task1-task3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "class node:\n",
        "  def __init__(self,data):\n",
        "     self.data=data\n",
        "     self.left = None\n",
        "     self.right=None\n",
        "class BST:\n",
        "  found = False\n",
        "  def __init__(self):\n",
        "    self.root=None\n",
        "  def insert_node(self,basenode,data):\n",
        "    if(basenode==None) :\n",
        "      newnode=node(data)\n",
        "      return newnode\n",
        "    if(data<basenode.data):\n",
        "      basenode.left=self.insert_node(basenode.left,data)\n",
        "    else:\n",
        "      basenode.right=self.insert_node(basenode.right,data)\n",
        "    return basenode  \n",
        "\n",
        "\n",
        "  def insert(self,data):\n",
        "    self.root =  self.insert_node(self.root,data)      \n",
        "\n",
        "  def DFS (self,basenode):\n",
        "    if (basenode==None):\n",
        "      print(\"the tree is empty\" )\n",
        "    if(basenode.left!=None):\n",
        "      self.DFS(basenode.left)  \n",
        "    print(basenode.data)  \n",
        "    if(basenode.right!=None):\n",
        "      self.DFS(basenode.right)  \n",
        "\n",
        "  def DFS_search (self,basenode,data):\n",
        "    if (basenode==None):\n",
        "      print(\"the tree is empty\" )\n",
        "    if(basenode.data==data):  \n",
        "       print(basenode.data) \n",
        "       self.found = True\n",
        "       return  \n",
        "    if(basenode.left!=None and self.found==False):\n",
        "      self.DFS_search(basenode.left,data)  \n",
        "    if(basenode.right!=None and self.found==False):\n",
        "      self.DFS_search(basenode.right,data)    \n",
        "\n",
        "\n",
        "  def BFS (self):\n",
        "    if(self.root==None):\n",
        "      print(\"the tree is empty\")\n",
        "      return\n",
        "    q=[self.root]\n",
        "    while(len(q)>0):\n",
        "       temp = q.pop(0)\n",
        "       print(temp.data)\n",
        "       if(temp.left!=None):\n",
        "         q.append(temp.left)\n",
        "       if(temp.right!=None):\n",
        "         q.append(temp.right)  \n",
        "\n",
        "\n",
        "  def BFS_search (self,data):\n",
        "    if(self.root==None):\n",
        "      print(\"the tree is empty\")\n",
        "      return\n",
        "    q=[self.root]\n",
        "    while(len(q)>0):\n",
        "       temp = q.pop(0)\n",
        "       if(temp.data==data):\n",
        "         print(\"Num found\")\n",
        "         return\n",
        "       if(temp.left!=None):\n",
        "         q.append(temp.left)\n",
        "       if(temp.right!=None):\n",
        "         q.append(temp.right)         \n"
      ],
      "metadata": {
        "id": "PqYQjQhjrHch"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Graphs"
      ],
      "metadata": {
        "id": "oKXFtfNMevAL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import defaultdict\n",
        "class Graph:\n",
        "  def __init__(self):\n",
        "      self.graph = defaultdict(list)\n",
        "  def addEdge(self,u,v):\n",
        "    self.graph[u].append(v)\n",
        "  def bfs(self,s):\n",
        "    visited = []\n",
        "    queue=[s]\n",
        "    while(queue):\n",
        "      temp = queue.pop(0)\n",
        "      if(temp not in visited):\n",
        "        visited.append(temp)\n",
        "        print(temp)\n",
        "        for i in self.graph[temp]:\n",
        "          queue.append(i)\n",
        "\n",
        "  def dfs(self,s):\n",
        "    visited = []\n",
        "    stack=[s]\n",
        "    while(stack):\n",
        "      temp = stack.pop(-1)\n",
        "      if(temp not in visited):\n",
        "        visited.append(temp)\n",
        "        print(temp)\n",
        "        for i in self.graph[temp]:\n",
        "          stack.append(i)\n"
      ],
      "metadata": {
        "id": "Eybyyru5ewWX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "g = Graph()\n",
        "g.addEdge(0, 1)\n",
        "g.addEdge(0, 2)\n",
        "g.addEdge(1, 2)\n",
        "g.addEdge(2, 0)\n",
        "g.addEdge(2, 3)\n",
        "g.addEdge(3, 3)\n",
        "g.addEdge(0, 5)\n",
        " \n",
        "print(\"Following is Breadth First Traversal\"\n",
        "      \" (starting from vertex 2)\")\n",
        "g.dfs(1)\n",
        " "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JleRKDfWh91Y",
        "outputId": "be07664e-289d-43d0-d11e-28b26a57530b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Following is Breadth First Traversal (starting from vertex 2)\n",
            "1\n",
            "2\n",
            "3\n",
            "0\n",
            "5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "total = 1000\n",
        "a = random.sample(range(0, 50000), total)"
      ],
      "metadata": {
        "id": "fvoav5wZMq7F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "C0Zq1tlpn03r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Task#2**"
      ],
      "metadata": {
        "id": "bCFqyCx8mred"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "A = 1000\n",
        "B = 40000\n",
        "C=  80000\n",
        "D=  200000\n",
        "E=  1000000\n",
        "\n",
        "import random\n",
        "A = random.sample(range(0, 5000000), A)\n",
        "B = random.sample(range(0, 5000000), B)\n",
        "C = random.sample(range(0, 5000000), C)\n",
        "D = random.sample(range(0, 5000000), D)\n",
        "E = random.sample(range(0, 5000000), E)"
      ],
      "metadata": {
        "id": "ZRM-S-YJm1id"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a = BST()\n",
        "for i in A:\n",
        "  a.insert(i)\n",
        "\n",
        "b = BST()\n",
        "for i in B:\n",
        "  b.insert(i)\n",
        "\n",
        "c = BST()\n",
        "for i in C:\n",
        "  c.insert(i)    \n",
        "\n",
        "d = BST()\n",
        "for i in D:\n",
        "  d.insert(i)\n",
        "\n",
        "e = BST()\n",
        "for i in E:\n",
        "  e.insert(i)  "
      ],
      "metadata": {
        "id": "upKgA6JrnNUC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "g=A[len(A)-220]"
      ],
      "metadata": {
        "id": "tfOrPZqkoSD2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dic={}"
      ],
      "metadata": {
        "id": "ubGn6PKXozSW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "L=E\n",
        "T=e"
      ],
      "metadata": {
        "id": "OoK-ddbYM2P4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=L[len(L)-220]\n",
        "total = len(L)"
      ],
      "metadata": {
        "id": "7YWb4GBPVSID"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "start = time.time()\n",
        "T.BFS_search(x)\n",
        "end = time.time()\n",
        "diff=end-start"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BC27soiRNlnm",
        "outputId": "9d33101f-e09a-44fc-9c72-6839f4dc85ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num found\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dic[\"BFS_\"+str(total)]=diff\n",
        "dic"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ENdwqy5ckZcj",
        "outputId": "c37e6275-6e01-4969-8e52-0b891bdcca9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'BFS_1000': 0.0010328292846679688,\n",
              " 'DFS_1000': 0.0006282329559326172,\n",
              " 'BFS_40000': 0.02685236930847168,\n",
              " 'DFS_40000': 0.03754734992980957,\n",
              " 'BFS_80000': 0.15070676803588867,\n",
              " 'DFS_80000': 0.02941727638244629,\n",
              " 'BFS_200000': 0.49098992347717285,\n",
              " 'DFS_200000': 0.18863821029663086,\n",
              " 'BFS_1000000': 11.934588432312012}"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "start = time.time()\n",
        "T.DFS_search(T.root,x)\n",
        "T.found=False\n",
        "end = time.time()\n",
        "diff=end-start"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OcKJJZ-pkGn5",
        "outputId": "3c479597-17f9-46ff-c1d3-d9965b158119"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1532630\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dic[\"DFS_\"+str(total)]=diff\n",
        "dic"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LaiJ8EbJSBEs",
        "outputId": "904c71de-5df6-452e-dd98-d64bea16a46a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'BFS_1000': 0.0010328292846679688,\n",
              " 'DFS_1000': 0.0006282329559326172,\n",
              " 'BFS_40000': 0.02685236930847168,\n",
              " 'DFS_40000': 0.03754734992980957,\n",
              " 'BFS_80000': 0.15070676803588867,\n",
              " 'DFS_80000': 0.02941727638244629,\n",
              " 'BFS_200000': 0.49098992347717285,\n",
              " 'DFS_200000': 0.18863821029663086,\n",
              " 'BFS_1000000': 11.934588432312012,\n",
              " 'DFS_1000000': 0.514847993850708}"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res=dic.copy()"
      ],
      "metadata": {
        "id": "QTGdsL27Wj0U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "res"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "__LEEOjOWw5z",
        "outputId": "5354b056-f095-4546-f3d5-84219cc89e9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'BFS_1000': 0.0010328292846679688,\n",
              " 'DFS_1000': 0.0006282329559326172,\n",
              " 'BFS_40000': 0.02685236930847168,\n",
              " 'DFS_40000': 0.03754734992980957,\n",
              " 'BFS_80000': 0.15070676803588867,\n",
              " 'DFS_80000': 0.02941727638244629,\n",
