Python Data Structures and Algorithms

This repository provides Python implementations of various data structures, algorithms, design patterns, and solutions to popular coding problems. It aims to build a strong foundation in Python programming, improve problem-solving skills, and explore optimization techniques for real-world and competitive programming challenges on platforms like LeetCode, Codeforces, and HackerRank.

Repository Structure

The repository is organized into the following folders:

1. fundamentals

This folder contains basic Python concepts and fundamental programming techniques to build a strong foundation.

• Built-in functions and modules
• Closures, nested functions, and decorators
• Context managers and file handling
• Python data structures, comprehensions, and string operations
• Functions, control flow, and operators
• Module imports and custom modules

2. object_oriented_programming

This folder focuses on object-oriented programming concepts and patterns.

• Classes, objects, and inheritance
• Encapsulation and polymorphism
• Static and class methods
• Dunder (magic) methods

3. algorithms

This folder includes implementations of essential algorithms, covering:

• Sorting (Quick Sort, Merge Sort, etc.)
• Searching (Binary Search, Linear Search)
• Divide and Conquer
• Greedy Algorithms
• Dynamic Programming
• Backtracking
• Graph Algorithms (BFS, DFS)

4. dsa

This folder includes implementations of core data structures and algorithms for DSA practice.

• Arrays
• Linked Lists
• Stacks
• Queues
• Hash Tables
• Trees
• Graphs

5. design_patterns

This folder organizes design patterns into three categories:

Creational Patterns

• Abstract Factory, Builder, Factory, Prototype, Singleton

Structural Patterns

• Adapter, Bridge, Composite, Decorator, Facade, Flyweight, Proxy

Behavioral Patterns

• Chain of Responsibility, Command, Interpreter, Iterator, Mediator, Memento, Observer, State, Strategy, Template, Visitor

6. concurrency_parallelism

This folder demonstrates concurrency and parallelism concepts in Python.

• Asynchronous Programming (asyncio)
• Threading (py_threading.py)
• Multiprocessing (multi_processing.py)
• Using concurrent.futures for thread and process pools
• Green threads with gevent

7. memory_management

This folder explores memory management and optimization techniques in Python.

• Garbage collection
• Heap and stack memory differentiation
• Memory usage optimization
• Weak references

8. problems

This folder contains solutions to coding problems from platforms such as LeetCode, Codeforces, and HackerRank.

9. testing_debugging

This folder focuses on testing and debugging techniques in Python.

Debugging

• Examples using Python's pdb debugger

Profiling and Optimization

• Code profiling with cProfile
• Optimization using functools.lru_cache
• Benchmarking with timeit

Unit Testing

• Example unit tests for mathematical operations

Contact

Feel free to reach out if you have any questions or suggestions: