This comprehensive guide covers 50 essential Python interview questions that every Python developer should know. These questions cover fundamental concepts, data structures, object-oriented programming, advanced features, and best practices commonly asked in technical interviews.

## Core Concepts

Understanding Python's core concepts is essential for any Python developer. These questions test your knowledge of data types, variables, operators, and Python's unique features.

## Data Structures

Python provides powerful built-in data structures. Master these questions to demonstrate your understanding of lists, dictionaries, tuples, sets, and their use cases.

## Object-Oriented Programming

Python supports OOP with classes, inheritance, polymorphism, and encapsulation. These questions cover class design, method resolution order, and Python's OOP features.

## Advanced Features

Python's advanced features include decorators, generators, context managers, and metaprogramming. These questions test your ability to use Python's powerful features effectively.

## Best Practices & Libraries

Production-ready Python code requires understanding best practices, popular libraries, and Pythonic code style. These questions cover PEP guidelines, testing, and common libraries.

Comprehensive collection of 50 essential Python interview questions covering data structures, OOP, decorators, generators, and advanced Python concepts. Free Python interview questions with answers. Python interview prep guide.

Python Interview Questions: 50 Essential Questions for Developers

FastAPI is probably the best thing that happened to Python web development in recent years. If you're building APIs in 2025, you should definitely know FastAPI.

## Why FastAPI is Awesome

FastAPI is built on modern Python features. It uses type hints (which you probably already know), async/await for handling multiple requests, and automatically creates API documentation for you. The best part? It's incredibly fast - almost as fast as Node.js.

## Getting Started is Super Easy

Setting up FastAPI takes just a few minutes. You install it, write a simple function, and boom - you have a working API. No complicated setup, no confusing configuration files.

## Building Your First API

Let's create a simple todo API. I'll show you how easy it is to create endpoints, handle requests, and return responses. You'll be surprised how little code you need.

## Real-World Features

FastAPI handles authentication, database connections, file uploads, and more. I'll show you practical examples that you can actually use in your projects.

Learn how to build fast, modern APIs with FastAPI. Perfect for 2025 - it's fast, easy to use, and automatically generates documentation. Build production-ready APIs in minutes.

Building APIs with FastAPI: The Modern Python Way

Pandas is the tool that makes working with data in Python actually enjoyable. If you're dealing with CSV files, databases, or any structured data, Pandas is your best friend.

## What is Pandas?

Pandas gives you DataFrames - think of them as super-powered Excel spreadsheets in Python. You can filter, group, analyze, and transform data with just a few lines of code.

## Reading Data

Pandas can read almost anything - CSV files, Excel files, JSON, SQL databases. One line of code and your data is ready to work with.

## Cleaning Data

Real data is messy. Missing values, duplicates, wrong formats - Pandas has tools for all of it. I'll show you the most common problems and how to fix them.

## Analyzing Data

Once your data is clean, the fun begins. Group by categories, calculate averages, find patterns - Pandas makes it all easy.

Learn Pandas the easy way. Clean data, analyze it, and make sense of it. Whether you're working with CSV files or databases, Pandas makes data analysis simple and fun.

Python Data Analysis with Pandas: Made Simple

Decorators are one of those Python features that seem magical at first, but once you understand them, they become incredibly useful. They let you modify or extend functions without changing their code.

## What Are Decorators?

Think of decorators as wrappers around your functions. They take a function, do something with it (like add timing, logging, or validation), and return a new function. The cool part? You use them with a simple @ symbol.

## How They Work

Decorators are just functions that take other functions as arguments. Once you understand this, everything clicks. I'll show you step by step how Python processes decorators.

## Common Use Cases

You'll see decorators everywhere - timing functions, logging, authentication, caching. I'll show you practical examples that you'll actually use in real projects.

## Building Your Own

Once you understand how decorators work, building your own is easy. I'll show you how to create decorators that add useful functionality to your functions.

Master Python decorators - one of the most powerful features. Learn how they work, when to use them, and build your own decorators. Once you understand decorators, Python becomes way more powerful.

Python Decorators: Understanding the Magic

Generators are one of Python's most elegant features. They let you create iterators without storing everything in memory. Perfect for large datasets, infinite sequences, or when you want cleaner code.

## What Are Generators?

Generators are functions that use yield instead of return. When you call them, they don't run immediately - they return a generator object. You iterate over it, and it produces values one at a time.

## Why Use Generators?

They're memory efficient - instead of creating a huge list, generators produce values on-demand. They're also lazy, meaning they only compute what you need. This makes them perfect for large files or infinite sequences.

## Generator Expressions

Just like list comprehensions, but for generators. They use parentheses instead of square brackets and are super memory efficient. I'll show you when to use them.

## Real-World Examples

I'll show you practical examples - reading large files, processing data streams, creating infinite sequences. These are patterns you'll use in real projects.

Learn Python generators and why they're so useful. They save memory, make your code cleaner, and are perfect for working with large datasets. This is a must-know concept for Python developers.

Python Generators: Memory-Efficient Iteration

Async/await in Python lets you write code that can do multiple things at once without blocking. Perfect for web requests, file I/O, or any operation where you're waiting for something.

## Why Async?

When your code makes a network request or reads a file, it waits. With async, while one request is waiting, your code can handle other requests. This makes your programs much faster.

## The Basics

The async keyword makes a function a coroutine, and await pauses execution until something completes. It's similar to JavaScript's async/await if you know that. The syntax is clean and easy to read.

## Running Async Code

You need an event loop to run async code. I'll show you how to use asyncio.run() and how to create tasks that run concurrently.

## Real Examples

I'll show you practical examples - making multiple API calls, reading files concurrently, building async web scrapers. These are real patterns you'll use.

Master async/await in Python. Learn how to write asynchronous code that's fast and efficient. Perfect for web scraping, API calls, and I/O operations. This is how modern Python handles concurrency.

Python Async/Await: Modern Asynchronous Programming

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Python Tutorials - Programming Guide

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