100% Free
🎯 3200+ Questions. One Goal: Your Success Sign In Sign Up

Top Python Interview Questions

Curated Python interview questions and answers across difficulty levels.

Last updated:

Python Interview Questions & Answers

Skip to Questions

Welcome to our comprehensive collection of Python interview questions and answers. This page contains expertly curated interview questions covering all aspects of Python, from fundamental concepts to advanced topics. Whether you're preparing for an entry-level position or a senior role, you'll find questions tailored to your experience level.

Our Python interview questions are designed to help you:

  • Understand core concepts and best practices in Python
  • Prepare for technical interviews at all experience levels
  • Master both theoretical knowledge and practical application
  • Build confidence for your next Python interview

Each question includes detailed answers and explanations to help you understand not just what the answer is, but why it's correct. We cover topics ranging from basic Python concepts to advanced scenarios that you might encounter in senior-level interviews.

Use the filters below to find questions by difficulty level (Entry, Junior, Mid, Senior, Expert) or focus specifically on code challenges. Each question is carefully crafted to reflect real-world interview scenarios you'll encounter at top tech companies, startups, and MNCs.

Questions

117 questions
Q1:

What are Python's key features?

Entry

Answer

Python features:
• Interpreted, dynamically typed, high-level
• Object-oriented and functional support
• Large standard library and community
• Automatic memory management
• Portable and readable syntax
Permalink
Q2:

What is the difference between Python 2 and Python 3?

Entry

Answer

Key differences:
• print is a function in Python 3
• / returns float in Python 3
• Strings are Unicode by default
• Most libraries support Python 3 only
Permalink
Q3:

What are Python data types?

Entry

Answer

Python data types:
• Numeric: int, float, complex
• Sequence: list, tuple, range
• Text: str
• Mapping: dict
• Set: set, frozenset
• Boolean: bool
• NoneType: None
Permalink
Q4:

Explain Python variable scoping rules.

Entry

Answer

Scopes:
• Local
• Enclosing
• Global
• Built-in
Use global and nonlocal to modify outer variables.
Permalink
Q5:

How does Python manage memory?

Entry

Answer

Python uses automatic memory management:
• Reference counting
• Garbage collector for circular references
• No manual allocation/deallocation needed.
Permalink
Q6:

What are lists, tuples, sets, and dictionaries?

Entry

Answer

List: ordered, mutable
Tuple: ordered, immutable
Set: unique elements, unordered
Dictionary: key-value pairs, mutable
Permalink
Q7:

Explain Python functions.

Entry

Answer

Functions use def keyword.
Support default, keyword, and variable-length args.
Support lambdas and decorators.
Functions are first-class objects.
Permalink
Q8:

What are Python modules and packages?

Entry

Answer

Module: single .py file.
Package: directory with __init__.py.
Provide modularity and namespace management.
Permalink
Q9:

Explain Python exception handling.

Entry

Answer

Use try/except/finally/else.
Create custom exceptions by subclassing Exception.
Ensures robust error handling.
Permalink
Q10:

What are Python decorators?

Entry

Answer

Decorators modify functions dynamically.
Use @decorator syntax.
Common for logging, authorization, caching.
Permalink
Q11:

How does Python handle file operations?

Entry

Answer

Use open() with modes: r, w, a, rb, wb.
Use with statement for auto cleanup.
Supports read(), write(), seek().
Permalink
Q12:

Explain Python iterators and generators.

Entry

Answer

Iterator: implements __iter__() and __next__().
Generator: uses yield for lazy evaluation.
Efficient for large datasets.
Permalink
Q13:

How do Python comprehensions work?

Entry

Answer

List: [x*x for x in range(5)]
Set: {x for x in range(5)}
Dict: {x: x*x}
Fast, concise, readable.
Permalink
Q14:

Explain *args and **kwargs.

Entry

Answer

*args: variable positional args.
**kwargs: variable keyword args.
Useful for flexible function definitions.
Permalink
Q15:

What are Python's built-in structures for stacks and queues?

Entry

Answer

Stack: list with append/pop.
Queue: collections.deque.
queue.Queue for thread-safe queues.
Permalink
Q16:

How does Python support OOP?

Entry

Answer

Supports classes, inheritance, polymorphism.
Uses __init__, __str__, __repr__, and super().
Supports multiple inheritance.
Permalink
Q17:

How does Python handle memory for objects?

