LeetCode Solutions in Python

A comprehensive collection of LeetCode problems organized by patterns and problem types.

📁 Repository Structure

Core Problem Patterns

• 01_arrays_and_strings - Array manipulation, string processing
• 02_two_pointers - Two pointer technique problems
• 03_sliding_window - Sliding window pattern problems
• 04_hash_table - Hash table and frequency counting
• 05_stack_and_queue - Stack and queue data structures
• 06_linked_list - Linked list operations and algorithms
• 07_trees - Binary trees, BST, tree traversals
• 08_graphs - Graph algorithms (DFS, BFS, shortest path)
• 09_dynamic_programming - DP problems and optimization
• 10_backtracking - Backtracking and recursive solutions
• 11_greedy - Greedy algorithm problems
• 12_binary_search - Binary search variations
• 13_sorting - Sorting algorithms and problems
• 14_math - Mathematical problems and number theory
• 15_bit_manipulation - Bit manipulation techniques
• 16_union_find - Union-Find data structure problems
• 17_trie - Trie (prefix tree) problems
• 18_segment_tree - Segment tree and advanced data structures
• 19_advanced - Complex algorithms and advanced topics

Utilities

• utils - Common data structures and helper functions
• templates - Code templates for different problem types

🚀 Getting Started

1. Each problem folder contains solutions with:

   • Problem statement
   • Multiple solution approaches
   • Time and space complexity analysis
   • Test cases

2. Use the templates in the templates/ folder for quick problem setup

3. Check utils/ for common data structures and helper functions

📊 Problem Difficulty Distribution

• Easy: Fundamental concepts and basic implementations
• Medium: Requires understanding of algorithms and data structures
• Hard: Complex algorithms, optimization, and advanced techniques

🎯 Learning Path

1. Start with arrays_and_strings for basic concepts
2. Move to two_pointers and sliding_window for optimization techniques
3. Practice trees and graphs for recursive thinking
4. Master dynamic_programming for optimization problems
5. Explore advanced topics for complex algorithms

📝 Naming Convention

• Files: problem_number_problem_name.py
• Example: 0001_two_sum.py
• Each file includes problem description, solution, and complexity analysis

🔧 Tools and Setup

• Python 3.8+
• Recommended IDE: VS Code with Python extension
• Testing: Use built-in Python unittest or pytest

📚 Resources

• LeetCode
• NeetCode
• AlgoExpert

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Happy coding! 🎉