BDDS_17march_01.ipynb

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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lovnishverma/Python-Getting-Started/blob/main/BDDS_17march_01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### variables"
      ],
      "metadata": {
        "id": "bl5F05_MgM8d"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lssSDnsEflzM",
        "outputId": "54695951-1b43-409e-c8c0-5e4c9f3875c7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n"
          ]
        }
      ],
      "source": [
        "# single line comments\n",
        "a=10      # declare variable a with value 10\n",
        "print (a)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sal=25000.50\n",
        "print(sal)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hLCXUsPtgKw6",
        "outputId": "3b8a0cc3-2f9b-4923-9223-08788562708b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "25000.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print ( type(a) , type(sal))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_D4UMuRgHDn",
        "outputId": "3ab4b61b-ac99-45e9-e793-7851eedefb4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'int'> <class 'float'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nm1 = \"Ram Sita\"\n",
        "print(nm1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6B5ihiqHgq0n",
        "outputId": "4653faba-5ca9-4822-b9d2-cfe990c91f34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ram Sita\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print( a,sal, nm1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dZjIZPk2hDiN",
        "outputId": "5359736e-7858-40ce-e66f-ebdd7cb33c70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10 25000.5 Ram Sita\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print \\\n",
        "(a,sal,nm1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4wFmk8LNhQKc",
        "outputId": "b2efd1f2-69ad-4311-fba0-3e596600969b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10 25000.5 Ram Sita\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bigstr = \" this is my world and  \\\n",
        "i rule my verses\"\n",
        "print(bigstr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o2BSnvi6hdBA",
        "outputId": "b49fb087-944a-406e-d05f-560a1c93d9f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " this is my world and  i rule my verses\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m=10 ; n=20 ;  j=30;\n",
        "print(m,n,j)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rf_YvcyfhpLL",
        "outputId": "c6b8f25b-ee64-42bc-8677-ea5ef986e5e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10 20 30\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### control statements"
      ],
      "metadata": {
        "id": "9NfLjUCWiFll"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m=1\n",
        "m = int (input (\" enter number : \"))\n",
        "if m>5 :\n",
        "      print ('greater')\n",
        "      print( ' more ')\n",
        "else :\n",
        "    print ('smaller ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WWFOwomBhs4g",
        "outputId": "11e9c9d6-0001-4e03-c40a-83f47e20031c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " enter number : 3\n",
            "smaller \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# loop\n",
        "cnt = 0\n",
        "while (cnt<10) :\n",
        "    print (cnt)\n",
        "    cnt = cnt+1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Gz9wy6fi8wq",
        "outputId": "8fb4af8f-6377-4443-d09a-5719487eed63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for cnt in range(3) :\n",
        "    print (cnt)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fKXRINGwjEHY",
        "outputId": "7f5652a1-3745-4c15-b23e-7de71cdbb0cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "1\n",
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for cnt in range( 5 ,11 ,2 ) :\n",
        "    print (cnt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rlFl5G6VjhjD",
        "outputId": "ed7b31dc-eec2-4b17-d85a-843ac80ef79e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n",
            "7\n",
            "9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DDly0F5ujmSx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### using Numpy"
      ],
      "metadata": {
        "id": "09zrC4cTj3Uv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "gJuQ7Vvvj4-E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n1 = np.array([1,2,3,4])\n",
        "print ( n1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JOx7L72pkD13",
        "outputId": "1ca1df2e-0f84-4ced-f47a-774707aa7350"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 2 3 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n1 + 10"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n296NXPIkD4-",
        "outputId": "11153f55-0b25-4841-d9e5-dc781a2495a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([11, 12, 13, 14])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n2 = np.random.randint(10,20,5)\n",
        "print(n2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nXytWcPwkVso",
        "outputId": "d5c183df-b444-4095-c02d-0a56f327df4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[15 12 15 17 16]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# add 5 to each element of n2 array\n",
        "print (n2)\n",
        "# option 1\n",
        "# create n4 array having same size as n2 with blank values\n",
        "n4 = np.zeros(5)\n",
        "for i in range (5):\n",
        "    n4[i] = n2[i] + 5\n",
        "print(n4)\n",
        "# option 2\n",
        "n3 = n2 + 5\n",
        "print(n3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kiCQVZgakqx_",
        "outputId": "3bc611a7-da99-414b-91e9-c2fa2112765d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[20 17 20 22 21]\n",
            "[25. 22. 25. 27. 26.]\n",
            "[25 22 25 27 26]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "N1kvJL5nk8Nb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### function"
      ],
      "metadata": {
        "id": "89W5wYw_m-UC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def addarray ( n2 , n ):\n",
        "  n4 = np.zeros( len(n2) )\n",
        "  for i in range (len(n2)):\n",
        "      n4[i] = n2[i] + n\n",
        "  # print(n4)"
      ],
      "metadata": {
        "id": "RB3V1Bb8m_py"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n5 = np.random.randint(1,20,8)\n",
        "print(n5)\n",
        "addarray(n5 , 6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rd-KbsCAnT0D",
        "outputId": "adea0e60-1612-4316-98d1-77c9a7de7422"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 8 11  7 14  4 14  9 15]\n",
            "[14. 17. 13. 20. 10. 20. 15. 21.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# scenario 1\n",
        "n5 = np.random.randint(1,200,1000000)\n",
        "# print(n5)\n",
        "%timeit addarray(n5 , 6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zExfjppon88V",
        "outputId": "f1a01a53-35ae-4967-a98d-0ab244e2fb0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "436 ms ± 54.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# scenario 2\n",
        "%timeit n6 = n5+6"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ONiGSI7jn8_I",
        "outputId": "8f0d2397-0ece-44f4-cf95-c499fd10715d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.35 ms ± 106 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n5[ 999998:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dqC3u1-wpYCp",
        "outputId": "311c951a-f60b-4014-a118-bde4eea6713e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([107, 111])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "67e2Vtblpc5F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### list, tuple , dictionary"
      ],
      "metadata": {
        "id": "ePZ52nqDqFdb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "l1 = [10,20,30]\n",
        "print(l1, type (l1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7GHAqZWHqIKx",