Entry

Answer

Uses reference counting.
Circular refs handled by GC.
Immutable objects are interned for performance.
Permalink
Q18:

How do you manage packages in Python?

Entry

Answer

Use pip to install packages.
Use virtual environments for isolation.
requirements.txt for dependency tracking.
Permalink
Q19:

Explain Python's Global Interpreter Lock (GIL).

Entry

Answer

GIL allows only one thread to execute Python bytecode at a time.
Limits CPU-bound threading.
I/O-bound threads work well.
Multiprocessing bypasses GIL.
Permalink
Q20:

What are __init__.py and __main__ used for?

Entry

Answer

__init__.py marks a folder as a package.
__main__ allows a script to run directly using main block.
Permalink
Q21:

Explain Python's multiple inheritance and MRO.

Junior

Answer

Python supports multiple inheritance.
MRO (Method Resolution Order) decides method lookup order.
Uses C3 linearization to avoid ambiguity.
Call C.mro() to check the resolution order.
Permalink
Q22:

What are Python magic or dunder methods?

Junior

Answer

Special methods like __init__, __str__, __repr__, __eq__, __add__.
Used for operator overloading and customizing built-in behavior.
Example: obj1 + obj2 calls obj1.__add__(obj2).
Permalink
Q23:

What are Python metaclasses?

Junior

Answer

Metaclass is the class of a class.
Defines how classes are created.
Useful for validation, auto-registration, or enforcing patterns.
Custom metaclasses extend type.
Permalink
Q24:

Difference between iterators and iterables.

Junior

Answer

Iterable: implements __iter__().
Iterator: implements __next__().
for-loops use iter() and next() internally.
Permalink
Q25:

How do generators improve performance?

Junior

Answer

Generators use yield for lazy evaluation.
Memory-efficient for large datasets.
Useful for pipelines and streaming data.
Permalink
Q26:

Explain Python decorators in depth.

Junior

Answer

Decorators modify behavior of functions or classes.
Can be parameterized.
Used for logging, caching, auth, timing.
Use @decorator syntax.
Permalink
Q27:

How does Python handle closures?

Junior

Answer

Closures capture variables from enclosing scopes.
Used in decorators, factories, callbacks.
inner() retains access to outer() variables even after outer() finishes.
Permalink
Q28:

Explain with statement and context managers.

Junior

Answer

Context managers use __enter__ and __exit__.
Automatically handle resource cleanup.
Used for files, network connections, locks.
Can create custom managers using contextlib.
Permalink
Q29:

How do you perform logging in Python?

Junior

Answer

Use logging module.
Levels: DEBUG, INFO, WARNING, ERROR, CRITICAL.
Supports logging to console, files, remote servers.
Preferred over print() in production.
Permalink
Q30:

How do you handle exceptions and create custom exceptions?

Junior

Answer

Use try/except/finally.
Create custom exceptions by subclassing Exception.
raise MyError("msg").
Ensures clean error handling.
Permalink
Q31:

How do you perform unit testing in Python?

Junior

Answer

Use unittest or pytest frameworks.
Mock dependencies using unittest.mock.
Test functions, classes, and APIs.
Permalink
Q32:

Advanced use of *args and **kwargs.

Junior

Answer

*args passes variable positional args.
**kwargs passes variable keyword args.
Used to forward arguments to other functions.
Common in decorators and wrappers.
Permalink
Q33:

Explain Python's itertools module.

Junior

Answer

Provides fast, memory-efficient iterators.
Functions: count(), cycle(), combinations(), product().
Useful for looping, combinatorics, pipelines.
Permalink
Q34:

Explain functools and lru_cache.

Junior

Answer

functools offers higher-order tools.
lru_cache caches results of expensive functions.
Improves performance for recursive or repeated calculations.
Permalink
Q35:

How do you handle file operations with context managers?

Junior

Answer

Use with open("file") for auto-close.
Supports read(), write(), binary modes.
Prevents file descriptor leaks.
Permalink
Q36:

How do you connect Python with databases?

Junior

Answer

Use sqlite3, psycopg2, PyMySQL, or ORMs like SQLAlchemy.
Use parameterized queries to prevent SQL injection.
Permalink
Q37:

How do you implement serialization and deserialization?

