dictionaries mapping names to objects.
Conclude by emphasizing that understanding namespaces helps manage variable scope and avoid errors.

Sample Answer:
A namespace in Python is a system that stores the mapping between names and objects to keep them unique within a particular scope. It prevents naming conflicts by allowing the same name to exist in different namespaces without clashing. Python has local namespaces inside functions, global namespaces for modules, and built-in namespaces for standard functions. Namespaces act like dictionaries linking names to objects, helping you manage variable access and avoid errors.

Q5) Explain the difference between a list and a tuple?

How to Answer:
Define both lists and tuples as data structures in Python.
Explain that lists are mutable while tuples are immutable.
Mention differences in performance and use cases.
Highlight memory consumption and built-in methods.
Conclude with when to use each type.

Sample Answer:
Lists and tuples are both used to store collections of items in Python. Lists are mutable, meaning you can modify, add, or remove elements after creation, making them flexible but slightly slower. Tuples are immutable, so their contents cannot be changed, which makes them faster and more memory-efficient. Lists have many built-in methods, while tuples have fewer. Use lists when you need to change data and tuples for fixed collections.

Also Read: Types of Data Structures in Python: List, Tuple, Sets & Dictionary

Q6) Difference between pickling and unpickling?

How to Answer:
Define pickling as the process of converting Python objects to byte streams.
Explain unpickling as restoring the original object from the byte stream.
Mention the Python pickle module and its main functions.
Discuss why this process is useful, e.g., saving data or transmitting objects.
Finish with a note on use cases.

Sample Answer:
Pickling in Python is the process of converting an object into a byte stream using the pickle module, which allows the object to be saved to a file or sent over a network. Unpickling reverses this process, restoring the original object from the byte stream. The functions pickle.dump() and pickle.load() handle pickling and unpickling, respectively. This process is useful for storing complex data and later retrieving it exactly as it was.

Q7) What are decorators in Python?

How to Answer:
Define what decorators are and their purpose in Python.
Explain how decorators modify or extend functions without changing their code.
Mention the syntax using the @ symbol.
Describe typical use cases like logging or access control.
Conclude with their role in writing clean and reusable code.

Sample Answer:
Decorators in Python are special functions that modify or enhance other functions without changing their original code. They use the @ syntax above a function definition and wrap the function with additional behavior. Common uses include adding logging, enforcing access control, or timing function execution. Decorators help keep code clean, modular, and reusable by separating concerns.

Q8) Difference between generators and iterators?

How to Answer:
Begin by defining what an iterator is in Python.
Explain that generators are a simple way to create iterators using the yield keyword.
Highlight that generators produce items lazily, one at a time, without storing the entire sequence in memory.
Mention that both support iteration but differ in implementation and use cases.
Use a table to clearly show key differences.
Conclude by stating when to use each.

Sample Answer:
An iterator in Python is an object that allows traversal over all elements of a collection, implementing the __iter__() and __next__() methods. Generators are a simpler way to create iterators using functions with the yield statement, which produces items one at a time and saves memory.

Use generators when working with large datasets or streams where memory efficiency matters. For more complex iteration, custom iterators can be used.

Q9) How to convert a number into a string?

How to Answer:
Explain that converting numbers to strings is common in Python.
Mention the built-in str() function for this conversion.
Give examples of converting other number formats like octal or hexadecimal.
Note the importance of this for formatting and output.
Close by emphasizing simplicity.

Sample Answer:
In Python, converting numbers to strings is done using the built-in str() function. For example, str(123) returns '123'. For specific formats, functions like oct() and hex() convert numbers to octal and hexadecimal string representations, respectively. This conversion is useful when formatting output or combining numbers with text. It’s a straightforward operation supported by Python’s core functions.

Also Read: Python Program to Convert List to String

Q10) What is the use of the // operator in Python?

How to Answer:
Define the floor division operator //.
Explain that it divides two numbers and returns the integer quotient, rounding down.
Contrast it with regular division /.
Provide a simple example.
Mention its common use cases.

Sample Answer:
The floor division operator // in Python divides two numbers and returns the quotient rounded down to the nearest whole number (integer). Unlike regular division / which returns a float, // gives an integer result. For example, 7 //2 returns 3. It’s commonly used when you need an integer result from division, such as in indexing or discrete calculations.

Q11) Does Python have a Switch or Case statement like in C?

How to Answer:
Start by stating that Python does not have a built-in switch or case statement.
Explain common alternatives like using if-elif-else chains or dictionaries.
Mention how dictionaries can map keys to functions or values.
Provide a brief example.
Conclude with Python’s design philosophy favoring explicit code.

Sample Answer:
Python does not have a built-in switch or case statement like some other languages. Instead, similar functionality can be achieved using if-elif-else chains or dictionaries that map keys to functions or values. For example, a dictionary can store functions for each case, and you call the appropriate function based on a key. This approach fits Python’s emphasis on clear and readable code.

Q12) What is the range() function and what are its parameters?

How to Answer:
Define what the range() function does.
Explain its parameters and how it generates sequences of integers.
Mention default values and usage in loops.
Give examples of different parameter combinations.
Emphasize its importance in iteration.