        "outputId": "422a734a-d326-458a-9e32-180329f3a1d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10, 20, 30] <class 'list'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l2 = [\"amit\" , 200, l1 , [\"ram\",\"sita\"]]\n",
        "print(l2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c6nA9r8lqM0f",
        "outputId": "abf464e4-101d-4acb-971b-efaacfb79864"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['amit', 200, [10, 20, 30], ['ram', 'sita']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l2[0:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vvfysfTaqZEK",
        "outputId": "4d3afc47-74f9-4289-bf76-b17453c4f5a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['amit', 200, [10, 20, 30]]"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l2[2][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-fLODTovqgBm",
        "outputId": "0ae986b4-a226-4045-ddcf-a8aceaf2a6aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GzCDpSEeqqvo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### tuple"
      ],
      "metadata": {
        "id": "Nlt2OkB7q-oq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "t1 = (\"10\",\"20\")"
      ],
      "metadata": {
        "id": "mWB1emd0rAbM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a=10\n",
        "b=20\n",
        "print(a,b)\n",
        "# exchange the value\n",
        "a,b = b, a\n",
        "print(a,b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ZKTGXzprDCp",
        "outputId": "eeacd685-2b53-4ff9-ff61-1d3661955e4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10 20\n",
            "20 10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=10\n",
        "b=20\n",
        "c=30\n",
        "print (a , b, c )\n",
        "a, b , c  = c, a, b\n",
        "print (a , b, c )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-VBhTgvxrZ0o",
        "outputId": "7fc58af4-9fa6-45ed-8114-2482a89ba650"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10 20 30\n",
            "30 10 20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### dictionary"
      ],
      "metadata": {
        "id": "2ll7yL5rrqHZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d1 = { \"name\": \"Ram\", \"age\": 200}"
      ],
      "metadata": {
        "id": "RbFCDYL5rsUQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(d1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BFcKgssVrxjT",
        "outputId": "82ba979e-6abd-46a3-bc2e-6f0cfe1c4148"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'name': 'Ram', 'age': 200}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d1[\"name\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "UkuWqtkYryL5",
        "outputId": "8ce4cfd8-616b-4573-ec8b-0f9c702ffe88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Ram'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d1.values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_WP1tozZr4Qw",
        "outputId": "ebe9c2fb-ae9f-4d46-c444-963b8687e019"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_values(['Ram', 200])"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d1[\"age\"] = 20"
      ],
      "metadata": {
        "id": "xaS_GMZLsX57"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d1.values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jt-ZTdWgsfao",
        "outputId": "6f03b84d-79c3-4f98-cbdf-21f9b4df0b27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_values(['Ram', 20])"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GFHpkGAniIkh",
        "outputId": "76f4f711-6801-46f6-c80a-999f41054fe2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10 20 30\n",
            "30 10 20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GyCX1i23iOkv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Pandas"
      ],
      "metadata": {
        "id": "8sMEvd49oQVQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "pd.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "wYv2fgYdiOn1",
        "outputId": "da137127-b862-489e-be4d-2af002c7e07e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.2.2'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url1 = \"/content/sample_data/california_housing_test.csv\"\n",
        "df1 =  pd.read_csv(url1)\n",
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "QnXtsbBOpx9M",
        "outputId": "ad20c62b-d129-4631-b030-6f3c93997bc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0    -122.05     37.37                27.0       3885.0           661.0   \n",
              "1    -118.30     34.26                43.0       1510.0           310.0   \n",
              "2    -117.81     33.78                27.0       3589.0           507.0   \n",
              "3    -118.36     33.82                28.0         67.0            15.0   \n",
              "4    -119.67     36.33                19.0       1241.0           244.0   \n",
              "\n",
              "   population  households  median_income  median_house_value  \n",
              "0      1537.0       606.0         6.6085            344700.0  \n",
              "1       809.0       277.0         3.5990            176500.0  \n",
              "2      1484.0       495.0         5.7934            270500.0  \n",
              "3        49.0        11.0         6.1359            330000.0  \n",
              "4       850.0       237.0         2.9375             81700.0  "
            ],
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              "      <th>longitude</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-118.30</td>\n",
              "      <td>34.26</td>\n",
              "      <td>43.0</td>\n",
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              "      <td>176500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-117.81</td>\n",
              "      <td>33.78</td>\n",
              "      <td>27.0</td>\n",
              "      <td>3589.0</td>\n",
              "      <td>507.0</td>\n",
              "      <td>1484.0</td>\n",
              "      <td>495.0</td>\n",
              "      <td>5.7934</td>\n",
              "      <td>270500.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-118.36</td>\n",
              "      <td>33.82</td>\n",
              "      <td>28.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>15.0</td>\n",
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              "      <td>11.0</td>\n",
              "      <td>6.1359</td>\n",
              "      <td>330000.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-119.67</td>\n",
              "      <td>36.33</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1241.0</td>\n",
              "      <td>244.0</td>\n",
              "      <td>850.0</td>\n",
              "      <td>237.0</td>\n",
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              "    async function quickchart(key) {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 3000,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9949362939550161,\n        \"min\": -124.18,\n        \"max\": -114.49,\n        \"num_unique_values\": 607,\n        \"samples\": [\n          -121.15,\n          -121.46,\n          -121.02\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1296695233438325,\n        \"min\": 32.56,\n        \"max\": 41.92,\n        \"num_unique_values\": 587,\n        \"samples\": [\n          40.17,\n          33.69,\n          39.61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.555395554955755,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          14.0,\n          49.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2155.59333162558,\n        \"min\": 6.0,\n        \"max\": 30450.0,\n        \"num_unique_values\": 2215,\n        \"samples\": [\n          1961.0,\n          1807.0,\n          680.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 415.6543681363232,\n        \"min\": 2.0,\n        \"max\": 5419.0,\n        \"num_unique_values\": 1055,\n        \"samples\": [\n          532.0,\n          764.0,\n          2162.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1030.5430124122422,\n        \"min\": 5.0,\n        \"max\": 11935.0,\n        \"num_unique_values\": 1802,\n        \"samples\": [\n          947.0,\n          1140.0,\n          2019.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 365.42270980552604,\n        \"min\": 2.0,\n        \"max\": 4930.0,\n        \"num_unique_values\": 1026,\n        \"samples\": [\n          646.0,\n          629.0,\n          504.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.854511729691481,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 2578,\n        \"samples\": [\n          1.725,\n          0.7403,\n          2.6964\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 113119.68746964433,\n        \"min\": 22500.0,\n        \"max\": 500001.0,\n        \"num_unique_values\": 1784,\n        \"samples\": [\n          71900.0,\n          63000.0,\n          115800.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qQcMzmoFr3SH",
