python-dsalgo 0.1.0

A Python package for data structures and algorithms

Python Data Structures and Algorithms Package

This repository contains a Python package implementing various data structures and algorithms. It's designed as a beginner project to help understand and practice fundamental concepts of python package creation.

Table of Contents

• Python Data Structures and Algorithms Package
  • Table of Contents
  • About
  • Installation
  • Usage
  • Modules
    • data_structure
    • searching
    • sorting
  • Contributing
  • License

About

The Python Data Structures and Algorithms package includes implementations of common data structures (like lists, stacks, queues, trees, and graphs) and algorithms (like searching and sorting). This package is an educational resource.

Installation

First, clone the repository and navigate to the directory:

git clone https://github.com/HeyBuddy-NSK/python-dsalgo.git
cd python-dsalgo

Then, install the package:

pip install .

OR

Install directly using pip.

pip install python-dsalgo

Usage

After installing the package, you can use it in your Python scripts. Here's an example of how to use the sorting module:

from python_dsalgo.sorting import quick_sort

arr = [3, 6, 8, 10, 1, 2, 1]
sorted_arr = quick_sort(arr)
print(sorted_arr)

Modules

data_structure

• singly_linked_list.py : Implementation of singly linked list.

  • Functions / Methods:

    • append(data) : Appends data to the end of list.
    • add_at_start(data) : Adds data to the start of list.
    • delete_with_value(Target_value) : Delete node from list with given value by user
    • print_list() : Prints whole list.
    • search(key_data) : Performs search operation for given data in the list.
    • get_length() : returns length of list.

  • Usage

    from python_dsalgo.data_structure import SinglyLinkedList
    
    sll = SinglyLinkedList()
    sll.append(1)
    sll.append(2)
    sll.append(3)
    sll.print_list()
    

• doubly_linked_list.py : Implementation of doubly linked list.

  • Functions / Methods:

    • append(data) : Appends data to the end of list.
    • add_at_start(data) : Adds data to the start of list.
    • delete_with_value(Target_value) : Delete node from list with given value by user
    • print_list() : Prints whole list.
    • search(key_data) : Performs search operation for given data in the list.

  • Usage

    from python_dsalgo.data_structure import DoublyLinkedList
    
    dll = DoublyLinkedList()
    dll.append(1)
    dll.append(2)
    dll.append(3)
    dll.display()
    

• circular_singly_linked_list.py : Implementation of circular singly linked list.

  • Functions / Methods:

    • append(data) : Appends data to the end of list.
    • add_at_start(data) : Adds data to the start of list.
    • delete_with_value(Target_value) : Delete node from list with given value by user
    • print_list() : Prints whole list.

  • Usage

    from python_dsalgo.data_structure import CircularSinglyLinkedList
    
    csll = CircularSinglyLinkedList()
    csll.append(1)
    csll.append(2)
    csll.append(3)
    csll.print_list()
    

• circular_doubly_linked_list.py : Implementation of circular doubly linked list

  • Functions / Methods:

    • append(data) : Appends data to the end of list.
    • delete_with_value(Target_value) : Delete node from list with given value by user
    • print_list() : Prints whole list.

  • Usage

    from python_dsalgo.data_structure import CircularDoublyLinkedList
    
    cdll = CircularDoublyLinkedList()
    cdll.append(1)
    cdll.append(2)
    cdll.append(3)
    cdll.print_list()
    

• stack.py: Implementation of stacks.

  • Functions / Methods :

    • push(data) : Add / push data to the top of stack.
    • pop() : Pops / deletes data from the top of stack.
    • isEmpty() : Returns True if stack empty else returns False.
    • peek() : Returns value at the top of stack.
    • size() : Get size of stack.

  • Usage

    from python_dsalgo.data_structure import Stack
    
    stack = Stack()
    stack.push(1)
    stack.push(2)
    stack.push(3)
    print(stack.pop())
    

• queue.py: Implementation of queues.