Sample Answer:
The range() function in Python generates a sequence of integers used commonly in loops. It can take one, two, or three arguments: range(stop), range(start, stop), or range(start, stop, step). By default, it starts at 0 and increments by 1. For example, range(5) produces numbers 0 to 4, and range(2, 10, 2) produces 2, 4, 6, 8. It’s essential for controlling iteration counts.

Q13) What is the use of %s?

How to Answer:
Explain that %s is a placeholder for string formatting.
Describe how it converts any value into a string in formatted output.
Mention its use in the old-style string formatting method.
Give a simple example.
Note it’s one of several format specifiers.

Sample Answer:
The %s format specifier in Python is used as a placeholder in strings to insert values converted to strings. It’s part of the older style of string formattiing using the % operator. For example, "Hello %s" % "World" outputs "Hello World". %s automatically converts any value to a string, making it useful for building formatted text.

Q14) Is it mandatory for a Python function to return a value?

How to Answer:
State that Python functions do not have to explicitly return a value.
Explain that if no return statement is used, the function returns None by default.
Mention the difference from languages with procedures or void functions.
Provide a brief example.
Conclude on flexibility.

Sample Answer:
In Python, it’s not mandatory for a function to return a value explicitly. If a function doesn’t have a return statement, it returns None by default. This differs from some languages where procedures or void functions exist. For example, a function without a return still executes but yields None when called. This allows flexible function design.

Also Read: Python Recursive Function Concept: Python Tutorial for Beginners

Q15) Does Python have a main() function?

How to Answer:
Explain that Python does have a main() function concept but not mandatory.
Describe the common idiom using if __name__ == "__main__": to execute code when the script runs directly.
Mention how this controls program entry.
Give a simple example.
Finish by noting its role in script modularity.

Sample Answer:
Python doesn’t require a main() function like some other languages, but the convention is to use the if __name__ == "__main__": block. Code inside this block runs only when the script is executed directly, not when imported as a module. For example, this allows you to organize code and control program entry. It’s a useful pattern for modular and reusable scripts.

Also Read: Python In-Built Function [With Syntax and Examples] 

Q16) What are the key features of Python?

How to Answer:
Start by defining Python as a versatile programming language.
List core features such as interpreted nature, dynamic typing, and simplicity.
Explain features like object-oriented support and readability.
Mention Python’s large standard library and portability.
Conclude by emphasizing why these features make Python popular.

Sample Answer:
Python is a high-level, interpreted programming language known for its simplicity and readability. Key features include dynamic typing, which allows flexible variable usage; support for multiple programming paradigms including object-oriented and procedural; an extensive standard library offering ready-to-use modules; portability across platforms; and easy syntax resembling English. These features make Python ideal for beginners and powerful for complex applications.

Also Read: Top 7 Python Features Every Python Developer Should Know 

Q17) Is Python case-sensitive?

How to Answer:
Define case sensitivity in programming languages.
Explain that Python differentiates between uppercase and lowercase letters.
Give a simple example showing two differently cased variable names.
Mention errors caused by incorrect case usage.
Conclude on the importance of consistent naming.

Sample Answer:
 Python is a case-sensitive language, meaning it treats uppercase and lowercase letters as distinct. For example, variables named myVar and myvar would be considered different. Accessing a variable with the wrong case leads to a NameError. Therefore, consistent naming conventions are important to avoid bugs and confusion in Python code.

Q18) What are python modules? Name some commonly used built-in modules in Python?

How to Answer:
Define what a module is in Python.
Explain that it is a file containing Python code like functions and classes.
Mention how modules promote code reuse and organization.
Give examples of built-in and user-defined modules.
Conclude with how to import and use modules.

Sample Answer:
A Python module is a file with a .py extension that contains Python code, such as functions, classes, and variables. Modules help organize code logically and allow reuse across different programs. Python includes many built-in modules like math and os, and developers can create custom modules. You can import modules using the import statement to access their contents.

Q19) Explain “self” in Python.

How to Answer:
Explain that self represents the instance of a class.
Describe its role as the first parameter in instance methods.
Mention how self differentiates instance variables from local variables.
Give an example of self usage in a method.
Conclude on its importance for object-oriented programming in Python.

Sample Answer:
In Python, self refers to the instance of the class and is used as the first parameter in instance methods. It allows access to the object’s attributes and methods from within the class. For example, in def set_name(self, name):, self.name refers to the instance variable. Using self helps distinguish instance data from local variables and is essential for object-oriented programming.

 Q20) What is PEP 8?

How to Answer:
Define PEP 8 as the style guide for Python code.
Explain its purpose: improving code readability and consistency.
Mention some key guidelines like indentation, naming conventions, and line length.
State its importance for collaboration and maintainability.
Conclude by encouraging adherence to PEP 8.

Sample Answer:
PEP 8 is the official Python style guide that provides conventions for writing clean and readable code. It covers guidelines on indentation, naming conventions, spacing, and maximum line length. Following PEP 8 ensures consistency across Python projects, making code easier to read and maintain, especially in collaborative environments.

Q21) Is indentation mandatory in Python?