Junior

Answer

Use json.dumps()/json.loads().
pickle for Python object serialization.
Avoid untrusted pickle data for security.
Permalink
Q38:

Explain Python's datetime and time modules.

Junior

Answer

datetime provides date/time objects and timedelta.
time handles timestamps and sleep.
Supports formatting and parsing with strptime().
Permalink
Q39:

How do you implement caching in Python?

Junior

Answer

Use lru_cache or dictionaries for in-memory caching.
Use Redis/Memcached for distributed caching.
Improves performance of repeated calls.
Permalink
Q40:

How do you profile and optimize Python code?

Junior

Answer

Use cProfile or timeit.
Use line profiling for detailed analysis.
Optimize loops, data structures.
Use NumPy or Cython for heavy computation.
Permalink
Q41:

What are Python descriptors and how are they used?

Mid

Answer

Descriptors define __get__, __set__, and __delete__ methods.
Used for managing attribute access, validation, computed attributes, and reusable logic.
They power @property functionality in Python.
Permalink
Q42:

What is __slots__ in Python and why use it?

Mid

Answer

__slots__ restricts dynamic attribute creation.
Saves memory by preventing per-instance __dict__.
Useful for memory-sensitive applications or many small objects.
Permalink
Q43:

Explain Python metaclasses and use cases.

Mid

Answer

Metaclasses control class creation.
Used for validation, enforcing interfaces, singletons, auto-registration.
Defined by extending type and overriding __new__.
Permalink
Q44:

How does Python handle threading and concurrency?

Mid

Answer

threading handles I/O-bound tasks.
GIL limits CPU-bound threads.
multiprocessing enables true parallelism.
concurrent.futures simplifies thread/process pool usage.
Permalink
Q45:

How do you implement asynchronous programming in Python?

Mid

Answer

Use async/await with asyncio.
async def defines async functions.
await pauses execution.
Ideal for I/O-bound workloads like network or DB operations.
Permalink
Q46:

How do Python coroutines work?

Mid

Answer

Coroutines defined with async def.
Execution suspends at await points.
Useful for pipelines, cooperative multitasking, and event-driven systems.
Permalink
Q47:

How do you implement concurrent futures for parallel tasks?

Mid

Answer

Use ThreadPoolExecutor or ProcessPoolExecutor.
submit() runs tasks asynchronously.
Retrieve results using result() or as_completed().
Permalink
Q48:

How does Python handle sockets and networking?

Mid

Answer

socket module supports TCP/UDP networking.
Used for client-server communication.
asyncio supports asynchronous networking for high concurrency.
Permalink
Q49:

How do you make HTTP requests in Python?

Mid

Answer

Use requests for synchronous HTTP calls.
Use aiohttp or httpx for asynchronous requests.
Supports headers, auth, JSON, and streaming.
Permalink
Q50:

What are Python design patterns?

Mid

Answer

Patterns include Singleton, Factory, Observer, Strategy, Decorator.
Improve maintainability, structure, and scalability.
Permalink
Q51:

How do Python weak references work?

Mid

Answer

weakref allows referencing objects without preventing GC.
Useful for caching and avoiding memory leaks.
Permalink
Q52:

How do you handle file and directory operations?

Mid

Answer

Use os and pathlib for creating, deleting, and navigating files/directories.
Use shutil for copy/move/archive operations.
Permalink
Q53:

How do you serialize and deserialize custom Python objects?

Mid

Answer

Use json.dumps() with custom default handlers.
pickle supports full object serialization.
Avoid untrusted pickle data for security.
Permalink
Q54:

How do you profile Python code?

Mid

Answer

Use cProfile, profile, or timeit.
Use line_profiler for detailed bottleneck analysis.
Optimize slow loops and hotspots.
Permalink
Q55:

How do you implement caching in advanced scenarios?

Mid

Answer

Use lru_cache for memoization.
Use Redis or Memcached for distributed caching.
Support TTL and size-based eviction.
Permalink
Q56:

How do you handle logging in distributed Python applications?

Mid

Answer

Use structured JSON logging.
Forward logs to ELK, Fluentd, or centralized servers.
Include correlation IDs for traceability.
Permalink
Q57:

How do you implement custom context managers?

