DSA-BASICS/queue_implementation(without_class).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3f9cdf3f",
   "metadata": {},
   "source": [
    "Like stack, queue is a linear data structure but it stores items in First In First Out (FIFO) manner. With a queue the least recently added item is removed first. A good example of queue is any queue of consumers for a resource where the consumer that came first is served first."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "80262df0",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "29a82c86",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa671f91",
   "metadata": {},
   "source": [
    "Operations associated with queue are: \n",
    " \n",
    "\n",
    "- Enqueue: Adds an item to the queue. If the queue is full, and you try to push, then it is said to be an Overflow condition – Time Complexity : O(1)\n",
    "- Dequeue: Removes an item from the queue. The items are popped in the same order in which they are pushed. If the queue is empty, and you try to pop, then it is said to be an Underflow condition – Time Complexity : O(1)\n",
    "- Front: Get the front item from queue – Time Complexity : O(1)\n",
    "- Rear: Get the last item from queue – Time Complexity : O(1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cefb7959",
   "metadata": {},
   "source": [
    "# Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a0da234",
   "metadata": {},
   "source": [
    "There are various ways to implement a queue in Python. This article covers the implementation of queue using data structures and modules from Python library.\n",
    "Queue in Python can be implemented by the following ways:\n",
    " \n",
    "\n",
    "- list\n",
    "- collections.deque\n",
    "- queue.Queue\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1bfad3a",
   "metadata": {},
   "source": [
    "# Implementation using list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec4d7d19",
   "metadata": {},
   "source": [
    "List is a Python’s built-in data structure that can be used as a queue. Instead of enqueue() and dequeue(), append() and pop() function is used. However, lists are quite slow for this purpose because inserting or deleting an element at the beginning requires shifting all of the other elements by one, requiring O(n) time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d59fb510",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial queue\n",
      "['a', 'b', 'c']\n",
      "\n",
      "Elements dequeued from queue\n",
      "a\n",
      "queue after first item popped out ['b', 'c']\n",
      "b\n",
      "queue after first item popped out ['c']\n",
      "c\n",
      "\n",
      "Queue after removing elements\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "# Python program to\n",
    "# demonstrate queue implementation\n",
    "# using list\n",
    "\n",
    "# Initializing a queue\n",
    "queue = []\n",
    "\n",
    "# Adding elements to the queue\n",
    "queue.append('a')\n",
    "queue.append('b')\n",
    "queue.append('c')\n",
    "\n",
    "print(\"Initial queue\")\n",
    "print(queue)\n",
    "\n",
    "# Removing elements from the queue\n",
    "print(\"\\nElements dequeued from queue\")\n",
    "print(queue.pop(0))\n",
    "print(\"queue after first item popped out\", queue)\n",
    "print(queue.pop(0))\n",
    "print(\"queue after first item popped out\", queue)\n",
    "print(queue.pop(0))\n",
    "\n",
    "print(\"\\nQueue after removing elements\")\n",
    "print(queue)\n",
    "\n",
    "# Uncommenting print(queue.pop(0))\n",
    "# will raise and IndexError\n",
    "# as the queue is now empty\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6b1ae8e",
   "metadata": {},
   "source": [
    "# Implementation using collections.deque"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a164b3c2",
   "metadata": {},
   "source": [
    "- Queue in Python can be implemented using deque class from the collections module. Deque is preferred over list in the cases where we need quicker append and pop operations from both the ends of container, as deque provides an O(1) time complexity for append and pop operations as compared to list which provides O(n) time complexity. - - -- - Instead of enqueue and deque, append() and popleft() functions are used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "30ece955",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial queue\n",