        "outputId": "d0945cf4-d435-4cf4-9823-7823fbd62946"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url2 = \"https://raw.githubusercontent.com/sarwansingh/Python/refs/heads/master/ClassExamples/data/iris.csv\"\n",
        "df2 =  pd.read_csv(url2 , header= None  , names=['a','b','c','d','e'])\n",
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Wr9Cggksr8ws",
        "outputId": "289fa418-9198-4c7c-a650-cce123e6cccb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     a    b    c    d            e\n",
              "0  5.1  3.5  1.4  0.2  Iris-setosa\n",
              "1  4.9  3.0  1.4  0.2  Iris-setosa\n",
              "2  4.7  3.2  1.3  0.2  Iris-setosa\n",
              "3  4.6  3.1  1.5  0.2  Iris-setosa\n",
              "4  5.0  3.6  1.4  0.2  Iris-setosa"
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              "      <td>5.1</td>\n",
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              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "      <td>Iris-setosa</td>\n",
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              "      <th>3</th>\n",
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              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "      async function convertToInteractive(key) {\n",
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              "                                                    [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",
              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ad2486b0-0b79-4be3-ba27-58ed214e4c5d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2",
              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"a\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"b\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522617,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"d\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008414,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"e\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 [1:3 ]  # show me rows from rowid 1 to <3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "UkPv0lxEtR_P",
        "outputId": "5c5341cb-217e-4fd7-abc8-380e6f5c06d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     a    b    c    d            e\n",
              "1  4.9  3.0  1.4  0.2  Iris-setosa\n",
              "2  4.7  3.2  1.3  0.2  Iris-setosa"
            ],
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              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
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              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-af58cc67-7234-4eb1-8941-9f67901cebd3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-af58cc67-7234-4eb1-8941-9f67901cebd3');\n",
              "        const dataTable =\n",
              "          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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-aea60897-0233-43f2-aa92-963405c3efce button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df2 [1:3 ]\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"a\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14142135623730964,\n        \"min\": 4.7,\n        \"max\": 4.9,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4.7,\n          4.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"b\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14142135623730964,\n        \"min\": 3.0,\n        \"max\": 3.2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.2,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07071067811865465,\n        \"min\": 1.3,\n        \"max\": 1.4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.3,\n          1.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"d\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.2,\n        \"max\": 0.2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"e\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-setosa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 [1:3 ,1:4]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "XPIZtynmuL1T",
        "outputId": "0dc71e06-a5a1-4f2f-bd77-96f7836925a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "InvalidIndexError",
          "evalue": "(slice(1, 3, None), slice(1, 4, None))",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3805\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3806\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: '(slice(1, 3, None), slice(1, 4, None))' is an invalid key",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mInvalidIndexError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-45c7a5a7f849>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf2\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4101\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4102\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4103\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4104\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3815\u001b[0m             \u001b[0;31m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3816\u001b[0m             \u001b[0;31m#  the TypeError.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3817\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_indexing_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3818\u001b[0m             \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3819\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_check_indexing_error\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   6057\u001b[0m             \u001b[0;31m# if key is not a scalar, directly raise an error (the code below\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6058\u001b[0m             \u001b[0;31m# would convert to numpy arrays and raise later any way) - GH29926\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6059\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6060\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6061\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mcache_readonly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mInvalidIndexError\u001b[0m: (slice(1, 3, None), slice(1, 4, None))"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.iloc[1:3 ,1:4] # [row, col]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "TCkNU7hVuL5a",
        "outputId": "c915ef41-5a67-48b6-f161-73ee06fee4ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     b    c    d\n",
              "1  3.0  1.4  0.2\n",
              "2  3.2  1.3  0.2"
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>1</th>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c2dfa139-8209-46d0-a7b2-3fb313131daa')\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-c2dfa139-8209-46d0-a7b2-3fb313131daa');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-46ca0cc5-4e8a-418a-898a-1ff040aa78c1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"b\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14142135623730964,\n        \"min\": 3.0,\n        \"max\": 3.2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.2,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07071067811865465,\n        \"min\": 1.3,\n        \"max\": 1.4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.3,\n          1.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"d\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.2,\n        \"max\": 0.2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2[['b','c']]   # get all rows and column b and c"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "va-4dEsPvWdA",
        "outputId": "dc18e742-f049-48b2-d221-39957d9715c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       b    c\n",
              "0    3.5  1.4\n",
              "1    3.0  1.4\n",
              "2    3.2  1.3\n",
              "3    3.1  1.5\n",
              "4    3.6  1.4\n",
              "..   ...  ...\n",
              "145  3.0  5.2\n",
              "146  2.5  5.0\n",
              "147  3.0  5.2\n",
              "148  3.4  5.4\n",
              "149  3.0  5.1\n",
              "\n",
              "[150 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d99a3380-e688-4ee8-99de-fb21dbe4b013\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d99a3380-e688-4ee8-99de-fb21dbe4b013')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d99a3380-e688-4ee8-99de-fb21dbe4b013 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d99a3380-e688-4ee8-99de-fb21dbe4b013');\n",
              "        const dataTable =\n",
              "          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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-205d3f3b-7d63-4369-bc3d-333ac80ea557\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-205d3f3b-7d63-4369-bc3d-333ac80ea557')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-205d3f3b-7d63-4369-bc3d-333ac80ea557 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df2[['b','c']]   # get all rows and column b and c\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"b\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522617,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2[['b','c']][:3]   # get column b and c  and rowid <3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "KGruWbzvvxFy",
        "outputId": "5540077e-4df2-4269-8ce6-f7f9648c004d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     b    c\n",
              "0  3.5  1.4\n",
              "1  3.0  1.4\n",
              "2  3.2  1.3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6678c7e1-f34f-493f-bff2-d7702f21587d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6678c7e1-f34f-493f-bff2-d7702f21587d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6678c7e1-f34f-493f-bff2-d7702f21587d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6678c7e1-f34f-493f-bff2-d7702f21587d');\n",
              "        const dataTable =\n",
              "          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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-701a10fa-98c5-4bc5-b591-e3f85c0d39d3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-701a10fa-98c5-4bc5-b591-e3f85c0d39d3')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-701a10fa-98c5-4bc5-b591-e3f85c0d39d3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df2[['b','c']][:3]   # get column b and c  and rowid <3\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"b\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2516611478423583,\n        \"min\": 3.0,\n        \"max\": 3.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3.5,\n          3.0,\n          3.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0577350269189625,\n        \"min\": 1.3,\n        \"max\": 1.4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.3,\n          1.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2[[1:3]][:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "6fB9pxAbwSuR",
        "outputId": "edd55f67-407b-4840-a631-6e88d6b85e60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "invalid syntax (<ipython-input-28-877420851e5a>, line 1)",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-28-877420851e5a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    df2[[1:3]][:3]\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualization"
      ],
      "metadata": {
        "id": "ldlvKJQKxA4x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "url ='https://raw.githubusercontent.com/sarwansingh/Python/master/ClassExamples/data/iris.csv'\n",
        "#To import the Iris dataset:\n",
        "nameslist = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species']\n",
        "iris =  pd.read_csv(url, header=None, names=nameslist) # the iris dataset is now a Pandas DataFrame\n",
        "print (iris.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BIIdX8QrxDKd",
        "outputId": "fc7f4337-679a-4f4c-feaa-612953165281"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(150, 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SfFXhv1vyAT-",
        "outputId": "0920e0d8-1740-4263-bb6d-527f4c3f85b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SepalLength  SepalWidth  PetalLength  PetalWidth      Species\n",
              "0          5.1         3.5          1.4         0.2  Iris-setosa\n",
              "1          4.9         3.0          1.4         0.2  Iris-setosa\n",
              "2          4.7         3.2          1.3         0.2  Iris-setosa\n",
              "3          4.6         3.1          1.5         0.2  Iris-setosa\n",
              "4          5.0         3.6          1.4         0.2  Iris-setosa"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SepalLength</th>\n",
              "      <th>SepalWidth</th>\n",
              "      <th>PetalLength</th>\n",
              "      <th>PetalWidth</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-20dde101-a071-4c70-9f11-ab3aeb2fe963 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-20dde101-a071-4c70-9f11-ab3aeb2fe963');\n",
              "        const dataTable =\n",
              "          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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
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              "  </button>\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4279def0-52b6-42a6-8f2a-f6a2ad976288 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "iris",
              "summary": "{\n  \"name\": \"iris\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"SepalLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522617,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008414,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris[45:53]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "L97q7eVjyHdd",
        "outputId": "ebe3ca62-66ea-449a-811d-82e72bd3ede0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    SepalLength  SepalWidth  PetalLength  PetalWidth          Species\n",
              "45          4.8         3.0          1.4         0.3      Iris-setosa\n",
              "46          5.1         3.8          1.6         0.2      Iris-setosa\n",
              "47          4.6         3.2          1.4         0.2      Iris-setosa\n",
              "48          5.3         3.7          1.5         0.2      Iris-setosa\n",
              "49          5.0         3.3          1.4         0.2      Iris-setosa\n",
              "50          7.0         3.2          4.7         1.4  Iris-versicolor\n",
              "51          6.4         3.2          4.5         1.5  Iris-versicolor\n",
              "52          6.9         3.1          4.9         1.5  Iris-versicolor"
            ],
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SepalLength</th>\n",
              "      <th>SepalWidth</th>\n",
              "      <th>PetalLength</th>\n",
              "      <th>PetalWidth</th>\n",
              "      <th>Species</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>5.3</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