Mid

Answer

Define __enter__ and __exit__ methods.
Or use contextlib for simplified managers.
Ensures proper cleanup of resources.
Permalink
Q58:

How do you implement async iterators and async generators?

Mid

Answer

Async generator uses async def with yield.
Async iterator defines __aiter__ and __anext__.
Useful for streaming async data.
Permalink
Q59:

What are the main libraries for data analysis in Python?

Mid

Answer

NumPy for numerical computation,
pandas for DataFrame-based manipulation,
SciPy for scientific computing,
Matplotlib and Seaborn for visualization.
Permalink
Q60:

What is NumPy and why is it important?

Mid

Answer

NumPy provides multi-dimensional arrays and vectorized operations.
Allows fast computation and broadcasting.
Foundation for scientific and ML libraries.
Permalink
Q61:

What is pandas and how is it used?

Mid

Answer

pandas offers Series and DataFrame structures.
Supports filtering, grouping, merging, reshaping.
Ideal for cleaning and preprocessing data.
Permalink
Q62:

What are Python data visualization tools?

Mid

Answer

Matplotlib for low-level charts,
Seaborn for statistical visualizations,
Plotly/Bokeh for interactive plots.
Permalink
Q63:

How do you handle missing data in pandas?

Mid

Answer

Identify missing values with isnull/notnull.
Fill with mean/median/mode or custom values.
Drop rows or columns when appropriate.
Permalink
Q64:

How do you handle categorical data?

Mid

Answer

Convert categories to numeric using encoding.
One-hot for nominal, label encoding for ordinal.
Required for ML algorithms.
Permalink
Q65:

How do you normalize or standardize data?

Mid

Answer

Normalization scales values to 0–1.
Standardization gives mean 0 and std 1.
Ensures equal feature contribution.
Permalink
Q66:

What is scikit-learn and why is it used?

Mid

Answer

scikit-learn provides ML algorithms,
preprocessing tools,
model evaluation and pipelines.
Permalink
Q67:

What are common machine learning algorithms in Python?

Mid

Answer

Supervised: Linear/Logistic Regression, SVM, Trees, RF.
Unsupervised: K-means, PCA.
Ensemble: Boosting, Bagging.
Permalink
Q68:

How do you split datasets for training and testing?

Mid

Answer

Divide data into training and test sets.
Optionally add validation set.
Prevents overfitting and checks generalization.
Permalink
Q69:

What are pipelines in machine learning?

Mid

Answer

Combine preprocessing and models into a workflow.
Ensures consistent transformations.
Improves reproducibility.
Permalink
Q70:

How do you evaluate machine learning models?

Mid

Answer

Classification: Accuracy, Precision, Recall, F1, AUC.
Regression: MSE, MAE, R2.
Used to compare and select models.
Permalink
Q71:

How do you handle overfitting and underfitting?

Mid

Answer

Overfitting: Reduce complexity, regularization, cross-validation.
Underfitting: Increase model complexity or features.
Permalink
Q72:

How do you save and load machine learning models?

Mid

Answer

Use pickle or joblib for serialization.
Framework-specific save/load for deep learning.
Allows reuse without retraining.
Permalink
Q73:

How do you implement feature selection?

Mid

Answer

Use correlation, recursive feature elimination,
or model-based selectors.
Improves performance and reduces dimensionality.
Permalink
Q74:

How do you handle time series data?

Mid

Answer

Use datetime indexing, resampling, rolling windows.
Model trends, seasonality.
Use pandas and statsmodels.
Permalink
Q75:

How do you perform cross-validation?

Mid

Answer

Split data into multiple folds.
Train and test repeatedly.
Ensures robust model evaluation.
Permalink
Q76:

What are Python tools for NLP?

Mid

Answer

NLTK for tokenization and parsing.
spaCy for fast NLP pipelines.
Text preprocessing for ML.
Permalink
Q77:

How do you handle large datasets in Python?

Mid

Answer

Use chunking or lazy loading.
Use vectorized NumPy/pandas operations.
Use Dask or PySpark for distributed computing.
Permalink
Q78:

How do you deploy Python ML models?

Mid

Answer

Expose models using Flask, FastAPI, or Django.
Containerize with Docker.
Use CI/CD and cloud platforms for production.
Permalink
Q79:

What are the main Python web frameworks and their use cases?

