Data Structure - Linked Lists.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linked Lists\n",
    "\n",
    "* Better for new insertions/deletions (compared to regular Python lists)\n",
    "    * __*NOTE: Only really efficient when items are inserted to the head (prepend) or tail (append)*__; __O(1) time__\n",
    "    * __O(n) time__ when insertions/deletions occur anywhere else in list\n",
    "* Not great for trying to access a specific index (will have to start from index 0 and count up to the request index one by one (linear / sequential search)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "image/png": {
       "width": 950
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML \n",
    "Image(filename=\"aa_Screenshots/linked-lists.png\", width=950)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "### Will need to build from scratch in Python, unlike Java..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A class object for storing a single node of a linked list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Node:\n",
    "    ## Attributes\n",
    "    data = None # actual list element\n",
    "    next_node = None # pointer to the next node of the list\n",
    "\n",
    "    ## Constructor\n",
    "    def __init__(self, data):\n",
    "        self.data = data\n",
    "    \n",
    "    ## What to display when object name is invoked\n",
    "    def __repr__(self):\n",
    "        return \"<Node data %s>\" % self.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A class object that defines a __singly__ linked list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinkedList:\n",
    "    ## Constructor\n",
    "    def __init__(self):\n",
    "        self.head = None\n",
    "\n",
    "\n",
    "    ## Boolean check to see if list is empty or not\n",
    "    def is_empty(self):\n",
    "        return self.head == None\n",
    "\n",
    "\n",
    "    ## Output the size (numbr of nodes) of the list; O(n) time\n",
    "    def size(self):\n",
    "        current = self.head\n",
    "        count = 0 # increments for each node visited\n",
    "\n",
    "        # Go through list until the very end (tail node)\n",
    "        while current != None:\n",
    "            count += 1\n",
    "\n",
    "            current = current.next_node # go to the next node in LinkedList\n",
    "\n",
    "        return count\n",
    "\n",
    "\n",
    "\n",
    "    # Returns the head element\n",
    "    def get_head(self):\n",
    "        return self.head\n",
    "\n",
    "\n",
    "\n",
    "    # Returns the tail element\n",
    "    def get_tail(self):\n",
    "        current = self.head\n",
    "        \n",
    "        while current != None:\n",
    "            if current.next_node == None:\n",
    "                return current\n",
    "            else:\n",
    "                current = current.next_node\n",
    "\n",
    "\n",
    "    # Prints out a string of all the nodes (similar to __repr__()), but also includes the index number of each node\n",
    "    def get_index_positions(self):\n",
    "        nodes = [] # regular list containing all the nodes\n",
    "        current = self.head\n",
    "        index = 0\n",
    "        \n",
    "        while current != None:\n",
    "            # Add head element to regular list\n",
    "            if current is self.head:\n",
    "                nodes.append(\"[Head: %s] (Index %d)\" % (current.data, index))\n",
    "            \n",
    "            # Add tail element to regular list\n",
    "            elif current.next_node is None:\n",
    "                nodes.append(\"[Tail: %s] (Index %d)\" % (current.data, index))\n",
    "\n",
    "            # Add all of the other elements b/w the head & tail\n",
    "            else:\n",
    "                nodes.append(\"[%s] (Index %d)\" % (current.data, index))\n",
    "\n",
    "            index += 1\n",
    "            \n",
    "            current = current.next_node # go to the next node in LinkedList\n",
    "        \n",
    "        return '--> '.join(nodes)\n",
    "\n",
    "\n",
    "    # With a given index position, return the node at the specified index\n",
    "    def get_node_at_index(self, index):\n",
    "        current = self.head\n",
    "        current_position = 0\n",
    "\n",
    "\n",
    "        # Makes sure index is an integer number\n",
    "        try:\n",
    "            int(index) == index\n",
    "        except:\n",
    "            return \"Please enter an integer for the index position\"\n",
    "\n",
    "\n",
    "        while current != None:\n",
    "            # If negative number or if index # is greater than the last index number in list:\n",
    "            if index < 0 or index > self.size():\n",
    "                return \"Index is out of bounds, enter a valid index\"\n",
    "                break\n",
    "            \n",
    "            # Once position equals the specified index number:\n",
    "            elif current_position == index:\n",
    "                return current\n",
    "\n",
    "            else:\n",