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              "      <th>49</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>7.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.4</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>6.4</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe7167aa-a9c1-4639-9cda-774a6de7120b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fe7167aa-a9c1-4639-9cda-774a6de7120b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fe7167aa-a9c1-4639-9cda-774a6de7120b');\n",
              "        const dataTable =\n",
              "          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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
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              "\n",
              "\n",
              "<div id=\"df-1a243c86-f266-46d9-8a77-fa51031c01d8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1a243c86-f266-46d9-8a77-fa51031c01d8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1a243c86-f266-46d9-8a77-fa51031c01d8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"iris[45:53]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"SepalLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.972386607417911,\n        \"min\": 4.6,\n        \"max\": 7.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.1,\n          7.0,\n          4.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28504385627478446,\n        \"min\": 3.0,\n        \"max\": 3.8,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3.0,\n          3.8,\n          3.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6816233653399157,\n        \"min\": 1.4,\n        \"max\": 4.9,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.4,\n          1.6,\n          4.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6468329437674438,\n        \"min\": 0.2,\n        \"max\": 1.5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.2,\n          1.5,\n          0.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Iris-versicolor\",\n          \"Iris-setosa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris [ 98:104]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "CKCLR2uoyMhy",
        "outputId": "7d563e57-f2b3-4a1d-b1b0-df36568faf83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     SepalLength  SepalWidth  PetalLength  PetalWidth          Species\n",
              "98           5.1         2.5          3.0         1.1  Iris-versicolor\n",
              "99           5.7         2.8          4.1         1.3  Iris-versicolor\n",
              "100          6.3         3.3          6.0         2.5   Iris-virginica\n",
              "101          5.8         2.7          5.1         1.9   Iris-virginica\n",
              "102          7.1         3.0          5.9         2.1   Iris-virginica\n",
              "103          6.3         2.9          5.6         1.8   Iris-virginica"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"iris [ 98:104]\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"SepalLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.68044103344816,\n        \"min\": 5.1,\n        \"max\": 7.1,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5.7,\n          7.1,\n          6.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2732520204255892,\n        \"min\": 2.5,\n        \"max\": 3.3,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2.5,\n          2.8,\n          2.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1811011811017718,\n        \"min\": 3.0,\n        \"max\": 6.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3.0,\n          4.1,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5154286242213044,\n        \"min\": 1.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.1,\n          1.3,\n          1.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Iris-virginica\",\n          \"Iris-versicolor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Statistical summary of data"
      ],
      "metadata": {
        "id": "y0XwXQgVyjbU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# count of Species in iris dataset\n",
        "iris['Species'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "8xi9oHgDyTHm",
        "outputId": "1ad7964c-0cf6-4067-dfd3-d281c17f19a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Species\n",
              "Iris-setosa        50\n",
              "Iris-versicolor    50\n",
              "Iris-virginica     50\n",
              "Name: count, dtype: int64"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Species</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Iris-setosa</th>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iris-versicolor</th>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iris-virginica</th>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "jexAlYwPyn4b",
        "outputId": "ddf71fa5-14e0-445b-fba9-08267cbf1a2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SepalLength  SepalWidth  PetalLength  PetalWidth\n",
              "count   150.000000  150.000000   150.000000  150.000000\n",
              "mean      5.843333    3.054000     3.758667    1.198667\n",
              "std       0.828066    0.433594     1.764420    0.763161\n",
              "min       4.300000    2.000000     1.000000    0.100000\n",
              "25%       5.100000    2.800000     1.600000    0.300000\n",
              "50%       5.800000    3.000000     4.350000    1.300000\n",
              "75%       6.400000    3.300000     5.100000    1.800000\n",
              "max       7.900000    4.400000     6.900000    2.500000"
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              "      <td>150.000000</td>\n",
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              "      <td>5.843333</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>6.400000</td>\n",
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              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cb132a2f-c45e-4be5-a971-278568330b29 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cb132a2f-c45e-4be5-a971-278568330b29');\n",
              "        const dataTable =\n",
              "          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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9dcb361a-f149-4e26-a952-358498a907d2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9dcb361a-f149-4e26-a952-358498a907d2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9dcb361a-f149-4e26-a952-358498a907d2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"iris\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"SepalLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.24711349471842,\n        \"min\": 0.8280661279778629,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.843333333333334,\n          5.8,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.08647211421483,\n        \"min\": 0.4335943113621737,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0540000000000003,\n          3.0,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.835227940958106,\n        \"min\": 1.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.758666666666666,\n          4.35,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.63663424340991,\n        \"min\": 0.1,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.1986666666666668,\n          1.3,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j6T0yU-sypgw",
        "outputId": "c93da146-aca0-4068-aea3-50b6de437074"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Xyo0uvOzKji",
        "outputId": "51bd625c-9310-4e59-88ed-2d94a6ccbd8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 5 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   SepalLength  150 non-null    float64\n",
            " 1   SepalWidth   150 non-null    float64\n",
            " 2   PetalLength  150 non-null    float64\n",
            " 3   PetalWidth   150 non-null    float64\n",
            " 4   Species      150 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "ZIje3OXszL15"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iris.plot(kind=\"scatter\", x=\"SepalLength\", y=\"SepalWidth\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "qai2MQd0zqzz",