              " 'BFS_200000': 0.49098992347717285,\n",
              " 'DFS_200000': 0.18863821029663086,\n",
              " 'BFS_1000000': 11.934588432312012,\n",
              " 'DFS_1000000': 0.514847993850708}"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.DataFrame(res.values(),res.keys())\n",
        "df.plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "X7Jo9sM6kRPc",
        "outputId": "2a0083cb-22af-410a-def5-884371af8ff3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.transpose()\n",
        "df.columns"
      ],
      "metadata": {
        "id": "f3DqCV8HoicI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d70d6e9a-00f7-4741-8c67-49099ec1cfbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['BFS_1000', 'DFS_1000', 'BFS_40000', 'DFS_40000', 'BFS_80000',\n",
              "       'DFS_80000', 'BFS_200000', 'DFS_200000', 'BFS_1000000', 'DFS_1000000'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "BFS = df[df.columns[df.columns.str.contains(\"BFS\")]]\n",
        "DFS = df[df.columns[df.columns.str.contains(\"DFS\")]]"
      ],
      "metadata": {
        "id": "k_4VDS0hv_w-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "BFS=BFS.transpose()\n",
        "DFS=DFS.transpose()"
      ],
      "metadata": {
        "id": "bx8_s94NxEGe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "BFS.rename(columns={0: 'BFS Time'}, inplace=True)\n",
        "BFS.index = BFS.index.str.strip('BFS_')\n",
        "\n",
        "DFS.rename(columns={0: 'DFS Time'}, inplace=True)\n",
        "DFS.index = DFS.index.str.strip('DFS_')"
      ],
      "metadata": {
        "id": "EqRu-uzoxS9M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DFS"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
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              "         DFS Time\n",
              "1000     0.000628\n",
              "40000    0.037547\n",
              "80000    0.029417\n",
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        "BFS"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          BFS Time\n",
              "1000      0.001033\n",
              "40000     0.026852\n",
              "80000     0.150707\n",
              "200000    0.490990\n",
              "1000000  11.934588"
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              "  </style>\n",
              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-61e77f4b-b30a-46e3-ba17-adc9ba532ab5');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
          "execution_count": 81
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    },
    {
      "cell_type": "code",
      "source": [
        "Data = BFS.join(DFS)"
      ],
      "metadata": {
        "id": "IXnKf5QGzF7_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Data.plot.bar()"
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      "metadata": {
        "colab": {
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          "height": 316
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        "id": "T4uH6dwD0fWo",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**TASK 3**"
      ],
      "metadata": {
        "id": "M4RpVngO4DGm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install jedi\n",
        "!pip install setuptools==64.0.2\n",
        "!pip install binarytree"
      ],
      "metadata": {
        "id": "1lbAgVmn0ney",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "outputId": "f600ab48-313a-401b-ad77-7186db4a45cb"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting jedi\n",
            "  Downloading jedi-0.18.2-py2.py3-none-any.whl (1.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m21.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.8/dist-packages (from jedi) (0.8.3)\n",
            "Installing collected packages: jedi\n",
            "Successfully installed jedi-0.18.2\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting setuptools==64.0.2\n",
            "  Downloading setuptools-64.0.2-py3-none-any.whl (1.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m14.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: setuptools\n",
            "  Attempting uninstall: setuptools\n",
            "    Found existing installation: setuptools 57.4.0\n",
            "    Uninstalling setuptools-57.4.0:\n",
            "      Successfully uninstalled setuptools-57.4.0\n",
            "Successfully installed setuptools-64.0.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pkg_resources"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting binarytree\n",
            "  Downloading binarytree-6.5.1-py3-none-any.whl (18 kB)\n",
            "Collecting setuptools-scm[toml]>=5.0.1\n",
