Python Interview Questions and Answers for Freshers 2026

Interview Preparation

Python Interview Questions and Answers for Freshers — 2026

These are the most commonly asked Python interview questions for freshers in 2026 — compiled by Vtricks faculty based on real interview feedback from students placed at companies like Amazon, Google, Infosys, Capgemini, TCS, Zoho in Bangalore.

There are currently 6,800+ active Python job openings in Bangalore. Freshers can expect ₹4–7 LPA at companies across Bangalore's tech corridor — Whitefield, Electronic City, Koramangala, and the CBD. Preparation matters: candidates who practise these questions consistently perform significantly better in technical rounds.

Interview Tips from Vtricks Faculty

Always explain your reasoning process — interviewers want to see how you think, not just the final answer.
Use real examples from projects you have worked on when answering scenario-based questions.
If you don't know the answer, say so honestly and describe how you would find the answer — this is better than guessing.
For Bangalore companies specifically: be ready to answer follow-up questions — they often go 2-3 levels deep on any concept.
Always ask clarifying questions before answering complex scenario-based questions — this demonstrates professional problem-solving approach.

Easy — basic concept check

Medium — applied knowledge

Hard — senior/deep dive

All 20 Questions

Python Interview Questions — Freshers

Q1. What is Python and why is it popular?

Conceptual Easy

ANSWER

Python is a high-level, interpreted, general-purpose programming language known for its simple and readable syntax. It is popular because it has a large standard library, extensive third-party packages (NumPy, Pandas, Django, Flask), strong community support, and is used across web development, data science, automation, AI, and scripting. Major companies like Google, Netflix, Instagram, and Dropbox use Python extensively.

Q2. What is the difference between a list and a tuple in Python?

Technical Easy

ANSWER

A list is mutable — you can change, add, or remove elements after creation using methods like append(), remove(), and pop(). A tuple is immutable — once created, its elements cannot be changed. Lists use square brackets [], tuples use parentheses (). Tuples are faster than lists and are used for fixed data like coordinates or database records. Use tuples when you want to protect data from accidental modification.

Q3. What is a dictionary in Python and how do you use it?

Technical Easy

ANSWER

A dictionary is a collection of key-value pairs, unordered (insertion-ordered from Python 3.7+), and mutable. Keys must be unique and immutable. You access values using keys: my_dict['key']. Common methods: dict.get(key), dict.keys(), dict.values(), dict.items(), dict.update(). Example: student = {'name': 'Rahul', 'age': 22, 'course': 'Python'} — student['name'] returns 'Rahul'. Dictionaries are used for fast lookups, JSON-like data, and counting occurrences.

Q4. Explain the difference between == and 'is' in Python.

Technical Easy

ANSWER

== checks if two objects have the same value — it compares content. 'is' checks if two variables point to the exact same object in memory — it compares identity. Example: a = [1,2,3]; b = [1,2,3]; a == b returns True (same values), but a is b returns False (different objects). However: a = 'hello'; b = 'hello'; a is b may return True because Python caches small strings (string interning). Always use == for value comparison and 'is' only for None checks (if x is None).

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Q5. What is a Python decorator and how does it work?

Technical Medium

ANSWER

A decorator is a function that takes another function as input, adds some behaviour, and returns a modified function. They use the @decorator_name syntax above a function definition. Common use cases: logging, authentication checks, timing functions, caching. Example: @login_required in Django checks if a user is logged in before executing a view function. Under the hood, @decorator is equivalent to: function = decorator(function). Python's built-in decorators include @staticmethod, @classmethod, and @property.

Q6. What is the difference between deep copy and shallow copy?

Technical Medium

ANSWER

A shallow copy creates a new object but references the same nested objects as the original. Changing a nested object in the copy affects the original. A deep copy creates a completely independent copy — all nested objects are also copied. Use copy.copy() for shallow copy and copy.deepcopy() for deep copy. Example: if you have a list of lists, a shallow copy shares the inner lists, so modifying an inner list in the copy modifies the original. Use deep copy when you need complete independence from the original object.

Q7. What are Python generators and when do you use them?

Technical Medium

ANSWER

A generator is a function that yields values one at a time using the yield keyword instead of returning all values at once. Generators are memory efficient because they generate values on demand (lazy evaluation) rather than storing all values in memory. Use generators when: processing large files line by line, implementing infinite sequences, building data pipelines. Example: def count_up(n): i=0; while i

Q8. What is the difference between *args and **kwargs?

Technical Easy

ANSWER

*args allows a function to accept any number of positional arguments as a tuple. **kwargs allows a function to accept any number of keyword arguments as a dictionary. Example: def my_func(*args, **kwargs): print(args, kwargs) — calling my_func(1,2,3, name='Rahul', city='Bangalore') prints (1,2,3) and {'name':'Rahul', 'city':'Bangalore'}. Use *args when you don't know how many positional arguments will be passed and **kwargs for optional keyword configuration parameters.

Q9. How does Python handle memory management?

Technical Medium

ANSWER

Python uses automatic memory management through: Reference counting — each object tracks how many references point to it; when count reaches 0, memory is freed. Garbage collection — handles circular references that reference counting cannot resolve, using a generational collector. Memory pools — Python pre-allocates memory pools for small objects (integers, strings) for efficiency. The gc module allows manual control. Python's Global Interpreter Lock (GIL) ensures thread-safe memory operations but limits true multi-threading for CPU-bound tasks.

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Q10. What is the difference between a module and a package in Python?

Conceptual Easy

ANSWER

A module is a single Python file (.py) that contains functions, classes, and variables. You import it using import module_name. A package is a directory containing multiple modules along with an __init__.py file that marks it as a package. Example: 'math' is a module. 'numpy' is a package with multiple sub-modules. You can import specific items: from numpy import array. Packages allow organising large codebases into logical namespaces and sub-directories.

Q11. Explain list comprehension and when to use it.

Technical Easy

ANSWER

List comprehension creates a new list by applying an expression to each item in an iterable, with an optional filter. Syntax: [expression for item in iterable if condition]. Example: squares = [x**2 for x in range(10) if x % 2 == 0] — creates a list of squares of even numbers from 0 to 9. Use list comprehension when: the logic is simple and readable, you want to filter and transform in one line. Avoid when the logic is complex — use a regular for loop for readability.

Q12. What is exception handling in Python?

Technical Easy

ANSWER

Exception handling manages runtime errors gracefully using try-except-else-finally blocks. try block contains code that might raise an exception. except catches specific exceptions: except ValueError, except TypeError. else runs if no exception occurred. finally always runs regardless of whether an exception occurred — used for cleanup like closing files or database connections. Example: try: result = 10/0 except ZeroDivisionError: print('Cannot divide by zero') finally: print('Done'). Always catch specific exceptions rather than bare except.

Q13. What is the difference between append() and extend() in a list?

Technical Easy

ANSWER

append() adds a single element to the end of a list — including another list as a single element. extend() adds all elements from an iterable to the end of the list. Example: a = [1,2,3]; a.append([4,5]) gives [1,2,3,[4,5]] — the inner list is one element. a = [1,2,3]; a.extend([4,5]) gives [1,2,3,4,5] — the elements are added individually. Use extend() when you want to merge two lists. Use append() when adding a single item.

Q14. What is a virtual environment in Python and why use it?

Conceptual Easy

ANSWER

A virtual environment is an isolated Python environment that has its own interpreter, libraries, and scripts separate from the system Python. Use it to: avoid conflicts between project dependencies (Project A needs Django 3.2, Project B needs Django 4.0), keep projects reproducible with requirements.txt, prevent accidental modification of system-wide packages. Create with: python -m venv venv. Activate with: source venv/bin/activate (Linux/Mac) or venv\Scripts\activate (Windows). Install packages inside the venv with pip install.

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Q15. What is the purpose of __init__.py in a Python package?