How to Answer:
Explain that indentation defines code blocks in Python.
Mention that it replaces braces or keywords used in other languages.
State that improper indentation causes syntax errors.
 Highlight its role in readability.
Conclude by stressing its mandatory nature.

Sample Answer:
In Python, indentation is mandatory and defines blocks of code such as those inside loops, functions, or conditionals. Unlike other languages that use braces, Python uses spaces or tabs to indicate code structure. Incorrect indentation leads to syntax errors. Proper indentation enhances code readability and is essential for Python code to run correctly.

Also Read: What is an Indentation Error in Python? Causes, Examples, Benefits and Challenges

Q22) Explain the difference between Python arrays and lists.

How to Answer:
Define arrays and lists as data containers.
Explain arrays store homogeneous data and lists store heterogeneous data.
Mention arrays require importing the array module, while lists are built-in.
Discuss differences in memory usage and flexibility.
Conclude with typical use cases.

Sample Answer:
Arrays in Python are collections of elements of the same data type and require importing the array module. Lists are built-in and can hold items of different types. Arrays use less memory and are more efficient for numeric data, while lists offer more flexibility and are used more commonly for general purposes.

Refer to the below-mentioned table for difference-

Q23) What is __init__?

How to Answer:
Define __init__ as the constructor method in Python classes.
Explain its role in initializing new objects.
Describe how it sets initial attribute values.
Provide a simple example.
Conclude by noting its importance in class design.

Sample Answer:
The __init__ method in Python is a special constructor method automatically called when creating a new object. It initializes the object’s attributes with given values. For example, def __init__(self, name):  self.name = name sets the name attribute on object creation. It’s essential for setting up objects correctly.  

Q24) Explain the functionality of “break,” “continue,” and “pass.”

How to Answer:
Define each keyword’s function in loops or code blocks.
Explain that break exits the loop entirely.
State continue skips the current iteration.
Describe pass as a placeholder doing nothing.
Give brief examples and usage contexts.

Sample Answer:
 break terminates the nearest enclosing loop immediately, stopping further iterations. continue skips the rest of the current loop iteration and moves to the next. pass is a no-operation placeholder used where code is syntactically required but no action is needed yet. These keywords control flow and aid in writing clear loops.

Refer to the below-mentioned table to understand the functionality of break, continue and pass.

Also Read: Break Pass and Continue Statement in Python - A Complete Guide

Q25) How to write comments in Python?

How to Answer:
Explain that comments are notes in code ignored during execution.
Mention single-line comments start with #.
Describe multi-line comments using triple quotes (docstrings).
State their purpose for code documentation and clarity.
Conclude on best practices.

Sample Answer:
 Comments in Python are annotations ignored by the interpreter, used to explain code. Single-line comments begin with #. Multi-line comments can be written using triple quotes (''' or """) and are often used as docstrings. Comments improve code readability and maintenance.

Q26) What are the generators in Python?

How to Answer:
Define generators as special iterators created with yield.
Explain how they produce items one at a time.
Mention memory efficiency and lazy evaluation.
Provide a simple generator example.
Conclude with their use in handling large data.

Sample Answer:
Generators are functions that yield items one at a time using the yield keyword, allowing iteration without storing the entire sequence in memory. This makes them memory-efficient and suitable for large datasets. For example, a generator function can yield numbers in a range without creating a full list.

Q27) How can you capitalize the first letter of a string in Python?

How to Answer:
State that the capitalize() method changes the first character to uppercase.
Explain it leaves the rest of the string lowercase.
Give a simple example.
Mention when it’s useful.
Conclude with its simplicity.

Sample Answer:
The capitalize() method in Python returns a copy of the string with the first character converted to uppercase and the rest to lowercase. For example, "hello".capitalize() returns "Hello". It’s useful for formatting text in a consistent way.

Q28) What are “docstrings” in Python?

How to Answer:
State that the capitalize() method changes the first character to uppercase.
Explain it leaves the rest of the string lowercase.
Give a simple example.
Mention when it’s useful.
Conclude with its simplicity.

Sample Answer:
The capitalize() method in Python returns a copy of the string with the first character converted to uppercase and the rest to lowercase. For example, "hello".capitalize() returns "Hello". It’s useful for formatting text in a consistent way.

Properties of ‘docstrings’ in Python-

• Convenient for associating documentation with Python. 
• Specified in the source code.
• Helps in understanding the capabilities of a module or function. 
• Describes the actual job of the function.
• They are put under triple quotation marks.

Also Read: 16+ Essential Python String Methods You Should Know (With Examples)

Q29) Explain the functions of “is,” “not,” and “in” operators?

How to Answer:
Define each operator and its function.
Explain is checks identity, not negates boolean values, in checks membership.
Provide brief examples.
Mention typical use cases.

Conclude on their importance in conditionals.

Sample Answer:
The is operator checks if two variables point to the same object in memory. The not operator negates a boolean expression. The in operator tests if a value exists within a sequence like a list or string. For example, a is b tests identity, not True is False, and 'x' in 'text' is True. These operators are commonly used in conditions and control flow.

Properties of ‘is’, ‘not’ and ‘in’ operators include-

Q30) How to copy an object in Python?