      "deque(['a', 'b', 'c'])\n",
      "\n",
      "Elements dequeued from the queue\n",
      "a\n",
      "b\n",
      "c\n",
      "\n",
      "Queue after removing elements\n",
      "deque([])\n"
     ]
    }
   ],
   "source": [
    "# Python program to\n",
    "# demonstrate queue implementation\n",
    "# using collections.dequeue\n",
    "\n",
    "\n",
    "from collections import deque\n",
    "\n",
    "# Initializing a queue\n",
    "q = deque()\n",
    "\n",
    "# Adding elements to a queue\n",
    "q.append('a')\n",
    "q.append('b')\n",
    "q.append('c')\n",
    "\n",
    "print(\"Initial queue\")\n",
    "print(q)\n",
    "\n",
    "# Removing elements from a queue\n",
    "print(\"\\nElements dequeued from the queue\")\n",
    "print(q.popleft())\n",
    "print(q.popleft())\n",
    "print(q.popleft())\n",
    "\n",
    "print(\"\\nQueue after removing elements\")\n",
    "print(q)\n",
    "\n",
    "# Uncommenting q.popleft()\n",
    "# will raise an IndexError\n",
    "# as queue is now empty\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b82bf7",
   "metadata": {},
   "source": [
    "# Implementation using queue.Queue"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7750b260",
   "metadata": {},
   "source": [
    "Queue is built-in module of Python which is used to implement a queue. queue.Queue(maxsize) initializes a variable to a maximum size of maxsize. A maxsize of zero ‘0’ means a infinite queue. This Queue follows FIFO rule. \n",
    "There are various functions available in this module: "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c11c8c9",
   "metadata": {},
   "source": [
    "- maxsize – Number of items allowed in the queue.\n",
    "- empty() – Return True if the queue is empty, False otherwise.\n",
    "- full() – Return True if there are maxsize items in the queue. If the queue was initialized with maxsize=0 (the default), then full() never returns True.\n",
    "- get() – Remove and return an item from the queue. If queue is empty, wait until an item is available.\n",
    "- get_nowait() – Return an item if one is immediately available, else raise QueueEmpty.\n",
    "- put(item) – Put an item into the queue. If the queue is full, wait until a free slot is available before adding the item.\n",
    "- put_nowait(item) – Put an item into the queue without blocking. If no free slot is immediately available, raise QueueFull.\n",
    "- qsize() – Return the number of items in the queue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0135d75b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "\n",
      "Full:  True\n",
      "\n",
      "Elements dequeued from the queue\n",
      "a\n",
      "b\n",
      "c\n",
      "\n",
      "Empty:  True\n",
      "\n",
      "Empty:  False\n",
      "Full:  False\n"
     ]
    }
   ],
   "source": [
    "# Python program to\n",
    "# demonstrate implementation of\n",
    "# queue using queue module\n",
    "\n",
    "\n",
    "from queue import Queue\n",
    "\n",
    "# Initializing a queue\n",
    "q = Queue(maxsize = 3)\n",
    "\n",
    "# qsize() give the maxsize\n",
    "# of the Queue\n",
    "print(q.qsize())\n",
    "\n",
    "# Adding of element to queue\n",
    "q.put('a')\n",
    "q.put('b')\n",
    "q.put('c')\n",
    "\n",
    "# Return Boolean for Full\n",
    "# Queue\n",
    "print(\"\\nFull: \", q.full())\n",
    "\n",
    "# Removing element from queue\n",
    "print(\"\\nElements dequeued from the queue\")\n",
    "print(q.get())\n",
    "print(q.get())\n",
    "print(q.get())\n",
    "\n",
    "# Return Boolean for Empty\n",
    "# Queue\n",
    "print(\"\\nEmpty: \", q.empty())\n",
    "\n",
    "q.put(1)\n",
    "print(\"\\nEmpty: \", q.empty())\n",
    "print(\"Full: \", q.full())\n",
    "\n",
    "# This would result into Infinite\n",
    "# Loop as the Queue is empty.\n",
    "# print(q.get())\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "b79d12c8",
   "metadata": {},
   "outputs": [],
   "source": []
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