Technical Easy

ANSWER

__init__.py marks a directory as a Python package, allowing it to be imported as a module. It can be empty or contain package-level initialisation code, import statements, or define __all__ to control what is exported when someone does from package import *. In Python 3.3+, __init__.py is optional (namespace packages), but it is still best practice to include it for explicit package definition and to control the public API of your package.

Q16. Explain the concept of object-oriented programming in Python.

Conceptual Medium

ANSWER

OOP organises code around objects — instances of classes — that bundle data (attributes) and behaviour (methods) together. Python supports four OOP principles: Encapsulation — bundling data and methods, controlling access with private attributes (prefix with __). Inheritance — a child class inherits attributes and methods from a parent class (class Dog(Animal)). Polymorphism — different classes can share the same method name with different implementations. Abstraction — hiding complex implementation details and showing only what is necessary.

Q17. What is the difference between range() and xrange() in Python?

Technical Easy

ANSWER

In Python 2, range() returned a list in memory while xrange() returned an iterator (memory efficient). In Python 3, range() behaves like Python 2's xrange() — it returns a range object that generates numbers on demand without storing them all in memory. So in Python 3, xrange() no longer exists. range(1000000) in Python 3 does not create a million-element list — it creates a lightweight range object. This makes Python 3's range() memory efficient for large iterations.

Q18. How do you read and write files in Python?

Technical Easy

ANSWER

Use the open() function with different modes: 'r' for reading, 'w' for writing (overwrites), 'a' for appending, 'b' for binary files. Best practice is using the with statement which automatically closes the file. Example: with open('file.txt', 'r') as f: content = f.read(). For line-by-line reading: for line in f: print(line). For writing: with open('output.txt', 'w') as f: f.write('Hello Bangalore'). For CSV files, use the csv module or Pandas read_csv() and to_csv().

Q19. What are Python's built-in data types?

Conceptual Easy

ANSWER

Python's built-in data types are: Numeric — int (integers), float (decimal numbers), complex (complex numbers). Text — str (strings). Boolean — bool (True or False). Sequence — list (mutable ordered), tuple (immutable ordered), range (sequence of numbers). Mapping — dict (key-value pairs). Set — set (unordered unique elements), frozenset (immutable set). Binary — bytes, bytearray, memoryview. Python is dynamically typed — you don't declare types; Python infers them at runtime.

Q20. What is a lambda function and when should you use it?

Technical Easy

ANSWER

A lambda function is an anonymous single-expression function defined inline using the lambda keyword. Syntax: lambda arguments: expression. Example: square = lambda x: x**2 — square(5) returns 25. Use lambda functions for short, throwaway functions passed as arguments to higher-order functions like map(), filter(), sorted(). Example: sorted(students, key=lambda s: s['age']). Avoid lambda when the logic is complex or reused — define a named function instead for readability.

Company Insights

What Python Companies in Bangalore Actually Ask

Based on interview feedback from Vtricks students placed at Bangalore companies in 2026:

Round 1 — Written/Online Test

Most Bangalore companies start with a written or online test covering python fundamentals, multiple choice questions on Python and Django, and basic problem-solving questions. Duration: 30–60 minutes. Companies like Amazon and Google use platforms like HackerRank or their own internal assessments.

Round 2 — Technical Interview (Most Important)

This is where most candidates are filtered. Expect: direct questions from this list, hands-on tasks (write a SQL query, debug a piece of code, explain a dashboard you built), and scenario-based questions where you walk through how you would solve a real problem. Be prepared to share your screen and code live.

Round 3 — Managerial / HR Round

Focuses on: why you chose python as a career, how you handle ambiguous requirements, a project you are proud of (have this ready in detail — situation, what you did, result), and salary expectations. Research the company's tech stack and recent news before this round.

Tools You Must Be Able to Demonstrate

Python — be ready to use this live in an interview
Django — be ready to use this live in an interview
Flask — be ready to use this live in an interview
Pandas — be ready to use this live in an interview
NumPy — be ready to use this live in an interview
Scikit-learn — be ready to use this live in an interview

More Resources

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Top Python Interview Questions
and Answers for Freshers