How to Answer:
Explain that assignment copies references, not objects.
Describe shallow copy as copying the object but referencing nested objects.
Define deep copy as a recursive copy of all nested objects.
Mention the copy module and its functions.
Conclude on when to use each type.

Sample Answer:
Assigning one variable to another copies the reference, not the object itself. A shallow copy creates a new object but copies references to nested objects, so changes in nested objects affect both copies. A deep copy recursively copies all nested objects, creating an independent clone. Python’s copy module provides copy() for shallow and deepcopy() for deep copying. 

Q31) what is encapsulation?

How to Answer:
Define encapsulation as bundling data and methods in classes.
Explain it restricts direct access to some components.
Mention using private variables with underscores.
Describe benefits like data hiding and modularity.
Conclude on its role in OOP.
Sample Answer:
Encapsulation is an object-oriented principle that bundles data (attributes) and methods (functions) within a class while restricting direct access to some parts using private variables (prefixed with underscores). This protects data integrity and hides internal details, making code modular and easier to maintain.

Q32) What is a dictionary in pthon?

How to Answer:
Define a dictionary as a key-value data structure.
Explain keys must be unique and immutable.
Mention common uses for fast lookup and data storage.
Provide a simple example.
Conclude on versatility.

Sample Answer:
A dictionary in Python is an unordered collection of key-value pairs, where keys are unique and immutable. Dictionaries provide fast data lookup and are useful for storing related information. For example, {'name': 'Ajay', 'age': 25} is a dictionary mapping keys to values.
Example
dictionary = {
‘a’: 1,
‘b’: 2
}

print(dictionary[‘b’])

Q33) What are functions?

How to Answer:
Define functions as reusable blocks of code performing a specific task.
Explain function definition and calling.
Mention parameters and return values.
Give a brief example.
Conclude on benefits like code reuse and clarity.

Sample Answer:
Functions in Python are reusable blocks of code defined with the def keyword that perform specific tasks. They can accept parameters and return values. For example, def greet(name): print("Hello", name) defines a function called with greet("Ajay"). Functions improve code modularity and readability.

Example
def dog():
print(“my name is tommy”)

dog();

Q34) What are local variables and global variables in Python?

How to Answer:
Define local variables as those declared inside functions.
Define global variables as those declared outside functions.
Explain scope differences and lifetime.
Mention how globals can be accessed inside functions with global keyword.
Conclude on importance of scope awareness.

Sample Answer:
Local variables are defined within a function and accessible only there during its execution. Global variables are declared outside functions and accessible throughout the program. To modify a global variable inside a function, you use the global keyword. Understanding local and global scopes helps avoid variable conflicts.

Q35) What is the pass statement in Python used for? 

How to Answer:
Describe pass as a placeholder statement doing nothing.
Explain it’s used when syntax requires code but no action is needed yet.
Give examples such as empty functions or loops.
Mention it prevents errors during development.
Conclude on its usefulness.

Sample Answer:
The pass statement in Python is a null operation used as a placeholder where code is syntactically required but no action is needed. For example, in an empty function or loop, pass avoids syntax errors and allows the code to run until implementation is added.

Q36) How to check if all the characters in a string are alphanumeric? 

How to Answer:
Explain isalnum() checks if all characters in a string are alphanumeric.
State it returns True if only letters and numbers are present, False otherwise.
Give simple examples.
Mention common use cases in validation.
Conclude on ease of use.

Sample Answer:
The isalnum() method returns True if all characters in a string are letters or numbers and there’s at least one character. For example, 'Hello123'.isalnum() is True, but 'Hello 123!'.isalnum() is False. It’s useful for input validation.

Q37) How to remove all leading whitespaces in a string? 

How to Answer:
Define leading whitespace as spaces or tabs at the start of a string.
Explain using the strip(), lstrip() methods.
Provide examples of each.
Mention typical uses in cleaning input data.
Conclude on string handling.

Sample Answer:
Leading whitespace refers to spaces or tabs at the beginning of a string. Python’s lstrip() method removes only leading whitespace, while strip() removes whitespace from both ends. For example, ' hello'.lstrip() returns 'hello'. These methods help clean and standardize string input.

Q38)  Is Python a functional programming language or object-oriented?

• How to Answer:
  Briefly define functional programming (FP) and object-oriented programming (OOP).
  Explain Python supports both paradigms.
  Describe FP features like first-class functions and immutability.
  Describe OOP features like classes, objects, inheritance.
  Conclude on Python’s flexibility.
• Sample Answer:
  Functional programming emphasizes pure functions, immutability, and treating functions as first-class objects, focusing on expressions and declarations. Object-oriented programming organizes code around objects and classes with encapsulation, inheritance, and polymorphism. Python supports both FP and OOP, allowing developers to choose or combine approaches as needed.

If you're still building your Python skills, now is the perfect time to strengthen that foundation. Check out the Programming with Python: Introduction for Beginners free course by upGrad to build the foundation you need before getting into programming.

Q39) Define *args and **kwargs in Python

How to Answer:
Define *args as a way to pass variable positional arguments.
Define **kwargs as a way to pass variable keyword arguments.
Explain how they allow flexible function calls.
Give examples for each.
Conclude on their usefulness in function design.