Senior

Answer

Flask: Lightweight and flexible, ideal for microservices.
Django: Full-featured framework with ORM, admin, and authentication.
FastAPI: High-performance async API framework.
Pyramid/Tornado: Used for scalable or asynchronous workloads.
Permalink
Q80:

How does Flask handle routing and requests?

Senior

Answer

Routes map URLs to Python functions.
Decorators define endpoint behavior.
Request/response objects manage headers, parameters, and bodies.
Permalink
Q81:

How does Django handle URL routing?

Senior

Answer

URL patterns map URLs to views.
Supports dynamic parameters, namespaces, and reverse resolution.
Maintains separation of logic and presentation.
Permalink
Q82:

What is the difference between function-based and class-based views in Django?

Senior

Answer

Function-based views: simple and direct.
Class-based views: reusable patterns, mixins, and structured handling of HTTP methods.
Permalink
Q83:

How do Python frameworks handle templates?

Senior

Answer

Templates separate business logic from UI.
Use engines like Jinja2 or Django Template Language.
Support inheritance, loops, conditions, and reusable components.
Permalink
Q84:

How do you handle forms and input validation?

Senior

Answer

Form libraries parse, validate, and sanitize input.
Flask uses WTForms; Django has built-in forms with validation rules.
Protects against invalid and malicious input.
Permalink
Q85:

How do you implement authentication and authorization?

Senior

Answer

Authentication verifies identity; authorization controls access.
Use sessions, JWTs, OAuth, or framework built-ins.
Django provides full authentication system; Flask uses extensions.
Permalink
Q86:

How do you build REST APIs in Python frameworks?

Senior

Answer

Map CRUD operations to HTTP verbs.
Use serializers for JSON conversion.
Follow REST principles like statelessness, versioning, and proper endpoint design.
Permalink
Q87:

How do you handle database access with ORM?

Senior

Answer

ORM maps classes to database tables.
Django ORM and SQLAlchemy handle queries, joins, and relationships.
Supports migrations and validation.
Permalink
Q88:

How do you perform database migrations?

Senior

Answer

Migrations track schema changes.
Auto-generated scripts modify tables and fields.
Keep environments consistent across dev, staging, and production.
Permalink
Q89:

How do you implement caching in web applications?

Senior

Answer

Caching reduces database load and speeds responses.
Use Redis or Memcached.
Supports page, fragment, and function-level caching.
Permalink
Q90:

How do you implement logging for web applications?

Senior

Answer

Log request context, errors, and performance metrics.
Integrate with monitoring tools.
Helps diagnose issues and maintain observability.
Permalink
Q91:

How do you secure Python web applications?

Senior

Answer

Mitigate XSS, CSRF, SQL injection, and other attacks.
Use HTTPS, secure cookies, and proper session handling.
Rely on framework security middleware.
Permalink
Q92:

How do you handle REST API versioning?

Senior

Answer

Use URL-based (/v1/), header-based, or media-type versioning.
Ensures backward compatibility as APIs evolve.
Permalink
Q93:

How do you implement pagination, filtering, and sorting in APIs?

Senior

Answer

Pagination splits large data sets.
Filtering uses query parameters.
Sorting results ensures consistent ordering and performance.
Permalink
Q94:

How do you test Python web applications?

Senior

Answer

Unit tests for logic, integration tests for endpoints.
Use framework test clients to simulate requests.
Mock external dependencies.
Permalink
Q95:

How do you handle file uploads and downloads?

Senior

Answer

Validate file types and sizes.
Store files securely.
Use streaming for large downloads.
CDNs improve performance.
Permalink
Q96:

How do you implement middleware in Python frameworks?

Senior

Answer

Middleware intercepts requests and responses.
Used for logging, authentication, caching, error handling.
Stackable for modular behavior.
Permalink
Q97:

How do you handle asynchronous tasks in web frameworks?

Senior

Answer

Use Celery or RQ for background tasks.
Async endpoints prevent blocking.
Improves responsiveness for heavy workloads.
Permalink
Q98:

How do you deploy Python web applications?

Senior

Answer

Use WSGI servers (Gunicorn, uWSGI) or ASGI servers (Uvicorn, Daphne).
Reverse proxy with Nginx.
Containerize with Docker and automate via CI/CD.
Permalink
Q99:

How do you implement asynchronous programming with asyncio?