            "  Downloading setuptools_scm-7.1.0-py3-none-any.whl (43 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.8/43.8 KB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: setuptools>=60.8.2 in /usr/local/lib/python3.8/dist-packages (from binarytree) (64.0.2)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.8/dist-packages (from binarytree) (0.10.1)\n",
            "Requirement already satisfied: tomli>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from setuptools-scm[toml]>=5.0.1->binarytree) (2.0.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from setuptools-scm[toml]>=5.0.1->binarytree) (4.5.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from setuptools-scm[toml]>=5.0.1->binarytree) (23.0)\n",
            "Installing collected packages: setuptools-scm, binarytree\n",
            "Successfully installed binarytree-6.5.1 setuptools-scm-7.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from binarytree import build\n",
        "x=[51,\n",
        " 43,\n",
        " 83,\n",
        " 20,\n",
        " None,\n",
        " 58,\n",
        " 93,\n",
        " 9,\n",
        " 37,\n",
        " None,\n",
        " None,\n",
        " 51,\n",
        " None,\n",
        " 88,\n",
        " None,\n",
        " None,\n",
        " 16,\n",
        " 36,\n",
        " None,\n",
        " None,\n",
        " None,\n",
        " None,\n",
        " None,\n",
        " None,\n",
        " None,\n",
        " None,\n",
        " None,\n",
        " None,\n",
        " None,\n",
        " None,\n",
        " None,\n",
        " None,\n",
        " None,\n",
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        " None,\n",
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        " None,\n",
        " None,\n",
        " None,\n",
        " None,\n",
        " None,\n",
        " None,\n",
        " 31,\n",
        " 34]\n",
        "root=build(x)"
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      "metadata": {
        "id": "Hql9xH7O_jbj"
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      "execution_count": 20,
      "outputs": []
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    {
      "cell_type": "code",
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        "root"
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        "outputId": "fdbf0a82-d907-4368-cc62-d2c64a14cdde"
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          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install anytree"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "clA3uQLO7oBE",
        "outputId": "6f1ad8c0-4334-4254-d61a-e3c5b42b028f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting anytree\n",
            "  Downloading anytree-2.8.0-py2.py3-none-any.whl (41 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.7/41.7 KB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.8/dist-packages (from anytree) (1.15.0)\n",
            "Installing collected packages: anytree\n",
            "Successfully installed anytree-2.8.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from anytree import Node, RenderTree\n",
        "\n",
        "# Create the nodes of the tree\n",
        "root = Node(51)\n",
        "n1 = Node(43, parent=root)\n",
        "n2 = Node(83, parent=root)\n",
        "n3 = Node(20, parent=n1)\n",
        "n4 = Node(58, parent=n2)\n",
        "n5 = Node(93, parent=n2)\n",
        "n6 = Node(9, parent=n3)\n",
        "n7 = Node(37, parent=n3)\n",
        "n8 = Node(16, parent=n6)\n",
        "n9 = Node(36, parent=n7)\n",
        "n10 = Node(88, parent=n5)\n",
        "\n",
        "# Print the tree\n",
        "for pre, _, node in RenderTree(root):\n",
        "    print(\"%s%s\" % (pre, node.name))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6ZX-z0895DU",
        "outputId": "4a1358ca-f736-4284-b9db-844f2aa92a4d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "51\n",
            "├── 43\n",
            "│   └── 20\n",
            "│       ├── 9\n",
            "│       │   └── 16\n",
            "│       └── 37\n",
            "│           └── 36\n",
            "└── 83\n",
            "    ├── 58\n",
            "    └── 93\n",
            "        └── 88\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from anytree.exporter import DotExporter\n",
        "DotExporter(root).to_picture(\"udo.png\")\n"
      ],
      "metadata": {
        "id": "3AGt_xfg-Frt"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install treelib"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zVL6HH7AHDI4",
        "outputId": "6cc568aa-a3cb-4686-f561-447931f50caa"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting treelib\n",
            "  Downloading treelib-1.6.1.tar.gz (24 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.8/dist-packages (from treelib) (0.16.0)\n",
            "Building wheels for collected packages: treelib\n",