Sample Answer:
In Python, *args allows a function to accept any number of positional arguments as a tuple, while **kwargs lets it accept any number of keyword arguments as a dictionary. This flexibility enables functions to handle varying inputs. For example, def func(*args, **kwargs): can process both unnamed and named arguments.

Q40) What is the difference between libraries and modules in Python? 

How to Answer:
Define modules as single Python files containing code.
Define libraries as collections of modules packaged together.
Explain libraries provide pre-built functionality and tools.
Give examples like math (module) vs NumPy (library).
Conclude on their roles in development.

Sample Answer:
A module is a single Python file containing functions, classes, or variables, while a library is a collection of related modules packaged to provide extensive functionality. For example, math is a module, whereas NumPy is a library comprising multiple modules for numerical operations. Libraries simplify development by offering reusable tools.

Having trouble interpreting and analyzing data? Check out upGrad’s free Learn Python Libraries: NumPy, Matplotlib & Pandas course. Gain the skills to handle complex datasets and create powerful visualizations. Start learning today!

After going through the python interview questions for experienced/freshers in this section, practice coding regularly and review the answers to strengthen your skills. Once comfortable, move on to the next section with more challenging python interview questions and answers designed for experienced candidates. 

This will help you prepare thoroughly for python top interview questions in upcoming interviews.

Python Interview Questions for Experienced 

This section dives into Python interview questions and answers designed for experienced professionals. It covers deeper concepts, advanced features, and real-world scenarios that you’re likely to face in senior roles. If you want to stand out, mastering these python top interview questions will sharpen your expertise and prepare you for challenging technical discussions.

Q41) What is GIL?

How to Answer:
Start by defining GIL (Global Interpreter Lock) in Python.
Explain its role in managing thread execution.
Mention its effect on multi-threading and CPU-bound tasks.
Describe why it limits true parallelism in Python threads.
Conclude on its impact and common workarounds.

Sample Answer:
The Global Interpreter Lock (GIL) is a mutex in CPython that allows only one thread to execute Python bytecode at a time. It ensures thread safety but prevents multi-threaded Python programs from achieving true parallelism in CPU-bound tasks. While it simplifies memory management, it limits the use of multiple processors with threads. To overcome this, developers often use multiprocessing or external libraries.

Q42) Before the use of the ‘in’ operator, which method was used to check the presence of a key in a dictionary?

How to Answer:
Define the purpose of has_key() in earlier Python versions.
Explain that it checked if a dictionary contained a specific key.
Mention that it was removed in Python 3.
State the modern alternative using the in operator.
Conclude on best practice.

Sample Answer:
The has_key() method was used in Python 2 to check if a dictionary contained a specific key. It has been removed in Python 3, where the preferred way is to use the in operator, like key in dict. This approach is more readable and consistent with other container types in Python.

Q43) How to change data type of list?

How to Answer:
Explain that Python provides built-in functions to convert lists to other types.
List examples such as converting a list to a tuple, set, dictionary, or string.
Mention the functions or methods used for each conversion.
Give brief examples.
Conclude on Python’s flexibility in data handling.

Sample Answer:
In Python, you can change a list’s data type using built-in functions. For example, tuple(my_list) converts a list to a tuple; set(my_list) converts to a set; dict() can convert a list of key-value pairs to a dictionary; and ''.join(my_list) converts a list of strings to a single string. These conversions help adapt data for different uses.

Q44) Explore memory management in Python

How to Answer:
Describe that Python has an automatic memory manager.
Explain the concept of a private heap for Python objects.
Mention the role of the garbage collector in reclaiming unused memory.
Discuss how memory is allocated and deallocated automatically.
Conclude on how this eases developer workload.

Sample Answer:
Python manages memory automatically using a private heap where all objects and data structures are stored. The Python Memory Manager handles allocation and deallocation of this memory. Additionally, Python includes a built-in garbage collector that recycles memory from objects no longer in use, freeing developers from manual memory management tasks.

Handling complex problems without clear strategies can slow your progress. Explore upGrad’s Data Structures & Algorithms free course to build strong problem-solving skills. Start today!

Q45) What is PYTHONPATH?

How to Answer:
Define PYTHONPATH as an environment variable.
Explain its role in specifying directories for Python to search for modules.
Mention how it extends the default module search paths.
Give examples of when it might be used.
Conclude on its importance in module management.

Sample Answer:
PYTHONPATH is an environment variable that specifies additional directories where Python looks for modules and packages during import. It supplements the default search paths, allowing users to include custom or third-party module locations. Setting PYTHONPATH is helpful in managing modules that aren’t installed in standard locations.

Q46) Explain the use of “help()” and “dir()” functions.

How to Answer:
Explain that help() shows documentation for modules, functions, classes, etc.
Describe that dir() lists the attributes and methods of an object.
Mention typical uses in exploration and debugging.
Provide examples for each.
Conclude on their value for developers.

Sample Answer:
The help() function in Python displays documentation for objects like modules, classes, or functions, assisting developers in understanding their usage. The dir() function returns a list of attributes and methods associated with an object, helping to explore its capabilities. Both are valuable tools for interactive programming and debugging.

Q47) What is a statement in Python?

How to Answer:
Define a statement as an instruction executed by Python.
Explain that statements perform actions like assignments, loops, or function calls.
Give examples of common Python statements.
Mention that expressions can be part of statements.
Conclude on their role in program flow.

Sample Answer:
A statement in Python is an instruction that the interpreter executes, such as variable assignment, loops (for, while), function calls, or conditionals (if). For example, x = 5 and print(x) are statements. Statements control the flow and behavior of a program.

Q48) What is ==  in Python?

How to Answer:
Define == as the equality comparison operator.
Explain it checks if two values or objects are equal in value.
Mention difference from is operator which checks identity.
Provide a simple example.
Conclude on its use in conditions.

Sample Answer:
The == operator in Python compares two values or objects for equality, returning True if their contents are the same. For example, 5 == 5 is True. It differs from is, which checks if two variables point to the same object in memory. == is widely used in conditional statements.

Q49) What are the escape sequences in Python?

How to Answer:
Define escape sequences as special characters in strings preceded by a backslash.
List common escape sequences like \n (newline), \t (tab), and \\ (backslash).
Explain their use in formatting strings.
Give an example string using escape sequences.
Conclude on their importance in string handling.

Sample Answer:
Escape sequences in Python are special characters in strings introduced by a backslash (\). Examples include \n for newline, \t for tab, and \\ for a literal backslash. For instance, "Hello\nWorld" prints Hello and World on separate lines. Escape sequences help format and control string output.

Also Read: 16+ Essential Python String Methods You Should Know (With Examples)

After reviewing the experienced python interview questions and answers, focus on solving real coding problems to apply your knowledge. Next, dive into the python coding interview questions section to sharpen your problem-solving skills and get ready for python top interview questions in technical rounds.

Python Coding Interview Questions

Q50) How do you do data abstraction in Python?

How to Answer:
Define data abstraction as hiding internal details while showing essential features.
Explain how Python supports abstraction via classes and methods.
Give an example of abstraction in a class interface.
Mention its benefit in simplifying complex systems.
Conclude on its role in OOP.

Sample Answer:
Data abstraction is an object-oriented concept that hides complex implementation details while exposing only necessary features. In Python, abstraction is achieved through classes that provide methods to interact with data without exposing the inner workings. This simplifies usage and protects data integrity.

Also Read: Difference between Abstraction and Encapsulation Simplified

Q51) What are the best python project ideas for the beginner level?

How to Answer:
List simple project ideas suitable for beginners.
Explain briefly what each project entails.
Mention how these projects build fundamental skills.
Encourage starting with manageable tasks.
Conclude on learning through practice.

Sample Answer:This section contains python interview questions and answers focused on coding problems. These python coding interview questions are designed for both python interview questions for experienced/freshers. By practicing these python top interview questions, you’ll strengthen your coding skills and be ready to face technical rounds with confidence.
Beginner Python projects include creating a code generator that substitutes letters to encode text, building a simple web browser UI to load URLs, making a countdown calculator to compute time between dates, and writing sorting algorithms. These projects develop basic programming skills like string manipulation, UI design, and algorithmic thinking.

Also Read: Top Python IDEs: Choosing the Best IDE for Your Python Development Needs

Q52) What are the best python project ideas for the intermediate level?

How to Answer:
Suggest projects with moderate complexity.
Briefly describe features or challenges involved.
Mention skills gained, such as real-time data handling or UI interaction.
Encourage tackling these after basics are mastered.
Conclude on project-driven learning.

Sample Answer:
Intermediate projects include building a real-time clock website with time zone selectors, making a clickable Tic-Tac-Toe game with UI, and scraping web data for analysis. These projects help develop skills in web development, event handling, and data collection, bridging the gap to advanced Python programming.

Also Read: 50 Python Project Ideas With Source Code [2025 Guide]

Q53) Which sorting technique is used by sort() and sorted() functions of python?

How to Answer:
Start by naming the sorting algorithm used by Python's built-in sorting functions.
Explain the basics of the algorithm and why it is efficient.
Mention its stability and time complexity.
Conclude on why it is suited for real-world data.

Sample Answer:
The sort() and sorted() functions in Python use the TimSort algorithm, which is a hybrid sorting algorithm derived from merge sort and insertion sort. It is designed to perform efficiently on many kinds of real-world data. TimSort is stable, has a worst-case time complexity of O(n log n), and adapts well to partially sorted data, making it both fast and reliable.

Q54) Is Python a compiled language or an interpreted language?

How to Answer:
Explain the difference between compiled and interpreted languages briefly.
Describe Python’s approach involving compilation to bytecode and interpretation.
Mention the role of the Python Virtual Machine (PVM).
Conclude that Python blends both concepts.

Sample Answer:
Python is often described as both compiled and interpreted. When Python code runs, it is first compiled into bytecode, which is an intermediate, platform-independent representation. This bytecode is then executed by the Python Virtual Machine (PVM), which interprets it. Thus, Python combines compilation to bytecode with interpretation at runtime.