Expert

Answer

Asyncio provides an event loop for managing asynchronous tasks.
Enables non-blocking I/O.
Uses coroutines, tasks, and futures for high-performance concurrency.
Permalink
Q100:

What are Python coroutines and how are they useful?

Expert

Answer

Coroutines can pause and resume execution.
Ideal for high-latency operations without blocking the thread.
Used in scalable servers and data pipelines.
Permalink
Q101:

How do Python tasks and futures work in concurrency?

Expert

Answer

Tasks schedule coroutines on the event loop.
Futures represent results not yet available.
Useful for coordinating parallel async operations.
Permalink
Q102:

How do Python threads differ from processes?

Expert

Answer

Threads share memory and suit I/O-bound tasks.
Processes have separate memory and suit CPU-bound tasks.
Processes bypass GIL for true parallelism.
Permalink
Q103:

How do you handle synchronization in Python concurrency?

Expert

Answer

Use locks, semaphores, events, and conditions.
Prevent race conditions and ensure safe shared-resource access.
Critical in multi-threaded applications.
Permalink
Q104:

What are Python design patterns and their use cases?

Expert

Answer

Singleton, Factory, Observer, Strategy, Decorator.
Provide modularity, scalability, and maintainability.
Applied in complex architecture and large systems.
Permalink
Q105:

How do Python descriptors, properties, and slots optimize classes?

Expert

Answer

Descriptors control attribute access.
Properties provide clean getters/setters.
Slots reduce memory by avoiding __dict__.
Useful for high-performance apps.
Permalink
Q106:

How do you profile Python applications?

Expert

Answer

Use cProfile, timeit, and line profiling.
Identify slow functions and optimize algorithms.
Apply vectorization and efficient data structures.
Permalink
Q107:

How do you optimize memory in Python?

Expert

Answer

Use generators for lazy evaluation.
Reduce object creation.
Use slots, weak references, and optimized structures.
Permalink
Q108:

How do Python weak references help manage memory?

Expert

Answer

Weak references allow referencing objects without preventing GC.
Useful in caching and preventing memory leaks in long-running apps.
Permalink
Q109:

How do Python context managers improve resource handling?

Expert

Answer

Automatically clean up resources via __enter__ and __exit__.
Prevent leaks for file, DB, and network operations.
Essential for robust resource management.
Permalink
Q110:

How do you handle advanced exception management in Python?

Expert

Answer

Use structured try/except/finally.
Create custom exception hierarchies.
Enable cleaner recovery and better debugging.
Permalink
Q111:

How do you implement logging for large Python applications?

Expert

Answer

Use structured logging with context.
Supports file, console, and remote handlers.
Enhances monitoring and traceability in production.
Permalink
Q112:

How do you handle multiprocessing and parallelism efficiently?

Expert

Answer

Use ProcessPoolExecutor for CPU-bound tasks.
Distribute workloads across processes.
Avoid shared state unless using managers or queues.
Permalink
Q113:

How do you implement caching in advanced Python applications?

Expert

Answer

Use in-memory or distributed caches like Redis.
Apply eviction strategies and TTL policies.
Boosts performance for repeated computations.
Permalink
Q114:

How do you debug and trace Python applications?

Expert

Answer

Use debuggers, logs, and profiling.
Trace executions to find runtime issues.
Unit tests help detect early failures.
Permalink
Q115:

How are configuration and environment variables managed in Python?

Expert

Answer

Use env variables, config files, or libraries.
Keep secrets secure.
Supports portability across environments.
Permalink
Q116:

How do you handle concurrency in Python web frameworks?

Expert

Answer

Async frameworks enable non-blocking requests.
Background workers handle long tasks.
Improves scalability and responsiveness.
Permalink
Q117:

How do you integrate Python with external libraries efficiently?

Expert

Answer

Use standard APIs and dependency management.
Ensure compatibility and maintainability.
Supports performant, modular architectures.
Permalink

Curated Sets for Python

No curated sets yet. Group questions into collections from the admin panel to feature them here.

People Also Ask - Related Python Questions

What are Python's key features?
What is the difference between Python 2 and Python 3?
What are Python data types?
Explain Python variable scoping rules.
How does Python manage memory?
What are lists, tuples, sets, and dictionaries?
Ready to level up? Start Practice