DSA in Python

A collection of Data Structures and Algorithms (DSA) implementations written mainly in Python, with a few C examples included.

Table of Contents

• Overview
• Contents
• Getting Started
• Folder Structure
• Learning Goals
• Contributing
• License

Overview

This repository is a personal collection of DSA practice code. It includes beginner-friendly implementations across searching, sorting, graphs, dynamic programming, and related problem-solving patterns.

Contents

• LinearSearch/
  • linear_search.py - Linear Search implementation in Python.
  • linear_searchh.c - Linear Search implementation in C.
• BinarySearch/
  • bn_search.py - Binary Search implementation in Python.
  • bn_search.c - Binary Search implementation in C.
  • first_last_index_arr.c - Find first and last index of element in sorted array (C).
  • first_last_index_arr.py - Find first and last index of element in sorted array.
  • right_or_left_minimum.py - Find right or left minimum in rotated sorted array.
• BinaryTree/
  • binary_tree.py - Binary Tree implementation in Python.
  • binary_tree_tuple.py - Binary Tree using tuples in Python.
  • height_of_tree.py - Find the height of a binary tree.
  • max_depth.py - Find the maximum depth of a binary tree.
  • min_depth.py - Find the minimum depth of a binary tree.
  • nmbr_of_nodes.py - Count number of nodes in a binary tree.
• BinarySearchTrees/
  • display.py - Display Binary Search Tree values.
  • is_balanced.py - Check if a Binary Search Tree is balanced.
  • key_value_pairs_bst.py - Binary Search Tree with key-value pairs.
  • maximum_key.py - Find maximum key in a Binary Search Tree.
  • minimum_key.py - Find minimum key in a Binary Search Tree.
  • str_bst.py - Binary Search Tree with string values.
  • validate_bst.py - Validate whether a tree is a Binary Search Tree.
• RotationCount/
  • rotation_count_linear.py - Rotation count in a rotated sorted array (Python, linear approach).
  • rotation_count_linear.c - Rotation count in a rotated sorted array (C, linear approach).
• TwoPointers/
  • move_zeros.py - Move all zeros to the end using two pointers.
• HashTables/
  • basic_hash_table.py - Basic hash table implementation.
  • handling_collsion_hash.py - Handle collisions in hash tables.
  • hashing_function.py - Basic hashing function to compute index.
  • improved_hash_table.py - Improved hash table with better collision handling.
  • insert.py - Insert key-value data into hash table structure.
  • list_kv.py - List-based key-value storage example.
• BubbleSort/
  • bubble_sort.py - Bubble Sort implementation in Python.
• InsertionSort/
  • insertion_sort.py - Insertion Sort implementation in Python.
• MergeSort/
  • merge_sort.py - Merge Sort implementation in Python.
• QuickSort/
  • quick_sort.py - Quick Sort implementation in Python.
• CustomComparisionFunc/
  • notebook_decreasing.py - Custom comparison function example for sorting in decreasing order.
• DynanicProgramming/
  • lcs_dp.py - Longest common subsequence implementation using dynamic programming.
• Memoization/
  • largest_common_subsequence.py - Longest common subsequence implementation.
  • lcs_with_memo.py - Longest common subsequence using memoization.
• KnapSackProblem/
  • knap_sack.py - Basic knapsack problem implementation.
  • knap_sack_dp.py - Knapsack problem using dynamic programming.
• Graph/
  • graph_list.py - Graph implementation using adjacency list.
  • weighted_directed_graph.py - Weighted directed graph implementation.
• PathFindingAlgorithms/
  • breadth_first_search.py - Breadth-first search implementation.
  • depth_first_search.py - Depth-first search implementation.
• DijkstraAlgorithm/
  • dijkstra_algo.py - Dijkstra shortest path algorithm implementation.
• Questions/
  • minimum_update_distance.py - Minimum edit/update distance problem implementation.
  • subarray_sum.py - Subarray sum problem implementation.

Getting Started

Prerequisites

• Python 3.x installed
• GCC compiler installed (for running the C file)

Run an Example

Run one Python file from the project root:

python LinearSearch/linear_search.py

Compile and Run a C File

Compile and run one C file from the project root:

gcc LinearSearch/linear_searchh.c -o LinearSearch/linear_searchh
./LinearSearch/linear_searchh

Folder Structure

DSA/
├── BinarySearch/
│   ├── bn_search.c
│   ├── bn_search.py
│   ├── first_last_index_arr.c
│   ├── first_last_index_arr.py
│   └── right_or_left_minimum.py
├── BinarySearchTrees/
│   ├── display.py
│   ├── is_balanced.py
│   ├── key_value_pairs_bst.py
│   ├── maximum_key.py
│   ├── minimum_key.py
│   ├── str_bst.py
│   └── validate_bst.py
├── BinaryTree/
│   ├── binary_tree.py
│   ├── binary_tree_tuple.py
│   ├── height_of_tree.py
│   ├── max_depth.py
│   ├── min_depth.py
│   └── nmbr_of_nodes.py
├── BubbleSort/
│   └── bubble_sort.py
├── HashTables/
│   ├── basic_hash_table.py
│   ├── handling_collsion_hash.py
│   ├── hashing_function.py
│   ├── improved_hash_table.py
│   ├── insert.py
│   └── list_kv.py
├── InsertionSort/
│   └── insertion_sort.py
├── MergeSort/
│   └── merge_sort.py
├── QuickSort/
│   └── quick_sort.py
├── CustomComparisionFunc/
│   └── notebook_decreasing.py
├── DynanicProgramming/
│   └── lcs_dp.py
├── Memoization/
│   ├── largest_common_subsequence.py
│   └── lcs_with_memo.py
├── KnapSackProblem/
│   ├── knap_sack.py
│   └── knap_sack_dp.py
├── Graph/
│   ├── graph_list.py
│   └── weighted_directed_graph.py
├── PathFindingAlgorithms/
│   ├── breadth_first_search.py
│   └── depth_first_search.py
├── DijkstraAlgorithm/
│   └── dijkstra_algo.py
├── Questions/
│   ├── minimum_update_distance.py
│   └── subarray_sum.py
├── LinearSearch/
│   ├── linear_search.py
│   └── linear_searchh.c
├── RotationCount/
│   ├── rotation_count_linear.py
│   └── rotation_count_linear.c
└── TwoPointers/
    └── move_zeros.py

Learning Goals

• Understand how searching works in unsorted data.
• Compare Linear Search with faster methods like Binary Search.
• Practice writing clean, beginner-friendly Python code.

Contributing

Contributions are welcome.

1. Fork the repository
2. Create a new branch
3. Add your algorithm or improvements
4. Submit a pull request

License

This project is for educational purpose.