    "                current = current.next_node\n",
    "                current_position += 1\n",
    "\n",
    "\n",
    "\n",
    "    ## Allows you to add new node with data, prepending to BEGINNING of list as new head element; O(1) time\n",
    "    def prepend_item(self, data):\n",
    "        new_node = Node(data)\n",
    "        new_node.next_node = self.head # have the new node point to the current head element\n",
    "        self.head = new_node # THEN, prepend insert to beginning of list & make the new insert the new head element\n",
    "\n",
    "\n",
    "\n",
    "    ## Allows you to add new node with data, appending to END of list as new head element; O(1) time\n",
    "    def append_item(self, data):\n",
    "        if self.is_empty():\n",
    "            self.prepend_item(data)\n",
    "            \n",
    "        else:\n",
    "            new_node = Node(data)\n",
    "\n",
    "            current = self.head\n",
    "\n",
    "            while current != None:\n",
    "                if current.next_node == None:\n",
    "                    current.next_node = new_node\n",
    "                    break\n",
    "                else:\n",
    "                    current = current.next_node\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    ## Search for the 1st matching node of the passed in data value (\"key\"); O(n) time\n",
    "    ## \"key\" parameter is the actual value being searched for in the linkedlist\n",
    "    def search(self, key):\n",
    "        current = self.head\n",
    "\n",
    "        while current != None:\n",
    "            if current.data == key:\n",
    "                return current\n",
    "            else:\n",
    "                current = current.next_node # go to the next node in LinkedList\n",
    "            \n",
    "        return \"Not in list\" # Return if finished w/ above while loop & key is not found in list\n",
    "\n",
    "    # def insert_test(self):\n",
    "    #     current = self.head\n",
    "    #     count = 0 # increments for each node visited\n",
    "\n",
    "    #     # Go through list until the very end (tail node)\n",
    "    #     while current != None:\n",
    "    #         print(count)\n",
    "    #         count += 1\n",
    "\n",
    "    #         current = current.next_node # go to the next node in LinkedList\n",
    "\n",
    "    #     return count\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    ## Insert new nodes in list, before specified index position\n",
    "    def insert(self, data, index):\n",
    "        current = self.head\n",
    "        previous = None\n",
    "        current_position = 0\n",
    "\n",
    "\n",
    "        # Makes sure index is an integer number\n",
    "        try:\n",
    "            int(index) == index\n",
    "        except:\n",
    "            return \"Please enter an integer for the index position\"\n",
    "\n",
    "\n",
    "        # O(1) time to insert; O(n) to search for index position ---> overall O(n) time\n",
    "        # Need to change pointers for node BEFORE and AFTER new insertion\n",
    "        while current != None:\n",
    "\n",
    "            # If negative number or 0, insert at the very beginning of list ---> just invoke prepend_item() method\n",
    "            if index <= 0:\n",
    "                self.prepend_item(data)\n",
    "                break\n",
    "            \n",
    "            # If index number greater than list size, insert at the very end of list ---> just invoke append_item() method\n",
    "            elif index > self.size():\n",
    "                self.append_item(data)\n",
    "                break\n",
    "            \n",
    "            # Once position equals the specified index number:\n",
    "            elif current_position == index:\n",
    "                new_insert = Node(data) # create new node\n",
    "\n",
    "                # EXAMPLE: \n",
    "                #           1 (previous) --> 3 (previous.next_node / current)\n",
    "                #           1 (previous) --> 2 (previous.next_node / new_insert)\n",
    "                #           2 (new_insert) --> 3 (new_insert.next_node / current)\n",
    "                #           1 (previous) --> 2 (previous.next_node / new_insert) --> 3 (new_insert.next_node / current)\n",
    "                \n",
    "                previous.next_node = new_insert\n",
    "                new_insert.next_node = current\n",
    "                break\n",
    "\n",
    "            else:\n",
    "                previous = current\n",
    "                current = current.next_node\n",
    "                current_position += 1\n",
    "\n",
    "        return self.__repr__()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    ## Deletes 1st matching node of the passed in data value (\"key\"); O(n) time; updating node pointers of before and after nodes\n",
    "    ## Returns node or None if key doesn't exist\n",
    "    ## O(1) time to delete; O(n) to search ---> overall O(n) time\n",
    "    def delete(self, key):\n",