  • Functions / Methods :
    • enqueue(data) : Inserts data into queue.
    • dequeue() : Deletes data from queue.
    • front() : Returns value from the front of queue.
    • rear() : Returns value from the rear of queue.
    • isEmpty() : Checks if queue is empty or not. Returns True if queue is empty else False.
    • size() : Returns size of queue.
  • Usage

    from python_dsalgo.data_structure import Queue
    
    queue = Queue()
    queue.enqueue(1)
    queue.enqueue(2)
    queue.enqueue(3)
    print(queue.dequeue())
    

• binary_search_tree.py: Implementation of binary search tree.

  • Functions / Methods :

    • insert(key) : Insert a new node with the given key / data.
    • search(key) : Search for a node with the given key.
    • delete(key) : Delete a node with the given key.
    • inorder() : Perform an inorder traversal.

  • Usage

    from Python_dsalgo.data_structure import BinarySearchTree
    
    bst = BinarySearchTree()
    bst.insert(10)
    bst.insert(5)
    bst.insert(15)
    bst.inorder()
    

• graph_AL.py: Implementation of graph adjacency list representation.

  • Functions / Methods :

    • add_edge(u,v) : Addes edge in graph between two vertices u and v.
    • print_graph() : prints adjacency list representation of graph.

  • Usage

    from python_dsalgo.data_structure import GraphAL
    
    graph = GraphAL()
    graph.add_edge(0, 1)
    graph.add_edge(0, 2)
    graph.print_graph()
    

• graph_AM.py : Implementation of graph adjacency matrix representation.

  • Functions / Methods :

    • add_edge(u,v) : Addes edge in graph between two vertices u and v.
    • print_graph() : Prints Adjacency Matrix representation of graph.

  • Usage

    from python_dsalgo.data_structure import GraphAM
    
    # While creating object you have to give how many virtices you will. e.g here it is 3.
    graph = GraphAM(3)
    graph.add_edge(0, 1)
    graph.add_edge(0, 2)
    graph.print_graph()
    

• hash_table.py : Implementation of hash table.

  • Functions / Methods :

    • insert(value) : Inserts Hash value in the table.
    • search(value) : This function searches for the value in table.
    • delete(value) : This function deletes value from the function.
    • print_table() : This function prints whole table contents.

  • Usage

    from python_dsalgo.data_structure import HashTable
    
    ht = HashTable()
    ht.insert('value1')
    ht.insert('value2')
    print(ht.search('value2'))
    ht.print_table()
    

• max_heap.py : implementation of max heap.

  • Functions / Methods :

    • insert(data) : Inserts value given by user into the heap structure.
    • delete_node(data) : Deletes node with given value by user.
    • get_max() : Returns maximum value from heap.
    • print_all() : Prints max heap.

  • Usage

    from python_dsalgo.data_structure import MaxHeap
    
    max_heap = MaxHeap()
    max_heap.insert(10)
    max_heap.insert(5)
    max_heap.insert(20)
    print(max_heap.get_max())
    max_heap.print_all()
    

• min_heap.py : implementation of min heap.

  • Functions / Methods :

    • insert(data) : Inserts value given by user into the heap structure.
    • delete_node(data) : Deletes node with given value by user.
    • get_min() : Returns minimum value from heap.
    • print_all() : Prints min heap.

  • Usage

    from python_dsalgo.data_structure import MinHeap
    
    min_heap = MinHeap()
    min_heap.insert(10)
    min_heap.insert(5)
    min_heap.insert(20)
    print(min_heap.get_min())
    min_heap.print_all()
    

searching

• binary_search: Implementation of searching algorithm binary search.

  • Method binary_search(array,target) : Returns index of element if found.

  • Usage

    from python_dsalgo.searching import binary_search
    
    arr = [1, 2, 3, 4, 5]
    index = binary_search(arr, 3)
    print(index)
    

• linear_search: Implementation of searching algorithm linear search.

  • Method linear_search(array,target) : Returns index of element if found.

  • Usage

    from python_dsalgo.searching import linear_search
    
    arr = [1, 2, 3, 4, 5]
    index = linear_search(arr, 3)
    print(index)
    

• bfs : Implementation of breadth first search algorithm.

  • Method bfs(graph, starting_vertex) : Returns order list in wich order graph was visited.

