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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:

Email
GitHub