Q55) What is the difference between xrange and range function?

How to Answer:
Explain the difference between range() and xrange() in Python 2.
Mention that xrange() returns an iterator, while range() returns a list.
Note that xrange() is more memory efficient.
State that xrange() was removed in Python 3 and range() behaves like xrange() now.

Sample Answer:
In Python 2, range() returns a list containing all numbers in the specified range, which can use significant memory for large ranges. xrange() returns an iterator that generates numbers on demand, making it more memory efficient. In Python 3, xrange() was removed, and range() now behaves like xrange() did, returning an iterator instead of a list.

Q56) What is the zip function?

How to Answer:
Define the purpose of zip() in Python.
Explain how it combines elements from multiple iterables into tuples.
Mention that the result is an iterator in Python 3.
Give a simple example.
Conclude on its use in parallel iteration.

Sample Answer:
The zip() function takes multiple iterables and returns an iterator of tuples, where each tuple contains elements from each iterable at the corresponding position. For example, zip([1,2], ['a','b']) produces (1, 'a') and (2, 'b'). It is commonly used for parallel iteration over multiple sequences.

Q57) How is Exceptional handling done in Python?

How to Answer:
Explain the purpose of exception handling.
Mention the try, except, and finally blocks.
Describe how errors are caught and managed.
Give a simple example structure.
Conclude on its importance in robust programs.

Sample Answer:
Exception handling in Python is done using try, except, and finally blocks. The try block contains code that may raise an exception. The except block handles specific exceptions if they occur, preventing program crashes. The finally block contains code that executes regardless of whether an exception occurred, often used for cleanup. This mechanism helps build reliable and fault-tolerant programs.

Q58) What are the limitations of Python?

How to Answer:
List some known limitations of Python.
Explain how these affect certain use cases.
Mention design trade-offs like speed vs ease of use.
Conclude that awareness helps choose Python appropriately.

Sample Answer:
Python has limitations including slower execution speed compared to compiled languages like C or C++, and less efficiency in mobile computing. Its dynamic typing can lead to higher memory usage, and it depends heavily on third-party libraries for some functionality. Additionally, Python’s Global Interpreter Lock (GIL) limits true multi-threading. Knowing these limitations helps determine when Python is the best choice.

Q59) Do runtime errors exist in Python? Explain with an example.

How to Answer:
Confirm that runtime errors occur during execution.
Explain what causes runtime errors with examples.
Give a simple example causing a runtime error.
Mention how exception handling can catch them.

Sample Answer:
Yes, runtime errors occur in Python when the program is running, often due to unexpected conditions. For example, dividing by zero causes a ZeroDivisionError. Another example is missing parentheses in a print statement in Python 3, causing a syntax error at runtime. These errors can be caught using exception handling to prevent crashes.

After practicing the python coding interview questions and answers, start applying your skills to real-world situations. Move on to the python scenario-based interview questions section to prepare for complex problems and excel in python top interview questions during interviews.

Also Read: Python Developer Salary in India 2025

 Python Scenario-Based Interview Questions

This section covers python scenario-based interview questions designed for basic python interview questions for experienced/freshers. These questions focus on real-world problems to test practical skills and decision-making. Studying these basic python interview questions and answers will help you tackle complex situations confidently and prepare effectively for python top interview questions in technical rounds.

Q60) How would you handle exception management in a Python web application?

How to Answer:
Start by explaining the importance of handling exceptions to prevent app crashes.
Describe centralizing error handling using middleware or decorators.
Mention logging errors for debugging and user-friendly error messages.
Explain catching specific exceptions for targeted responses.
Conclude on how proper management improves stability and user experience.

Sample Answer:
In a Python web application, exception management should be centralized to catch and handle errors gracefully without crashing the app. This can be done using middleware or decorators that wrap view functions to intercept exceptions. Logging detailed error information aids debugging, while returning friendly error messages improves user experience. Catching specific exceptions allows tailored handling, ensuring the application remains stable and responsive.

Q61) How can you optimize Python code that processes large datasets to improve performance?

How to Answer:
Begin with the challenge of processing large datasets efficiently.
Explain the use of generators to handle data lazily.
Mention leveraging optimized libraries like NumPy or Pandas.
Suggest profiling code to find bottlenecks.
Discuss multiprocessing or async approaches for parallelism.

Sample Answer:
To optimize Python code for large datasets, using generators lets you process data one item at a time without loading it all into memory. Libraries like NumPy and Pandas offer fast, optimized operations for numerical data. Profiling helps identify slow sections, which can be improved or parallelized using multiprocessing or asynchronous programming to speed up execution.

Q62) Describe how you would design a Python class to model a real-world bank account system.

How to Answer:
Explain creating a class with attributes for account details.
Describe methods for deposits, withdrawals, and balance checks.
Mention data validation and encapsulation to protect data.
Discuss using inheritance for specialized account types.
Conclude with benefits like code reuse and clarity.

Sample Answer:
I would create a BankAccount class with attributes like account_number, owner, and balance. Methods would include deposit(), withdraw(), and get_balance(), with checks to prevent overdrafts. Encapsulation would protect the balance attribute. Inheritance could create specialized accounts, such as SavingsAccount, that add interest calculations. This design ensures maintainability and clear structure.

