datastax 0.2.0

A python library to handle dataStructures

updated: Monday, 24th January 2022

Simplicity meets intelligence

dataStax

What's New?

• Added Threaded Binary Trees
• Added LRU Cache
• Added Proper and effective testcases

Table of Contents

• Introduction
• Problem Statement
• Benefits
• Requirements
• Installation
• Usage
• What's Next

Introduction

• This is a very simple yet powerful project to implement day to day abstract data structures
• A pure implementation of Python in representing Tree, Linkedlist and Array based datastructures in basic command prompt
• It helps visualize each data structure for better understanding
• Students can be beneficial in using this Package
• This project is still under construction

Problem Statement

• Often at the beginning of B.Tech Course, CS students face a lot of problems understanding the internal architecture of complex ADTs.
• While solving coding challenges locally where test cases have to be written using these ADTs, it becomes really cumbersome to write these data structures from scratch.
• Also, when writing programs which implements these ADS, we encounter lots of errors just because we are unable to preview what's actually going on under the hood.

Benefits

• Instant installation
• Quick Updates
• Very small size
• No extra modules required
• Written purely from scratch
• Easy Documentation [Upcoming]
• Command Line Demo

Requirements

• Runs on latest Python 3.7+
• (WARNING: Though the module might run on py 3.7 error free, but it has been tested for 3.9+)
• (Suggesting you to always update to the latest python version)
• This Library requires no extra modules

Installation

1. Use the python package manager pip to install datastax.

pip install datastax

Usage

Demo

• To get a demo of the library use the following command

  • Windows:

  > py -m datastax 
  

  • Unix based systems:

  $ python3 -m datastax
  

  • Result

  Available modules are:
  1. LinkedLists
  2. Trees
  3. Arrays
  
  Usage
  > py datastax <data-structure> [data]
  Data Structures:
  ->  trees          Hierarchical DS
  ->  linkedlists    Linear DS
  ->  arrays         Fixed Size Linear DS
  

• Then follow as the instruction guides

> py -m datastax linkedlist 1 2 3 4
  Visuals for LinkedLists:

  1. Singly Linked List:
  Node[1] -> Node[2] -> Node[3] -> Node[4] -> NULL

  2. Doubly Linked List:
  NULL <-> Node[1] <-> Node[2] <-> Node[3] <-> Node[4] <-> NULL
  ...

Practical Usage

• Queue

from datastax.arrays import Queue

# Building a Queue Data Structure with fixed capacity
queue = Queue(capacity=5)

# Enqueueing items inside queue
for item in ('item 1', 'item 2'):
    queue.enqueue(item)

# Performing Dequeue Operation 
queue.dequeue()

queue.enqueue('item 3')
print(queue)

$ OUTPUT:

         ┌──────────╥──────────┬──────────┐
FRONT -> │    ╳     ║  item 2  │  item 3  │ <- REAR
         └──────────╨──────────┴──────────┘
      

---

• BinaryTree

from datastax.trees import BinaryTree

bt = BinaryTree([1, 2, 3, 4, 5])
print(bt)

$ OUTPUT:

             1           
       ┌─────┴─────┐     
       2           3     
    ┌──┴──┐              
    4     5              

---

• MinHeapTree

from datastax.trees import MinHeapTree

MiHT = MinHeapTree([1, 2, 4, 2, 6, 5, 9, 18, 3, 2])
print(MiHT)

$ OUTPUT

                        1                       
            ┌───────────┴───────────┐           
            2                       4           
      ┌─────┴─────┐           ┌─────┴─────┐     
      2           2           5           9     
   ┌──┴──┐     ┌──┘                             
  18     3     6    

---

• ThreadedBinaryTree

from datastax.trees import ThreadedBinaryTree as Tbt

tbt = Tbt(['a', 'b', 'c', 'd', 'e'], insertion_logic="BinaryTree")
print(tbt)

$ OUTPUT               
                                   ┌───┐
   ┌───────────────────────────> DUMMY │<──────────────┐
   │                           ┌───┴───┘               │
   │                           a                       │        
   │           ┌───────────────┴───────────────┐       │        
   │           b           │           │       c       │        
   │   ┌───────┴───────┐   │           └───────┴───────┘        
   │   d   │       │   e   │                                    
   └───┴───┘       └───┴───┘                                    

What's Next

• Implementation of Segment Trees
• Proper tests using UnitTest Lib
• Enhanced Documentation
• Implementation of Other Abstract data types like LFU_CACHE, SKIP_LIST
• Beautification of README.md

Upcoming

from datastax.trees import SumSegmentTree as St

st = St([1, 3, 5, 7, 9, 11])
print(st)

$ OUTPUT               
                       36                       
            ┌───────────┴───────────┐           
            9                      27           
      ┌─────┴─────┐           ┌─────┴─────┐     
      4           5          16          11     
   ┌──┴──┐     ┌──┴──┐                          
   1     3     7     9