  • Usage

    from python_dsalgo.searching import bfs
    
    graph = {
        'A': ['B', 'C'],
        'B': ['D', 'E'],
        'C': ['F'],
        'D': [],
        'E': ['F'],
        'F': []
    }
    
    order_list = bfs(graph, 'A')
    print(order_list)
    

• dfs : Implementation of depth first search algorithm.

  • Method dfs(graph, starting_vertex) : Returns order list in wich order graph was visited.

  • Usage

    from python_dsalgo.searching import dfs
    
    graph = {
        'A': ['B', 'C'],
        'B': ['D', 'E'],
        'C': ['F'],
        'D': [],
        'E': ['F'],
        'F': []
    }
    order_list = dfs(graph, 'A')
    print(order_list)
    

sorting

• bubble_sort : Implementation of sorting algorithm bubble sort.

  • Method bubble_sort(array) : Returns sorted array.

  • Usage

    from python_dsalgo.sorting import bubble_sort
    
    arr = [64, 34, 25, 12, 22, 11, 90]
    sorted_arr = bubble_sort(arr)
    print(sorted_arr)
    

• bucket_sort : Implementation of sorting algorithm bucket sort.

  • Method bucket_sort(array) : Returns sorted array.

  • Usage

    from python_dsalgo.sorting import bucket_sort
    
    arr = [0.25, 0.36, 0.58, 0.41, 0.29, 0.22, 0.93, 0.44, 0.60, 0.75]
    sorted_arr = bucket_sort(arr)
    print(sorted_arr)
    

• counting_sort : Implementation of sorting algorithm counting sort.

  • Method counting_sort(array) : Returns sorted array.

  • Usage

    from python_dsalgo.sorting import counting_sort
    
    arr = [4, 2, 2, 8, 3, 3, 1]
    sorted_arr = counting_sort(arr)
    print(sorted_arr)
    

• heap_sort : Implementation of sorting algorithm heap sort.

  • Method heap_sort(array) : Returns sorted array.

  • Usage

    from python_dsalgo.sorting import heap_sort
    
    arr = [12, 11, 13, 5, 6, 7]
    sorted_arr = heap_sort(arr)
    print(sorted_arr)
    

• insertion_sort : Implementation of sorting algorithm insertion sort.

  • Method insertion_sort(array) : Returns sorted array.

  • Usage

    from python_dsalgo.sorting import insertion_sort
    
    arr = [12, 11, 13, 5, 6]
    sorted_arr = insertion_sort(arr)
    print(sorted_arr)
    

• merge_sort : Implementation of sorting algorithm merge sort.

  • Method merge_sort(array) : Returns sorted array.

  • Usage

    from python_dsalgo.sorting import merge_sort
    
    arr = [12, 11, 13, 5, 6, 7]
    sorted_arr = merge_sort(arr)
    print(sorted_arr)
    

• quick_sort : Implementation of sorting algorithm quick sort.

  • Method quick_sort(array) : Returns sorted array.

  • Usage

    from ptyhon_dsalgo.sorting import quick_sort
    
    arr = [10, 7, 8, 9, 1, 5]
    sorted_arr = quick_sort(arr)
    print(sorted_arr)
    

• radix_sort : Implementation of sorting algorithm radix sort.

  • Method radix_sort(array) : Returns sorted array.

  • Usage

    from python_dsalgo.sorting import radix_sort
    
    arr = [170, 45, 75, 90, 802, 24, 2, 66]
    sorted_arr = radix_sort(arr)
    print(sorted_arr)
    

• selection_sort : Implementation of sorting algorithm selection sort.

  • Method selection_sort(array) : Returns sorted array.

  • Usage

    from python_dsalgo.sorting import selection_sort
    
    arr = [64, 25, 12, 22, 11]
    sorted_arr = selection_sort(arr)
    print(sorted_arr)
    

• shell_sort : Implementation of sorting algorithm shell sort.

  • Method shell_sort(array) : Returns sorted array.

  • Usage

    from python_dsalgo.sorting import shell_sort
    
    arr = [12, 34, 54, 2, 3]
    sorted_arr = shell_sort(arr)
    print(sorted_arr)
    

Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Create a new Pull Request.

License

This project is licensed under the MIT License. See the LICENSE file for details.