        "outputId": "5fe7d76d-ea36-4070-8a1d-b2248d9c589b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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4PrewpMnzmNph2NS4ACBShL3A6devn/Lz8/Xxxx/r2muv1cyZM/XVV1/VOW1aWppmzJihwYMHKz09XStWrFCHDh307LPP1vv+CxYs0KFDh3yvnTt3hioVBNngbic0OH5o96Qmz2Nqh2FT4wKASBH2AqdFixbq27evhg0bpoyMDJ166ql67LHHGjVvbGyshgwZoi1bttQ7TVxcnO8urdoXIkN6v2Ql1XMxbVJCrM5I7dDkeUztMGxqXAAQKcJe4BytpqZGVVVVjZq2urpamzZtUufOnUMcFcJl1ewxxxQstXdEBWseUzsMmxoXAESCsDbbXLBggc477zx1795dZWVlWrJkiR544AG9++67mjhxombMmKGuXbsqIyNDknTXXXdp5MiR6tu3rw4ePKiHHnpIK1eu1KefflrvdTtHo9lmZFq/uVi5hSUa2j2pziM3wZjH1A7DpsYFAE4K9Pc7rM/BKSoq0owZM/Tdd9/J6/Vq0KBBvuJGkgoLC9Ws2X8PMpWUlOjqq6/Wnj17lJSUpGHDhunDDz9sdHGDyHVGaodGFzZ25+nV3swCwtS4AMBkto7g7N27VzfddJNWr16toqIiHf0W1dXVQQsw2DiCAwBA5HHkCM4VV1yhwsJC3XrrrercuTNPEwYAAEaxVeB88MEHWr9+vQYPHhzkcAAAAJrO1l1UKSkpx5yWAgAAMIWtAudPf/qTbrnlFm3fvj3I4SDYnOhEbafTtxPLCDR3t6wrN3Fim5j4OQHQdI0+RZWUlOR3rU1FRYX69OmjhIQExcb6P3PkwIEDwYsQtjjRidpOp28nlhFo7m5ZV27ixDYx8XMCIHgafRfV4sWLG/2mM2fOtB1QqEXLXVQz/vyJsrfs82vWGOPxaHTf9np51oigLGPIXf+ss7FlUkKs8m47O2zLCDR3t6wrN3Fim5j4OQFQv5DdRWVy0QJ/tZ2oj/bTTtRNfa5KY7p2B/rcmmAsI9Dc3bKu3MSJbWLi5wRAcNm6BicmJkZFRUXHDN+/f79iYmKaHBSaxolO1HY6fTuxjEBzd8u6chMntomJnxMAwWWrwKnvrFZVVZVatGjRpIDQdE50orbT6duJZQSau1vWlZs4sU1M/JwACK6AnoPz+OOPS5I8Ho9eeOEFtW7d2jeuurpa69atU//+/YMbIQJW24m6vusFgnEovbZrd33XlQTjlIudZQSau1vWlZs4sU1M/JwACK6AWjX06tVLkrRjxw5169bN73RUixYt1LNnT9111106/fTTgx9pkETLRcaHKo/ohqV5Ib3jY+f+Sl3w5AchvTPIzjICzd0t68pNnNgmJn5OANQv0N9vW72oxo8frxUrVigpKfIOrUdLgVPLiU7Udjp9O7GMQHN3y7pyEye2iYmfEwDHcqTAiWTRVuAAAOAGIbtN/MYbb2x0EI8++mijpwUAAAi2Rhc4eXl5fn/n5ubqhx9+UL9+/SRJ33zzjWJiYjRs2LDgRggAABCgRhc4a9as8f370UcfVZs2bbR48WLfdTglJSW68sordcYZZwQ/SgAAgADYugana9eu+uc//6mBAwf6Df/iiy909tlna/fu3UELMNi4BgcAgMgTsmtwjl5IcfGxjy0vLi5WWVmZnbcEGrS1uFw7DlQGdOeKnXmA48kqKFL+roMRfyecW/IA6mOrwLnooot05ZVX6pFHHtGIET82mfv44481f/58XXzxxUENENHNTgdnuj4jFNzSEd4teQDHY+sUVWVlpW666Sa9+OKLOnLkx52kefPmmjVrlh566CG1amXu/5Y5RRVZ7HRwpuszQsEtHeHdkgeiT6C/37Z6USUkJOipp57S/v37lZeXp7y8PB04cEBPPfWU0cUNIkttB+fqo2rwn3ZwDsY8wPE0piN8JHBLHkBj2CpwarVq1UqDBg3SoEGDKGwQdHY6ONP1GaHglo7wbskDaIxGX4Nz8cUXKzMzU4mJice9zmbFihVNDgyw08GZrs8IBbd0hHdLHkBjNLrA8Xq98ng8vn8DoWangzNdnxEKbukI75Y8gMagFxWMZqeDM12fEQpu6QjvljwQfULabDM9PV1nnXWWxo8fr5EjRyo2NvJ+LChwIpOdDs50fUYouKUjvFvyQPQIaYFzxRVXKCsrSzt27FDLli2Vlpam8ePH68wzz9SIESMUExPTpOCdQIEDAEDkCWmBU2v79u16//33lZWVpbVr12rnzp1q3bq1Ro8erTPPPFPz58+3FbwTKHAAAIg8jhQ4R9u6datefPFFLVq0SOXl5aqurm7qW4YMBQ4AAJHHkV5UkrRjxw6tXbvW9yoqKtLIkSOVnp5u9y0BAACCIqAC5+WXX/YVNPv27dOoUaOUnp6uq6++WqeddlpEXnQMAADcJ6AC54orrlD37t11yy23aNasWRQ0TeREt2s7yzCxyzCdwSNfNG/DQPcpU9dVoHHZycPU70VEnoCuwXnmmWe0du1aZWVl6fvvv9eYMWM0btw4paena9iwYb4HAZrMhGtwnOh2bWcZJnYZpjN45IvmbRjoPmXqugo0Ljt5mPq9CHM4dpHxV1995buLau3ataqqqtLo0aM1fvx43XTTTXbe0hEmFDhOdLu2swwTuwzTGTzyRfM2DHSfMnVdBRqXnTxM/V6EORzpJi5JAwYM0LXXXqvXXntNeXl5uv766/XBBx/o5ptvtvuWUcGJbtd2lmFil2E6g0e+aN6Gge5Tpq6rQOOyk4ep34uIbLYKnKKiIr322mu69tprddJJJyklJUUPP/ywhgwZottuuy3YMbqKE92u7SzDxC7DdAaPfNG8DQPdp0xdV4HGZScPU78XEdkCusj4uuuu09q1a1VQUKDmzZtrxIgR+vnPf67x48dr1KhRio+PD1WcruFEt2s7yzCxyzCdwSNfNG/DQPcpU9dVoHHZycPU70VEtoCO4OTl5WnKlCl65513VFJSovXr1+vuu+/WmWeeSXHTSLXdrmOOuiA7xuPR2NQOQbmi384yarsM1yVcXYadWFcIrWjehoHuU6auq0DjspOHqd+LiGwBFTg5OTm67777NHHiRCUk0HXWrkXThmh03/Z+w0b3ba9F04aEdRmrZo855gu59o6PcHFiXSG0onkbBrpPmbquAo3LTh6mfi8icjX6LqpVq1Y1+k0vuOAC2wGFmgl3UdVyotu1nWWY2GWYzuCRL5q3YaD7lKnrKtC47ORh6vciwi9kt4k3a9a4gz0ej4deVAAAIKhC1ouqpqamSYEBAAA4xfZzcAAAAExlu5t4RUWFsrKyVFhYqMOHD/uNmzNnTpMDAwAAsMtWgZOXl6dJkyapsrJSFRUVatu2rfbt26eEhAQlJydT4AAAgLCydYrqN7/5jX72s5+ppKRELVu21EcffaQdO3Zo2LBhevjhh4MdI5pga3G51hQUhfQx5HaW8donhZr3Wp6Wb9wZsrgAUzixHwbKiX2Q/RzhZKvZ5gknnKCPP/5Y/fr10wknnKCcnByddNJJ+vjjjzVz5kz9+9//DkWsQREtd1GZ2pl3066DuuipD/VDzX8/ds2bebRq9mgN6OoNSlyAKUzsXu3EPsh+jlBwpNlmbGys77bx5ORkFRYWSpK8Xq927qRSN8GcpfnK3rLPb1j2ln26YWleWJdx9JeeJP1QY+mCJ7ODFhdgCif2w0A5sQ+yn8MEtgqcIUOGaMOGDZKk9PR03XbbbXr11Vc1b948nXzyyUENEIEztTPva58UHvOlV+uHGovD2HAVE7tXO7EPsp/DFLYKnPvuu0+dO3eWJN17771KSkrStddeq+LiYj333HNBDRCBM7Uzb862/Q3Ok/3tvgbHA5HExO7VTuyD7Ocwha27qIYPH+77d3Jyst55552gBYSmM7Uzb1qvdlqZt7veeUb3aV/vOCDSmNi92ol9kP0cpmjSg/6Kioq0fv16rV+/XsXFxcefAY4wtTPv1BHd1byZ55jh0o8XIF46PKXJcQGmMLF7tRP7IPs5TGGrwCkrK9Pll1+url27Kj09Xenp6erSpYt++ctf6tChQ8GOETaY2pl31ezRx3z51d5dAbiNid2rndgH2c9hAlu3iU+dOlV5eXlatGiR0tLSJEk5OTmaO3euBg8erGXLlgU90GCJltvEa5namXf5xp3K/nafRvdpz//o4Homdq92Yh9kP0cwhayb+E+1atVK7777rsaMGeM3fP369Tr33HNVUWHOw6yOFm0FDgAAbuDIc3DatWsnr/fYhzV5vV4lJSXZeUsAAICgsVXgLFy4UDfeeKP27NnjG7Znzx7Nnz9ft956a9CCAwAAsMPWKaohQ4Zoy5YtqqqqUvfu3SVJhYWFiouLU2pqqt+0ubm5wYk0SDhFBQBA5An099vWc3CmTJliZzYAAABH2DqCEyxPP/20nn76aW3fvl2SNHDgQN12220677zz6p1n+fLluvXWW7V9+3alpqbqgQce0KRJkxq9zFAewdlaXK4dByobfadEoNObKqugSPm7Dmpo9ySdkdqhUfNE67pyKo9At4kTcTnxObGzDBO99kmhcrbtD+juIyfWlYn7oZ2YTMzDjmjL3ZG7qCTp4MGD+utf/6pvv/1W8+fPV9u2bZWbm6uOHTuqa9eujXqPN998UzExMUpNTZVlWVq8eLEeeugh5eXlaeDAgcdM/+GHH2rs2LHKyMjQ+eefryVLluiBBx5Qbm5uo3tghaLACbRjsIkdhu3Ysb9CU57MVknlEd+wpIRYrZo9Rint6n6Ka7SuK6fyCHSbOBGXE58TO8swkZ0u3E6sKxP3QzsxmZiHHdGauyMFzueff64JEybI6/Vq+/btKigoUO/evbVw4UIVFhbq5ZdfthW8JLVt21YPPfSQZs2adcy4qVOnqqKiQm+99ZZv2MiRIzV48GA988wzjXr/UBQ4M/78ibK37PNrqhfj8Wh03/Z6edaIJk9vqiF3/dPvS7JWUkKs8m47u855onVdOZVHoNvEibic+JzYWYaJ+v7+H3U2qmzezKMt99V9pNqJdWXifmgnJhPzsCNac3fkNvEbb7xRV1xxhTZv3qz4+Hjf8EmTJmndunV23lLV1dVatmyZKioqfA8PPFpOTo4mTJjgN+ycc85RTk5Ove9bVVWl0tJSv1cwBdox2MQOw3ZkFRTV+SUpSSWVR7T+J/9LqBWt68qpPALdJk7E5cTnxM4yTGSnC7cT68rE/dBOTCbmYUc05x4oWwXOhg0b9Otf//qY4V27dvW7dbwxNm3apNatWysuLk7XXHONXn/9dQ0YMKDOaffs2aOOHTv6DevYsWODy8zIyJDX6/W9UlKC+zTNQDsGm9hh2I78XQcbHJ9bWHLMsGhdV07lEeg2cSIuJz4ndpZhIjtduJ1YVybuh3ZiMjEPO6I590DZKnDi4uLqPBLyzTffqEOHwC7s69evn/Lz8/Xxxx/r2muv1cyZM/XVV1/ZCatOCxYs0KFDh3yvnTuP/V9QUwTaMdjEDsN2DO52QoPjh3Y/9oGP0bqunMoj0G3iRFxOfE7sLMNEab3aNTi+ri7cTqwrE/dDOzGZmIcd0Zx7oGwVOBdccIHuuusuHTny46FOj8ejwsJC3XzzzbrkkksCeq8WLVqob9++GjZsmDIyMnTqqafqscceq3PaTp06ae/evX7D9u7dq06dOtX7/nFxcUpMTPR7BVOgHYNN7DBsR3q/ZCXVc2FaUkJsnXdlROu6ciqPQLeJE3E58TmxswwT2enC7cS6MnE/tBOTiXnYEc25B8pWgfPII4+ovLxcycnJ+s9//qP09HT16dNHrVu31r333tukgGpqalRVVVXnuLS0NK1evdpv2HvvvVfvNTtOCbRjsIkdhu1YNXvMMV+WtXdj1Cda15VTeQS6TZyIy4nPiZ1lmMhOF24n1pWJ+6GdmEzMw45ozj0QTXoOzgcffKDPP/9c5eXlGjZsmM4666yA5l+wYIHOO+88de/eXWVlZb7bvt99911NnDhRM2bMUNeuXZWRkSHpx9vE09PTdf/992vy5MlatmyZ7rvvvrDfJl4r0I7BJnYYtmP95mLlFpYE9DyNaF1XTuUR6DZxIi4nPid2lmEiO124nVhXJu6HdmIyMQ87oi33kN4mnpOTo/379+v888/3DVu8eLFuv/12VVZWasqUKVq0aJHi4uIa9X6zZs3S6tWr9d1338nr9WrQoEG6+eabNXHiREnSuHHj1LNnT2VmZvrmWb58uRYuXOh70N+DDz5ozIP+AABAaIS0wDnvvPM0btw43XzzzZJ+vANq2LBhmjlzpk466SQ99NBD+vWvf6077rjDdgKhRoEDAEDkCelzcPLz8/1OQy1btkwjRozQ888/rxtvvFGPP/64/vKXvwQeNQAAQBAFVOCUlJT4PYcmKyvLr2/UaaedFvTbsAEAAAIVUIHTsWNHbdu2TZJ0+PBh5ebmauTIkb7xZWVlio2NjJ4WAADAvQIqcCZNmqRbbrlF69ev14IFC5SQkKAzzjjDN/7zzz9Xnz59gh6kW20tLteagiLXPiYb4RPoZ8vUz6ITeZi4DDtM3YZuwLqNTM0Dmfjuu+/WxRdfrPT0dLVu3VqLFy9WixYtfONffPFFnX125DS2Cxc3dHWFmdzSrd2JPExchh2mbkM3YN1GNlvPwTl06JBat26tmJgYv+EHDhxQ69at/Yoe05hwF5UburrCTG7p1u5EHiYuww5Tt6EbsG7N4kg3ca/Xe0xxI0lt27Y1urgxQbR2dUXouaVbuxN5mLgMO0zdhm7Auo18tgoc2BetXV0Rem7p1u5EHiYuww5Tt6EbsG4jHwWOw6K1qytCzy3d2p3Iw8Rl2GHqNnQD1m3ko8BxWLR2dUXouaVbuxN5mLgMO0zdhm7Auo18FDhhEI1dXeEMt3RrdyIPE5dhh6nb0A1Yt5GtSd3EI5EJd1HViuSurjCbW7q1O5GHicuww9Rt6AasWzOEtNmmG5hU4AAAgMZx5DZxAAAAk1HgAAAA16HAAQAArkOBAwAAXCegZpsAIsPW4nLtOFDZ6Ls+sgqKlL/roIZ2T9IZqR1CsgwnOJGHnbwDjcvEdSuZG5eJTF1XpsYVChQ4gIsE2v14x/4KTXkyWyWVR3zDkhJitWr2GKW0q/tJriZ2WHYiDzt5BxqXievW5LhMZOq6MjWuUOIUFeAic5bmK3vLPr9h2Vv26YaleXVOf/SPrySVVB7RBU9+ELRlOMGJPOzkHWhcJq5bydy4TGTqujI1rlCiwAFcItDux1kFRcf8+NYqqTyi9T/5n57dZTjBiTzs5B1oXCauW5PjMpGp68rUuEKNAgdwiUC7H+fvOtjg9LmFJU1ehhOcyMNO3oHGZeK6lcyNy0SmritT4wo1ChzAJQLtfjy42wkNTj+0e1KTl+EEJ/Kwk3egcZm4biVz4zKRqevK1LhCjQIHcIlAux+n90tWUj0XFyYlxNZ5t4+JHZadyMNO3oHGZeK6NTkuE5m6rkyNK9QocAAXCbT78arZY475Ea69yydYy3CCE3nYyTvQuExct5K5cZnI1HVlalyhRLNNwIUC7X68fnOxcgtLAnp+jIkdlp3Iw07egcZl4rqVzI3LRKauK1Pjagy6iR8HBQ4AAJGHbuIAACDqUeAAAADXocABAACuQ4EDAABch2abwP9xU5ddJ3J5YvVmZX+7T2ekdtB14/uGZBkmbhM7MZmYB+B2FDiIem7qsutELh9uKdb/e+ET3985Ww/owXcL9NrVI3V6n3ZBWYaJ28ROTCbmAUQLTlEh6rmpy64Tufy0uPmpqc9/FLRlmLhN7MRkYh5AtKDAQVRzU5ddJ3J5YvXmBsc/tWZLk5dh4jaxE5OJeQDRhAIHUc1NXXadyCX7230Njl//k1Mxdpm4TezEZGIeQDShwEFUc1OXXSdyGd2nfYPjG9seoSEmbhM7MZmYBxBNKHAQ1dzUZdeJXK4/K7XB8cG4m8rEbWInJhPzAKIJBQ6inpu67DqRy2tXjwxouB0mbhM7MZmYBxAtaLYJ/J9I7rJ7NCdyeWrNFq3fXBzS5+CYuE3sxGRiHkCkoZv4cVDgAAAQeegmDgAAoh4FDgAAcB0KHAAA4DoUOAAAwHVotgkYzolO1G7pkG1iTIBJomkfocABDOVEJ2q3dMg2MSbAJNG4j3CKCjCUE52o3dIh28SYAJNE4z5CgQMYyIlO1G7pkG1iTIBJonUfocABDOREJ2q3dMg2MSbAJNG6j1DgAAZyohO1WzpkmxgTYJJo3UcocAADOdGJ2i0dsk2MCTBJtO4jFDiAoZzoRO2WDtkmxgSYJBr3EZptAoZzohO1WzpkmxgTYJJI3kfoJn4cFDgAAEQeuokDAICoR4EDAABchwIHAAC4DgUOAABwnbAWOBkZGTrttNPUpk0bJScna8qUKSooKGhwnszMTHk8Hr9XfHy8QxEjWLYWl2tNQVFIHxHuxDKcYCePQOdxy7pyQlZBkR5b/Y3W/6RpYSRim8PtwtpNPCsrS7Nnz9Zpp52mH374Qb///e919tln66uvvlKrVvXfvpaYmOhXCHmOengRzGVqh2wTOdHp2y3rygk79ldoypPZKqk84huWlBCrVbPHKKVdw0+KNQnbHNHCqNvEi4uLlZycrKysLI0dO7bOaTIzMzVv3jwdPHjQ1jK4TTy8Zvz5E2Vv2efX9C3G49Hovu318qwREbMMJ9jJI9B53LKunDDkrn/6FTe1khJilXfb2WGIyB62OSJVRN8mfujQIUlS27ZtG5yuvLxcPXr0UEpKii688EJ9+eWX9U5bVVWl0tJSvxfCw9QO2SZyotO3W9aVE7IKiuosbiSppPJIxJyuYpsjmhhT4NTU1GjevHkaPXq0Tj755Hqn69evn1588UW98cYbeuWVV1RTU6NRo0Zp165ddU6fkZEhr9fre6WkpIQqBRyHqR2yTeREp2+3rCsn5O862OD43MISZwJpIrY5ookxBc7s2bP1xRdfaNmyZQ1Ol5aWphkzZmjw4MFKT0/XihUr1KFDBz377LN1Tr9gwQIdOnTI99q5c2cowkcjmNoh20ROdPp2y7pywuBuJzQ4fmj3JGcCaSK2OaKJEQXO9ddfr7feektr1qxRt27dApo3NjZWQ4YM0ZYtW+ocHxcXp8TERL8XwsPUDtkmcqLTt1vWlRPS+yUrqZ4LcJMSYnVGageHI7KHbY5oEtYCx7IsXX/99Xr99df1/vvvq1evXgG/R3V1tTZt2qTOnTuHIEIEm6kdsk3kRKdvt6wrJ6yaPeaYIqf2LqpIwjZHtAjrXVTXXXedlixZojfeeEP9+vXzDfd6vWrZsqUkacaMGeratasyMjIkSXfddZdGjhypvn376uDBg3rooYe0cuVKffrppxowYMBxl8ldVGYwtUO2iZzo9O2WdeWE9ZuLlVtYoqHdkyLmyE1d2OaINBHVTby+59e89NJLuuKKKyRJ48aNU8+ePZWZmSlJ+s1vfqMVK1Zoz549SkpK0rBhw3TPPfdoyJDG/e+DAgcAgMgTUQVOOFDgAAAQeSL6OTgAAADBQIEDAABchwIHAAC4TlibbSJ6bS0u144DldzB0QhZBUXK33Uw4u/aAQAnUeDAUXQybjy3dK8GgHDgFBUcNWdpvrK37PMblr1ln25YmhemiMx1dHEj/djY8YInPwhTRAAQOShw4Bg6GTeeW7pXA0C4UODAMXQybjy3dK8GgHChwIFj6GTceG7pXg0A4UKBA8fQybjx3NK9GgDChQIHjqKTceO5pXs1AIQDvagQFnQybjy3dK8GgKag2eZxUOAAABB5aLYJAACiHgUOAABwHQocAADgOhQ4AADAdWi2GWGitQt3tOYtRXfuJmJ7AJGBAidCRGsX7mjNW4ru3E3E9gAiC6eoIkS0duGO1ryl6M7dRGwPILJQ4ESAaO3CHa15S9Gdu4nYHkDkocCJANHahTta85aiO3cTsT2AyEOBEwGitQt3tOYtRXfuJmJ7AJGHAicCRGsX7mjNW4ru3E3E9gAiDwVOhIjWLtzRmrcU3bmbiO0BRBaabUaYaO3CHa15S9Gdu4nYHkB40E38OCK9wAEAIBrRTRwAAEQ9ChwAAOA6FDgAAMB1KHAAAIDr0GwTgLGyCoqUv+ughnZP0hmpHUKyDLqDA+5EgQPAODv2V2jKk9kqqTziG5aUEKtVs8copV3DTxVuLLqDA+7GKSoAxjm6uJGkksojuuDJD4K2DLqDA+5GgQPAKFkFRccUN7VKKo9o/U+OuNhFd3DA/ShwABglf9fBBsfnFpY0eRl0BwfcjwIHgFEGdzuhwfFDuyc1eRl0BwfcjwIHgFHS+yUrqZ6LfJMSYoNyNxXdwQH3o8ABYJxVs8ccU+TU3kUVLHQHB9yNZpsAjLV+c7FyC0tC+hwcuoMDkYFu4sdBgQMAQOShmzgAAIh6FDgAAMB1KHAAAIDrUOAAAADXocABAACuQ4EDAABchwIHAAC4DgUOAABwHQocAADgOhQ4AADAdShwAACA61DgAAAA16HAAQAArkOBAwAAXIcCBwAAuA4FDgAAcB0KHAAA4DoUOAAAwHXCWuBkZGTotNNOU5s2bZScnKwpU6aooKDguPMtX75c/fv3V3x8vE455RT94x//cCBahNPW4nKtKSjStn0V4Q4FABABwlrgZGVlafbs2froo4/03nvv6ciRIzr77LNVUVH/j9iHH36oadOmadasWcrLy9OUKVM0ZcoUffHFFw5GDqccrDysGX/+RGc+kqUrX9qg8Q+v1Yw/f6JDlUfCHRoAwGAey7KscAdRq7i4WMnJycrKytLYsWPrnGbq1KmqqKjQW2+95Rs2cuRIDR48WM8888xxl1FaWiqv16tDhw4pMTExaLEjNGb8+RNlb9mn6p98TGM8Ho3u214vzxoRxsgAAE4K9PfbqGtwDh06JElq27ZtvdPk5ORowoQJfsPOOecc5eTk1Dl9VVWVSktL/V6IDFuLy7Vuc7FfcSNJ1ZaldZuLOV0FAKiXMQVOTU2N5s2bp9GjR+vkk0+ud7o9e/aoY8eOfsM6duyoPXv21Dl9RkaGvF6v75WSkhLUuBE6Ow5UNjh++34KHABA3YwpcGbPnq0vvvhCy5YtC+r7LliwQIcOHfK9du7cGdT3R+j0aJvQ4Pie7Vo5FAkAINI0D3cAknT99dfrrbfe0rp169StW7cGp+3UqZP27t3rN2zv3r3q1KlTndPHxcUpLi4uaLHCOb07tNbY1A71XoPTqz0FDgCgbmE9gmNZlq6//nq9/vrrev/999WrV6/jzpOWlqbVq1f7DXvvvfeUlpYWqjARRoumDdHovu39ho3u216Lpg0JU0QAgEgQ1iM4s2fP1pIlS/TGG2+oTZs2vutovF6vWrZsKUmaMWOGunbtqoyMDEnS3LlzlZ6erkceeUSTJ0/WsmXLtHHjRj333HNhywOh402I1cuzRmjbvgpt31+hnu1aceQGAHBcYb1N3OPx1Dn8pZde0hVXXCFJGjdunHr27KnMzEzf+OXLl2vhwoXavn27UlNT9eCDD2rSpEmNWia3iQMAEHkC/f026jk4TqDAAQAg8kT0c3AAAACCgQIHAAC4DgUOAABwHQocAADgOhQ4AADAdShwAACA61DgAAAA16HAAQAArkOBAwAAXMeIbuJOqn1wc2lpaZgjAQAAjVX7u93YBgxRV+CUlZVJklJSUsIcCQAACFRZWZm8Xu9xp4u6XlQ1NTXavXu32rRpU2+zT5OVlpYqJSVFO3fujKpeWtGat0Tu0Zh7tOYtkXs05t7YvC3LUllZmbp06aJmzY5/hU3UHcFp1qyZunXrFu4wmiwxMTGqdoBa0Zq3RO7RmHu05i2RezTm3pi8G3PkphYXGQMAANehwAEAAK5DgRNh4uLidPvttysuLi7coTgqWvOWyD0ac4/WvCVyj8bcQ5V31F1kDAAA3I8jOAAAwHUocAAAgOtQ4AAAANehwAEAAK5DgWOo+++/Xx6PR/Pmzat3mszMTHk8Hr9XfHy8c0EGyR133HFMHv37929wnuXLl6t///6Kj4/XKaecon/84x8ORRtcgebulm1e63//93/1y1/+Uu3atVPLli11yimnaOPGjQ3Os3btWg0dOlRxcXHq27evMjMznQk2iALNe+3atcdsd4/Hoz179jgYddP17Nmzzjxmz55d7zxu2dcDzd0t+3p1dbVuvfVW9erVSy1btlSfPn109913H7efVDD286h7knEk2LBhg5599lkNGjTouNMmJiaqoKDA93cktp+QpIEDB+pf//qX7+/mzev/aH744YeaNm2aMjIydP7552vJkiWaMmWKcnNzdfLJJzsRblAFkrvknm1eUlKi0aNHa/z48Xr77bfVoUMHbd68WUlJSfXOs23bNk2ePFnXXHONXn31Va1evVpXXXWVOnfurHPOOcfB6O2zk3etgoICvye9JicnhzLUoNuwYYOqq6t9f3/xxReaOHGiLr300jqnd9O+Hmjukjv29QceeEBPP/20Fi9erIEDB2rjxo268sor5fV6NWfOnDrnCdp+bsEoZWVlVmpqqvXee+9Z6enp1ty5c+ud9qWXXrK8Xq9jsYXK7bffbp166qmNnv6yyy6zJk+e7Dfs9NNPt379618HObLQCzR3t2xzy7Ksm2++2RozZkxA8/zud7+zBg4c6Dds6tSp1jnnnBPM0ELKTt5r1qyxJFklJSWhCSpM5s6da/Xp08eqqampc7yb9vWjHS93t+zrkydPtn71q1/5Dbv44out6dOn1ztPsPZzTlEZZvbs2Zo8ebImTJjQqOnLy8vVo0cPpaSk6MILL9SXX34Z4ghDY/PmzerSpYt69+6t6dOnq7CwsN5pc3Jyjlk/55xzjnJyckIdZkgEkrvknm2+atUqDR8+XJdeeqmSk5M1ZMgQPf/88w3O44ZtbyfvWoMHD1bnzp01ceJEZWdnhzjS0Dp8+LBeeeUV/epXv6r3yIQbtnddGpO75I59fdSoUVq9erW++eYbSdJnn32mDz74QOedd1698wRru1PgGGTZsmXKzc1VRkZGo6bv16+fXnzxRb3xxht65ZVXVFNTo1GjRmnXrl0hjjS4Tj/9dGVmZuqdd97R008/rW3btumMM85QWVlZndPv2bNHHTt29BvWsWPHiLseQQo8d7dsc0naunWrnn76aaWmpurdd9/Vtddeqzlz5mjx4sX1zlPfti8tLdV//vOfUIccFHby7ty5s5555hn97W9/09/+9jelpKRo3Lhxys3NdTDy4Fq5cqUOHjyoK664ot5p3LSv/1RjcnfLvn7LLbfoF7/4hfr376/Y2FgNGTJE8+bN0/Tp0+udJ2j7eUDHexAyhYWFVnJysvXZZ5/5hh3vFNXRDh8+bPXp08dauHBhCCJ0TklJiZWYmGi98MILdY6PjY21lixZ4jfsySeftJKTk50IL6SOl/vRInmbx8bGWmlpaX7DbrjhBmvkyJH1zpOammrdd999fsP+/ve/W5KsysrKkMQZbHbyrsvYsWOtX/7yl8EMzVFnn322df755zc4jVv39cbkfrRI3deXLl1qdevWzVq6dKn1+eefWy+//LLVtm1bKzMzs955grWfcwTHEJ9++qmKioo0dOhQNW/eXM2bN1dWVpYef/xxNW/e3O/itPrUVsdbtmxxIOLQOeGEE3TiiSfWm0enTp20d+9ev2F79+5Vp06dnAgvpI6X+9EieZt37txZAwYM8Bt20kknNXiKrr5tn5iYqJYtW4YkzmCzk3ddRowYEZHbXZJ27Nihf/3rX7rqqqsanM6N+3pjcz9apO7r8+fP9x3FOeWUU3T55ZfrN7/5TYNnKoK1n1PgGOKss87Spk2blJ+f73sNHz5c06dPV35+vmJiYo77HtXV1dq0aZM6d+7sQMShU15erm+//bbePNLS0rR69Wq/Ye+9957S0tKcCC+kjpf70SJ5m48ePdrvDhFJ+uabb9SjR49653HDtreTd13y8/MjcrtL0ksvvaTk5GRNnjy5wencsL2P1tjcjxap+3plZaWaNfMvNWJiYlRTU1PvPEHb7raPOyHkjj5Fdfnll1u33HKL7+8777zTevfdd61vv/3W+vTTT61f/OIXVnx8vPXll1+GIVr7fvvb31pr1661tm3bZmVnZ1sTJkyw2rdvbxUVFVmWdWze2dnZVvPmza2HH37Y+vrrr63bb7/dio2NtTZt2hSuFGwLNHe3bHPLsqxPPvnEat68uXXvvfdamzdvtl599VUrISHBeuWVV3zT3HLLLdbll1/u+3vr1q1WQkKCNX/+fOvrr7+2nnzySSsmJsZ65513wpGCLXby/uMf/2itXLnS2rx5s7Vp0yZr7ty5VrNmzax//etf4UihSaqrq63u3btbN9988zHj3LyvW1ZgubtlX585c6bVtWtX66233rK2bdtmrVixwmrfvr31u9/9zjdNqPZzChyDHV3gpKenWzNnzvT9PW/ePKt79+5WixYtrI4dO1qTJk2ycnNznQ+0iaZOnWp17tzZatGihdW1a1dr6tSp1pYtW3zjj87bsizrL3/5i3XiiSdaLVq0sAYOHGj9/e9/dzjq4Ag0d7ds81pvvvmmdfLJJ1txcXFW//79reeee85v/MyZM6309HS/YWvWrLEGDx5stWjRwurdu7f10ksvORdwkASa9wMPPGD16dPHio+Pt9q2bWuNGzfOev/99x2OOjjeffddS5JVUFBwzDg37+uWFVjubtnXS0tLrblz51rdu3e34uPjrd69e1t/+MMfrKqqKt80odrPPZZ1nMcJAgAARBiuwQEAAK5DgQMAAFyHAgcAALgOBQ4AAHAdChwAAOA6FDgAAMB1KHAAAIDrUOAAAADXocABEHE8Ho9WrlwZ7jAaJZJiBdyEAgdAoxUXF+vaa69V9+7dFRcXp06dOumcc85RdnZ2WOMyoYi44447NHjw4LDGAOC/moc7AACR45JLLtHhw4e1ePFi9e7dW3v37tXq1au1f//+cIcGAH44ggOgUQ4ePKj169frgQce0Pjx49WjRw+NGDFCCxYs0AUXXOCb5qqrrlKHDh2UmJioM888U5999pnvPWqPcjz77LNKSUlRQkKCLrvsMh06dMg3zYYNGzRx4kS1b99eXq9X6enpys3NbVLsL7zwgk466STFx8erf//+euqpp3zjtm/fLo/HoxUrVmj8+PFKSEjQqaeeqpycHL/3eP75530xX3TRRXr00Ud1wgknSJIyMzN155136rPPPpPH45HH41FmZqZv3n379umiiy5SQkKCUlNTtWrVqiblA+D4KHAANErr1q3VunVrrVy5UlVVVXVOc+mll6qoqEhvv/22Pv30Uw0dOlRnnXWWDhw44Jtmy5Yt+stf/qI333xT77zzjvLy8nTdddf5xpeVlWnmzJn64IMP9NFHHyk1NVWTJk1SWVmZrbhfffVV3Xbbbbr33nv19ddf67777tOtt96qxYsX+033hz/8QTfddJPy8/N14oknatq0afrhhx8kSdnZ2brmmms0d+5c5efna+LEibr33nt9806dOlW//e1vNXDgQH333Xf67rvvNHXqVN/4O++8U5dddpk+//xzTZo0SdOnT/dbJwBCoEl90AFElb/+9a9WUlKSFR8fb40aNcpasGCB9dlnn1mWZVnr16+3EhMTre+//95vnj59+ljPPvusZVmWdfvtt1sxMTHWrl27fOPffvttq1mzZtZ3331X5zKrq6utNm3aWG+++aZvmCTr9ddfr/fvo5e/ZMkSv2F33323lZaWZlmWZW3bts2SZL3wwgu+8V9++aUlyfr6668ty7KsqVOnWpMnT/Z7j+nTp1ter9f39+23326deuqpxyxfkrVw4ULf3+Xl5ZYk6+23364zXgDBwREcAI12ySWXaPfu3Vq1apXOPfdcrV27VkOHDlVmZqY+++wzlZeXq127dr6jPa1bt9a2bdv07bff+t6je/fu6tq1q+/vtLQ01dTUqKCgQJK0d+9eXX311UpNTZXX61ViYqLKy8tVWFgYcLwVFRX69ttvNWvWLL+Y7rnnHr+YJGnQoEG+f3fu3FmSVFRUJEkqKCjQiBEj/KY/+u+G/PS9W7VqpcTERN97AwgNLjIGEJD4+HhNnDhREydO1K233qqrrrpKt99+u6677jp17txZa9euPWae2mtVGmPmzJnav3+/HnvsMfXo0UNxcXFKS0vT4cOHA461vLxc0o/Xz5x++ul+42JiYvz+jo2N9f3b4/FIkmpqagJeZl1++t617x+s9wZQNwocAE0yYMAArVy5UkOHDtWePXvUvHlz9ezZs97pCwsLtXv3bnXp0kWS9NFHH6lZs2bq16+fpB+vd3nqqac0adIkSdLOnTu1b98+W7F17NhRXbp00datWzV9+nRb7yFJ/fr104YNG/yGHf13ixYtVF1dbXsZAIKLAgdAo+zfv1+XXnqpfvWrX2nQoEFq06aNNm7cqAcffFAXXnihJkyYoLS0NE2ZMkUPPvigTjzxRO3evVt///vfddFFF2n48OGSfjwCNHPmTD388MMqLS3VnDlzdNlll6lTp06SpNTUVP3P//yPhg8frtLSUs2fP18tW7Y8bnzbtm1Tfn6+37DU1FTdeeedmjNnjrxer84991xVVVVp48aNKikp0Y033tio3G+44QaNHTtWjz76qH72s5/p/fff19tvv+070iNJPXv29MXQrVs3tWnTRnFxcY1cuwCCLtwXAQGIDN9//711yy23WEOHDrW8Xq+VkJBg9evXz1q4cKFVWVlpWZZllZaWWjfccIPVpUsXKzY21kpJSbGmT59uFRYWWpb13wtxn3rqKatLly5WfHy89fOf/9w6cOCAbzm5ubnW8OHDrfj4eCs1NdVavny51aNHD+uPf/yjbxrVcZFxXa/169dblmVZr776qjV48GCrRYsWVlJSkjV27FhrxYoVlmX99yLjvLw83/uVlJRYkqw1a9b4hj333HNW165drZYtW1pTpkyx7rnnHqtTp05+6+eSSy6xTjjhBEuS9dJLL9UZq2VZltfr9Y0HEBoey7KsMNVWAKLMHXfcoZUrVx5zpCUSXX311fr3v/+t9evXhzsUAHXgFBUANMLDDz+siRMnqlWrVnr77be1ePFivwcGAjALBQ4ANMInn3yiBx98UGVlZerdu7cef/xxXXXVVeEOC0A9OEUFAABchwf9AQAA16HAAQAArkOBAwAAXIcCBwAAuA4FDgAAcB0KHAAA4DoUOAAAwHUocAAAgOv8fzUzwezP8YeDAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "U3BNEZJ_zq2s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7cONQ7Sk0MJm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# using seaborn library to plot\n",
        "g = sns.FacetGrid(iris, hue=\"Species\" )\n",
        "g.map(plt.scatter, \"SepalLength\", \"SepalWidth\")\n",
        "g.add_legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "wjozUWrq0MMU",
        "outputId": "1090f5ac-26ec-4ecd-8f54-130618b78117"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 443x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax= sns.boxplot(x=\"Species\", y=\"PetalLength\", data=iris)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "hwffasHC0prH",
        "outputId": "be8a383f-87f9-4cce-b59d-22e8dd74bcea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax= sns.boxplot(  y=\"PetalLength\", data=iris)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "vZIRrc_H0puQ",
        "outputId": "37743264-fe9a-469c-fdb9-b7f443376f55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax= sns.boxplot(  x=\"PetalLength\", data=iris)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Fr7e9lk62Bu2",
        "outputId": "06f628a1-1818-4505-91e8-a4ead2a0ecfc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import matplotlib.pyplot as plt\n",
        "\n",
        "# labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n",
        "# sizes = [15, 30, 45, 10]\n",
        "\n",
        "# fig, ax = plt.subplots()\n",
        "# ax.pie(sizes, labels=labels)"
      ],
      "metadata": {
        "id": "1mSI2hio2BxX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Machine Learning"
      ],
      "metadata": {
        "id": "5beGMHP83MSl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(iris.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGOIFhjk3PKj",
        "outputId": "a7b4f0d0-a0f5-4dc0-db59-d127ed2dc64b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(150, 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# apply logisticregression on iris dataset\n",
        "# Step 1\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "ZzbmTmDp3Rtr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# divide data in X (input) and Y (output)\n",
        "# Step 2\n",
        "X = iris.iloc[:,0:4]\n",
        "Y = iris.iloc[:,4]\n",
        "print (X.shape, Y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GVka4cXA3mbe",
        "outputId": "a6135b60-2f25-4442-9bde-e8eadf70cca1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(150, 4) (150,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# step 3\n",
        "model.fit(X,Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 79
        },
        "id": "PX9DyImr3md-",
        "outputId": "92911035-d90e-4465-f17c-f7a940adfa7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "iris[48:52]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "Tit_wPoY35wV",
        "outputId": "f85153f6-79d2-4075-d115-a9602db62935"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    SepalLength  SepalWidth  PetalLength  PetalWidth          Species\n",
              "48          5.3         3.7          1.5         0.2      Iris-setosa\n",
              "49          5.0         3.3          1.4         0.2      Iris-setosa\n",
              "50          7.0         3.2          4.7         1.4  Iris-versicolor\n",
              "51          6.4         3.2          4.5         1.5  Iris-versicolor"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b008e74e-60d5-45bd-9d61-1781a73d93f2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SepalLength</th>\n",
              "      <th>SepalWidth</th>\n",
              "      <th>PetalLength</th>\n",
              "      <th>PetalWidth</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>5.3</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>7.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.4</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>6.4</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b008e74e-60d5-45bd-9d61-1781a73d93f2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b008e74e-60d5-45bd-9d61-1781a73d93f2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b008e74e-60d5-45bd-9d61-1781a73d93f2');\n",
              "        const dataTable =\n",
              "          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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-74fe7923-196b-4179-9b69-16ad77a268ce\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-74fe7923-196b-4179-9b69-16ad77a268ce')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-74fe7923-196b-4179-9b69-16ad77a268ce button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"iris[48:52]\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"SepalLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9358596760910973,\n        \"min\": 5.0,\n        \"max\": 7.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5.0,\n          6.4,\n          5.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23804761428476168,\n        \"min\": 3.2,\n        \"max\": 3.7,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3.7,\n          3.3,\n          3.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8209429791548482,\n        \"min\": 1.4,\n        \"max\": 4.7,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.4,\n          4.5,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.722841614740048,\n        \"min\": 0.2,\n        \"max\": 1.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.2,\n          1.4,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Iris-versicolor\",\n          \"Iris-setosa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# step 4\n",
        "result = model.predict([[5.3,\t3.7\t,1.5,\t0.2 ]])\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TfqF249b4DaG",
        "outputId": "f8d17c2a-dca1-441f-f339-f01f14f3088d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Iris-setosa']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = model.predict([[6.4,\t3.2,\t4.5,\t1.5 ]])\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NvqPmATr4M-p",
        "outputId": "d5c3c84e-2fd2-46e2-feb1-de9d6bba626a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Iris-versicolor']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = model.predict([[3.4,\t4.2,\t4.5,\t2.5 ]])\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QZ7SluKL4UBV",
        "outputId": "ac55010f-370c-4d2a-9c81-3e5043820343"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Iris-virginica']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Make front end"
      ],
      "metadata": {
        "id": "H7B-dr3D5Zht"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://aimljan23f1.glitch.me/irisp"
      ],
      "metadata": {
        "id": "OdOV3ICa5WxA"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "j307TDIt4UEP"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}