    "        current = self.head\n",
    "        previous = None\n",
    "        found = False\n",
    "\n",
    "        while current != None and not found:\n",
    "            # If you want to delete head node element:\n",
    "            if current.data == key and current is self.head:\n",
    "                # changing the pointer nodes will automatically remove any nodes that no longer have anything pointing to it\n",
    "                self.head = current.next_node \n",
    "                found = True\n",
    "\n",
    "            # if the node after is the key:\n",
    "            elif current.data == key:\n",
    "                previous.next_node = current.next_node\n",
    "                found = True\n",
    "            \n",
    "            else:\n",
    "                previous = current\n",
    "                current = current.next_node # go to the next node in LinkedList\n",
    "        return current\n",
    "\n",
    "\n",
    "\n",
    "    ## What to display when LinkedList object name is invoked\n",
    "    ## Returns a string representation of list; O(n) time\n",
    "    def __repr__(self):\n",
    "        nodes = [] # regular list containing all the nodes\n",
    "        current = self.head\n",
    "        count = 0\n",
    "        \n",
    "        while current != None:\n",
    "            # Add head element to regular list\n",
    "            if current is self.head:\n",
    "                nodes.append(\"[Head: %s]\" % current.data)\n",
    "            \n",
    "            # Add tail element to regular list\n",
    "            elif current.next_node is None:\n",
    "                nodes.append(\"[Tail: %s]\" % current.data)\n",
    "\n",
    "            # Add all of the other elements b/w the head & tail\n",
    "            else:\n",
    "                nodes.append(\"[%s]\" % current.data)\n",
    "\n",
    "            count += 1\n",
    "            \n",
    "            current = current.next_node # go to the next node in LinkedList\n",
    "        \n",
    "        return '--> '.join(nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ls = LinkedList()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Node data 10>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N1 = Node(10)\n",
    "N1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Node data 20>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N2 = Node(20)\n",
    "N2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Node data 20>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N1.next_node = N2\n",
    "N1.next_node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "ls.head = N1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Now combining `Node` & `LinkedList` classes:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ls2 = LinkedList()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "## ADD ITEMS AT THE BEGINNING OF LIST\n",
    "\n",
    "ls2.prepend_item(30) # added 1st; TAIL (old)\n",
    "ls2.prepend_item(20) # added 2nd\n",
    "ls2.prepend_item(10) # added 3rd / last; HEAD (old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "## ADD ITEMS AT THE END OF LIST\n",
    "\n",
    "ls2.append_item(40) # added 1st\n",
    "ls2.append_item(50) # added 2nd\n",
    "ls2.append_item(60) # added 3rd / last; TAIL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls2.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Head: 10]--> [20]--> [30]--> [40]--> [50]--> [Tail: 60]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Node data 20>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls2.search(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Not in list'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls2.search(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[Head: 10]--> [15]--> [20]--> [30]--> [40]--> [50]--> [Tail: 60]'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls2.insert(15, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[Head: 10]--> [15]--> [20]--> [30]--> [40]--> [50]--> [60]--> [Tail: 70]'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls2.insert(70, 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[Head: 5]--> [10]--> [15]--> [20]--> [30]--> [40]--> [50]--> [60]--> [Tail: 70]'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls2.insert(5, -2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[Head: 5] (Index 0)--> [10] (Index 1)--> [15] (Index 2)--> [20] (Index 3)--> [30] (Index 4)--> [40] (Index 5)--> [50] (Index 6)--> [60] (Index 7)--> [Tail: 70] (Index 8)'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls2.get_index_positions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Head element: <Node data 5>\n",
      "Tail element: <Node data 70>\n"
     ]
    }
   ],
   "source": [
    "print(\"Head element:\", ls2.get_head())\n",
    "print(\"Tail element:\", ls2.get_tail())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.10 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "3b806cc711b465e66e8252dff191d8681f8b41038e9cbc25fa942edafd26a2d7"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}