Q63) Explain a scenario where you would prefer to use a Python generator instead of a list.

How to Answer:
Start by defining generators and their lazy evaluation.
Explain scenarios involving large or streaming data.
Discuss memory efficiency and performance benefits.
Give examples like file reading or infinite sequences.
Conclude on the practicality of generators.

Sample Answer:
Generators are preferred when working with large datasets or streams, such as reading lines from a huge log file, where loading all data into a list would be inefficient or impossible. Because generators yield one item at a time, they save memory and improve performance, making them ideal for such use cases.

Q64) How would you implement logging in a Python application that runs as a background service?

How to Answer:
Explain using Python’s logging module for flexible logging.
Mention configuring log levels and output formats.
Discuss using rotating file handlers for long-running services.
Describe logging exceptions and key events.
Conclude on monitoring and troubleshooting benefits.

Sample Answer:
For a background service, I’d use Python’s logging module configured to write logs to files with rotation to manage file size. Setting appropriate log levels like INFO and ERROR helps capture essential events. Including timestamps and exception details ensures thorough monitoring, aiding in troubleshooting and maintaining service health.

Q65) How can you manage configuration settings in a Python project that needs different environments like development and production?

How to Answer:
Start by explaining the need for environment-specific configurations.
Describe using separate config files or environment variables.
Mention libraries like dotenv for managing environment variables.
Explain how code detects and loads appropriate settings.
Conclude on security and flexibility advantages.

Sample Answer:
Managing different configurations can be done by using separate config files or environment variables for development, testing, and production. Tools like python-dotenv help load environment variables securely. The application detects the current environment and loads the corresponding settings, keeping sensitive data separate and making deployment flexible.

Q66) Describe how you would handle database transactions in a Python application to ensure data integrity.

How to Answer:
Define database transactions and their importance.
Explain using transaction blocks to group operations atomically.
Mention rollback on errors to avoid partial updates.
Discuss using ORMs like SQLAlchemy or database connectors.
Conclude on maintaining consistency and reliability.

Sample Answer:
Database transactions group multiple operations so they either all succeed or all fail, ensuring data integrity. In Python, using ORMs like SQLAlchemy or database drivers, you wrap critical operations in transaction blocks. If an error occurs, the transaction is rolled back to prevent partial data changes, maintaining consistency.

Q67) How would you approach testing a Python function that interacts with an external API?

How to Answer:
Explain why testing with actual APIs is unreliable.
Describe using mocking to simulate API responses.
Mention tools like unittest.mock or responses.
Highlight testing different scenarios like success and failure.
Conclude on ensuring reliable and fast tests.

Sample Answer:
Since real API calls can be slow and unreliable during tests, I’d use mocking tools like unittest.mock to simulate API responses. This lets me test various scenarios—successful calls, failures, timeouts—without network dependency, making tests fast, reliable, and isolated.

Q68) In a multithreaded Python program, how would you avoid race conditions?

How to Answer:
Define race conditions and their impact.
Explain using synchronization tools like Locks.
Describe acquiring and releasing locks around critical sections.
Mention thread-safe data structures where applicable.
Conclude on ensuring data consistency.

Sample Answer:
Race conditions happen when multiple threads access and modify shared data simultaneously. To avoid this, I use Locks from the threading module to ensure only one thread modifies the data at a time. Acquiring a lock before a critical section and releasing it afterward prevents conflicts and keeps data consistent.

Q69) How would you structure a Python project to be scalable and maintainable?

How to Answer:
Explain organizing code into modules and packages.
Mention using virtual environments and dependency management.
Discuss following PEP 8 and writing modular, documented code.
Highlight including tests and automation.
Conclude on fostering collaboration and growth.

Sample Answer:
I’d structure the project by dividing code into logical modules and packages with clear responsibilities. Using virtual environments isolates dependencies. Adhering to PEP 8 style guidelines improves readability. Including automated tests and continuous integration ensures reliability. This structure supports scalability and makes collaboration easier.

Also Read: Explore 12 Real-World Applications of Python [2025]

After working through these basic python interview questions for experienced/freshers, and python scenario-based interview questions, it’s important to put your knowledge into action. 

Don’t just memorize answers, practice coding regularly to strengthen problem-solving skills and improve speed. Review python interview questions and answers to fill any gaps and revisit challenging topics. 

Staying consistent with these steps will prepare you well for python top interview questions and give you the confidence to succeed in interviews and beyond.

Conclusion

This blog includes common python interview questions and answers, covering important topics like data types, functions, object-oriented programming, and coding challenges. 

However, preparing for such interviews can feel overwhelming, especially when you encounter tricky questions, including basic python interview questions, that require clear, practical responses.

To help bridge this gap, upGrad’s personalized career guidance can help you explore the right learning path based on your goals. You can also visit your nearest upGrad center and start hands-on training today!  

Explore our Popular Data Science Courses

Read our popular Data Science Articles

Top Data Science Skills to Learn

References:

1. https://www.liquidweb.com/blog/latest-python-version/