            "  Building wheel for treelib (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for treelib: filename=treelib-1.6.1-py3-none-any.whl size=18368 sha256=0753b8782c31f9e2b93a7889383ef8401938f8a221044c87c753e02e4faf3c83\n",
            "  Stored in directory: /root/.cache/pip/wheels/71/df/8b/6b005e3bb9b275c24dfc392cda334f43f132e85a6f17cfad3a\n",
            "Successfully built treelib\n",
            "Installing collected packages: treelib\n",
            "Successfully installed treelib-1.6.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from treelib import Node, Tree\n",
        "\n",
        "# Create the tree object and add the root node\n",
        "tree = Tree()\n",
        "tree.create_node(tag=51, identifier=51)\n",
        "\n",
        "# Add the remaining nodes to the tree\n",
        "tree.create_node(tag=43, identifier=43, parent=51)\n",
        "tree.create_node(tag=83, identifier=83, parent=51)\n",
        "tree.create_node(tag=20, identifier=20, parent=43)\n",
        "tree.create_node(tag=58, identifier=58, parent=83)\n",
        "tree.create_node(tag=93, identifier=93, parent=83)\n",
        "tree.create_node(tag=9, identifier=9, parent=20)\n",
        "tree.create_node(tag=37, identifier=37, parent=20)\n",
        "tree.create_node(tag=16, identifier=16, parent=9)\n",
        "tree.create_node(tag=36, identifier=36, parent=37)\n",
        "tree.create_node(tag=88, identifier=88, parent=93)\n",
        "\n",
        "# Show the tree using the Treelib built-in tree representation\n",
        "tree.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQpiihhIELcA",
        "outputId": "8c8f4caa-fad4-4d06-bbb3-cb21197e4570"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "51\n",
            "├── 43\n",
            "│   └── 20\n",
            "│       ├── 9\n",
            "│       │   └── 16\n",
            "│       └── 37\n",
            "│           └── 36\n",
            "└── 83\n",
            "    ├── 58\n",
            "    └── 93\n",
            "        └── 88\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install turtle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0wQRSAp2HTXv",
        "outputId": "5604c990-cdfb-4901-9a2b-dcc9e3239d37"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting turtle\n",
            "  Downloading turtle-0.0.2.tar.gz (11 kB)\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py egg_info\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25herror\n",
            "\u001b[1;31merror\u001b[0m: \u001b[1mmetadata-generation-failed\u001b[0m\n",
            "\n",
            "\u001b[31m×\u001b[0m Encountered error while generating package metadata.\n",
            "\u001b[31m╰─>\u001b[0m See above for output.\n",
            "\n",
            "\u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\n",
            "\u001b[1;36mhint\u001b[0m: See above for details.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import graphviz  # doctest: +NO_EXE\n",
        "dot = graphviz.Digraph(comment='The njnj Table')\n",
        "dot  #doctest: +ELLIPSIS"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "Z-1XrSjvH8h6",
        "outputId": "62819d52-df57-40d0-fc5e-5168dd09fbdd"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.dot.Digraph at 0x7f96c7980340>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.43.0 (0)\n -->\n<!-- Title: %3 Pages: 1 -->\n<svg width=\"8pt\" height=\"8pt\"\n viewBox=\"0.00 0.00 8.00 8.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 4)\">\n<title>%3</title>\n<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-4 4,-4 4,4 -4,4\"/>\n</g>\n</svg>\n"
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import graphviz\n",
        "\n",
        "dot = graphviz.Digraph(comment='Tree')\n",
        "\n",
        "# Add nodes\n",
        "dot.node('A', '51')\n",
        "dot.node('B', '43')\n",
        "dot.node('C', '83')\n",
        "dot.node('D', '20')\n",
        "dot.node('E', '58')\n",
        "dot.node('F', '93')\n",
        "dot.node('G', '9')\n",
        "dot.node('H', '37')\n",
        "dot.node('I', '16')\n",
        "dot.node('J', '36')\n",
        "dot.node('K', '88')\n",
        "\n",
        "# Add edges\n",
        "dot.edges(['AB', 'AC', 'BD', 'CE', 'CF', 'DG', 'DH', 'GI', 'HJ', 'FK'])\n",
        "\n",
        "# Render and save the graph\n",
        "dot\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "Fv9I0sun_2IA",
        "outputId": "b99eecb1-e40e-4469-9579-ebfd82c5c3ed"
      },
      "execution_count": 36,
      "outputs": [
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          "metadata": {},
          "execution_count": 36
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      "cell_type": "code",
      "source": [],
      "metadata": {
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      "execution_count": null,
      "outputs": []
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}